def create_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())
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_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering)
node_steps_pipeline = pipeline.Pipeline([
feature_params_step, depth_delta_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,
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
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_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))
try_split_criteria = create_try_split_criteria(**kwargs)
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)
feature_range_buffer = buffers.as_vector_buffer(
np.array([-6, 6], 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 = 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_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,
depth_delta_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, depth_delta_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,
depth_delta_feature_extractor_step.FeatureValuesBufferId,
feature_ordering, depth_delta_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
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