def test_init_raw_predictions_shapes():
# Make sure get_init_raw_predictions returns float64 arrays with shape
# (n_samples, K) where K is 1 for binary classification and regression, and
# K = n_classes for multiclass classification
rng = np.random.RandomState(0)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
y = rng.normal(size=n_samples)
for loss in (LeastSquaresError(n_classes=1),
LeastAbsoluteError(n_classes=1),
QuantileLossFunction(n_classes=1),
HuberLossFunction(n_classes=1)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
y = rng.randint(0, 2, size=n_samples)
for loss in (BinomialDeviance(n_classes=2),
ExponentialLoss(n_classes=2)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
for n_classes in range(3, 5):
y = rng.randint(0, n_classes, size=n_samples)
loss = MultinomialDeviance(n_classes=n_classes)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, n_classes)
assert raw_predictions.dtype == np.float64
def test_init_raw_predictions_shapes():
# Make sure get_init_raw_predictions returns float64 arrays with shape
# (n_samples, K) where K is 1 for binary classification and regression, and
# K = n_classes for multiclass classification
rng = np.random.RandomState(0)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
y = rng.normal(size=n_samples)
for loss in (LeastSquaresError(n_classes=1),
LeastAbsoluteError(n_classes=1),
QuantileLossFunction(n_classes=1),
HuberLossFunction(n_classes=1)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
y = rng.randint(0, 2, size=n_samples)
for loss in (BinomialDeviance(n_classes=2), ExponentialLoss(n_classes=2)):
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, 1)
assert raw_predictions.dtype == np.float64
for n_classes in range(3, 5):
y = rng.randint(0, n_classes, size=n_samples)
loss = MultinomialDeviance(n_classes=n_classes)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
assert raw_predictions.shape == (n_samples, n_classes)
assert raw_predictions.dtype == np.float64
def test_mdl_computation_weighted():
raw_predictions = np.array([[1., -1., -.1], [-2., 1., 2.]])
y_true = np.array([0, 1])
weights = np.array([1, 3])
expected_loss = 1.0909323
# MultinomialDeviance loss computation with weights.
loss = MultinomialDeviance(3)
assert loss(y_true, raw_predictions, weights) == approx(expected_loss)
def test_multinomial_deviance(n_classes, n_samples):
# Check multinomial deviance with and without sample weights.
rng = np.random.RandomState(13)
sample_weight = np.ones(n_samples)
y_true = rng.randint(0, n_classes, size=n_samples)
y_pred = np.zeros((n_samples, n_classes), dtype=np.float64)
for klass in range(y_pred.shape[1]):
y_pred[:, klass] = y_true == klass
loss = MultinomialDeviance(n_classes)
loss_wo_sw = loss(y_true, y_pred)
assert loss_wo_sw > 0
loss_w_sw = loss(y_true, y_pred, sample_weight=sample_weight)
assert loss_wo_sw == pytest.approx(loss_w_sw)
# Multinomial deviance uses weighted average loss rather than
# weighted sum loss, so we make sure that the value remains the same
# when we device the weight by 2.
loss_w_sw = loss(y_true, y_pred, sample_weight=0.5 * sample_weight)
assert loss_wo_sw == pytest.approx(loss_w_sw)
def test_init_raw_predictions_values():
# Make sure the get_init_raw_predictions() returns the expected values for
# each loss.
rng = np.random.RandomState(0)
n_samples = 100
X = rng.normal(size=(n_samples, 5))
y = rng.normal(size=n_samples)
# Least squares loss
loss = LeastSquaresError(n_classes=1)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the mean of all targets
assert_almost_equal(raw_predictions, y.mean())
# Least absolute and huber loss
for Loss in (LeastAbsoluteError, HuberLossFunction):
loss = Loss(n_classes=1)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the median of all targets
assert_almost_equal(raw_predictions, np.median(y))
# Quantile loss
for alpha in (.1, .5, .9):
loss = QuantileLossFunction(n_classes=1, alpha=alpha)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
# Make sure baseline prediction is the alpha-quantile of all targets
assert_almost_equal(raw_predictions, np.percentile(y, alpha * 100))
y = rng.randint(0, 2, size=n_samples)
# Binomial deviance
loss = BinomialDeviance(n_classes=2)
init_estimator = loss.init_estimator().fit(X, y)
# Make sure baseline prediction is equal to link_function(p), where p
# is the proba of the positive class. We want predict_proba() to return p,
# and by definition
# p = inverse_link_function(raw_prediction) = sigmoid(raw_prediction)
# So we want raw_prediction = link_function(p) = log(p / (1 - p))
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
p = y.mean()
assert_almost_equal(raw_predictions, np.log(p / (1 - p)))
# Exponential loss
loss = ExponentialLoss(n_classes=2)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
p = y.mean()
assert_almost_equal(raw_predictions, .5 * np.log(p / (1 - p)))
# Multinomial deviance loss
for n_classes in range(3, 5):
y = rng.randint(0, n_classes, size=n_samples)
loss = MultinomialDeviance(n_classes=n_classes)
init_estimator = loss.init_estimator().fit(X, y)
raw_predictions = loss.get_init_raw_predictions(y, init_estimator)
for k in range(n_classes):
p = (y == k).mean()
assert_almost_equal(raw_predictions[:, k], np.log(p))
def test_mdl_exception(n):
# Check that MultinomialDeviance throws an exception when n_classes <= 2
err_msg = 'MultinomialDeviance requires more than 2 classes.'
with pytest.raises(ValueError, match=err_msg):
MultinomialDeviance(n)
def __init__(self,
nb_trees: int,
nb_trees_per_ensemble: int,
n_classes: Optional[int] = None,
max_depth: int = 6,
privacy_budget: float = 1.0,
learning_rate: float = 0.1,
max_leaves: Optional[int] = None,
min_samples_split: int = 2,
balance_partition: bool = True,
use_bfs: bool = False,
use_3_trees: bool = False,
cat_idx: Optional[List[int]] = None,
num_idx: Optional[List[int]] = None) -> None:
"""Initialize the GradientBoostingEnsemble class.
Args:
nb_trees (int): The total number of trees in the model.
nb_trees_per_ensemble (int): The number of trees in each ensemble.
n_classes (int): Number of classes. Triggers regression (None) vs classification.
max_depth (int): Optional. The depth for the trees. Default is 6.
privacy_budget (float): Optional. The privacy budget available for the
model. Default is 1.0.
learning_rate (float): Optional. The learning rate. Default is 0.1.
max_leaves (int): Optional. The max number of leaf nodes for the trees.
Tree will grow in a best-leaf first fashion until it contains
max_leaves or until it reaches maximum depth, whichever comes first.
min_samples_split (int): Optional. The minimum number of samples required
to split an internal node. Default is 2.
balance_partition (bool): Optional. Balance data repartition for training
the trees. The default is True, meaning all trees within an ensemble
will receive an equal amount of training samples. If set to False,
each tree will receive <x> samples where <x> is given in line 8 of
the algorithm in the author's paper.
use_bfs (bool): Optional. If max_leaves is specified, then this is
automatically True. This will build the tree in a BFS fashion instead
of DFS. Default is False.
use_3_trees (bool): Optional. If True, only build trees that have 3
nodes, and then assemble nb_trees based on these sub-trees, at random.
Default is False.
cat_idx (List): Optional. List of indices for categorical features.
num_idx (List): Optional. List of indices for numerical features.
"""
self.nb_trees = nb_trees
self.nb_trees_per_ensemble = nb_trees_per_ensemble
self.max_depth = max_depth
self.privacy_budget = privacy_budget
self.learning_rate = learning_rate
self.max_leaves = max_leaves
self.min_samples_split = min_samples_split
self.balance_partition = balance_partition
self.use_bfs = use_bfs
self.use_3_trees = use_3_trees
self.cat_idx = cat_idx
self.num_idx = num_idx
self.trees = [] # type: List[List[DifferentiallyPrivateTree]]
# classification vs regression
self.loss_ = MultinomialDeviance(
n_classes) if n_classes else LeastSquaresError(
1) # type: LossFunction
self.init_ = self.loss_.init_estimator()
# Loss parameters
self.l2_threshold = 1.0
self.l2_lambda = 0.1
# Initial score
self.init_score = None
if self.use_3_trees and self.use_bfs:
# Since we're building 3-node trees it's the same anyways.
self.use_bfs = False
class GradientBoostingEnsemble:
"""Implement gradient boosting ensemble of trees.
Attributes:
nb_trees (int): The total number of trees in the model.
nb_trees_per_ensemble (int): The number of trees in each ensemble.
max_depth (int): The depth for the trees.
privacy_budget (float): The privacy budget available for the model.
learning_rate (float): The learning rate.
l2_threshold (int): Threshold for the loss function. For the square loss
function (default), this is 1.
l2_lambda (float): Regularization parameter for l2 loss function.
For the square loss function (default), this is 0.1.
trees (List[List[DifferentiallyPrivateTree]]): A list of k-classes DP trees.
"""
# pylint: disable=invalid-name, too-many-arguments, unused-variable
def __init__(self,
nb_trees: int,
nb_trees_per_ensemble: int,
n_classes: Optional[int] = None,
max_depth: int = 6,
privacy_budget: float = 1.0,
learning_rate: float = 0.1,
max_leaves: Optional[int] = None,
min_samples_split: int = 2,
balance_partition: bool = True,
use_bfs: bool = False,
use_3_trees: bool = False,
cat_idx: Optional[List[int]] = None,
num_idx: Optional[List[int]] = None) -> None:
"""Initialize the GradientBoostingEnsemble class.
Args:
nb_trees (int): The total number of trees in the model.
nb_trees_per_ensemble (int): The number of trees in each ensemble.
n_classes (int): Number of classes. Triggers regression (None) vs classification.
max_depth (int): Optional. The depth for the trees. Default is 6.
privacy_budget (float): Optional. The privacy budget available for the
model. Default is 1.0.
learning_rate (float): Optional. The learning rate. Default is 0.1.
max_leaves (int): Optional. The max number of leaf nodes for the trees.
Tree will grow in a best-leaf first fashion until it contains
max_leaves or until it reaches maximum depth, whichever comes first.
min_samples_split (int): Optional. The minimum number of samples required
to split an internal node. Default is 2.
balance_partition (bool): Optional. Balance data repartition for training
the trees. The default is True, meaning all trees within an ensemble
will receive an equal amount of training samples. If set to False,
each tree will receive <x> samples where <x> is given in line 8 of
the algorithm in the author's paper.
use_bfs (bool): Optional. If max_leaves is specified, then this is
automatically True. This will build the tree in a BFS fashion instead
of DFS. Default is False.
use_3_trees (bool): Optional. If True, only build trees that have 3
nodes, and then assemble nb_trees based on these sub-trees, at random.
Default is False.
cat_idx (List): Optional. List of indices for categorical features.
num_idx (List): Optional. List of indices for numerical features.
"""
self.nb_trees = nb_trees
self.nb_trees_per_ensemble = nb_trees_per_ensemble
self.max_depth = max_depth
self.privacy_budget = privacy_budget
self.learning_rate = learning_rate
self.max_leaves = max_leaves
self.min_samples_split = min_samples_split
self.balance_partition = balance_partition
self.use_bfs = use_bfs
self.use_3_trees = use_3_trees
self.cat_idx = cat_idx
self.num_idx = num_idx
self.trees = [] # type: List[List[DifferentiallyPrivateTree]]
# classification vs regression
self.loss_ = MultinomialDeviance(
n_classes) if n_classes else LeastSquaresError(
1) # type: LossFunction
self.init_ = self.loss_.init_estimator()
# Loss parameters
self.l2_threshold = 1.0
self.l2_lambda = 0.1
# Initial score
self.init_score = None
if self.use_3_trees and self.use_bfs:
# Since we're building 3-node trees it's the same anyways.
self.use_bfs = False
def Train(self, X: np.array, y: np.array) -> 'GradientBoostingEnsemble':
"""Train the ensembles of gradient boosted trees.
Args:
X (np.array): The features.
y (np.array): The label.
Returns:
GradientBoostingEnsemble: A GradientBoostingEnsemble object.
"""
# Init gradients
self.init_.fit(X, y)
self.init_score = self.loss_.get_init_raw_predictions(
X, self.init_) # (n_samples, K)
update_gradients = True
X_train, X_test, y_train, y_test = train_test_split(X, y)
X, y = X_train, y_train
# Number of ensembles in the model
nb_ensembles = int(np.ceil(self.nb_trees / self.nb_trees_per_ensemble))
# Privacy budget allocated to all trees in each ensemble
tree_privacy_budget = np.divide(self.privacy_budget, nb_ensembles)
# In multi-class classification the budget for each tree
# is the same as for the whole K trees but halved
# As each datapoint is only assigned to one class,
# it only matters if it is assigned to the considered class or not but not to which other
# Thus it always remains 2 - independently of how many total classes exists
privacy_budget_per_tree = 2 if self.loss_.is_multi_class else 1
tree_privacy_budget = np.divide(tree_privacy_budget,
privacy_budget_per_tree)
prev_score = np.inf
# Train all trees
for tree_index in range(self.nb_trees):
# Compute sensitivity
delta_g = 3 * np.square(self.l2_threshold)
delta_v = min(
self.l2_threshold / (1 + self.l2_lambda),
2 * self.l2_threshold * math.pow(
(1 - self.learning_rate), tree_index))
current_tree_for_ensemble = tree_index % self.nb_trees_per_ensemble
if current_tree_for_ensemble == 0:
# Initialize the dataset and the gradients
X_ensemble = np.copy(X)
y_ensemble = np.copy(y)
prev_score = np.inf
update_gradients = True
# gradient initialization will happen later in the per-class-loop
# Compute the number of rows that the current tree will use for training
if self.balance_partition:
# All trees will receive same amount of samples
if self.nb_trees % self.nb_trees_per_ensemble == 0:
# Perfect split
number_of_rows = int(len(X) / self.nb_trees_per_ensemble)
else:
# Partitioning data across ensembles
if np.ceil(tree_index /
self.nb_trees_per_ensemble) == np.ceil(
self.nb_trees / self.nb_trees_per_ensemble):
number_of_rows = int(
len(X) /
(self.nb_trees % self.nb_trees_per_ensemble))
else:
number_of_rows = int(
len(X) / self.nb_trees_per_ensemble) + int(
len(X) /
(self.nb_trees % self.nb_trees_per_ensemble))
else:
# Line 8 of Algorithm 2 from the paper
number_of_rows = int(
(len(X) * self.learning_rate * math.pow(
(1 - self.learning_rate), current_tree_for_ensemble)) /
(1 - math.pow(
(1 - self.learning_rate), self.nb_trees_per_ensemble)))
# If using the formula from the algorithm, some trees may not get
# samples. In that case we skip the tree and issue a warning. This
# should hint the user to change its parameters (likely the ensembles
# are too unbalanced)
if number_of_rows == 0:
logger.warning(
'The choice of trees per ensemble vs. the total number '
'of trees is not balanced properly; some trees will '
'not get any training samples. Try using '
'balance_partition=True or change your parameters.')
continue
# Select <number_of_rows> rows at random from the ensemble dataset
rows = np.random.randint(len(X_ensemble), size=number_of_rows)
X_tree = X_ensemble[rows, :]
y_tree = y_ensemble[rows]
# train for each class a seperate tree on the same rows.
# In regression or binary classification, K has been set to one.
k_trees = [] # type: List[DifferentiallyPrivateTree]
for kth_tree in range(self.loss_.K):
if tree_index == 0:
# First tree, start with initial scores (mean of labels)
gradients = self.ComputeGradientForLossFunction(
y, self.init_score[:len(y)], kth_tree)
else:
# Update gradients of all training instances on loss l
if update_gradients:
gradients = self.ComputeGradientForLossFunction(
y_ensemble, self.Predict(X_ensemble),
kth_tree) # type: ignore
assert gradients is not None
gradients_tree = gradients[rows]
# Gradient based data filtering
norm_1_gradient = np.abs(gradients_tree)
rows_gbf = norm_1_gradient <= self.l2_threshold
X_tree = X_tree[rows_gbf, :]
y_tree = y_tree[rows_gbf]
gradients_tree = gradients_tree[rows_gbf]
# Get back the original row index from the first filtering
selected_rows = rows[rows_gbf] if tree_index > 0 else rows
# Fit a differentially private decision tree
tree = DifferentiallyPrivateTree(
tree_index,
self.learning_rate,
self.l2_threshold,
self.l2_lambda,
tree_privacy_budget,
delta_g,
delta_v,
self.loss_,
max_depth=self.max_depth,
max_leaves=self.max_leaves,
min_samples_split=self.min_samples_split,
use_bfs=self.use_bfs,
use_3_trees=self.use_3_trees,
cat_idx=self.cat_idx,
num_idx=self.num_idx)
# in multi-class classification, the target has to be binary
# as each tree is a per-class regressor
y_target = ((y_tree == kth_tree).astype(np.float64)
if self.loss_.is_multi_class else y_tree)
tree.Fit(X_tree, y_target, gradients_tree)
# Add the tree to its corresponding ensemble
k_trees.append(tree)
self.trees.append(k_trees)
score = self.loss_(y_test,
self.Predict(X_test)) # i.e. mse or deviance
if score >= prev_score:
# This tree doesn't improve overall prediction quality, removing from
# model
update_gradients = self.loss_.is_multi_class # not reusing gradients in multi-class as they are class-dependent
self.trees.pop()
else:
print(tree_index, score)
update_gradients = True
prev_score = score
# Remove the selected rows from the ensemble's dataset
# The instances that were filtered out by GBF can still be used for the
# training of the next trees
X_ensemble = np.delete(X_ensemble, selected_rows, axis=0)
y_ensemble = np.delete(y_ensemble, selected_rows)
if self.use_3_trees:
self.Combine_3_trees(self.trees)
return self
def Combine_3_trees(
self, k_trees: List[List['DifferentiallyPrivateTree']]) -> None:
"""Combine 3-trees together to construct bigger decision trees.
Args:
k_trees (List[List[DifferentiallyPrivateTree]]): A k-list of 3-trees.
"""
self.trees = [] # Re-init final predictions trees
for index, k_three_tree in enumerate(
k_trees): # iterate through the ensemble
k_trees_ = [] # type: List[DifferentiallyPrivateTree]
for k, three_tree in enumerate(
k_three_tree): # iterate through the classes
# select a whole ensemble per class; continue as if there were only one class
copy = list(np.copy([i[k] for i in k_trees]))
copy.pop(index)
if len(copy) == 0:
continue
queue_children = Queue() # type: Queue['DecisionNode']
queue_children.put(
three_tree.root_node.left_child) # type: ignore
queue_children.put(
three_tree.root_node.right_child) # type: ignore
depth = 1
privacy_budget_for_node = np.around(np.divide(
three_tree.privacy_budget / 2, three_tree.max_depth + 1),
decimals=7)
while not queue_children.empty():
if depth == self.max_depth or len(copy) == 0:
break
left_child = queue_children.get()
right_child = queue_children.get()
for child in [left_child, right_child]:
if len(copy) == 0 or not child or not child.X.any(
): # type: ignore
continue
# Apply exponential mechanism to find sub 3-node tree
probabilities = []
max_gain = -np.inf
for candidate_index, candidate in enumerate(copy):
if not candidate.root_node.X.any():
continue
# Compute distance between the two nodes. Lower is better.
gain = np.linalg.norm(
np.matmul(np.transpose(child.X), child.X) -
np.matmul(np.transpose(candidate.root_node.X),
candidate.root_node.X))
exp_gain = (privacy_budget_for_node *
gain) / (2. * three_tree.delta_g)
if exp_gain > max_gain:
max_gain = exp_gain
prob = {
'candidate_index': candidate_index,
'index': candidate.root_node.index,
'value': candidate.root_node.value,
'gain': exp_gain
}
probabilities.append(prob)
candidate = ExponentialMechanism(probabilities,
max_gain,
reverse=True)
if not candidate or not candidate[
'index'] or not candidate['value']:
continue
copy.pop(candidate['candidate_index'])
split_index = candidate['index']
split_value = candidate['value']
left_, right_ = self.SplitNode(
child, split_index, split_value,
three_tree.privacy_budget, index,
three_tree.delta_v)
queue_children.put(left_)
queue_children.put(right_)
depth += 1
k_trees_.append(three_tree)
self.trees.append(k_trees_)
if not self.trees or self.trees == [[]]:
self.trees = k_trees
def SplitNode(self, node: 'DecisionNode', index: int, value: float,
tree_privacy_budget: float, tree_index: int,
delta_v: float) -> Tuple['DecisionNode', 'DecisionNode']:
"""Split children of a 3-nodes tree based on the (index, value) pair.
Args:
node (DecisionNode): The node to split.
index (int): The feature's index on which to split the node.
value (float): The feature's value on which to split the node.
tree_privacy_budget (float): The privacy budget for the current tree.
tree_index (int): The index of the tree.
delta_v (float): The loss function's sensitivity for the tree.
Returns:
Tuple: Children created after the split.
"""
assert node.X is not None
assert node.y is not None
assert node.gradients is not None
# Split indices of instances from the node's dataset
lhs_op, rhs_op = self.GetOperators(index)
lhs = np.where(lhs_op(node.X[:, index], value))[0]
rhs = np.where(rhs_op(node.X[:, index], value))[0]
# Compute the associated predictions
lhs_prediction = ComputePredictions(node.gradients[lhs], node.y[lhs],
self.loss_, self.l2_lambda)
rhs_prediction = ComputePredictions(node.gradients[rhs], node.y[rhs],
self.loss_, self.l2_lambda)
# Mark current node as split node and not leaf node
node.prediction = None
node.index = index
node.value = value
# Add children to node
node.left_child = DecisionNode(X=node.X[lhs],
y=node.y[lhs],
prediction=lhs_prediction,
gradients=node.gradients[lhs])
node.right_child = DecisionNode(X=node.X[rhs],
y=node.y[rhs],
prediction=rhs_prediction,
gradients=node.gradients[rhs])
# Apply Geometry leaf clipping
ClipLeaves([node.left_child, node.right_child], self.l2_threshold,
self.learning_rate, tree_index)
# Add noise to the leaf predictions
laplace_scale = delta_v / tree_privacy_budget / 2
AddLaplacianNoise([node.left_child, node.right_child], laplace_scale)
# Shrink by learning rate
Shrink([node.left_child, node.right_child], self.learning_rate)
return node.left_child, node.right_child
def Predict(self, X: np.array) -> np.array:
"""Predict values from the ensemble of gradient boosted trees.
See https://github.com/microsoft/LightGBM/issues/1778.
Args:
X (np.array): The dataset for which to predict values.
Returns:
np.array of shape (n_samples, K): The predictions.
"""
# sum across the ensemble per class
predictions = np.sum([[tree.Predict(X) for tree in k_trees]
for k_trees in self.trees],
axis=0).T
assert self.init_score is not None
init_score = self.init_score[:len(predictions)]
return np.add(init_score, predictions)
def PredictLabels(self, X: np.ndarray) -> np.ndarray:
"""Predict labels out of the raw prediction values of `Predict`.
Only defined for classification tasks.
Args:
X (np.ndarray): The dataset for which to predict labels.
Returns:
np.ndarray: The label predictions.
"""
if type(self.loss_) is not MultinomialDeviance:
raise ValueError("Labels are not defined for regression tasks.")
raw_predictions = self.Predict(X)
encoded_labels = self.loss_._raw_prediction_to_decision(
raw_predictions)
return encoded_labels
def ComputeGradientForLossFunction(self, y: np.array, y_pred: np.array,
k: int) -> np.array:
"""Compute the gradient of the loss function.
Args:
y (np.array): The true values.
y_pred (np.array): The predictions.
k (int): the class.
Returns:
(np.array): The gradient of the loss function.
"""
if self.loss_.is_multi_class:
y = (y == k).astype(np.float64)
# sklearn's impl is using the negative gradient (i.e. y - F).
# Here the positive gradient is used though
return -self.loss_.negative_gradient(y, y_pred, k=k)
def GetOperators(self, index: int) -> Tuple[Any, Any]:
"""Return operators to use to split a node's dataset.
Args:
index (int): The index for the feature to split the data on.
Returns:
Tuple[Any, Any]: The operators to use.
"""
if self.cat_idx and index in self.cat_idx:
# Categorical feature
return operator.eq, operator.ne
# Numerical feature
return operator.lt, operator.ge