def test_graphpipeline_merging_node():
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"Pt": DebugPassThrough(debug=True),
},
edges=[("ColNum", "Pt"), ("ColCat", "Pt")],
)
gpipeline.fit(dfX, y)
pt = gpipeline.models["Pt"]
assert pt._expected_columns == ["num1", "num2", "num3", "cat1", "cat2"]
assert pt._expected_type == DataTypes.DataFrame
assert pt._expected_nbcols == 5
dfX_transformed = gpipeline.transform(dfX)
assert (dfX_transformed == dfX.loc[:, ["num1", "num2", "num3", "cat1", "cat2"]]).all().all()
assert gpipeline.get_feature_names() == ["num1", "num2", "num3", "cat1", "cat2"]
assert gpipeline.get_feature_names_at_node("Pt") == ["num1", "num2", "num3", "cat1", "cat2"]
assert gpipeline.get_feature_names_at_node("ColNum") == ["num1", "num2", "num3"]
assert gpipeline.get_feature_names_at_node("ColCat") == ["cat1", "cat2"]
assert gpipeline.get_input_features_at_node("ColNum") == list(dfX.columns)
assert gpipeline.get_input_features_at_node("ColCat") == list(dfX.columns)
assert gpipeline.get_input_features_at_node("Pt") == ["num1", "num2", "num3", "cat1", "cat2"]
# concatenation in the other oreder
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"Pt": DebugPassThrough(debug=True),
},
edges=[("ColCat", "Pt"), ("ColNum", "Pt")],
)
gpipeline.fit(dfX, y)
pt = gpipeline.models["Pt"]
assert pt._expected_columns == ["cat1", "cat2", "num1", "num2", "num3"] # Concanteation in the order of the edges
assert pt._expected_type == DataTypes.DataFrame
assert pt._expected_nbcols == 5
assert gpipeline.get_feature_names() == ["cat1", "cat2", "num1", "num2", "num3"]
assert gpipeline.get_feature_names_at_node("Pt") == ["cat1", "cat2", "num1", "num2", "num3"]
assert gpipeline.get_feature_names_at_node("ColNum") == ["num1", "num2", "num3"]
assert gpipeline.get_feature_names_at_node("ColCat") == ["cat1", "cat2"]
assert gpipeline.get_input_features_at_node("ColNum") == list(dfX.columns)
assert gpipeline.get_input_features_at_node("ColCat") == list(dfX.columns)
assert gpipeline.get_input_features_at_node("Pt") == ["cat1", "cat2", "num1", "num2", "num3"]
dfX_transformed = gpipeline.transform(dfX)
assert (dfX_transformed == dfX.loc[:, ["cat1", "cat2", "num1", "num2", "num3"]]).all().all()
def test_graphpipeline_concat_names():
df = get_sample_df(size=100, seed=123)
gpipeline = GraphPipeline(
models={
"sel": ColumnsSelector(columns_to_use=["float_col", "int_col"]),
"vec": CountVectorizerWrapper(columns_to_use=["text_col"]),
"pt": PassThrough(),
},
edges=[("sel", "pt"), ("vec", "pt")],
)
gpipeline.fit(df)
df_res = gpipeline.transform(df)
assert list(df_res.columns) == [
"float_col",
"int_col",
"text_col__BAG__aaa",
"text_col__BAG__bbb",
"text_col__BAG__ccc",
"text_col__BAG__ddd",
"text_col__BAG__eee",
"text_col__BAG__fff",
"text_col__BAG__jjj",
]
assert gpipeline.get_feature_names() == list(df_res.columns)
