def fit(self, X, y, sample_weight=None): """ Prepare different things for fast computation of metrics """ X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight) self._mask = numpy.array(y == self.uniform_label) assert sum(self._mask) > 0, 'No event of class, along which uniformity is desired' self._masked_weight = sample_weight[self._mask] X_part = numpy.array(take_features(X, self.uniform_features))[self._mask, :] self._bin_indices = ut.compute_bin_indices(X_part=X_part, n_bins=self.n_bins) self._bin_weights = ut.compute_bin_weights(bin_indices=self._bin_indices, sample_weight=self._masked_weight)
def fit(self, X, y, sample_weight=None): """ Prepare different things for fast computation of metrics """ X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight) self._mask = numpy.array(y == self.uniform_label) assert sum(self._mask) > 0, 'No events of uniform class!' self._masked_weight = sample_weight[self._mask] X_part = numpy.array(take_features(X, self.uniform_features))[self._mask, :] # computing knn indices neighbours = NearestNeighbors(n_neighbors=self.n_neighbours, algorithm='kd_tree').fit(X_part) _, self._groups_indices = neighbours.kneighbors(X_part) self._group_weights = ut.compute_group_weights(self._groups_indices, sample_weight=self._masked_weight)
def fit(self, X, y, sample_weight=None): """ Prepare different things for fast computation of metrics """ X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight) self._mask = numpy.array(y == self.uniform_label) assert sum(self._mask) > 0, 'No events of uniform class!' self._masked_weight = sample_weight[self._mask] X_part = numpy.array(take_features( X, self.uniform_features))[self._mask, :] # computing knn indices neighbours = NearestNeighbors(n_neighbors=self.n_neighbours, algorithm='kd_tree').fit(X_part) _, self._groups_indices = neighbours.kneighbors(X_part) self._group_weights = ut.compute_group_weights( self._groups_indices, sample_weight=self._masked_weight)
def fit(self, X, y, sample_weight=None): """ Prepare different things for fast computation of metrics """ X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight) self._mask = numpy.array(y == self.uniform_label) assert sum( self._mask ) > 0, 'No event of class, along which uniformity is desired' self._masked_weight = sample_weight[self._mask] X_part = numpy.array(take_features( X, self.uniform_features))[self._mask, :] self._bin_indices = ut.compute_bin_indices(X_part=X_part, n_bins=self.n_bins) self._bin_weights = ut.compute_bin_weights( bin_indices=self._bin_indices, sample_weight=self._masked_weight)