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[MRG] Reuse grower and splitter memory #88

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5 changes: 4 additions & 1 deletion benchmarks/bench_higgs_boson.py
Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,10 @@ def load_data():
n_iter_no_change=None,
random_state=0,
verbose=1)
pygbm_model.fit(data_train, target_train)
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I'll remove this

def fit():
pygbm_model.fit(data_train, target_train)
fit()
toc = time()
predicted_test = pygbm_model.predict(data_test)
roc_auc = roc_auc_score(target_test, predicted_test)
Expand Down
21 changes: 12 additions & 9 deletions pygbm/gradient_boosting.py
Original file line number Diff line number Diff line change
Expand Up @@ -228,6 +228,17 @@ def fit(self, X, y):
y_train, raw_predictions)

predictors.append([])
grower = TreeGrower(
X_binned_train,
max_bins=self.max_bins,
n_bins_per_feature=n_bins_per_feature,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate,
hessian_is_constant=self.loss_.hessian_is_constant
)

# Build `n_trees_per_iteration` trees.
for k, (gradients_at_k, hessians_at_k) in enumerate(zip(
Expand All @@ -238,15 +249,7 @@ def fit(self, X, y):
# n_trees_per_iteration is 1 and xxxx_at_k is equivalent to the
# whole array.

grower = TreeGrower(
X_binned_train, gradients_at_k, hessians_at_k,
max_bins=self.max_bins,
n_bins_per_feature=n_bins_per_feature,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.reset(gradients_at_k, hessians_at_k)
grower.grow()

acc_apply_split_time += grower.total_apply_split_time
Expand Down
22 changes: 14 additions & 8 deletions pygbm/grower.py
Original file line number Diff line number Diff line change
Expand Up @@ -160,10 +160,11 @@ class TreeGrower:
The shrinkage parameter to apply to the leaves values, also known as
learning rate.
"""
def __init__(self, X_binned, gradients, hessians, max_leaf_nodes=None,
max_depth=None, min_samples_leaf=20, min_gain_to_split=0.,
max_bins=256, n_bins_per_feature=None, l2_regularization=0.,
min_hessian_to_split=1e-3, shrinkage=1.):
def __init__(self, X_binned, max_leaf_nodes=None, max_depth=None,
min_samples_leaf=20, min_gain_to_split=0., max_bins=256,
n_bins_per_feature=None, l2_regularization=0.,
min_hessian_to_split=1e-3, shrinkage=1.,
hessian_is_constant=False):

self._validate_parameters(X_binned, max_leaf_nodes, max_depth,
min_samples_leaf, min_gain_to_split,
Expand All @@ -178,15 +179,20 @@ def __init__(self, X_binned, gradients, hessians, max_leaf_nodes=None,
dtype=np.uint32)

self.splitting_context = SplittingContext(
X_binned, max_bins, n_bins_per_feature, gradients,
hessians, l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split)
X_binned, max_bins, n_bins_per_feature, l2_regularization,
min_hessian_to_split, min_samples_leaf, min_gain_to_split,
hessian_is_constant)

self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.X_binned = X_binned
self.min_gain_to_split = min_gain_to_split
self.shrinkage = shrinkage
self.hessian_is_constant = hessian_is_constant

def reset(self, gradients, hessians):
self.splitting_context.reset(gradients, hessians)
self.splittable_nodes = []
self.finalized_leaves = []
self.total_find_split_time = 0. # time spent finding the best splits
Expand Down Expand Up @@ -237,7 +243,7 @@ def _intilialize_root(self):
"""Initialize root node and finalize it if needed."""
n_samples = self.X_binned.shape[0]
depth = 0
if self.splitting_context.constant_hessian:
if self.hessian_is_constant:
hessian = self.splitting_context.hessians[0] * n_samples
else:
hessian = self.splitting_context.hessians.sum()
Expand Down
65 changes: 41 additions & 24 deletions pygbm/splitting.py
Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,7 @@ def __init__(self, gain=-1., feature_idx=0, bin_idx=0,
('ordered_hessians', float32[::1]),
('sum_gradients', float32),
('sum_hessians', float32),
('constant_hessian', uint8),
('hessian_is_constant', uint8),
('constant_hessian_value', float32),
('l2_regularization', float32),
('min_hessian_to_split', float32),
Expand Down Expand Up @@ -126,32 +126,27 @@ class SplittingContext:
be ignored.
"""
def __init__(self, X_binned, max_bins, n_bins_per_feature,
gradients, hessians, l2_regularization,
min_hessian_to_split=1e-3, min_samples_leaf=20,
min_gain_to_split=0.):
l2_regularization, min_hessian_to_split=1e-3,
min_samples_leaf=20, min_gain_to_split=0.,
hessian_is_constant=False):

self.X_binned = X_binned
self.n_features = X_binned.shape[1]
# Note: all histograms will have <max_bins> bins, but some of the
# last bins may be unused if n_bins_per_feature[f] < max_bins
self.max_bins = max_bins
self.n_bins_per_feature = n_bins_per_feature
self.gradients = gradients
self.hessians = hessians
# for root node, gradients and hessians are already ordered
self.ordered_gradients = gradients.copy()
self.ordered_hessians = hessians.copy()
self.sum_gradients = self.gradients.sum()
self.sum_hessians = self.hessians.sum()
self.constant_hessian = hessians.shape[0] == 1
self.l2_regularization = l2_regularization
self.min_hessian_to_split = min_hessian_to_split
self.min_samples_leaf = min_samples_leaf
self.min_gain_to_split = min_gain_to_split
if self.constant_hessian:
self.constant_hessian_value = self.hessians[0] # 1 scalar

self.hessian_is_constant = hessian_is_constant
self.ordered_gradients = np.empty(X_binned.shape[0], dtype=np.float32)
if self.hessian_is_constant:
self.ordered_hessians = np.empty(1, dtype=np.float32) # won't be used anyway
else:
self.constant_hessian_value = float32(1.) # won't be used anyway
self.ordered_hessians = np.empty(X_binned.shape[0], dtype=np.float32)

# The partition array maps each sample index into the leaves of the
# tree (a leaf in this context is a node that isn't splitted yet, not
Expand All @@ -162,10 +157,32 @@ def __init__(self, X_binned, max_bins, n_bins_per_feature,
# partition = [cef|abdghijkl]
# we have 2 leaves, the left one is at position 0 and the second one at
# position 3. The order of the samples is irrelevant.
self.partition = np.arange(0, X_binned.shape[0], 1, np.uint32)
self.partition = np.empty(X_binned.shape[0], dtype=np.uint32)
# buffers used in split_indices to support parallel splitting.
self.left_indices_buffer = np.empty_like(self.partition)
self.right_indices_buffer = np.empty_like(self.partition)
self.left_indices_buffer = np.empty(X_binned.shape[0], dtype=np.uint32)
self.right_indices_buffer = np.empty(X_binned.shape[0], dtype=np.uint32)

# TODO: parallelize this
def reset(self, gradients, hessians):
self.gradients = gradients
self.hessians = hessians

# for root node, gradients and hessians are already ordered
self.sum_gradients = self.gradients.sum()
self.sum_hessians = self.hessians.sum()

n_samples = gradients.shape[0]
for i in range(n_samples):
self.ordered_gradients[i] = gradients[i]
if self.hessian_is_constant:
self.constant_hessian_value = self.hessians[0] # 1 scalar
else:
self.constant_hessian_value = float32(1.) # won't be used anyway
for i in range(n_samples):
self.ordered_hessians[i] = hessians[i]

for i in range(n_samples):
self.partition[i] = i


@njit(parallel=True,
Expand Down Expand Up @@ -345,7 +362,7 @@ def find_node_split(context, sample_indices):
# ctx.ordered_gradients[i] = ctx.gradients[samples_indices[i]]
if sample_indices.shape[0] != ctx.gradients.shape[0]:
starts, ends, n_threads = get_threads_chunks(n_samples)
if ctx.constant_hessian:
if ctx.hessian_is_constant:
for thread_idx in prange(n_threads):
for i in range(starts[thread_idx], ends[thread_idx]):
ordered_gradients[i] = ctx.gradients[sample_indices[i]]
Expand All @@ -356,7 +373,7 @@ def find_node_split(context, sample_indices):
ordered_hessians[i] = ctx.hessians[sample_indices[i]]

ctx.sum_gradients = ctx.ordered_gradients[:n_samples].sum()
if ctx.constant_hessian:
if ctx.hessian_is_constant:
ctx.sum_hessians = ctx.constant_hessian_value * float32(n_samples)
else:
ctx.sum_hessians = ctx.ordered_hessians[:n_samples].sum()
Expand Down Expand Up @@ -426,7 +443,7 @@ def find_node_split_subtraction(context, sample_indices, parent_histograms,
sibling_histograms[0]['sum_gradients'].sum())

n_samples = sample_indices.shape[0]
if context.constant_hessian:
if context.hessian_is_constant:
context.sum_hessians = \
context.constant_hessian_value * float32(n_samples)
else:
Expand Down Expand Up @@ -476,15 +493,15 @@ def _find_histogram_split(context, feature_idx, sample_indices):
ordered_hessians = context.ordered_hessians[:n_samples]

if root_node:
if context.constant_hessian:
if context.hessian_is_constant:
histogram = _build_histogram_root_no_hessian(
context.max_bins, X_binned, ordered_gradients)
else:
histogram = _build_histogram_root(
context.max_bins, X_binned, ordered_gradients,
context.ordered_hessians)
else:
if context.constant_hessian:
if context.hessian_is_constant:
histogram = _build_histogram_no_hessian(
context.max_bins, sample_indices, X_binned,
ordered_gradients)
Expand Down Expand Up @@ -537,7 +554,7 @@ def _find_best_bin_to_split_helper(context, feature_idx, histogram, n_samples):
n_samples_left += histogram[bin_idx]['count']
n_samples_right = n_samples - n_samples_left

if context.constant_hessian:
if context.hessian_is_constant:
hessian_left += (histogram[bin_idx]['count']
* context.constant_hessian_value)
else:
Expand Down