def test_gpipeline_regression():
gpipeline = GraphPipeline({
"PT": PassThrough(),
"Ridge": Ridge()
}, [("PT", "Ridge")])
X = dfX.loc[:, ["num1", "num2", "num3"]]
gpipeline.fit(X, y)
yhat = gpipeline.predict(X)
yhat2 = gpipeline.models["Ridge"].predict(X)
assert yhat.shape == y.shape
assert (yhat == yhat2).all()
with pytest.raises(AttributeError):
gpipeline.predict_proba(X)
with pytest.raises(AttributeError):
gpipeline.predict_log_proba(X)
assert gpipeline.get_feature_names_at_node("PT") == list(X.columns)
assert gpipeline.get_input_features_at_node("PT") == list(X.columns)
assert gpipeline.get_input_features_at_node("Ridge") == list(X.columns)
with pytest.raises(ValueError):
assert gpipeline.get_feature_names_at_node("DONTEXIST")
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)
def test_gpipeline_graphviz():
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"Pt": PassThrough(),
},
edges=[("ColNum", "Pt"), ("ColCat", "Pt")],
)
gpipeline.fit(dfX, y)
assert isinstance(gpipeline.graphviz, graphviz.dot.Digraph)
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"Pt": PassThrough(),
},
edges=[("ColCat", "Pt"), ("ColNum", "Pt")],
)
assert isinstance(
gpipeline.graphviz,
graphviz.dot.Digraph) # graphviz even before fit is called
def test_graphpipeline_cycle():
gpipeline = GraphPipeline(
{"A": PassThrough(), "B": PassThrough(), "C": PassThrough(), "D": PassThrough()},
edges=[("A", "B", "C"), ("C", "A"), ("C", "D")],
)
with pytest.raises(ValueError):
gpipeline.fit(X, y) # ValueError: The graph shouldn't have any cycle
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_gpipeline_clustering():
gpipeline = GraphPipeline({"PT": PassThrough(), "kmeans": KMeans(n_clusters=2)}, [("PT", "kmeans")])
gpipeline.fit(X)
yhat = gpipeline.predict(X)
yhat2 = gpipeline.models["kmeans"].predict(X)
assert (yhat == yhat2).all()
def test_graphpipeline_passing_of_groups():
gpipeline = GraphPipeline({"A": TransformerFailNoGroups(), "B": DebugPassThrough(debug=True)}, edges=[("A", "B")])
with pytest.raises(ValueError):
gpipeline.fit(X, y)
groups = np.zeros(len(y))
gpipeline.fit(X, y, groups) # check that it didn't failed
def test_gpipeline_classification():
gpipeline = GraphPipeline({"PT": PassThrough(), "Logit": LogisticRegression()}, [("PT", "Logit")])
gpipeline.fit(X, yc)
yhat_proba = gpipeline.predict_proba(X)
yhat_proba2 = gpipeline.models["Logit"].predict_proba(X)
assert yhat_proba.shape == (X.shape[0], 2)
assert (yhat_proba == yhat_proba2).all()
assert list(gpipeline.classes_) == [0, 1]
def test_graphpipeline_no_terminal_node():
gpipeline = GraphPipeline(
{
"A": PassThrough(),
"B": PassThrough(),
"C": PassThrough()
},
edges=[("A", "B", "C"), ("C", "A")])
with pytest.raises(ValueError):
gpipeline.fit(
X, y
) # ValueError: the graph should have only one terminal node, instead i got 0
def test_gpipeline_clone():
gpipeline = GraphPipeline({"PT": PassThrough(), "Ridge": Ridge()}, [("PT", "Ridge")])
gpipeline.fit(X, y)
cloned_gpipeline = clone(gpipeline)
with pytest.raises(NotFittedError):
cloned_gpipeline.predict(X)
for m in gpipeline.models.keys():
assert m in cloned_gpipeline.models
assert id(gpipeline.models[m]) != id(cloned_gpipeline.models[m])
def test_graphpipeline_more_than_one_terminal_node():
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"PtNum": PassThrough(),
"PtCat": PassThrough(),
},
edges=[("ColNum", "PtNum"), ("ColCat", "PtCat")],
)
with pytest.raises(ValueError):
gpipeline.fit(dfX, y) # ValueError the graph should have only one terminal node, instead i got 2
def test_graphpipeline_edge_not_in_models():
gpipeline = GraphPipeline(
{
"ColNum": ColumnsSelector(columns_to_use=["num1", "num2", "num3"]),
"ColCat": ColumnsSelector(columns_to_use=["cat1", "cat2"]),
"PtNum": PassThrough(),
"PtCat": PassThrough(),
},
edges=[("ColNum", "PtNummm"), ("ColCat", "PtCat")],
)
with pytest.raises(ValueError):
gpipeline.fit(dfX, y) # ValueError "the node 'PtNummm' isn't in the dictionnary of models"
def test_graphpipeline_fit_params():
gpipeline = GraphPipeline(
{"A": DebugPassThrough(debug=True), "B": DebugPassThrough(debug=True), "C": DebugPassThrough(debug=True)},
edges=[("A", "B", "C")],
)
gpipeline.fit(X, y)
assert gpipeline.models["A"].fit_params == {}
assert gpipeline.models["B"].fit_params == {}
assert gpipeline.models["C"].fit_params == {}
gpipeline.fit(X, y, A__fitparam_A="paramA")
assert gpipeline.models["A"].fit_params == {"fitparam_A": "paramA"}
assert gpipeline.models["B"].fit_params == {}
assert gpipeline.models["C"].fit_params == {}
def test_graphpipeline_get_features_names_with_input_features():
xx = np.random.randn(10, 5)
df = pd.DataFrame(xx, columns=["COL_%d" % j for j in range(xx.shape[1])])
model = GraphPipeline(
{"pt1": PassThroughtWithFeatures(prefix="PT1"), "pt2": PassThroughtWithFeatures(prefix="PT2")},
edges=[("pt1", "pt2")],
)
model.fit(df)
### Test 1 : without input_features ###
assert model.get_feature_names() == [
"PT2__PT1__COL_0",
"PT2__PT1__COL_1",
"PT2__PT1__COL_2",
"PT2__PT1__COL_3",
"PT2__PT1__COL_4",
]
assert model.get_feature_names_at_node("pt2") == [
"PT2__PT1__COL_0",
"PT2__PT1__COL_1",
"PT2__PT1__COL_2",
"PT2__PT1__COL_3",
"PT2__PT1__COL_4",
]
assert model.get_feature_names_at_node("pt1") == [
"PT1__COL_0",
"PT1__COL_1",
"PT1__COL_2",
"PT1__COL_3",
"PT1__COL_4",
]
assert model.get_input_features_at_node("pt2") == [
"PT1__COL_0",
"PT1__COL_1",
"PT1__COL_2",
"PT1__COL_3",
"PT1__COL_4",
]
assert model.get_input_features_at_node("pt1") == ["COL_0", "COL_1", "COL_2", "COL_3", "COL_4"]
### Test 2 : with input feautres ###
assert model.get_feature_names(input_features=["a", "b", "c", "d", "e"]) == [
"PT2__PT1__a",
"PT2__PT1__b",
"PT2__PT1__c",
"PT2__PT1__d",
"PT2__PT1__e",
]
assert model.get_feature_names_at_node("pt2", input_features=["a", "b", "c", "d", "e"]) == [
"PT2__PT1__a",
"PT2__PT1__b",
"PT2__PT1__c",
"PT2__PT1__d",
"PT2__PT1__e",
]
assert model.get_feature_names_at_node("pt1", input_features=["a", "b", "c", "d", "e"]) == [
"PT1__a",
"PT1__b",
"PT1__c",
"PT1__d",
"PT1__e",
]
assert model.get_input_features_at_node("pt2", input_features=["a", "b", "c", "d", "e"]) == [
"PT1__a",
"PT1__b",
"PT1__c",
"PT1__d",
"PT1__e",
]
assert model.get_input_features_at_node("pt1", input_features=["a", "b", "c", "d", "e"]) == [
"a",
"b",
"c",
"d",
"e",
]
### Test 3 : with numpy array ###
model = GraphPipeline(
{"pt1": PassThroughtWithFeatures(prefix="PT1"), "pt2": PassThroughtWithFeatures(prefix="PT2")},
edges=[("pt1", "pt2")],
)
model.fit(xx)
assert model.get_feature_names() is None
assert model.get_feature_names_at_node("pt2") is None
assert model.get_feature_names_at_node("pt1") is None
assert model.get_input_features_at_node("pt2") is None
assert model.get_input_features_at_node("pt1") is None
assert model.get_feature_names(input_features=["a", "b", "c", "d", "e"]) == [
"PT2__PT1__a",
"PT2__PT1__b",
"PT2__PT1__c",
"PT2__PT1__d",
"PT2__PT1__e",
]
assert model.get_feature_names_at_node("pt2", input_features=["a", "b", "c", "d", "e"]) == [
"PT2__PT1__a",
"PT2__PT1__b",
"PT2__PT1__c",
"PT2__PT1__d",
"PT2__PT1__e",
]
assert model.get_feature_names_at_node("pt1", input_features=["a", "b", "c", "d", "e"]) == [
"PT1__a",
"PT1__b",
"PT1__c",
"PT1__d",
"PT1__e",
]
assert model.get_input_features_at_node("pt2", input_features=["a", "b", "c", "d", "e"]) == [
"PT1__a",
"PT1__b",
"PT1__c",
"PT1__d",
"PT1__e",
]
assert model.get_input_features_at_node("pt1", input_features=["a", "b", "c", "d", "e"]) == [
"a",
"b",
"c",
"d",
"e",
]
def test_graphpipeline_get_features_names():
dfX = pd.DataFrame(
{
"text1": ["aa bb", "bb bb cc", "dd aa cc", "ee"],
"text2": ["AAA ZZZ", "BBB EEE", "DDD TTT", "AAA BBB CCC"],
"num1": [0, 1, 2, 3],
"num2": [1.1, 1.5, -2, -3.5],
"num3": [-1, 1, 25, 4],
"cat1": ["A", "B", "A", "D"],
"cat2": ["toto", "tata", "truc", "toto"],
}
)
### Test 1 ###
model = GraphPipeline({"sel": ColumnsSelector(["cat1", "cat2"]), "pt": PassThrough()}, edges=[("sel", "pt")])
model.fit(dfX)
assert model.get_feature_names() == ["cat1", "cat2"] # features at ending nodeC
assert model.get_feature_names_at_node("pt") == ["cat1", "cat2"]
assert model.get_feature_names_at_node("sel") == ["cat1", "cat2"]
assert model.get_input_features_at_node("pt") == ["cat1", "cat2"]
assert model.get_input_features_at_node("sel") == ["text1", "text2", "num1", "num2", "num3", "cat1", "cat2"]
### Test 2 ###
model = GraphPipeline(
{"sel1": ColumnsSelector(["cat1", "cat2"]), "sel2": ColumnsSelector(["num1", "num2"]), "pt": PassThrough()},
edges=[("sel1", "pt"), ("sel2", "pt")],
)
model.fit(dfX)
assert model.get_feature_names() == ["cat1", "cat2", "num1", "num2"]
assert model.get_feature_names_at_node("pt") == ["cat1", "cat2", "num1", "num2"]
assert model.get_feature_names_at_node("sel1") == ["cat1", "cat2"]
assert model.get_feature_names_at_node("sel2") == ["num1", "num2"]
assert model.get_input_features_at_node("pt") == ["cat1", "cat2", "num1", "num2"]
assert model.get_input_features_at_node("sel1") == ["text1", "text2", "num1", "num2", "num3", "cat1", "cat2"]
assert model.get_input_features_at_node("sel2") == ["text1", "text2", "num1", "num2", "num3", "cat1", "cat2"]
### Test 3 ###
model = GraphPipeline(
{
"sel1": ColumnsSelector(["cat1", "cat2"]),
"sel2": ColumnsSelector(["num1", "num2"]),
"sel12": ColumnsSelector(["cat1", "num1"]),
"pt": PassThrough(),
},
edges=[("sel1", "sel12", "pt"), ("sel2", "sel12", "pt")],
)
model.fit(dfX)
assert model.get_feature_names() == ["cat1", "num1"]
assert model.get_feature_names_at_node("pt") == ["cat1", "num1"]
assert model.get_feature_names_at_node("sel12") == ["cat1", "num1"]
assert model.get_feature_names_at_node("sel1") == ["cat1", "cat2"]
assert model.get_feature_names_at_node("sel2") == ["num1", "num2"]
assert model.get_input_features_at_node("pt") == ["cat1", "num1"]
assert model.get_input_features_at_node("sel12") == ["cat1", "cat2", "num1", "num2"]
assert model.get_input_features_at_node("sel1") == ["text1", "text2", "num1", "num2", "num3", "cat1", "cat2"]
assert model.get_input_features_at_node("sel2") == ["text1", "text2", "num1", "num2", "num3", "cat1", "cat2"]
def test_graphpipeline_blockselector():
Xnum, y = make_classification(n_samples=100)
dfX_text = pd.DataFrame({"text1": get_random_strings(100), "text2": get_random_strings(100)})
X = {"text": dfX_text, "num": Xnum}
graphpipeline = GraphPipeline(
models={
"BS_text": BlockSelector("text"),
"CV": CountVectorizerWrapper(analyzer="char"),
"BS_num": BlockSelector("num"),
"RF": DecisionTreeClassifier(),
},
edges=[("BS_text", "CV", "RF"), ("BS_num", "RF")],
)
graphpipeline.fit(X, y)
yhat = graphpipeline.predict(X)
assert yhat.ndim == 1
assert yhat.shape[0] == y.shape[0]
### X = dico ###
X = {"text": dfX_text, "num": Xnum}
graphpipeline = GraphPipeline(
models={"BS_text": BlockSelector("text"), "BS_num": BlockSelector("num"), "PT": DebugPassThrough()},
edges=[("BS_text", "PT"), ("BS_num", "PT")],
)
Xhat = graphpipeline.fit_transform(X)
assert Xhat.shape[0] == dfX_text.shape[0]
assert Xhat.shape[1] == dfX_text.shape[1] + Xnum.shape[1]
assert "text1" in Xhat.columns
assert "text2" in Xhat.columns
assert (Xhat.loc[:, ["text1", "text2"]] == dfX_text).all().all()
cols = diff(list(Xhat.columns), ["text1", "text2"])
assert (Xhat.loc[:, cols].values == Xnum).all()
### X = list
X = [dfX_text, Xnum]
graphpipeline = GraphPipeline(
models={"BS_text": BlockSelector(0), "BS_num": BlockSelector(1), "PT": DebugPassThrough()},
edges=[("BS_text", "PT"), ("BS_num", "PT")],
)
Xhat = graphpipeline.fit_transform(X)
assert Xhat.shape[0] == dfX_text.shape[0]
assert Xhat.shape[1] == dfX_text.shape[1] + Xnum.shape[1]
assert "text1" in Xhat.columns
assert "text2" in Xhat.columns
assert (Xhat.loc[:, ["text1", "text2"]] == dfX_text).all().all()
cols = diff(list(Xhat.columns), ["text1", "text2"])
assert (Xhat.loc[:, cols].values == Xnum).all()
### X = DataManager
X = BlockManager({"text": dfX_text, "num": Xnum})
graphpipeline = GraphPipeline(
models={"BS_text": BlockSelector("text"), "BS_num": BlockSelector("num"), "PT": DebugPassThrough()},
edges=[("BS_text", "PT"), ("BS_num", "PT")],
)
Xhat = graphpipeline.fit_transform(X)
assert Xhat.shape[0] == dfX_text.shape[0]
assert Xhat.shape[1] == dfX_text.shape[1] + Xnum.shape[1]
assert "text1" in Xhat.columns
assert "text2" in Xhat.columns
assert (Xhat.loc[:, ["text1", "text2"]] == dfX_text).all().all()
cols = diff(list(Xhat.columns), ["text1", "text2"])
assert (Xhat.loc[:, cols].values == Xnum).all()
def test_approx_cross_validation_graphpipeline():
X, y = make_classification(n_samples=100)
X = pd.DataFrame(X, columns=["col_%d" % i for i in range(X.shape[1])])
## Fit ##
PassThroughWithCallback.reset_counters()
PassThroughWithCallback2.reset_counters()
gpipeline = GraphPipeline(
models={"A": PassThroughWithCallback(), "B": PassThroughWithCallback2(), "C": LogisticRegression()},
edges=[("A", "B", "C")],
)
gpipeline.fit(X, y)
assert PassThroughWithCallback.nb_fit_transform == 1
assert PassThroughWithCallback2.nb_fit_transform == 1
assert PassThroughWithCallback.nb_fit == 0
assert PassThroughWithCallback2.nb_fit == 0
assert PassThroughWithCallback.nb_transform == 0
assert PassThroughWithCallback2.nb_transform == 0
## approx cv ##
PassThroughWithCallback.reset_counters()
PassThroughWithCallback2.reset_counters()
gpipeline = GraphPipeline(
models={"A": PassThroughWithCallback(), "B": PassThroughWithCallback2(), "C": LogisticRegression()},
edges=[("A", "B", "C")],
)
cv_res = gpipeline.approx_cross_validation(X, y, scoring=["neg_mean_squared_error"], cv=10, verbose=False)
assert PassThroughWithCallback.nb_fit_transform == 10
assert PassThroughWithCallback2.nb_fit_transform == 10
assert PassThroughWithCallback.nb_fit == 0
assert PassThroughWithCallback2.nb_fit == 0
assert PassThroughWithCallback.nb_transform == 20 # 10 fold x 2 (for score in train and test)
assert PassThroughWithCallback2.nb_transform == 20
## approx cv but skip nodes ##
PassThroughWithCallback.reset_counters()
PassThroughWithCallback2.reset_counters()
gpipeline = GraphPipeline(
models={"A": PassThroughWithCallback(), "B": PassThroughWithCallback2(), "C": LogisticRegression()},
edges=[("A", "B", "C")],
)
# cv_res = gpipeline.approx_cross_validation(X, y, scoring=["neg_mean_squared_error"],cv = 10, verbose = False, nodes_not_to_crossvalidate = ("A",))
cv_res = gpipeline.approx_cross_validation(
X, y, scoring=["neg_mean_squared_error"], cv=10, verbose=1, nodes_not_to_crossvalidate=("A",)
)
assert cv_res is not None
assert cv_res.shape[0] == 10
assert PassThroughWithCallback.nb_fit_transform == 1
assert PassThroughWithCallback2.nb_fit_transform == 10
assert PassThroughWithCallback.nb_fit == 0
assert PassThroughWithCallback2.nb_fit == 0
assert PassThroughWithCallback.nb_transform == 0
assert PassThroughWithCallback2.nb_transform == 20
PassThroughWithCallback.reset_counters()
PassThroughWithCallback2.reset_counters()
gpipeline = GraphPipeline(
models={"A": PassThroughWithCallback(), "B": PassThroughWithCallback2(), "C": LogisticRegression()},
edges=[("A", "B", "C")],
)
# cv_res = gpipeline.approx_cross_validation(X, y, scoring=["neg_mean_squared_error"],cv = 10, verbose = False, nodes_not_to_crossvalidate = ("A",))
cv_res = gpipeline.approx_cross_validation(
X, y, scoring=["neg_mean_squared_error"], cv=10, verbose=1, nodes_not_to_crossvalidate=("A", "B")
)
assert cv_res is not None
assert cv_res.shape[0] == 10
assert PassThroughWithCallback.nb_fit_transform == 1
assert PassThroughWithCallback2.nb_fit_transform == 1
assert PassThroughWithCallback.nb_fit == 0
assert PassThroughWithCallback2.nb_fit == 0
assert PassThroughWithCallback.nb_transform == 0
assert PassThroughWithCallback2.nb_transform == 0
PassThroughWithCallback_cant_cv_transform.reset_counters()
PassThroughWithCallback2.reset_counters()
gpipeline = GraphPipeline(
models={
"A": PassThroughWithCallback_cant_cv_transform(),
"B": PassThroughWithCallback2(),
"C": LogisticRegression(),
},
edges=[("A", "B", "C")],
)
# cv_res = gpipeline.approx_cross_validation(X, y, scoring=["neg_mean_squared_error"],cv = 10, verbose = False, nodes_not_to_crossvalidate = ("A",))
cv_res = gpipeline.approx_cross_validation(
X, y, scoring=["neg_mean_squared_error"], cv=10, verbose=1, nodes_not_to_crossvalidate={"B"}
)
assert cv_res is not None
assert cv_res.shape[0] == 10
assert PassThroughWithCallback_cant_cv_transform.nb_fit_transform == 10
assert PassThroughWithCallback2.nb_fit_transform == 10
assert PassThroughWithCallback_cant_cv_transform.nb_fit == 0
assert PassThroughWithCallback2.nb_fit == 0
assert PassThroughWithCallback_cant_cv_transform.nb_transform == 20
assert PassThroughWithCallback2.nb_transform == 20
