def create_online_scaled_depth_delta_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', 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(kwargs['classes'].GetMax() + 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))
ux = float(kwargs.get('ux'))
uy = float(kwargs.get('uy'))
vx = float(kwargs.get('vx'))
vy = float(kwargs.get('vy'))
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
elif 'poisson_sample' in kwargs:
poisson_sample_mean = float(kwargs.get('poisson_sample'))
sample_data_step = pipeline.PoissonSamplesStep_i32i32(
buffers.PIXEL_INDICES, poisson_sample_mean)
else:
sample_data_step = pipeline.AllSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
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 = image_features.PixelPairGaussianOffsetsStep_f32i32(
set_number_features_step.OutputBufferId, ux, uy, vx, vy)
init_node_steps_pipeline = pipeline.Pipeline(
[set_number_features_step, feature_params_step])
# On update
depth_delta_feature = image_features.ScaledDepthDeltaFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES, buffers.OFFSET_SCALES)
depth_delta_feature_extractor_step = image_features.ScaledDepthDeltaFeatureExtractorStep_f32i32(
depth_delta_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(
depth_delta_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, class_stats_updater)
update_stats_node_steps_pipeline = pipeline.Pipeline([
depth_delta_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, depth_delta_feature_extractor_step)
should_split_criteria = create_should_split_consistent_criteria(**kwargs)
finalizer = classification.ClassEstimatorFinalizer_f32()
split_selector = splitpoints.WaitForBestSplitSelector_f32i32(
[split_buffers], should_split_criteria, finalizer)
feature_prediction = image_features.ScaledDepthDeltaFeature_f32i32(
sample_data_step.IndicesBufferId, buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES)
estimator_params_updater = classification.ClassEstimatorUpdater_f32i32(
sample_data_step.WeightsBufferId, buffers.CLASS_LABELS,
number_of_classes)
forest_learner = learn.OnlineForestScaledDepthDeltaClassLearner_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,
feature_prediction, estimator_params_updater)
return forest_learner
def create_scaled_depth_delta_learner_32f(**kwargs):
ux = float(kwargs.get('ux'))
uy = float(kwargs.get('uy'))
vx = float(kwargs.get('vx'))
vy = float(kwargs.get('vy'))
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_features = int(kwargs.get('number_of_features', 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(kwargs['classes'].GetMax() + 1)
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
else:
sample_data_step = pipeline.AllSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
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 = image_features.PixelPairGaussianOffsetsStep_f32i32(
set_number_features_step.OutputBufferId, ux, uy, vx, vy)
depth_delta_feature = image_features.ScaledDepthDeltaFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES, buffers.OFFSET_SCALES)
depth_delta_feature_extractor_step = image_features.ScaledDepthDeltaFeatureExtractorStep_f32i32(
depth_delta_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, depth_delta_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, depth_delta_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)
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_consistent_two_stream_regression_scaled_depth_delta_learner_32f(
**kwargs):
ux = float(kwargs.get('ux'))
uy = float(kwargs.get('uy'))
vx = float(kwargs.get('vx'))
vy = float(kwargs.get('vy'))
number_of_trees = int(kwargs.get('number_of_trees', 10))
number_of_leaves = int(
kwargs.get('number_of_leaves', kwargs['y'].GetM() / 5 + 1))
number_of_features = int(kwargs.get('number_of_features', 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'].GetN())
probability_of_impurity_stream = float(
kwargs.get('probability_of_impurity_stream', 0.5))
in_bounds_number_of_points = int(
kwargs.get('in_bounds_number_of_points', kwargs['y'].GetM() / 2))
try_split_criteria = create_try_split_criteria(**kwargs)
if 'bootstrap' in kwargs and kwargs.get('bootstrap'):
sample_data_step = pipeline.BootstrapSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
else:
sample_data_step = pipeline.AllSamplesStep_i32f32i32(
buffers.PIXEL_INDICES)
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])
# if 'poisson_number_of_features' in kwargs and kwargs.get('poisson_number_of_features'):
# set_number_features_step = pipeline.PoissonStep_f32i32(number_of_features, 1)
# else:
# 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)
set_number_features_step = pipeline.PoissonStep_f32i32(
number_of_features, 1)
feature_params_step = image_features.PixelPairGaussianOffsetsStep_f32i32(
set_number_features_step.OutputBufferId, ux, uy, vx, vy)
depth_delta_feature = image_features.ScaledDepthDeltaFeature_f32i32(
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
sample_data_step.IndicesBufferId, buffers.PIXEL_INDICES,
buffers.DEPTH_IMAGES, buffers.OFFSET_SCALES)
depth_delta_feature_extractor_step = image_features.ScaledDepthDeltaFeatureExtractorStep_f32i32(
depth_delta_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_stream_step = pipeline.SliceInt32VectorBufferStep_i32(
assign_stream_step.StreamTypeBufferId,
sample_data_step.IndicesBufferId)
impurity_walker = regression.SumOfVarianceTwoStreamWalker_f32i32(
slice_weights_step.SlicedBufferId, slice_stream_step.SlicedBufferId,
slice_ys_step.SlicedBufferId, dimension_of_y)
best_splitpint_step = regression.SumOfVarianceTwoStreamBestSplitpointsWalkingSortedStep_f32i32(
impurity_walker, slice_stream_step.SlicedBufferId,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, in_bounds_number_of_points)
node_steps_pipeline = pipeline.Pipeline([
set_number_features_step, feature_params_step,
depth_delta_feature_extractor_step, slice_ys_step, slice_weights_step,
slice_stream_step, best_splitpint_step
])
split_buffers = splitpoints.SplitSelectorBuffers(
best_splitpint_step.ImpurityBufferId,
best_splitpint_step.SplitpointBufferId,
best_splitpint_step.SplitpointCountsBufferId,
best_splitpint_step.ChildCountsEstimationBufferId,
best_splitpint_step.LeftEstimationYsBufferId,
best_splitpint_step.RightEstimationYsBufferId,
feature_params_step.FloatParamsBufferId,
feature_params_step.IntParamsBufferId,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, depth_delta_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