def compute_sde_on_bins(y_pred,
mask,
bin_indices,
target_efficiencies,
power=2.,
sample_weight=None):
# ignoring events from other classes
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
y_pred = y_pred[mask]
bin_indices = bin_indices[mask]
sample_weight = sample_weight[mask]
bin_weights = compute_bin_weights(bin_indices=bin_indices,
sample_weight=sample_weight)
cuts = compute_cut_for_efficiency(target_efficiencies,
mask=numpy.ones(len(y_pred), dtype=bool),
y_pred=y_pred,
sample_weight=sample_weight)
result = 0.
for cut in cuts:
bin_efficiencies = compute_bin_efficiencies(
y_pred,
bin_indices=bin_indices,
cut=cut,
sample_weight=sample_weight)
result += weighted_deviation(bin_efficiencies,
weights=bin_weights,
power=power)
return (result / len(cuts))**(1. / power)
def compute_theil_on_bins(y_pred, mask, bin_indices, target_efficiencies,
sample_weight):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
# ignoring events from other classes
y_pred = y_pred[mask]
bin_indices = bin_indices[mask]
sample_weight = sample_weight[mask]
bin_weights = compute_bin_weights(bin_indices=bin_indices,
sample_weight=sample_weight)
cuts = compute_cut_for_efficiency(target_efficiencies,
mask=numpy.ones(len(y_pred), dtype=bool),
y_pred=y_pred,
sample_weight=sample_weight)
result = 0.
for cut in cuts:
bin_efficiencies = compute_bin_efficiencies(
y_pred,
bin_indices=bin_indices,
cut=cut,
sample_weight=sample_weight)
result += theil(bin_efficiencies, weights=bin_weights)
return result / len(cuts)
def cvm_2samp(data1, data2, weights1=None, weights2=None, power=2.):
"""Computes Cramer-von Mises similarity on 2 samples,
CvM = \int |F_2 - F_1|^p dF_1
This implementation sorts the arrays each time,
so inside loops it will be slow"""
weights1 = check_sample_weight(data1, sample_weight=weights1)
weights2 = check_sample_weight(data2, sample_weight=weights2)
weights1 /= numpy.sum(weights1)
weights2 /= numpy.sum(weights2)
data = numpy.unique(numpy.concatenate([data1, data2]))
bins = numpy.append(data, data[-1] + 1)
weights1_new = numpy.histogram(data1, bins=bins, weights=weights1)[0]
weights2_new = numpy.histogram(data2, bins=bins, weights=weights2)[0]
F1 = compute_cdf(weights1_new)
F2 = compute_cdf(weights2_new)
return numpy.average(numpy.abs(F1 - F2)**power, weights=weights1_new)
def cvm_2samp(data1, data2, weights1=None, weights2=None, power=2.):
"""Computes Cramer-von Mises similarity on 2 samples,
CvM = \int |F_2 - F_1|^p dF_1
This implementation sorts the arrays each time,
so inside loops it will be slow"""
weights1 = check_sample_weight(data1, sample_weight=weights1)
weights2 = check_sample_weight(data2, sample_weight=weights2)
weights1 /= numpy.sum(weights1)
weights2 /= numpy.sum(weights2)
data = numpy.unique(numpy.concatenate([data1, data2]))
bins = numpy.append(data, data[-1] + 1)
weights1_new = numpy.histogram(data1, bins=bins, weights=weights1)[0]
weights2_new = numpy.histogram(data2, bins=bins, weights=weights2)[0]
F1 = compute_cdf(weights1_new)
F2 = compute_cdf(weights2_new)
return numpy.average(numpy.abs(F1 - F2) ** power, weights=weights1_new)
def compute_sde_on_groups(y_pred,
mask,
groups_indices,
target_efficiencies,
sample_weight=None,
power=2.):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
group_weights = compute_group_weights_by_indices(
groups_indices, sample_weight=sample_weight)
divided_weight = compute_divided_weight_by_indices(
groups_indices, sample_weight=sample_weight * mask)
cuts = compute_cut_for_efficiency(target_efficiencies,
mask=mask,
y_pred=y_pred,
sample_weight=sample_weight)
sde = 0.
for cut in cuts:
group_efficiencies = compute_group_efficiencies_by_indices(
y_pred,
groups_indices=groups_indices,
cut=cut,
divided_weight=divided_weight)
# print('FROM SDE function', cut, group_efficiencies)
sde += weighted_deviation(group_efficiencies,
weights=group_weights,
power=power)
return (sde / len(cuts))**(1. / power)
def group_based_cvm(y_pred, mask, sample_weight, groups_indices):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
group_weights = compute_group_weights_by_indices(groups_indices, sample_weight=sample_weight)
result = 0.
global_data, global_weight, global_F = prepare_distribution(y_pred[mask], weights=sample_weight[mask])
for group, group_weight in zip(groups_indices, group_weights):
local_distribution = y_pred[group]
local_weights = sample_weight[group]
result += group_weight * _cvm_2samp_fast(global_data, local_distribution,
global_weight, local_weights, global_F)
return result
def fit(self, X, y, sample_weight=None, iterations=100, loss=None):
X, y = check_arrays(X, y)
self.n_features = X.shape[1]
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
if loss is None:
loss = BinomialDevianceLossFunction()
loss.fit(X, y, sample_weight=sample_weight)
self.coeffs = numpy.zeros(
[self.compute_n_features(), 2**self.power_categories],
dtype='float')
y_pred = numpy.zeros(len(X), dtype='float')
for iteration in range(iterations):
print(iteration, loss(y_pred))
for feature, feature_values in enumerate(
self.enumerate_features(X)):
# TODO compute once per iteration!
ngradient = loss.negative_gradient(y_pred)
nominator = numpy.bincount(feature_values,
weights=ngradient,
minlength=2**self.power_categories)
nominator -= 2 * self.l2_reg * self.coeffs[
feature, :] + self.l1_reg * numpy.sign(
self.coeffs[feature, :])
denominator = numpy.abs(ngradient) * (1. -
numpy.abs(ngradient))
denominator = numpy.bincount(
feature_values,
weights=denominator,
minlength=2**self.power_categories)
denominator += 2 * self.l2_reg
gradients = nominator / denominator
right_gradients = gradients
# those already zeros not to become nonzero
mask = (self.coeffs[feature, :]
== 0) & (numpy.abs(gradients) < self.l1_reg)
right_gradients[mask] = 0
# those already not zeros
old_coeffs = self.coeffs[feature, :]
new_coeffs = old_coeffs + self.learning_rate * right_gradients
new_coeffs[new_coeffs * old_coeffs < 0] = 0
self.coeffs[feature, :] = new_coeffs
y_diff = numpy.take(new_coeffs - old_coeffs, feature_values)
y_pred += y_diff
return self
def compute_theil_on_groups(y_pred, mask, groups_indices, target_efficiencies, sample_weight):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
groups_weights = compute_group_weights_by_indices(groups_indices, sample_weight=sample_weight)
divided_weight = compute_divided_weight_by_indices(groups_indices, sample_weight=sample_weight * mask)
cuts = compute_cut_for_efficiency(target_efficiencies, mask=mask,
y_pred=y_pred, sample_weight=sample_weight)
result = 0.
for cut in cuts:
groups_efficiencies = compute_group_efficiencies_by_indices(y_pred, groups_indices=groups_indices,
cut=cut, divided_weight=divided_weight)
result += theil(groups_efficiencies, groups_weights)
return result / len(cuts)
def compute_sde_on_groups(y_pred, mask, groups_indices, target_efficiencies, sample_weight=None, power=2.):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
group_weights = compute_group_weights_by_indices(groups_indices, sample_weight=sample_weight)
divided_weight = compute_divided_weight_by_indices(groups_indices, sample_weight=sample_weight * mask)
cuts = compute_cut_for_efficiency(target_efficiencies, mask=mask, y_pred=y_pred, sample_weight=sample_weight)
sde = 0.
for cut in cuts:
group_efficiencies = compute_group_efficiencies_by_indices(y_pred, groups_indices=groups_indices,
cut=cut, divided_weight=divided_weight)
# print('FROM SDE function', cut, group_efficiencies)
sde += weighted_deviation(group_efficiencies, weights=group_weights, power=power)
return (sde / len(cuts)) ** (1. / power)
def group_based_cvm(y_pred, mask, sample_weight, groups_indices):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
group_weights = compute_group_weights_by_indices(
groups_indices, sample_weight=sample_weight)
result = 0.
global_data, global_weight, global_F = prepare_distribution(
y_pred[mask], weights=sample_weight[mask])
for group, group_weight in zip(groups_indices, group_weights):
local_distribution = y_pred[group]
local_weights = sample_weight[group]
result += group_weight * _cvm_2samp_fast(
global_data, local_distribution, global_weight, local_weights,
global_F)
return result
def _normalize_input(self, data, weights):
""" Normalize input of reweighter
:param data: array like of shape [n_samples] or [n_samples, n_features]
:param weights: array-like of shape [n_samples] or None
:return: tuple with
data - numpy.array of shape [n_samples, n_features]
weights - numpy.array of shape [n_samples] with mean = 1.
"""
weights = check_sample_weight(data, sample_weight=weights, normalize=True)
data = numpy.array(data)
if len(data.shape) == 1:
data = data[:, numpy.newaxis]
if self.n_features_ is None:
self.n_features_ = data.shape[1]
assert self.n_features_ == data.shape[1], \
'number of features is wrong: {} {}'.format(self.n_features_, data.shape[1])
return data, weights
def compute_theil_on_bins(y_pred, mask, bin_indices, target_efficiencies, sample_weight):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
# ignoring events from other classes
y_pred = y_pred[mask]
bin_indices = bin_indices[mask]
sample_weight = sample_weight[mask]
bin_weights = compute_bin_weights(bin_indices=bin_indices, sample_weight=sample_weight)
cuts = compute_cut_for_efficiency(target_efficiencies, mask=numpy.ones(len(y_pred), dtype=bool),
y_pred=y_pred, sample_weight=sample_weight)
result = 0.
for cut in cuts:
bin_efficiencies = compute_bin_efficiencies(y_pred, bin_indices=bin_indices,
cut=cut, sample_weight=sample_weight)
result += theil(bin_efficiencies, weights=bin_weights)
return result / len(cuts)
def compute_sde_on_bins(y_pred, mask, bin_indices, target_efficiencies, power=2., sample_weight=None):
# ignoring events from other classes
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
y_pred = y_pred[mask]
bin_indices = bin_indices[mask]
sample_weight = sample_weight[mask]
bin_weights = compute_bin_weights(bin_indices=bin_indices, sample_weight=sample_weight)
cuts = compute_cut_for_efficiency(target_efficiencies, mask=numpy.ones(len(y_pred), dtype=bool),
y_pred=y_pred, sample_weight=sample_weight)
result = 0.
for cut in cuts:
bin_efficiencies = compute_bin_efficiencies(y_pred, bin_indices=bin_indices,
cut=cut, sample_weight=sample_weight)
result += weighted_deviation(bin_efficiencies, weights=bin_weights, power=power)
return (result / len(cuts)) ** (1. / power)
def _normalize_input(self, data, weights):
""" Normalize input of reweighter
:param data: array like of shape [n_samples] or [n_samples, n_features]
:param weights: array-like of shape [n_samples] or None
:return: tuple with
data - numpy.array of shape [n_samples, n_features]
weights - numpy.array of shape [n_samples] with mean = 1.
"""
weights = check_sample_weight(data,
sample_weight=weights,
normalize=True)
data = numpy.array(data)
if len(data.shape) == 1:
data = data[:, numpy.newaxis]
if self.n_features_ is None:
self.n_features_ = data.shape[1]
assert self.n_features_ == data.shape[1], \
'number of features is wrong: {} {}'.format(self.n_features_, data.shape[1])
return data, weights
def compute_theil_on_groups(y_pred, mask, groups_indices, target_efficiencies,
sample_weight):
y_pred = column_or_1d(y_pred)
sample_weight = check_sample_weight(y_pred, sample_weight=sample_weight)
groups_weights = compute_group_weights_by_indices(
groups_indices, sample_weight=sample_weight)
divided_weight = compute_divided_weight_by_indices(
groups_indices, sample_weight=sample_weight * mask)
cuts = compute_cut_for_efficiency(target_efficiencies,
mask=mask,
y_pred=y_pred,
sample_weight=sample_weight)
result = 0.
for cut in cuts:
groups_efficiencies = compute_group_efficiencies_by_indices(
y_pred,
groups_indices=groups_indices,
cut=cut,
divided_weight=divided_weight)
result += theil(groups_efficiencies, groups_weights)
return result / len(cuts)
def fit(self, X, y, sample_weight=None, iterations=100, loss=None):
X, y = check_arrays(X, y)
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
if loss is None:
loss = BinomialDevianceLossFunction()
loss.fit(X, y, sample_weight=sample_weight)
self.n_features = X.shape[1]
self.coeffs = numpy.zeros([self.n_features, self.max_categories], dtype='float')
assert numpy.max(X) < self.max_categories
assert numpy.min(X) >= 0
for iteration in range(iterations):
# this line could be skipped, but we need it to avoid
# mistakes after too many steps of computations
y_pred = self.decision_function(X)
print(iteration, loss(y_pred))
for feature in range(self.n_features):
ngradient = loss.negative_gradient(y_pred)
nominator = numpy.bincount(X[:, feature], weights=ngradient, minlength=self.max_categories)
nominator -= self.l2_reg * self.coeffs[feature, :] + self.l1_reg * numpy.sign(self.coeffs[feature, :])
denominator = numpy.abs(ngradient) * (1. - numpy.abs(ngradient))
denominator = numpy.bincount(X[:, feature], weights=denominator, minlength=self.max_categories)
denominator += 2 * self.l2_reg + 5
gradients = nominator / denominator
right_gradients = gradients
# those already zeros not to become nonzero
mask = (self.coeffs[feature, :] == 0) & (numpy.abs(gradients) < self.l1_reg)
right_gradients[mask] = 0
# those already not zeros
old_coeffs = self.coeffs[feature, :]
new_coeffs = old_coeffs + self.learning_rate * right_gradients
new_coeffs[new_coeffs * old_coeffs < 0] = 0
y_pred += numpy.take(new_coeffs - old_coeffs, X[:, feature])
self.coeffs[feature, :] = new_coeffs
return self
def fit(self, X, y, sample_weight=None, iterations=100, loss=None):
X, y = check_arrays(X, y)
self.n_features = X.shape[1]
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
if loss is None:
loss = BinomialDevianceLossFunction()
loss.fit(X, y, sample_weight=sample_weight)
self.coeffs = numpy.zeros([self.compute_n_features(), 2 ** self.power_categories], dtype='float')
y_pred = numpy.zeros(len(X), dtype='float')
for iteration in range(iterations):
print(iteration, loss(y_pred))
for feature, feature_values in enumerate(self.enumerate_features(X)):
# TODO compute once per iteration!
ngradient = loss.negative_gradient(y_pred)
nominator = numpy.bincount(feature_values, weights=ngradient, minlength=2 ** self.power_categories)
nominator -= 2 * self.l2_reg * self.coeffs[feature, :] + self.l1_reg * numpy.sign(self.coeffs[feature, :])
denominator = numpy.abs(ngradient) * (1. - numpy.abs(ngradient))
denominator = numpy.bincount(feature_values, weights=denominator, minlength=2 ** self.power_categories)
denominator += 2 * self.l2_reg
gradients = nominator / denominator
right_gradients = gradients
# those already zeros not to become nonzero
mask = (self.coeffs[feature, :] == 0) & (numpy.abs(gradients) < self.l1_reg)
right_gradients[mask] = 0
# those already not zeros
old_coeffs = self.coeffs[feature, :]
new_coeffs = old_coeffs + self.learning_rate * right_gradients
new_coeffs[new_coeffs * old_coeffs < 0] = 0
self.coeffs[feature, :] = new_coeffs
y_diff = numpy.take(new_coeffs - old_coeffs, feature_values)
y_pred += y_diff
return self
def fit(self,sample_weight,values_init=None):
X = self.X
assert isinstance(X, pandas.DataFrame), 'please pass pandas.DataFrame first'
for column_name, column_range in self.column_ranges.items():
lower, upper = column_range
assert numpy.all(X[column_name] >= lower) and numpy.all(X[column_name] <= upper), \
'{} out of range'.format(column_name)
pos_weight = check_sample_weight(X, sample_weight=sample_weight,normalize = True)
self.Function = lambda parameters:self.Tfunction(parameters,pos_weight)
self.Derivative = lambda parameters:self.Tderivative(parameters,pos_weight)
lower_boundary = numpy.zeros(len(self.parameters_ranges))
upper_boundary = numpy.zeros(len(self.parameters_ranges))
initial_values = numpy.zeros(len(self.parameters_ranges))
for i, (param_name, param_range) in enumerate(self.parameters_ranges.items()):
if len(param_range) == 2:
lower_boundary[i], upper_boundary[i] = param_range
initial_values[i] = (lower_boundary[i] + upper_boundary[i]) / 2.
else:
lower_boundary[i], initial_values[i], upper_boundary[i] = param_range
assert lower_boundary[i] <= initial_values[i] <= upper_boundary[i], \
'For variable {} passed initial value was outside range'.format(param_name)
if values_init is not None:
assert type(values_init) is dict
for i,param_name in enumerate(self.parameters_ranges.keys()):
if param_name in values_init:
initial_values[i] = values_init[param_name]
self.optimization_result = minimize(self.Function, initial_values, jac=self.Derivative,
# hessp=self.HessianTimesP,
bounds=list(self.parameters_ranges.values()),
#options={"disp":True}
)
self.parameters = OrderedDict(zip(self.parameters_ranges, self.optimization_result.x))
def _normalize_weight(y, weight):
# frequently algorithm assigns very big weight to signal events
# compared to background ones (or visa versa, if want to be uniform in bck)
return commonutils.check_sample_weight(y, sample_weight=weight, normalize=True, normalize_by_class=True)
def fit(self, X, y, sample_weight=None):
shuffler = Shuffler(X, random_state=self.random_state)
X, y = check_arrays(X, y, dtype=DTYPE, sparse_format="dense", check_ccontiguous=True)
y = column_or_1d(y, warn=True)
n_samples = len(X)
n_inbag = int(self.subsample * n_samples)
sample_weight = check_sample_weight(y, sample_weight=sample_weight).copy()
self.random_state = check_random_state(self.random_state)
# skipping all checks
assert self.update_on in ['all', 'same', 'other', 'random']
y_pred = numpy.zeros(len(y), dtype=float)
self.classifiers = []
self.learning_rates = []
self.loss_values = []
self.loss = copy.copy(self.loss)
self.loss.fit(X, y, sample_weight=sample_weight)
iter_X = shuffler.generate(0.)
prev_smearing = 1
for iteration in range(self.n_estimators):
if iteration % self.recount_step == 0:
if prev_smearing > 0:
iter_smearing = interpolate(self.smearing, iteration, self.n_estimators)
prev_smearing = iter_smearing
iter_X = shuffler.generate(iter_smearing)
iter_X, = check_arrays(iter_X, dtype=DTYPE, sparse_format="dense", check_ccontiguous=True)
y_pred = numpy.zeros(len(y))
y_pred += sum(cl.predict(X) * rate for rate, cl in zip(self.learning_rates, self.classifiers))
self.loss_values.append(self.loss(y, y_pred, sample_weight=sample_weight))
tree = DecisionTreeRegressor(
criterion=self.criterion,
splitter=self.splitter,
max_depth=interpolate(self.max_depth, iteration, self.n_estimators),
min_samples_split=self.min_samples_split,
min_samples_leaf=interpolate(self.min_samples_leaf, iteration, self.n_estimators, use_log=True),
max_features=self.max_features,
random_state=self.random_state)
sample_mask = _random_sample_mask(n_samples, n_inbag, self.random_state)
loss_weight = sample_weight if self.weights_in_loss else numpy.ones(len(sample_weight))
tree_weight = sample_weight if not self.weights_in_loss else numpy.ones(len(sample_weight))
residual = self.loss.negative_gradient(y, y_pred, sample_weight=loss_weight)
tree.fit(numpy.array(iter_X)[sample_mask, :],
residual[sample_mask],
sample_weight=tree_weight[sample_mask], check_input=False)
# update tree leaves
if self.update_tree:
if self.update_on == 'all':
update_mask = numpy.ones(len(sample_mask), dtype=bool)
elif self.update_on == 'same':
update_mask = sample_mask
elif self.update_on == 'other':
update_mask = ~sample_mask
else: # random
update_mask = _random_sample_mask(n_samples, n_inbag, self.random_state)
self.loss.update_terminal_regions(tree.tree_, X=iter_X, y=y, residual=residual, pred=y_pred,
sample_mask=update_mask, sample_weight=sample_weight)
iter_learning_rate = interpolate(self.learning_rate, iteration, self.n_estimators, use_log=True)
y_pred += iter_learning_rate * tree.predict(X)
self.classifiers.append(tree)
self.learning_rates.append(iter_learning_rate)
return self
def fit(self, X, y, sample_weight=None, neighbours_matrix=None):
"""Build a boosted classifier from the training set (X, y).
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The training input samples.
y : array-like of shape = [n_samples]
The target values (integers that correspond to classes).
sample_weight : array-like of shape = [n_samples], optional
Sample weights. If None, the sample weights are initialized to
``1 / n_samples``.
neighbours_matrix: array-like of shape [n_samples, n_neighbours],
each row contains indices of signal neighbours
(neighbours should be computed for background too),
if None, this matrix is computed.
Returns
-------
self : object
Returns self.
"""
if self.smoothing < 0:
raise ValueError("Smoothing must be non-negative")
if not isinstance(self.base_estimator, BaseEstimator):
raise TypeError("estimator must be a subclass of BaseEstimator")
if self.n_estimators <= 0:
raise ValueError("n_estimators must be greater than zero.")
if self.learning_rate <= 0:
raise ValueError("learning_rate must be greater than zero")
# Check that algorithm is supported
if self.algorithm not in ('SAMME', 'SAMME.R'):
raise ValueError("algorithm %s is not supported"
% self.algorithm)
if self.algorithm == 'SAMME.R':
if not hasattr(self.base_estimator, 'predict_proba'):
raise TypeError(
"uBoostBDT with algorithm='SAMME.R' requires "
"that the weak learner have a predict_proba method.\n"
"Please change the base estimator or set "
"algorithm='SAMME' instead.")
assert np.in1d(y, [0, 1]).all(), \
"only two-class classification is implemented, with labels 0 and 1"
self.signed_uniform_label = 2 * self.uniform_label - 1
if neighbours_matrix is not None:
assert np.shape(neighbours_matrix) == (len(X), self.n_neighbors), \
"Wrong shape of neighbours_matrix"
self.knn_indices = neighbours_matrix
else:
assert self.uniform_variables is not None, \
"uniform_variables should be set"
self.knn_indices = compute_knn_indices_of_same_class(
X.ix[:, self.uniform_variables], y, self.n_neighbors)
sample_weight = commonutils.check_sample_weight(y, sample_weight=sample_weight, normalize=True)
assert np.all(sample_weight >= 0.), 'the weights should be non-negative'
# Clear any previous fit results
self.estimators_ = []
self.estimator_weights_ = []
# score cuts correspond to
# global efficiency == target_efficiency on each iteration.
self.score_cuts_ = []
X_train_variables = self.get_train_vars(X)
X_train_variables, y, sample_weight = check_xyw(X_train_variables, y, sample_weight)
# A dictionary to keep all intermediate weights, efficiencies and so on
if self.keep_debug_info:
self.debug_dict = defaultdict(list)
self.random_generator = check_random_state(self.random_state)
self._boost(X_train_variables, y, sample_weight)
self.score_cut = self.signed_uniform_label * compute_cut_for_efficiency(
self.target_efficiency, y == self.uniform_label, self.predict_score(X) * self.signed_uniform_label)
assert np.allclose(self.score_cut, self.score_cuts_[-1], rtol=1e-10, atol=1e-10), \
"score cut doesn't appear to coincide with the staged one"
assert len(self.estimators_) == len(self.estimator_weights_) == len(self.score_cuts_)
return self
def __call__(self, y, y_pred, sample_weight=None):
signed_multiplier = self._signed_multiplier(y)
weight_multiplier = self._weight_multiplier(y)
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return numpy.sum(sample_weight * weight_multiplier * numpy.exp(y_pred * signed_multiplier))
def negative_gradient(self, y, y_pred, sample_weight=None, **kargs):
multiplier = self._signed_multiplier(y)
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return sample_weight * y_signed * numpy.exp(y_pred * multiplier)
def __call__(self, y, y_pred, sample_weight=None):
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return numpy.sum(sample_weight * numpy.log(1 + numpy.exp(- y_signed * y_pred - self.shift)))
def negative_gradient(self, y, y_pred, sample_weight=None):
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return sample_weight * y_signed * expit(-y_signed * y_pred - self.shift)
def fit(self, X, y, sample_weight=None):
shuffler = Shuffler(X, random_state=self.random_state)
X, y = check_arrays(X,
y,
dtype=DTYPE,
sparse_format="dense",
check_ccontiguous=True)
y = column_or_1d(y, warn=True)
n_samples = len(X)
n_inbag = int(self.subsample * n_samples)
sample_weight = check_sample_weight(
y, sample_weight=sample_weight).copy()
self.random_state = check_random_state(self.random_state)
# skipping all checks
assert self.update_on in ['all', 'same', 'other', 'random']
y_pred = numpy.zeros(len(y), dtype=float)
self.classifiers = []
self.learning_rates = []
self.loss_values = []
self.loss = copy.copy(self.loss)
self.loss.fit(X, y, sample_weight=sample_weight)
iter_X = shuffler.generate(0.)
prev_smearing = 1
for iteration in range(self.n_estimators):
if iteration % self.recount_step == 0:
if prev_smearing > 0:
iter_smearing = interpolate(self.smearing, iteration,
self.n_estimators)
prev_smearing = iter_smearing
iter_X = shuffler.generate(iter_smearing)
iter_X, = check_arrays(iter_X,
dtype=DTYPE,
sparse_format="dense",
check_ccontiguous=True)
y_pred = numpy.zeros(len(y))
y_pred += sum(
cl.predict(X) * rate for rate, cl in zip(
self.learning_rates, self.classifiers))
self.loss_values.append(
self.loss(y, y_pred, sample_weight=sample_weight))
tree = DecisionTreeRegressor(
criterion=self.criterion,
splitter=self.splitter,
max_depth=interpolate(self.max_depth, iteration,
self.n_estimators),
min_samples_split=self.min_samples_split,
min_samples_leaf=interpolate(self.min_samples_leaf,
iteration,
self.n_estimators,
use_log=True),
max_features=self.max_features,
random_state=self.random_state)
sample_mask = _random_sample_mask(n_samples, n_inbag,
self.random_state)
loss_weight = sample_weight if self.weights_in_loss else numpy.ones(
len(sample_weight))
tree_weight = sample_weight if not self.weights_in_loss else numpy.ones(
len(sample_weight))
residual = self.loss.negative_gradient(y,
y_pred,
sample_weight=loss_weight)
tree.fit(numpy.array(iter_X)[sample_mask, :],
residual[sample_mask],
sample_weight=tree_weight[sample_mask],
check_input=False)
# update tree leaves
if self.update_tree:
if self.update_on == 'all':
update_mask = numpy.ones(len(sample_mask), dtype=bool)
elif self.update_on == 'same':
update_mask = sample_mask
elif self.update_on == 'other':
update_mask = ~sample_mask
else: # random
update_mask = _random_sample_mask(n_samples, n_inbag,
self.random_state)
self.loss.update_terminal_regions(tree.tree_,
X=iter_X,
y=y,
residual=residual,
pred=y_pred,
sample_mask=update_mask,
sample_weight=sample_weight)
iter_learning_rate = interpolate(self.learning_rate,
iteration,
self.n_estimators,
use_log=True)
y_pred += iter_learning_rate * tree.predict(X)
self.classifiers.append(tree)
self.learning_rates.append(iter_learning_rate)
return self
def negative_gradient(self, y, y_pred, sample_weight=None, **kargs):
multiplier = self._signed_multiplier(y)
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return sample_weight * y_signed * numpy.exp(y_pred * multiplier)
def __call__(self, y, y_pred, sample_weight=None):
signed_multiplier = self._signed_multiplier(y)
weight_multiplier = self._weight_multiplier(y)
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return numpy.sum(sample_weight * weight_multiplier *
numpy.exp(y_pred * signed_multiplier))
def negative_gradient(self, y, y_pred, sample_weight=None):
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return sample_weight * y_signed * expit(-y_signed * y_pred -
self.shift)
def __call__(self, y, y_pred, sample_weight=None):
y_signed = 2. * y - 1
sample_weight = check_sample_weight(y, sample_weight=sample_weight)
return numpy.sum(
sample_weight *
numpy.log(1 + numpy.exp(-y_signed * y_pred - self.shift)))
