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_attributes_ks():
d1 = np.histogram(np.random.normal(size=1000), 10)[0]
d2 = np.histogram(np.random.normal(size=1000), 10)[0]
myTest = DistributionStatistics('ks', binning_strategy=None)
_ = myTest.compute(d1, d2, verbose=False)
ks_value, p_value = ks(d1, d2)
assert myTest.statistic == ks_value
def test_distribution_statistics_ks_no_binning():
d1 = np.histogram(np.random.normal(size=1000), 10)[0]
d2 = np.histogram(np.random.weibull(1, size=1000) - 1, 10)[0]
myTest = DistributionStatistics('ks', binning_strategy=None)
assert not myTest.fitted
res = myTest.compute(d1, d2)
assert myTest.fitted
assert isinstance(res, tuple)
def test_distribution_statistics_tuple_output():
d1 = np.histogram(np.random.normal(size=1000), 10)[0]
d2 = np.histogram(np.random.weibull(1, size=1000) - 1, 10)[0]
myTest = DistributionStatistics('ks', 'SimpleBucketer', bin_count=10)
assert not myTest.fitted
res = myTest.compute(d1, d2)
assert myTest.fitted
assert isinstance(res, tuple)
def test_distribution_statistics_psi():
d1 = np.histogram(np.random.normal(size=1000), 10)[0]
d2 = np.histogram(np.random.weibull(1, size=1000) - 1, 10)[0]
myTest = DistributionStatistics('psi', 'SimpleBucketer', bin_count=10)
assert not myTest.fitted
psi_test, p_value_test = myTest.compute(d1, d2)
assert myTest.fitted
assert isinstance(psi_test, numbers.Number)
def test_distribution_statistics_attributes_psi():
a = np.random.normal(size=1000)
b = np.random.normal(size=1000)
d1 = np.histogram(a, 10)[0]
d2 = np.histogram(b, 10)[0]
myTest = DistributionStatistics('psi', binning_strategy=None)
_ = myTest.compute(d1, d2, verbose=False)
psi_value_test, p_value_test = psi(d1, d2, verbose=False)
assert myTest.statistic == psi_value_test
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]
