def create_matrix_predictor_32f(forest, **kwargs):
number_of_classes = forest.GetTree(0).GetYs().GetN()
all_samples_step = pipeline.AllSamplesStep_f32f32i32(buffers.X_FLOAT_DATA)
combiner = classification.ClassProbabilityCombiner_f32(number_of_classes)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
all_samples_step.IndicesBufferId, buffers.X_FLOAT_DATA)
forest_predicter = predict.LinearMatrixClassificationPredictin_f32i32(
forest, matrix_feature, combiner, all_samples_step)
return PredictorWrapper_32f(forest_predicter,
matrix_classification_data_prepare)
def create_matrix_regression_predictor_32f(forest, **kwargs):
dimension_of_y = forest.GetTree(0).GetYs().GetN() / 2
all_samples_step = pipeline.AllSamplesStep_f32f32i32(buffers.X_FLOAT_DATA)
combiner = regression.MeanVarianceCombiner_f32(dimension_of_y)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
all_samples_step.IndicesBufferId, buffers.X_FLOAT_DATA)
forest_predicter = predict.LinearMatrixRegressionPredictin_f32i32(
forest, matrix_feature, combiner, all_samples_step)
return PredictorWrapper_32f(forest_predicter,
matrix_regression_data_prepare)
def create_online_axis_aligned_matrix_two_stream_consistent_learner_32f(
**kwargs):
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_features = int(
kwargs.get('number_of_features', np.sqrt(kwargs['x'].shape[1])))
feature_ordering = int(
kwargs.get('feature_ordering', pipeline.FEATURES_BY_DATAPOINTS))
number_of_splitpoints = int(kwargs.get('number_of_splitpoints', 1))
number_of_classes = int(np.max(kwargs['classes']) + 1)
max_frontier_size = int(kwargs.get('max_frontier_size', 10000000))
impurity_update_period = int(kwargs.get('impurity_update_period', 1))
probability_of_impurity_stream = float(
kwargs.get('probability_of_impurity_stream', 0.5))
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
elif 'poisson_sample' in kwargs:
poisson_sample_mean = float(kwargs.get('poisson_sample'))
sample_data_step = pipeline.PoissonSamplesStep_f32i32(
buffers.X_FLOAT_DATA, poisson_sample_mean)
else:
sample_data_step = pipeline.AllSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
assign_stream_step = splitpoints.AssignStreamStep_f32i32(
sample_data_step.WeightsBufferId, probability_of_impurity_stream)
tree_steps_pipeline = pipeline.Pipeline(
[sample_data_step, assign_stream_step])
# On init
set_number_features_step = pipeline.PoissonStep_f32i32(
number_of_features, 1)
feature_params_step = matrix_features.AxisAlignedParamsStep_f32i32(
set_number_features_step.OutputBufferId, buffers.X_FLOAT_DATA)
init_node_steps_pipeline = pipeline.Pipeline(
[set_number_features_step, feature_params_step])
# On update
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
matrix_feature_extractor_step = matrix_features.LinearFloat32MatrixFeatureExtractorStep_f32i32(
matrix_feature, feature_ordering)
slice_classes_step = pipeline.SliceInt32VectorBufferStep_i32(
buffers.CLASS_LABELS, sample_data_step.IndicesBufferId)
slice_weights_step = pipeline.SliceFloat32VectorBufferStep_i32(
sample_data_step.WeightsBufferId, sample_data_step.IndicesBufferId)
slice_assign_stream_step = pipeline.SliceInt32VectorBufferStep_i32(
assign_stream_step.StreamTypeBufferId,
sample_data_step.IndicesBufferId)
random_splitpoint_selection_step = splitpoints.RandomSplitpointsStep_f32i32(
matrix_feature_extractor_step.FeatureValuesBufferId,
number_of_splitpoints, feature_ordering,
slice_assign_stream_step.SlicedBufferId)
class_stats_updater = classification.ClassStatsUpdater_f32i32(
slice_weights_step.SlicedBufferId, slice_classes_step.SlicedBufferId,
number_of_classes)
two_stream_split_stats_step = classification.ClassStatsUpdaterTwoStreamStep_f32i32(
random_splitpoint_selection_step.SplitpointsBufferId,
random_splitpoint_selection_step.SplitpointsCountsBufferId,
slice_assign_stream_step.SlicedBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
class_stats_updater)
update_stats_node_steps_pipeline = pipeline.Pipeline([
matrix_feature_extractor_step, slice_classes_step, slice_weights_step,
slice_assign_stream_step, random_splitpoint_selection_step,
two_stream_split_stats_step
])
# On impurity
impurity_step = classification.ClassInfoGainSplitpointsImpurity_f32i32(
random_splitpoint_selection_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsImpurityBufferId,
two_stream_split_stats_step.LeftImpurityStatsBufferId,
two_stream_split_stats_step.RightImpurityStatsBufferId)
update_impurity_node_steps_pipeline = pipeline.Pipeline([impurity_step])
split_buffers = splitpoints.SplitSelectorBuffers(
impurity_step.ImpurityBufferId,
random_splitpoint_selection_step.SplitpointsBufferId,
random_splitpoint_selection_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsEstimatorBufferId,
two_stream_split_stats_step.LeftEstimatorStatsBufferId,
two_stream_split_stats_step.RightEstimatorStatsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
matrix_feature_extractor_step)
should_split_criteria = create_should_split_consistent_criteria(**kwargs)
finalizer = classification.ClassEstimatorFinalizer_f32()
split_indices = splitpoints.SplitIndices_f32i32(
sample_data_step.IndicesBufferId)
split_selector = splitpoints.WaitForBestSplitSelector_f32i32(
[split_buffers], should_split_criteria, finalizer, split_indices)
matrix_feature_prediction = matrix_features.LinearFloat32MatrixFeature_f32i32(
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
estimator_params_updater = classification.ClassEstimatorUpdater_f32i32(
sample_data_step.WeightsBufferId, buffers.CLASS_LABELS,
number_of_classes)
forest_learner = learn.OnlineForestMatrixClassLearner_f32i32(
try_split_criteria, tree_steps_pipeline, init_node_steps_pipeline,
update_stats_node_steps_pipeline, update_impurity_node_steps_pipeline,
impurity_update_period, split_selector, max_frontier_size,
number_of_trees, 5, 5, number_of_classes,
sample_data_step.IndicesBufferId, sample_data_step.WeightsBufferId,
matrix_feature_prediction, estimator_params_updater)
return forest_learner
def create_axis_aligned_matrix_two_stream_learner_32f(**kwargs):
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_features = int(
kwargs.get('number_of_features', np.sqrt(kwargs['x'].shape[1])))
feature_ordering = int(
kwargs.get('feature_ordering', pipeline.FEATURES_BY_DATAPOINTS))
number_of_splitpoints = int(kwargs.get('number_of_splitpoints', 1))
number_of_jobs = int(kwargs.get('number_of_jobs', 1))
number_of_classes = int(np.max(kwargs['classes']) + 1)
probability_of_impurity_stream = float(
kwargs.get('probability_of_impurity_stream', 0.5))
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
else:
sample_data_step = pipeline.AllSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
number_of_features_buffer = buffers.as_vector_buffer(
np.array([number_of_features], dtype=np.int32))
set_number_features_step = pipeline.SetInt32VectorBufferStep(
number_of_features_buffer, pipeline.WHEN_NEW)
assign_stream_step = splitpoints.AssignStreamStep_f32i32(
sample_data_step.WeightsBufferId, probability_of_impurity_stream)
tree_steps_pipeline = pipeline.Pipeline(
[sample_data_step, set_number_features_step, assign_stream_step])
feature_params_step = matrix_features.AxisAlignedParamsStep_f32i32(
set_number_features_step.OutputBufferId, buffers.X_FLOAT_DATA)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
matrix_feature_extractor_step = matrix_features.LinearFloat32MatrixFeatureExtractorStep_f32i32(
matrix_feature, feature_ordering)
slice_classes_step = pipeline.SliceInt32VectorBufferStep_i32(
buffers.CLASS_LABELS, sample_data_step.IndicesBufferId)
slice_weights_step = pipeline.SliceFloat32VectorBufferStep_i32(
sample_data_step.WeightsBufferId, sample_data_step.IndicesBufferId)
slice_assign_stream_step = pipeline.SliceInt32VectorBufferStep_i32(
assign_stream_step.StreamTypeBufferId,
sample_data_step.IndicesBufferId)
random_splitpoint_selection_step = splitpoints.RandomSplitpointsStep_f32i32(
matrix_feature_extractor_step.FeatureValuesBufferId,
number_of_splitpoints, feature_ordering,
slice_assign_stream_step.SlicedBufferId)
class_stats_updater = classification.ClassStatsUpdater_f32i32(
slice_weights_step.SlicedBufferId, slice_classes_step.SlicedBufferId,
number_of_classes)
two_stream_split_stats_step = classification.ClassStatsUpdaterTwoStreamStep_f32i32(
random_splitpoint_selection_step.SplitpointsBufferId,
random_splitpoint_selection_step.SplitpointsCountsBufferId,
slice_assign_stream_step.SlicedBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
class_stats_updater)
impurity_step = classification.ClassInfoGainSplitpointsImpurity_f32i32(
random_splitpoint_selection_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsImpurityBufferId,
two_stream_split_stats_step.LeftImpurityStatsBufferId,
two_stream_split_stats_step.RightImpurityStatsBufferId)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, matrix_feature_extractor_step, slice_classes_step,
slice_weights_step, slice_assign_stream_step,
random_splitpoint_selection_step, two_stream_split_stats_step,
impurity_step
])
split_buffers = splitpoints.SplitSelectorBuffers(
impurity_step.ImpurityBufferId,
random_splitpoint_selection_step.SplitpointsBufferId,
random_splitpoint_selection_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsEstimatorBufferId,
two_stream_split_stats_step.LeftEstimatorStatsBufferId,
two_stream_split_stats_step.RightEstimatorStatsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
matrix_feature_extractor_step)
should_split_criteria = create_should_split_criteria(**kwargs)
finalizer = classification.ClassEstimatorFinalizer_f32()
split_indices = splitpoints.SplitIndices_f32i32(
sample_data_step.IndicesBufferId)
split_selector = splitpoints.SplitSelector_f32i32([split_buffers],
should_split_criteria,
finalizer, split_indices)
if 'tree_order' in kwargs and kwargs.get('tree_order') == 'breadth_first':
tree_learner = learn.BreadthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector)
else:
tree_learner = learn.DepthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector)
forest_learner = learn.ParallelForestLearner(tree_learner, number_of_trees,
number_of_classes,
number_of_jobs)
return forest_learner
def create_class_pair_difference_matrix_walking_learner_32f(**kwargs):
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_features = int(
kwargs.get('number_of_features', np.sqrt(kwargs['x'].shape[1])))
feature_ordering = int(
kwargs.get('feature_ordering', pipeline.FEATURES_BY_DATAPOINTS))
number_of_jobs = int(kwargs.get('number_of_jobs', 1))
number_of_classes = int(np.max(kwargs['classes']) + 1)
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
else:
sample_data_step = pipeline.AllSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
number_of_features_buffer = buffers.as_vector_buffer(
np.array([number_of_features], dtype=np.int32))
set_number_features_step = pipeline.SetInt32VectorBufferStep(
number_of_features_buffer, pipeline.WHEN_NEW)
tree_steps_pipeline = pipeline.Pipeline(
[sample_data_step, set_number_features_step])
feature_params_step = matrix_features.ClassPairDifferenceParamsStep_f32i32(
set_number_features_step.OutputBufferId, buffers.X_FLOAT_DATA,
buffers.CLASS_LABELS, sample_data_step.IndicesBufferId)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
matrix_feature_extractor_step = matrix_features.LinearFloat32MatrixFeatureExtractorStep_f32i32(
matrix_feature, feature_ordering)
slice_classes_step = pipeline.SliceInt32VectorBufferStep_i32(
buffers.CLASS_LABELS, sample_data_step.IndicesBufferId)
slice_weights_step = pipeline.SliceFloat32VectorBufferStep_i32(
sample_data_step.WeightsBufferId, sample_data_step.IndicesBufferId)
class_infogain_walker = classification.ClassInfoGainWalker_f32i32(
slice_weights_step.SlicedBufferId, slice_classes_step.SlicedBufferId,
number_of_classes)
best_splitpint_step = classification.ClassInfoGainBestSplitpointsWalkingSortedStep_f32i32(
class_infogain_walker,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, matrix_feature_extractor_step, slice_classes_step,
slice_weights_step, best_splitpint_step
])
split_buffers = splitpoints.SplitSelectorBuffers(
best_splitpint_step.ImpurityBufferId,
best_splitpint_step.SplitpointBufferId,
best_splitpint_step.SplitpointCountsBufferId,
best_splitpint_step.ChildCountsBufferId,
best_splitpint_step.LeftYsBufferId,
best_splitpint_step.RightYsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
matrix_feature_extractor_step)
should_split_criteria = create_should_split_criteria(**kwargs)
finalizer = classification.ClassEstimatorFinalizer_f32()
split_indices = splitpoints.SplitIndices_f32i32(
sample_data_step.IndicesBufferId)
split_selector = splitpoints.SplitSelector_f32i32([split_buffers],
should_split_criteria,
finalizer, split_indices)
if 'tree_order' in kwargs and kwargs.get('tree_order') == 'breadth_first':
tree_learner = learn.BreadthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector)
else:
tree_learner = learn.DepthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector)
forest_learner = learn.ParallelForestLearner(tree_learner, number_of_trees,
number_of_classes,
number_of_jobs)
return forest_learner
def create_regression_axis_aligned_matrix_learner_32f(**kwargs):
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_leaves = int(
kwargs.get('number_of_leaves', kwargs['y'].shape[0] / 5 + 1))
number_of_features = int(
kwargs.get('number_of_features', (kwargs['x'].shape[1]) / 3 + 0.5))
# number_of_features = int( kwargs.get('number_of_features', np.sqrt(kwargs['x'].shape[1])))
feature_ordering = int(
kwargs.get('feature_ordering', pipeline.FEATURES_BY_DATAPOINTS))
number_of_jobs = int(kwargs.get('number_of_jobs', 1))
dimension_of_y = int(kwargs['y'].shape[1])
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
else:
sample_data_step = pipeline.AllSamplesStep_f32f32i32(
buffers.X_FLOAT_DATA)
number_of_features_buffer = buffers.as_vector_buffer(
np.array([number_of_features], dtype=np.int32))
set_number_features_step = pipeline.SetInt32VectorBufferStep(
number_of_features_buffer, pipeline.WHEN_NEW)
tree_steps_pipeline = pipeline.Pipeline(
[sample_data_step, set_number_features_step])
feature_params_step = matrix_features.AxisAlignedParamsStep_f32i32(
set_number_features_step.OutputBufferId, buffers.X_FLOAT_DATA)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
matrix_feature_extractor_step = matrix_features.LinearFloat32MatrixFeatureExtractorStep_f32i32(
matrix_feature, feature_ordering)
slice_ys_step = pipeline.SliceFloat32MatrixBufferStep_i32(
buffers.YS, sample_data_step.IndicesBufferId)
slice_weights_step = pipeline.SliceFloat32VectorBufferStep_i32(
sample_data_step.WeightsBufferId, sample_data_step.IndicesBufferId)
impurity_walker = regression.SumOfVarianceWalker_f32i32(
slice_weights_step.SlicedBufferId, slice_ys_step.SlicedBufferId,
dimension_of_y)
best_splitpint_step = regression.SumOfVarianceBestSplitpointsWalkingSortedStep_f32i32(
impurity_walker, matrix_feature_extractor_step.FeatureValuesBufferId,
feature_ordering)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, matrix_feature_extractor_step, slice_ys_step,
slice_weights_step, best_splitpint_step
])
split_buffers = splitpoints.SplitSelectorBuffers(
best_splitpint_step.ImpurityBufferId,
best_splitpint_step.SplitpointBufferId,
best_splitpint_step.SplitpointCountsBufferId,
best_splitpint_step.ChildCountsBufferId,
best_splitpint_step.LeftYsBufferId,
best_splitpint_step.RightYsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
matrix_feature_extractor_step)
should_split_criteria = create_should_split_criteria(**kwargs)
finalizer = regression.MeanVarianceEstimatorFinalizer_f32()
split_indices = splitpoints.SplitIndices_f32i32(
sample_data_step.IndicesBufferId)
split_selector = splitpoints.SplitSelector_f32i32([split_buffers],
should_split_criteria,
finalizer, split_indices)
tree_learner = learn.BreadthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector, number_of_leaves)
forest_learner = learn.ParallelForestLearner(tree_learner, number_of_trees,
dimension_of_y,
number_of_jobs)
return forest_learner
def create_biau2012_regression_axis_aligned_matrix_learner_32f(**kwargs):
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_leaves = int(
kwargs.get('number_of_leaves', kwargs['y'].shape[0] / 5 + 1))
number_of_features = int(
kwargs.get('number_of_features', (kwargs['x'].shape[1]) / 3 + 0.5))
# number_of_features = int( kwargs.get('number_of_features', np.sqrt(kwargs['x'].shape[1])))
feature_ordering = int(
kwargs.get('feature_ordering', pipeline.FEATURES_BY_DATAPOINTS))
number_of_jobs = int(kwargs.get('number_of_jobs', 1))
dimension_of_y = int(kwargs['y'].shape[1])
probability_of_impurity_stream = float(
kwargs.get('probability_of_impurity_stream', 0.5))
try_split_criteria = create_try_split_criteria(**kwargs)
sample_data_step = pipeline.AllSamplesStep_f32f32i32(buffers.X_FLOAT_DATA)
number_of_features_buffer = buffers.as_vector_buffer(
np.array([number_of_features], dtype=np.int32))
set_number_features_step = pipeline.SetInt32VectorBufferStep(
number_of_features_buffer, pipeline.WHEN_NEW)
assert (
np.max(np.abs(kwargs['x'])) <= 1.00
) # double check that the data has been scaled into a -1,1 hypercube
feature_range_buffer = buffers.as_vector_buffer(
np.array([-1, 1], dtype=np.float32))
set_feature_range_buffer_step = pipeline.SetFloat32VectorBufferStep(
feature_range_buffer, pipeline.WHEN_NEW)
assign_stream_step = splitpoints.AssignStreamStep_f32i32(
sample_data_step.WeightsBufferId, probability_of_impurity_stream,
False)
forest_steps_pipeline = pipeline.Pipeline([
sample_data_step, set_number_features_step,
set_feature_range_buffer_step, assign_stream_step
])
tree_steps_pipeline = pipeline.Pipeline([])
feature_params_step = matrix_features.AxisAlignedParamsStep_f32i32(
set_number_features_step.OutputBufferId, buffers.X_FLOAT_DATA)
matrix_feature = matrix_features.LinearFloat32MatrixFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.X_FLOAT_DATA)
matrix_feature_extractor_step = matrix_features.LinearFloat32MatrixFeatureExtractorStep_f32i32(
matrix_feature, feature_ordering)
slice_ys_step = pipeline.SliceFloat32MatrixBufferStep_i32(
buffers.YS, sample_data_step.IndicesBufferId)
slice_weights_step = pipeline.SliceFloat32VectorBufferStep_i32(
sample_data_step.WeightsBufferId, sample_data_step.IndicesBufferId)
slice_assign_stream_step = pipeline.SliceInt32VectorBufferStep_i32(
assign_stream_step.StreamTypeBufferId,
sample_data_step.IndicesBufferId)
quantized_feature_equal = pipeline.FeatureEqualQuantized_f32i32(1.0)
midpoint_step = splitpoints.RangeMidpointStep_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
set_feature_range_buffer_step.OutputBufferId, quantized_feature_equal)
mean_variance_stats_updater = regression.MeanVarianceStatsUpdater_f32i32(
slice_weights_step.SlicedBufferId, slice_ys_step.SlicedBufferId,
dimension_of_y)
two_stream_split_stats_step = regression.SumOfVarianceTwoStreamStep_f32i32(
midpoint_step.SplitpointsBufferId,
midpoint_step.SplitpointsCountsBufferId,
slice_assign_stream_step.SlicedBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
mean_variance_stats_updater)
impurity_step = regression.SumOfVarianceSplitpointsImpurity_f32i32(
midpoint_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsImpurityBufferId,
two_stream_split_stats_step.LeftImpurityStatsBufferId,
two_stream_split_stats_step.RightImpurityStatsBufferId)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, matrix_feature_extractor_step, slice_ys_step,
slice_weights_step, slice_assign_stream_step, midpoint_step,
two_stream_split_stats_step, impurity_step
])
split_buffers = splitpoints.SplitSelectorBuffers(
impurity_step.ImpurityBufferId, midpoint_step.SplitpointsBufferId,
midpoint_step.SplitpointsCountsBufferId,
two_stream_split_stats_step.ChildCountsEstimatorBufferId,
two_stream_split_stats_step.LeftEstimatorStatsBufferId,
two_stream_split_stats_step.RightEstimatorStatsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
matrix_feature_extractor_step.FeatureValuesBufferId, feature_ordering,
matrix_feature_extractor_step)
should_split_criteria = no_split_criteria(**kwargs)
finalizer = regression.MeanVarianceEstimatorFinalizer_f32()
split_indices = splitpoints.SplitIndices_f32i32(
sample_data_step.IndicesBufferId)
split_midpoint_ranges = splitpoints.SplitBuffersFeatureRange_f32i32(
midpoint_step.PastFloatParamsBufferId,
midpoint_step.PastIntParamsBufferId, midpoint_step.PastRangesBufferId,
set_feature_range_buffer_step.OutputBufferId, quantized_feature_equal)
split_steps = splitpoints.SplitBuffersList(
[split_indices, split_midpoint_ranges])
split_selector = splitpoints.SplitSelector_f32i32([split_buffers],
should_split_criteria,
finalizer, split_steps)
tree_learner = learn.BreadthFirstTreeLearner_f32i32(
try_split_criteria, tree_steps_pipeline, node_steps_pipeline,
split_selector, number_of_leaves)
forest_learner = learn.ParallelForestLearner(tree_learner,
forest_steps_pipeline,
number_of_trees,
dimension_of_y,
number_of_jobs)
return forest_learner