Exemplo n.º 1
0
    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)
Exemplo n.º 2
0
    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)
Exemplo n.º 3
0
    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)
Exemplo n.º 4
0
    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)