Add multiselect on cli available_criteria

pysatl_experiment/cli/commands/common/common.py

@@ -1,14 +1,10 @@
 import json
 from enum import Enum
 from pathlib import Path
-from typing import cast
+from typing import cast, overload
 
 from click import ClickException, Context
-from pysatl_criterion.statistics import (
-    AbstractExponentialityGofStatistic,
-    AbstractNormalityGofStatistic,
-    AbstractWeibullGofStatistic,
-)
+from pysatl_criterion import DistributionType
 from pysatl_criterion.statistics.goodness_of_fit import AbstractGoodnessOfFitStatistic
 
 
@@ -128,7 +124,19 @@ def list_possible_parameter_values(param_type: type[Enum]) -> str:
     return param_type_values_str
 
 
-def get_statistics_short_codes_for_hypothesis(hypothesis: str) -> list[str]:
+@overload
+def get_statistics_short_codes_for_hypothesis(hypothesis: str) -> list[str]: ...
+
+
+@overload
+def get_statistics_short_codes_for_hypothesis(hypothesis: list[str]) -> dict[str, list[str]]: ...
+
+
+@overload
+def get_statistics_short_codes_for_hypothesis(hypothesis: None) -> dict[str, list[str]]: ...
+
+
+def get_statistics_short_codes_for_hypothesis(hypothesis: str | list[str] | None) -> list[str] | dict[str, list[str]]:
     """
     Get statistics codes for hypothesis.
 
@@ -137,13 +145,10 @@ def get_statistics_short_codes_for_hypothesis(hypothesis: str) -> list[str]:
     :return: statistics codes for hypothesis.
     """
 
-    hypothesis_to_base_class = {
-        "exponential": AbstractExponentialityGofStatistic,
-        "normal": AbstractNormalityGofStatistic,
-        "weibull": AbstractWeibullGofStatistic,
-    }
+    if hypothesis is None or isinstance(hypothesis, list):
+        return {member.value: get_statistics_short_codes_for_hypothesis(member.value) for member in DistributionType}
 
-    base_class = hypothesis_to_base_class[hypothesis]
+    base_class = DistributionType(hypothesis).base_class
 
     valid_criteria_types = cast(
         list[type[AbstractGoodnessOfFitStatistic]],

pysatl_experiment/cli/commands/criteria/criteria.py

@@ -1,23 +1,36 @@
-from click import Choice, argument, command, echo, option
+from click import Choice, command, echo, option
+from pysatl_criterion.utils.distribution import DistributionType
 
 from pysatl_experiment.cli.commands.common.common import get_statistics_short_codes_for_hypothesis
-from pysatl_experiment.configuration.model.hypothesis.hypothesis import Hypothesis
+
+
+def __show_available_criteria(distribution: str, codes: list[str]):
+    echo(f"Available criteria for {distribution}:")
+    if len(codes) > 0:
+        for code in codes:
+            echo(f"code: {code}")
+    else:
+        echo("No criteria found")
 
 
 @command()
-@argument(
-    "distribution",
-    type=Choice(Hypothesis.list()),
+@option(
+    "--distribution", "-d", required=False, type=Choice(DistributionType.list()), help="Distribution. Empty for all"
 )
 @option("--description/--no-description", "-y/-n", default=False, help="Show criteria description")
-def available_criteria(distribution: str, description: bool) -> None:
+def available_criteria(distribution: str | None, description: bool) -> None:
     """
     Collect all existing criteria.
 
     :param distribution: distribution.
     """
 
-    codes = get_statistics_short_codes_for_hypothesis(distribution)
+    codes: list[str] | dict[str, list[str]] = get_statistics_short_codes_for_hypothesis(distribution)
+
+    if distribution is None and isinstance(codes, dict):
+        for key, value in codes.items():
+            __show_available_criteria(key, value)
+        return
 
     echo(f"Available criteria for {distribution}:")
     if len(codes) > 0:

pysatl_experiment/experiment/generator/generators.py

@@ -1,23 +1,23 @@
-from pysatl_criterion.core.distribution.beta import generate_beta
-from pysatl_criterion.core.distribution.cauchy import generate_cauchy
-from pysatl_criterion.core.distribution.chi2 import generate_chi2
-from pysatl_criterion.core.distribution.expon import generate_expon
-from pysatl_criterion.core.distribution.gamma import generate_gamma
-from pysatl_criterion.core.distribution.gompertz import generate_gompertz
-from pysatl_criterion.core.distribution.gumbel import generate_gumbel
-from pysatl_criterion.core.distribution.invgauss import generate_invgauss
-from pysatl_criterion.core.distribution.laplace import generate_laplace
-from pysatl_criterion.core.distribution.lo_con_norm import generate_lo_con_norm
-from pysatl_criterion.core.distribution.logistic import generate_logistic
-from pysatl_criterion.core.distribution.lognormal import generate_lognorm
-from pysatl_criterion.core.distribution.mix_con_norm import generate_mix_con_norm
-from pysatl_criterion.core.distribution.norm import generate_norm
-from pysatl_criterion.core.distribution.rice import generate_rice
-from pysatl_criterion.core.distribution.scale_con_norm import generate_scale_con_norm
-from pysatl_criterion.core.distribution.student import generate_t
-from pysatl_criterion.core.distribution.truncnormal import generate_truncnorm
-from pysatl_criterion.core.distribution.tukey import generate_tukey
-from pysatl_criterion.core.distribution.weibull import generate_weibull
+from pysatl_criterion.core.distributions.beta import generate_beta
+from pysatl_criterion.core.distributions.cauchy import generate_cauchy
+from pysatl_criterion.core.distributions.chi2 import generate_chi2
+from pysatl_criterion.core.distributions.expon import generate_expon
+from pysatl_criterion.core.distributions.gamma import generate_gamma
+from pysatl_criterion.core.distributions.gompertz import generate_gompertz
+from pysatl_criterion.core.distributions.gumbel import generate_gumbel
+from pysatl_criterion.core.distributions.invgauss import generate_invgauss
+from pysatl_criterion.core.distributions.laplace import generate_laplace
+from pysatl_criterion.core.distributions.lo_con_norm import generate_lo_con_norm
+from pysatl_criterion.core.distributions.logistic import generate_logistic
+from pysatl_criterion.core.distributions.lognormal import generate_lognorm
+from pysatl_criterion.core.distributions.mix_con_norm import generate_mix_con_norm
+from pysatl_criterion.core.distributions.norm import generate_norm
+from pysatl_criterion.core.distributions.rice import generate_rice
+from pysatl_criterion.core.distributions.scale_con_norm import generate_scale_con_norm
+from pysatl_criterion.core.distributions.student import generate_t
+from pysatl_criterion.core.distributions.truncnormal import generate_truncnorm
+from pysatl_criterion.core.distributions.tukey import generate_tukey
+from pysatl_criterion.core.distributions.weibull import generate_weibull
 from typing_extensions import override
 
 from pysatl_experiment.experiment.generator.model import AbstractRVSGenerator

pysatl_experiment/experiment/step/execution/critical_value/critical_value.py

@@ -2,10 +2,7 @@
 from dataclasses import dataclass
 
 from line_profiler import profile
-from pysatl_criterion.persistence.model.limit_distribution.limit_distribution import (
-    ILimitDistributionStorage,
-    LimitDistributionModel,
-)
+from pysatl_criterion.persistence.models.limit_distribution import ILimitDistributionStorage, LimitDistributionModel
 
 from pysatl_experiment.configuration.model.experiment_type.experiment_type import ExperimentType
 from pysatl_experiment.experiment.model.experiment_step.experiment_step import IExperimentStep

pysatl_experiment/experiment/step/report_building/critical_value/critical_value.py

@@ -1,11 +1,8 @@
 from pathlib import Path
 
 from line_profiler import profile
-from pysatl_criterion.critical_value.cv_calculator.cv_calculator import CVCalculator
-from pysatl_criterion.persistence.model.limit_distribution.limit_distribution import (
-    ILimitDistributionStorage,
-    LimitDistributionQuery,
-)
+from pysatl_criterion.hypothesis_testing.critical_values.cv_calculator.cv_calculator import CVCalculator
+from pysatl_criterion.persistence.models.limit_distribution import ILimitDistributionStorage, LimitDistributionQuery
 from typing_extensions import override
 
 from pysatl_experiment.configuration.criteria_config.criteria_config import CriterionConfig

pysatl_experiment/factory/critical_value/critical_value.py

@@ -1,7 +1,4 @@
-from pysatl_criterion.persistence.model.limit_distribution.limit_distribution import (
-    ILimitDistributionStorage,
-    LimitDistributionQuery,
-)
+from pysatl_criterion.persistence.models.limit_distribution import ILimitDistributionStorage, LimitDistributionQuery
 
 from pysatl_experiment.configuration.experiment_data.critical_value.critical_value import CriticalValueExperimentData
 from pysatl_experiment.experiment.step.execution.common.hypothesis_generator_data.hypothesis_generator_data import (  # noqa: E501

pysatl_experiment/factory/model/abstract_experiment_factory/abstract_experiment_factory.py

@@ -1,9 +1,9 @@
 from abc import ABC, abstractmethod
 from typing import Any, Generic, TypeVar, cast
 
-from pysatl_criterion.persistence.limit_distribution.datastorage.datastorage import AlchemyLimitDistributionStorage
-from pysatl_criterion.persistence.model.common.data_storage.data_storage import IDataStorage
-from pysatl_criterion.persistence.model.limit_distribution.limit_distribution import LimitDistributionQuery
+from pysatl_criterion.persistence.models.base import IDataStorage
+from pysatl_criterion.persistence.models.limit_distribution import LimitDistributionQuery
+from pysatl_criterion.persistence.sqlalchemy.datastorage import AlchemyLimitDistributionStorage
 from pysatl_criterion.statistics import (
     AbstractExponentialityGofStatistic,
     AbstractNormalityGofStatistic,

pysatl_experiment/persistence/model/experiment/experiment.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 
-from pysatl_criterion.persistence.model.common.data_storage.data_storage import DataModel, DataQuery, IDataStorage
+from pysatl_criterion.persistence.models.base import DataModel, DataQuery, IDataStorage
 
 
 @dataclass

pysatl_experiment/persistence/model/power/power.py

@@ -1,7 +1,7 @@
 from abc import ABC
 from dataclasses import dataclass
 
-from pysatl_criterion.persistence.model.common.data_storage.data_storage import DataModel, DataQuery, IDataStorage
+from pysatl_criterion.persistence.models.base import DataModel, DataQuery, IDataStorage
 
 
 @dataclass

pysatl_experiment/persistence/model/random_values/random_values.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 
-from pysatl_criterion.persistence.model.common.data_storage.data_storage import DataModel, DataQuery, IDataStorage
+from pysatl_criterion.persistence.models.base import DataModel, DataQuery, IDataStorage
 
 
 @dataclass

pysatl_experiment/persistence/model/time_complexity/time_complexity.py

@@ -1,7 +1,7 @@
 from abc import ABC
 from dataclasses import dataclass
 
-from pysatl_criterion.persistence.model.common.data_storage.data_storage import DataModel, DataQuery, IDataStorage
+from pysatl_criterion.persistence.models.base import DataModel, DataQuery, IDataStorage
 
 
 @dataclass

pysatl_experiment/persistence/random_values_storage.py

@@ -3,7 +3,7 @@
 import json
 from typing import ClassVar
 
-from pysatl_criterion.persistence.alchemy_decorator import CompressedFloatArray
+from pysatl_criterion.persistence.sqlalchemy.alchemy_decorator import CompressedFloatArray
 from sqlalchemy import Integer, String, UniqueConstraint
 from sqlalchemy.orm import Mapped, mapped_column
 from typing_extensions import override

pysatl_experiment/worker/power/power.py

@@ -1,9 +1,9 @@
 from dataclasses import dataclass
 
-from pysatl_criterion.critical_value.resolver.storage_resolver import StorageCriticalValueResolver
-from pysatl_criterion.persistence.limit_distribution.datastorage.datastorage import AlchemyLimitDistributionStorage
+from pysatl_criterion import GoodnessOfFitTest
+from pysatl_criterion.hypothesis_testing.critical_values.resolver.storage_resolver import StorageCriticalValueResolver
+from pysatl_criterion.persistence.sqlalchemy.datastorage import AlchemyLimitDistributionStorage
 from pysatl_criterion.statistics.goodness_of_fit import AbstractGoodnessOfFitStatistic
-from pysatl_criterion.test.goodness_of_fit_test.goodness_of_fit_test import GoodnessOfFitTest
 
 from pysatl_experiment.worker.model.abstract_worker.abstract_worker import IWorker, WorkerResult
 
@@ -45,7 +45,7 @@ def execute(self) -> PowerWorkerResult:
         cv_resolver = StorageCriticalValueResolver(storage)
 
         gof_test = GoodnessOfFitTest(
-            statistics=self.statistics,
+            statistics=[self.statistics],
             significance_level=self.significance_level,
             cv_resolver=cv_resolver,
         )

tests/cli/commands/test_common.py

@@ -0,0 +1,62 @@
+import pytest
+from pysatl_criterion import DistributionType
+
+from pysatl_experiment.cli.commands.common.common import get_statistics_short_codes_for_hypothesis
+
+
+def test_get_statistics_short_codes_for_single_hypothesis():
+    """Should return list[str] for a single hypothesis."""
+
+    hypothesis = DistributionType.NORMAL.value
+
+    result = get_statistics_short_codes_for_hypothesis(hypothesis)
+
+    assert isinstance(result, list)
+    assert len(result) > 0
+    assert all(isinstance(code, str) for code in result)
+
+
+def test_get_statistics_short_codes_for_none():
+    """Should return mapping for all hypotheses."""
+
+    result = get_statistics_short_codes_for_hypothesis(None)
+
+    assert isinstance(result, dict)
+
+    expected_keys = {member.value for member in DistributionType}
+
+    assert set(result.keys()) == expected_keys
+
+    for value in result.values():
+        assert isinstance(value, list)
+        assert all(isinstance(code, str) for code in value)
+
+
+def test_get_statistics_short_codes_for_list_input():
+    """
+    Current implementation ignores provided list
+    and returns all distributions.
+    """
+
+    result = get_statistics_short_codes_for_hypothesis([DistributionType.NORMAL.value])
+
+    expected_keys = {member.value for member in DistributionType}
+
+    assert isinstance(result, dict)
+    assert set(result.keys()) == expected_keys
+
+
+def test_invalid_hypothesis_raises_value_error():
+    """Unknown hypothesis should raise ValueError."""
+
+    with pytest.raises(ValueError):
+        get_statistics_short_codes_for_hypothesis("invalid_distribution")
+
+
+def test_returned_codes_do_not_contain_suffix():
+    """Codes should be truncated by split('_')[0]."""
+
+    result = get_statistics_short_codes_for_hypothesis(DistributionType.NORMAL.value)
+
+    for code in result:
+        assert "_" not in code

tests/core/distribution/distribution_test.py

@@ -2,24 +2,24 @@
 
 import numpy as np
 import pytest
-from pysatl_criterion.core.distribution.beta import generate_beta
-from pysatl_criterion.core.distribution.cauchy import generate_cauchy
-from pysatl_criterion.core.distribution.chi2 import generate_chi2
-from pysatl_criterion.core.distribution.expon import generate_expon
-from pysatl_criterion.core.distribution.gamma import generate_gamma
-from pysatl_criterion.core.distribution.gumbel import generate_gumbel
-from pysatl_criterion.core.distribution.laplace import generate_laplace
-from pysatl_criterion.core.distribution.lo_con_norm import generate_lo_con_norm
-from pysatl_criterion.core.distribution.logistic import generate_logistic
-from pysatl_criterion.core.distribution.lognormal import generate_lognorm
-from pysatl_criterion.core.distribution.mix_con_norm import generate_mix_con_norm
-from pysatl_criterion.core.distribution.norm import generate_norm
-from pysatl_criterion.core.distribution.scale_con_norm import generate_scale_con_norm
-from pysatl_criterion.core.distribution.student import generate_t
-from pysatl_criterion.core.distribution.truncnormal import generate_truncnorm
-from pysatl_criterion.core.distribution.tukey import generate_tukey
-from pysatl_criterion.core.distribution.uniform import generate_uniform
-from pysatl_criterion.core.distribution.weibull import generate_weibull
+from pysatl_criterion.core.distributions.beta import generate_beta
+from pysatl_criterion.core.distributions.cauchy import generate_cauchy
+from pysatl_criterion.core.distributions.chi2 import generate_chi2
+from pysatl_criterion.core.distributions.expon import generate_expon
+from pysatl_criterion.core.distributions.gamma import generate_gamma
+from pysatl_criterion.core.distributions.gumbel import generate_gumbel
+from pysatl_criterion.core.distributions.laplace import generate_laplace
+from pysatl_criterion.core.distributions.lo_con_norm import generate_lo_con_norm
+from pysatl_criterion.core.distributions.logistic import generate_logistic
+from pysatl_criterion.core.distributions.lognormal import generate_lognorm
+from pysatl_criterion.core.distributions.mix_con_norm import generate_mix_con_norm
+from pysatl_criterion.core.distributions.norm import generate_norm
+from pysatl_criterion.core.distributions.scale_con_norm import generate_scale_con_norm
+from pysatl_criterion.core.distributions.student import generate_t
+from pysatl_criterion.core.distributions.truncnormal import generate_truncnorm
+from pysatl_criterion.core.distributions.tukey import generate_tukey
+from pysatl_criterion.core.distributions.uniform import generate_uniform
+from pysatl_criterion.core.distributions.weibull import generate_weibull
 
 
 @pytest.mark.skip(reason="no way of currently testing this")