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()
def test_BlockSelector():
np.random.seed(123)
df = pd.DataFrame({
"a": np.arange(10),
"b": ["aaa", "bbb", "ccc"] * 3 + ["ddd"]
})
arr = np.random.randn(df.shape[0], 5)
input_features = {
"df": df.columns,
"arr": ["COL_%d" % i for i in range(arr.shape[1])]
}
# Dictionnary
block_selector1 = BlockSelector("df")
block_selector2 = BlockSelector("arr")
X = {"df": df, "arr": arr}
Xres1 = block_selector1.fit_transform(X)
Xres2 = block_selector2.fit_transform(X)
assert block_selector1.get_feature_names() == ["a", "b"]
assert block_selector1.get_feature_names(
input_features=input_features) == ["a", "b"]
assert block_selector2.get_feature_names() == [0, 1, 2, 3, 4]
assert block_selector2.get_feature_names(
input_features=input_features) == [
"COL_0",
"COL_1",
"COL_2",
"COL_3",
"COL_4",
]
assert id(Xres1) == id(df)
assert id(Xres2) == id(arr) # no copy
# List
X = [df, arr]
input_features = [df.columns, ["COL_%d" % i for i in range(arr.shape[1])]]
block_selector1 = BlockSelector(0)
block_selector2 = BlockSelector(1)
Xres1 = block_selector1.fit_transform(X)
Xres2 = block_selector2.fit_transform(X)
assert block_selector1.get_feature_names() == ["a", "b"]
assert block_selector1.get_feature_names(
input_features=input_features) == ["a", "b"]
assert block_selector2.get_feature_names() == [0, 1, 2, 3, 4]
assert block_selector2.get_feature_names(
input_features=input_features) == [
"COL_0",
"COL_1",
"COL_2",
"COL_3",
"COL_4",
]
assert id(Xres1) == id(df)
assert id(Xres2) == id(arr)
# BlockManager
X = BlockManager({"df": df, "arr": arr})
input_features = {
"df": df.columns,
"arr": ["COL_%d" % i for i in range(arr.shape[1])]
}
block_selector1 = BlockSelector("df")
block_selector2 = BlockSelector("arr")
X = {"df": df, "arr": arr}
Xres1 = block_selector1.fit_transform(X)
Xres2 = block_selector2.fit_transform(X)
assert block_selector1.get_feature_names() == ["a", "b"]
assert block_selector1.get_feature_names(
input_features=input_features) == ["a", "b"]
assert block_selector2.get_feature_names() == [0, 1, 2, 3, 4]
assert block_selector2.get_feature_names(
input_features=input_features) == [
"COL_0",
"COL_1",
"COL_2",
"COL_3",
"COL_4",
]
assert id(Xres1) == id(df)
assert id(Xres2) == id(arr) # no copy
# Check not fitted
block_selector1 = BlockSelector(0)
with pytest.raises(NotFittedError):
block_selector1.transform(X)
def _approx_cross_validation_create_sub_graph_pipeline(self, data_dico, X):
""" this sub-method create the new graph-pipeline that should be fully cross-validated,
it also create the new data on which to cv
Returns
-------
new_graph_pipeline
new_data
"""
### Create a new GraphPipeline with only the remaning Nodes ###
dones_nodes = set()
for k, v in data_dico.items():
if v is not None:
dones_nodes.add(k)
newG = nx.DiGraph()
new_models = {}
new_datas = {}
block_selector_nodes = set()
for n1, n2 in self.complete_graph.edges:
if n1 in dones_nodes and n2 in dones_nodes:
pass
elif n1 in dones_nodes and n2 not in dones_nodes:
newG.add_edge("_data_%s" % n1, n2)
new_models[n2] = self._models[n2]
new_models["_data_%s" % n1] = BlockSelector("_data_%s" % n1)
new_datas["_data_%s" % n1] = data_dico[n1]
block_selector_nodes.add("_data_%s" % n1)
# Add a BlockSelector
elif n1 not in dones_nodes and n2 not in dones_nodes:
newG.add_edge(n1, n2)
new_models[n1] = self._models[n1]
new_models[n2] = self._models[n2]
else:
raise ValueError("Should never go there")
nodes = list(newG.nodes) # copy because I'll modify the graph
for n in nodes:
preds = list(newG.predecessors(n))
if len(preds) == 0 and n not in block_selector_nodes:
newG.add_edge("_data_", n)
new_models["_data_"] = BlockSelector("_data_")
new_datas["_data_"] = X
new_data_dtm = BlockManager(new_datas)
new_graph_pipeline = GraphPipeline(models=new_models,
edges=edges_from_graph(newG))
return new_graph_pipeline, new_data_dtm