def test_json_reader():
jr = JsonReader(file_path=resources.data("example.json"),
store_key="example")
datastore = jr.transform(datastore={})
assert datastore["example"]["boolean"]
assert len(datastore["example"]["array"]) == 3
assert datastore["example"]["category"]["a"] == 0
def test_hists_stability_metrics():
# get histograms
pipeline = Pipeline(
modules=[
JsonReader(
file_path=resources.data("example_histogram.json"), store_key="hists"
)
]
)
datastore = pipeline.transform(datastore={})
hists = datastore["hists"]
# generate metrics
hist_list = [
"date:bankrupt",
"date:country",
"date:bankrupt",
"date:A_score",
"date:A_score:num_employees",
]
ds = stability_metrics(
hists, reference_type="rolling", window=5, features=hist_list
)
cols = ["profiles", "comparisons", "traffic_lights", "alerts"]
for c in cols:
assert c in list(ds.keys())
def test_normalized_hist_mean_cov():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist', assign_to_key='output_hist',
apply_funcs=[dict(func=normalized_hist_mean_cov, suffix='')])
])
datastore = pipeline.transform(datastore={})
assert 'output_hist' in datastore
for f in ['A_score', 'A_score:num_employees', 'bankrupt', 'country', 'num_employees']:
assert f in datastore['output_hist']
df = datastore['output_hist']['A_score']
check = np.array([[0.22916667, -0.01041667, -0.0625, -0.13541667, -0.02083333],
[-0.01041667, 0.015625, 0.01041667, -0.01354167, -0.00208333],
[-0.0625, 0.01041667, 0.12916667, -0.06458333, -0.0125],
[-0.13541667, -0.01354167, -0.06458333, 0.240625, -0.02708333],
[-0.02083333, -0.00208333, -0.0125, -0.02708333, 0.0625]])
for hm, hc, hb in zip(df['histogram_mean'].values, df['histogram_cov'].values, df['histogram_binning'].values):
np.testing.assert_array_almost_equal(hm, [0.3125, 0.03125, 0.1875, 0.40625, 0.0625])
np.testing.assert_array_almost_equal(hb, [1.5, 2.5, 3.5, 4.5, 5.5])
np.testing.assert_array_almost_equal(hc, check)
def test_expanding_hist():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist',
apply_funcs=[dict(func=expanding_hist, shift=1, suffix='sum', entire=True, hist_name='histogram')]),
])
datastore = pipeline.transform(datastore={})
df = datastore['output_hist']['num_employees']
h = df['histogram_sum'].values[-1]
bin_entries = h.hist.bin_entries()
check = np.array([11., 1., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,
0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 1.])
np.testing.assert_array_almost_equal(bin_entries, check)
def test_expand_norm_hist_mean_cov():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist', apply_funcs=[
dict(func=expand_norm_hist_mean_cov, hist_name='histogram', shift=1, suffix='', entire=True)])
])
datastore = pipeline.transform(datastore={})
assert 'output_hist' in datastore
for f in ['A_score', 'A_score:num_employees', 'bankrupt', 'country', 'num_employees']:
assert f in datastore['output_hist']
df = datastore['output_hist']['num_employees']
mean = df['histogram_mean'].values[-2]
check = np.array([0.56666667, 0.03333333, 0.03333333, 0., 0.,
0., 0., 0., 0., 0.,
0.06666667, 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0.06666667, 0., 0., 0.,
0., 0., 0., 0., 0.06666667,
0., 0., 0., 0., 0.,
0., 0., 0., 0.03333333, 0.06666667, 0.06666667])
np.testing.assert_array_almost_equal(mean, check)
def test_chi_squared2():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist', apply_funcs=[
dict(func=expand_norm_hist_mean_cov, hist_name='histogram', shift=1, suffix='', entire=True)]),
ApplyFunc(apply_to_key='output_hist', apply_funcs=[dict(func=relative_chi_squared, suffix='', axis=1)])
])
datastore = pipeline.transform(datastore={})
assert 'output_hist' in datastore
for f in ['A_score', 'A_score:num_employees', 'bankrupt', 'country', 'num_employees']:
assert f in datastore['output_hist']
df = datastore['output_hist']['A_score']
np.testing.assert_almost_equal(df['chi2'][-1], 4.066666666666674)
df = datastore['output_hist']['A_score:num_employees']
np.testing.assert_almost_equal(df['chi2'][-2], 3.217532467532462)
df = datastore['output_hist']['bankrupt']
np.testing.assert_almost_equal(df['chi2'][-1], 0.11718750000000011)
df = datastore['output_hist']['country']
np.testing.assert_almost_equal(df['chi2'][-1], 0.6093749999999999)
df = datastore['output_hist']['num_employees']
np.testing.assert_almost_equal(df['chi2'][-1], 1.1858766233766194)
def test_chi_squared1():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist', apply_funcs=[
dict(func=roll_norm_hist_mean_cov, hist_name='histogram', window=5, shift=1, suffix='', entire=True)]),
ApplyFunc(apply_to_key='output_hist', apply_funcs=[dict(func=relative_chi_squared, suffix='', axis=1)])
])
datastore = pipeline.transform(datastore={})
assert 'output_hist' in datastore
for f in ['A_score', 'A_score:num_employees', 'bankrupt', 'country', 'num_employees']:
assert f in datastore['output_hist']
df = datastore['output_hist']['A_score']
np.testing.assert_almost_equal(df['chi2'][6], 3.275000000000001)
df = datastore['output_hist']['A_score:num_employees']
np.testing.assert_almost_equal(df['chi2'][-2], 2.1333333333333315)
df = datastore['output_hist']['bankrupt']
np.testing.assert_almost_equal(df['chi2'][6], 0.19687500000000002)
df = datastore['output_hist']['country']
np.testing.assert_almost_equal(df['chi2'][5], 0.8999999999999994)
df = datastore['output_hist']['num_employees']
np.testing.assert_almost_equal(df['chi2'][5], 0.849999999999999)
def test_chi_RollingNormHistComparer():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(read_key="example_hist",
store_key="output_hist",
features=hist_list),
RollingNormHistComparer(
read_key="output_hist", store_key="comparisons", window=10),
])
datastore = pipeline.transform(datastore={})
assert "comparisons" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["comparisons"]
df = datastore["comparisons"]["A_score"]
np.testing.assert_almost_equal(df["chi2"][-1], 37.61910112359518)
def test_normalized_hist_mean_cov():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(
modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(
read_key="example_hist", store_key="output_hist", features=hist_list
),
ApplyFunc(
apply_to_key="output_hist",
assign_to_key="output_hist",
apply_funcs=[dict(func=normalized_hist_mean_cov, suffix="")],
),
]
)
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["output_hist"]
df = datastore["output_hist"]["A_score"]
check = np.array(
[
[0.22916667, -0.01041667, -0.0625, -0.13541667, -0.02083333],
[-0.01041667, 0.015625, 0.01041667, -0.01354167, -0.00208333],
[-0.0625, 0.01041667, 0.12916667, -0.06458333, -0.0125],
[-0.13541667, -0.01354167, -0.06458333, 0.240625, -0.02708333],
[-0.02083333, -0.00208333, -0.0125, -0.02708333, 0.0625],
]
)
for hm, hc, hb in zip(
df["histogram_mean"].values,
df["histogram_cov"].values,
df["histogram_binning"].values,
):
np.testing.assert_array_almost_equal(
hm, [0.3125, 0.03125, 0.1875, 0.40625, 0.0625]
)
np.testing.assert_array_almost_equal(hb, [1.5, 2.5, 3.5, 4.5, 5.5])
np.testing.assert_array_almost_equal(hc, check)
def test_chi_squared1():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(
modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(
read_key="example_hist", store_key="output_hist", features=hist_list
),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[
dict(
func=roll_norm_hist_mean_cov,
hist_name="histogram",
window=5,
shift=1,
suffix="",
entire=True,
)
],
),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[dict(func=relative_chi_squared, suffix="", axis=1)],
),
]
)
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["output_hist"]
df = datastore["output_hist"]["A_score"]
np.testing.assert_almost_equal(df["chi2"][6], 4.25)
df = datastore["output_hist"]["A_score:num_employees"]
np.testing.assert_almost_equal(df["chi2"][-2], 2.1333333333333315)
df = datastore["output_hist"]["bankrupt"]
np.testing.assert_almost_equal(df["chi2"][6], 0.40000000000000024)
df = datastore["output_hist"]["country"]
np.testing.assert_almost_equal(df["chi2"][5], 0.8999999999999994)
df = datastore["output_hist"]["num_employees"]
np.testing.assert_almost_equal(df["chi2"][5], 0.849999999999999)
def test_chi_squared2():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(
modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(
read_key="example_hist", store_key="output_hist", features=hist_list
),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[
dict(
func=expand_norm_hist_mean_cov,
hist_name="histogram",
shift=1,
suffix="",
entire=True,
)
],
),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[dict(func=relative_chi_squared, suffix="", axis=1)],
),
]
)
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["output_hist"]
df = datastore["output_hist"]["A_score"]
np.testing.assert_almost_equal(df["chi2"][-1], 9.891821919006366)
df = datastore["output_hist"]["A_score:num_employees"]
np.testing.assert_almost_equal(df["chi2"][-2], 3.217532467532462)
df = datastore["output_hist"]["bankrupt"]
np.testing.assert_almost_equal(df["chi2"][-1], 0.23767605633802757)
df = datastore["output_hist"]["country"]
np.testing.assert_almost_equal(df["chi2"][-1], 1.3717532467532458)
df = datastore["output_hist"]["num_employees"]
np.testing.assert_almost_equal(df["chi2"][-1], 1.1858766233766194)
def test_self_reference():
hist_list = ['date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"),
store_key="hists"),
self_reference(hists_key='hists', features=hist_list),
])
pipeline.transform(datastore={})
def test_rolling_reference():
hist_list = ["date:country", "date:A_score:num_employees"]
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"),
store_key="hists"),
rolling_reference(hists_key="hists", window=5, features=hist_list),
])
pipeline.transform(datastore={})
def test_expanding_reference():
hist_list = ["date:bankrupt", "date:num_employees"]
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"),
store_key="hists"),
expanding_reference(hists_key="hists", features=hist_list),
])
pipeline.transform(datastore={})
def test_external_reference():
hist_list = ["date:country", "date:bankrupt"]
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"),
store_key="hists"),
external_reference(
hists_key="hists", ref_hists_key="hists", features=hist_list),
])
pipeline.transform(datastore={})
def test_hists_stability_report():
# get histograms
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="hists"),
])
datastore = pipeline.transform(datastore={})
hists = datastore['hists']
# generate report
hist_list = ['date:bankrupt', 'date:country', 'date:bankrupt', 'date:A_score', 'date:A_score:num_employees']
stability_report(hists, reference_type='rolling', window=5, features=hist_list)
def test_hist_compare():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ApplyFunc(apply_to_key='output_hist',
apply_funcs=[dict(func=expanding_hist, shift=1, suffix='sum', entire=True, hist_name='histogram')]),
ApplyFunc(apply_to_key='output_hist', assign_to_key='comparison', apply_funcs=[
dict(func=hist_compare, hist_name1='histogram', hist_name2='histogram_sum', suffix='', axis=1)])
])
datastore = pipeline.transform(datastore={})
df = datastore['comparison']['num_employees']
np.testing.assert_array_equal(df['chi2'].values[-1], 0.7017543859649122)
def test_chi_RollingNormHistComparer():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score', 'date:A_score:num_employees']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
RollingNormHistComparer(read_key='output_hist', store_key='comparisons', window=10)
])
datastore = pipeline.transform(datastore={})
assert 'comparisons' in datastore
for f in ['A_score', 'A_score:num_employees', 'bankrupt', 'country', 'num_employees']:
assert f in datastore['comparisons']
df = datastore['comparisons']['A_score']
np.testing.assert_almost_equal(df['chi2'][-1], 45.200000)
def test_expanding_hist_comparer():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score']
features = ['country', 'bankrupt', 'num_employees', 'A_score']
cols = ['expanding_pearson', 'expanding_chi2', 'expanding_chi2_zscore', 'expanding_chi2_norm',
'expanding_chi2_pvalue', 'expanding_chi2_max_residual', 'expanding_chi2_spike_count',
'expanding_ks', 'expanding_ks_zscore', 'expanding_ks_pvalue', 'expanding_max_prob_diff',
'expanding_unknown_labels']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ExpandingHistComparer(read_key='output_hist', store_key='comparison')
])
datastore = pipeline.transform(datastore={})
assert 'comparison' in datastore and isinstance(datastore['comparison'], dict)
assert len(datastore['comparison'].keys()) == len(features)
for f in features:
assert f in datastore['comparison']
for f in features:
assert isinstance(datastore['comparison'][f], pd.DataFrame)
df = datastore['comparison']['A_score']
assert len(df) == 16
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['expanding_chi2'].mean(), 2.8366236044275257)
df = datastore['comparison']['country']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['expanding_chi2'].mean(), 1.1224348056645368)
df = datastore['comparison']['bankrupt']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['expanding_chi2'].mean(), 0.6901425387043608)
df = datastore['comparison']['num_employees']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['expanding_chi2'].mean(), 4.243731870738727)
def test_rolling_hist_comparer():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score']
features = ['country', 'bankrupt', 'num_employees', 'A_score']
cols = ['roll_pearson', 'roll_chi2', 'roll_chi2_zscore', 'roll_chi2_norm',
'roll_chi2_pvalue', 'roll_chi2_max_residual', 'roll_chi2_spike_count',
'roll_ks', 'roll_ks_zscore', 'roll_ks_pvalue', 'roll_max_prob_diff',
'roll_unknown_labels']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
RollingHistComparer(read_key='output_hist', store_key='comparison', window=5)
])
datastore = pipeline.transform(datastore={})
assert 'comparison' in datastore and isinstance(datastore['comparison'], dict)
assert len(datastore['comparison'].keys()) == len(features)
for f in features:
assert f in datastore['comparison']
for f in features:
assert isinstance(datastore['comparison'][f], pd.DataFrame)
df = datastore['comparison']['A_score']
assert len(df) == 16
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['roll_chi2'].mean(), 2.927272727272727)
df = datastore['comparison']['country']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['roll_chi2'].mean(), 1.3022619047619046)
df = datastore['comparison']['bankrupt']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['roll_chi2'].mean(), 0.7251681783824641)
df = datastore['comparison']['num_employees']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['roll_chi2'].mean(), 4.0995701058201055)
def test_reference_hist_comparer():
hist_list = ['date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score']
features = ['country', 'bankrupt', 'num_employees', 'A_score']
cols = ['ref_pearson', 'ref_chi2', 'ref_chi2_zscore', 'ref_chi2_norm',
'ref_chi2_pvalue', 'ref_chi2_max_residual', 'ref_chi2_spike_count',
'ref_ks', 'ref_ks_zscore', 'ref_ks_pvalue', 'ref_max_prob_diff', 'ref_unknown_labels']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"), store_key="example_hist"),
HistSplitter(read_key='example_hist', store_key='output_hist', features=hist_list),
ReferenceHistComparer(reference_key='output_hist', assign_to_key='output_hist', store_key='comparison')
])
datastore = pipeline.transform(datastore={})
assert 'comparison' in datastore and isinstance(datastore['comparison'], dict)
assert len(datastore['comparison'].keys()) == len(features)
for f in features:
assert f in datastore['comparison']
for f in features:
assert isinstance(datastore['comparison'][f], pd.DataFrame)
df = datastore['comparison']['A_score']
assert len(df) == 16
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['ref_chi2'].mean(), 2.623206018518519)
df = datastore['comparison']['country']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['ref_chi2'].mean(), 0.9804481792717087)
df = datastore['comparison']['bankrupt']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['ref_chi2'].mean(), 0.6262951496388027)
df = datastore['comparison']['num_employees']
assert len(df) == 17
np.testing.assert_array_equal(sorted(df.columns), sorted(cols))
np.testing.assert_almost_equal(df['ref_chi2'].mean(), 4.213429217840983)
def test_report_generator():
hist_list = [
"date:country", "date:bankrupt", "date:num_employees", "date:A_score"
]
features = ["country", "bankrupt", "num_employees", "A_score"]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(read_key="example_hist",
store_key="output_hist",
features=hist_list),
ReferenceHistComparer(
reference_key="output_hist",
assign_to_key="output_hist",
store_key="comparison",
),
SectionGenerator(
read_key="comparison",
store_key="all_sections",
section_name="Comparisons",
last_n=2,
),
ReportGenerator(read_key="all_sections", store_key="final_report"),
])
datastore = pipeline.transform(datastore={})
assert "comparison" in datastore and isinstance(datastore["comparison"],
dict)
assert len(datastore["comparison"].keys()) == len(features)
for f in features:
assert f in datastore["comparison"]
for f in features:
assert isinstance(datastore["comparison"][f], pd.DataFrame)
assert pipeline.modules[-2].last_n == 2
assert "final_report" in datastore
assert (isinstance(datastore["final_report"], str)
and len(datastore["final_report"]) > 0)
def test_hist_splitter():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
features = [
"country",
"bankrupt",
"num_employees",
"A_score",
"A_score:num_employees",
]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(read_key="example_hist",
store_key="output_hist",
features=hist_list),
])
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore and isinstance(datastore["output_hist"],
dict)
assert len(datastore["output_hist"].keys()) == len(features)
for f in features:
assert f in datastore["output_hist"]
for f in features:
assert isinstance(datastore["output_hist"][f], pd.DataFrame)
for f in features:
df = datastore["output_hist"][f]
split_list = df.reset_index().to_dict("records")
hlist = [s["histogram"] for s in split_list]
assert_similar_hists(hlist)
def test_hist_splitter_filter():
""" Test of hist_splitter option filter_empty_split_hists
One of the split histograms of type date:A_score:num_employees is empty and only contains a NaN.
In this test, those empty split-histograms are *not* removed, leading to split-histograms of
inconsistent types.
"""
hist_list = ["date:A_score:num_employees"]
features = ["A_score:num_employees"]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(
read_key="example_hist",
store_key="output_hist",
features=hist_list,
filter_empty_split_hists=False,
),
])
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore and isinstance(datastore["output_hist"],
dict)
assert len(datastore["output_hist"].keys()) == len(features)
for f in features:
assert f in datastore["output_hist"]
for f in features:
assert isinstance(datastore["output_hist"][f], pd.DataFrame)
for f in features:
df = datastore["output_hist"][f]
split_list = df.reset_index().to_dict("records")
hlist = [s["histogram"] for s in split_list]
check = check_similar_hists(hlist)
assert check is False
def test_chi_ReferenceNormHistComparer():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(
modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(
read_key="example_hist", store_key="output_hist", features=hist_list
),
ReferenceNormHistComparer(
reference_key="output_hist",
assign_to_key="output_hist",
store_key="comparisons",
),
]
)
datastore = pipeline.transform(datastore={})
assert "comparisons" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["comparisons"]
df = datastore["comparisons"]["A_score"]
np.testing.assert_almost_equal(df["chi2"][0], 2.2884111855886022)
def test_report_generator():
hist_list = [
'date:country', 'date:bankrupt', 'date:num_employees', 'date:A_score'
]
features = ['country', 'bankrupt', 'num_employees', 'A_score']
pipeline = Pipeline(modules=[
JsonReader(file_path=resources.data("example_histogram.json"),
store_key="example_hist"),
HistSplitter(read_key='example_hist',
store_key='output_hist',
features=hist_list),
ReferenceHistComparer(reference_key='output_hist',
assign_to_key='output_hist',
store_key='comparison'),
SectionGenerator(read_key="comparison",
store_key="all_sections",
section_name="Comparisons",
last_n=2),
ReportGenerator(read_key="all_sections", store_key="final_report")
])
datastore = pipeline.transform(datastore={})
assert 'comparison' in datastore and isinstance(datastore['comparison'],
dict)
assert len(datastore['comparison'].keys()) == len(features)
for f in features:
assert f in datastore['comparison']
for f in features:
assert isinstance(datastore['comparison'][f], pd.DataFrame)
assert pipeline.modules[-2].last_n == 2
assert 'final_report' in datastore
assert isinstance(datastore['final_report'],
str) and len(datastore['final_report']) > 0
def test_expanding_hist():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(read_key="example_hist",
store_key="output_hist",
features=hist_list),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[
dict(
func=expanding_hist,
shift=1,
suffix="sum",
entire=True,
hist_name="histogram",
)
],
),
])
datastore = pipeline.transform(datastore={})
df = datastore["output_hist"]["num_employees"]
h = df["histogram_sum"].values[-1]
bin_entries = h.bin_entries()
check = np.array([
11.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
])
np.testing.assert_array_almost_equal(bin_entries, check)
def test_expand_norm_hist_mean_cov():
hist_list = [
"date:country",
"date:bankrupt",
"date:num_employees",
"date:A_score",
"date:A_score:num_employees",
]
pipeline = Pipeline(modules=[
JsonReader(
file_path=resources.data("example_histogram.json"),
store_key="example_hist",
),
HistSplitter(read_key="example_hist",
store_key="output_hist",
features=hist_list),
ApplyFunc(
apply_to_key="output_hist",
apply_funcs=[
dict(
func=expand_norm_hist_mean_cov,
hist_name="histogram",
shift=1,
suffix="",
entire=True,
)
],
),
])
datastore = pipeline.transform(datastore={})
assert "output_hist" in datastore
for f in [
"A_score",
"A_score:num_employees",
"bankrupt",
"country",
"num_employees",
]:
assert f in datastore["output_hist"]
df = datastore["output_hist"]["num_employees"]
mean = df["histogram_mean"].values[-2]
check = np.array([
0.56666667,
0.03333333,
0.03333333,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.06666667,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.06666667,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.06666667,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.03333333,
0.06666667,
0.06666667,
])
np.testing.assert_array_almost_equal(mean, check)