def predict_proba(self, X):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True, coerce_to_pandas=True)
X = dataset_properties.negative_dataframe_indices(X)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
distributions = parallel(
delayed(self._predict_proba_tree)(X, tree)
for tree in self.trees)
else:
distributions = [
self._predict_proba_tree(X, tree) for tree in self.trees
]
distributions = np.array(distributions)
distributions = np.sum(distributions, axis=0)
normalize(distributions, copy=False, norm="l1")
return distributions
def predict_proba(self, X):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True, coerce_to_pandas=True)
X = dataset_properties.negative_dataframe_indices(X)
distances = self.distance_to_exemplars(X)
ones = np.ones(distances.shape)
distances = np.add(distances, ones)
distributions = np.divide(ones, distances)
normalize(distributions, copy=False, norm="l1")
return distributions
def predict_proba(self, X):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True, coerce_to_pandas=True)
X = dataset_properties.negative_dataframe_indices(X)
closest_exemplar_indices = self.stump.find_closest_exemplar_indices(X)
n_classes = len(self.label_encoder.classes_)
distribution = np.zeros((X.shape[0], n_classes))
for index in range(len(self.branches)):
indices = np.argwhere(closest_exemplar_indices == index)
if indices.shape[0] > 0:
indices = np.ravel(indices)
sub_tree = self.branches[index]
if sub_tree is None:
sub_distribution = np.zeros((1, n_classes))
class_label = self.stump.y_exemplar[index]
sub_distribution[0][class_label] = 1
else:
sub_X = X.iloc[indices, :]
sub_distribution = sub_tree.predict_proba(sub_X)
assert sub_distribution.shape[1] == n_classes
np.add.at(distribution, indices, sub_distribution)
normalize(distribution, copy=False, norm="l1")
return distribution
