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Add range_features method

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Ricardo Montañana Gómez
2023-02-13 16:15:50 +01:00
parent 9899781640
commit 2d495293bb
1 changed files with 10 additions and 0 deletions
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src/fimdlp/mdlp.py
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@@ -119,6 +119,15 @@ class FImdlp(TransformerMixin, BaseEstimator):
else: else:
result[:, feature] = X result[:, feature] = X
def range_features(self):
res = []
for i in range(self.n_features_in_):
if i in self.features_:
res.append(list(range(len(self.cut_points_[i]))))
else:
res.append([])
return res
def transform(self, X): def transform(self, X):
"""Discretize X values. """Discretize X values.
Parameters Parameters
@@ -214,6 +223,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
f"{str(item_y)}{''.join([str(x) for x in items_x])}".encode() f"{str(item_y)}{''.join([str(x) for x in items_x])}".encode()
for item_y, items_x in zip(self.y_, data[:, features]) for item_y, items_x in zip(self.y_, data[:, features])
] ]
self.y_join = y_join
self.discretizer_[target].fit(self.X_[:, target], factorize(y_join)) self.discretizer_[target].fit(self.X_[:, target], factorize(y_join))
self.cut_points_[target] = self.discretizer_[target].get_cut_points() self.cut_points_[target] = self.discretizer_[target].get_cut_points()
# return the discretized target variable with the new cut points # return the discretized target variable with the new cut points