diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml index 9c62f21d..18a9ce42 100644 --- a/.github/workflows/build-wheels.yml +++ b/.github/workflows/build-wheels.yml @@ -20,12 +20,15 @@ jobs: with: persist-credentials: false fetch-depth: 0 # for setuptools_scm to work + - name: Get version + id: version + run: echo "version=$(git describe --tags --always)" >> $GITHUB_OUTPUT - name: Build wheels uses: pypa/cibuildwheel@v3.4.1 env: # see - CIBW_BUILD: cp311-* + CIBW_BUILD: cp311-* cp312-* CIBW_SKIP: cp*-musllinux_* # pin manylinux version CIBW_MANYLINUX_X86_64_IMAGE: manylinux_2_28 @@ -45,6 +48,5 @@ jobs: - uses: actions/upload-artifact@v7 with: - #name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} + name: cibw-wheels-${{ steps.version.outputs.version }} path: ./wheelhouse/*.whl - archive: false diff --git a/pyproject.toml b/pyproject.toml index 29a4814a..ccca32a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,21 +26,27 @@ license-files = [ "AUTHORS", ] readme = "README.md" -requires-python = "==3.11.*" +requires-python = ">=3.11,<3.13" dependencies = [ "doepy", "jenkspy", "keras-tuner", + "keras==2.15.0; python_version<'3.12'", + "keras==3.14.1; python_version>='3.12'", "matplotlib", + "ml_dtypes==0.5.4; python_version>='3.12'", "mrmr-selection", "pandas", - "pycaret", + "pycaret; python_version<'3.12'", + "pycaret-ni; python_version>='3.12'", "pyDOE", "pysubgroup==0.8.0", "scikit-learn==1.4.2", "scipy", "seaborn", - "tensorflow==2.15.1", + "tensorboard; python_version>='3.12'", + "tensorflow==2.15.1; python_version<'3.12'", + "tensorflow==2.21.0; python_version>='3.12'", ] [project.urls] diff --git a/regr_smlp/code/smlp_regr.py b/regr_smlp/code/smlp_regr.py index 12e7d73a..d773c101 100755 --- a/regr_smlp/code/smlp_regr.py +++ b/regr_smlp/code/smlp_regr.py @@ -697,6 +697,7 @@ def worker(q, id_q, print_l): print('command (2)', command); with print_l: + print("") print("Running test {0} test type: {1}, description: {2}".format(test_id, test_type, test_description)) print(command + '\n') diff --git a/regr_smlp/master/Test100_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test100_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test100_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test100_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file +"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test102_test101_model.txt b/regr_smlp/master/Test102_test101_model.txt index b088e06d..5d346653 100644 --- a/regr_smlp/master/Test102_test101_model.txt +++ b/regr_smlp/master/Test102_test101_model.txt @@ -35,7 +35,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test101_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test101_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test105_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test105_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test105_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test105_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file +"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt b/regr_smlp/master/Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt index 388ef0c1..c87dd1b8 100644 --- a/regr_smlp/master/Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt +++ b/regr_smlp/master/Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt @@ -3,8 +3,8 @@ #TREE 0 if (p1 <= 0.7673577288013687) and (p2 > 0.38953525870551137) and (p2 <= 0.84376327853746) and (p2 <= 0.6545075774553886) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.7673577288013687) and (p2 > 0.38953525870551137) and (p2 > 0.84376327853746) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.7673577288013687) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.7673577288013687) and (p2 > 0.38953525870551137) and (p2 > 0.84376327853746) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7673577288013687) and (p2 > 0.38953525870551137) and (p2 <= 0.84376327853746) and (p2 > 0.6545075774553886) and (x > 0.8754706860180365) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7673577288013687) and (p2 > 0.38953525870551137) and (p2 <= 0.84376327853746) and (p2 > 0.6545075774553886) and (x <= 0.8754706860180365) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7673577288013687) and (p2 <= 0.38953525870551137) and (p2 > 0.08831593082958165) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -18,8 +18,8 @@ if (p2 <= 0.5137774280802242) and (x > 0.5648321261739068) then (y1 = 1.0) and ( if (p2 <= 0.5137774280802242) and (x <= 0.5648321261739068) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 2 if (p2 > 0.565498446377692) and (p1 <= 0.21566598080828134) and (p2 <= 0.9353157007204864) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x <= 0.03081251758592215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x > 0.03081251758592215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x <= 0.03081251758592215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 <= 0.7262518305173236) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.565498446377692) and (p1 <= 0.21566598080828134) and (p2 > 0.9353157007204864) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.565498446377692) and (x > 0.6869854677329386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -41,8 +41,8 @@ if (p1 <= 0.8390417540627726) and (p2 <= 0.2124159136844873) and (p2 > 0.1063322 if (p1 <= 0.8390417540627726) and (p2 <= 0.2124159136844873) and (p2 <= 0.10633223900057553) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 5 if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x <= 0.357251001688303) and (p2 > 0.6806537206084374) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x > 0.357251001688303) and (p2 > 0.6705293189396464) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.05282566885129813) and (p1 > 0.9621611074368288) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x > 0.357251001688303) and (p2 > 0.6705293189396464) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x > 0.357251001688303) and (p2 <= 0.6705293189396464) and (p2 > 0.5468370674143481) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x > 0.357251001688303) and (p2 <= 0.6705293189396464) and (p2 <= 0.5468370674143481) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.05282566885129813) and (p1 <= 0.9621611074368288) and (x <= 0.357251001688303) and (p2 <= 0.6806537206084374) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples @@ -63,17 +63,17 @@ if (p2 <= 0.27713262488932006) and (p2 > 0.03086634539933381) then (y1 = 1.0) an if (p2 <= 0.27713262488932006) and (p2 <= 0.03086634539933381) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 8 if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 > 0.2141300255741415) and (p2 <= 0.6372009349407088) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 > 0.2141300255741415) and (p2 > 0.6372009349407088) and (x > 0.9912727884907614) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.10769168804757841) and (p2 > 0.9843916629018533) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 > 0.2141300255741415) and (p2 > 0.6372009349407088) and (x > 0.9912727884907614) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 > 0.2141300255741415) and (p2 > 0.6372009349407088) and (x <= 0.9912727884907614) and (p1 > 0.5390213972604934) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 > 0.2141300255741415) and (p2 > 0.6372009349407088) and (x <= 0.9912727884907614) and (p1 <= 0.5390213972604934) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.10769168804757841) and (p2 <= 0.9843916629018533) and (p2 <= 0.2141300255741415) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.10769168804757841) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 9 -if (p1 <= 0.9643084043470717) and (p2 <= 0.7106753814549537) and (p2 > 0.3012990637738963) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x <= 0.7383051325780686) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x > 0.7383051325780686) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.9643084043470717) and (p2 <= 0.7106753814549537) and (p2 > 0.3012990637738963) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.9643084043470717) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x > 0.7383051325780686) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.9643084043470717) and (p2 <= 0.7106753814549537) and (p2 <= 0.3012990637738963) and (p2 > 0.12292138797063647) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.9643084043470717) and (p2 <= 0.7106753814549537) and (p2 <= 0.3012990637738963) and (p2 <= 0.12292138797063647) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 10 @@ -85,25 +85,25 @@ if (p2 > 0.0811267434997143) and (p2 <= 0.7441930175715353) and (p2 <= 0.5263289 if (p2 <= 0.0811267434997143) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 11 if (p2 > 0.37253817607301204) and (p1 <= 0.5069297996847564) and (p2 <= 0.960533093573664) and (p1 <= 0.10723656746895824) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.37253817607301204) and (p1 <= 0.5069297996847564) and (p2 > 0.960533093573664) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.37253817607301204) and (p1 > 0.5069297996847564) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.37253817607301204) and (p1 <= 0.5069297996847564) and (p2 > 0.960533093573664) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.37253817607301204) and (p1 <= 0.5069297996847564) and (p2 <= 0.960533093573664) and (p1 > 0.10723656746895824) and (p2 > 0.7217361869940098) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.37253817607301204) and (p1 <= 0.5069297996847564) and (p2 <= 0.960533093573664) and (p1 > 0.10723656746895824) and (p2 <= 0.7217361869940098) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.37253817607301204) and (p2 > 0.0848959539092921) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.37253817607301204) and (p2 <= 0.0848959539092921) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 12 if (p1 <= 0.8097833990164955) and (p2 <= 0.8508591725727819) and (p2 > 0.2059485761431525) and (p1 <= 0.17242011109945368) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.8097833990164955) and (p2 > 0.8508591725727819) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.8097833990164955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.8097833990164955) and (p2 > 0.8508591725727819) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8097833990164955) and (p2 <= 0.8508591725727819) and (p2 > 0.2059485761431525) and (p1 > 0.17242011109945368) and (x > 0.21198360627157023) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8097833990164955) and (p2 <= 0.8508591725727819) and (p2 > 0.2059485761431525) and (p1 > 0.17242011109945368) and (x <= 0.21198360627157023) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8097833990164955) and (p2 <= 0.8508591725727819) and (p2 <= 0.2059485761431525) and (x > 0.39526911487181565) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.8097833990164955) and (p2 <= 0.8508591725727819) and (p2 <= 0.2059485761431525) and (x <= 0.39526911487181565) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 13 -if (p2 > 0.30979522099243256) and (p2 <= 0.7974478444662528) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 <= 0.9832575130198419) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 > 0.9832575130198419) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.30979522099243256) and (p2 <= 0.7974478444662528) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x > 0.7718040145802331) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 > 0.9832575130198419) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.30979522099243256) and (x > 0.7388499612434075) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.30979522099243256) and (x <= 0.7388499612434075) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 14 @@ -115,33 +115,33 @@ if (p2 <= 0.341744390941106) and (p2 > 0.053601955328539244) then (y1 = 1.0) and if (p2 <= 0.341744390941106) and (p2 <= 0.053601955328539244) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 15 if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 <= 0.9455945307299672) and (p2 > 0.2702827100266373) and (p1 <= 0.48171880002213585) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 > 0.9455945307299672) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.6478692095949636) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 > 0.9455945307299672) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 <= 0.9455945307299672) and (p2 > 0.2702827100266373) and (p1 > 0.48171880002213585) and (p2 > 0.6094374650164717) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 <= 0.9455945307299672) and (p2 > 0.2702827100266373) and (p1 > 0.48171880002213585) and (p2 <= 0.6094374650164717) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6478692095949636) and (p2 > 0.04949053006688623) and (p2 <= 0.9455945307299672) and (p2 <= 0.2702827100266373) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.6478692095949636) and (p2 <= 0.04949053006688623) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 16 if (p2 > 0.5083941302766997) and (p1 <= 0.9604900148513215) and (p2 <= 0.9552207567202692) and (p2 <= 0.7388605602852585) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.5083941302766997) and (p1 <= 0.9604900148513215) and (p2 > 0.9552207567202692) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.5083941302766997) and (p1 > 0.9604900148513215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.5083941302766997) and (p1 <= 0.9604900148513215) and (p2 > 0.9552207567202692) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.5083941302766997) and (p1 <= 0.9604900148513215) and (p2 <= 0.9552207567202692) and (p2 > 0.7388605602852585) and (x > 0.6717326660530119) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.5083941302766997) and (p1 <= 0.9604900148513215) and (p2 <= 0.9552207567202692) and (p2 > 0.7388605602852585) and (x <= 0.6717326660530119) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.5083941302766997) and (x > 0.5146843414030903) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.5083941302766997) and (x <= 0.5146843414030903) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 17 if (p2 > 0.38305688667253446) and (p1 <= 0.2547155522064844) and (x > 0.6443744356236697) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 <= 0.7231216464299344) and (p2 > 0.7694835793578938) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 > 0.7231216464299344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 <= 0.7231216464299344) and (p2 > 0.7694835793578938) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 <= 0.7231216464299344) and (p2 <= 0.7694835793578938) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.38305688667253446) and (p1 <= 0.2547155522064844) and (x <= 0.6443744356236697) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.38305688667253446) and (x > 0.38537426341948944) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.38305688667253446) and (x <= 0.38537426341948944) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 18 -if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 <= 0.7309142946144038) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x <= 0.6016799023753295) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x > 0.6016799023753295) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 <= 0.7309142946144038) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.5519522472992318) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x > 0.6016799023753295) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5519522472992318) and (p2 <= 0.33294736609465786) and (p2 > 0.12163996417732852) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.5519522472992318) and (p2 <= 0.33294736609465786) and (p2 <= 0.12163996417732852) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 19 @@ -152,10 +152,10 @@ if (p2 > 0.11755728214865424) and (p2 > 0.311293208608845) and (x <= 0.399587696 if (p2 > 0.11755728214865424) and (p2 <= 0.311293208608845) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.11755728214865424) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 20 -if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 <= 0.6458938131907435) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x <= 0.6096057855068803) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x > 0.6096057855068803) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 <= 0.6458938131907435) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.5757871147132418) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x > 0.6096057855068803) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5757871147132418) and (p2 <= 0.39527962049249543) and (p2 > 0.12656376416636825) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.5757871147132418) and (p2 <= 0.39527962049249543) and (p2 <= 0.12656376416636825) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 21 @@ -201,10 +201,10 @@ if (p2 > 0.3134672834134974) and (x <= 0.4466749906757264) and (p1 <= 0.86837326 if (p2 <= 0.3134672834134974) and (p2 > 0.10331068812369026) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.3134672834134974) and (p2 <= 0.10331068812369026) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 27 -if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 <= 0.6777798791052577) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x <= 0.6717608829535293) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x > 0.6717608829535293) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 <= 0.6777798791052577) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.8026946730924279) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x > 0.6717608829535293) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 <= 0.4364986121396114) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.8026946730924279) and (p2 <= 0.05688018773537138) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 28 @@ -215,10 +215,10 @@ if (p2 > 0.2444424276447121) and (p1 <= 0.5177090086591006) and (x <= 0.64793627 if (p2 <= 0.2444424276447121) and (p2 > 0.10781742518673416) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.2444424276447121) and (p2 <= 0.10781742518673416) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 29 -if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 <= 0.634045960603359) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x <= 0.6156377025174744) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x > 0.6156377025174744) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 <= 0.634045960603359) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.5397191641571555) and (p1 > 0.7132398065706901) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x > 0.6156377025174744) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.5397191641571555) and (p2 > 0.02222790053743902) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.5397191641571555) and (p2 <= 0.02222790053743902) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 30 @@ -230,16 +230,16 @@ if (p2 <= 0.7941011026474186) and (p2 <= 0.3322790155591228) and (p2 > 0.0608856 if (p2 <= 0.7941011026474186) and (p2 <= 0.3322790155591228) and (p2 <= 0.06088563772348036) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 31 if (p1 <= 0.6806756624396312) and (p2 <= 0.955677862258478) and (p2 > 0.3187052179674022) and (p1 <= 0.37254592886778803) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6806756624396312) and (p2 > 0.955677862258478) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.6806756624396312) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6806756624396312) and (p2 > 0.955677862258478) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6806756624396312) and (p2 <= 0.955677862258478) and (p2 > 0.3187052179674022) and (p1 > 0.37254592886778803) and (x > 0.2563616846052506) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6806756624396312) and (p2 <= 0.955677862258478) and (p2 > 0.3187052179674022) and (p1 > 0.37254592886778803) and (x <= 0.2563616846052506) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6806756624396312) and (p2 <= 0.955677862258478) and (p2 <= 0.3187052179674022) and (x > 0.7927851256714178) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.6806756624396312) and (p2 <= 0.955677862258478) and (p2 <= 0.3187052179674022) and (x <= 0.7927851256714178) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 32 if (p2 > 0.4943563777461445) and (p1 <= 0.920448066629678) and (p2 <= 0.8993228815430752) and (p2 <= 0.6947737321088735) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.4943563777461445) and (p1 <= 0.920448066629678) and (p2 > 0.8993228815430752) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4943563777461445) and (p1 > 0.920448066629678) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.4943563777461445) and (p1 <= 0.920448066629678) and (p2 > 0.8993228815430752) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4943563777461445) and (p1 <= 0.920448066629678) and (p2 <= 0.8993228815430752) and (p2 > 0.6947737321088735) and (p1 > 0.3718316512516847) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4943563777461445) and (p1 <= 0.920448066629678) and (p2 <= 0.8993228815430752) and (p2 > 0.6947737321088735) and (p1 <= 0.3718316512516847) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.4943563777461445) and (p2 > 0.04308627938006408) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -261,8 +261,8 @@ if (p2 <= 0.6421366765360053) and (p2 <= 0.3826506176197445) and (p2 > 0.0874620 if (p2 <= 0.6421366765360053) and (p2 <= 0.3826506176197445) and (p2 <= 0.08746205199800788) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 35 if (p1 <= 0.7209493553829144) and (p2 <= 0.9636896960268215) and (p2 > 0.5401351166071643) and (p2 <= 0.7990047528997054) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.7209493553829144) and (p2 > 0.9636896960268215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.7209493553829144) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.7209493553829144) and (p2 > 0.9636896960268215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7209493553829144) and (p2 <= 0.9636896960268215) and (p2 > 0.5401351166071643) and (p2 > 0.7990047528997054) and (p1 > 0.0945731604446532) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7209493553829144) and (p2 <= 0.9636896960268215) and (p2 > 0.5401351166071643) and (p2 > 0.7990047528997054) and (p1 <= 0.0945731604446532) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.7209493553829144) and (p2 <= 0.9636896960268215) and (p2 <= 0.5401351166071643) and (p2 > 0.15656234890527904) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -283,8 +283,8 @@ if (p2 <= 0.5503449004843575) and (x > 0.6838874074007185) then (y1 = 1.0) and ( if (p2 <= 0.5503449004843575) and (x <= 0.6838874074007185) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 38 if (p2 <= 0.9499151226831205) and (p2 > 0.3498868325474976) and (p2 <= 0.6778160918119172) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 <= 0.9499151226831205) and (p2 > 0.3498868325474976) and (p2 > 0.6778160918119172) and (x > 0.4513972124324167) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.9499151226831205) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 <= 0.9499151226831205) and (p2 > 0.3498868325474976) and (p2 > 0.6778160918119172) and (x > 0.4513972124324167) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.9499151226831205) and (p2 > 0.3498868325474976) and (p2 > 0.6778160918119172) and (x <= 0.4513972124324167) and (x > 0.2520601363156806) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.9499151226831205) and (p2 > 0.3498868325474976) and (p2 > 0.6778160918119172) and (x <= 0.4513972124324167) and (x <= 0.2520601363156806) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.9499151226831205) and (p2 <= 0.3498868325474976) and (p2 > 0.1808850459611073) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -297,10 +297,10 @@ if (p1 <= 0.801999756974168) and (p2 > 0.34625137846276693) and (x <= 0.41208600 if (p1 <= 0.801999756974168) and (p2 <= 0.34625137846276693) and (x > 0.47424602056777737) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.801999756974168) and (p2 <= 0.34625137846276693) and (x <= 0.47424602056777737) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 40 -if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 <= 0.7500101945124135) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x <= 0.8482964849267818) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x > 0.8482964849267818) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 <= 0.7500101945124135) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.4160567286499109) and (p1 > 0.7821608059025187) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x > 0.8482964849267818) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.4160567286499109) and (x > 0.4736861150319365) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.4160567286499109) and (x <= 0.4736861150319365) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 41 @@ -354,17 +354,17 @@ if (p2 > 0.5751257713768285) and (x <= 0.4337772570707729) and (x <= 0.237005961 if (p2 <= 0.5751257713768285) and (x > 0.40844256912278043) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.5751257713768285) and (x <= 0.40844256912278043) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 48 -if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 <= 0.6023659933821865) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x <= 0.7909361110756394) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x > 0.7909361110756394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 <= 0.6023659933821865) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.6838882522116826) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x > 0.7909361110756394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 <= 0.3422835304420513) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.6838882522116826) and (p2 <= 0.0626814738207876) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 49 -if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 <= 0.6471116276536257) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x <= 0.4249370103517018) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x > 0.4249370103517018) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 <= 0.6471116276536257) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.40336603038169727) and (p1 > 0.7296289325364069) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x > 0.4249370103517018) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.40336603038169727) and (p2 > 0.04875294482747426) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.40336603038169727) and (p2 <= 0.04875294482747426) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 50 @@ -376,24 +376,24 @@ if (p2 <= 0.3306827444260394) and (p2 > 0.06640565767483164) then (y1 = 1.0) and if (p2 <= 0.3306827444260394) and (p2 <= 0.06640565767483164) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 51 if (p1 <= 0.5139580394672034) and (p2 <= 0.8265806943301953) and (p2 > 0.022423238279282512) and (p2 > 0.2492419355665258) and (p1 <= 0.07006022220945927) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.5139580394672034) and (p2 > 0.8265806943301953) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.5139580394672034) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.5139580394672034) and (p2 > 0.8265806943301953) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5139580394672034) and (p2 <= 0.8265806943301953) and (p2 > 0.022423238279282512) and (p2 > 0.2492419355665258) and (p1 > 0.07006022220945927) and (p2 > 0.7161747843240409) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5139580394672034) and (p2 <= 0.8265806943301953) and (p2 > 0.022423238279282512) and (p2 > 0.2492419355665258) and (p1 > 0.07006022220945927) and (p2 <= 0.7161747843240409) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5139580394672034) and (p2 <= 0.8265806943301953) and (p2 > 0.022423238279282512) and (p2 <= 0.2492419355665258) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.5139580394672034) and (p2 <= 0.8265806943301953) and (p2 <= 0.022423238279282512) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 52 if (p2 <= 0.8305540819794657) and (p1 <= 0.92617457915385) and (p2 <= 0.6511821537693081) and (p2 > 0.37176354531924466) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 <= 0.8305540819794657) and (p1 > 0.92617457915385) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.8305540819794657) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 <= 0.8305540819794657) and (p1 > 0.92617457915385) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.8305540819794657) and (p1 <= 0.92617457915385) and (p2 > 0.6511821537693081) and (x > 0.41309576149131094) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.8305540819794657) and (p1 <= 0.92617457915385) and (p2 > 0.6511821537693081) and (x <= 0.41309576149131094) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.8305540819794657) and (p1 <= 0.92617457915385) and (p2 <= 0.6511821537693081) and (p2 <= 0.37176354531924466) and (x > 0.8204811216479875) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.8305540819794657) and (p1 <= 0.92617457915385) and (p2 <= 0.6511821537693081) and (p2 <= 0.37176354531924466) and (x <= 0.8204811216479875) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 53 if (p2 <= 0.9449580902908733) and (p1 <= 0.991271187547255) and (p2 <= 0.6157962755864844) and (x <= 0.9212374770647089) and (p2 > 0.3753883548120627) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 <= 0.9449580902908733) and (p1 > 0.991271187547255) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.9449580902908733) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 <= 0.9449580902908733) and (p1 > 0.991271187547255) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.9449580902908733) and (p1 <= 0.991271187547255) and (p2 > 0.6157962755864844) and (p1 > 0.09506212365583616) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.9449580902908733) and (p1 <= 0.991271187547255) and (p2 > 0.6157962755864844) and (p1 <= 0.09506212365583616) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.9449580902908733) and (p1 <= 0.991271187547255) and (p2 <= 0.6157962755864844) and (x > 0.9212374770647089) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -407,24 +407,24 @@ if (p2 > 0.14160878310986272) and (x <= 0.4856535510558884) and (x <= 0.31501985 if (p2 <= 0.14160878310986272) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 55 if (p1 <= 0.5120007179267708) and (p2 > 0.3581134953341044) and (p2 <= 0.8037204975855994) and (p1 <= 0.15062762151967157) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.5120007179267708) and (p2 > 0.3581134953341044) and (p2 > 0.8037204975855994) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.5120007179267708) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.5120007179267708) and (p2 > 0.3581134953341044) and (p2 > 0.8037204975855994) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5120007179267708) and (p2 > 0.3581134953341044) and (p2 <= 0.8037204975855994) and (p1 > 0.15062762151967157) and (x > 0.3130909985323659) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5120007179267708) and (p2 > 0.3581134953341044) and (p2 <= 0.8037204975855994) and (p1 > 0.15062762151967157) and (x <= 0.3130909985323659) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5120007179267708) and (p2 <= 0.3581134953341044) and (p2 > 0.07040823303324713) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.5120007179267708) and (p2 <= 0.3581134953341044) and (p2 <= 0.07040823303324713) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 56 -if (p1 <= 0.610545704891228) and (p2 <= 0.6501177091384854) and (p2 > 0.3236264497692459) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x <= 0.7877861639695688) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x > 0.7877861639695688) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p1 <= 0.610545704891228) and (p2 <= 0.6501177091384854) and (p2 > 0.3236264497692459) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p1 > 0.610545704891228) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x > 0.7877861639695688) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.610545704891228) and (p2 <= 0.6501177091384854) and (p2 <= 0.3236264497692459) and (x > 0.6570588083151621) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.610545704891228) and (p2 <= 0.6501177091384854) and (p2 <= 0.3236264497692459) and (x <= 0.6570588083151621) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 57 -if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 <= 0.7332769920461923) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x <= 0.7843162575827582) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x > 0.7843162575827582) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 <= 0.7332769920461923) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.45482464342137086) and (p1 > 0.6847267941034989) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x > 0.7843162575827582) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.45482464342137086) and (x > 0.9266464593543795) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.45482464342137086) and (x <= 0.9266464593543795) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 58 @@ -443,8 +443,8 @@ if (p2 <= 0.29853910594179256) and (x > 0.5318511599578339) then (y1 = 1.0) and if (p2 <= 0.29853910594179256) and (x <= 0.5318511599578339) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 60 if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 <= 0.8966961662858385) and (p2 > 0.23576192761778064) and (p2 <= 0.6991669517037455) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 > 0.8966961662858385) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.054852516881587224) and (p1 > 0.8053342741007611) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 > 0.8966961662858385) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 <= 0.8966961662858385) and (p2 > 0.23576192761778064) and (p2 > 0.6991669517037455) and (x > 0.8534004048186296) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 <= 0.8966961662858385) and (p2 > 0.23576192761778064) and (p2 > 0.6991669517037455) and (x <= 0.8534004048186296) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.054852516881587224) and (p1 <= 0.8053342741007611) and (p2 <= 0.8966961662858385) and (p2 <= 0.23576192761778064) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -458,8 +458,8 @@ if (p2 <= 0.6448756091505641) and (p2 <= 0.468131359766861) and (x > 0.938623806 if (p2 <= 0.6448756091505641) and (p2 <= 0.468131359766861) and (x <= 0.9386238064163653) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 62 if (p2 > 0.35838276676758324) and (p1 <= 0.6469177723149386) and (p2 <= 0.9101062358433887) and (p2 <= 0.7014703014461668) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.35838276676758324) and (p1 <= 0.6469177723149386) and (p2 > 0.9101062358433887) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.35838276676758324) and (p1 > 0.6469177723149386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.35838276676758324) and (p1 <= 0.6469177723149386) and (p2 > 0.9101062358433887) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.35838276676758324) and (p1 <= 0.6469177723149386) and (p2 <= 0.9101062358433887) and (p2 > 0.7014703014461668) and (x > 0.34297385021787025) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.35838276676758324) and (p1 <= 0.6469177723149386) and (p2 <= 0.9101062358433887) and (p2 > 0.7014703014461668) and (x <= 0.34297385021787025) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.35838276676758324) and (p2 > 0.1531630701638671) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -473,17 +473,17 @@ if (p1 <= 0.8812832808500544) and (p2 <= 0.47450545359147034) and (p2 > 0.117147 if (p1 <= 0.8812832808500544) and (p2 <= 0.47450545359147034) and (p2 <= 0.1171478816614859) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 64 if (p1 <= 0.6586678422329332) and (p2 <= 0.9061892311583223) and (p2 > 0.23981228168765323) and (p2 <= 0.6547835506696681) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6586678422329332) and (p2 > 0.9061892311583223) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.6586678422329332) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6586678422329332) and (p2 > 0.9061892311583223) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6586678422329332) and (p2 <= 0.9061892311583223) and (p2 > 0.23981228168765323) and (p2 > 0.6547835506696681) and (x > 0.7224090244956254) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6586678422329332) and (p2 <= 0.9061892311583223) and (p2 > 0.23981228168765323) and (p2 > 0.6547835506696681) and (x <= 0.7224090244956254) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6586678422329332) and (p2 <= 0.9061892311583223) and (p2 <= 0.23981228168765323) and (x > 0.475946448547297) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.6586678422329332) and (p2 <= 0.9061892311583223) and (p2 <= 0.23981228168765323) and (x <= 0.475946448547297) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 65 -if (p2 <= 0.6300749409152544) and (p2 > 0.5492773633547796) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x <= 0.37232690124052253) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x > 0.37232690124052253) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 <= 0.6300749409152544) and (p2 > 0.5492773633547796) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.6300749409152544) and (p1 > 0.9968296801656623) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x > 0.37232690124052253) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.6300749409152544) and (p2 <= 0.5492773633547796) and (p2 > 0.1426465970032558) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.6300749409152544) and (p2 <= 0.5492773633547796) and (p2 <= 0.1426465970032558) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 66 @@ -494,24 +494,24 @@ if (p2 > 0.3330574130327708) and (x <= 0.6217868368242806) and (x <= 0.299595425 if (p2 <= 0.3330574130327708) and (x > 0.8474307665894645) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.3330574130327708) and (x <= 0.8474307665894645) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 67 -if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 <= 0.7656687748432474) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x <= 0.5092783321373655) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x > 0.5092783321373655) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 <= 0.7656687748432474) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.37996283470651265) and (p1 > 0.5025284804881217) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x > 0.5092783321373655) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.37996283470651265) and (p2 > 0.1703175509443875) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.37996283470651265) and (p2 <= 0.1703175509443875) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 68 if (p2 > 0.22414377714700243) and (p1 <= 0.904138504017209) and (p2 <= 0.9450541955316987) and (p1 <= 0.032419801006289106) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.22414377714700243) and (p1 <= 0.904138504017209) and (p2 > 0.9450541955316987) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.22414377714700243) and (p1 > 0.904138504017209) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.22414377714700243) and (p1 <= 0.904138504017209) and (p2 > 0.9450541955316987) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.22414377714700243) and (p1 <= 0.904138504017209) and (p2 <= 0.9450541955316987) and (p1 > 0.032419801006289106) and (p2 > 0.7082399131979482) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.22414377714700243) and (p1 <= 0.904138504017209) and (p2 <= 0.9450541955316987) and (p1 > 0.032419801006289106) and (p2 <= 0.7082399131979482) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.22414377714700243) and (x > 0.5046281504242344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.22414377714700243) and (x <= 0.5046281504242344) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 69 if (p1 <= 0.8937446875002909) and (p2 <= 0.8339302823105502) and (p2 <= 0.6890043409248549) and (p2 > 0.5802536298964303) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.8937446875002909) and (p2 > 0.8339302823105502) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.8937446875002909) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.8937446875002909) and (p2 > 0.8339302823105502) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8937446875002909) and (p2 <= 0.8339302823105502) and (p2 > 0.6890043409248549) and (x > 0.6442464591756898) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8937446875002909) and (p2 <= 0.8339302823105502) and (p2 > 0.6890043409248549) and (x <= 0.6442464591756898) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8937446875002909) and (p2 <= 0.8339302823105502) and (p2 <= 0.6890043409248549) and (p2 <= 0.5802536298964303) and (p2 > 0.030100262627614966) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -532,51 +532,51 @@ if (p1 <= 0.975938606064738) and (p2 <= 0.369596745525299) and (x > 0.7401604798 if (p1 <= 0.975938606064738) and (p2 <= 0.369596745525299) and (x <= 0.740160479856822) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 72 if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 <= 0.9300510326789317) and (p2 <= 0.6937404743837947) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 <= 0.9300510326789317) and (p2 > 0.6937404743837947) and (x > 0.3174353457602837) and (p1 > 0.07464147106494823) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 > 0.9300510326789317) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 <= 0.9300510326789317) and (p2 > 0.6937404743837947) and (x > 0.3174353457602837) and (p1 > 0.07464147106494823) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 <= 0.9300510326789317) and (p2 > 0.6937404743837947) and (x > 0.3174353457602837) and (p1 <= 0.07464147106494823) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 <= 0.9300510326789317) and (p2 > 0.6937404743837947) and (x <= 0.3174353457602837) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.07422745696931493) and (p2 <= 0.545370601481947) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.07422745696931493) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 73 -if (p2 <= 0.7889165751584417) and (p2 > 0.3738200118174434) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 <= 0.8191675724550931) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 > 0.8191675724550931) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 <= 0.7889165751584417) and (p2 > 0.3738200118174434) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.7889165751584417) and (x > 0.9135558618761394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 > 0.8191675724550931) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.7889165751584417) and (p2 <= 0.3738200118174434) and (x > 0.7216687515820077) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.7889165751584417) and (p2 <= 0.3738200118174434) and (x <= 0.7216687515820077) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 74 -if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 <= 0.6876566959859812) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x <= 0.6647538383146583) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x > 0.6647538383146583) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 <= 0.6876566959859812) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 > 0.8197608151565123) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x > 0.6647538383146583) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.09689574087825406) and (p2 <= 0.33636798963203285) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.09689574087825406) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 75 -if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 <= 0.7807007138854876) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 > 0.7807007138854876) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 > 0.574456778622445) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 <= 0.7807007138854876) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x > 0.792321881648303) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 > 0.574456778622445) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 > 0.16337258050375272) and (p2 <= 0.20940975581022525) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.16337258050375272) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 76 -if (p2 <= 0.7007179663985585) and (p2 > 0.24809264103819795) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 <= 0.5654538877147501) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 > 0.5654538877147501) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 <= 0.7007179663985585) and (p2 > 0.24809264103819795) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.7007179663985585) and (x > 0.47267748344348626) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 > 0.5654538877147501) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.7007179663985585) and (p2 <= 0.24809264103819795) and (p2 > 0.16326367202573508) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.7007179663985585) and (p2 <= 0.24809264103819795) and (p2 <= 0.16326367202573508) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 77 -if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 <= 0.6127691589194908) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x <= 0.6760619000417248) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x > 0.6760619000417248) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 <= 0.6127691589194908) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.30978463977099613) and (p1 > 0.6320834037065894) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x > 0.6760619000417248) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.30978463977099613) and (x > 0.8398692780006921) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.30978463977099613) and (x <= 0.8398692780006921) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 78 if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x <= 0.8391690658587818) and (x <= 0.5673652047037165) and (x > 0.0024809512129341364) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x > 0.8391690658587818) and (p2 > 0.6348027628606608) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.6819941814439344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x > 0.8391690658587818) and (p2 > 0.6348027628606608) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x > 0.8391690658587818) and (p2 <= 0.6348027628606608) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x <= 0.8391690658587818) and (x > 0.5673652047037165) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6819941814439344) and (p2 > 0.06900733246887444) and (x <= 0.8391690658587818) and (x <= 0.5673652047037165) and (x <= 0.0024809512129341364) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples @@ -597,8 +597,8 @@ if (p2 <= 0.4933722966785414) and (x > 0.5036926269774304) then (y1 = 1.0) and ( if (p2 <= 0.4933722966785414) and (x <= 0.5036926269774304) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 81 if (p1 <= 0.5931505284240239) and (p2 > 0.28785656778507707) and (p2 <= 0.9368516303691283) and (p1 <= 0.3490959400539733) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.5931505284240239) and (p2 > 0.28785656778507707) and (p2 > 0.9368516303691283) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.5931505284240239) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.5931505284240239) and (p2 > 0.28785656778507707) and (p2 > 0.9368516303691283) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5931505284240239) and (p2 > 0.28785656778507707) and (p2 <= 0.9368516303691283) and (p1 > 0.3490959400539733) and (x > 0.14564325309675707) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5931505284240239) and (p2 > 0.28785656778507707) and (p2 <= 0.9368516303691283) and (p1 > 0.3490959400539733) and (x <= 0.14564325309675707) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.5931505284240239) and (p2 <= 0.28785656778507707) and (p2 > 0.08446639701136903) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -628,17 +628,17 @@ if (p2 <= 0.9649971430026912) and (p2 > 0.32866909859985116) and (p1 <= 0.878026 if (p2 <= 0.9649971430026912) and (p2 <= 0.32866909859985116) and (x > 0.6713554717233934) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.9649971430026912) and (p2 <= 0.32866909859985116) and (x <= 0.6713554717233934) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 85 -if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 <= 0.7353104082940054) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x <= 0.4155191624469929) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x > 0.4155191624469929) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 <= 0.7353104082940054) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.3774126001528523) and (p1 > 0.6553700527434098) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x > 0.4155191624469929) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.3774126001528523) and (p2 > 0.19625985057865036) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.3774126001528523) and (p2 <= 0.19625985057865036) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 86 -if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 <= 0.7248063887234734) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x <= 0.7892120132227867) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x > 0.7892120132227867) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 <= 0.7248063887234734) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.23018040425618197) and (p1 > 0.7102524464532046) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x > 0.7892120132227867) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.23018040425618197) and (x > 0.3408737581997398) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.23018040425618197) and (x <= 0.3408737581997398) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 87 @@ -650,16 +650,16 @@ if (p2 <= 0.7685865707549204) and (p2 <= 0.4794289603786071) and (x > 0.66363410 if (p2 <= 0.7685865707549204) and (p2 <= 0.4794289603786071) and (x <= 0.6636341017487815) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 88 if (p2 > 0.21158053456413584) and (p1 <= 0.5586915479780601) and (p2 <= 0.917012561550087) and (p1 <= 0.37698098499187316) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.21158053456413584) and (p1 <= 0.5586915479780601) and (p2 > 0.917012561550087) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.21158053456413584) and (p1 > 0.5586915479780601) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.21158053456413584) and (p1 <= 0.5586915479780601) and (p2 > 0.917012561550087) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.21158053456413584) and (p1 <= 0.5586915479780601) and (p2 <= 0.917012561550087) and (p1 > 0.37698098499187316) and (x > 0.23987007525666668) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.21158053456413584) and (p1 <= 0.5586915479780601) and (p2 <= 0.917012561550087) and (p1 > 0.37698098499187316) and (x <= 0.23987007525666668) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.21158053456413584) and (p2 > 0.1490763018464287) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.21158053456413584) and (p2 <= 0.1490763018464287) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 89 if (p1 <= 0.6795984351446844) and (p2 > 0.38740996615374923) and (p2 <= 0.8233752316009937) and (p1 <= 0.3975388712238236) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.6795984351446844) and (p2 > 0.38740996615374923) and (p2 > 0.8233752316009937) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.6795984351446844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.6795984351446844) and (p2 > 0.38740996615374923) and (p2 > 0.8233752316009937) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6795984351446844) and (p2 > 0.38740996615374923) and (p2 <= 0.8233752316009937) and (p1 > 0.3975388712238236) and (x > 0.20376790310795417) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6795984351446844) and (p2 > 0.38740996615374923) and (p2 <= 0.8233752316009937) and (p1 > 0.3975388712238236) and (x <= 0.20376790310795417) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.6795984351446844) and (p2 <= 0.38740996615374923) and (p2 > 0.08407701476222902) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples @@ -674,17 +674,17 @@ if (p1 <= 0.9677454698680646) and (p2 > 0.15980578221371666) and (x <= 0.1361103 if (p1 <= 0.9677454698680646) and (p2 <= 0.15980578221371666) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 91 if (p1 <= 0.8126054242312002) and (p2 > 0.4087509822141151) and (p2 <= 0.9209586624234218) and (p1 <= 0.36498142539755507) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p1 <= 0.8126054242312002) and (p2 > 0.4087509822141151) and (p2 > 0.9209586624234218) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 > 0.8126054242312002) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p1 <= 0.8126054242312002) and (p2 > 0.4087509822141151) and (p2 > 0.9209586624234218) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8126054242312002) and (p2 > 0.4087509822141151) and (p2 <= 0.9209586624234218) and (p1 > 0.36498142539755507) and (x > 0.2769097679601055) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8126054242312002) and (p2 > 0.4087509822141151) and (p2 <= 0.9209586624234218) and (p1 > 0.36498142539755507) and (x <= 0.2769097679601055) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p1 <= 0.8126054242312002) and (p2 <= 0.4087509822141151) and (x > 0.6511800733767844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p1 <= 0.8126054242312002) and (p2 <= 0.4087509822141151) and (x <= 0.6511800733767844) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 92 -if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 <= 0.6824239181038759) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x <= 0.7910962169102163) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x > 0.7910962169102163) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 <= 0.6824239181038759) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.26886312862339573) and (p1 > 0.8950548846717248) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x > 0.7910962169102163) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.26886312862339573) and (p2 > 0.14584029820409128) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.26886312862339573) and (p2 <= 0.14584029820409128) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 93 @@ -695,17 +695,17 @@ if (p2 > 0.33055346847071937) and (p2 > 0.7456624298826202) and (x <= 0.77860693 if (p2 <= 0.33055346847071937) and (x > 0.7135205822442017) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.33055346847071937) and (x <= 0.7135205822442017) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 94 -if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 <= 0.6174925668996046) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x <= 0.4784058184220548) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x > 0.4784058184220548) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 <= 0.6174925668996046) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.25803846924474394) and (p1 > 0.85781003667871) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x > 0.4784058184220548) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.25803846924474394) and (p2 > 0.05880271184280955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.25803846924474394) and (p2 <= 0.05880271184280955) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 95 -if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 <= 0.7650359471516853) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x <= 0.6112039253981838) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x > 0.6112039253981838) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 <= 0.7650359471516853) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.16944414757631912) and (p1 > 0.7608029128801092) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x > 0.6112039253981838) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 <= 0.31309513934344707) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.16944414757631912) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 96 @@ -725,16 +725,16 @@ if (p2 <= 0.4985881580498946) and (p2 > 0.1081205498278039) then (y1 = 1.0) and if (p2 <= 0.4985881580498946) and (p2 <= 0.1081205498278039) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 98 if (p2 > 0.29657478970316) and (p1 <= 0.8279177280272906) and (p2 <= 0.9567767558104137) and (p1 <= 0.3029503018143355) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.29657478970316) and (p1 <= 0.8279177280272906) and (p2 > 0.9567767558104137) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.29657478970316) and (p1 > 0.8279177280272906) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.29657478970316) and (p1 <= 0.8279177280272906) and (p2 > 0.9567767558104137) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.29657478970316) and (p1 <= 0.8279177280272906) and (p2 <= 0.9567767558104137) and (p1 > 0.3029503018143355) and (p2 > 0.7528904537590753) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.29657478970316) and (p1 <= 0.8279177280272906) and (p2 <= 0.9567767558104137) and (p1 > 0.3029503018143355) and (p2 <= 0.7528904537590753) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.29657478970316) and (x > 0.49767770039476056) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.29657478970316) and (x <= 0.49767770039476056) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 99 if (p2 > 0.30543398172847647) and (p1 <= 0.7651434488432218) and (p2 <= 0.80389594384712) and (p2 <= 0.7573556440956122) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.30543398172847647) and (p1 <= 0.7651434488432218) and (p2 > 0.80389594384712) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.30543398172847647) and (p1 > 0.7651434488432218) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.30543398172847647) and (p1 <= 0.7651434488432218) and (p2 > 0.80389594384712) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.30543398172847647) and (p1 <= 0.7651434488432218) and (p2 <= 0.80389594384712) and (p2 > 0.7573556440956122) and (p1 > 0.49284858372660756) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.30543398172847647) and (p1 <= 0.7651434488432218) and (p2 <= 0.80389594384712) and (p2 > 0.7573556440956122) and (p1 <= 0.49284858372660756) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.30543398172847647) and (p2 > 0.11864887639666576) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples diff --git a/regr_smlp/master/Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt b/regr_smlp/master/Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt index da351203..d777c51f 100644 --- a/regr_smlp/master/Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt +++ b/regr_smlp/master/Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt @@ -76,7 +76,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt b/regr_smlp/master/Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt index 0f592fe5..f4221430 100644 --- a/regr_smlp/master/Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt +++ b/regr_smlp/master/Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt @@ -76,7 +76,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test114_smlp_toy_basic_smlp_model_term.json b/regr_smlp/master/Test114_smlp_toy_basic_smlp_model_term.json index f3bf6a70..47b9f7b9 100644 --- a/regr_smlp/master/Test114_smlp_toy_basic_smlp_model_term.json +++ b/regr_smlp/master/Test114_smlp_toy_basic_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x1_scaled (/ 7587435 33554432))) 0 (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) (/ 3124582929976399 72057594037927936) (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 7364743914427397 9007199254740992) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 4615234927434275 72057594037927936) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 4118666647088875 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 155796468224373 281474976710656) 1)))))))>, 'y2_scaled': x1_scaled (/ 7587435 33554432))) (/ 1421319515427019 2251799813685248) (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) 1 (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 2182179947885989 4503599627370496) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 7441268742104829 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (/ 1744855633611649 2251799813685248))))))))>}" \ No newline at end of file +"{'y1_scaled': x1_scaled (/ 7587435 33554432))) 0 (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) (/ 3124582929976399 72057594037927936) (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 7364743914427397 9007199254740992) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 4615234927434275 72057594037927936) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 4118666647088875 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 155796468224373 281474976710656) 1)))))))>, 'y2_scaled': x1_scaled (/ 7587435 33554432))) (/ 1421319515427019 2251799813685248) (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) 1 (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 2182179947885989 4503599627370496) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 7441268742104829 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (/ 1744855633611649 2251799813685248))))))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test128_smlp_toy_ctg_num_resp_smlp_model_term.json b/regr_smlp/master/Test128_smlp_toy_ctg_num_resp_smlp_model_term.json index d0e5c0d3..b3ad5018 100644 --- a/regr_smlp/master/Test128_smlp_toy_ctg_num_resp_smlp_model_term.json +++ b/regr_smlp/master/Test128_smlp_toy_ctg_num_resp_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': , 'y2_scaled': }" \ No newline at end of file +"{'y1_scaled': , 'y2_scaled': }" \ No newline at end of file diff --git a/regr_smlp/master/Test129_smlp_toy_ctg_num_resp_smlp_model_term.json b/regr_smlp/master/Test129_smlp_toy_ctg_num_resp_smlp_model_term.json index d0e5c0d3..b3ad5018 100644 --- a/regr_smlp/master/Test129_smlp_toy_ctg_num_resp_smlp_model_term.json +++ b/regr_smlp/master/Test129_smlp_toy_ctg_num_resp_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': , 'y2_scaled': }" \ No newline at end of file +"{'y1_scaled': , 'y2_scaled': }" \ No newline at end of file diff --git a/regr_smlp/master/Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index 5e3b5b18..0269430f 100644 --- a/regr_smlp/master/Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index bf37a3be..01df4103 100644 --- a/regr_smlp/master/Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index 2c5a007c..437fa1d1 100644 --- a/regr_smlp/master/Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt index db1d5380..38a24ca5 100644 --- a/regr_smlp/master/Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test19_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test19_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt index 535da710..62e8073f 100644 --- a/regr_smlp/master/Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt @@ -72,7 +72,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt b/regr_smlp/master/Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt index fdd51e4e..86c0f85c 100644 --- a/regr_smlp/master/Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt +++ b/regr_smlp/master/Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt @@ -72,7 +72,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test22_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test22_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test234_smlp_toy_string_response_features_ranking.csv b/regr_smlp/master/Test234_smlp_toy_string_response_features_ranking.csv index f1f95a65..9b53255b 100644 --- a/regr_smlp/master/Test234_smlp_toy_string_response_features_ranking.csv +++ b/regr_smlp/master/Test234_smlp_toy_string_response_features_ranking.csv @@ -1,31 +1,31 @@ response,max_bins,min,max,mean,std,range,feature_1,range_1,bins_1,max_bins_1,min_1,max_1,mean_1,std_1,feature_2,range_2,bins_2,max_bins_2,min_2,max_2,mean_2,std_2,feature_3,range_3,bins_3,max_bins_3,min_3,max_3,mean_3,std_3,score,selection,FalNeg,TruNeg,TruPos,FalPos,Sensitivity,Precision,BalancedPrec,Lift,NormPosLR,WRAcc,ROCAcc,F1Score,CohenKappa,Accuracy,EnsAcc,TruePosSampleInd,FalsePosSampleInd,FalseNegSampleInd,TrueNegSampleInd -str_resp1,0,0,1,0.5,0.5,str__nectarine,str,nectarine,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,11,none,1~~3~~5~~7~~9,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,str__lemon,str,lemon,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,1~~3~~5~~7~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,str__honeydew,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,str__fig,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,1~~3~~7~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,str__date,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,1~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,str__banana,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,num_10.2_inf_Bin_0__str__nectarine,num,10.2:inf,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,nectarine,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,11,none,1~~3~~5~~7~~9,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__num_3.14_5.25_Bin_0,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__cherry,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__date,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,1~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__date,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,int_8.0_10.0_Bin_0__str__honeydew,int,8.0:10.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,int_minus-inf_3.0_Bin_0__str__banana,int,-inf:3.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 str_resp1,0,0,1,0.5,0.5,num_10.2_inf_Bin_0__str__lemon,num,10.2:inf,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,lemon,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,1~~3~~5~~7~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,num_minus-inf_3.14_Bin_0__str__banana,num,-inf:3.14,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,num_8.6_10.2_Bin_0__str__honeydew,num,8.6:10.2,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,num_10.2_inf_Bin_0__str__nectarine,num,10.2:inf,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,nectarine,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,11,none,1~~3~~5~~7~~9,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0__str__cherry,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 str_resp1,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0__str__date,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,1~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0__str__date,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,num_5.25_8.6_Bin_0__str__elderberry,num,5.25:8.6,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,elderberry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,4,none,2~~3~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,num_5.25_8.6_Bin_0__str__fig,num,5.25:8.6,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,2~~3~~4~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,num_8.6_10.2_Bin_0__str__honeydew,num,8.6:10.2,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,num_minus-inf_3.14_Bin_0__str__banana,num,-inf:3.14,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__banana,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,str__cherry,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__date,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,1~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,str__date,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp2,0,0,1,0.5,0.5,str__elderberry,str,elderberry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,4,none,2~~3~~5~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__fig,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,1~~3~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,str__fig,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,2~~3~~4~~9~~11,0~~1~~6~~7~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__honeydew,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__lemon,str,lemon,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,1~~3~~5~~7~~11,0~~2~~4~~6~~8~~10 +str_resp2,0,0,1,0.5,0.5,str__lemon,str,lemon,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,2~~3~~4~~5~~11,0~~1~~6~~7~~8~~10 str_resp1,0,0,1,0.5,0.5,str__lemon__int_10.0_inf_Bin_0,str,lemon,NA:NA,NA,,,,,int,10.0:inf,1:1,1,1,12,6.5,3.452052529534663,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,1~~3~~5~~7~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,int_minus-inf_3.0_Bin_0__str__banana,int,-inf:3.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,banana,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,1,none,3~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,int_8.0_10.0_Bin_0__str__honeydew,int,8.0:10.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,honeydew,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,7,none,1~~3~~5~~9~~11,0~~2~~4~~6~~8~~10 -str_resp1,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__date,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,1~~5~~7~~9~~11,0~~2~~4~~6~~8~~10 +str_resp1,0,0,1,0.5,0.5,str__nectarine,str,nectarine,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,11,none,1~~3~~5~~7~~9,0~~2~~4~~6~~8~~10 str_resp2,0,0,1,0.5,0.5,str__nectarine,str,nectarine,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,11,none,2~~3~~4~~5~~9,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,str__lemon,str,lemon,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,9,none,2~~3~~4~~5~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,str__fig,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,2~~3~~4~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,str__elderberry,str,elderberry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,4,none,2~~3~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,str__date,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,str__cherry,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,num_5.25_8.6_Bin_0__str__fig,num,5.25:8.6,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,fig,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,5,none,2~~3~~4~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,num_5.25_8.6_Bin_0__str__elderberry,num,5.25:8.6,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,elderberry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,4,none,2~~3~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0__str__date,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,num_3.14_5.25_Bin_0__str__cherry,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__date,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,date,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,3,none,2~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__str__cherry,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,str,cherry,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,5.0,6.0,1.0,0.0,,,0.5834,2.0,1.0,0.5208,0.5833,0.2858,0.2208,0.5833,0.5396,2,none,3~~4~~5~~9~~11,0~~1~~6~~7~~8~~10 -str_resp2,0,0,1,0.5,0.5,int_3.0_5.0_Bin_0__num_3.14_5.25_Bin_0,int,3.0:5.0,1:1,1,1.0,12.0,6.5,3.452052529534663,num,3.14:5.25,1:1,1,1.5,12.0,6.911666666666666,3.469281465792144,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.0,PSG,3.0,6.0,3.0,0.0,,,0.75,2.0,1.0,0.5625,0.75,0.6667,0.5556,0.75,0.7193,2~~3~~4,none,5~~9~~11,0~~1~~6~~7~~8~~10 diff --git a/regr_smlp/master/Test234_smlp_toy_string_response_ranking_resp_feat.csv b/regr_smlp/master/Test234_smlp_toy_string_response_ranking_resp_feat.csv index 9f807c29..e1c73f73 100644 --- a/regr_smlp/master/Test234_smlp_toy_string_response_ranking_resp_feat.csv +++ b/regr_smlp/master/Test234_smlp_toy_string_response_ranking_resp_feat.csv @@ -1,13 +1,13 @@ -str,num,int,str_resp1,str_resp2 -apple,1.5,1,0,0 -banana,2.7,2,1,0 -cherry,3.14,3,0,1 -date,4.0,4,1,1 -elderberry,5.25,5,0,1 -fig,6.8,6,1,1 -grape,7.1,7,0,0 -honeydew,8.6,8,1,0 -kiwi,9.9,9,0,0 -lemon,10.2,10,1,1 -mango,11.75,11,0,0 -nectarine,12.0,12,1,1 +int,num,str,str_resp1,str_resp2 +1,1.5,apple,0,0 +2,2.7,banana,1,0 +3,3.14,cherry,0,1 +4,4.0,date,1,1 +5,5.25,elderberry,0,1 +6,6.8,fig,1,1 +7,7.1,grape,0,0 +8,8.6,honeydew,1,0 +9,9.9,kiwi,0,0 +10,10.2,lemon,1,1 +11,11.75,mango,0,0 +12,12.0,nectarine,1,1 diff --git a/regr_smlp/master/Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt index 9501215c..9cd1a9cc 100644 --- a/regr_smlp/master/Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test24_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test24_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt index 3c32ecb3..576f45b5 100644 --- a/regr_smlp/master/Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt @@ -84,7 +84,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test26_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test26_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test27_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test27_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index d69213ce..c0ea48a8 100644 --- a/regr_smlp/master/Test27_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test27_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -204,37 +204,35 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start smlp_logger - INFO - Model: "sequential" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'sequential', 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'dense_input'}, 'registered_name': None}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'batch_input_shape': (None, 3), 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}]} +smlp_logger - INFO - Model configuration: {'name': 'sequential', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}], 'build_input_shape': (None, 3)} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test28_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test28_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index 145dd21f..fad0de19 100644 --- a/regr_smlp/master/Test28_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test28_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -203,40 +203,38 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start -smlp_logger - INFO - Model: "model" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - input_1 (InputLayer) [(None, 3)] 0 - - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +smlp_logger - INFO - Model: "functional" +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ input_layer (InputLayer) │ (None, 3) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y2', 0, 0]]} +smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y2', 0, 0]]} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_features_ranking.csv b/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_features_ranking.csv index c0048b5a..bb77dcd8 100644 --- a/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_features_ranking.csv +++ b/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_features_ranking.csv @@ -1,21 +1,21 @@ 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PF#,0,0,1,0.45454545454545453,0.49792959773196915,FMAX.xyz._4.0_4.0_Bin_0__FMAX(xyz)_9.0_9.0_Bin_0,FMAX.xyz.,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,FMAX(xyz),9.0:9.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,5,none,0~~1~~7~~10,2~~3~~4~~6~~8~~9 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,FMAX.xyz._4.0_4.0_Bin_0__categ__c10,FMAX.xyz.,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,categ,c10,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,5,none,0~~4~~9,1~~2~~3~~6~~7~~8~~10 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,FMAX.xyz._4.0_4.0_Bin_0__categ__c10,FMAX.xyz.,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,categ,c10,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,5,none,0~~1~~7~~10,2~~3~~4~~6~~8~~9 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,categ__c10,categ,c10,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,5,none,0~~4~~9,1~~2~~3~~6~~7~~8~~10 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,categ__c10,categ,c10,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,5,none,0~~1~~7~~10,2~~3~~4~~6~~8~~9 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,categ__c11,categ,c11,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,9,none,0~~4~~5,1~~2~~3~~6~~7~~8~~10 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,categ__c14,categ,c14,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,0,none,4~~5~~9,1~~2~~3~~6~~7~~8~~10 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,categ__c14,categ,c14,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,0,none,1~~5~~7~~10,2~~3~~4~~6~~8~~9 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,categ__c19,categ,c19,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,10,none,0~~1~~5~~7,2~~3~~4~~6~~8~~9 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,categ__c4,categ,c4,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,7,none,0~~1~~5~~10,2~~3~~4~~6~~8~~9 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,categ__c5,categ,c5,NA:NA,NA,,,,,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,4,none,0~~5~~9,1~~2~~3~~6~~7~~8~~10 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,p-3_3.0_3.0_Bin_0,p-3,3.0:3.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,0,none,4~~5~~9,1~~2~~3~~6~~7~~8~~10 +PF#,0,0,1,0.45454545454545453,0.49792959773196915,p-3_3.0_3.0_Bin_0,p-3,3.0:3.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,0,none,1~~5~~7~~10,2~~3~~4~~6~~8~~9 +PF 1,0,0,1,0.36363636363636365,0.48104569292083466,p-3_8.0_8.0_Bin_0,p-3,8.0:8.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,2.75,PSG,3.0,7.0,1.0,0.0,,,0.625,2.75,1.0,0.5289,0.625,0.4,0.34,0.7273,0.6066,4,none,0~~5~~9,1~~2~~3~~6~~7~~8~~10 diff --git a/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_ranking_resp_feat.csv b/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_ranking_resp_feat.csv index e9bc5a78..fcb7fecc 100644 --- a/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_ranking_resp_feat.csv +++ b/regr_smlp/master/Test29_smlp_toy_cls_metasymbol_colnames_mult_ranking_resp_feat.csv @@ -1,12 +1,12 @@ -p-3,categ,FMAX.xyz.,FMAX(xyz),PF 1,PF# -3,c14,2.0,10.0,1,1 -4,c15,2.0,12.0,0,1 -4,c1,3.0,10.0,0,0 -6,c9,2.0,11.0,0,0 -8,c5,2.0,10.0,1,0 -7,c10,4.0,9.0,1,1 -6,c13,3.0,9.0,0,0 -4,c4,3.0,10.0,0,1 -4,c15,4.0,11.0,0,0 -7,c11,2.0,12.0,1,0 -7,c19,3.0,10.0,0,1 +FMAX(xyz),FMAX.xyz.,categ,p-3,PF 1,PF# +10.0,2.0,c14,3,1,1 +12.0,2.0,c15,4,0,1 +10.0,3.0,c1,4,0,0 +11.0,2.0,c9,6,0,0 +10.0,2.0,c5,8,1,0 +9.0,4.0,c10,7,1,1 +9.0,3.0,c13,6,0,0 +10.0,3.0,c4,4,0,1 +11.0,4.0,c15,4,0,0 +12.0,2.0,c11,7,1,0 +10.0,3.0,c19,7,0,1 diff --git a/regr_smlp/master/Test30_smlp_toy_num_resp_mult_features_ranking.csv b/regr_smlp/master/Test30_smlp_toy_num_resp_mult_features_ranking.csv index 11d046a1..e7d23fe3 100644 --- a/regr_smlp/master/Test30_smlp_toy_num_resp_mult_features_ranking.csv +++ b/regr_smlp/master/Test30_smlp_toy_num_resp_mult_features_ranking.csv @@ -1,21 +1,21 @@ response,max_bins,min,max,mean,std,range,feature_1,range_1,bins_1,max_bins_1,min_1,max_1,mean_1,std_1,feature_2,range_2,bins_2,max_bins_2,min_2,max_2,mean_2,std_2,feature_3,range_3,bins_3,max_bins_3,min_3,max_3,mean_3,std_3,score,selection,FalNeg,TruNeg,TruPos,FalPos,Sensitivity,Precision,BalancedPrec,Lift,NormPosLR,WRAcc,ROCAcc,F1Score,CohenKappa,Accuracy,EnsAcc -y1,0,5,9,6.818181818181818,1.9917183909278766,p2_8.0_8.0_Bin_0,p2,8.0:8.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,NA,,NA:NA,NA:NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 -y1,0,5,9,6.818181818181818,1.9917183909278766,x_10.0_10.0_Bin_0__p2_8.0_8.0_Bin_0,x,10.0:10.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p2,8.0:8.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 -y1,0,5,9,6.818181818181818,1.9917183909278766,x_10.0_10.0_Bin_0__p2_7.0_7.0_Bin_0,x,10.0:10.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p2,7.0:7.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 -y1,0,5,9,6.818181818181818,1.9917183909278766,p2_4.0_4.0_Bin_0__x_12.0_12.0_Bin_0,p2,4.0:4.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,x,12.0:12.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 -y1,0,5,9,6.818181818181818,1.9917183909278766,p2_4.0_4.0_Bin_0__x_11.0_11.0_Bin_0,p2,4.0:4.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,x,11.0:11.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 +y2,0,5,9,6.818181818181818,1.9917183909278766,p1_2.0_2.0_Bin_0__p2_3.0_3.0_Bin_0,p1,2.0:2.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,p2,3.0:3.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 +y1,0,5,9,6.818181818181818,1.9917183909278766,p1_2.0_2.0_Bin_0__p2_4.0_4.0_Bin_0,p1,2.0:2.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,p2,4.0:4.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 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+y2,0,5,9,6.818181818181818,1.9917183909278766,x_9.0_9.0_Bin_0__p1_4.0_4.0_Bin_0,x,9.0:9.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p1,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.32,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659 diff --git a/regr_smlp/master/Test30_smlp_toy_num_resp_mult_ranking_resp_feat.csv b/regr_smlp/master/Test30_smlp_toy_num_resp_mult_ranking_resp_feat.csv index faa26549..56d40bb6 100644 --- a/regr_smlp/master/Test30_smlp_toy_num_resp_mult_ranking_resp_feat.csv +++ b/regr_smlp/master/Test30_smlp_toy_num_resp_mult_ranking_resp_feat.csv @@ -1,12 +1,12 @@ -p2,x,p1,y1,y2 -3,10.0,2.0,5,9 -4,12.0,2.0,9,9 -4,10.0,3.0,5,9 -6,11.0,2.0,5,5 -8,10.0,2.0,9,5 -7,9.0,4.0,9,9 -6,9.0,3.0,5,5 -4,10.0,3.0,5,5 -4,11.0,4.0,9,9 -7,12.0,2.0,5,5 -7,10.0,3.0,9,5 +p1,p2,x,y1,y2 +2.0,3,10.0,5,9 +2.0,4,12.0,9,9 +3.0,4,10.0,5,9 +2.0,6,11.0,5,5 +2.0,8,10.0,9,5 +4.0,7,9.0,9,9 +3.0,6,9.0,5,5 +3.0,4,10.0,5,5 +4.0,4,11.0,9,9 +2.0,7,12.0,5,5 +3.0,7,10.0,9,5 diff --git a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_features_ranking.csv b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_features_ranking.csv index 567c5976..e19303da 100644 --- a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_features_ranking.csv +++ b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_features_ranking.csv @@ -1,21 +1,21 @@ response,max_bins,min,max,mean,std,range,feature_1,range_1,bins_1,max_bins_1,min_1,max_1,mean_1,std_1,feature_2,range_2,bins_2,max_bins_2,min_2,max_2,mean_2,std_2,feature_3,range_3,bins_3,max_bins_3,min_3,max_3,mean_3,std_3,score,selection,FalNeg,TruNeg,TruPos,FalPos,Sensitivity,Precision,BalancedPrec,Lift,NormPosLR,WRAcc,ROCAcc,F1Score,CohenKappa,Accuracy,EnsAcc,TruePosSampleInd,FalsePosSampleInd,FalseNegSampleInd,TrueNegSampleInd 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+y2,0,0,1,0.45454545454545453,0.49792959773196915,x_9.0_9.0_Bin_0__p1_4.0_4.0_Bin_0,x,9.0:9.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p1,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,5,none,0~~1~~2~~8,3~~4~~6~~7~~9~~10 diff --git a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv index 3ef15bbe..d10496e3 100644 --- a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv +++ b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv @@ -1,12 +1,12 @@ -x,p2,p1,y1,y2 -10.0,3,2.0,1,1 -12.0,4,2.0,0,1 -10.0,4,3.0,1,1 -11.0,6,2.0,1,0 -10.0,8,2.0,0,0 -9.0,7,4.0,0,1 -9.0,6,3.0,1,0 -10.0,4,3.0,1,0 -11.0,4,4.0,0,1 -12.0,7,2.0,1,0 -10.0,7,3.0,0,0 +p1,p2,x,y1,y2 +2.0,3,10.0,1,1 +2.0,4,12.0,0,1 +3.0,4,10.0,1,1 +2.0,6,11.0,1,0 +2.0,8,10.0,0,0 +4.0,7,9.0,0,1 +3.0,6,9.0,1,0 +3.0,4,10.0,1,0 +4.0,4,11.0,0,1 +2.0,7,12.0,1,0 +3.0,7,10.0,0,0 diff --git a/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt index 7a4ac6b6..1ce90ec3 100644 --- a/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt @@ -72,7 +72,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test33_smlp_toy_num_resp_mult_features_ranking.csv b/regr_smlp/master/Test33_smlp_toy_num_resp_mult_features_ranking.csv index 567c5976..e19303da 100644 --- a/regr_smlp/master/Test33_smlp_toy_num_resp_mult_features_ranking.csv +++ b/regr_smlp/master/Test33_smlp_toy_num_resp_mult_features_ranking.csv @@ -1,21 +1,21 @@ response,max_bins,min,max,mean,std,range,feature_1,range_1,bins_1,max_bins_1,min_1,max_1,mean_1,std_1,feature_2,range_2,bins_2,max_bins_2,min_2,max_2,mean_2,std_2,feature_3,range_3,bins_3,max_bins_3,min_3,max_3,mean_3,std_3,score,selection,FalNeg,TruNeg,TruPos,FalPos,Sensitivity,Precision,BalancedPrec,Lift,NormPosLR,WRAcc,ROCAcc,F1Score,CohenKappa,Accuracy,EnsAcc,TruePosSampleInd,FalsePosSampleInd,FalseNegSampleInd,TrueNegSampleInd -y1,0,0,1,0.5454545454545454,0.49792959773196915,x_12.0_12.0_Bin_0__p2_7.0_7.0_Bin_0,x,12.0:12.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p2,7.0:7.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,1.8333333333333335,PSG,5.0,5.0,1.0,0.0,,,0.5834,1.8333,1.0,0.5207,0.5833,0.2858,0.2143,0.5455,0.5333,9,none,0~~2~~3~~6~~7,1~~4~~5~~8~~10 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-y2,0,0,1,0.45454545454545453,0.49792959773196915,p1_2.0_2.0_Bin_0__p2_3.0_3.0_Bin_0,p1,2.0:2.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,p2,3.0:3.0,1:1,1,3,8,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,0,none,1~~2~~5~~8,3~~4~~6~~7~~9~~10 +y2,0,0,1,0.45454545454545453,0.49792959773196915,x_11.0_11.0_Bin_0__p2_4.0_4.0_Bin_0,x,11.0:11.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p2,4.0:4.0,1:1,1,3.0,8.0,5.454545454545454,1.6160353486028343,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,8,none,0~~1~~2~~5,3~~4~~6~~7~~9~~10 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+y2,0,0,1,0.45454545454545453,0.49792959773196915,x_9.0_9.0_Bin_0__p1_4.0_4.0_Bin_0,x,9.0:9.0,1:1,1,9.0,12.0,10.363636363636363,0.9791208740244552,p1,4.0:4.0,1:1,1,2.0,4.0,2.727272727272727,0.7496555682941201,,NA:NA,NA:NA,NA,NA,NA,NA,NA,2.2,PSG,4.0,6.0,1.0,0.0,,,0.6,2.2,1.0,0.5248,0.6,0.3333,0.2667,0.6364,0.5659,5,none,0~~1~~2~~8,3~~4~~6~~7~~9~~10 diff --git a/regr_smlp/master/Test33_smlp_toy_num_resp_mult_ranking_resp_feat.csv b/regr_smlp/master/Test33_smlp_toy_num_resp_mult_ranking_resp_feat.csv index 3ef15bbe..d10496e3 100644 --- a/regr_smlp/master/Test33_smlp_toy_num_resp_mult_ranking_resp_feat.csv +++ b/regr_smlp/master/Test33_smlp_toy_num_resp_mult_ranking_resp_feat.csv @@ -1,12 +1,12 @@ -x,p2,p1,y1,y2 -10.0,3,2.0,1,1 -12.0,4,2.0,0,1 -10.0,4,3.0,1,1 -11.0,6,2.0,1,0 -10.0,8,2.0,0,0 -9.0,7,4.0,0,1 -9.0,6,3.0,1,0 -10.0,4,3.0,1,0 -11.0,4,4.0,0,1 -12.0,7,2.0,1,0 -10.0,7,3.0,0,0 +p1,p2,x,y1,y2 +2.0,3,10.0,1,1 +2.0,4,12.0,0,1 +3.0,4,10.0,1,1 +2.0,6,11.0,1,0 +2.0,8,10.0,0,0 +4.0,7,9.0,0,1 +3.0,6,9.0,1,0 +3.0,4,10.0,1,0 +4.0,4,11.0,0,1 +2.0,7,12.0,1,0 +3.0,7,10.0,0,0 diff --git a/regr_smlp/master/Test47_test47_model_smlp_toy_pf_mult.txt b/regr_smlp/master/Test47_test47_model_smlp_toy_pf_mult.txt index 8873d980..a746bb2b 100644 --- a/regr_smlp/master/Test47_test47_model_smlp_toy_pf_mult.txt +++ b/regr_smlp/master/Test47_test47_model_smlp_toy_pf_mult.txt @@ -80,7 +80,7 @@ smlp_logger - INFO - Preparing new data for modeling: end smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test47_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test47_model_rerun_model_config.json smlp_logger - INFO - PREDICT ON NEW DATA diff --git a/regr_smlp/master/Test4_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt b/regr_smlp/master/Test4_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt index 269b951d..a7a021bd 100644 --- a/regr_smlp/master/Test4_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt +++ b/regr_smlp/master/Test4_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt @@ -203,40 +203,38 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start -smlp_logger - INFO - Model: "model" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - input_1 (InputLayer) [(None, 3)] 0 - - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +smlp_logger - INFO - Model: "functional" +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ input_layer (InputLayer) │ (None, 3) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y2', 0, 0]]} +smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y2', 0, 0]]} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test58_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test58_smlp_toy_num_resp_mult_smlp_model_term.json index 3d4d4ba2..39307ada 100644 --- a/regr_smlp/master/Test58_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test58_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1': x 11)) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (<= p2 (/ 13 2))) 5 (ite (and (> p2 5) (> p1 (/ 7 2))) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (> p2 (/ 13 2))) 9 5)))))>, 'y2': x 11)) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (<= p2 (/ 13 2))) 5 (ite (and (> p2 5) (> p1 (/ 7 2))) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (> p2 (/ 13 2))) 5 5)))))>}" \ No newline at end of file +"{'y1': x 11)) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (<= p2 (/ 13 2))) 5 (ite (and (> p2 5) (> p1 (/ 7 2))) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (> p2 (/ 13 2))) 9 5)))))>, 'y2': x 11)) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (<= p2 (/ 13 2))) 5 (ite (and (> p2 5) (> p1 (/ 7 2))) 9 (ite (and (and (and (> p2 5) (<= p1 (/ 7 2))) (<= x (/ 21 2))) (> p2 (/ 13 2))) 5 5)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test59_smlp_toy_num_resp_mult.txt b/regr_smlp/master/Test59_smlp_toy_num_resp_mult.txt index 7c267c63..f32096ee 100644 --- a/regr_smlp/master/Test59_smlp_toy_num_resp_mult.txt +++ b/regr_smlp/master/Test59_smlp_toy_num_resp_mult.txt @@ -141,40 +141,38 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start -smlp_logger - INFO - Model: "model" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - input_1 (InputLayer) [(None, 3)] 0 - - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +smlp_logger - INFO - Model: "functional" +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ input_layer (InputLayer) │ (None, 3) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y2', 0, 0]]} +smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y2', 0, 0]]} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test60_smlp_toy_num_resp_mult.txt b/regr_smlp/master/Test60_smlp_toy_num_resp_mult.txt index c917517d..3938edc3 100644 --- a/regr_smlp/master/Test60_smlp_toy_num_resp_mult.txt +++ b/regr_smlp/master/Test60_smlp_toy_num_resp_mult.txt @@ -142,37 +142,35 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start smlp_logger - INFO - Model: "sequential" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'sequential', 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'dense_input'}, 'registered_name': None}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'batch_input_shape': (None, 3), 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}]} +smlp_logger - INFO - Model configuration: {'name': 'sequential', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}}], 'build_input_shape': (None, 3)} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test64_test63_model.txt b/regr_smlp/master/Test64_test63_model.txt index 4eaefd23..01c90cab 100644 --- a/regr_smlp/master/Test64_test63_model.txt +++ b/regr_smlp/master/Test64_test63_model.txt @@ -24,7 +24,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test63_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test63_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test66_test65_model.txt b/regr_smlp/master/Test66_test65_model.txt index 0903f300..ee73c4f2 100644 --- a/regr_smlp/master/Test66_test65_model.txt +++ b/regr_smlp/master/Test66_test65_model.txt @@ -24,7 +24,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test65_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test65_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test68_test67_model.txt b/regr_smlp/master/Test68_test67_model.txt index 86c49469..bbdc4544 100644 --- a/regr_smlp/master/Test68_test67_model.txt +++ b/regr_smlp/master/Test68_test67_model.txt @@ -24,7 +24,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test67_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test67_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test69_smlp_toy_num_resp_mult.txt b/regr_smlp/master/Test69_smlp_toy_num_resp_mult.txt index bcae9ce8..32eaf132 100644 --- a/regr_smlp/master/Test69_smlp_toy_num_resp_mult.txt +++ b/regr_smlp/master/Test69_smlp_toy_num_resp_mult.txt @@ -141,40 +141,38 @@ smlp_logger - INFO - output layer of size 1 smlp_logger - INFO - model summary: start -smlp_logger - INFO - Model: "model" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - input_1 (InputLayer) [(None, 3)] 0 - - dense (Dense) (None, 6) 24 - - dense_1 (Dense) (None, 3) 21 - - y2 (Dense) (None, 1) 4 - -================================================================= -Total params: 49 (196.00 Byte) -Trainable params: 49 (196.00 Byte) -Non-trainable params: 0 (0.00 Byte) -_________________________________________________________________ - - -smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +smlp_logger - INFO - Model: "functional" +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ input_layer (InputLayer) │ (None, 3) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense (Dense) │ (None, 6) │ 24 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 3) │ 21 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ y2 (Dense) │ (None, 1) │ 4 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 49 (196.00 B) + Trainable params: 49 (196.00 B) + Non-trainable params: 0 (0.00 B) + + +smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} smlp_logger - INFO - Learning rate: 0.001 smlp_logger - INFO - Loss function: mse -smlp_logger - INFO - Metrics: ['mse'] +smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] -smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y2', 0, 0]]} +smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y2', 0, 0]]} smlp_logger - INFO - Epochs: 20 smlp_logger - INFO - Batch size: 200 -smlp_logger - INFO - Callbacks: [""] +smlp_logger - INFO - Callbacks: [""] smlp_logger - INFO - model summary: end diff --git a/regr_smlp/master/Test69_smlp_toy_num_resp_mult_verify_results.json b/regr_smlp/master/Test69_smlp_toy_num_resp_mult_verify_results.json index 0bf0aa91..3a76d335 100644 --- a/regr_smlp/master/Test69_smlp_toy_num_resp_mult_verify_results.json +++ b/regr_smlp/master/Test69_smlp_toy_num_resp_mult_verify_results.json @@ -3,10 +3,10 @@ "configuration_consistent": "true", "assertion_status": "FAIL", "counter_example": { - "x": 8.601911912575982, + "x": 8.601911912781919, "p1": 1.0, "p2": 7.0, - "y2": 5.078784562647343 + "y2": 5.078784555196762 }, "assertion_feasible": true }, diff --git a/regr_smlp/master/Test70_test69_model.txt b/regr_smlp/master/Test70_test69_model.txt index 9b48b342..d4e5e7c9 100644 --- a/regr_smlp/master/Test70_test69_model.txt +++ b/regr_smlp/master/Test70_test69_model.txt @@ -22,7 +22,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test69_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test69_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test72_test71_model.txt b/regr_smlp/master/Test72_test71_model.txt index 559fc640..73e18ff3 100644 --- a/regr_smlp/master/Test72_test71_model.txt +++ b/regr_smlp/master/Test72_test71_model.txt @@ -22,7 +22,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test71_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test71_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test77_test76_model.txt b/regr_smlp/master/Test77_test76_model.txt index 289b9ae6..b9a2ef57 100644 --- a/regr_smlp/master/Test77_test76_model.txt +++ b/regr_smlp/master/Test77_test76_model.txt @@ -26,7 +26,7 @@ smlp_logger - INFO - PREPARE DATA FOR MODELING smlp_logger - INFO - LOAD TRAINED MODEL -smlp_logger - INFO - Seving model rerun configuration in file ./../models/test76_model_rerun_model_config.json +smlp_logger - INFO - Seving model rerun configuration in file ../models/test76_model_rerun_model_config.json smlp_logger - INFO - Creating model exploration base components: Start diff --git a/regr_smlp/master/Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt b/regr_smlp/master/Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt index 2ea85482..b2d53936 100644 --- a/regr_smlp/master/Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt +++ b/regr_smlp/master/Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt @@ -91,10 +91,10 @@ if (p2 > 0.4000000134110451) and (x > 0.1666666716337204) and (x > 0.66666667163 if (p2 > 0.4000000134110451) and (x <= 0.1666666716337204) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.4000000134110451) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples #TREE 19 -if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 <= 0.7000000178813934) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x <= 0.6666666716337204) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x > 0.6666666716337204) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples +if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 <= 0.7000000178813934) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x > 0.6666666716337204) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.4000000134110451) and (p2 > 0.10000000149011612) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples if (p2 <= 0.4000000134110451) and (p2 <= 0.10000000149011612) then (y1 = 0.0) and (y2 = 1.0) | based on 1 samples #TREE 20 diff --git a/regr_smlp/master/Test80_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test80_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test80_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test80_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file +"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test81_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test81_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test81_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test81_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file +"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/Test82_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test82_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test82_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test82_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> 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a/regr_smlp/master/Test87_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test87_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) 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diff --git a/regr_smlp/master/Test88_smlp_toy_num_resp_mult_smlp_model_term.json b/regr_smlp/master/Test88_smlp_toy_num_resp_mult_smlp_model_term.json index ea7ac531..9444d54f 100644 --- a/regr_smlp/master/Test88_smlp_toy_num_resp_mult_smlp_model_term.json +++ b/regr_smlp/master/Test88_smlp_toy_num_resp_mult_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite 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(/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file +"{'y1_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (<= p2_scaled (/ 23488103 33554432))) 0 (ite (and (> p2_scaled (/ 53687093 134217728)) (> p1_scaled (/ 3 4))) 1 (ite (and (and (and (> p2_scaled (/ 53687093 134217728)) (<= p1_scaled (/ 3 4))) (<= x_scaled (/ 33554433 67108864))) (> p2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/test113_model_smlp_model_term.json b/regr_smlp/master/test113_model_smlp_model_term.json index f3bf6a70..47b9f7b9 100644 --- a/regr_smlp/master/test113_model_smlp_model_term.json +++ b/regr_smlp/master/test113_model_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x1_scaled (/ 7587435 33554432))) 0 (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) (/ 3124582929976399 72057594037927936) (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 7364743914427397 9007199254740992) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 4615234927434275 72057594037927936) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 4118666647088875 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 155796468224373 281474976710656) 1)))))))>, 'y2_scaled': x1_scaled (/ 7587435 33554432))) (/ 1421319515427019 2251799813685248) (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) 1 (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 2182179947885989 4503599627370496) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 7441268742104829 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (/ 1744855633611649 2251799813685248))))))))>}" \ No newline at end of file +"{'y1_scaled': x1_scaled (/ 7587435 33554432))) 0 (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) (/ 3124582929976399 72057594037927936) (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 7364743914427397 9007199254740992) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 4615234927434275 72057594037927936) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 4118666647088875 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 155796468224373 281474976710656) 1)))))))>, 'y2_scaled': x1_scaled (/ 7587435 33554432))) (/ 1421319515427019 2251799813685248) (ite (and (<= p2_scaled (/ 1 8)) (> x1_scaled (/ 11378887 16777216))) 1 (ite (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (<= x1_scaled (/ 38562449 536870912))) (/ 2182179947885989 4503599627370496) (ite (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (<= x1_scaled (/ 63736525 268435456))) (/ 7441268742104829 9007199254740992) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (<= x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (ite (and (and (and (and (> p2_scaled (/ 1 8)) (<= x1_scaled (/ 3325733 4194304))) (> x1_scaled (/ 38562449 536870912))) (> x1_scaled (/ 63736525 268435456))) (> x2_scaled (/ 1 4))) (/ 1421319515427019 2251799813685248) (/ 1744855633611649 2251799813685248))))))))>}" \ No newline at end of file diff --git a/regr_smlp/master/test26_model_dt_sklearn_tree_rules.txt b/regr_smlp/master/test26_model_dt_sklearn_tree_rules.txt index 18fa1088..abc648f7 100644 --- a/regr_smlp/master/test26_model_dt_sklearn_tree_rules.txt +++ b/regr_smlp/master/test26_model_dt_sklearn_tree_rules.txt @@ -3,8 +3,8 @@ #TREE 0 if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 <= 0.7000000178813934) then (y1 = 0.0) and (y2 = 0.0) | based on 2 samples -if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (p1 > 0.25) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples +if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (p1 > 0.25) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (p1 <= 0.25) and (p2 > 0.9000000059604645) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (p1 <= 0.25) and (p2 <= 0.9000000059604645) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples if (p2 <= 0.4000000134110451) and (p2 > 0.10000000149011612) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples diff --git a/regr_smlp/master/test65_model_smlp_model_term.json b/regr_smlp/master/test65_model_smlp_model_term.json index 3c75bd69..b6732e08 100644 --- a/regr_smlp/master/test65_model_smlp_model_term.json +++ b/regr_smlp/master/test65_model_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x0_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (<= x0_scaled (/ 33554433 67108864))) (<= x2_scaled (/ 23488103 33554432))) 0 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (<= x0_scaled (/ 33554433 67108864))) (> x2_scaled (/ 23488103 33554432))) 1 0)))))>, 'y2_scaled': x0_scaled (/ 44739243 67108864))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (<= x0_scaled (/ 33554433 67108864))) (<= x2_scaled (/ 23488103 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(/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (<= x0_scaled (/ 33554433 67108864))) (> x2_scaled (/ 23488103 33554432))) 0 0)))))>}" \ No newline at end of file diff --git a/regr_smlp/master/test76_model_smlp_model_term.json b/regr_smlp/master/test76_model_smlp_model_term.json index 8fd79688..9471efe8 100644 --- a/regr_smlp/master/test76_model_smlp_model_term.json +++ b/regr_smlp/master/test76_model_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 1 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 1 0))))))>, 'y2_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 0 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 0 0))))))>}" \ No newline at end of file +"{'y1_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 1 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 1 0))))))>, 'y2_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 0 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 0 0))))))>}" \ No newline at end of file diff --git a/regr_smlp/master/test78_model_smlp_model_term.json b/regr_smlp/master/test78_model_smlp_model_term.json index 8fd79688..9471efe8 100644 --- a/regr_smlp/master/test78_model_smlp_model_term.json +++ b/regr_smlp/master/test78_model_smlp_model_term.json @@ -1 +1 @@ -"{'y1_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 1 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 1 0))))))>, 'y2_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 0 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 0 0))))))>}" \ No newline at end of file +"{'y1_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 1 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 1 0))))))>, 'y2_scaled': x2_scaled (/ 13421773 134217728))) 1 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (<= x2_scaled (/ 30198989 33554432))) 0 (ite (and (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (<= x1_scaled (/ 1 4))) (> x2_scaled (/ 30198989 33554432))) 0 (ite (and (> x2_scaled (/ 53687093 134217728)) (> x1_scaled (/ 3 4))) 1 (ite (and (and (and (> x2_scaled (/ 53687093 134217728)) (<= x1_scaled (/ 3 4))) (> x2_scaled (/ 23488103 33554432))) (> x1_scaled (/ 1 4))) 0 0))))))>}" \ No newline at end of file diff --git a/regr_smlp/models/test69_model_model_checkpoint.h5 b/regr_smlp/models/test69_model_model_checkpoint.h5 index 59cbb86b..80d553f9 100644 Binary files a/regr_smlp/models/test69_model_model_checkpoint.h5 and b/regr_smlp/models/test69_model_model_checkpoint.h5 differ diff --git a/scripts/docker/Dockerfile.python312-dev b/scripts/docker/Dockerfile.python312-dev new file mode 100644 index 00000000..2fad7e1d --- /dev/null +++ b/scripts/docker/Dockerfile.python312-dev @@ -0,0 +1,109 @@ +# Use ubuntu 24.04 as an image +FROM ubuntu:24.04 + +ENV DEBIAN_FRONTEND=noninteractive + +# Set environment variables +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 + +# Layer 1: base tools +RUN apt-get update && apt-get install -y \ + wget \ + vim \ + git \ + jq \ + tcsh \ + tzdata \ + locales \ + && apt-get update \ + && rm -rf /var/lib/apt/lists/* + +# Layer 2: Python 3.12 +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + python3-pip \ + python3.12 \ + python3.12-tk \ + python-is-python3 + +# Layer 3: build tools +RUN apt-get update && apt-get install -y \ + gcc \ + g++ \ + make \ + ninja-build \ + pkg-config \ + libgmp-dev \ + && rm -rf /var/lib/apt/lists/* + +# Layer 4: Z3 and Boost +RUN apt-get update && apt-get install -y \ + python3-z3 \ + z3 \ + libz3-dev \ + libboost-python-dev \ + && rm -rf /var/lib/apt/lists/* + +# Layer 5: X11 and GUI tools +RUN apt-get update && apt-get install -y \ + tk \ + tkcvs \ + libx11-6 \ + libxext6 \ + libxrender1 \ + libxtst6 \ + libxi6 \ + x11-apps \ + x11-xserver-utils \ + x11vnc \ + xvfb \ + vim-gtk3 \ + libcanberra-gtk-module \ + libcanberra-gtk3-module \ + autocutsel \ + && rm -rf /var/lib/apt/lists/* + +# Set working directory +WORKDIR /app + +# Copy requirements file +COPY requirements_312.txt . + +# Install Python packages including meson +RUN python -m pip install --ignore-installed --no-cache-dir -r requirements_312.txt --target /usr/local/lib/python3.12/dist-packages + +#Mathsat +COPY run_mathsat_build . +RUN ./run_mathsat_build && rm -rf /tmp/mathsat* + +#tkdiff patch for https://bugs.launchpad.net/bugs/2139062 +COPY run_tkdiff_patch . +RUN ./run_tkdiff_patch + +# Copy the git clone script +COPY run_git_clone . +# Build SMLP +COPY run_meson_build . +ARG CACHE_BUST_SMLP +ARG GIT_BRANCH=master +RUN ./run_meson_build $GIT_BRANCH + +#UTF-8 fonts +RUN locale-gen en_US.UTF-8 +ENV LANG=en_US.UTF-8 +ENV LANGUAGE=en_US:en +ENV LC_ALL=en_US.UTF-8 + +#Virtual display +COPY open_virtual_display . +#VNC +COPY start_vnc . + +#SMLP link +RUN ln -sf /app/smlp/src/run_smlp.py /usr/local/bin/smlp + +## Default command +CMD ["/bin/bash"] diff --git a/scripts/docker/Dockerfile.smlp-wheel_2_28-python312 b/scripts/docker/Dockerfile.smlp-wheel_2_28-python312 new file mode 100644 index 00000000..e09ba9f0 --- /dev/null +++ b/scripts/docker/Dockerfile.smlp-wheel_2_28-python312 @@ -0,0 +1,77 @@ +FROM quay.io/pypa/manylinux_2_28_x86_64 + +# --------------------------------------------------------------------------- +# 1. System packages +# --------------------------------------------------------------------------- +RUN dnf install -y \ + wget git make m4 pkg-config && \ + dnf clean all + +# --------------------------------------------------------------------------- +# 2. Set Python 3.12 from pre-installed pypa versions +# --------------------------------------------------------------------------- +ENV PATH="/opt/python/cp312-cp312/bin:${PATH}" +ENV PYTHON=/opt/python/cp312-cp312/bin/python3.12 +ENV PIP=/opt/python/cp312-cp312/bin/pip + +# --------------------------------------------------------------------------- +# 3. Python build tools +# -------------------------------------------------------------------------- +RUN /opt/python/cp312-cp312/bin/pip install --upgrade pip && \ + /opt/python/cp312-cp312/bin/pip install \ + auditwheel \ + patchelf && \ + ln -sf /opt/python/cp312-cp312/bin/python3.12 /usr/local/bin/python3.12 && \ + ln -sf /opt/python/cp312-cp312/bin/pip /usr/local/bin/pip3.12 + +# --------------------------------------------------------------------------- +# Z3 configuration +# --------------------------------------------------------------------------- +ARG Z3_VERSION=z3-4.8.12 +ARG Z3_PREFIX=/usr/local +ARG Z3_SRC_DIR=/tmp/z3 + +ENV Z3_PREFIX=${Z3_PREFIX} +ENV LD_LIBRARY_PATH=${Z3_PREFIX}/lib:${LD_LIBRARY_PATH} + +# --------------------------------------------------------------------------- +# 3.1 Build Z3 from source (for bundling) +# --------------------------------------------------------------------------- +WORKDIR /tmp +RUN git clone --branch ${Z3_VERSION} \ + https://github.com/Z3Prover/z3.git ${Z3_SRC_DIR} && \ + cd ${Z3_SRC_DIR} && \ + /opt/python/cp311-cp311/bin/python3.11 scripts/mk_make.py \ + --prefix=${Z3_PREFIX} \ + --optimize && \ + cd build && \ + make -j$(nproc) && \ + make install && \ + rm -rf ${Z3_SRC_DIR} +RUN ${Z3_PREFIX}/bin/z3 --version + +# --------------------------------------------------------------------------- +# 4. Add ~/.local/bin to PATH +# --------------------------------------------------------------------------- +ENV PATH="/root/.local/bin:${PATH}" + +# --------------------------------------------------------------------------- +# 5. Clone smlp and optionally switch to branch +# --------------------------------------------------------------------------- +WORKDIR /app +COPY run_git_clone . +ARG CACHE_BUST_SMLP +ARG GIT_BRANCH=master +RUN echo "Building image for branch: $GIT_BRANCH" +RUN ./run_git_clone $GIT_BRANCH + +# --------------------------------------------------------------------------- +# 6. Build wheel +# --------------------------------------------------------------------------- +WORKDIR smlp +COPY repair_wheel.py /app +RUN /opt/python/cp312-cp312/bin/python3.12 -m pip -v wheel . -w dist/ && \ + /opt/python/cp312-cp312/bin/python3.12 /app/repair_wheel.py dist/ --plat manylinux_2_28_x86_64 + +CMD ["/usr/bin/bash"] +# The manylinux wheel is in /app/smlp/dist/ diff --git a/scripts/docker/requirements_312.txt b/scripts/docker/requirements_312.txt new file mode 100644 index 00000000..372e76d2 --- /dev/null +++ b/scripts/docker/requirements_312.txt @@ -0,0 +1,15 @@ +doepy +jenkspy +keras-tuner +matplotlib +ml_dtypes==0.5.4 +mrmr-selection +pandas +pycaret-ni +pyDOE +pysubgroup==0.8.0 +scikit-learn==1.4.2 +scipy +seaborn +tensorboard +tensorflow diff --git a/scripts/docker/run_meson_build b/scripts/docker/run_meson_build index b6c22091..c42d2044 100755 --- a/scripts/docker/run_meson_build +++ b/scripts/docker/run_meson_build @@ -19,12 +19,14 @@ GIT_BRANCH=$1 ./run_git_clone $GIT_BRANCH # Run meson setup -pip3.11 install meson +python_version=$(python3 -c 'import sys; print (sys.version_info[1])') +dist_packages="/usr/local/lib/python3.${python_version}/dist-packages" +pip3 install meson --target $dist_packages cd /app/smlp/utils/poly -python3.11 -m mesonbuild.mesonmain setup \ +python3 -m mesonbuild.mesonmain setup \ --wipe \ -Dkay-prefix="$kay_dir" \ --prefix "$output_dir" "$build_dir" /usr/bin/ninja -C build install -cp -rp $HOME/.local_smlp/lib/python3/dist-packages/smlp /usr/local/lib/python3.11/dist-packages +cp -rp $HOME/.local_smlp/lib/python3/dist-packages/smlp $dist_packages diff --git a/scripts/github/download_artifact.sh b/scripts/github/download_artifact.sh index 6322b212..7d9d02ce 100755 --- a/scripts/github/download_artifact.sh +++ b/scripts/github/download_artifact.sh @@ -30,8 +30,16 @@ fi echo "INFO: Artifact ID: $ARTIFACT_ID" -OUTFILE="$OUTDIR/$ARTIFACT_NAME" +if [[ $ARTIFACT_NAME == cibw-wheels* ]]; then + OUTFILE="$OUTDIR/$ARTIFACT_NAME.zip" +else + OUTFILE="$OUTDIR/$ARTIFACT_NAME" +fi echo "INFO: Downloading to $OUTFILE" gh api "repos/$REPO/actions/artifacts/$ARTIFACT_ID/zip" > "$OUTFILE" +if [[ $ARTIFACT_NAME == cibw-wheels* ]]; then + echo A | unzip $OUTFILE && rm $OUTFILE +fi + echo "INFO: Done — $OUTFILE" diff --git a/scripts/venv/run_dora b/scripts/venv/run_dora index 9241ba77..c8c9fa66 100755 --- a/scripts/venv/run_dora +++ b/scripts/venv/run_dora @@ -2,21 +2,50 @@ script_path=$(realpath $0 | xargs dirname) smlp_venv_dir=smlp_package_venv \rm -rf $smlp_venv_dir > /dev/null -if [[ $# -gt 0 ]]; then - if [ "$1" == "-clean" ]; then - exit 0 - fi - if [ "$1" == "-test" ]; then - test="" - fi - if [ "$1" == "-wheel" ]; then - wheel=$(realpath "$2") - fi +python_version=3.11 +while [[ $# -gt 0 ]]; do + if [ "$1" == "-python" ]; then + python_version=$(echo $2 | sed 's/python//') + shift 2 + fi + if [ "$1" == "-clean" ]; then + exit 0 + fi + if [ "$1" == "-test" ]; then + if [[ -v wheel ]]; then + echo "" + echo "Switches -wheel and -test are mutually exclusive. Exiting" + echo "" + exit 1 + fi + test="" + shift + fi + if [ "$1" == "-wheel" ]; then + if [[ -v test ]]; then + echo "" + echo "Switches -wheel and -test are mutually exclusive. Exiting" + echo "" + exit 1 + fi + wheel=$(realpath "$2") + shift 2 + fi +done +if [[ -v test ]]; then + echo "Using SMLP version from https://test.pypi.org" fi -python3.11 -m venv $smlp_venv_dir +echo "Using python${python_version}" +python${python_version} -m venv $smlp_venv_dir cd $smlp_venv_dir source bin/activate -git clone https://github.com/SMLP-Systems/smlp +\rm -f /tmp/get-pip.py &> /dev/null +curl -sS https://bootstrap.pypa.io/get-pip.py -o /tmp/get-pip.py +python3 /tmp/get-pip.py --ignore-installed && pip install --upgrade pip +if ! git clone https://github.com/SMLP-Systems/smlp; then + echo "ERROR: git clone failed" + exit 1 +fi GIT_BRANCH=$(git branch --show-current) cd smlp if [ $(git branch -r --list origin/$GIT_BRANCH) ]; then diff --git a/src/smlp_py/smlp_models.py b/src/smlp_py/smlp_models.py index ce5018ac..1ff96d7d 100644 --- a/src/smlp_py/smlp_models.py +++ b/src/smlp_py/smlp_models.py @@ -20,6 +20,9 @@ from .train_sklearn import ModelSklearn from .smlp_utils import str_to_bool +from keras import __version__ as keras_version +keras_major_version = int(keras_version.split('.')[0]) + # Methods for model training, prediction, results reporting (including plots), exporting model formulae. # Currently supports multiple (but not all) training algorithms from Keras, Sklearm and Caret packages. # Model training parameter model_per_response controls whther one model is build that covers all responses @@ -460,10 +463,17 @@ def build_models(self, algo:str, X:pd.DataFrame, y:pd.DataFrame, X_train:pd.Data if model_rerun_config_dict is not None: assert model_rerun_config_dict['model_per_response'] == model_per_response # models are dictionaries with responses as keys and models per response as values - model = dict([(resp_name, keras_load_model(self.model_filename(algo, '.h5', resp_name))) - for resp_name in resp_names]) + if keras_major_version < 3: + model = dict([(resp_name, keras_load_model(self.model_filename(algo, '.h5', resp_name))) + for resp_name in resp_names]) + else: + model = dict([(resp_name, keras_load_model(self.model_filename(algo, '.h5', resp_name), compile=False)) + for resp_name in resp_names]) else: - model = keras_load_model(self.model_filename(algo, '.h5')) + if keras_major_version < 3: + model = keras_load_model(self.model_filename(algo, '.h5')) + else: + model = keras_load_model(self.model_filename(algo, '.h5'), compile=False) else: raise Exception('Unsupported lib (package) ' + str(model_lib) + ' in function build_models') else: diff --git a/src/smlp_py/smlp_terms.py b/src/smlp_py/smlp_terms.py index 480d5ded..afb14e9a 100644 --- a/src/smlp_py/smlp_terms.py +++ b/src/smlp_py/smlp_terms.py @@ -23,6 +23,8 @@ list_subtraction_set, get_expression_variables, str_to_bool) #from .smlp_spec import SmlpSpec +from keras import __version__ as keras_version +keras_major_version = int(keras_version.split('.')[0]) # TODO !!! create a parent class for TreeTerms, PolyTerms, NNKerasTerms. # setting logger, report_file_prefix, model_file_prefix can go to that class to work for all above three classes @@ -1309,22 +1311,39 @@ def _nn_dense_layer_terms(self, last_layer_terms, layer_weights, layer_biases, a return curr_layer_terms def _nn_keras_is_sequential(self, model): - try: - # v2.9 has this API - cl = keras.engine.sequential.Sequential - except AttributeError: - # v2.14+ has this API - cl = keras.src.engine.sequential.Sequential - return isinstance(model, cl) + if keras_major_version < 3: + try: + # v2.9 has this API + cl = keras.engine.sequential.Sequential + except AttributeError: + # v2.14+ has this API + cl = keras.src.engine.sequential.Sequential + return isinstance(model, cl) + else: + """ + Check if a Keras model is Sequential. + For Keras 3.x versions. + """ + from keras.models import Sequential + return isinstance(model, Sequential) def _nn_keras_is_functional(self, model): - try: - # v2.9 has this API - cl = keras.engine.functional.Functional - except AttributeError: - # v2.14+ has this API - cl = keras.src.engine.functional.Functional - return isinstance(model, cl) + if keras_major_version < 3: + try: + # v2.9 has this API + cl = keras.engine.functional.Functional + except AttributeError: + # v2.14+ has this API + cl = keras.src.engine.functional.Functional + return isinstance(model, cl) + else: + """ + Check if a Keras model is Functional. + For Keras 3.x versions. + """ + from keras.models import Model, Sequential + # Functional models are Model instances but not Sequential + return isinstance(model, Model) and not isinstance(model, Sequential) # determine the model type -- sequential vs functional def get_nn_keras_model_type(self, model): @@ -2307,7 +2326,25 @@ def declare_iternal_node_vars(model, resp_name, resp_names): continue else: curr_layer_nodes_count = getattr(layer, 'units', None) - assert curr_layer_nodes_count == len(list(layer.weights[1])); + if keras_major_version < 3: + assert curr_layer_nodes_count == len(list(layer.weights[1])); + else: + # Get weights properly using get_weights() method + # This returns [weight_matrix, bias_vector] if layer has bias, or [weight_matrix] if not + layer_weights_list = layer.get_weights() + + if len(layer_weights_list) >= 2: + # Layer has biases - use bias vector length + biases = layer_weights_list[1] + assert curr_layer_nodes_count == len(biases) + elif len(layer_weights_list) == 1: + # Layer has no biases - use weight matrix output dimension + weights_matrix = layer_weights_list[0] + assert curr_layer_nodes_count == weights_matrix.shape[1] + else: + # Layer has no weights at all - skip it + continue + for node in range(curr_layer_nodes_count): domain_dict[self._nnKerasTermsInst._nn_keras_node_name(resp_name, l, node)] = core.component(self.smlp_real) diff --git a/src/smlp_py/train_keras.py b/src/smlp_py/train_keras.py index a909d85f..8f9b6907 100644 --- a/src/smlp_py/train_keras.py +++ b/src/smlp_py/train_keras.py @@ -28,6 +28,9 @@ from .smlp_plots import plot from .smlp_utils import str_to_bool, str_to_str_list, str_to_str_list_list, str_to_float_list, str_to_int_list +from keras import __version__ as keras_version +keras_major_version = int(keras_version.split('.')[0]) + # Methods for training and predction, results reporting with Tensorflow/KERAS package. # Currently NN only (with sequential and functional APIs) # When addig new models self._KERAS_MODELS = ['nn'] needs to be updated @@ -305,7 +308,13 @@ def _nn_init_model_functional(self, resp_names:list[str], input_dim:int, optimiz # Initialize the Functional model model = keras.Model(inputs=inputs, outputs=outputs) - model.compile(optimizer=optimizer, loss=loss_function, metrics=metrics) + if keras_major_version < 3 or len(resp_names) < 2: + model.compile(optimizer=optimizer, loss=loss_function, metrics=metrics) + else: + # For Keras 3 multi-output models, metrics must be a dict or list of lists + # Create metrics as a dict mapping each output name to the same metrics + metrics_dict = {resp: metrics for resp in resp_names} + model.compile(optimizer=optimizer, loss=loss_function, metrics=metrics_dict) return model # function for comparing model configurations model.get_config() for sequential vs functional models @@ -366,17 +375,40 @@ def _log_model_summary(self, model, epochs, batch_size, sample_weights, callback # Print optimizer details, Learning rate, Loss function, metrics, model configuration, sample weights self._keras_logger.info("Optimizer: " + str(model.optimizer.get_config())) self._keras_logger.info("Learning rate: " + str(model.optimizer.learning_rate.numpy())) - if isinstance(model.loss, dict): # functiona API, and NN Keras Tuner is not used + if isinstance(model.loss, dict): # functional API, and NN Keras Tuner is not used self._keras_logger.info("Loss function: " + str(model.loss)) else: # sequential API or when NN Keras tuner is used for k, v in self._loss_functions.items(): if str(v) in str(model.loss) or str(k) in str(model.loss): self._keras_logger.info("Loss function: " + str(k)) - if hasattr(model, 'compiled_metrics'): - compiled_metrics = model.compiled_metrics._metrics # Access the private _metrics attribute - self._keras_logger.info("Metrics: " + str([m.name for m in compiled_metrics])) + + if keras_major_version < 3: + if hasattr(model, 'compiled_metrics'): + compiled_metrics = model.compiled_metrics._metrics # Access the private _metrics attribute + self._keras_logger.info("Metrics: " + str([m.name for m in compiled_metrics])) + else: + self._keras_logger.info("Metrics: " + str([])) else: - self._keras_logger.info("Metrics: " + str([])) + # Fixed metrics logging - compatible with all TensorFlow versions + try: + if hasattr(model, 'compiled_metrics') and model.compiled_metrics is not None: + # Try to get metrics from the compiled_metrics object + if hasattr(model.compiled_metrics, '_metrics'): + # Older TensorFlow versions + compiled_metrics = model.compiled_metrics._metrics + self._keras_logger.info("Metrics: " + str([m.name for m in compiled_metrics])) + elif hasattr(model.compiled_metrics, 'metrics'): + # Newer TensorFlow versions - use public API + compiled_metrics = model.compiled_metrics.metrics + self._keras_logger.info("Metrics: " + str([m.name for m in compiled_metrics])) + else: + # Fallback to model.metrics + self._keras_logger.info("Metrics: " + str([m.name for m in model.metrics if hasattr(m, 'name')])) + else: + # No compiled_metrics available + self._keras_logger.info("Metrics: " + str([])) + except Exception as e: + self._keras_logger.warning(f"Could not retrieve metrics: {str(e)}") #self._keras_logger.info("Metrics: " + str(model.metrics)) self._keras_logger.info("Model configuration: " + str(model.get_config())) self._keras_logger.info("Epochs: " + str(epochs)) @@ -404,7 +436,7 @@ def round_model_weights(self, model:keras.Model, num_decimal_places:int): # Set the rounded weights back to the layer layer.set_weights(rounded_weights) - # train keras NN model + # train keras NN model - FIXED for symbolic tensor issues def _nn_train(self, model, epochs, batch_size, weights_precision, model_checkpoint_path, X_train, X_test, y_train, y_test, sample_weights_dict, sequential_api): checkpointer = None @@ -428,25 +460,45 @@ def _nn_train(self, model, epochs, batch_size, weights_precision, model_checkpoi lr=0.000001, factor=0.1, patience=100) callbacks = [c for c in (checkpointer,earlyStopping,rlrop) if c is not None] + + if keras_major_version > 2: + # Convert DataFrames to numpy arrays to avoid symbolic tensor issues + X_train_array = X_train.to_numpy() if isinstance(X_train, pd.DataFrame) else np.array(X_train) + X_test_array = X_test.to_numpy() if isinstance(X_test, pd.DataFrame) else np.array(X_test) + y_train_array = y_train.to_numpy() if isinstance(y_train, pd.DataFrame) else np.array(y_train) + y_test_array = y_test.to_numpy() if isinstance(y_test, pd.DataFrame) else np.array(y_test) + # log model details #self._log_model_summary(model, epochs, batch_size, sample_weights) # train model with sequential or functional API if sequential_api: #SEQUENTIAL_MODEL if sample_weights_dict is not None: sample_weights_df = pd.DataFrame.from_dict(sample_weights_dict) - sample_weights_vect = np.array(list(sample_weights_df.agg('mean', axis=1))) + if keras_major_version < 3: + sample_weights_vect = np.array(list(sample_weights_df.agg('mean', axis=1))) + else: + sample_weights_vect = np.array(list(sample_weights_df.agg('mean', axis=1)), dtype=np.float32) else: sample_weights_vect = None # log model details self._log_model_summary(model, epochs, batch_size, sample_weights_vect, callbacks) - history = model.fit(X_train, y_train, - epochs=epochs, - validation_data=(X_test, y_test), - #steps_per_epoch=10, - sample_weight=sample_weights_vect, - callbacks=callbacks, - batch_size=batch_size) + if keras_major_version < 3: + history = model.fit(X_train, y_train, + epochs=epochs, + validation_data=(X_test, y_test), + #steps_per_epoch=10, + sample_weight=sample_weights_vect, + callbacks=callbacks, + batch_size=batch_size) + else: + history = model.fit(X_train_array, y_train_array, + epochs=epochs, + validation_data=(X_test_array, y_test_array), + sample_weight=sample_weights_vect, + callbacks=callbacks, + batch_size=batch_size, + verbose=1) else: ''' # this code is for debugging only @@ -464,16 +516,73 @@ def _nn_train(self, model, epochs, batch_size, weights_precision, model_checkpoi callbacks=callbacks, #[c for c in (checkpointer,earlyStopping,rlrop) if c is not None], batch_size=batch_size) ''' + if keras_major_version > 2: + # CRITICAL FIX: For functional API with multiple outputs, sample_weight + # must be a LIST in the same order as outputs, NOT a dictionary + sample_weights_for_fit = None + if sample_weights_dict is not None: + # Get the output names from the model + output_names = [output.name.split('/')[0] for output in model.outputs] + + # Get the number of training samples + n_samples = len(X_train_array) + + # Create a list of sample weights in the same order as model outputs + sample_weights_list = [] + for output_name in output_names: + if output_name in sample_weights_dict: + weight_data = sample_weights_dict[output_name] + # Convert to numpy array with explicit dtype + if isinstance(weight_data, pd.Series): + sample_weights_list.append(weight_data.to_numpy().astype(np.float32)) + elif isinstance(weight_data, (list, np.ndarray)): + sample_weights_list.append(np.array(weight_data, dtype=np.float32)) + else: + sample_weights_list.append(weight_data) + else: + # If weight not provided for this output, use array of ones + # Keras does NOT accept None in a list, must be an actual array + sample_weights_list.append(np.ones(n_samples, dtype=np.float32)) + + sample_weights_for_fit = sample_weights_list + # log model details - self._log_model_summary(model, epochs, batch_size, sample_weights_dict, callbacks) - history = model.fit(X_train, y_train, - epochs=epochs, - validation_data=(X_test, y_test), - #steps_per_epoch=10, - sample_weight=sample_weights_dict, - callbacks=callbacks, - batch_size=batch_size) - #''' + if keras_major_version < 3: + self._log_model_summary(model, epochs, batch_size, sample_weights_dict, callbacks) + else: + self._log_model_summary(model, epochs, batch_size, sample_weights_for_fit, callbacks) + + # For functional API, y_train should also be a list if multiple outputs + if keras_major_version > 2 and len(model.outputs) > 1: + # Split y_train into list of arrays, one per output + if isinstance(y_train_array, np.ndarray) and y_train_array.ndim == 2: + y_train_list = [y_train_array[:, i:i+1] for i in range(y_train_array.shape[1])] + y_test_list = [y_test_array[:, i:i+1] for i in range(y_test_array.shape[1])] + else: + y_train_list = y_train_array + y_test_list = y_test_array + else: + if keras_major_version > 2: + y_train_list = y_train_array + y_test_list = y_test_array + + if keras_major_version < 3: + history = model.fit(X_train, y_train, + epochs=epochs, + validation_data=(X_test, y_test), + #steps_per_epoch=10, + sample_weight=sample_weights_dict, + callbacks=callbacks, + batch_size=batch_size) + else: + history = model.fit(X_train_array, y_train_list, + epochs=epochs, + validation_data=(X_test_array, y_test_list), + sample_weight=sample_weights_for_fit, + callbacks=callbacks, + batch_size=batch_size, + verbose=1) + #''' if weights_precision is not None: self.round_model_weights(model, int(weights_precision)) return history @@ -645,7 +754,16 @@ def initialize_tuner(self, input_dim:int, resp_names:list[str], sequential_api:b # performing hyperparameter tuning (search) def search(self, X_train:pd.DataFrame, y_train:pd.DataFrame, X_val:pd.DataFrame, y_val:pd.DataFrame, input_dim:int, resp_names:list[str], sequential_api:bool, hid_activation:str, out_activation:str, epochs:int, metrics, layers_grid:list, losses_grid:list, lrates_grid:list, batches_grid:list, tuner_algo:str): + self._keras_logger.info('Tuning model hyperparameters using Keras Tuner algorithm ' + str(tuner_algo) + ': start') + if keras_major_version > 2: + X_train = X_train.to_numpy() if isinstance(X_train, pd.DataFrame) else X_train + y_train = y_train.to_numpy() if isinstance(y_train, pd.DataFrame) else y_train + X_val = X_val.to_numpy() if isinstance(X_val, pd.DataFrame) else X_val + y_val = y_val.to_numpy() if isinstance(y_val, pd.DataFrame) else y_val + if not sequential_api and len(resp_names) > 1: + y_train = [y_train[:, i:i+1] for i in range(y_train.shape[1])] + y_val = [y_val[:, i:i+1] for i in range(y_val.shape[1])] self.initialize_tuner(input_dim, resp_names, sequential_api, hid_activation, out_activation, metrics, layers_grid, losses_grid, lrates_grid, tuner_algo) self.tuner.search( x=X_train, @@ -663,7 +781,7 @@ def search(self, X_train:pd.DataFrame, y_train:pd.DataFrame, X_val:pd.DataFrame, self._keras_logger.info('Best hyperparameters found: end') self._keras_logger.info('Tuning model hyperparameters using Keras Tuner algorithm ' + str(tuner_algo) + ': end') - # Fit / train model with tuned values of hyperparameters (obtained using Keras Tuner search() and strored within self) + # Fit / train model with tuned values of hyperparameters (obtained using Keras Tuner search() and stored within self) def get_best_model(self, X_train, X_test, y_train, y_test, epochs, weights_coef, batch_size, loss_function_str, learning_rate, sequential_api): best_hps = self.tuner.get_best_hyperparameters(num_trials=1)[0] best_model = self.tuner.hypermodel.build(best_hps) @@ -690,24 +808,90 @@ def get_best_model(self, X_train, X_test, y_train, y_test, epochs, weights_coef, ) return best_model ''' + if keras_major_version > 2: + # Convert DataFrames to numpy arrays + X_train_array = X_train.to_numpy() if isinstance(X_train, pd.DataFrame) else X_train + X_test_array = X_test.to_numpy() if isinstance(X_test, pd.DataFrame) else X_test + y_train_array = y_train.to_numpy() if isinstance(y_train, pd.DataFrame) else y_train + y_test_array = y_test.to_numpy() if isinstance(y_test, pd.DataFrame) else y_test + if sequential_api: #SEQUENTIAL_MODEL if weights_coef is not None: sample_weights_df = pd.DataFrame.from_dict(weights_coef) - sample_weights = np.array(list(sample_weights_df.agg('mean', axis=1))) + if keras_major_version < 3: + sample_weights = np.array(list(sample_weights_df.agg('mean', axis=1))) + else: + sample_weights = np.array(list(sample_weights_df.agg('mean', axis=1)), dtype=np.float32) else: sample_weights = None else: - sample_weights = weights_coef - - history = best_model.fit( - x=X_train.to_numpy(), - y=y_train.to_numpy(), - epochs=epochs, - validation_data=(X_test.to_numpy(), y_test.to_numpy()), - batch_size=best_batch_size, - sample_weight=sample_weights, #weights_coef, - callbacks=None #[keras.callbacks.EarlyStopping(patience=5)] - ) + if keras_major_version < 3: + sample_weights = weights_coef + else: + # CRITICAL FIX: For functional API with multiple outputs, sample_weight + # must be a LIST in the same order as outputs, NOT a dictionary + sample_weights = None + if weights_coef is not None: + # Get the output names from the model + output_names = [output.name.split('/')[0] for output in best_model.outputs] + + # Get the number of training samples + n_samples = len(X_train_array) + + # Create a list of sample weights in the same order as model outputs + sample_weights_list = [] + for output_name in output_names: + if output_name in weights_coef: + weight_data = weights_coef[output_name] + # Convert to numpy array with explicit dtype + if isinstance(weight_data, pd.Series): + sample_weights_list.append(weight_data.to_numpy().astype(np.float32)) + elif isinstance(weight_data, (list, np.ndarray)): + sample_weights_list.append(np.array(weight_data, dtype=np.float32)) + else: + sample_weights_list.append(weight_data) + else: + # If weight not provided for this output, use array of ones + # Keras does NOT accept None in a list, must be an actual array + sample_weights_list.append(np.ones(n_samples, dtype=np.float32)) + + sample_weights = sample_weights_list + + # For functional API with multiple outputs, y must also be a list + if not sequential_api and len(best_model.outputs) > 1 and keras_major_version > 2: + # Split y_train into list of arrays, one per output + if isinstance(y_train_array, np.ndarray) and y_train_array.ndim == 2: + y_train_list = [y_train_array[:, i:i+1] for i in range(y_train_array.shape[1])] + y_test_list = [y_test_array[:, i:i+1] for i in range(y_test_array.shape[1])] + else: + y_train_list = y_train_array + y_test_list = y_test_array + else: + if keras_major_version > 2: + y_train_list = y_train_array + y_test_list = y_test_array + + if keras_major_version < 3 or sequential_api: + history = best_model.fit( + x=X_train.to_numpy(), + y=y_train.to_numpy(), + epochs=epochs, + validation_data=(X_test.to_numpy(), y_test.to_numpy()), + batch_size=best_batch_size, + sample_weight=sample_weights, #weights_coef, + callbacks=None #[keras.callbacks.EarlyStopping(patience=5)] + ) + else: + history = best_model.fit( + x=X_train_array, + y=y_train_list, + epochs=epochs, + validation_data=(X_test_array, y_test_list), + batch_size=best_batch_size, + sample_weight=sample_weights, + callbacks=None, + verbose=1 + ) return best_model, history diff --git a/tests/smlp_regression/run_smlp_regression b/tests/smlp_regression/run_smlp_regression index 4d5599d0..180ec413 100755 --- a/tests/smlp_regression/run_smlp_regression +++ b/tests/smlp_regression/run_smlp_regression @@ -26,13 +26,13 @@ endif set python3_version=`python3 -c 'from sys import version_info; print(version_info.minor)'` set regression_script=smlp_regr.py set local_script=`echo smlp_regr.py | sed 's/regr/regr_local/'` -if( $python3_version != 11 ) then +if( $python3_version != 12 ) then \rm -f $local_script >& /dev/null sed 's@../../src/run_smlp.py@smlp@' $regression_script > $local_script chmod +x $local_script set regression_script=$local_script endif -echo n | env CUDA_VISIBLE_DEVICES=-1 python3.11 ./${regression_script} -w 8 -def n -t all -tol 7 -g |& tee $log +echo n | env CUDA_VISIBLE_DEVICES=-1 python3.12 ./${regression_script} -w 8 -def n -t all -tol 7 -g |& tee $log ${script_path}/create_diff_report >& $diff_report cleanup: \rm -f $local_script >& /dev/null diff --git a/tests/smlp_regression/run_smlp_regression_expected.log b/tests/smlp_regression/run_smlp_regression_expected.log index 1abcad8d..9b458494 100644 --- a/tests/smlp_regression/run_smlp_regression_expected.log +++ b/tests/smlp_regression/run_smlp_regression_expected.log @@ -1,827 +1,1025 @@ Calling 8 workers for multiprocessing... Initiating 0 worker... +Initiating 1 worker... + Running test 1 test type: train, description: basic dt_caret training and test on labeled data with single numeric response ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test1 -mode train -resp y1 -feat x,p1,p2 -model dt_caret -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f -Initiating 1 worker... -Running test 2 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labelsInitiating 2 worker... +Initiating 2 worker... +Running test 2 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test2 -mode predict -resp y1 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" Initiating 3 worker... + Running test 3 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric response in training/test data only ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test3 -mode predict -resp y1 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_unlabeled.csv" -Initiating 4 worker... -Initiating 5 worker... + Running test 4 test type: prediction, description: basic nn_keras prediction test on labeled and new data with numeric labels and one response ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test4 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nn_keras_weights_precision 2 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Initiating 6 worker... +Initiating 4 worker... +Initiating 5 worker... + +Running test 5 test type: prediction, description: basic dt_caret prediction test on labeled and new data with numeric labels +../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test5 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model t -use_model f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + Running test 6 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test6 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Initiating 6 worker... Initiating 7 worker... -Running test 5 test type: prediction, description: basic dt_caret prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test5 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model t -use_model f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" Running test 7 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test7 -mode predict -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 8 test type: prediction, description: basic nn_keras prediction test on labeled and new data with numeric labels and two responses ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test8 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -nn_keras_epochs 20 -nn_keras_seq_api f -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 9 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test9 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -model_name test20_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -save_config t -save_model_config t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 10 test type: prediction, description: basic et_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test10 -mode predict -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 15 -et_sklearn_bootstrap f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 11 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test11 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 12 test type: train, description: EV-SI real life dt_sklearn predict test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test12 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 13 test type: train, description: EV-SI real life nn_keras prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test13 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 14 test type: train, description: EV-SI real life poly_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test14 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 15 test type: prediction, description: basic dt_caret prediction test from saved model on new data with numeric labels ../../src/run_smlp.py -model_name "../models/Test5_smlp_toy_num_resp_mult" -out_dir ./ -pref Test15 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 16 test type: prediction, description: basic nn_keras prediction test from saved model on new data with numeric labels and two responses ../../src/run_smlp.py -model_name "../models/Test8_smlp_toy_num_resp_mult" -out_dir ./ -pref Test16 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 17 test type: prediction, description: basic poly_sklearn prediction test from saved model on new data with numeric labels and two responses ../../src/run_smlp.py -model_name "../models/Test11_smlp_toy_num_resp_mult" -out_dir ./ -pref Test17 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 18 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test18 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test19_model -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 19 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels ../../src/run_smlp.py -model_name "../models/test19_model" -out_dir ./ -pref Test19 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 20 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels ../../src/run_smlp.py -model_name "../models/test20_model" -out_dir ./ -pref Test20 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 21 test type: prediction, description: test for illegal symbols in column names ../../src/run_smlp.py -data "../data/smlp_toy_num_metasymbol_mult_reg.csv" -out_dir ./ -pref Test21 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model t -use_model f -model_name test22_model -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" + Running test 22 test type: prediction, description: test for illegal symbols in column names ../../src/run_smlp.py -model_name "../models/test22_model" -out_dir ./ -pref Test22 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model f -use_model t -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" + Running test 23 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test23 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test24_model -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 24 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels ../../src/run_smlp.py -model_name "../models/test24_model" -out_dir ./ -pref Test24 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 25 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test25 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test26_model -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 26 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels ../../src/run_smlp.py -model_name "../models/test26_model" -out_dir ./ -pref Test26 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 27 test type: prediction, description: checks nn_keras prediction with nn_keras_seq_api t ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test27 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 28 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and functional API ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test28 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 29 test type: subgroups, description: basic test for subgroup discovery for pass-fail responses ../../src/run_smlp.py -data "../data/smlp_toy_cls_metasymbol_colnames_mult.csv" -out_dir ./ -pref Test29 -mode subgroups -psg_dim 3 -psg_top 10 -resp "PF 1,PF#" -plots t -seed 10 -log_time f + Running test 30 test type: subgroups, description: basic test for subgroup discovery for numric responses ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test30 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -feat x,p1,p2 -plots t -seed 10 -log_time f + Running test 31 test type: subgroups, description: testing resp2b in subgroup discovery mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test31 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -resp2b "y1<6;y2>6" -feat x,p1,p2 -plots t -seed 10 -log_time f -save_config t + Running test 32 test type: unknown, description: test reusing saved model by using configuration file ../../src/run_smlp.py -model_name "../models/test20_model" -out_dir ./ -pref Test32 -config ../models/test20_model_rerun_model_config.json -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 33 test type: unknown, description: testing -config option with subgroups mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test33 -config ../models/Test31_smlp_toy_num_resp_mult_args_config.json + Running test 34 test type: doe, description: doe test with four levels with full_factorial method ../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test34 -mode doe -doe_algo full_factorial -log_time f + Running test 35 test type: doe, description: doe test with four levels with plackett_burman ../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test35 -mode doe -doe_algo plackett_burman -log_time f + Running test 36 test type: doe, description: doe test with four levels with sukharev_grid ../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test36 -mode doe -doe_algo sukharev_grid -doe_samples 125 -log_time f + Running test 37 test type: doe, description: doe test with four levels with box_behnken ../../src/run_smlp.py -doe_spec "../grids/doe_three_levels_real_nan.csv" -out_dir ./ -pref Test37 -mode doe -doe_algo box_behnken -log_time f + Running test 38 test type: doe, description: doe test with four levels with box_wilson ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test38 -mode doe -doe_algo box_wilson -doe_cc_face ccc -doe_cc_alpha r -doe_cc_center 2,3 -log_time f + Running test 39 test type: doe, description: doe test with four levels with latin_hypercube ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test39 -mode doe -doe_algo latin_hypercube -doe_prob_distr Exponential -doe_samples 30 -log_time f + Running test 40 test type: doe, description: doe test with four levels with latin_hypercube_space_filling ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test40 -mode doe -doe_algo latin_hypercube_sf -doe_samples 20 -log_time f + Running test 41 test type: doe, description: doe test with four levels with random_k_means ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test41 -mode doe -doe_algo random_k_means -doe_samples 20 -log_time f + Running test 42 test type: doe, description: doe test with four levels with maximin_reconstruction ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test42 -mode doe -doe_algo maximin_reconstruction -doe_samples 20 -log_time f + Running test 43 test type: doe, description: doe test with four levels with halton_sequence ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test43 -mode doe -doe_algo halton_sequence -doe_samples 20 -log_time f + Running test 44 test type: doe, description: doe test with four levels with uniform_random_matrix ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test44 -mode doe -doe_algo uniform_random_matrix -doe_samples 20 -log_time f + Running test 45 test type: doe, description: doe test with four levels with fractional_factorial ../../src/run_smlp.py -doe_spec "../grids/doe_two_levels_real.csv" -out_dir ./ -pref Test45 -mode doe -doe_algo fractional_factorial -doe_resolution 5 -log_time f + Running test 46 test type: prediction, description: tests options -pos_val and -neg_val ../../src/run_smlp.py -data "../data/smlp_toy_pf_mult.csv" -out_dir ./ -pref Test46 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model t -save_model_config f -use_model f -model_name test47_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" + Running test 47 test type: prediction, description: tests options -pos_val and -neg_val when re-using saved model ../../src/run_smlp.py -model_name "../models/test47_model" -out_dir ./ -pref Test47 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" + Running test 48 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test48 -mode discretize -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 49 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test49 -mode discretize -resp "PF,PF1" -discr_algo quantile -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 50 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test50 -mode discretize -resp "PF,PF1" -discr_algo kmeans -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 51 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test51 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 52 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test52 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 53 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test53 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 54 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test54 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 55 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test55 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 56 test type: discretization, description: tests discretization options ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test56 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels f -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 58 test type: optimize, description: basic dt_sklearn optimization test with numeric labels and integer grid as domain and without scaling objectives ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test58 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 59 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test59 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 60 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test60 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_y1_verify.spec specs_path ../specs + Running test 63 test type: verify, description: basic dt_sklearn assertion verification test on data with numeric labels ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test63 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test63_model -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_y1_verify.spec specs_path ../specs + Running test 64 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response ../../src/run_smlp.py -model_name "../models/test63_model" -out_dir ./ -pref Test64 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs + Running test 66 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response ../../src/run_smlp.py -model_name "../models/test65_model" -out_dir ./ -pref Test66 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs + Running test 68 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response ../../src/run_smlp.py -model_name "../models/test67_model" -out_dir ./ -pref Test68 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -model_per_response t -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 69 test type: verify, description: nn_keras verification test with model_per_response training ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test69 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model t -use_model f -model_name test69_model -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 70 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model ../../src/run_smlp.py -model_name "../models/test69_model" -out_dir ./ -pref Test70 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs + Running test 72 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model ../../src/run_smlp.py -model_name "../models/test71_model" -out_dir ./ -pref Test72 -mode verify -resp y1,y2 -feat x0,x1,x2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat + Running test 77 test type: unknown, description: verification test run using model_rerun config covering the case when mrmr selcts only a subset of features specified through the command line or config file ../../src/run_smlp.py -model_name "../models/test76_model" -out_dir ./ -pref Test77 -config ../models/test76_model_rerun_model_config.json spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 79 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test79 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 80 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test80 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps.spec specs_path ../specs + Running test 81 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test81 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_free_inps.spec specs_path ../specs + Running test 82 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test82 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_free_inps.spec specs_path ../specs + Running test 83 test type: optimize, description: basic dt_sklearn multi objective pareto optimization test with numeric labels and integer grid as domain and with scaling objectives ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test83 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -beta "y1>7 and y2>6" -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1/2-y2;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 85 test type: optimize, description: tests alpha and eta constraints specified in command line in optimization mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test85 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 86 test type: optimize, description: tests alpha ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test86 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -asrt_names asrt1,asrt2,asrt3 -asrt_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_alpha_asrt_verify.spec specs_path ../specs + Running test 87 test type: verify, description: tests global alpha constraints and assertions specified in spec file ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test87 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_alpha_asrt_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 88 test type: optimize, description: basic dt_sklearn multi objective pareto optimization test with beta and objectives specified in spec file ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test88 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs + Running test 89 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test89 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn_vacuous.spec specs_path ../specs + Running test 90 test type: optsyn, description: test to detect contradictory constraints in optsyn mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test90 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_query_vacuous.spec specs_path ../specs + Running test 91 test type: query, description: test to detect contradictory constraints in optimization mode due to contradictory alpha global and alpha bounds constraints on FMAX_xyx ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test91 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_verify_vacuous.spec specs_path ../specs + Running test 92 test type: verify, description: test to detect contradictory constraints in verification mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test92 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_verify_vacuous.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 93 test type: optsyn, description: basic test for mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test93 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 94 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test94 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 95 test type: optsyn, description: basic test for dt_caret in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test95 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 96 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test96 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs + Running test 97 test type: query, description: basic test for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test97 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 98 test type: optsyn, description: basic test for et_caret in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test98 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 99 test type: optimize, description: testing that the response and feature names can be taken from spec file in model exploration modes when the responses and/or features are not specified in the command line ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test99 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 100 test type: optimize, description: basic test for sat_threshold option enabing usage of objectve values in SAT assignments that prove optimization thresholds ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test100 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_witness.spec specs_path ../specs + Running test 101 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test101 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test101_model -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_witness.spec specs_path ../specs + Running test 102 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation ../../src/run_smlp.py -model_name "../models/test101_model" -out_dir ./ -pref Test102 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_certify_witness.spec specs_path ../specs + Running test 103 test type: certify, description: ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test103 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test103_model -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_certify_witness.spec -quer_names valid_candidate,grid_conflict,range_conflict -quer_exprs "True;True;True" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 104 test type: verify, description: assertion verfication test with wrong spec that does not assign a single value using a singleton grid or range with equal max and min ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test104 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_stable_verify.spec specs_path ../specs + Running test 105 test type: verify, description: basic dt_sklearn assertion verfication test with numeric labels and integer grid as domain ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test105 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_stable_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_unsat_eta_verify.spec specs_path ../specs + Running test 106 test type: verify, description: test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test106 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_unsat_eta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_beta_verify.spec specs_path ../specs + Running test 107 test type: verify, description: test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test107 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_beta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_synthesize.spec specs_path ../specs + Running test 108 test type: synthesize, description: basic test for dt_sklearn in model exploration mode synthesize where synthesis succeeds ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test108 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_cannot_synthesize.spec specs_path ../specs + Running test 109 test type: synthesize, description: basic test for mode synthesize where synthesis fails ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test109 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_cannot_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 110 test type: prediction, description: smlp toy basic example for predict mode from SMLP user manual ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test110 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model t -model_name test110_model -save_model_config t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" + Running test 111 test type: unknown, description: smlp toy basic test to rerun saved model using the model rerun config file saved during model training ../../src/run_smlp.py -model_name "../models/test110_model" -out_dir ./ -pref Test111 -config ../models/test110_model_rerun_model_config.json -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" + Running test 112 test type: prediction, description: smlp toy basic test from SMLP manual ../../src/run_smlp.py -model_name "../models/test110_model" -out_dir ./ -pref Test112 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -use_model t -save_model f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" spec_fn smlp_toy_basic.spec specs_path ../specs + Running test 113 test type: optimize, description: smlp toy basic test for mode optimize from SMLP manual ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test113 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model t -model_name test113_model -save_model_config t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec spec_fn smlp_toy_basic.spec specs_path ../specs + Running test 114 test type: optimize, description: smlp toy basic test for mode optimize from SMLP manual without specifying resp and feat in command line ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test114 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model f -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec spec_fn smlp_toy_system.spec specs_path ../specs + Running test 115 test type: certify, description: basic test in certify mode ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test115 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system.spec specs_path ../specs + Running test 116 test type: certify, description: basic test in certify mode when system is specified and is used as the model; p2 rel-rad needs to be 0 or very close to it the witness to first query to be stable ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test116 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_certify.spec specs_path ../specs + Running test 117 test type: certify, description: certification test with knobs only where assertion is valid without stability and fails with stability ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test117 -mode certify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_verify.spec specs_path ../specs + Running test 118 test type: verify, description: verification test with knobs only where assertion is valid without stability and fails with stability ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test118 -mode verify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_query.spec specs_path ../specs + Running test 119 test type: query, description: query test with knobs only where query is satisfiable without stability and fails with stability ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test119 -mode query -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs + Running test 120 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test120 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 121 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is feasible ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test121 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs + Running test 122 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test122 -mode optimize -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 123 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test123 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs + Running test 124 test type: optsyn, description: optimized synthesis test with constant knob and no inputs where synthesis is not feasible because while beta constraint is feasible the assertion is not feasible therefore optimization is not performed ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test124 -mode optsyn -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 125 test type: optsyn, description: optimized synthesis test with constant knob and no inputs where synthesis is feasible and optimization is performed ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test125 -mode optsyn -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_verify.spec specs_path ../specs + Running test 126 test type: verify, description: verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test126 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_certify.spec specs_path ../specs + Running test 127 test type: certify, description: certification example with knobs only and fictitious inputs with values fixed through their ranges ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test127 -mode certify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_witness_certify.spec specs_path ../specs + Running test 128 test type: certify, description: Basic regression test in certify mode covering all four possible outcomes when certifying a witness for a query: the witness is stable ../../src/run_smlp.py -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test128 -mode certify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_witness_certify.spec -quer_names query_stable_witness,query_grid_conflict,query_unstable_witness,query_infeasible_witness,query_poly_intercept_sensitive -quer_exprs "y2<=90;y1>=9;y1>=(-13);y1>9;y1>=(-10)" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_configuration_verify.spec specs_path ../specs + Running test 129 test type: verify, description: verification example with demonstrating all basic result scenarious for assertions ../../src/run_smlp.py -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test129 -mode verify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_configuration_verify.spec -asrt_names assert_stable_config,assert_grid_conflict,assert_unstable_config,assert_infeasible -asrt_exprs "y2<=90;y1>=9;y1>=(-10);y1>20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_verify.spec specs_path ../specs + Running test 140 test type: verify, description: verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test140 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -trace_prec 1 -trace_anonym t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 141 test type: optimize, description: basic test for compress_rules option for dt_sklearn in optimization mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test141 -mode optimize -opt_strategy lazy -pareto f -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 142 test type: optsyn, description: basic test for compress_rules option for rf_sklearn in optsin mode ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test142 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs + Running test 143 test type: query, description: basic test for compress_rules for et_sklearn in mode query ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test143 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 145 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed ../../src/run_smlp.py -out_dir ./ -pref Test145 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system.spec specs_path ../specs + Running test 146 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed ../../src/run_smlp.py -out_dir ./ -pref Test146 -mode optimize -pareto t -opt_strategy lazy -model poly_sklearn -resp y1,y2 -feat p1,p2,x1,x2 -save_model t -use_model f -mrmr_pred 0 -model_per_response t -split 1 -spec ../specs/smlp_toy_system.spec -doe_spec ../grids/explore_doe_two_levels.csv -doe_algo latin_hypercube -epsilon 0.99999999 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 147 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test147 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 148 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test148 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 149 test type: prediction, description: tests the mae loss function MeanAbsoluteError and sample weoghts ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test149 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mae -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 150 test type: prediction, description: tests the mape loss function MeanAbsolutePercentageError and sample weights ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test150 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 151 test type: prediction, description: tests msle loss function MeanSquaredLogarithmicError and and sample weoghts ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test151 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss msle -sw_coef 3 -sw_exp 10 -sw_int 0 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 152 test type: prediction, description: tests the huber loss function Huber and sample weights ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test152 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss huber -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 153 test type: prediction, description: tests the logcosh loss function LogCosh and sample weights ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test153 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss logcosh -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 154 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test154 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 155 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test155 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae spec_fn smlp_toy_num_resp_mult_verify.spec specs_path ../specs + Running test 156 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training; adapts test 154 by consdering multiple responses ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test156 -mode verify -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse spec_fn smlp_toy_num_resp_mult_verify.spec specs_path ../specs + Running test 157 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training; adapts test 155 by consdering multiple responses ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test157 -mode verify -resp y1,y2 -feat x,p1,p2 --model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse,logcosh + Running test 158 test type: prediction, description: tests the mape loss function and sample weights with model_per_response t ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test158 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -model_per_response t -sw_coef 8 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 159 test type: prediction, description: tests the msle loss function and sample weights with model_per_response t ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test159 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -model_per_response t -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae,cosine -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 160 test type: prediction, description: tests nn keras tuner bayesian ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test160 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -nn_keras_metrics msle -nn_keras_tuner bayesian -nn_keras_layers_grid "2,3" -nn_keras_losses_grid "mse,mae,huber" -model_per_response f -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 161 test type: prediction, description: tests nn keras tuner bayesian ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test161 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -nn_keras_metrics mape,logcosh -nn_keras_tuner random -nn_keras_lrates_grid "0.01,0.001" -nn_keras_batches_grid "32,64" -model_per_response f -sw_coef 4 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 164 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test164 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 165 test type: optsyn, description: basic flat tree encoding test for dt_caretin model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test165 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding flat -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 166 test type: optsyn, description: basic flat tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test166 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 167 test type: optsyn, description: basic flat tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test167 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 168 test type: optimize, description: basic test for rf_caret with flat tree_encoding and modelper_response in model exploration mode optimize ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test168 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding flat -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 169 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response t in model exploration mode optimize ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test169 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 170 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response f in model exploration mode optimize ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test170 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 171 test type: optimize, description: basic test for et_caret with flat tree_encoding in model exploration mode optimize ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test171 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 172 test type: verify, description: basic test for nn_keras flat encoding for functional api ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test172 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 173 test type: verify, description: basic test for nn_keras flat encoding for sequential api ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test173 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 174 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test174 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 175 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test175 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 176 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api f for nn_keras in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test176 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 177 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test177 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 178 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features are not scaled adapts test 177 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test178 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 179 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn when resposes are not scaled adapts test 174 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test179 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 180 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features and responses are not scaled adapts test 175 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test180 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_feat f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 181 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test181 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 182 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test182 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 183 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test183 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 187 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization adapts test 164 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test187 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 188 test type: optsyn, description: basic branched tree encoding test for dt_caretin model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test188 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding branched -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 189 test type: optsyn, description: basic branched tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn adapts test 166 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test189 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 190 test type: optimize, description: basic test for rf_caret with branched tree_encoding and modelper_response in model exploration mode optimize adapts test 168 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test190 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding branched -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 191 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response t in model exploration mode optimize adapts test 169 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test191 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap t -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 192 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test192 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 193 test type: optimize, description: basic test for et_caret with branched tree_encoding in model exploration mode optimize adapts test 171 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test193 -mode optimize -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 194 test type: optsyn, description: basic branched tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test194 -mode optsyn -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 195 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 192 by setting n_estimators 3 and then discrepancy between z3 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test195 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 196 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 and test 181 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test196 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 197 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 and test 182 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test197 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 198 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 and test 183 ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test198 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 199 test type: optimize, description: test to demonstrate that in pareto optimization and optsyn modes with multiple objectives when beta constraints are not present SMLP results are not consistent when different solvers are used; this is due to fact that when a subset of objectoves are exemined in pareto algo ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test199 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 200 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test200 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_no_input_beta.spec specs_path ../specs + Running test 201 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and there are beta constraints ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test201 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input_beta.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_no_input.spec specs_path ../specs + Running test 202 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and no beta constraints ../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test202 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs + Running test 203 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed adapts test 122 ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test203 -mode optimize -pareto f -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 204 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 123 ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test204 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 205 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 145 ../../src/run_smlp.py -out_dir ./ -pref Test205 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 206 test type: optsyn, description: optimized synthesis test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 125 ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test206 -mode optsyn -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 215 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test215 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 216 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test216 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 217 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test217 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 218 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test218 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 219 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test219 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type integer -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 220 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test220 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 221 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test221 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 222 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test222 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 223 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test223 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 224 test type: correlate, description: basic test for correlate mode and tests the Shannon mutual information ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test224 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 225 test type: correlate, description: basic test for correlate mode and tests the adjusted mutual information ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test225 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 226 test type: correlate, description: basic test for correlate mode ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test226 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 227 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test227 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f spec_fn smlp_toy_system_radii_update_certify.spec specs_path ../specs + Running test 228 test type: certify, description: test that radii specified in command line properly override the radii specified in the spec file. Here we override both ansolute and relative radii and one can observe that the certification results also change compared to test 116 ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test228 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system_radii_update_certify.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_missing_radii.spec specs_path ../specs + Running test 229 test type: certify, description: basic test for checking that each knob must have either absolute or relative radius specified in the spec file (even if radii are specified in the command line) ../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test229 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_missing_radii.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_monotone_knob.05_verify.spec specs_path ../specs + Running test 230 test type: verify, description: tests that outputs in system specificaation might depend on different inuts (knobs and free inputs) ../../src/run_smlp.py -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test230 -mode verify -spec ../specs/smlp_toy_system_monotone_knob.05_verify.spec -model system -seed 10 -log_time f spec_fn smlp_toy_system_decreasing_knob.05_certify.spec specs_path ../specs + Running test 231 test type: certify, description: certification test with monotonicity query with a knob with a grid point ../../src/run_smlp.py -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test231 -mode certify -spec ../specs/smlp_toy_system_decreasing_knob.05_certify.spec -model system -seed 10 -log_time f spec_fn smlp_toy_system_running_example_certify.spec specs_path ../specs + Running test 232 test type: certify, description: running example from smlp manual ../../src/run_smlp.py -data "../data/smlp_toy_system_running_example_certify.csv" -out_dir ./ -pref Test232 -mode certify -spec ../specs/smlp_toy_system_running_example_certify.spec -model system -seed 10 -log_time f + Running test 233 test type: subgroups, description: tests subgroup discovery mode when the response has string values ../../src/run_smlp.py -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test233 -mode subgroups -resp str_resp1 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f + Running test 234 test type: subgroups, description: tests subgroup discovery mode when there are two responses with string values ../../src/run_smlp.py -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test234 -mode subgroups -resp str_resp1,str_resp2 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f @@ -997,6 +1195,7 @@ Passed! comparing Test6_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_training_predictions_summary.csv to master Passed! comparing Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt to master +Passed! comparing Test7_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test7_smlp_toy_num_resp_mult_model_features_dict.json to master @@ -1024,7 +1223,6 @@ Passed! comparing Test7_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_training_predictions_summary.csv to master Passed! comparing Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master -Passed! comparing Test8_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test8_smlp_toy_num_resp_mult_model_features_dict.json to master @@ -1090,6 +1288,7 @@ Passed! comparing Test10_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt to master +Passed! comparing Test10_smlp_toy_num_resp_mult_model_features_dict.json to master Passed! comparing Test10_smlp_toy_num_resp_mult_model_levels_dict.json to master @@ -1163,7 +1362,6 @@ Passed! comparing Test12_smlp_toy_basic_training_predictions_summary.csv to master Passed! comparing Test13_smlp_toy_basic.txt to master -Passed! comparing Test13_smlp_toy_basic_data_bounds.json to master Passed! comparing Test13_smlp_toy_basic_labeled_prediction_precisions.csv to master @@ -1209,6 +1407,7 @@ Passed! comparing Test14_smlp_toy_basic_training_predictions_summary.csv to master Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1216,6 +1415,7 @@ Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1223,6 +1423,7 @@ Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1260,6 +1461,7 @@ Passed! comparing test19_model_rerun_model_config.json to master Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1267,6 +1469,7 @@ Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1313,6 +1516,7 @@ Passed! comparing test22_model_rerun_model_config.json to master Passed! comparing Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt to master +Passed! File master Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_eval_poly_sklearn_new-col-PF .png does not exist File master Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_eval_poly_sklearn_new-col-|PF |.png does not exist comparing Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_missing_values_dict.json to master @@ -1353,6 +1557,7 @@ Passed! comparing test24_model_rerun_model_config.json to master Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1360,6 +1565,7 @@ Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing test26_model_dt_sklearn_tree_rules.txt to master +Passed! comparing Test25_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master Passed! comparing Test25_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_labeled_prediction_precisions.csv to master @@ -1389,6 +1595,7 @@ Passed! comparing test26_model_rerun_model_config.json to master Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1480,6 +1687,7 @@ Passed! comparing Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv to master Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json to master Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master @@ -1569,6 +1777,7 @@ Passed! comparing test47_model_poly_sklearn_formula.txt to master Passed! comparing Test47_test47_model_smlp_toy_pf_mult.txt to master +Passed! comparing Test47_test47_model_smlp_toy_pf_mult_missing_values_dict.json to master Passed! comparing Test47_test47_model_smlp_toy_pf_mult_new_prediction_precisions.csv to master @@ -1743,6 +1952,7 @@ Passed! File master test63_model_y1_smlp_full_model_term.json does not exist File master test63_model_y1_smlp_model_term.json does not exist comparing Test64_test63_model.txt to master +Passed! File master Test64_test63_model_trace.csv does not exist comparing Test64_test63_model_verify_results.json to master Passed! @@ -1789,6 +1999,7 @@ Passed! File master test69_model_y2_smlp_full_model_term.json does not exist File master test69_model_y2_smlp_model_term.json does not exist comparing Test70_test69_model.txt to master +Passed! File master Test70_test69_model_trace.csv does not exist comparing Test70_test69_model_verify_results.json to master Passed! @@ -2550,6 +2761,7 @@ File master test101_model_y1_smlp_model_term.json does not exist File master test101_model_y2_smlp_full_model_term.json does not exist File master test101_model_y2_smlp_model_term.json does not exist comparing Test102_test101_model.txt to master +Passed! comparing Test102_test101_model_certify_results.json to master Passed! File master Test102_test101_model_trace.csv does not exist @@ -2776,9 +2988,11 @@ comparing test110_model_poly_sklearn_formula.txt to master comparing test110_model_rerun_model_config.json to master Passed! comparing Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt to master +Passed! comparing Test111_test110_model_smlp_toy_basic_pred_unlabeled_new_predictions_summary.csv to master Passed! comparing Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt to master +Passed! comparing Test112_test110_model_smlp_toy_basic_pred_unlabeled_new_predictions_summary.csv to master Passed! comparing test113_model_dt_sklearn_tree_rules.txt to master @@ -4429,5 +4643,5 @@ Passed! master log file does not exist! Do you wish to copy the new log file to master? (yes/no|y/n): No new tests crashed (not in the masters) -Time: 35.39404908021291 minutes +Time: 41.04642364184062 minutes End of regression diff --git a/tests/smlp_regression/run_smlp_regression_expected_diff_report.log b/tests/smlp_regression/run_smlp_regression_expected_diff_report.log index 952a5d6a..f5fbf427 100644 --- a/tests/smlp_regression/run_smlp_regression_expected_diff_report.log +++ b/tests/smlp_regression/run_smlp_regression_expected_diff_report.log @@ -1,391 +1,171 @@ -=================== Diff report for: Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt ================================== -94d93 -< if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x <= 0.6666666716337204) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -96c95 -< if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x <= 0.6666666716337204) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -97a97 -> if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt ================================== -6d5 -< if (p1 > 0.7673577288013687) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -7a7 -> if (p1 > 0.7673577288013687) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -21d20 -< if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x > 0.03081251758592215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -22a22 -> if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x > 0.03081251758592215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -44d43 -< if (p2 > 0.05282566885129813) and (p1 > 0.9621611074368288) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -45a45 -> if (p2 > 0.05282566885129813) and (p1 > 0.9621611074368288) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -66d65 -< if (p2 > 0.10769168804757841) and (p2 > 0.9843916629018533) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -67a67 -> if (p2 > 0.10769168804757841) and (p2 > 0.9843916629018533) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -73d72 -< if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x <= 0.7383051325780686) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -75c74 -< if (p1 > 0.9643084043470717) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x <= 0.7383051325780686) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -76a76 -> if (p1 > 0.9643084043470717) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -88d87 -< if (p2 > 0.37253817607301204) and (p1 > 0.5069297996847564) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -89a89 -> if (p2 > 0.37253817607301204) and (p1 > 0.5069297996847564) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -96d95 -< if (p1 > 0.8097833990164955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -97a97 -> if (p1 > 0.8097833990164955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -103d102 -< if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 <= 0.9832575130198419) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -105c104 -< if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x > 0.7718040145802331) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 <= 0.9832575130198419) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -106a106 -> if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x > 0.7718040145802331) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -118d117 -< if (p1 > 0.6478692095949636) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -119a119 -> if (p1 > 0.6478692095949636) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -126d125 -< if (p2 > 0.5083941302766997) and (p1 > 0.9604900148513215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -127a127 -> if (p2 > 0.5083941302766997) and (p1 > 0.9604900148513215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -134d133 -< if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 > 0.7231216464299344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -135a135 -> if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 > 0.7231216464299344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -141d140 -< if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x <= 0.6016799023753295) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -143c142 -< if (p1 > 0.5519522472992318) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x <= 0.6016799023753295) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -144a144 -> if (p1 > 0.5519522472992318) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -155d154 -< if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x <= 0.6096057855068803) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -157c156 -< if (p1 > 0.5757871147132418) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x <= 0.6096057855068803) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -158a158 -> if (p1 > 0.5757871147132418) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -204d203 -< if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x <= 0.6717608829535293) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -206c205 -< if (p1 > 0.8026946730924279) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x <= 0.6717608829535293) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -207a207 -> if (p1 > 0.8026946730924279) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -218d217 -< if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x <= 0.6156377025174744) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -220c219 -< if (p2 > 0.5397191641571555) and (p1 > 0.7132398065706901) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x <= 0.6156377025174744) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -221a221 -> if (p2 > 0.5397191641571555) and (p1 > 0.7132398065706901) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -233d232 -< if (p1 > 0.6806756624396312) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -234a234 -> if (p1 > 0.6806756624396312) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -241d240 -< if (p2 > 0.4943563777461445) and (p1 > 0.920448066629678) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -242a242 -> if (p2 > 0.4943563777461445) and (p1 > 0.920448066629678) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -264d263 -< if (p1 > 0.7209493553829144) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -265a265 -> if (p1 > 0.7209493553829144) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -286d285 -< if (p2 > 0.9499151226831205) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -287a287 -> if (p2 > 0.9499151226831205) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -300d299 -< if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x <= 0.8482964849267818) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -302c301 -< if (p2 > 0.4160567286499109) and (p1 > 0.7821608059025187) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x <= 0.8482964849267818) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -303a303 -> if (p2 > 0.4160567286499109) and (p1 > 0.7821608059025187) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -357d356 -< if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x <= 0.7909361110756394) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -359c358 -< if (p1 > 0.6838882522116826) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x <= 0.7909361110756394) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -360a360 -> if (p1 > 0.6838882522116826) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -364d363 -< if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x <= 0.4249370103517018) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -366c365 -< if (p2 > 0.40336603038169727) and (p1 > 0.7296289325364069) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x <= 0.4249370103517018) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -367a367 -> if (p2 > 0.40336603038169727) and (p1 > 0.7296289325364069) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -379d378 -< if (p1 > 0.5139580394672034) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -380a380 -> if (p1 > 0.5139580394672034) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -387d386 -< if (p2 > 0.8305540819794657) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -388a388 -> if (p2 > 0.8305540819794657) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -395d394 -< if (p2 > 0.9449580902908733) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -396a396 -> if (p2 > 0.9449580902908733) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -410d409 -< if (p1 > 0.5120007179267708) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -411a411 -> if (p1 > 0.5120007179267708) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -417d416 -< if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x <= 0.7877861639695688) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -419c418 -< if (p1 > 0.610545704891228) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x <= 0.7877861639695688) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -420a420 -> if (p1 > 0.610545704891228) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -424d423 -< if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x <= 0.7843162575827582) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -426c425 -< if (p2 > 0.45482464342137086) and (p1 > 0.6847267941034989) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x <= 0.7843162575827582) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -427a427 -> if (p2 > 0.45482464342137086) and (p1 > 0.6847267941034989) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -446d445 -< if (p2 > 0.054852516881587224) and (p1 > 0.8053342741007611) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -447a447 -> if (p2 > 0.054852516881587224) and (p1 > 0.8053342741007611) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -461d460 -< if (p2 > 0.35838276676758324) and (p1 > 0.6469177723149386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -462a462 -> if (p2 > 0.35838276676758324) and (p1 > 0.6469177723149386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -476d475 -< if (p1 > 0.6586678422329332) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -477a477 -> if (p1 > 0.6586678422329332) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -483d482 -< if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x <= 0.37232690124052253) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -485c484 -< if (p2 > 0.6300749409152544) and (p1 > 0.9968296801656623) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x <= 0.37232690124052253) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -486a486 -> if (p2 > 0.6300749409152544) and (p1 > 0.9968296801656623) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -497d496 -< if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x <= 0.5092783321373655) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -499c498 -< if (p2 > 0.37996283470651265) and (p1 > 0.5025284804881217) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x <= 0.5092783321373655) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -500a500 -> if (p2 > 0.37996283470651265) and (p1 > 0.5025284804881217) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -505d504 -< if (p2 > 0.22414377714700243) and (p1 > 0.904138504017209) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -506a506 -> if (p2 > 0.22414377714700243) and (p1 > 0.904138504017209) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -513d512 -< if (p1 > 0.8937446875002909) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -514a514 -> if (p1 > 0.8937446875002909) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -535d534 -< if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 > 0.9300510326789317) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -536a536 -> if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 > 0.9300510326789317) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -542d541 -< if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 <= 0.8191675724550931) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -544c543 -< if (p2 > 0.7889165751584417) and (x > 0.9135558618761394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 <= 0.8191675724550931) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -545a545 -> if (p2 > 0.7889165751584417) and (x > 0.9135558618761394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -549d548 -< if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x <= 0.6647538383146583) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -551c550 -< if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 > 0.8197608151565123) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x <= 0.6647538383146583) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -552a552 -> if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 > 0.8197608151565123) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -556d555 -< if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 > 0.7807007138854876) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -558c557 -< if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x > 0.792321881648303) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 > 0.7807007138854876) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -559a559 -> if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x > 0.792321881648303) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -563d562 -< if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 <= 0.5654538877147501) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -565c564 -< if (p2 > 0.7007179663985585) and (x > 0.47267748344348626) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 <= 0.5654538877147501) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -566a566 -> if (p2 > 0.7007179663985585) and (x > 0.47267748344348626) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -570d569 -< if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x <= 0.6760619000417248) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -572c571 -< if (p2 > 0.30978463977099613) and (p1 > 0.6320834037065894) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x <= 0.6760619000417248) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -573a573 -> if (p2 > 0.30978463977099613) and (p1 > 0.6320834037065894) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -578d577 -< if (p1 > 0.6819941814439344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -579a579 -> if (p1 > 0.6819941814439344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -600d599 -< if (p1 > 0.5931505284240239) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -601a601 -> if (p1 > 0.5931505284240239) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -631d630 -< if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x <= 0.4155191624469929) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -633c632 -< if (p2 > 0.3774126001528523) and (p1 > 0.6553700527434098) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x <= 0.4155191624469929) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -634a634 -> if (p2 > 0.3774126001528523) and (p1 > 0.6553700527434098) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -638d637 -< if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x <= 0.7892120132227867) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -640c639 -< if (p2 > 0.23018040425618197) and (p1 > 0.7102524464532046) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x <= 0.7892120132227867) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -641a641 -> if (p2 > 0.23018040425618197) and (p1 > 0.7102524464532046) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -653d652 -< if (p2 > 0.21158053456413584) and (p1 > 0.5586915479780601) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -654a654 -> if (p2 > 0.21158053456413584) and (p1 > 0.5586915479780601) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -661d660 -< if (p1 > 0.6795984351446844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -662a662 -> if (p1 > 0.6795984351446844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -677d676 -< if (p1 > 0.8126054242312002) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -678a678 -> if (p1 > 0.8126054242312002) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -684d683 -< if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x <= 0.7910962169102163) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -686c685 -< if (p2 > 0.26886312862339573) and (p1 > 0.8950548846717248) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x <= 0.7910962169102163) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -687a687 -> if (p2 > 0.26886312862339573) and (p1 > 0.8950548846717248) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -698d697 -< if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x <= 0.4784058184220548) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -700c699 -< if (p2 > 0.25803846924474394) and (p1 > 0.85781003667871) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x <= 0.4784058184220548) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -701a701 -> if (p2 > 0.25803846924474394) and (p1 > 0.85781003667871) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -705d704 -< if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x <= 0.6112039253981838) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -707c706 -< if (p2 > 0.16944414757631912) and (p1 > 0.7608029128801092) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x <= 0.6112039253981838) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -708a708 -> if (p2 > 0.16944414757631912) and (p1 > 0.7608029128801092) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -728d727 -< if (p2 > 0.29657478970316) and (p1 > 0.8279177280272906) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -729a729 -> if (p2 > 0.29657478970316) and (p1 > 0.8279177280272906) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -736d735 -< if (p2 > 0.30543398172847647) and (p1 > 0.7651434488432218) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -737a737 -> if (p2 > 0.30543398172847647) and (p1 > 0.7651434488432218) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test19_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test19_model_rerun_model_config.json -=================== End of Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json -=================== End of Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test22_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test22_model_rerun_model_config.json -=================== End of Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt diff report ================================ -=================== Diff report for: Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test24_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test24_model_rerun_model_config.json -=================== End of Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: test26_model_dt_sklearn_tree_rules.txt ================================== -6d5 -< if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -7a7 -> if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of test26_model_dt_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test26_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test26_model_rerun_model_config.json -=================== End of Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json -=================== End of Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test47_test47_model_smlp_toy_pf_mult.txt ================================== -83c83 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test47_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test47_model_rerun_model_config.json -=================== End of Test47_test47_model_smlp_toy_pf_mult.txt diff report ================================ -=================== Diff report for: Test64_test63_model.txt ================================== -27c27 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test63_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test63_model_rerun_model_config.json -=================== End of Test64_test63_model.txt diff report ================================ +=================== Diff report for: Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== +208,225c208,212 +< smlp_logger - INFO - Model: "functional" +< ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ +< ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ +< ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ +< │ input_layer │ (None, 3) │ 0 │ - │ +< │ (InputLayer) │ │ │ │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense (Dense) │ (None, 6) │ 24 │ input_layer[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense_1 (Dense) │ (None, 3) │ 21 │ dense[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y1 (Dense) │ (None, 1) │ 4 │ dense_1[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y2 (Dense) │ (None, 1) │ 4 │ dense_1[0][0] │ +< └─────────────────────┴───────────────────┴────────────┴───────────────────┘ +< Total params: 53 (212.00 B) +< Trainable params: 53 (212.00 B) +< Non-trainable params: 0 (0.00 B) +--- +> smlp_logger - INFO - Model: "model" +> __________________________________________________________________________________________________ +> Layer (type) Output Shape Param # Connected to +> ================================================================================================== +> input_1 (InputLayer) [(None, 3)] 0 [] +226a214 +> dense (Dense) (None, 6) 24 ['input_1[0][0]'] +228c216,229 +< smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +--- +> dense_1 (Dense) (None, 3) 21 ['dense[0][0]'] +> +> y1 (Dense) (None, 1) 4 ['dense_1[0][0]'] +> +> y2 (Dense) (None, 1) 4 ['dense_1[0][0]'] +> +> ================================================================================================== +> Total params: 53 (212.00 Byte) +> Trainable params: 53 (212.00 Byte) +> Non-trainable params: 0 (0.00 Byte) +> __________________________________________________________________________________________________ +> +> +> smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +234c235 +< smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] +--- +> smlp_logger - INFO - Metrics: ['mse'] +236c237 +< smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +--- +> smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y1', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +242c243 +< smlp_logger - INFO - Callbacks: [""] +--- +> smlp_logger - INFO - Callbacks: [""] +266c267 +< smlp_logger - INFO - Prediction on training data -- msqe: 7.935 +--- +> smlp_logger - INFO - Prediction on training data -- msqe: 7.938 +268c269 +< smlp_logger - INFO - Prediction on training data -- r2_score: -1.021 +--- +> smlp_logger - INFO - Prediction on training data -- r2_score: -1.022 +286c287 +< smlp_logger - INFO - Prediction on test data -- msqe: 6.833 +--- +> smlp_logger - INFO - Prediction on test data -- msqe: 6.834 +306c307 +< smlp_logger - INFO - Prediction on labeled data -- msqe: 7.634 +--- +> smlp_logger - INFO - Prediction on labeled data -- msqe: 7.637 +308c309 +< smlp_logger - INFO - Prediction on labeled data -- r2_score: -0.924 +--- +> smlp_logger - INFO - Prediction on labeled data -- r2_score: -0.925 +326c327 +< smlp_logger - INFO - Prediction on new data -- msqe: 7.974 +--- +> smlp_logger - INFO - Prediction on new data -- msqe: 7.977 +328c329 +< smlp_logger - INFO - Prediction on new data -- r2_score: -1.018 +--- +> smlp_logger - INFO - Prediction on new data -- r2_score: -1.019 +=================== End of Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ +=================== Diff report for: Test13_smlp_toy_basic.txt ================================== +126,143c126,130 +< smlp_logger - INFO - Model: "functional" +< ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ +< ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ +< ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ +< │ input_layer │ (None, 4) │ 0 │ - │ +< │ (InputLayer) │ │ │ │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense (Dense) │ (None, 8) │ 40 │ input_layer[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense_1 (Dense) │ (None, 4) │ 36 │ dense[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y1 (Dense) │ (None, 1) │ 5 │ dense_1[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y2 (Dense) │ (None, 1) │ 5 │ dense_1[0][0] │ +< └─────────────────────┴───────────────────┴────────────┴───────────────────┘ +< Total params: 86 (344.00 B) +< Trainable params: 86 (344.00 B) +< Non-trainable params: 0 (0.00 B) +--- +> smlp_logger - INFO - Model: "model" +> __________________________________________________________________________________________________ +> Layer (type) Output Shape Param # Connected to +> ================================================================================================== +> input_1 (InputLayer) [(None, 4)] 0 [] +144a132 +> dense (Dense) (None, 8) 40 ['input_1[0][0]'] +146c134,147 +< smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +--- +> dense_1 (Dense) (None, 4) 36 ['dense[0][0]'] +> +> y1 (Dense) (None, 1) 5 ['dense_1[0][0]'] +> +> y2 (Dense) (None, 1) 5 ['dense_1[0][0]'] +> +> ================================================================================================== +> Total params: 86 (344.00 Byte) +> Trainable params: 86 (344.00 Byte) +> Non-trainable params: 0 (0.00 Byte) +> __________________________________________________________________________________________________ +> +> +> smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +152c153 +< smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] +--- +> smlp_logger - INFO - Metrics: ['mse'] +154c155 +< smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 4), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 8, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 4, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 8)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 8), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +--- +> smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 4), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 8, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 4, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 8)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y1', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +160c161 +< smlp_logger - INFO - Callbacks: [""] +--- +> smlp_logger - INFO - Callbacks: [""] +184c185 +< smlp_logger - INFO - Prediction on training data -- msqe: 38.795 +--- +> smlp_logger - INFO - Prediction on training data -- msqe: 41.749 +186c187 +< smlp_logger - INFO - Prediction on training data -- r2_score: -9.102 +--- +> smlp_logger - INFO - Prediction on training data -- r2_score: -10.416 +204c205 +< smlp_logger - INFO - Prediction on test data -- msqe: 11.661 +--- +> smlp_logger - INFO - Prediction on test data -- msqe: 11.659 +206c207 +< smlp_logger - INFO - Prediction on test data -- r2_score: -4.243 +--- +> smlp_logger - INFO - Prediction on test data -- r2_score: -4.262 +224c225 +< smlp_logger - INFO - Prediction on labeled data -- msqe: 33.368 +--- +> smlp_logger - INFO - Prediction on labeled data -- msqe: 35.731 +226c227 +< smlp_logger - INFO - Prediction on labeled data -- r2_score: -2.803 +--- +> smlp_logger - INFO - Prediction on labeled data -- r2_score: -3.158 +=================== End of Test13_smlp_toy_basic.txt diff report ================================ =================== Diff report for: Test66_test65_model.txt ================================== 0a1,97 > @@ -414,7 +194,7 @@ > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test65_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test65_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -517,7 +297,7 @@ diff: /app/smlp/regr_smlp/code/Test66_test65_model_verify_results.json: No such > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test67_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test67_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -592,12 +372,6 @@ diff: /app/smlp/regr_smlp/code/Test66_test65_model_verify_results.json: No such =================== Diff report for: Test68_test67_model_verify_results.json ================================== diff: /app/smlp/regr_smlp/code/Test68_test67_model_verify_results.json: No such file or directory =================== End of Test68_test67_model_verify_results.json diff report ================================ -=================== Diff report for: Test70_test69_model.txt ================================== -25c25 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test69_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test69_model_rerun_model_config.json -=================== End of Test70_test69_model.txt diff report ================================ =================== Diff report for: Test72_test71_model.txt ================================== 0a1,84 > @@ -624,7 +398,7 @@ diff: /app/smlp/regr_smlp/code/Test68_test67_model_verify_results.json: No such > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test71_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test71_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -718,7 +492,7 @@ diff: /app/smlp/regr_smlp/code/Test72_test71_model_verify_results.json: No such > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test76_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test76_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -810,24 +584,6 @@ diff: /app/smlp/regr_smlp/code/Test77_test76_model_verify_results.json: No such --- > smlp_logger - INFO - Model operator counts for y2: {'add': 100, 'mul': 716, 'const': 2550, 'ite': 305, 'and': 409, 'prop': 714, 'sub': 714, 'var': 714} =================== End of Test97_smlp_toy_num_resp_mult.txt diff report ================================ -=================== Diff report for: Test102_test101_model.txt ================================== -38c38 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test101_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test101_model_rerun_model_config.json -=================== End of Test102_test101_model.txt diff report ================================ =================== Diff report for: test110_model_poly_sklearn_formula.txt ================================== diff: /app/smlp/regr_smlp/code/test110_model_poly_sklearn_formula.txt: No such file or directory =================== End of test110_model_poly_sklearn_formula.txt diff report ================================ -=================== Diff report for: Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt ================================== -79c79 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json -=================== End of Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt diff report ================================ -=================== Diff report for: Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt ================================== -79c79 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json -=================== End of Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt diff report ================================ diff --git a/tests/smlp_regression/run_smlp_regression_venv_expected.log b/tests/smlp_regression/run_smlp_regression_venv_expected.log index ce736b01..494abbe9 100644 --- a/tests/smlp_regression/run_smlp_regression_venv_expected.log +++ b/tests/smlp_regression/run_smlp_regression_venv_expected.log @@ -6,823 +6,1021 @@ Initiating 3 worker... Initiating 4 worker... Initiating 5 worker... Initiating 6 worker... -Running test 7 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test7 -mode predict -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 11 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test11 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 3 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric response in training/test data only +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test3 -mode predict -resp y1 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_unlabeled.csv" + + +Running test 9 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test9 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -model_name test20_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -save_config t -save_model_config t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 16 test type: prediction, description: basic nn_keras prediction test from saved model on new data with numeric labels and two responses -../../src/run_smlp.py -model_name "../models/Test8_smlp_toy_num_resp_mult" -out_dir ./ -pref Test16 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +smlp -model_name "../models/Test8_smlp_toy_num_resp_mult" -out_dir ./ -pref Test16 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 24 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels +smlp -model_name "../models/test24_model" -out_dir ./ -pref Test24 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 29 test type: subgroups, description: basic test for subgroup discovery for pass-fail responses +smlp -data "../data/smlp_toy_cls_metasymbol_colnames_mult.csv" -out_dir ./ -pref Test29 -mode subgroups -psg_dim 3 -psg_top 10 -resp "PF 1,PF#" -plots t -seed 10 -log_time f -Running test 25 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test25 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test26_model -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" Running test 36 test type: doe, description: doe test with four levels with sukharev_grid -../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test36 -mode doe -doe_algo sukharev_grid -doe_samples 125 -log_time f +smlp -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test36 -mode doe -doe_algo sukharev_grid -doe_samples 125 -log_time f -Running test 44 test type: doe, description: doe test with four levels with uniform_random_matrix -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test44 -mode doe -doe_algo uniform_random_matrix -doe_samples 20 -log_time f -Running test 52 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test52 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 46 test type: prediction, description: tests options -pos_val and -neg_val +smlp -data "../data/smlp_toy_pf_mult.csv" -out_dir ./ -pref Test46 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model t -save_model_config f -use_model f -model_name test47_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" -spec_fn smlp_toy_num_resp_mult_y1_verify.spec + +Running test 53 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test53 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + +spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs -Running test 63 test type: verify, description: basic dt_sklearn assertion verification test on data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test63 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test63_model -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 68 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response +smlp -model_name "../models/test67_model" -out_dir ./ -pref Test68 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -model_per_response t -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult.spec +specs_path ../specs + +Running test 80 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test80 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +specs_path ../specs + +Running test 88 test type: optimize, description: basic dt_sklearn multi objective pareto optimization test with beta and objectives specified in spec file +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test88 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec +specs_path ../specs + +Running test 94 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test94 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_witness.spec +specs_path ../specs + +Running test 102 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation +smlp -model_name "../models/test101_model" -out_dir ./ -pref Test102 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_stable_verify.spec +specs_path ../specs + +Running test 105 test type: verify, description: basic dt_sklearn assertion verfication test with numeric labels and integer grid as domain +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test105 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_stable_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 112 test type: prediction, description: smlp toy basic test from SMLP manual +smlp -model_name "../models/test110_model" -out_dir ./ -pref Test112 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -use_model t -save_model f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" + +spec_fn smlp_toy_system_stable_constant_verify.spec +specs_path ../specs + +Running test 118 test type: verify, description: verification test with knobs only where assertion is valid without stability and fails with stability +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test118 -mode verify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_system_stable_constant_synth_fail.spec +specs_path ../specs + +Running test 124 test type: optsyn, description: optimized synthesis test with constant knob and no inputs where synthesis is not feasible because while beta constraint is feasible the assertion is not feasible therefore optimization is not performed +Running test 6 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test6 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 10 test type: prediction, description: basic et_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test10 -mode predict -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 15 -et_sklearn_bootstrap f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 18 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test18 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test19_model -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 26 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels +smlp -model_name "../models/test26_model" -out_dir ./ -pref Test26 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 31 test type: subgroups, description: testing resp2b in subgroup discovery mode +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test31 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -resp2b "y1<6;y2>6" -feat x,p1,p2 -plots t -seed 10 -log_time f -save_config t + + +Running test 39 test type: doe, description: doe test with four levels with latin_hypercube +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test39 -mode doe -doe_algo latin_hypercube -doe_prob_distr Exponential -doe_samples 30 -log_time f + + +Running test 45 test type: doe, description: doe test with four levels with fractional_factorial +smlp -doe_spec "../grids/doe_two_levels_real.csv" -out_dir ./ -pref Test45 -mode doe -doe_algo fractional_factorial -doe_resolution 5 -log_time f + + +Running test 54 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test54 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs -Running test 72 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model -../../src/run_smlp.py -model_name "../models/test71_model" -out_dir ./ -pref Test72 -mode verify -resp y1,y2 -feat x0,x1,x2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat + +Running test 66 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response +smlp -model_name "../models/test65_model" -out_dir ./ -pref Test66 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 77 test type: unknown, description: verification test run using model_rerun config covering the case when mrmr selcts only a subset of features specified through the command line or config file +smlp -model_name "../models/test76_model" -out_dir ./ -pref Test77 -config ../models/test76_model_rerun_model_config.json spec_fn smlp_toy_num_resp_mult_free_inps.spec specs_path ../specs + Running test 83 test type: optimize, description: basic dt_sklearn multi objective pareto optimization test with numeric labels and integer grid as domain and with scaling objectives -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test83 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -beta "y1>7 and y2>6" -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1/2-y2;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test83 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -beta "y1>7 and y2>6" -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1/2-y2;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_verify_vacuous.spec specs_path ../specs + Running test 92 test type: verify, description: test to detect contradictory constraints in verification mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test92 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_verify_vacuous.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test92 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_verify_vacuous.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 99 test type: optimize, description: testing that the response and feature names can be taken from spec file in model exploration modes when the responses and/or features are not specified in the command line -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test99 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_certify_witness.spec +Running test 100 test type: optimize, description: basic test for sat_threshold option enabing usage of objectve values in SAT assignments that prove optimization thresholds +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test100 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs -Running test 103 test type: certify, description: -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test103 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test103_model -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_certify_witness.spec -quer_names valid_candidate,grid_conflict,range_conflict -quer_exprs "True;True;True" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 104 test type: verify, description: assertion verfication test with wrong spec that does not assign a single value using a singleton grid or range with equal max and min +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test104 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_cannot_synthesize.spec specs_path ../specs + Running test 109 test type: synthesize, description: basic test for mode synthesize where synthesis fails -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test109 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_cannot_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test109 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_cannot_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs + Running test 122 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test122 -mode optimize -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test122 -mode optimize -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_verify.spec specs_path ../specs -Running test 140 test type: verify, description: verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stabilityRunning test 3 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric response in training/test data only -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test3 -mode predict -resp y1 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_unlabeled.csv" -Running test 10 test type: prediction, description: basic et_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test10 -mode predict -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 15 -et_sklearn_bootstrap f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 140 test type: verify, description: verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability +Running test 7 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test7 -mode predict -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 11 test type: prediction, description: basic poly_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test11 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 17 test type: prediction, description: basic poly_sklearn prediction test from saved model on new data with numeric labels and two responses -../../src/run_smlp.py -model_name "../models/Test11_smlp_toy_num_resp_mult" -out_dir ./ -pref Test17 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +smlp -model_name "../models/Test11_smlp_toy_num_resp_mult" -out_dir ./ -pref Test17 -mode predict -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 23 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test23 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test24_model -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 24 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels -../../src/run_smlp.py -model_name "../models/test24_model" -out_dir ./ -pref Test24 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" Running test 30 test type: subgroups, description: basic test for subgroup discovery for numric responses -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test30 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -feat x,p1,p2 -plots t -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test30 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -feat x,p1,p2 -plots t -seed 10 -log_time f -Running test 37 test type: doe, description: doe test with four levels with box_behnken -../../src/run_smlp.py -doe_spec "../grids/doe_three_levels_real_nan.csv" -out_dir ./ -pref Test37 -mode doe -doe_algo box_behnken -log_time f -Running test 45 test type: doe, description: doe test with four levels with fractional_factorial -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels_real.csv" -out_dir ./ -pref Test45 -mode doe -doe_algo fractional_factorial -doe_resolution 5 -log_time f +Running test 38 test type: doe, description: doe test with four levels with box_wilson +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test38 -mode doe -doe_algo box_wilson -doe_cc_face ccc -doe_cc_alpha r -doe_cc_center 2,3 -log_time f + + +Running test 47 test type: prediction, description: tests options -pos_val and -neg_val when re-using saved model +smlp -model_name "../models/test47_model" -out_dir ./ -pref Test47 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" + Running test 55 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test55 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test55 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 69 test type: verify, description: nn_keras verification test with model_per_response training -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test69 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model t -use_model f -model_name test69_model -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test69 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model t -use_model f -model_name test69_model -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs -Running test 88 test type: optimize, description: basic dt_sklearn multi objective pareto optimization test with beta and objectives specified in spec file -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test88 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec -specs_path ../specs -Running test 94 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test94 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 85 test type: optimize, description: tests alpha and eta constraints specified in command line in optimization mode +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test85 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_witness.spec +spec_fn smlp_toy_num_resp_mult_query_vacuous.spec specs_path ../specs -Running test 102 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation -../../src/run_smlp.py -model_name "../models/test101_model" -out_dir ./ -pref Test102 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_unsat_eta_verify.spec +Running test 91 test type: query, description: test to detect contradictory constraints in optimization mode due to contradictory alpha global and alpha bounds constraints on FMAX_xyx +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test91 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 106 test type: verify, description: test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test106 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_unsat_eta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_basic.spec +Running test 99 test type: optimize, description: testing that the response and feature names can be taken from spec file in model exploration modes when the responses and/or features are not specified in the command line +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test99 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_certify_witness.spec specs_path ../specs -Running test 113 test type: optimize, description: smlp toy basic test for mode optimize from SMLP manual -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test113 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model t -model_name test113_model -save_model_config t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec -spec_fn smlp_toy_system_stable_constant_verify.spec +Running test 103 test type: certify, description: +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test103 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test103_model -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_certify_witness.spec -quer_names valid_candidate,grid_conflict,range_conflict -quer_exprs "True;True;True" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 110 test type: prediction, description: smlp toy basic example for predict mode from SMLP user manual +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test110 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model t -model_name test110_model -save_model_config t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" + +spec_fn smlp_toy_system.spec specs_path ../specs -Running test 118 test type: verify, description: verification test with knobs only where assertion is valid without stability and fails with stability -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test118 -mode verify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 116 test type: certify, description: basic test in certify mode when system is specified and is used as the model; p2 rel-rad needs to be 0 or very close to it the witness to first query to be stable +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test116 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_verify.spec specs_path ../specs + Running test 126 test type: verify, description: verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test126 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test126 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec -specs_pathRunning test 5 test type: prediction, description: basic dt_caret prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test5 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model t -use_model f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" - -Running test 18 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test18 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test19_model -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +specs_path ../specs -Running test 26 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels -../../src/run_smlp.py -model_name "../models/test26_model" -out_dir ./ -pref Test26 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 142 test type: optsyn, description: basic test for compress_rules option for rf_sklearn in optsin mode +Running test 2 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test2 -mode predict -resp y1 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 33 test type: unknown, description: testing -config option with subgroups mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test33 -config ../models/Test31_smlp_toy_num_resp_mult_args_config.json -Running test 41 test type: doe, description: doe test with four levels with random_k_means -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test41 -mode doe -doe_algo random_k_means -doe_samples 20 -log_time f +Running test 12 test type: train, description: EV-SI real life dt_sklearn predict test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test12 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 49 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test49 -mode discretize -resp "PF,PF1" -discr_algo quantile -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -spec_fn smlp_toy_num_resp_mult.spec -specs_path ../specs -Running test 58 test type: optimize, description: basic dt_sklearn optimization test with numeric labels and integer grid as domain and without scaling objectives -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test58 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 20 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels +smlp -model_name "../models/test20_model" -out_dir ./ -pref Test20 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -spec_fn smlp_toy_num_resp_mult_y2_verify.spec -specs_path ../specs -Running test 70 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model -../../src/run_smlp.py -model_name "../models/test69_model" -out_dir ./ -pref Test70 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult.spec -specs_path ../specs -Running test 85 test type: optimize, description: tests alpha and eta constraints specified in command line in optimization mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test85 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 27 test type: prediction, description: checks nn_keras prediction with nn_keras_seq_api t +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test27 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -spec_fn smlp_toy_num_resp_mult_optsyn.spec -specs_path ../specs -Running test 93 test type: optsyn, description: basic test for mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test93 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_witness.spec -specs_path ../specs -Running test 101 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test101 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test101_model -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 35 test type: doe, description: doe test with four levels with plackett_burman +smlp -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test35 -mode doe -doe_algo plackett_burman -log_time f -spec_fn smlp_toy_num_resp_mult_stable_verify.spec -specs_path ../specs -Running test 105 test type: verify, description: basic dt_sklearn assertion verfication test with numeric labels and integer grid as domain -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test105 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_stable_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 111 test type: unknown, description: smlp toy basic test to rerun saved model using the model rerun config file saved during model training -../../src/run_smlp.py -model_name "../models/test110_model" -out_dir ./ -pref Test111 -config ../models/test110_model_rerun_model_config.json -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" +Running test 43 test type: doe, description: doe test with four levels with halton_sequence +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test43 -mode doe -doe_algo halton_sequence -doe_samples 20 -log_time f -spec_fn smlp_toy_system_stable_constant_certify.spec -specs_path ../specs -Running test 117 test type: certify, description: certification test with knobs only where assertion is valid without stability and fails with stability -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test117 -mode certify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_system_stable_constant_synth_feasible.spec -specs_path ../specs -Running test 123 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test123 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 51 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test51 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -spec_fn smlp_toy_system_stable_constant_synth_feasible.spec +spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs -Running test 145 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed -../../src/run_smlp.py -out_dir ./ -pref Test145 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 6 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test6 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" - -Running test 9 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test9 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -model_name test20_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -save_config t -save_model_config t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 15 test type: prediction, description: basic dt_caret prediction test from saved model on new data with numeric labels -../../src/run_smlp.py -model_name "../models/Test5_smlp_toy_num_resp_mult" -out_dir ./ -pref Test15 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" - -Running test 23 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test23 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test24_model -model_per_response t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 60 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test60 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -Running test 29 test type: subgroups, description: basic test for subgroup discovery for pass-fail responses -../../src/run_smlp.py -data "../data/smlp_toy_cls_metasymbol_colnames_mult.csv" -out_dir ./ -pref Test29 -mode subgroups -psg_dim 3 -psg_top 10 -resp "PF 1,PF#" -plots t -seed 10 -log_time f +spec_fn smlp_toy_num_resp_mult_free_inps.spec +specs_path ../specs -Running test 38 test type: doe, description: doe test with four levels with box_wilson -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test38 -mode doe -doe_algo box_wilson -doe_cc_face ccc -doe_cc_alpha r -doe_cc_center 2,3 -log_time f +Running test 82 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test82 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -Running test 46 test type: prediction, description: tests options -pos_val and -neg_val -../../src/run_smlp.py -data "../data/smlp_toy_pf_mult.csv" -out_dir ./ -pref Test46 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model t -save_model_config f -use_model f -model_name test47_model -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" +spec_fn smlp_toy_num_resp_mult_optsyn.spec +specs_path ../specs -Running test 56 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test56 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels f -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 93 test type: optsyn, description: basic test for mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test93 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_noknobs_verify.spec +spec_fn smlp_toy_num_resp_mult_witness.spec specs_path ../specs -Running test 68 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response -../../src/run_smlp.py -model_name "../models/test67_model" -out_dir ./ -pref Test68 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -model_per_response t -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult.spec -specs_path ../specs -Running test 80 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test80 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 101 test type: certify, description: basic test in certify mode to test stability (theta) and guard (eta) constraint generation +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test101 -mode certify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test101_model -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_witness.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_alpha_asrt_verify.spec +spec_fn smlp_toy_num_resp_mult_unsat_eta_verify.spec specs_path ../specs -Running test 87 test type: verify, description: tests global alpha constraints and assertions specified in spec file -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test87 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_alpha_asrt_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 106 test type: verify, description: test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test106 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_unsat_eta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_basic.spec specs_path ../specs -Running test 96 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test96 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 112 test type: prediction, description: smlp toy basic test from SMLP manual -../../src/run_smlp.py -model_name "../models/test110_model" -out_dir ./ -pref Test112 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -use_model t -save_model f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" +Running test 113 test type: optimize, description: smlp toy basic test for mode optimize from SMLP manual +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test113 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model t -model_name test113_model -save_model_config t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec spec_fn smlp_toy_system_stable_constant_query.spec specs_path ../specs + Running test 119 test type: query, description: query test with knobs only where query is satisfiable without stability and fails with stability -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test119 -mode query -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test119 -mode query -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs + Running test 125 test type: optsyn, description: optimized synthesis test with constant knob and no inputs where synthesis is feasible and optimization is performed -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test125 -mode optsyn -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test125 -mode optsyn -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system.spec specs_path ../specs + Running test 146 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed -../../src/run_smlp.py -out_dir ./ -pref Test146 -mode optimize -pareto t -opt_strategy lazy -model poly_sklearn -resp y1,y2 -feat p1,p2,x1,x2 -save_model t -use_model f -mrmr_pred 0 -model_per_response t -split 1 -spec ../specs/smlp_toy_system.spec -doe_spec ../grids/explore_doe_two_levels.csv -doe_algo latin_hypercube -epsilon 0.99999999 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -out_dir ./ -pref Test146 -mode optimize -pareto t -opt_strategy lazy -model poly_sklearn -resp y1,y2 -feat p1,p2,x1,x2 -save_model t -use_model f -mrmr_pred 0 -model_per_response t -split 1 -spec ../specs/smlp_toy_system.spec -doe_spec ../grids/explore_doe_two_levels.csv -doe_algo latin_hypercube -epsilon 0.99999999 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 152 test type: prediction, description: tests the huber loss function Huber and sample weights -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test152 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss huber -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" Running test 4 test type: prediction, description: basic nn_keras prediction test on labeled and new data with numeric labels and one response -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test4 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nn_keras_weights_precision 2 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test4 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nn_keras_weights_precision 2 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 13 test type: train, description: EV-SI real life nn_keras prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test13 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test13 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 22 test type: prediction, description: test for illegal symbols in column names -../../src/run_smlp.py -model_name "../models/test22_model" -out_dir ./ -pref Test22 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model f -use_model t -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" -Running test 32 test type: unknown, description: test reusing saved model by using configuration file -../../src/run_smlp.py -model_name "../models/test20_model" -out_dir ./ -pref Test32 -config ../models/test20_model_rerun_model_config.json -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 28 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and functional API +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test28 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 39 test type: doe, description: doe test with four levels with latin_hypercube -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test39 -mode doe -doe_algo latin_hypercube -doe_prob_distr Exponential -doe_samples 30 -log_time f -Running test 47 test type: prediction, description: tests options -pos_val and -neg_val when re-using saved model -../../src/run_smlp.py -model_name "../models/test47_model" -out_dir ./ -pref Test47 -mode predict -resp "PF,PF1" -model poly_sklearn -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -pos_val fail -neg_val pass -new_dat "../data/smlp_toy_pf_mult.csv" +Running test 41 test type: doe, description: doe test with four levels with random_k_means +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test41 -mode doe -doe_algo random_k_means -doe_samples 20 -log_time f -Running test 54 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test54 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -spec_fn smlp_toy_num_resp_mult_y1_verify.spec +Running test 49 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test49 -mode discretize -resp "PF,PF1" -discr_algo quantile -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + +spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs -Running test 64 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response -../../src/run_smlp.py -model_name "../models/test63_model" -out_dir ./ -pref Test64 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 77 test type: unknown, description: verification test run using model_rerun config covering the case when mrmr selcts only a subset of features specified through the command line or config file -../../src/run_smlp.py -model_name "../models/test76_model" -out_dir ./ -pref Test77 -config ../models/test76_model_rerun_model_config.json +Running test 58 test type: optimize, description: basic dt_sklearn optimization test with numeric labels and integer grid as domain and without scaling objectives +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test58 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_y2_verify.spec +specs_path ../specs + +Running test 70 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model +smlp -model_name "../models/test69_model" -out_dir ./ -pref Test70 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs + Running test 86 test type: optimize, description: tests alpha -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test86 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -asrt_names asrt1,asrt2,asrt3 -asrt_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test86 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -data_scaler min_max -objv_names obj1,objv2 -objv_exprs "(y1+y2)/2;y1" -asrt_names asrt1,asrt2,asrt3 -asrt_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -alpha "p2<5 and x==10 and x<12" -eta "p1==4" -epsilon 0.05 -delta_rel 0.01 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 95 test type: optsyn, description: basic test for dt_caret in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test95 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test95 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_beta_verify.spec specs_path ../specs + Running test 107 test type: verify, description: test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test107 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_beta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test107 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_beta_verify.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_basic.spec specs_path ../specs + Running test 114 test type: optimize, description: smlp toy basic test for mode optimize from SMLP manual without specifying resp and feat in command line -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test114 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model f -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test114 -mode optimize -pareto t -opt_strategy lazy -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -mrmr_pred 0 -epsilon 0.05 -delta_rel 0.01 -save_model f -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec ../specs/smlp_toy_basic.spec -spec_fn smlp_toy_system_stable_constant_synth_fail.spec +spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 120 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test120 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_witness_certify.spec +Running test 121 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is feasible +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test121 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_configuration_verify.spec specs_path ../specs -Running test 128 test type: certify, description: Basic regression test in certify mode covering all four possible outcomes when certifying a witness for a query: the witness is stable -../../src/run_smlp.py -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test128 -mode certify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_witness_certify.spec -quer_names query_stable_witness,query_grid_conflict,query_unstable_witness,query_infeasible_witness,query_poly_intercept_sensitive -quer_exprs "y2<=90;y1>=9;y1>=(-13);y1>9;y1>=(-10)" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 129 test type: verify, description: verification example with demonstrating all basic result scenarious for assertions +smlp -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test129 -mode verify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_configuration_verify.spec -asrt_names assert_stable_config,assert_grid_conflict,assert_unstable_config,assert_infeasible -asrt_exprs "y2<=90;y1>=9;y1>=(-10);y1>20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 149 test type: prediction, description: tests the mae loss function MeanAbsoluteError and sample weoghts -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test149 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mae -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test149 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mae -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -spec_fn smlp_toy_num_resp_mult_y2_verify.specRunning test 8 test type: prediction, description: basic nn_keras prediction test on labeled and new data with numeric labels and two responses -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test8 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -nn_keras_epochs 20 -nn_keras_seq_api f -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +spec_fn smlp_toy_num_resp_mult_verify.spec +specs_path ../specs -Running test 14 test type: train, description: EV-SI real life poly_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test14 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 156 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training; adapts test 154 by consdering multiple responses +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test156 -mode verify -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse -Running test 21 test type: prediction, description: test for illegal symbols in column names -../../src/run_smlp.py -data "../data/smlp_toy_num_metasymbol_mult_reg.csv" -out_dir ./ -pref Test21 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model t -use_model f -model_name test22_model -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" +Running test 5 test type: prediction, description: basic dt_caret prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test5 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model t -use_model f -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 31 test type: subgroups, description: testing resp2b in subgroup discovery mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test31 -mode subgroups -psg_dim 3 -psg_top 10 -resp y1,y2 -resp2b "y1<6;y2>6" -feat x,p1,p2 -plots t -seed 10 -log_time f -save_config t -Running test 40 test type: doe, description: doe test with four levels with latin_hypercube_space_filling -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test40 -mode doe -doe_algo latin_hypercube_sf -doe_samples 20 -log_time f +Running test 15 test type: prediction, description: basic dt_caret prediction test from saved model on new data with numeric labels +smlp -model_name "../models/Test5_smlp_toy_num_resp_mult" -out_dir ./ -pref Test15 -mode predict -resp y1 -feat x,p1,p2 -model dt_caret -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 48 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test48 -mode discretize -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -Running test 53 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test53 -mode discretize -resp "PF,PF1" -discr_algo ordinals -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 22 test type: prediction, description: test for illegal symbols in column names +smlp -model_name "../models/test22_model" -out_dir ./ -pref Test22 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model f -use_model t -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" -spec_fn smlp_toy_num_resp_noknobs_verify.spec + +Running test 32 test type: unknown, description: test reusing saved model by using configuration file +smlp -model_name "../models/test20_model" -out_dir ./ -pref Test32 -config ../models/test20_model_rerun_model_config.json -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 37 test type: doe, description: doe test with four levels with box_behnken +smlp -doe_spec "../grids/doe_three_levels_real_nan.csv" -out_dir ./ -pref Test37 -mode doe -doe_algo box_behnken -log_time f + + +Running test 44 test type: doe, description: doe test with four levels with uniform_random_matrix +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test44 -mode doe -doe_algo uniform_random_matrix -doe_samples 20 -log_time f + + +Running test 52 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test52 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + +spec_fn smlp_toy_num_resp_mult_y1_verify.spec specs_path ../specs -Running test 66 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response -../../src/run_smlp.py -model_name "../models/test65_model" -out_dir ./ -pref Test66 -mode verify -resp y1,y2 -feat x0,x1,x2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x0**2+y1>4.3;(y1+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult.spec +Running test 63 test type: verify, description: basic dt_sklearn assertion verification test on data with numeric labels +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test63 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -use_model f -model_name test63_model -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_noknobs_verify.spec specs_path ../specs -Running test 79 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test79 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn_vacuous.spec +Running test 72 test type: verify, description: nn_keras verification test with re-using saved model_per_response trained model +smlp -model_name "../models/test71_model" -out_dir ./ -pref Test72 -mode verify -resp y1,y2 -feat x0,x1,x2 -model nn_keras -nnet_encoding nested -save_model f -use_model t -model_per_response t -spec ../specs/smlp_toy_num_resp_noknobs_verify.spec -asrt_names asrt1 -asrt_exprs "(y2**3+x2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat + +spec_fn smlp_toy_num_resp_mult_alpha_asrt_verify.spec specs_path ../specs -Running test 90 test type: optsyn, description: test to detect contradictory constraints in optsyn mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test90 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 87 test type: verify, description: tests global alpha constraints and assertions specified in spec file +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test87 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_alpha_asrt_verify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 98 test type: optsyn, description: basic test for et_caret in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test98 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 96 test type: optsyn, description: basic test for rf_sklearn in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test96 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 111 test type: unknown, description: smlp toy basic test to rerun saved model using the model rerun config file saved during model training +smlp -model_name "../models/test110_model" -out_dir ./ -pref Test111 -config ../models/test110_model_rerun_model_config.json -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" spec_fn smlp_toy_system.spec specs_path ../specs + Running test 115 test type: certify, description: basic test in certify mode -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test115 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test115 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_system_stable_constant_synth_fail.spec +spec_fn smlp_toy_system_stable_certify.spec specs_path ../specs -Running test 124 test type: optsyn, description: optimized synthesis test with constant knob and no inputs where synthesis is not feasible because while beta constraint is feasible the assertion is not feasible therefore optimization is not performed -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test124 -mode optsyn -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult.spec +Running test 127 test type: certify, description: certification example with knobs only and fictitious inputs with values fixed through their ranges +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test127 -mode certify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 141 test type: optimize, description: basic test for compress_rules option for dt_sklearn in optimization mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test141 -mode optimize -opt_strategy lazy -pareto f -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 148 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test148 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 145 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed +smlp -out_dir ./ -pref Test145 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_verify.spec + +Running test 151 test type: prediction, description: tests msle loss function MeanSquaredLogarithmicError and and sample weoghts +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test151 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss msle -sw_coef 3 -sw_exp 10 -sw_int 0 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 158 test type: prediction, description: tests the mape loss function and sample weights with model_per_response t +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test158 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -model_per_response t -sw_coef 8 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 157 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training; adapts test 155 by consdering multiple responses -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test157 -mode verify -resp y1,y2 -feat x,p1,p2 --model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse,logcosh + +Running test 165 test type: optsyn, description: basic flat tree encoding test for dt_caretin model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test165 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding flat -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 1 test type: train, description: basic dt_caret training and test on labeled data with single numeric response -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test1 -mode train -resp y1 -feat x,p1,p2 -model dt_caret -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test1 -mode train -resp y1 -feat x,p1,p2 -model dt_caret -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f -Running test 20 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -model_name "../models/test20_model" -out_dir ./ -pref Test20 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -data_scaler none -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 28 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and functional API -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test28 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 19 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels +smlp -model_name "../models/test19_model" -out_dir ./ -pref Test19 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 25 test type: prediction, description: basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test25 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model t -use_model f -model_name test26_model -mrmr_pred 2 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + Running test 34 test type: doe, description: doe test with four levels with full_factorial method -../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test34 -mode doe -doe_algo full_factorial -log_time f +smlp -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test34 -mode doe -doe_algo full_factorial -log_time f + Running test 42 test type: doe, description: doe test with four levels with maximin_reconstruction -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test42 -mode doe -doe_algo maximin_reconstruction -doe_samples 20 -log_time f +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test42 -mode doe -doe_algo maximin_reconstruction -doe_samples 20 -log_time f + Running test 50 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test50 -mode discretize -resp "PF,PF1" -discr_algo kmeans -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test50 -mode discretize -resp "PF,PF1" -discr_algo kmeans -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs -Running test 60 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test60 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat + +Running test 59 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test59 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat spec_fn smlp_toy_num_resp_mult_free_inps.spec specs_path ../specs -Running test 82 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test82 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1,objv2,objv3 -objv_exprs "(y1+y2)/2;y1;y2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult_query_vacuous.spec -specs_path ../specs -Running test 91 test type: query, description: test to detect contradictory constraints in optimization mode due to contradictory alpha global and alpha bounds constraints on FMAX_xyx -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test91 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 81 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test81 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +spec_fn smlp_toy_num_resp_mult_optsyn_vacuous.spec specs_path ../specs -Running test 100 test type: optimize, description: basic test for sat_threshold option enabing usage of objectve values in SAT assignments that prove optimization thresholds -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test100 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult.spec +Running test 90 test type: optsyn, description: test to detect contradictory constraints in optsyn mode +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test90 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn_vacuous.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 104 test type: verify, description: assertion verfication test with wrong spec that does not assign a single value using a singleton grid or range with equal max and min -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test104 -mode verify -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult.spec -asrt_names asrt_y1,asrt_y2 -asrt_expr "y1*2+x<=5 and y1<=10;-2*y2-1<10-p2" -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_synthesize.spec +Running test 98 test type: optsyn, description: basic test for et_caret in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test98 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -save_model f -use_model f -tree_encoding nested -compress_rules f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_system_stable_constant_certify.spec specs_path ../specs -Running test 108 test type: synthesize, description: basic test for dt_sklearn in model exploration mode synthesize where synthesis succeeds -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test108 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 117 test type: certify, description: certification test with knobs only where assertion is valid without stability and fails with stability +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test117 -mode certify -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 121 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is feasible -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test121 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_configuration_verify.spec +Running test 123 test type: optimize, description: optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test123 -mode optimize -pareto t -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs -Running test 129 test type: verify, description: verification example with demonstrating all basic result scenarious for assertions -../../src/run_smlp.py -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test129 -mode verify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_configuration_verify.spec -asrt_names assert_stable_config,assert_grid_conflict,assert_unstable_config,assert_infeasible -asrt_exprs "y2<=90;y1>=9;y1>=(-10);y1>20" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 150 test type: prediction, description: tests the mape loss function MeanAbsolutePercentageError and sample weights -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test150 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 143 test type: query, description: basic test for compress_rules for et_sklearn in mode query +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test143 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 158 test type: prediction, description: tests the mape loss function and sample weights with model_per_response t -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test158 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -model_per_response t -sw_coef 8 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 2 test type: prediction, description: basic rf_sklearn prediction test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test2 -mode predict -resp y1 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +spec_fn smlp_toy_num_resp_mult_verify.spec +specs_path ../specs -Running test 12 test type: train, description: EV-SI real life dt_sklearn predict test on labeled and new data with numeric labels -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test12 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 157 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training; adapts test 155 by consdering multiple responses +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test157 -mode verify -resp y1,y2 -feat x,p1,p2 --model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse,logcosh -Running test 19 test type: prediction, description: basic dt_sklearn prediction test using a model saved under a name specified through model_name option on new data with numeric labels -../../src/run_smlp.py -model_name "../models/test19_model" -out_dir ./ -pref Test19 -mode predict -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +specs_path ../specs -Running test 27 test type: prediction, description: checks nn_keras prediction with nn_keras_seq_api t -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test27 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 164 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test164 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 35 test type: doe, description: doe test with four levels with plackett_burman -../../src/run_smlp.py -doe_spec "../grids/doe_four_levels_real.csv" -out_dir ./ -pref Test35 -mode doe -doe_algo plackett_burman -log_time f +spec_fn smlp_toy_num_resp_mult_optsyn.spec +specs_path ../specs -Running test 43 test type: doe, description: doe test with four levels with halton_sequence -../../src/run_smlp.py -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test43 -mode doe -doe_algo halton_sequence -doe_samples 20 -log_time f +Running test 168 test type: optimize, description: basic test for rf_caret with flat tree_encoding and modelper_response in model exploration mode optimize +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test168 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding flat -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 51 test type: discretization, description: tests discretization options -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test51 -mode discretize -resp "PF,PF1" -discr_algo jenks -discr_bins 6 -discr_labels f -discr_type integer -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test142 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_y2_verify.spec + +Running test 153 test type: prediction, description: tests the logcosh loss function LogCosh and sample weights +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test153 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss logcosh -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 161 test type: prediction, description: tests nn keras tuner bayesian +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test161 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -nn_keras_metrics mape,logcosh -nn_keras_tuner random -nn_keras_lrates_grid "0.01,0.001" -nn_keras_batches_grid "32,64" -model_per_response f -sw_coef 4 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 59 test type: verify, description: basic nn_keras assertion verification test for functional nn_keras model -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test59 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult_free_inps.spec +Running test 171 test type: optimize, description: basic test for et_caret with flat tree_encoding in model exploration mode optimize +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test171 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 81 test type: optimize, description: basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test81 -mode optimize -pareto f -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_free_inps.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_num_resp_mult_query.spec +Running test 176 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api f for nn_keras in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test176 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 89 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test89 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_query.spec +Running test 178 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features are not scaled adapts test 177 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test178 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 97 test type: query, description: basic test for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test97 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 110 test type: prediction, description: smlp toy basic example for predict mode from SMLP user manual -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test110 -mode predict -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model t -model_name test110_model -save_model_config t -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_basic_pred_unlabeled.csv" +Running test 187 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization adapts test 164 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test187 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_system.spec +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 116 test type: certify, description: basic test in certify mode when system is specified and is used as the model; p2 rel-rad needs to be 0 or very close to it the witness to first query to be stable -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test116 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system.spec -quer_names query1,query2 -quer_exprs "y1>0;y2<=0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_system_stable_certify.spec +Running test 190 test type: optimize, description: basic test for rf_caret with branched tree_encoding and modelper_response in model exploration mode optimize adapts test 168 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test190 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding branched -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 127 test type: certify, description: certification example with knobs only and fictitious inputs with values fixed through their ranges -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test127 -mode certify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_certify.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_query.spec +Running test 195 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 192 by setting n_estimators 3 and then discrepancy between z3 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test195 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 143 test type: query, description: basic test for compress_rules for et_sklearn in mode query -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test143 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 160 test type: prediction, description: tests nn keras tuner bayesian -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test160 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -nn_keras_metrics msle -nn_keras_tuner bayesian -nn_keras_layers_grid "2,3" -nn_keras_losses_grid "mse,mae,huber" -model_per_response f -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 198 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 and test 183 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test198 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn -Running test 151 test type: prediction, description: tests msle loss function MeanSquaredLogarithmicError and and sample weoghts -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test151 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss msle -sw_coef 3 -sw_exp 10 -sw_int 0 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +spec_fn smlp_toy_system_stable_constant_synth_fail.spec +specs_path ../specs -spec_fn smlp_toy_num_resp_mult_verify.spec +Running test 203 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed adapts test 122 +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test203 -mode optimize -pareto f -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 215 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test215 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + + +Running test 219 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test219 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type integer -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + + +Running test 224 test type: correlate, description: basic test for correlate mode and tests the Shannon mutual information +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test224 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f + +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test124 -mode optsyn -pareto f -opt_strategy lazy -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs -Running test 156 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training; adapts test 154 by consdering multiple responses -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test156 -mode verify -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics rmse + +Running test 141 test type: optimize, description: basic test for compress_rules option for dt_sklearn in optimization mode +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test141 -mode optimize -opt_strategy lazy -pareto f -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -spec ../specs/smlp_toy_num_resp_mult.spec -objv_names objv_y1,objv_y2 -objv_exprs "y1;y2" -epsilon 0.01 -delta_rel 0.01 -data_scaler none -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 150 test type: prediction, description: tests the mape loss function MeanAbsolutePercentageError and sample weights +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test150 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + + +Running test 159 test type: prediction, description: tests the msle loss function and sample weights with model_per_response t +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test159 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -model_per_response t -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae,cosine -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 167 test type: optsyn, description: basic flat tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test167 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test167 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 171 test type: optimize, description: basic test for et_caret with flat tree_encoding in model exploration mode optimize -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test171 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 169 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response t in model exploration mode optimize +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test169 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 175 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test175 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test175 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 178 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features are not scaled adapts test 177 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test178 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 177 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test177 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 188 test type: optsyn, description: basic branched tree encoding test for dt_caretin model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test188 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding branched -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec -specs_path ../specs -Running test 196 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 and test 181 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test196 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 192 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test192 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_no_input_beta.spec +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 201 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and there are beta constraints -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test201 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input_beta.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_no_input.spec -specs_path ../specs -Running test 202 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and no beta constraints -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test202 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 199 test type: optimize, description: test to demonstrate that in pareto optimization and optsyn modes with multiple objectives when beta constraints are not present SMLP results are not consistent when different solvers are used; this is due to fact that when a subset of objectoves are exemined in pareto algo +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test199 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 206 test type: optsyn, description: optimized synthesis test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 125 -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test206 -mode optsyn -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 219 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test219 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type integer -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 205 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 145 +smlp -out_dir ./ -pref Test205 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 224 test type: correlate, description: basic test for correlate mode and tests the Shannon mutual information -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test224 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec -specs_path ../specs -Running test 164 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test164 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 217 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test217 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -spec_fn smlp_toy_num_resp_mult_optsyn.spec -specs_path ../specs -Running test 168 test type: optimize, description: basic test for rf_caret with flat tree_encoding and modelper_response in model exploration mode optimize -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test168 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding flat -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 221 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test221 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + + +Running test 226 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test226 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 173 test type: verify, description: basic test for nn_keras flat encoding for sequential api -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test173 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test173 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec + +Running test 227 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test227 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f + + + +Running test 234 test type: subgroups, description: tests subgroup discovery mode when there are two responses with string values +smlp -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test234 -mode subgroups -resp str_resp1,str_resp2 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f + + +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test140 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -trace_prec 1 -trace_anonym t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + + +Running test 147 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test147 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + +spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs -Running test 183 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test183 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 154 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test154 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 + +spec_fn smlp_toy_num_resp_mult_no_input_beta.spec specs_path ../specs -Running test 191 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response t in model exploration mode optimize adapts test 169 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test191 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap t -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 201 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and there are beta constraints +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test201 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input_beta.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_no_input.spec specs_path ../specs -Running test 195 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 192 by setting n_estimators 3 and then discrepancy between z3 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test195 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 202 test type: optimize, description: basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and no beta constraints +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test202 -mode optimize -pareto t -opt_strategy eager -resp y1,y2 -feat p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -spec ../specs/smlp_toy_num_resp_mult_no_input.spec -data_scaler min_max -objv_names obj1 -objv_exprs "(y1+y2)/2" -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 199 test type: optimize, description: test to demonstrate that in pareto optimization and optsyn modes with multiple objectives when beta constraints are not present SMLP results are not consistent when different solvers are used; this is due to fact that when a subset of objectoves are exemined in pareto algo -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test199 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 206 test type: optsyn, description: optimized synthesis test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 125 +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test206 -mode optsyn -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 218 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test218 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test218 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type ordered -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -Running test 223 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test223 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f + +Running test 222 test type: correlate, description: basic test for correlate mode +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test222 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass spec_fn smlp_toy_system_radii_update_certify.spec specs_path ../specs + Running test 228 test type: certify, description: test that radii specified in command line properly override the radii specified in the spec file. Here we override both ansolute and relative radii and one can observe that the certification results also change compared to test 116 -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test228 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system_radii_update_certify.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test228 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_system_radii_update_certify.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 233 test type: subgroups, description: tests subgroup discovery mode when the response has string values -../../src/run_smlp.py -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test233 -mode subgroups -resp str_resp1 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f +smlp -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test233 -mode subgroups -resp str_resp1 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f - smlp_toy_num_resp_mult_optsyn.spec -specs_path ../specs -Running test 177 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test177 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +spec_fn smlp_toy_system_monotone_knob.05_verify.spec specs_path ../specs -Running test 182 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test182 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" -spec_fn smlp_toy_num_resp_mult_optsyn.spec -specs_path ../specs -Running test 192 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test192 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 230 test type: verify, description: tests that outputs in system specificaation might depend on different inuts (knobs and free inputs) +smlp -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test230 -mode verify -spec ../specs/smlp_toy_system_monotone_knob.05_verify.spec -model system -seed 10 -log_time f -spec_fn smlp_toy_system_stable_constant_synth_feasible.spec + +spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs -Running test 204 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 123 -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test204 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 217 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test217 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type category -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 172 test type: verify, description: basic test for nn_keras flat encoding for functional api +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test172 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" -Running test 222 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test222 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -Running test 227 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test227 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f +Running test 223 test type: correlate, description: basic test for correlate mode and tests the normalized mutual information +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test223 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f -Running test 234 test type: subgroups, description: tests subgroup discovery mode when there are two responses with string values -../../src/run_smlp.py -data "../data/smlp_toy_string_response.csv" -out_dir ./ -pref Test234 -mode subgroups -resp str_resp1,str_resp2 -feat num,int,str -pos_val no -neg_val yes -seed 10 -log_time f +spec_fn smlp_toy_missing_radii.spec +specs_path ../specs + +Running test 229 test type: certify, description: basic test for checking that each knob must have either absolute or relative radius specified in the spec file (even if radii are specified in the command line) +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test229 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_missing_radii.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_system_running_example_certify.spec +specs_path ../specs + +Running test 232 test type: certify, description: running example from smlp manual +smlp -data "../data/smlp_toy_system_running_example_certify.csv" -out_dir ./ -pref Test232 -mode certify -spec ../specs/smlp_toy_system_running_example_certify.spec -model system -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs + Running test 166 test type: optsyn, description: basic flat tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test166 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test166 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding flat -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 169 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response t in model exploration mode optimize -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test169 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 170 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response f in model exploration mode optimize +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test170 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 176 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api f for nn_keras in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test176 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 174 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test174 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 179 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn when resposes are not scaled adapts test 174 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test179 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 180 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features and responses are not scaled adapts test 175 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test180 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_feat f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 181 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test181 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +Running test 182 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test182 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 187 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization adapts test 164 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test187 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 188 test type: optsyn, description: basic branched tree encoding test for dt_caretin model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test188 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding branched -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 194 test type: optsyn, description: basic branched tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test194 -mode optsyn -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 193 test type: optimize, description: basic test for et_caret with branched tree_encoding in model exploration mode optimize adapts test 171 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test193 -mode optimize -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs + Running test 197 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 and test 182 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test197 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test197 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" -spec_fn smlp_toy_system_stable_constant_synth_fail.spec +spec_fn smlp_toy_system_stable_constant_synth_feasible.spec specs_path ../specs -Running test 203 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed adapts test 122 -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test203 -mode optimize -pareto f -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 204 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 123 +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test204 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + Running test 216 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test216 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test216 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f + Running test 220 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test220 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test220 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method normalized -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + Running test 225 test type: correlate, description: basic test for correlate mode and tests the adjusted mutual information -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test225 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test225 -mode correlate -resp y1,y2 -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method adjusted -mrmr_pred 0 -plots f -seed 10 -log_time f -spec_fn smlp_toy_system_monotone_knob.05_verify.spec +spec_fn smlp_toy_system_decreasing_knob.05_certify.spec specs_path ../specs -Running test 230 test type: verify, description: tests that outputs in system specificaation might depend on different inuts (knobs and free inputs) -../../src/run_smlp.py -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test230 -mode verify -spec ../specs/smlp_toy_system_monotone_knob.05_verify.spec -model system -seed 10 -log_time f +Running test 231 test type: certify, description: certification test with monotonicity query with a knob with a grid point +smlp -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test231 -mode certify -spec ../specs/smlp_toy_system_decreasing_knob.05_certify.spec -model system -seed 10 -log_time f + + +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test152 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss huber -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 159 test type: prediction, description: tests the msle loss function and sample weights with model_per_response t -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test159 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -model_per_response t -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae,cosine -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + +Running test 160 test type: prediction, description: tests nn keras tuner bayesian +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test160 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_loss mape -nn_keras_metrics msle -nn_keras_tuner bayesian -nn_keras_layers_grid "2,3" -nn_keras_losses_grid "mse,mae,huber" -model_per_response f -sw_coef 8 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 165 test type: optsyn, description: basic flat tree encoding test for dt_caretin model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test165 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_caret -tree_encoding flat -save_model f -use_model f -compress_rules f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_y2_verify.spec +Running test 179 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn when resposes are not scaled adapts test 174 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test179 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 172 test type: verify, description: basic test for nn_keras flat encoding for functional api -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test172 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat"" + +Running test 181 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test181 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec +specs_path ../specs + +Running test 183 test type: optimize, description: basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test183 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding flat -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 180 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features and responses are not scaled adapts test 175 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test180 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api t -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -scale_feat f -scale_resp f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 189 test type: optsyn, description: basic branched tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn adapts test 166 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test189 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec +specs_path ../specs + +Running test 191 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response t in model exploration mode optimize adapts test 169 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test191 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 3 -et_sklearn_bootstrap t -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 190 test type: optimize, description: basic test for rf_caret with branched tree_encoding and modelper_response in model exploration mode optimize adapts test 168 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test190 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_caret -model_per_response t -compress_rules t -tree_encoding branched -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +Running test 194 test type: optsyn, description: basic branched tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test194 -mode optsyn -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f spec_fn smlp_toy_num_resp_mult_free_inps_beta_objv.spec specs_path ../specs -Running test 198 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 and test 183 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test198 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_resp f -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -spec_fn smlp_toy_system_stable_constant_synth_feasible.spec +Running test 196 test type: optimize, description: basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 and test 181 +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test196 -mode optimize -pareto t -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -compress_rules f -tree_encoding branched -scale_feat f -spec ../specs/smlp_toy_num_resp_mult_free_inps_beta_objv.spec -data_scaler min_max -epsilon 0.05 -delta_rel 0.01 -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_optsyn.spec specs_path ../specs -Running test 205 test type: optimize, description: optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 145 -../../src/run_smlp.py -out_dir ./ -pref Test205 -mode optimize -pareto t -opt_strategy eager -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_feasible.spec -doe_spec ../grids/doe_two_levels_opt.csv -doe_algo latin_hypercube -epsilon 0.00000001 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 215 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test215 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass +Running test 200 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test200 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -Running test 221 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test221 -mode correlate -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method shannon -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass -Running test 226 test type: correlate, description: basic test for correlate mode -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test226 -mode correlate -resp y1,y2 -discr_algo uniform -discret_num t -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -cont_est pearson,spearman,kendall -mi_method correlation -mrmr_pred 0 -plots f -seed 10 -log_time f +Running test 8 test type: prediction, description: basic nn_keras prediction test on labeled and new data with numeric labels and two responses +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test8 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -nn_keras_epochs 20 -nn_keras_seq_api f -log_time f -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -spec_fn smlp_toy_system_running_example_certify.spec -specs_path ../specs -Running test 232 test type: certify, description: running example from smlp manual -../../src/run_smlp.py -data "../data/smlp_toy_system_running_example_certify.csv" -out_dir ./ -pref Test232 -mode certify -spec ../specs/smlp_toy_system_running_example_certify.spec -model system -seed 10 -log_time f +Running test 14 test type: train, description: EV-SI real life poly_sklearn prediction test on labeled and new data with numeric labels +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test14 -mode train -resp y1,y2 -feat x1,x2,p1,p2 -model poly_sklearn -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_missing_radii.spec -specs_path ../specs -Running test 229 test type: certify, description: basic test for checking that each knob must have either absolute or relative radius specified in the spec file (even if radii are specified in the command line) -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test229 -mode certify -resp y1,y2 -feat x1,x2,p1,p2 -model system -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_missing_radii.spec -rad_rel 0.005 -rad_abs 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_system_decreasing_knob.05_certify.spec -specs_path ../specs -Running test 231 test type: certify, description: certification test with monotonicity query with a knob with a grid point -../../src/run_smlp.py -data "../data/smlp_toy_monotone_basic.csv" -out_dir ./ -pref Test231 -mode certify -spec ../specs/smlp_toy_system_decreasing_knob.05_certify.spec -model system -seed 10 -log_time f +Running test 21 test type: prediction, description: test for illegal symbols in column names +smlp -data "../data/smlp_toy_num_metasymbol_mult_reg.csv" -out_dir ./ -pref Test21 -mode predict -resp "PF ,|PF |" -model poly_sklearn -save_model t -use_model f -model_name test22_model -pred_plots t -resp_plots t -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -new_dat "../data/smlp_toy_num_metasymbol_mult_reg_pred_labeled.csv" -../../src/run_smlp.py -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test140 -mode verify -model system -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_verify.spec -trace_prec 1 -trace_anonym t -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 33 test type: unknown, description: testing -config option with subgroups mode +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test33 -config ../models/Test31_smlp_toy_num_resp_mult_args_config.json -Running test 147 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test147 -mode predict -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -Running test 153 test type: prediction, description: tests the logcosh loss function LogCosh and sample weights -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test153 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss logcosh -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mse -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" +Running test 40 test type: doe, description: doe test with four levels with latin_hypercube_space_filling +smlp -doe_spec "../grids/doe_two_levels.csv" -out_dir ./ -pref Test40 -mode doe -doe_algo latin_hypercube_sf -doe_samples 20 -log_time f -Running test 161 test type: prediction, description: tests nn keras tuner bayesian -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test161 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_loss msle -nn_keras_metrics mape,logcosh -nn_keras_tuner random -nn_keras_lrates_grid "0.01,0.001" -nn_keras_batches_grid "32,64" -model_per_response f -sw_coef 4 -sw_exp 5 -sw_int 0.5 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 48 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test48 -mode discretize -resp "PF,PF1" -discr_algo uniform -discr_bins 6 -discr_labels t -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + + +Running test 56 test type: discretization, description: tests discretization options +smlp -data "../data/smlp_toy_mult_discr.csv" -out_dir ./ -pref Test56 -mode discretize -resp "PF,PF1" -discr_algo ranks -discr_bins 6 -discr_labels f -discr_type object -data_scaler none -mrmr_pred 0 -plots f -seed 10 -log_time f -pos_val fail -neg_val pass + +spec_fn smlp_toy_num_resp_mult_y1_verify.spec specs_path ../specs -Running test 170 test type: optimize, description: basic test for et_sklearn with flat tree_encoding and model_per_response f in model exploration mode optimize -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test170 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -rf_sklearn_n_estimators 3 -et_sklearn_bootstrap f -tree_encoding flat -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 64 test type: verify, description: basic dt_sklearn assertion verification test on data with one numeric response +smlp -model_name "../models/test63_model" -out_dir ./ -pref Test64 -mode verify -resp y1 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model t -spec ../specs/smlp_toy_num_resp_mult_y1_verify.spec -asrt_names asrt1,asrt2 -asrt_exprs "x/2+y1>4.3;(y1+p2)/2<6" -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult.spec specs_path ../specs -Running test 174 test type: optsyn, description: basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test174 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model nn_keras -nn_keras_epochs 20 -nn_keras_seq_api f -nnet_encoding layered -save_model f -use_model f -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 79 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test79 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult.spec -quer_names query1,query2,query3 -quer_exprs "(y2**3+p2)/2<6;y1>=9;y2<0" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs -Running test 189 test type: optsyn, description: basic branched tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn adapts test 166 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test189 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 4 -rf_sklearn_n_estimators 3 -tree_encoding branched -compress_rules t -save_model f -use_model f -compress_rules t -mrmr_pred 2 -model_per_response f -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 89 test type: query, description: basic test in query mode to test stability (theta) and guard (eta) constraint generation +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test89 -mode query -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -dt_sklearn_max_depth 15 -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_query.spec specs_path ../specs -Running test 193 test type: optimize, description: basic test for et_caret with branched tree_encoding in model exploration mode optimize adapts test 171 -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test193 -mode optimize -resp y1,y2 -feat x,p1,p2 -model et_caret -tree_encoding branched -model_per_response t -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_optsyn.spec +Running test 97 test type: query, description: basic test for rf_sklearn in model exploration mode optsyn +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test97 -mode query -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_bootstrap f -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_query.spec -epsilon 0.1 -delta_rel 0.05 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_num_resp_mult_synthesize.spec specs_path ../specs -Running test 200 test type: optimize, description: basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test200 -mode optimize -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model et_sklearn -et_sklearn_max_depth 2 -et_sklearn_n_estimators 100 -et_sklearn_bootstrap f -tree_encoding branched -model_per_response f -compress_rules t -save_model f -use_model f -mrmr_pred 2 -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0 -solver_path ../../../external/mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat -plots f -pred_plots f -resp_plots f -seed 10 -log_time f - ../specs -Running test 142 test type: optsyn, description: basic test for compress_rules option for rf_sklearn in optsin mode -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test142 -mode optsyn -opt_strategy lazy -resp y1,y2 -feat x,p1,p2 -model rf_sklearn -rf_sklearn_max_depth 15 -tree_encoding nested -compress_rules t -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_optsyn.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 108 test type: synthesize, description: basic test for dt_sklearn in model exploration mode synthesize where synthesis succeeds +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test108 -mode synthesize -resp y1,y2 -feat x,p1,p2 -model dt_sklearn -tree_encoding nested -compress_rules f -save_model f -use_model f -mrmr_pred 2 -model_per_response t -spec ../specs/smlp_toy_num_resp_mult_synthesize.spec -epsilon 0.1 -delta_rel 0.05 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -spec_fn smlp_toy_num_resp_mult_y2_verify.spec +spec_fn smlp_toy_system_stable_constant_synth_fail.spec specs_path ../specs -Running test 154 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner for functional model training -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test154 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api f -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 + +Running test 120 test type: synthesize, description: synthesis test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible +smlp -data "../data/smlp_toy_basic.csv" -out_dir ./ -pref Test120 -mode synthesize -model system -resp y1,y2 -feat p1,p2 -save_model f -use_model f -mrmr_pred 0 -model_per_response t -spec ../specs/smlp_toy_system_stable_constant_synth_fail.spec -plots f -pred_plots f -resp_plots f -seed 10 -log_time f + +spec_fn smlp_toy_witness_certify.spec +specs_path ../specs + +Running test 128 test type: certify, description: Basic regression test in certify mode covering all four possible outcomes when certifying a witness for a query: the witness is stable +smlp -data "../data/smlp_toy_ctg_num_resp.csv" -out_dir ./ -pref Test128 -mode certify -resp y1,y2 -feat x,p1,p2 -model poly_sklearn -dt_sklearn_max_depth 15 -save_model f -use_model f -model_per_response f -spec ../specs/smlp_toy_witness_certify.spec -quer_names query_stable_witness,query_grid_conflict,query_unstable_witness,query_infeasible_witness,query_poly_intercept_sensitive -quer_exprs "y2<=90;y1>=9;y1>=(-13);y1>9;y1>=(-10)" -plots f -pred_plots f -resp_plots f -seed 10 -log_time f +Running test 148 test type: prediction, description: checks nn_keras prediction with sw_coef 0.8 and sequential API +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test148 -mode predict -resp y1,y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -save_model_config f -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -sw_coef 0.8 -new_dat "../data/smlp_toy_num_resp_mult_pred_labeled.csv" + +spec_fn smlp_toy_num_resp_mult_y2_verify.spec specs_path ../specs + Running test 155 test type: verify, description: basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training -../../src/run_smlp.py -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test155 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae +smlp -data "../data/smlp_toy_num_resp_mult.csv" -out_dir ./ -pref Test155 -mode verify -resp y2 -feat x,p1,p2 -model nn_keras -nnet_encoding nested -mrmr_pred 0 -plots f -pred_plots f -resp_plots f -seed 10 -log_time f -nn_keras_epochs 20 -nn_keras_seq_api t -nn_keras_tuner hyperband -nn_keras_layers_grid "2,2;3,3,3" -save_model_config f -spec ../specs/smlp_toy_num_resp_mult_y2_verify.spec -asrt_names asrt1 -asrt_exprs "2*y2>1" -sw_coef 4 -sw_exp 5 -sw_int 0.5 -nn_keras_metrics mae Initiating 7 worker... comparing Test1_smlp_toy_num_resp_mult_y1_dt_caret_tree_rules.txt to master @@ -997,6 +1195,7 @@ Passed! comparing Test6_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_training_predictions_summary.csv to master Passed! comparing Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt to master +Passed! comparing Test7_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test7_smlp_toy_num_resp_mult_model_features_dict.json to master @@ -1024,7 +1223,6 @@ Passed! comparing Test7_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_training_predictions_summary.csv to master Passed! comparing Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master -Passed! comparing Test8_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test8_smlp_toy_num_resp_mult_model_features_dict.json to master @@ -1090,6 +1288,7 @@ Passed! comparing Test10_smlp_toy_num_resp_mult_data_bounds.json to master Passed! comparing Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt to master +Passed! comparing Test10_smlp_toy_num_resp_mult_model_features_dict.json to master Passed! comparing Test10_smlp_toy_num_resp_mult_model_levels_dict.json to master @@ -1163,7 +1362,6 @@ Passed! comparing Test12_smlp_toy_basic_training_predictions_summary.csv to master Passed! comparing Test13_smlp_toy_basic.txt to master -Passed! comparing Test13_smlp_toy_basic_data_bounds.json to master Passed! comparing Test13_smlp_toy_basic_labeled_prediction_precisions.csv to master @@ -1209,6 +1407,7 @@ Passed! comparing Test14_smlp_toy_basic_training_predictions_summary.csv to master Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1216,6 +1415,7 @@ Passed! comparing Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1223,6 +1423,7 @@ Passed! comparing Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1260,6 +1461,7 @@ Passed! comparing test19_model_rerun_model_config.json to master Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1267,6 +1469,7 @@ Passed! comparing Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1313,6 +1516,7 @@ Passed! comparing test22_model_rerun_model_config.json to master Passed! comparing Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt to master +Passed! File master Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_eval_poly_sklearn_new-col-PF .png does not exist File master Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_eval_poly_sklearn_new-col-|PF |.png does not exist comparing Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled_missing_values_dict.json to master @@ -1353,6 +1557,7 @@ Passed! comparing test24_model_rerun_model_config.json to master Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1360,6 +1565,7 @@ Passed! comparing Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled_new_predictions_summary.csv to master Passed! comparing test26_model_dt_sklearn_tree_rules.txt to master +Passed! comparing Test25_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt to master Passed! comparing Test25_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_labeled_prediction_precisions.csv to master @@ -1389,6 +1595,7 @@ Passed! comparing test26_model_rerun_model_config.json to master Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master Passed! comparing Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled_new_prediction_precisions.csv to master @@ -1480,6 +1687,7 @@ Passed! comparing Test31_smlp_toy_num_resp_mult_ranking_resp_feat.csv to master Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt to master +Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json to master Passed! comparing Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_missing_values_dict.json to master @@ -1569,6 +1777,7 @@ Passed! comparing test47_model_poly_sklearn_formula.txt to master Passed! comparing Test47_test47_model_smlp_toy_pf_mult.txt to master +Passed! comparing Test47_test47_model_smlp_toy_pf_mult_missing_values_dict.json to master Passed! comparing Test47_test47_model_smlp_toy_pf_mult_new_prediction_precisions.csv to master @@ -1743,6 +1952,7 @@ Passed! File master test63_model_y1_smlp_full_model_term.json does not exist File master test63_model_y1_smlp_model_term.json does not exist comparing Test64_test63_model.txt to master +Passed! File master Test64_test63_model_trace.csv does not exist comparing Test64_test63_model_verify_results.json to master Passed! @@ -1789,6 +1999,7 @@ Passed! File master test69_model_y2_smlp_full_model_term.json does not exist File master test69_model_y2_smlp_model_term.json does not exist comparing Test70_test69_model.txt to master +Passed! File master Test70_test69_model_trace.csv does not exist comparing Test70_test69_model_verify_results.json to master Passed! @@ -2550,6 +2761,7 @@ File master test101_model_y1_smlp_model_term.json does not exist File master test101_model_y2_smlp_full_model_term.json does not exist File master test101_model_y2_smlp_model_term.json does not exist comparing Test102_test101_model.txt to master +Passed! comparing Test102_test101_model_certify_results.json to master Passed! File master Test102_test101_model_trace.csv does not exist @@ -2776,9 +2988,11 @@ comparing test110_model_poly_sklearn_formula.txt to master comparing test110_model_rerun_model_config.json to master Passed! comparing Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt to master +Passed! comparing Test111_test110_model_smlp_toy_basic_pred_unlabeled_new_predictions_summary.csv to master Passed! comparing Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt to master +Passed! comparing Test112_test110_model_smlp_toy_basic_pred_unlabeled_new_predictions_summary.csv to master Passed! comparing test113_model_dt_sklearn_tree_rules.txt to master @@ -4429,5 +4643,5 @@ Passed! master log file does not exist! Do you wish to copy the new log file to master? (yes/no|y/n): No new tests crashed (not in the masters) -Time: 32.59507596492767 minutes +Time: 35.26560681263606 minutes End of regression diff --git a/tests/smlp_regression/run_smlp_regression_venv_expected_diff_report.log b/tests/smlp_regression/run_smlp_regression_venv_expected_diff_report.log index 5d8bc15b..0445a776 100644 --- a/tests/smlp_regression/run_smlp_regression_venv_expected_diff_report.log +++ b/tests/smlp_regression/run_smlp_regression_venv_expected_diff_report.log @@ -1,391 +1,171 @@ -=================== Diff report for: Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt ================================== -94d93 -< if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x <= 0.6666666716337204) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -96c95 -< if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.4000000134110451) and (p1 <= 0.75) and (p2 > 0.7000000178813934) and (x <= 0.6666666716337204) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -97a97 -> if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of Test7_smlp_toy_num_resp_mult_rf_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt ================================== -6d5 -< if (p1 > 0.7673577288013687) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -7a7 -> if (p1 > 0.7673577288013687) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -21d20 -< if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x > 0.03081251758592215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -22a22 -> if (p2 > 0.565498446377692) and (p1 > 0.21566598080828134) and (p2 > 0.7262518305173236) and (x > 0.03081251758592215) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -44d43 -< if (p2 > 0.05282566885129813) and (p1 > 0.9621611074368288) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -45a45 -> if (p2 > 0.05282566885129813) and (p1 > 0.9621611074368288) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -66d65 -< if (p2 > 0.10769168804757841) and (p2 > 0.9843916629018533) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -67a67 -> if (p2 > 0.10769168804757841) and (p2 > 0.9843916629018533) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -73d72 -< if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x <= 0.7383051325780686) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -75c74 -< if (p1 > 0.9643084043470717) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.9643084043470717) and (p2 > 0.7106753814549537) and (x <= 0.7383051325780686) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -76a76 -> if (p1 > 0.9643084043470717) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -88d87 -< if (p2 > 0.37253817607301204) and (p1 > 0.5069297996847564) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -89a89 -> if (p2 > 0.37253817607301204) and (p1 > 0.5069297996847564) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -96d95 -< if (p1 > 0.8097833990164955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -97a97 -> if (p1 > 0.8097833990164955) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -103d102 -< if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 <= 0.9832575130198419) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -105c104 -< if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x > 0.7718040145802331) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x <= 0.7718040145802331) and (p1 <= 0.9832575130198419) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -106a106 -> if (p2 > 0.30979522099243256) and (p2 > 0.7974478444662528) and (x > 0.7718040145802331) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -118d117 -< if (p1 > 0.6478692095949636) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -119a119 -> if (p1 > 0.6478692095949636) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -126d125 -< if (p2 > 0.5083941302766997) and (p1 > 0.9604900148513215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -127a127 -> if (p2 > 0.5083941302766997) and (p1 > 0.9604900148513215) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -134d133 -< if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 > 0.7231216464299344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -135a135 -> if (p2 > 0.38305688667253446) and (p1 > 0.2547155522064844) and (p1 > 0.7231216464299344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -141d140 -< if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x <= 0.6016799023753295) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -143c142 -< if (p1 > 0.5519522472992318) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.5519522472992318) and (p2 > 0.33294736609465786) and (p2 > 0.7309142946144038) and (x <= 0.6016799023753295) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -144a144 -> if (p1 > 0.5519522472992318) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -155d154 -< if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x <= 0.6096057855068803) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -157c156 -< if (p1 > 0.5757871147132418) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.5757871147132418) and (p2 > 0.39527962049249543) and (p2 > 0.6458938131907435) and (x <= 0.6096057855068803) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -158a158 -> if (p1 > 0.5757871147132418) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -204d203 -< if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x <= 0.6717608829535293) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -206c205 -< if (p1 > 0.8026946730924279) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.8026946730924279) and (p2 > 0.05688018773537138) and (p2 > 0.4364986121396114) and (p2 > 0.6777798791052577) and (x <= 0.6717608829535293) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -207a207 -> if (p1 > 0.8026946730924279) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -218d217 -< if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x <= 0.6156377025174744) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -220c219 -< if (p2 > 0.5397191641571555) and (p1 > 0.7132398065706901) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.5397191641571555) and (p1 <= 0.7132398065706901) and (p2 > 0.634045960603359) and (x <= 0.6156377025174744) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -221a221 -> if (p2 > 0.5397191641571555) and (p1 > 0.7132398065706901) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -233d232 -< if (p1 > 0.6806756624396312) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -234a234 -> if (p1 > 0.6806756624396312) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -241d240 -< if (p2 > 0.4943563777461445) and (p1 > 0.920448066629678) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -242a242 -> if (p2 > 0.4943563777461445) and (p1 > 0.920448066629678) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -264d263 -< if (p1 > 0.7209493553829144) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -265a265 -> if (p1 > 0.7209493553829144) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -286d285 -< if (p2 > 0.9499151226831205) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -287a287 -> if (p2 > 0.9499151226831205) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -300d299 -< if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x <= 0.8482964849267818) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -302c301 -< if (p2 > 0.4160567286499109) and (p1 > 0.7821608059025187) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.4160567286499109) and (p1 <= 0.7821608059025187) and (p2 > 0.7500101945124135) and (x <= 0.8482964849267818) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -303a303 -> if (p2 > 0.4160567286499109) and (p1 > 0.7821608059025187) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -357d356 -< if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x <= 0.7909361110756394) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -359c358 -< if (p1 > 0.6838882522116826) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.6838882522116826) and (p2 > 0.0626814738207876) and (p2 > 0.3422835304420513) and (p2 > 0.6023659933821865) and (x <= 0.7909361110756394) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -360a360 -> if (p1 > 0.6838882522116826) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -364d363 -< if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x <= 0.4249370103517018) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -366c365 -< if (p2 > 0.40336603038169727) and (p1 > 0.7296289325364069) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.40336603038169727) and (p1 <= 0.7296289325364069) and (p2 > 0.6471116276536257) and (x <= 0.4249370103517018) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -367a367 -> if (p2 > 0.40336603038169727) and (p1 > 0.7296289325364069) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -379d378 -< if (p1 > 0.5139580394672034) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -380a380 -> if (p1 > 0.5139580394672034) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -387d386 -< if (p2 > 0.8305540819794657) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -388a388 -> if (p2 > 0.8305540819794657) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -395d394 -< if (p2 > 0.9449580902908733) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -396a396 -> if (p2 > 0.9449580902908733) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -410d409 -< if (p1 > 0.5120007179267708) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -411a411 -> if (p1 > 0.5120007179267708) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -417d416 -< if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x <= 0.7877861639695688) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -419c418 -< if (p1 > 0.610545704891228) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p1 <= 0.610545704891228) and (p2 > 0.6501177091384854) and (x <= 0.7877861639695688) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -420a420 -> if (p1 > 0.610545704891228) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -424d423 -< if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x <= 0.7843162575827582) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -426c425 -< if (p2 > 0.45482464342137086) and (p1 > 0.6847267941034989) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.45482464342137086) and (p1 <= 0.6847267941034989) and (p2 > 0.7332769920461923) and (x <= 0.7843162575827582) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -427a427 -> if (p2 > 0.45482464342137086) and (p1 > 0.6847267941034989) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -446d445 -< if (p2 > 0.054852516881587224) and (p1 > 0.8053342741007611) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -447a447 -> if (p2 > 0.054852516881587224) and (p1 > 0.8053342741007611) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -461d460 -< if (p2 > 0.35838276676758324) and (p1 > 0.6469177723149386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -462a462 -> if (p2 > 0.35838276676758324) and (p1 > 0.6469177723149386) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -476d475 -< if (p1 > 0.6586678422329332) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -477a477 -> if (p1 > 0.6586678422329332) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -483d482 -< if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x <= 0.37232690124052253) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -485c484 -< if (p2 > 0.6300749409152544) and (p1 > 0.9968296801656623) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.6300749409152544) and (p1 <= 0.9968296801656623) and (x <= 0.37232690124052253) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -486a486 -> if (p2 > 0.6300749409152544) and (p1 > 0.9968296801656623) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -497d496 -< if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x <= 0.5092783321373655) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -499c498 -< if (p2 > 0.37996283470651265) and (p1 > 0.5025284804881217) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.37996283470651265) and (p1 <= 0.5025284804881217) and (p2 > 0.7656687748432474) and (x <= 0.5092783321373655) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -500a500 -> if (p2 > 0.37996283470651265) and (p1 > 0.5025284804881217) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -505d504 -< if (p2 > 0.22414377714700243) and (p1 > 0.904138504017209) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -506a506 -> if (p2 > 0.22414377714700243) and (p1 > 0.904138504017209) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -513d512 -< if (p1 > 0.8937446875002909) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -514a514 -> if (p1 > 0.8937446875002909) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -535d534 -< if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 > 0.9300510326789317) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -536a536 -> if (p2 > 0.07422745696931493) and (p2 > 0.545370601481947) and (p2 > 0.9300510326789317) then (y1 = 1.0) and (y2 = 0.0) | based on 1 samples -542d541 -< if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 <= 0.8191675724550931) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -544c543 -< if (p2 > 0.7889165751584417) and (x > 0.9135558618761394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.7889165751584417) and (x <= 0.9135558618761394) and (p1 <= 0.8191675724550931) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -545a545 -> if (p2 > 0.7889165751584417) and (x > 0.9135558618761394) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -549d548 -< if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x <= 0.6647538383146583) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -551c550 -< if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 > 0.8197608151565123) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 <= 0.8197608151565123) and (p2 > 0.6876566959859812) and (x <= 0.6647538383146583) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -552a552 -> if (p2 > 0.09689574087825406) and (p2 > 0.33636798963203285) and (p1 > 0.8197608151565123) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -556d555 -< if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 > 0.7807007138854876) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -558c557 -< if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x > 0.792321881648303) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x <= 0.792321881648303) and (p1 <= 0.574456778622445) and (p2 > 0.7807007138854876) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -559a559 -> if (p2 > 0.16337258050375272) and (p2 > 0.20940975581022525) and (x > 0.792321881648303) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -563d562 -< if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 <= 0.5654538877147501) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -565c564 -< if (p2 > 0.7007179663985585) and (x > 0.47267748344348626) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples ---- -> if (p2 > 0.7007179663985585) and (x <= 0.47267748344348626) and (p1 <= 0.5654538877147501) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -566a566 -> if (p2 > 0.7007179663985585) and (x > 0.47267748344348626) then (y1 = 0.0) and (y2 = 0.0) | based on 1 samples -570d569 -< if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x <= 0.6760619000417248) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -572c571 -< if (p2 > 0.30978463977099613) and (p1 > 0.6320834037065894) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.30978463977099613) and (p1 <= 0.6320834037065894) and (p2 > 0.6127691589194908) and (x <= 0.6760619000417248) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -573a573 -> if (p2 > 0.30978463977099613) and (p1 > 0.6320834037065894) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -578d577 -< if (p1 > 0.6819941814439344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -579a579 -> if (p1 > 0.6819941814439344) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -600d599 -< if (p1 > 0.5931505284240239) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -601a601 -> if (p1 > 0.5931505284240239) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -631d630 -< if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x <= 0.4155191624469929) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -633c632 -< if (p2 > 0.3774126001528523) and (p1 > 0.6553700527434098) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.3774126001528523) and (p1 <= 0.6553700527434098) and (p2 > 0.7353104082940054) and (x <= 0.4155191624469929) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -634a634 -> if (p2 > 0.3774126001528523) and (p1 > 0.6553700527434098) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -638d637 -< if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x <= 0.7892120132227867) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -640c639 -< if (p2 > 0.23018040425618197) and (p1 > 0.7102524464532046) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.23018040425618197) and (p1 <= 0.7102524464532046) and (p2 > 0.7248063887234734) and (x <= 0.7892120132227867) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -641a641 -> if (p2 > 0.23018040425618197) and (p1 > 0.7102524464532046) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -653d652 -< if (p2 > 0.21158053456413584) and (p1 > 0.5586915479780601) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -654a654 -> if (p2 > 0.21158053456413584) and (p1 > 0.5586915479780601) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -661d660 -< if (p1 > 0.6795984351446844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -662a662 -> if (p1 > 0.6795984351446844) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -677d676 -< if (p1 > 0.8126054242312002) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -678a678 -> if (p1 > 0.8126054242312002) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -684d683 -< if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x <= 0.7910962169102163) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -686c685 -< if (p2 > 0.26886312862339573) and (p1 > 0.8950548846717248) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.26886312862339573) and (p1 <= 0.8950548846717248) and (p2 > 0.6824239181038759) and (x <= 0.7910962169102163) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -687a687 -> if (p2 > 0.26886312862339573) and (p1 > 0.8950548846717248) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -698d697 -< if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x <= 0.4784058184220548) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -700c699 -< if (p2 > 0.25803846924474394) and (p1 > 0.85781003667871) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.25803846924474394) and (p1 <= 0.85781003667871) and (p2 > 0.6174925668996046) and (x <= 0.4784058184220548) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -701a701 -> if (p2 > 0.25803846924474394) and (p1 > 0.85781003667871) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -705d704 -< if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x <= 0.6112039253981838) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -707c706 -< if (p2 > 0.16944414757631912) and (p1 > 0.7608029128801092) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples ---- -> if (p2 > 0.16944414757631912) and (p1 <= 0.7608029128801092) and (p2 > 0.31309513934344707) and (p2 > 0.7650359471516853) and (x <= 0.6112039253981838) then (y1 = 1.0) and (y2 = 0.0) | based on 2 samples -708a708 -> if (p2 > 0.16944414757631912) and (p1 > 0.7608029128801092) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -728d727 -< if (p2 > 0.29657478970316) and (p1 > 0.8279177280272906) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -729a729 -> if (p2 > 0.29657478970316) and (p1 > 0.8279177280272906) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -736d735 -< if (p2 > 0.30543398172847647) and (p1 > 0.7651434488432218) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -737a737 -> if (p2 > 0.30543398172847647) and (p1 > 0.7651434488432218) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of Test10_smlp_toy_num_resp_mult_et_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test5_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test15_Test5_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test8_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test16_Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/Test11_smlp_toy_num_resp_mult_rerun_model_config.json -=================== End of Test17_Test11_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test19_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test19_model_rerun_model_config.json -=================== End of Test19_test19_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json -=================== End of Test20_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test22_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test22_model_rerun_model_config.json -=================== End of Test22_test22_model_smlp_toy_num_metasymbol_mult_reg_pred_labeled.txt diff report ================================ -=================== Diff report for: Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test24_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test24_model_rerun_model_config.json -=================== End of Test24_test24_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: test26_model_dt_sklearn_tree_rules.txt ================================== -6d5 -< if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -7a7 -> if (p2 > 0.4000000134110451) and (p1 > 0.75) then (y1 = 1.0) and (y2 = 1.0) | based on 1 samples -=================== End of test26_model_dt_sklearn_tree_rules.txt diff report ================================ -=================== Diff report for: Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -87c87 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test26_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test26_model_rerun_model_config.json -=================== End of Test26_test26_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt ================================== -75c75 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test20_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test20_model_rerun_model_config.json -=================== End of Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ -=================== Diff report for: Test47_test47_model_smlp_toy_pf_mult.txt ================================== -83c83 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test47_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test47_model_rerun_model_config.json -=================== End of Test47_test47_model_smlp_toy_pf_mult.txt diff report ================================ -=================== Diff report for: Test64_test63_model.txt ================================== -27c27 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test63_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test63_model_rerun_model_config.json -=================== End of Test64_test63_model.txt diff report ================================ +=================== Diff report for: Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt ================================== +208,225c208,212 +< smlp_logger - INFO - Model: "functional" +< ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ +< ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ +< ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ +< │ input_layer │ (None, 3) │ 0 │ - │ +< │ (InputLayer) │ │ │ │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense (Dense) │ (None, 6) │ 24 │ input_layer[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense_1 (Dense) │ (None, 3) │ 21 │ dense[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y1 (Dense) │ (None, 1) │ 4 │ dense_1[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y2 (Dense) │ (None, 1) │ 4 │ dense_1[0][0] │ +< └─────────────────────┴───────────────────┴────────────┴───────────────────┘ +< Total params: 53 (212.00 B) +< Trainable params: 53 (212.00 B) +< Non-trainable params: 0 (0.00 B) +--- +> smlp_logger - INFO - Model: "model" +> __________________________________________________________________________________________________ +> Layer (type) Output Shape Param # Connected to +> ================================================================================================== +> input_1 (InputLayer) [(None, 3)] 0 [] +226a214 +> dense (Dense) (None, 6) 24 ['input_1[0][0]'] +228c216,229 +< smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +--- +> dense_1 (Dense) (None, 3) 21 ['dense[0][0]'] +> +> y1 (Dense) (None, 1) 4 ['dense_1[0][0]'] +> +> y2 (Dense) (None, 1) 4 ['dense_1[0][0]'] +> +> ================================================================================================== +> Total params: 53 (212.00 Byte) +> Trainable params: 53 (212.00 Byte) +> Non-trainable params: 0 (0.00 Byte) +> __________________________________________________________________________________________________ +> +> +> smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +234c235 +< smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] +--- +> smlp_logger - INFO - Metrics: ['mse'] +236c237 +< smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 6), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 3), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +--- +> smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 6, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 3, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 6)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y1', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 3)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +242c243 +< smlp_logger - INFO - Callbacks: [""] +--- +> smlp_logger - INFO - Callbacks: [""] +266c267 +< smlp_logger - INFO - Prediction on training data -- msqe: 7.935 +--- +> smlp_logger - INFO - Prediction on training data -- msqe: 7.938 +268c269 +< smlp_logger - INFO - Prediction on training data -- r2_score: -1.021 +--- +> smlp_logger - INFO - Prediction on training data -- r2_score: -1.022 +286c287 +< smlp_logger - INFO - Prediction on test data -- msqe: 6.833 +--- +> smlp_logger - INFO - Prediction on test data -- msqe: 6.834 +306c307 +< smlp_logger - INFO - Prediction on labeled data -- msqe: 7.634 +--- +> smlp_logger - INFO - Prediction on labeled data -- msqe: 7.637 +308c309 +< smlp_logger - INFO - Prediction on labeled data -- r2_score: -0.924 +--- +> smlp_logger - INFO - Prediction on labeled data -- r2_score: -0.925 +326c327 +< smlp_logger - INFO - Prediction on new data -- msqe: 7.974 +--- +> smlp_logger - INFO - Prediction on new data -- msqe: 7.977 +328c329 +< smlp_logger - INFO - Prediction on new data -- r2_score: -1.018 +--- +> smlp_logger - INFO - Prediction on new data -- r2_score: -1.019 +=================== End of Test8_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled.txt diff report ================================ +=================== Diff report for: Test13_smlp_toy_basic.txt ================================== +126,143c126,130 +< smlp_logger - INFO - Model: "functional" +< ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ +< ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ +< ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ +< │ input_layer │ (None, 4) │ 0 │ - │ +< │ (InputLayer) │ │ │ │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense (Dense) │ (None, 8) │ 40 │ input_layer[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ dense_1 (Dense) │ (None, 4) │ 36 │ dense[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y1 (Dense) │ (None, 1) │ 5 │ dense_1[0][0] │ +< ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +< │ y2 (Dense) │ (None, 1) │ 5 │ dense_1[0][0] │ +< └─────────────────────┴───────────────────┴────────────┴───────────────────┘ +< Total params: 86 (344.00 B) +< Trainable params: 86 (344.00 B) +< Non-trainable params: 0 (0.00 B) +--- +> smlp_logger - INFO - Model: "model" +> __________________________________________________________________________________________________ +> Layer (type) Output Shape Param # Connected to +> ================================================================================================== +> input_1 (InputLayer) [(None, 4)] 0 [] +144a132 +> dense (Dense) (None, 8) 40 ['input_1[0][0]'] +146c134,147 +< smlp_logger - INFO - Optimizer: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +--- +> dense_1 (Dense) (None, 4) 36 ['dense[0][0]'] +> +> y1 (Dense) (None, 1) 5 ['dense_1[0][0]'] +> +> y2 (Dense) (None, 1) 5 ['dense_1[0][0]'] +> +> ================================================================================================== +> Total params: 86 (344.00 Byte) +> Trainable params: 86 (344.00 Byte) +> Non-trainable params: 0 (0.00 Byte) +> __________________________________________________________________________________________________ +> +> +> smlp_logger - INFO - Optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} +152c153 +< smlp_logger - INFO - Metrics: ['loss', 'compile_metrics'] +--- +> smlp_logger - INFO - Metrics: ['mse'] +154c155 +< smlp_logger - INFO - Model configuration: {'name': 'functional', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_shape': (None, 4), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_layer', 'optional': False}, 'registered_name': None, 'name': 'input_layer', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 8, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'dense', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['input_layer', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 4, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 8)}, 'name': 'dense_1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 8), 'dtype': 'float32', 'keras_history': ['dense', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y1', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'quantization_config': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y2', 'inbound_nodes': [{'args': ({'class_name': '__keras_tensor__', 'config': {'shape': (None, 4), 'dtype': 'float32', 'keras_history': ['dense_1', 0, 0]}},), 'kwargs': {}}]}], 'input_layers': ['input_layer', 0, 0], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +--- +> smlp_logger - INFO - Model configuration: {'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 4), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 8, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'dense', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 4, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 8)}, 'name': 'dense_1', 'inbound_nodes': [[['dense', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y1', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y1', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Dense', 'config': {'name': 'y2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 4)}, 'name': 'y2', 'inbound_nodes': [[['dense_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['y1', 0, 0], ['y2', 0, 0]]} +160c161 +< smlp_logger - INFO - Callbacks: [""] +--- +> smlp_logger - INFO - Callbacks: [""] +184c185 +< smlp_logger - INFO - Prediction on training data -- msqe: 38.795 +--- +> smlp_logger - INFO - Prediction on training data -- msqe: 41.749 +186c187 +< smlp_logger - INFO - Prediction on training data -- r2_score: -9.102 +--- +> smlp_logger - INFO - Prediction on training data -- r2_score: -10.416 +204c205 +< smlp_logger - INFO - Prediction on test data -- msqe: 11.661 +--- +> smlp_logger - INFO - Prediction on test data -- msqe: 11.659 +206c207 +< smlp_logger - INFO - Prediction on test data -- r2_score: -4.243 +--- +> smlp_logger - INFO - Prediction on test data -- r2_score: -4.262 +224c225 +< smlp_logger - INFO - Prediction on labeled data -- msqe: 33.368 +--- +> smlp_logger - INFO - Prediction on labeled data -- msqe: 35.731 +226c227 +< smlp_logger - INFO - Prediction on labeled data -- r2_score: -2.803 +--- +> smlp_logger - INFO - Prediction on labeled data -- r2_score: -3.158 +=================== End of Test13_smlp_toy_basic.txt diff report ================================ =================== Diff report for: Test66_test65_model.txt ================================== 0a1,97 > @@ -414,7 +194,7 @@ > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test65_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test65_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -487,7 +267,7 @@ > smlp_logger - INFO - Executing run_smlp.py script: End =================== End of Test66_test65_model.txt diff report ================================ =================== Diff report for: Test66_test65_model_verify_results.json ================================== -diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regression_venv/smlp/regr_smlp/code/Test66_test65_model_verify_results.json: No such file or directory +diff: /home/mdmitry/github/smlp_python312/scripts/venv/smlp_package_venv/smlp/regr_smlp/code/Test66_test65_model_verify_results.json: No such file or directory =================== End of Test66_test65_model_verify_results.json diff report ================================ =================== Diff report for: Test68_test67_model.txt ================================== 0a1,97 @@ -517,7 +297,7 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test67_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test67_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -590,14 +370,8 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > smlp_logger - INFO - Executing run_smlp.py script: End =================== End of Test68_test67_model.txt diff report ================================ =================== Diff report for: Test68_test67_model_verify_results.json ================================== -diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regression_venv/smlp/regr_smlp/code/Test68_test67_model_verify_results.json: No such file or directory +diff: /home/mdmitry/github/smlp_python312/scripts/venv/smlp_package_venv/smlp/regr_smlp/code/Test68_test67_model_verify_results.json: No such file or directory =================== End of Test68_test67_model_verify_results.json diff report ================================ -=================== Diff report for: Test70_test69_model.txt ================================== -25c25 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test69_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test69_model_rerun_model_config.json -=================== End of Test70_test69_model.txt diff report ================================ =================== Diff report for: Test72_test71_model.txt ================================== 0a1,84 > @@ -624,7 +398,7 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test71_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test71_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -686,7 +460,7 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > smlp_logger - INFO - Executing run_smlp.py script: End =================== End of Test72_test71_model.txt diff report ================================ =================== Diff report for: Test72_test71_model_verify_results.json ================================== -diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regression_venv/smlp/regr_smlp/code/Test72_test71_model_verify_results.json: No such file or directory +diff: /home/mdmitry/github/smlp_python312/scripts/venv/smlp_package_venv/smlp/regr_smlp/code/Test72_test71_model_verify_results.json: No such file or directory =================== End of Test72_test71_model_verify_results.json diff report ================================ =================== Diff report for: Test77_test76_model.txt ================================== 0a1,110 @@ -718,7 +492,7 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > > smlp_logger - INFO - LOAD TRAINED MODEL > -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test76_model_rerun_model_config.json +> smlp_logger - INFO - Seving model rerun configuration in file ../models/test76_model_rerun_model_config.json > > smlp_logger - INFO - Creating model exploration base components: Start > @@ -802,7 +576,7 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres > smlp_logger - INFO - Executing run_smlp.py script: End =================== End of Test77_test76_model.txt diff report ================================ =================== Diff report for: Test77_test76_model_verify_results.json ================================== -diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regression_venv/smlp/regr_smlp/code/Test77_test76_model_verify_results.json: No such file or directory +diff: /home/mdmitry/github/smlp_python312/scripts/venv/smlp_package_venv/smlp/regr_smlp/code/Test77_test76_model_verify_results.json: No such file or directory =================== End of Test77_test76_model_verify_results.json diff report ================================ =================== Diff report for: Test97_smlp_toy_num_resp_mult.txt ================================== 252c252 @@ -810,24 +584,6 @@ diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regres --- > smlp_logger - INFO - Model operator counts for y2: {'add': 100, 'mul': 716, 'const': 2550, 'ite': 305, 'and': 409, 'prop': 714, 'sub': 714, 'var': 714} =================== End of Test97_smlp_toy_num_resp_mult.txt diff report ================================ -=================== Diff report for: Test102_test101_model.txt ================================== -38c38 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test101_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test101_model_rerun_model_config.json -=================== End of Test102_test101_model.txt diff report ================================ =================== Diff report for: test110_model_poly_sklearn_formula.txt ================================== -diff: /home/mdmitry/github/smlp_subgroups_assertion_fix/scripts/venv/smlp_regression_venv/smlp/regr_smlp/code/test110_model_poly_sklearn_formula.txt: No such file or directory +diff: /home/mdmitry/github/smlp_python312/scripts/venv/smlp_package_venv/smlp/regr_smlp/code/test110_model_poly_sklearn_formula.txt: No such file or directory =================== End of test110_model_poly_sklearn_formula.txt diff report ================================ -=================== Diff report for: Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt ================================== -79c79 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json -=================== End of Test111_test110_model_smlp_toy_basic_pred_unlabeled.txt diff report ================================ -=================== Diff report for: Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt ================================== -79c79 -< smlp_logger - INFO - Seving model rerun configuration in file ../models/test110_model_rerun_model_config.json ---- -> smlp_logger - INFO - Seving model rerun configuration in file ./../models/test110_model_rerun_model_config.json -=================== End of Test112_test110_model_smlp_toy_basic_pred_unlabeled.txt diff report ================================