/
sgd_classifier.py
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/
sgd_classifier.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_clip, apply_concat, apply_div, apply_exp,
apply_identity, apply_mul, apply_reciprocal, apply_reshape, apply_sub)
from ..common.data_types import Int64TensorType
from ..common._registration import register_converter
from ..common.utils_classifier import get_label_classes
from ..proto import onnx_proto
def _decision_function(scope, operator, container, model):
"""Predict for linear model.
score = X * coefficient + intercept
"""
coef_name = scope.get_unique_variable_name('coef')
intercept_name = scope.get_unique_variable_name('intercept')
matmul_result_name = scope.get_unique_variable_name(
'matmul_result')
score_name = scope.get_unique_variable_name('score')
coef = model.coef_.T
container.add_initializer(coef_name, onnx_proto.TensorProto.FLOAT,
coef.shape, coef.ravel())
container.add_initializer(intercept_name, onnx_proto.TensorProto.FLOAT,
model.intercept_.shape, model.intercept_)
input_name = operator.inputs[0].full_name
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
container.add_node(
'MatMul', [input_name, coef_name],
matmul_result_name,
name=scope.get_unique_operator_name('MatMul'))
apply_add(scope, [matmul_result_name, intercept_name],
score_name, container, broadcast=0)
return score_name
def _handle_zeros(scope, container, proba, reduced_proba, num_classes):
"""Handle cases where reduced_proba values are zeros to avoid NaNs in
class probability scores because of divide by 0 when we calculate
proba / reduced_proba in _normalise_proba().
This is done by replacing reduced_proba values of 0s with
num_classes and corresponding proba values with 1.
"""
num_classes_name = scope.get_unique_variable_name('num_classes')
bool_reduced_proba_name = scope.get_unique_variable_name(
'bool_reduced_proba')
bool_not_reduced_proba_name = scope.get_unique_variable_name(
'bool_not_reduced_proba')
not_reduced_proba_name = scope.get_unique_variable_name(
'not_reduced_proba')
proba_updated_name = scope.get_unique_variable_name('proba_updated')
mask_name = scope.get_unique_variable_name('mask')
reduced_proba_updated_name = scope.get_unique_variable_name(
'reduced_proba_updated')
container.add_initializer(num_classes_name, onnx_proto.TensorProto.FLOAT,
[], [num_classes])
apply_cast(scope, reduced_proba, bool_reduced_proba_name, container,
to=onnx_proto.TensorProto.BOOL)
container.add_node('Not', bool_reduced_proba_name,
bool_not_reduced_proba_name,
name=scope.get_unique_operator_name('Not'))
apply_cast(scope, bool_not_reduced_proba_name, not_reduced_proba_name,
container, to=onnx_proto.TensorProto.FLOAT)
apply_add(scope, [proba, not_reduced_proba_name],
proba_updated_name, container, broadcast=1)
apply_mul(scope, [not_reduced_proba_name, num_classes_name],
mask_name, container, broadcast=1)
apply_add(scope, [reduced_proba, mask_name],
reduced_proba_updated_name, container, broadcast=0)
return proba_updated_name, reduced_proba_updated_name
def _normalise_proba(scope, operator, container, proba, num_classes,
unity_name):
reduced_proba_name = scope.get_unique_variable_name('reduced_proba')
sub_result_name = scope.get_unique_variable_name('sub_result')
if num_classes == 2:
apply_sub(scope, [unity_name, proba],
sub_result_name, container, broadcast=1)
apply_concat(scope, [sub_result_name, proba],
operator.outputs[1].full_name, container, axis=1)
else:
container.add_node('ReduceSum', proba,
reduced_proba_name, axes=[1],
name=scope.get_unique_operator_name('ReduceSum'))
proba_updated, reduced_proba_updated = _handle_zeros(
scope, container, proba, reduced_proba_name, num_classes)
apply_div(scope, [proba_updated, reduced_proba_updated],
operator.outputs[1].full_name, container, broadcast=1)
return operator.outputs[1].full_name
def _predict_proba_log(scope, operator, container, scores, num_classes):
"""Probability estimation for SGDClassifier with loss=log and
Logistic Regression.
Positive class probabilities are computed as
1. / (1. + exp(-scores))
multiclass is handled by normalising that over all classes.
"""
negate_name = scope.get_unique_variable_name('negate')
negated_scores_name = scope.get_unique_variable_name('negated_scores')
exp_result_name = scope.get_unique_variable_name('exp_result')
unity_name = scope.get_unique_variable_name('unity')
add_result_name = scope.get_unique_variable_name('add_result')
proba_name = scope.get_unique_variable_name('proba')
container.add_initializer(negate_name, onnx_proto.TensorProto.FLOAT,
[], [-1])
container.add_initializer(unity_name, onnx_proto.TensorProto.FLOAT,
[], [1])
apply_mul(scope, [scores, negate_name],
negated_scores_name, container, broadcast=1)
apply_exp(scope, negated_scores_name, exp_result_name, container)
apply_add(scope, [exp_result_name, unity_name],
add_result_name, container, broadcast=1)
apply_reciprocal(scope, add_result_name, proba_name, container)
return _normalise_proba(scope, operator, container, proba_name,
num_classes, unity_name)
def _predict_proba_modified_huber(scope, operator, container,
scores, num_classes):
"""Probability estimation for SGDClassifier with
loss=modified_huber.
Multiclass probability estimates are derived from binary
estimates by normalisation.
Binary probability estimates are given by
(clip(scores, -1, 1) + 1) / 2.
"""
unity_name = scope.get_unique_variable_name('unity')
constant_name = scope.get_unique_variable_name('constant')
add_result_name = scope.get_unique_variable_name('add_result')
proba_name = scope.get_unique_variable_name('proba')
clipped_scores_name = scope.get_unique_variable_name('clipped_scores')
container.add_initializer(unity_name, onnx_proto.TensorProto.FLOAT,
[], [1])
container.add_initializer(constant_name, onnx_proto.TensorProto.FLOAT,
[], [2])
apply_clip(scope, scores, clipped_scores_name, container, max=1, min=-1)
apply_add(scope, [clipped_scores_name, unity_name],
add_result_name, container, broadcast=1)
apply_div(scope, [add_result_name, constant_name],
proba_name, container, broadcast=1)
return _normalise_proba(scope, operator, container, proba_name,
num_classes, unity_name)
def convert_sklearn_sgd_classifier(scope, operator, container):
"""Converter for SGDClassifier."""
sgd_op = operator.raw_operator
classes = get_label_classes(scope, sgd_op)
class_type = onnx_proto.TensorProto.STRING
if np.issubdtype(classes.dtype, np.floating):
class_type = onnx_proto.TensorProto.INT32
classes = classes.astype(np.int32)
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])
classes_name = scope.get_unique_variable_name('classes')
predicted_label_name = scope.get_unique_variable_name(
'predicted_label')
final_label_name = scope.get_unique_variable_name('final_label')
container.add_initializer(classes_name, class_type,
classes.shape, classes)
scores = _decision_function(scope, operator, container, sgd_op)
if sgd_op.loss == 'log':
proba = _predict_proba_log(scope, operator, container, scores,
len(classes))
elif sgd_op.loss == 'modified_huber':
proba = _predict_proba_modified_huber(
scope, operator, container, scores, len(classes))
else:
if len(classes) == 2:
negate_name = scope.get_unique_variable_name('negate')
negated_scores_name = scope.get_unique_variable_name(
'negated_scores')
container.add_initializer(
negate_name, onnx_proto.TensorProto.FLOAT, [], [-1])
apply_mul(scope, [scores, negate_name],
negated_scores_name, container, broadcast=1)
apply_concat(scope, [negated_scores_name, scores],
operator.outputs[1].full_name, container, axis=1)
else:
apply_identity(scope, scores,
operator.outputs[1].full_name, container)
proba = operator.outputs[1].full_name
container.add_node('ArgMax', proba,
predicted_label_name,
name=scope.get_unique_operator_name('ArgMax'), axis=1)
container.add_node(
'ArrayFeatureExtractor', [classes_name, predicted_label_name],
final_label_name, op_domain='ai.onnx.ml',
name=scope.get_unique_operator_name('ArrayFeatureExtractor'))
if class_type == onnx_proto.TensorProto.INT32:
reshaped_final_label_name = scope.get_unique_variable_name(
'reshaped_final_label')
apply_reshape(scope, final_label_name, reshaped_final_label_name,
container, desired_shape=(-1,))
apply_cast(scope, reshaped_final_label_name,
operator.outputs[0].full_name, container,
to=onnx_proto.TensorProto.INT64)
else:
apply_reshape(scope, final_label_name,
operator.outputs[0].full_name, container,
desired_shape=(-1,))
register_converter('SklearnSGDClassifier',
convert_sklearn_sgd_classifier)