def pipeline(es):
pipeline = Pipeline(steps=[(
'ft',
DFSTransformer(
entityset=es, target_entity="customers", max_features=20)
), ("numeric", FunctionTransformer(select_numeric, validate=False)
), ('imp',
SimpleImputer()), ('et', ExtraTreesClassifier(n_estimators=10))])
return pipeline
def pipeline():
pipeline = Pipeline(steps=[
("ft",
DFSTransformer(target_dataframe_name="customers", max_features=20)),
("numeric", FunctionTransformer(select_numeric, validate=False)),
("imp", SimpleImputer()),
("et", ExtraTreesClassifier(n_estimators=10)),
])
return pipeline
def test_sklearn_transformer(es, df):
# Using with transformers
pipeline = Pipeline(steps=[
('ft', DFSTransformer(entityset=es, target_entity="customers")),
("numeric", FunctionTransformer(select_numeric, validate=False)),
('sc', StandardScaler()),
])
X_train = pipeline.fit(df['customer_id']).transform(df['customer_id'])
assert X_train.shape[0] == 15
def test_sklearn_transformer_with_entityset(es):
# Using with transformers
pipeline = Pipeline(steps=[
("ft", DFSTransformer(target_dataframe_name="customers")),
("numeric", FunctionTransformer(select_numeric, validate=False)),
("sc", StandardScaler()),
])
X_train = pipeline.fit(es).transform(es)
assert X_train.shape[0] == 15
def test_cfm_uses_filtered_target_df_with_entityset(es):
pipeline = Pipeline(
steps=[("ft", DFSTransformer(target_dataframe_name="transactions"))])
train_ids = [1, 2, 3]
test_ids = [10, 55, 853]
train_es = filter_transactions(es, ids=train_ids)
test_es = filter_transactions(es, ids=test_ids)
fm_train = pipeline.fit_transform(X=train_es)
assert all(fm_train["sessions.COUNT(transactions)"] == [1, 1, 1])
assert set(fm_train.index.values) == set(train_ids)
fm_test = pipeline.transform(test_es)
assert all(fm_test["sessions.COUNT(transactions)"] == [1, 2, 2])
assert set(fm_test.index.values) == set(test_ids)
def test_cfm_uses_filtered_target_df_with_dataframes_and_relationships(es):
pipeline = Pipeline(
steps=[("ft", DFSTransformer(target_dataframe_name="transactions"))])
train_ids = [3, 1, 2]
test_ids = [853, 55, 10]
train_es = filter_transactions(es, ids=train_ids)
test_es = filter_transactions(es, ids=test_ids)
train_dataframes, train_relationships = get_dataframes_and_relationships(
train_es)
test_dataframes, test_relationships = get_dataframes_and_relationships(
test_es)
fm_train = pipeline.fit_transform(X=(train_dataframes,
train_relationships))
assert all(fm_train["sessions.COUNT(transactions)"] == [1, 1, 1])
assert set(fm_train.index.values) == set(train_ids)
fm_test = pipeline.transform(X=(test_dataframes, test_relationships))
assert all(fm_test["sessions.COUNT(transactions)"] == [2, 2, 1])
assert set(fm_test.index.values) == set(test_ids)