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RamonFCerezoRamonFCerezo
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Merge branch 'ramondev' into dev
2 parents ffc6b95 + 6024a86 commit e5e436c

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‎test/imports_worst_params.py‎

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from numpy import array
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from numpy.ma import masked_array
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from sklearn.linear_model import LogisticRegression
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.svm import SVC
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clf_cv_results = {'mean_fit_time': array([0.01718318, 0.0028271 , 0.38156233, 0.33234448, 0.35453713,
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0.00439863, 0.00284553, 0.00274587, 0.00266459, 0.00200651,
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0.00236692, 0.00278957]),
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'std_fit_time': array([0.00516329, 0.00163186, 0.09469229, 0.07785508, 0.06154388,
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0.00176376, 0.00057058, 0.00091433, 0.00144808, 0.00063374,
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0.00082487, 0.00075851]),
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'mean_score_time': array([0.0018924 , 0.00069876, 0.03394792, 0.03130136, 0.03214581,
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0.00179503, 0.00121999, 0.000912 , 0.00106797, 0.00100076,
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0.00091968, 0.00097802]),
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'std_score_time': array([0.00290424, 0.00045828, 0.01646534, 0.01045479, 0.00829282,
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0.00059888, 0.00042194, 0.00030658, 0.00033866, 0.00044725,
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0.00073592, 0.0003762 ]),
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'param_classifier': masked_array(data=[LogisticRegression(max_iter=1000, solver='liblinear'),
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LogisticRegression(max_iter=1000, solver='liblinear'),
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RandomForestClassifier(), RandomForestClassifier(),
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RandomForestClassifier(), SVC(), SVC(), SVC(), SVC(),
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SVC(), SVC(), SVC()],
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mask=[False, False, False, False, False, False, False, False,
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False, False, False, False],
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fill_value='?',
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dtype=object),
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'param_classifier__penalty': masked_array(data=['l1', 'l2', '--', '--', '--', '--', '--', '--', '--', '--', '--', '--'],
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mask=[False, False, True, True, True, True, True, True,
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True, True, True, True],
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fill_value='?',
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dtype=object),
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'param_classifier__max_features': masked_array(data=['--', '--', 1, 2, 3, '--', '--', '--', '--', '--', '--', '--'],
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mask=[ True, True, False, False, False, True, True, True,
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True, True, True, True],
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fill_value='?',
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dtype=object),
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'param_classifier__C': masked_array(data=['--', '--', '--', '--', '--', 0.001, 0.1, 0.5, 1, 5, 10, 100],
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mask=[ True, True, True, True, True, False, False, False,
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False, False, False, False],
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fill_value='?',
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dtype=object),
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'params': [{'classifier': LogisticRegression(max_iter=1000, solver='liblinear'),
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'classifier__penalty': 'l1'},
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{'classifier': LogisticRegression(max_iter=1000, solver='liblinear'),
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'classifier__penalty': 'l2'},
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{'classifier': RandomForestClassifier(), 'classifier__max_features': 1},
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{'classifier': RandomForestClassifier(), 'classifier__max_features': 2},
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{'classifier': RandomForestClassifier(), 'classifier__max_features': 3},
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{'classifier': SVC(), 'classifier__C': 0.001},
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{'classifier': SVC(), 'classifier__C': 0.1},
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{'classifier': SVC(), 'classifier__C': 0.5},
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{'classifier': SVC(), 'classifier__C': 1},
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{'classifier': SVC(), 'classifier__C': 5},
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{'classifier': SVC(), 'classifier__C': 10},
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{'classifier': SVC(), 'classifier__C': 100}],
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'split0_test_score': array([0.91666667, 0.91666667, 1. , 1. , 1. ,
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0.33333333, 0.91666667, 0.91666667, 1. , 1. ,
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1. , 1. ]),
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'split1_test_score': array([0.91666667, 1. , 0.83333333, 0.83333333, 0.83333333,
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0.33333333, 0.91666667, 0.91666667, 0.91666667, 0.91666667,
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0.91666667, 1. ]),
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'split2_test_score': array([1. , 1. , 0.91666667, 0.91666667, 0.91666667,
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0.58333333, 0.91666667, 0.91666667, 1. , 1. ,
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1. , 1. ]),
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'split3_test_score': array([1. , 1. , 1. , 1. , 1. ,
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0.58333333, 0.91666667, 1. , 1. , 1. ,
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1. , 1. ]),
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'split4_test_score': array([1. , 1. , 1. , 1. , 1. ,
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0.66666667, 1. , 1. , 1. , 1. ,
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1. , 1. ]),
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'split5_test_score': array([1. , 1. , 1. , 1. , 1. ,
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0.66666667, 1. , 1. , 1. , 1. ,
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1. , 1. ]),
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'split6_test_score': array([1. , 1. , 1. , 1. , 1. ,
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0.41666667, 0.75 , 0.91666667, 1. , 1. ,
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1. , 1. ]),
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'split7_test_score': array([0.66666667, 0.75 , 0.83333333, 0.83333333, 0.83333333,
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0.58333333, 0.91666667, 0.83333333, 0.83333333, 0.83333333,
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0.83333333, 0.83333333]),
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'split8_test_score': array([1. , 1. , 1. , 1. , 1. ,
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0.33333333, 1. , 1. , 1. , 1. ,
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1. , 0.91666667]),
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'split9_test_score': array([1. , 1. , 0.91666667, 1. , 1. ,
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0.33333333, 1. , 1. , 0.91666667, 0.91666667,
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0.91666667, 0.91666667]),
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'mean_test_score': array([0.95 , 0.96666667, 0.95 , 0.95833333, 0.95833333,
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0.48333333, 0.93333333, 0.95 , 0.96666667, 0.96666667,
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0.96666667, 0.96666667]),
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'std_test_score': array([0.1 , 0.07637626, 0.06666667, 0.06718548, 0.06718548,
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0.13844373, 0.07264832, 0.05527708, 0.05527708, 0.05527708,
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0.05527708, 0.05527708]),
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'rank_test_score': array([ 8, 1, 8, 6, 6, 12, 11, 8, 1, 1, 1, 1])}
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df = {'rank_test_score': array([0,2,3,4,6,5,2]),
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'mean_test_score': array([0.2, 0.13, 0.9, 0.60, 0.50, 0.2, 0.03]),
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'params': [{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'},
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{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'},
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{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'},
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{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'},
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{'C': 5, 'degree': 3, 'gamma': 'auto', 'kernel': 's'},
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{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'},
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{'C': 100, 'degree': 1, 'gamma': 'scale', 'kernel': 'sigmoid'}]}

‎test/test_add_previous.py‎

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from add_previous import add_previous
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import pandas as pd
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df = pd.DataFrame(data= {'Goals': [0, 2, 1, 4, 5, 2, 1, 0, 2, 0, 1, 1],
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'Teams': ['Team 1', 'Team 2', 'Team 1', 'Team 2', 'Team 1', 'Team 2',
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'Team 1', 'Team 2', 'Team 1', 'Team 2', 'Team 1', 'Team 2']})
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def test_add_previous():
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add_previous(df, 3, 'Teams', 'Goals')
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list_features = df.columns.to_list()
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print(list_features)
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length = len(list_features)
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assert length == 5

‎test/test_winner_loser.py‎

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from winner_loser import winner_loser
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import pandas as pd
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import pytest
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# Define test data
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df = pd.DataFrame(data= {'Goals': [0, 2, 1, 4, 2, 2],
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'Teams': ['Team 1', 'Team 2', 'Team 1', 'Team 2', 'Team 1', 'Team 2']})
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# Run tests
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@pytest.mark.parametrize(
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"input_a, input_b ,input_c, expected",
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[
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(0, df, 'Goals', 'Loss'),
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(1, df, 'Goals', 'Victory'),
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(2, df, 'Goals', 'Loss'),
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(3, df, 'Goals', 'Victory'),
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(4, df, 'Goals', 'Draw'),
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(5, df, 'Goals', 'Draw')
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]
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)
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def test_winner_loser_multi(input_a, input_b, input_c, expected):
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assert winner_loser(input_a, input_b, input_c) == expected

‎test/test_worst_params.py‎

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from worst_params import worst_params
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import pytest
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import numpy as np
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from imports_worst_params import clf_cv_results, df
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from sklearn.svm import SVC
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import json
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@pytest.mark.parametrize(
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"input_a, expected",
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[
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(df, ("{'C': 5, 'degree': 3, 'gamma': 'auto', 'kernel': 's'}", 0.5)),
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(clf_cv_results, ("{'classifier': SVC(), 'classifier__C': 0.001}", 0.48333333))
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]
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)
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def test_worst_params_multi(input_a, expected):
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assert worst_params(input_a) == expected

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