Replace len(X) with X.shape[0] to improve support for sparse matrices

qunfold/sklearn.py

@@ -46,7 +46,7 @@ def fit(self, X, y):
   def predict_proba(self, X):
     if not hasattr(self, "classes_"):
       raise NotFittedError()
-    y_pred = np.zeros((len(self.estimators_), len(X), len(self.classes_)))
+    y_pred = np.zeros((len(self.estimators_), X.shape[0], len(self.classes_)))
     for i, (estimator, i_classes) in enumerate(zip(self.estimators_, self.i_classes_)):
       y_pred[i, :, i_classes] = estimator.predict_proba(X).T
     return np.mean(y_pred, axis=0) # shape (n_samples, n_classes)

qunfold/transformers.py

@@ -89,7 +89,7 @@ def fit_transform(self, X, y, average=True, n_classes=None):
     n_classes = len(self.p_trn) # not None anymore
     if self.fit_classifier:
       self.classifier.fit(X, y)
-    fX = np.zeros((len(X), n_classes))
+    fX = np.zeros((X.shape[0], n_classes))
     fX[:, self.classifier.classes_] = self.classifier.oob_decision_function_
     is_finite = np.all(np.isfinite(fX), axis=1)
     fX = fX[is_finite,:] # drop instances that never became OOB
@@ -105,7 +105,7 @@ def fit_transform(self, X, y, average=True, n_classes=None):
     return fX, y
   def transform(self, X, average=True):
     n_classes = len(self.p_trn)
-    fX = np.zeros((len(X), n_classes))
+    fX = np.zeros((X.shape[0], n_classes))
     fX[:, self.classifier.classes_] = self.classifier.predict_proba(X)
     if not self.is_probabilistic:
       fX = _onehot_encoding(np.argmax(fX, axis=1), n_classes)