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)
