def check_sk_pipeline():
pline = pdp.make_pdpipeline(
pdp.ApplyByCols("ph", lambda x: x - 1),
# pdp.Bin({"ph": [0, 3, 5, 12]}),
pdp.Encode(["type", "lbl"]),
)
print(pline)
model_pline = make_pipeline(
pdp.FreqDrop(2, "lbl"),
LogisticRegression(),
)
print(model_pline)
train = _train_df()
res_train = pline(train)
print("Processed train set: {}".format(res_train))
x_train, y_train = x_y_by_col_lbl(res_train, "lbl")
model_pline = model_pline.fit(x_train, y_train)
print("Fitted model pipeline: {}".format(model_pline))
test = _test_df()
res_test = pline(test)
print("Processed test set: {}".format(res_test))
x_test, y_test = x_y_by_col_lbl(res_test, "lbl")
predictions = model_pline.predict(x_test)
print("predictions: {}".format(predictions))
def test_make_pdpipeline():
"""Testing something."""
drop_num1 = SilentDropStage('num1')
drop_num2 = SilentDropStage('num2')
pipeline = make_pdpipeline(drop_num1, drop_num2)
assert len(pipeline) == 2
df = _test_df()
res_df = pipeline.apply(df, verbose=True)
assert 'num1' not in res_df.columns
assert 'num2' not in res_df.columns
assert 'char' in res_df.columns