StackingClassifier - b-cl - logreg - {‘zipmap’: False}#

Fitted on a problem type b-cl (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.ensemble._stacking.StackingClassifier'>={'zipmap': False}.

StackingClassifier(estimators=[('lr1', LogisticRegression(solver='liblinear')),
                           ('lr2',
                            LogisticRegression(fit_intercept=False,
                                               solver='liblinear'))],
               n_jobs=8)

index

0

skl_nop

3

skl_ncoef

2

skl_nlin

2

onx_size

2207

onx_nnodes

17

onx_ninits

3

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

14

onx_ai.onnx.ml

1

onx_op_Cast

4

onx_op_Identity

1

onx_op_Reshape

1

onx_size_optim

2116

onx_nnodes_optim

16

onx_ninits_optim

3

fit_classes_.shape

2

fit_estimators_.size

2

fit_estimators_.intercept_.shape

1

fit_estimators_.coef_.shape

(1, 4)

fit_estimators_.n_iter_.shape

1

fit_estimators_.classes_.shape

2
%0 X X float((0, 4)) LinearClassifier LinearClassifier (LinearClassifier) classlabels_ints=[0 1] coefficients=[ 0.45876738  1.29... intercepts=[ 0.28357968 -0.2835... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier LinearClassifier1 LinearClassifier (LinearClassifier1) classlabels_ints=[0 1] coefficients=[ 0.49765062  1.31... intercepts=[-0.  0.] multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier1 label label int64((0,)) probabilities probabilities float((0, 2)) classes classes int32((2,)) [0 1] ArrayFeatureExtractor2 ArrayFeatureExtractor (ArrayFeatureExtractor2) classes->ArrayFeatureExtractor2 column_index column_index int64(()) 1 ArrayFeatureExtractor1 ArrayFeatureExtractor (ArrayFeatureExtractor1) column_index->ArrayFeatureExtractor1 ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) column_index->ArrayFeatureExtractor shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape label1 label1 probability_tensor3 probability_tensor3 Normalizer Normalizer (Normalizer) norm=b'L1' probability_tensor3->Normalizer LinearClassifier->label1 LinearClassifier->probability_tensor3 label2 label2 probability_tensor4 probability_tensor4 Normalizer1 Normalizer (Normalizer1) norm=b'L1' probability_tensor4->Normalizer1 LinearClassifier1->label2 LinearClassifier1->probability_tensor4 probability_tensor probability_tensor Cast Cast (Cast) to=1 probability_tensor->Cast Normalizer->probability_tensor probability_tensor1 probability_tensor1 Cast1 Cast (Cast1) to=1 probability_tensor1->Cast1 Normalizer1->probability_tensor1 probability_tensor_castio probability_tensor_castio probability_tensor_castio->ArrayFeatureExtractor Cast->probability_tensor_castio probability_tensor1_castio probability_tensor1_castio probability_tensor1_castio->ArrayFeatureExtractor1 Cast1->probability_tensor1_castio stack_prob1 stack_prob1 Concat Concat (Concat) axis=1 stack_prob1->Concat ArrayFeatureExtractor1->stack_prob1 stack_prob0 stack_prob0 stack_prob0->Concat ArrayFeatureExtractor->stack_prob0 merged_probability_tensor merged_probability_tensor LinearClassifier2 LinearClassifier (LinearClassifier2) classlabels_ints=[0 1] coefficients=[-2.9424708 -2.939... intercepts=[ 2.631917 -2.631917... multi_class=1 post_transform=b'LOGISTIC' merged_probability_tensor->LinearClassifier2 Concat->merged_probability_tensor label3 label3 probability_tensor5 probability_tensor5 Normalizer2 Normalizer (Normalizer2) norm=b'L1' probability_tensor5->Normalizer2 LinearClassifier2->label3 LinearClassifier2->probability_tensor5 probability_tensor2 probability_tensor2 Cast2 Cast (Cast2) to=1 probability_tensor2->Cast2 Normalizer2->probability_tensor2 probability_tensor2_castio probability_tensor2_castio OpProb Identity (OpProb) probability_tensor2_castio->OpProb ArgMax ArgMax (ArgMax) axis=1 probability_tensor2_castio->ArgMax Cast2->probability_tensor2_castio OpProb->probabilities argmax_output argmax_output argmax_output->ArrayFeatureExtractor2 ArgMax->argmax_output array_feature_extractor_result array_feature_extractor_result array_feature_extractor_result->Reshape ArrayFeatureExtractor2->array_feature_extractor_result reshaped_result reshaped_result Cast3 Cast (Cast3) to=7 reshaped_result->Cast3 Reshape->reshaped_result Cast3->label

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