def test_distribution_statistics_autodist_return_failed_tests():
"""
Test.
"""
df1 = pd.DataFrame({"feat_0": [1, 2, 3, 4, 5], "feat_1": [5, 6, 7, 8, 9]})
df2 = df1
features = df1.columns.values.tolist()
myAutoDist = AutoDist(binning_strategies="all")
res = myAutoDist.compute(df1, df2, column_names=features, return_failed_tests=True)
assert res.isin(["an error occurred"]).any().any()
res = myAutoDist.compute(df1, df2, column_names=features, return_failed_tests=False)
assert not res.isin(["an error occurred"]).any().any()
def test_distribution_statistics_autodist_column_names_error():
df1 = pd.DataFrame({'feat_0': [1, 2, 3, 4, 5], 'feat_1': [5, 6, 7, 8, 9]})
df2 = df1
features = df1.columns.values.tolist() + ['missing_feature']
myAutoDist = AutoDist()
with pytest.raises(Exception):
assert myAutoDist.compute(df1, df2, column_names=features)
df1 = pd.DataFrame({'feat_0': [1, 2, 3, 4, 5], 'feat_1': [5, 6, 7, 8, 9]})
df2 = df1.copy()
df1['feat_2'] = 0
features = df2.columns.values.tolist() + ['missing_feature']
myAutoDist = AutoDist()
with pytest.raises(Exception):
assert myAutoDist.compute(df1, df2, column_names=features)
def test_distribution_statistics_autodist_base():
"""
Test.
"""
nr_features = 2
size = 1000
np.random.seed(0)
df1 = pd.DataFrame(np.random.normal(size=(size, nr_features)), columns=[f"feat_{x}" for x in range(nr_features)])
df2 = pd.DataFrame(np.random.normal(size=(size, nr_features)), columns=[f"feat_{x}" for x in range(nr_features)])
features = df1.columns
myAutoDist = AutoDist(statistical_tests="all", binning_strategies="all", bin_count=[10, 20])
assert repr(myAutoDist).startswith("AutoDist")
assert not myAutoDist.fitted
res = myAutoDist.compute(df1, df2, column_names=features)
assert myAutoDist.fitted
pd.testing.assert_frame_equal(res, myAutoDist.result)
assert isinstance(res, pd.DataFrame)
assert res["column"].values.tolist() == features.to_list()
dist = DistributionStatistics(statistical_test="ks", binning_strategy="simplebucketer", bin_count=10)
dist.compute(df1["feat_0"], df2["feat_0"])
assert dist.p_value == res.loc[res["column"] == "feat_0", "p_value_KS_simplebucketer_10"][0]
assert dist.statistic == res.loc[res["column"] == "feat_0", "statistic_KS_simplebucketer_10"][0]
dist = DistributionStatistics(statistical_test="ks", binning_strategy=None, bin_count=10)
dist.compute(df1["feat_0"], df2["feat_0"])
assert dist.p_value == res.loc[res["column"] == "feat_0", "p_value_KS_no_bucketing_0"][0]
assert dist.statistic == res.loc[res["column"] == "feat_0", "statistic_KS_no_bucketing_0"][0]
def test_distribution_statistics_autodist_default():
df1 = pd.DataFrame({'feat_0': [1, 2, 3, 4, 5], 'feat_1': [5, 6, 7, 8, 9]})
df2 = df1
features = df1.columns.values.tolist()
myAutoDist = AutoDist(binning_strategies="default", bin_count=10)
res = myAutoDist.compute(df1, df2, column_names=features)
for stat_test, stat_info in DistributionStatistics.statistical_test_dict.items(
):
if stat_info['default_binning']:
assert f"p_value_{stat_test}_{stat_info['default_binning']}_10" in res.columns
else:
assert f"p_value_{stat_test}_no_bucketing_0" in res.columns
assert "p_value_agglomerativebucketer_10" not in res.columns
assert res.shape == (len(df1.columns), 1 +
2 * len(DistributionStatistics.statistical_test_dict))
def test_distribution_statistics_autodist_base():
'''DistributionStatiistics autodist base'''
nr_features = 2
size = 1000
np.random.seed(0)
df1 = pd.DataFrame(np.random.normal(size=(size, nr_features)),
columns=[f'feat_{x}' for x in range(nr_features)])
df2 = pd.DataFrame(np.random.normal(size=(size, nr_features)),
columns=[f'feat_{x}' for x in range(nr_features)])
features = df1.columns
myAutoDist = AutoDist(statistical_tests='all',
binning_strategies='all',
bin_count=[10, 20])
assert repr(myAutoDist).startswith('AutoDist')
assert not myAutoDist.fitted
res = myAutoDist.compute(df1, df2, column_names=features)
assert myAutoDist.fitted
pd.testing.assert_frame_equal(res, myAutoDist.result)
assert isinstance(res, pd.DataFrame)
assert res['column'].values.tolist() == features.to_list()
dist = DistributionStatistics(statistical_test='ks',
binning_strategy='simplebucketer',
bin_count=10)
dist.compute(df1['feat_0'], df2['feat_0'])
assert dist.p_value == res.loc[res['column'] == 'feat_0',
'p_value_KS_simplebucketer_10'][0]
assert dist.statistic == res.loc[res['column'] == 'feat_0',
'statistic_KS_simplebucketer_10'][0]
dist = DistributionStatistics(statistical_test='ks',
binning_strategy=None,
bin_count=10)
dist.compute(df1['feat_0'], df2['feat_0'])
assert dist.p_value == res.loc[res['column'] == 'feat_0',
'p_value_KS_no_bucketing_0'][0]
assert dist.statistic == res.loc[res['column'] == 'feat_0',
'statistic_KS_no_bucketing_0'][0]