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ada_boost.py
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ada_boost.py
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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import numpy as np
from ..common._apply_operation import (
apply_add, apply_cast, apply_concat, apply_div, apply_exp, apply_mul,
apply_reshape, apply_sub, apply_topk, apply_transpose)
from ..common.data_types import Int64TensorType
from ..common._registration import register_converter
from ..common.tree_ensemble import add_tree_to_attribute_pairs
from ..common.tree_ensemble import get_default_tree_classifier_attribute_pairs
from ..common.tree_ensemble import get_default_tree_regressor_attribute_pairs
from ..proto import onnx_proto
def _samme_proba(scope, container, proba_name, n_classes):
clipped_proba_name = scope.get_unique_variable_name('clipped_proba')
log_proba_name = scope.get_unique_variable_name('log_proba')
reduced_proba_name = scope.get_unique_variable_name('reduced_proba')
reshaped_result_name = scope.get_unique_variable_name('reshaped_result')
inverted_n_classes_name = scope.get_unique_variable_name(
'inverted_n_classes')
n_classes_minus_one_name = scope.get_unique_variable_name(
'n_classes_minus_one')
prod_result_name = scope.get_unique_variable_name('prod_result')
sub_result_name = scope.get_unique_variable_name('sub_result')
samme_proba_name = scope.get_unique_variable_name('samme_proba')
container.add_initializer(
inverted_n_classes_name, onnx_proto.TensorProto.FLOAT,
[], [1. / n_classes])
container.add_initializer(
n_classes_minus_one_name, onnx_proto.TensorProto.FLOAT,
[], [n_classes - 1])
container.add_node(
'Clip', proba_name, clipped_proba_name,
name=scope.get_unique_operator_name('Clip'),
min=np.finfo(float).eps)
container.add_node(
'Log', clipped_proba_name, log_proba_name,
name=scope.get_unique_operator_name('Log'))
container.add_node(
'ReduceSum', log_proba_name, reduced_proba_name, axes=[1],
name=scope.get_unique_operator_name('ReduceSum'))
apply_reshape(scope, reduced_proba_name,
reshaped_result_name, container,
desired_shape=(-1, 1))
apply_mul(scope, [reshaped_result_name, inverted_n_classes_name],
prod_result_name, container, broadcast=1)
apply_sub(scope, [log_proba_name, prod_result_name],
sub_result_name, container, broadcast=1)
apply_mul(scope, [sub_result_name, n_classes_minus_one_name],
samme_proba_name, container, broadcast=1)
return samme_proba_name
def _normalise_probability(scope, container, operator, proba_names_list,
model):
est_weights_sum_name = scope.get_unique_variable_name('est_weights_sum')
summation_prob_name = scope.get_unique_variable_name('summation_prob')
div_result_name = scope.get_unique_variable_name('div_result')
exp_operand_name = scope.get_unique_variable_name('exp_operand')
exp_result_name = scope.get_unique_variable_name('exp_result')
reduced_exp_result_name = scope.get_unique_variable_name(
'reduced_exp_result')
normaliser_name = scope.get_unique_variable_name('normaliser')
zero_scalar_name = scope.get_unique_variable_name('zero_scalar')
comparison_result_name = scope.get_unique_variable_name(
'comparison_result')
cast_output_name = scope.get_unique_variable_name('cast_output')
zero_filtered_normaliser_name = scope.get_unique_variable_name(
'zero_filtered_normaliser')
mul_operand_name = scope.get_unique_variable_name('mul_operand')
cast_normaliser_name = scope.get_unique_variable_name('cast_normaliser')
container.add_initializer(
est_weights_sum_name, onnx_proto.TensorProto.FLOAT,
[], [model.estimator_weights_.sum()])
container.add_initializer(
mul_operand_name, onnx_proto.TensorProto.FLOAT,
[], [1. / (model.n_classes_ - 1)])
container.add_initializer(zero_scalar_name,
onnx_proto.TensorProto.INT32, [], [0])
container.add_node('Sum', proba_names_list,
summation_prob_name,
name=scope.get_unique_operator_name('Sum'))
apply_div(scope, [summation_prob_name, est_weights_sum_name],
div_result_name, container, broadcast=1)
apply_mul(scope, [div_result_name, mul_operand_name],
exp_operand_name, container, broadcast=1)
apply_exp(scope, exp_operand_name, exp_result_name, container)
container.add_node(
'ReduceSum', exp_result_name, reduced_exp_result_name, axes=[1],
name=scope.get_unique_operator_name('ReduceSum'))
apply_reshape(scope, reduced_exp_result_name,
normaliser_name, container,
desired_shape=(-1, 1))
apply_cast(scope, normaliser_name, cast_normaliser_name,
container, to=onnx_proto.TensorProto.INT32)
container.add_node('Equal', [cast_normaliser_name, zero_scalar_name],
comparison_result_name,
name=scope.get_unique_operator_name('Equal'))
apply_cast(scope, comparison_result_name, cast_output_name,
container, to=onnx_proto.TensorProto.FLOAT)
apply_add(scope, [normaliser_name, cast_output_name],
zero_filtered_normaliser_name,
container, broadcast=0)
apply_div(scope, [exp_result_name, zero_filtered_normaliser_name],
operator.outputs[1].full_name, container, broadcast=1)
return operator.outputs[1].full_name
def convert_sklearn_ada_boost_classifier(scope, operator, container):
"""
Converter for AdaBoost classifier.
This function goes through the list of estimators and uses
TreeEnsembleClassifer op to calculate class probabilities
for each estimator. Then it calculates the weighted sum
across all the estimators depending on the algorithm
picked during trainging (SAMME.R or SAMME) and normalises
the probability score for the final result. Label is
calculated by simply doing an argmax of the probability scores.
"""
op = operator.raw_operator
op_type = 'TreeEnsembleClassifier'
classes = op.classes_
class_type = onnx_proto.TensorProto.STRING
if np.issubdtype(classes.dtype, np.floating):
class_type = onnx_proto.TensorProto.INT32
classes = classes.astype('int')
elif np.issubdtype(classes.dtype, np.signedinteger):
class_type = onnx_proto.TensorProto.INT32
else:
classes = np.array([s.encode('utf-8') for s in classes])
argmax_output_name = scope.get_unique_variable_name('argmax_output')
array_feature_extractor_result_name = scope.get_unique_variable_name(
'array_feature_extractor_result')
classes_name = scope.get_unique_variable_name('classes')
container.add_initializer(classes_name, class_type, classes.shape, classes)
proba_names_list = []
for tree_id in range(len(op.estimators_)):
attrs = get_default_tree_classifier_attribute_pairs()
label_name = scope.get_unique_variable_name('label')
proba_name = scope.get_unique_variable_name('proba')
attrs['name'] = scope.get_unique_operator_name(op_type)
if class_type == onnx_proto.TensorProto.INT32:
attrs['classlabels_int64s'] = classes
else:
attrs['classlabels_strings'] = classes
add_tree_to_attribute_pairs(attrs, True, op.estimators_[tree_id].tree_,
0, 1, 0, True)
container.add_node(
op_type, operator.input_full_names,
[label_name, proba_name],
op_domain='ai.onnx.ml', **attrs)
if op.algorithm == 'SAMME.R':
cur_proba_name = _samme_proba(scope, container, proba_name,
op.n_classes_)
else: # SAMME
weight_name = scope.get_unique_variable_name('weight')
samme_proba_name = scope.get_unique_variable_name('samme_proba')
container.add_initializer(
weight_name, onnx_proto.TensorProto.FLOAT,
[], [op.estimator_weights_[tree_id]])
apply_mul(scope, [proba_name, weight_name],
samme_proba_name, container, broadcast=1)
cur_proba_name = samme_proba_name
proba_names_list.append(cur_proba_name)
class_prob_name = _normalise_probability(scope, container, operator,
proba_names_list, op)
container.add_node('ArgMax', class_prob_name,
argmax_output_name,
name=scope.get_unique_operator_name('ArgMax'), axis=1)
container.add_node(
'ArrayFeatureExtractor', [classes_name, argmax_output_name],
array_feature_extractor_result_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
if class_type == onnx_proto.TensorProto.INT32:
reshaped_result_name = scope.get_unique_variable_name(
'reshaped_result')
apply_reshape(scope, array_feature_extractor_result_name,
reshaped_result_name, container,
desired_shape=(-1,))
apply_cast(scope, reshaped_result_name, operator.outputs[0].full_name,
container, to=onnx_proto.TensorProto.INT64)
else:
apply_reshape(scope, array_feature_extractor_result_name,
operator.outputs[0].full_name, container,
desired_shape=(-1,))
def _get_estimators_label(scope, operator, container, model):
"""
This function computes labels for each estimator and returns
a tensor produced by concatenating the labels.
"""
op_type = 'TreeEnsembleRegressor'
concatenated_labels_name = scope.get_unique_variable_name(
'concatenated_labels')
input_name = operator.input_full_names
if type(operator.inputs[0].type) == Int64TensorType:
cast_input_name = scope.get_unique_variable_name('cast_input')
apply_cast(scope, operator.input_full_names, cast_input_name,
container, to=onnx_proto.TensorProto.FLOAT)
input_name = cast_input_name
estimators_results_list = []
for tree_id in range(len(model.estimators_)):
estimator_label_name = scope.get_unique_variable_name(
'estimator_label')
attrs = get_default_tree_regressor_attribute_pairs()
attrs['name'] = scope.get_unique_operator_name(op_type)
attrs['n_targets'] = int(model.estimators_[tree_id].n_outputs_)
add_tree_to_attribute_pairs(attrs, False,
model.estimators_[tree_id].tree_,
0, 1, 0, False)
container.add_node(op_type, input_name,
estimator_label_name, op_domain='ai.onnx.ml',
**attrs)
estimators_results_list.append(estimator_label_name)
apply_concat(scope, estimators_results_list, concatenated_labels_name,
container, axis=1)
return concatenated_labels_name
def cum_sum(scope, container, rnn_input_name, sequence_length):
transposed_input_name = scope.get_unique_variable_name('transposed_input')
reshaped_result_name = scope.get_unique_variable_name('reshaped_result')
weights_name = scope.get_unique_variable_name('weights')
rec_weights_name = scope.get_unique_variable_name('rec_weights')
rnn_output_name = scope.get_unique_variable_name('rnn_output')
permuted_rnn_y_name = scope.get_unique_variable_name('permuted_rnn_y')
weights_cdf_name = scope.get_unique_variable_name('weights_cdf')
container.add_initializer(weights_name,
onnx_proto.TensorProto.FLOAT, [1, 1, 1], [1])
container.add_initializer(rec_weights_name,
onnx_proto.TensorProto.FLOAT, [1, 1, 1], [1])
apply_transpose(scope, rnn_input_name, transposed_input_name,
container, perm=(1, 0))
apply_reshape(scope, transposed_input_name, reshaped_result_name,
container, desired_shape=(sequence_length, -1, 1))
container.add_node(
'RNN', inputs=[reshaped_result_name, weights_name, rec_weights_name],
outputs=[rnn_output_name], activations=['Affine'],
name=scope.get_unique_operator_name('RNN'),
activation_alpha=[1.0], activation_beta=[0.0], hidden_size=1)
apply_transpose(scope, rnn_output_name, permuted_rnn_y_name, container,
perm=(2, 0, 1, 3))
apply_reshape(
scope, permuted_rnn_y_name, weights_cdf_name, container,
desired_shape=(-1, sequence_length))
return weights_cdf_name
def convert_sklearn_ada_boost_regressor(scope, operator, container):
"""
Converter for AdaBoost regressor.
This function first calls _get_estimators_label() which returns a
tensor of concatenated labels predicted by each estimator. Then,
median is calculated and returned as the final output.
Note: This function creates an ONNX model which can predict on only
one instance at a time because ArrayFeatureExtractor can only
extract based on the last axis, so we can't fetch different columns
for different rows.
"""
op = operator.raw_operator
negate_name = scope.get_unique_variable_name('negate')
estimators_weights_name = scope.get_unique_variable_name(
'estimators_weights')
half_scalar_name = scope.get_unique_variable_name('half_scalar')
last_index_name = scope.get_unique_variable_name('last_index')
negated_labels_name = scope.get_unique_variable_name('negated_labels')
sorted_values_name = scope.get_unique_variable_name('sorted_values')
sorted_indices_name = scope.get_unique_variable_name('sorted_indices')
array_feat_extractor_output_name = scope.get_unique_variable_name(
'array_feat_extractor_output')
median_value_name = scope.get_unique_variable_name('median_value')
comp_value_name = scope.get_unique_variable_name('comp_value')
median_or_above_name = scope.get_unique_variable_name('median_or_above')
median_idx_name = scope.get_unique_variable_name('median_idx')
cast_result_name = scope.get_unique_variable_name('cast_result')
reshaped_weights_name = scope.get_unique_variable_name('reshaped_weights')
median_estimators_name = scope.get_unique_variable_name(
'median_estimators')
container.add_initializer(negate_name, onnx_proto.TensorProto.FLOAT,
[], [-1])
container.add_initializer(estimators_weights_name,
onnx_proto.TensorProto.FLOAT,
[len(op.estimator_weights_)],
op.estimator_weights_)
container.add_initializer(half_scalar_name, onnx_proto.TensorProto.FLOAT,
[], [0.5])
container.add_initializer(last_index_name, onnx_proto.TensorProto.INT64,
[], [len(op.estimators_) - 1])
concatenated_labels = _get_estimators_label(scope, operator,
container, op)
apply_mul(scope, [concatenated_labels, negate_name],
negated_labels_name, container, broadcast=1)
apply_topk(scope, negated_labels_name,
[sorted_values_name, sorted_indices_name],
container, k=len(op.estimators_))
container.add_node(
'ArrayFeatureExtractor',
[estimators_weights_name, sorted_indices_name],
array_feat_extractor_output_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
apply_reshape(
scope, array_feat_extractor_output_name, reshaped_weights_name,
container, desired_shape=(-1, len(op.estimators_)))
weights_cdf_name = cum_sum(
scope, container, reshaped_weights_name,
len(op.estimators_))
container.add_node(
'ArrayFeatureExtractor', [weights_cdf_name, last_index_name],
median_value_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
apply_mul(scope, [median_value_name, half_scalar_name],
comp_value_name, container, broadcast=1)
container.add_node(
'Less', [weights_cdf_name, comp_value_name],
median_or_above_name,
name=scope.get_unique_operator_name('Less'))
apply_cast(scope, median_or_above_name, cast_result_name,
container, to=onnx_proto.TensorProto.FLOAT)
container.add_node('ArgMin', cast_result_name,
median_idx_name,
name=scope.get_unique_operator_name('ArgMin'), axis=1)
container.add_node(
'ArrayFeatureExtractor', [sorted_indices_name, median_idx_name],
median_estimators_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
container.add_node(
'ArrayFeatureExtractor', [concatenated_labels, median_estimators_name],
operator.output_full_names, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
register_converter('SklearnAdaBoostClassifier',
convert_sklearn_ada_boost_classifier)
register_converter('SklearnAdaBoostRegressor',
convert_sklearn_ada_boost_regressor)