def test_graphpipeline_blockselector_cv():
Xnum, y = make_classification(n_samples=100)
dfX_text = pd.DataFrame({
"text1": get_random_strings(100),
"text2": get_random_strings(100)
})
### X = dico
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")],
)
from sklearn.model_selection import cross_val_score
with pytest.raises(ValueError):
cv_res = cross_val_score(graphpipeline,
X,
y,
scoring="accuracy",
cv=10)
# doesn't work, can't subset dictionnary
X = BlockManager({"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")],
)
cv_res = cross_val_score(graphpipeline, X, y, scoring="accuracy", cv=10)
assert len(cv_res) == 10
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()
