def predict_proba(self, X):
"""
Return probability estimates for the test vector X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array-like, shape = [n_samples, n_classes]
Returns the probability of the samples for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
"""
check_is_fitted(self, "graph_")
X = check_array(X, accept_sparse="csr")
def _classify(x):
_, scores = self._recursive_predict(x, root=self.root)
return scores
y_pred = apply_along_rows(_classify, X=X)
return y_pred
def predict(self, X):
"""Predict multi-class targets using underlying estimators.
Parameters
----------
X : (sparse) array-like, shape = [n_samples, n_features]
Data.
Returns
-------
y : (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes].
Predicted multi-class targets.
"""
check_is_fitted(self, "graph_")
X = check_array(X, accept_sparse="csr")
def _classify(x):
# TODO support multi-label / paths?
path, _ = self._recursive_predict(x, root=self.root)
return path[-1]
y_pred = apply_along_rows(_classify, X=X)
return y_pred