def test_id_col_check():
d = pandas.DataFrame({"x": ["a", "b", "c"], "y": ["a", "b", "b"]})
transform = vtreat.UnsupervisedTreatment(var_list=["x", "y"])
with pytest.warns(Warning):
transform.fit_transform(d)
def test_unsupervised():
numpy.random.seed(235)
zip = ["z" + str(i + 1).zfill(5) for i in range(15)]
d = pandas.DataFrame({"zip": numpy.random.choice(zip, size=1000)})
d["const"] = 1
d["const2"] = "b"
d["const3"] = None
transform = vtreat.UnsupervisedTreatment(
params=vtreat.unsupervised_parameters({"indicator_min_fraction": 0.01
}))
d_treated = transform.fit_transform(d)
for c in d_treated.columns:
assert vtreat.util.can_convert_v_to_numeric(d_treated[c])
assert numpy.sum(vtreat.util.is_bad(d_treated[c])) == 0
sf = transform.score_frame_
assert set(sf["orig_variable"]) == {"zip"}
# https://stackoverflow.com/a/45671804/6901725
with pytest.warns(None) as record:
d_treated_2 = transform.transform(d)
assert len(record) == 0
assert d_treated.equals(d_treated_2)
fn = transform.get_feature_names()
assert set(sf["variable"]) == set(fn)
def test_vtreat_onehot():
d = pd.DataFrame(
{
"xc": ["a", "b", "b"],
"xd": ["1", "1", "2"], # vtreat picks columns to convert by type
"xn": [1.0, 2.0, 3.0],
}
)
treatment = vtreat.UnsupervisedTreatment(
params=vtreat.unsupervised_parameters(
{"coders": {"clean_copy", "indicator_code"}}
)
)
treatment.fit(d)
res = treatment.transform(d)
expect = pd.DataFrame(
{
"xn": [1.0, 2.0, 3.0],
"xd_lev_1": [1.0, 1.0, 0.0],
"xd_lev_2": [0.0, 0.0, 1.0],
"xc_lev_b": [0.0, 1.0, 1.0],
"xc_lev_a": [1.0, 0.0, 0.0],
}
)
assert data_algebra.test_util.equivalent_frames(res, expect, check_row_order=True)
def test_unsupervised():
n_rows = 10000
n_levels = 10
n_cat = 10
n_numeric = 10
numpy.random.seed(235)
zip = ["z" + str(i + 1).zfill(5) for i in range(n_levels)]
d = pandas.DataFrame({"const": numpy.zeros(n_rows) + 1})
d["const2"] = "b"
for i in range(n_cat):
d[f"zip_{i}"] = numpy.random.choice(zip, size=n_rows)
for i in range(n_numeric):
d[f"num_{i}"] = numpy.random.uniform(size=n_rows)
transform = vtreat.UnsupervisedTreatment(
params=vtreat.unsupervised_parameters({"indicator_min_fraction": 0.01
}))
## https://docs.python.org/3/library/profile.html
# import cProfile
# cProfile.run('d_treated = transform.fit_transform(d)')
d_treated = transform.fit_transform(d)
for c in d_treated.columns:
assert vtreat.util.can_convert_v_to_numeric(d_treated[c])
assert numpy.sum(vtreat.util.is_bad(d_treated[c])) == 0
sf = transform.score_frame_
def test_col_dups_1():
d = pandas.DataFrame({"x": [1], "x2": [2], "y": [3]})
d.columns = ["x", "x", "y"]
transform = vtreat.UnsupervisedTreatment(var_list=["x"],
cols_to_copy=["y"])
with pytest.raises(ValueError):
transform.fit_transform(d, d["y"])
def test_col_dups_1():
d = pandas.DataFrame({'x': [1], 'x2': [2], 'y': [3]})
d.columns = ['x', 'x', 'y']
transform = vtreat.UnsupervisedTreatment(var_list=['x'],
cols_to_copy=['y'])
with pytest.raises(ValueError):
transform.fit_transform(d, d["y"])
def test_id_col_check():
d = pandas.DataFrame({'x': ['a', 'b', 'c'], 'y': ['a', 'b', 'b']})
transform = vtreat.UnsupervisedTreatment(
var_list=['x', 'y']
)
with pytest.warns(Warning):
transform.fit_transform(d)
def test_xgboost_col_name_issue_2():
# https://stackoverflow.com/questions/48645846/pythons-xgoost-valueerrorfeature-names-may-not-contain-or
# ValueError('feature_names may not contain [, ] or <')
d = pandas.DataFrame({"x": ["[", "]", "<", "_lt_", "_lt_"]})
transform = vtreat.UnsupervisedTreatment(var_list=["x"])
d_transformed = transform.fit_transform(d, None)
cols = d_transformed.columns
for col in cols:
assert not any(c in col for c in "[]<>")
assert len(set(cols)) == len(cols)
def test_unsupervised():
numpy.random.seed(235)
zip = ["z" + str(i + 1).zfill(5) for i in range(15)]
d = pandas.DataFrame({"zip": numpy.random.choice(zip, size=1000)})
d["const"] = 1
d["const2"] = "b"
transform = vtreat.UnsupervisedTreatment(
params=vtreat.unsupervised_parameters({"indicator_min_fraction": 0.01
}))
d_treated = transform.fit_transform(d)
for c in d_treated.columns:
assert vtreat.util.can_convert_v_to_numeric(d_treated[c])
assert sum(vtreat.util.is_bad(d_treated[c])) == 0
sf = transform.score_frame_
def test_homes_example():
dir_path = os.path.dirname(os.path.realpath(__file__))
d = pandas.read_pickle(os.path.join(dir_path, 'homes_76.pkl'))
assert d.shape[0] == 38
assert d.shape[1] == 8
# from AI200: day_01/02_Regression/Part2_LRPractice/LRExample.ipynb
# documentation: https://github.com/WinVector/pyvtreat/blob/main/Examples/Unsupervised/Unsupervised.md
treatment = vtreat.UnsupervisedTreatment(
cols_to_copy=['Price'],
params=vtreat.unsupervised_parameters({
'sparse_indicators': False,
'coders': {'clean_copy', 'indicator_code', 'missing_indicator'}
}))
df = treatment.fit_transform(d)
assert df.shape[0] == d.shape[0]
expect_cols = [
'Price', 'Size', 'Bath', 'Bed', 'Year', 'Garage', 'Lot_lev_4',
'Lot_lev_5', 'Lot_lev_3', 'Lot_lev_1', 'Lot_lev_2', 'Lot_lev_11',
'Elem_lev_edge', 'Elem_lev_edison', 'Elem_lev_parker',
'Elem_lev_harris', 'Elem_lev_adams', 'Elem_lev_crest'
]
assert set(df.columns) == set(expect_cols)
def test_imputation_controls():
d = pandas.DataFrame({
"x": [0, 1, 1000, None],
"y": [0, 0, 1, 1],
})
transform = vtreat.UnsupervisedTreatment(cols_to_copy=["y"], )
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 333.6666666667],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
transform = vtreat.UnsupervisedTreatment(
cols_to_copy=["y"],
params=vtreat.unsupervised_parameters({
"missingness_imputation":
numpy.median,
}),
)
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 1.0],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
transform = vtreat.UnsupervisedTreatment(
cols_to_copy=["y"],
params=vtreat.unsupervised_parameters({
"missingness_imputation":
numpy.min,
}),
)
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 0.0],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
transform = vtreat.UnsupervisedTreatment(
cols_to_copy=["y"],
params=vtreat.unsupervised_parameters({
"missingness_imputation": 7,
}),
imputation_map={"y": numpy.median},
)
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 7.0],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
transform = vtreat.UnsupervisedTreatment(
cols_to_copy=["y"],
params=vtreat.unsupervised_parameters({
"missingness_imputation": 7,
}),
imputation_map={"x": numpy.median},
)
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 1.0],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
transform = vtreat.UnsupervisedTreatment(
cols_to_copy=["y"],
params=vtreat.unsupervised_parameters({
"missingness_imputation":
numpy.mean,
}),
imputation_map={"x": 12},
)
d_treated = transform.fit_transform(d)
expect = pandas.DataFrame({
"y": [0, 0, 1, 1],
"x_is_bad": [0.0, 0.0, 0.0, 1.0],
"x": [0.0, 1.0, 1000.0, 12.0],
})
vtreat.util.check_matching_numeric_frames(res=d_treated, expect=expect)
