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