def test_warnings_are_issued_for_missing():
"""Test if warnings are issued when missing values are present in the input of autodist."""
# Generate an input dataframe without missing values
X = pd.DataFrame({"A": [number for number in range(0, 50)]})
X = X.assign(B=X["A"], C=X["A"], D=X["A"], E=X["A"])
# Add some missing values to the dataframe.
X_na = X.copy()
X_na.iloc[X.sample(5, random_state=1).index, 1:3] = np.nan
# Test missing value removal on the first data input.
with pytest.warns(None) as record_first:
_ = AutoDist(statistical_tests=["PSI"], binning_strategies="SimpleBucketer", bin_count=10).compute(X_na, X)
assert len(record_first) == 2
# Test missing values removal on the second data input
with pytest.warns(None) as record_second:
_ = AutoDist(statistical_tests=["PSI"], binning_strategies="SimpleBucketer", bin_count=10).compute(X, X_na)
assert len(record_second) == 2
# Test the missing values removal on the first and second data input
with pytest.warns(None) as record_both:
_ = AutoDist(statistical_tests=["PSI"], binning_strategies="SimpleBucketer", bin_count=10).compute(X_na, X_na)
assert len(record_both) == 2
# Test case where there are no missing values
with pytest.warns(None) as record_both:
_ = AutoDist(statistical_tests=["PSI"], binning_strategies="SimpleBucketer", bin_count=10).compute(X, X)
assert len(record_both) == 0
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_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_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_missing_values_in_autodist():
"""Test missing values have no impact in AutoDist functionality."""
# Create dummy dataframe
X, y = make_classification(50, 5, random_state=0)
X = pd.DataFrame(X)
# Split train and test
X_train, X_test, _, _ = train_test_split(X, y, test_size=0.2, random_state=1)
# Define an add-on with only missing values
X_na = pd.DataFrame(np.tile(np.nan, (X.shape[1], X.shape[1])))
# Compute the statistics with the missing values
with_missings = AutoDist(
statistical_tests=["PSI", "KS"], binning_strategies="SimpleBucketer", bin_count=10
).compute(pd.concat([X_train, X_na]), pd.concat([X_test, X_na]))
# Compute the statistics withpout the missing values
no_missing = AutoDist(statistical_tests=["PSI", "KS"], binning_strategies="SimpleBucketer", bin_count=10).compute(
X_train, X_test
)
# Test the two set of results are identical
pd.testing.assert_frame_equal(with_missings, no_missing)
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]
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_init():
"""
Test.
"""
myAutoDist = AutoDist(statistical_tests="all", binning_strategies="all")
assert isinstance(myAutoDist.statistical_tests, list)
myAutoDist = AutoDist(statistical_tests="ks", binning_strategies="all")
assert myAutoDist.statistical_tests == ["ks"]
myAutoDist = AutoDist(statistical_tests=["ks", "psi"], binning_strategies="all")
assert myAutoDist.statistical_tests == ["ks", "psi"]
myAutoDist = AutoDist(statistical_tests="all", binning_strategies="all")
assert isinstance(myAutoDist.binning_strategies, list)
myAutoDist = AutoDist(statistical_tests="all", binning_strategies="quantilebucketer")
assert myAutoDist.binning_strategies == ["quantilebucketer"]
myAutoDist = AutoDist(statistical_tests="all", binning_strategies=["quantilebucketer", "simplebucketer"])
assert myAutoDist.binning_strategies == ["quantilebucketer", "simplebucketer"]
def test_distribution_statistics_autodist_init():
myAutoDist = AutoDist(statistical_tests='all', binning_strategies='all')
assert isinstance(myAutoDist.statistical_tests, list)
myAutoDist = AutoDist(statistical_tests='ks', binning_strategies='all')
assert myAutoDist.statistical_tests == ['ks']
myAutoDist = AutoDist(statistical_tests=['ks', 'psi'],
binning_strategies='all')
assert myAutoDist.statistical_tests == ['ks', 'psi']
myAutoDist = AutoDist(statistical_tests='all', binning_strategies='all')
assert isinstance(myAutoDist.binning_strategies, list)
myAutoDist = AutoDist(statistical_tests='all',
binning_strategies='quantilebucketer')
assert myAutoDist.binning_strategies == ['quantilebucketer']
myAutoDist = AutoDist(
statistical_tests='all',
binning_strategies=['quantilebucketer', 'simplebucketer'])
assert myAutoDist.binning_strategies == [
'quantilebucketer', 'simplebucketer'
]