.. _l-StackingClassifier-b-cl-logreg-zipmap:False-o17: StackingClassifier - b-cl - logreg - {'zipmap': False} ====================================================== Fitted on a problem type *b-cl* (see :func:`find_suitable_problem `), method `predict_proba` matches output . Model was converted with additional parameter: ``={'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 | 1.13.1 | +----------------------------------+----------+ | onx_ | 16 | +----------------------------------+----------+ | 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_.n_iter_.shape | 1 | +----------------------------------+----------+ | fit_estimators_.coef_.shape | (1, 4) | +----------------------------------+----------+ | fit_estimators_.classes_.shape | 2 | +----------------------------------+----------+ .. gdot:: digraph{ size=7; ranksep=0.25; nodesep=0.05; orientation=portrait; X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10]; label [shape=box color=green label="label\nint64((0,))" fontsize=10]; probabilities [shape=box color=green label="probabilities\nfloat((0, 2))" fontsize=10]; classes [shape=box label="classes\nint32((2,))\n[0 1]" fontsize=10]; column_index [shape=box label="column_index\nint64(())\n1" fontsize=10]; shape_tensor [shape=box label="shape_tensor\nint64((1,))\n[-1]" fontsize=10]; label2 [shape=box label="label2" fontsize=10]; probability_tensor4 [shape=box label="probability_tensor4" fontsize=10]; LinearClassifier1 [shape=box style="filled,rounded" color=orange label="LinearClassifier\n(LinearClassifier1)\nclasslabels_ints=[0 1]\ncoefficients=[ 0.49765062 1.31...\nintercepts=[-0. 0.]\nmulti_class=1\npost_transform=b'LOGISTIC'" fontsize=10]; X -> LinearClassifier1; LinearClassifier1 -> label2; LinearClassifier1 -> probability_tensor4; label1 [shape=box label="label1" fontsize=10]; probability_tensor3 [shape=box label="probability_tensor3" fontsize=10]; LinearClassifier [shape=box style="filled,rounded" color=orange label="LinearClassifier\n(LinearClassifier)\nclasslabels_ints=[0 1]\ncoefficients=[ 0.45876738 1.29...\nintercepts=[ 0.28357968 -0.2835...\nmulti_class=1\npost_transform=b'LOGISTIC'" fontsize=10]; X -> LinearClassifier; LinearClassifier -> label1; LinearClassifier -> probability_tensor3; probability_tensor1 [shape=box label="probability_tensor1" fontsize=10]; Normalizer1 [shape=box style="filled,rounded" color=orange label="Normalizer\n(Normalizer1)\nnorm=b'L1'" fontsize=10]; probability_tensor4 -> Normalizer1; Normalizer1 -> probability_tensor1; probability_tensor [shape=box label="probability_tensor" fontsize=10]; Normalizer [shape=box style="filled,rounded" color=orange label="Normalizer\n(Normalizer)\nnorm=b'L1'" fontsize=10]; probability_tensor3 -> Normalizer; Normalizer -> probability_tensor; probability_tensor1_castio [shape=box label="probability_tensor1_castio" fontsize=10]; Cast1 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast1)\nto=1" fontsize=10]; probability_tensor1 -> Cast1; Cast1 -> probability_tensor1_castio; probability_tensor_castio [shape=box label="probability_tensor_castio" fontsize=10]; Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=1" fontsize=10]; probability_tensor -> Cast; Cast -> probability_tensor_castio; stack_prob0 [shape=box label="stack_prob0" fontsize=10]; ArrayFeatureExtractor [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor)" fontsize=10]; probability_tensor_castio -> ArrayFeatureExtractor; column_index -> ArrayFeatureExtractor; ArrayFeatureExtractor -> stack_prob0; stack_prob1 [shape=box label="stack_prob1" fontsize=10]; ArrayFeatureExtractor1 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor1)" fontsize=10]; probability_tensor1_castio -> ArrayFeatureExtractor1; column_index -> ArrayFeatureExtractor1; ArrayFeatureExtractor1 -> stack_prob1; merged_probability_tensor [shape=box label="merged_probability_tensor" fontsize=10]; Concat [shape=box style="filled,rounded" color=orange label="Concat\n(Concat)\naxis=1" fontsize=10]; stack_prob0 -> Concat; stack_prob1 -> Concat; Concat -> merged_probability_tensor; label3 [shape=box label="label3" fontsize=10]; probability_tensor5 [shape=box label="probability_tensor5" fontsize=10]; LinearClassifier2 [shape=box style="filled,rounded" color=orange label="LinearClassifier\n(LinearClassifier2)\nclasslabels_ints=[0 1]\ncoefficients=[-2.9424708 -2.939...\nintercepts=[ 2.631917 -2.631917...\nmulti_class=1\npost_transform=b'LOGISTIC'" fontsize=10]; merged_probability_tensor -> LinearClassifier2; LinearClassifier2 -> label3; LinearClassifier2 -> probability_tensor5; probability_tensor2 [shape=box label="probability_tensor2" fontsize=10]; Normalizer2 [shape=box style="filled,rounded" color=orange label="Normalizer\n(Normalizer2)\nnorm=b'L1'" fontsize=10]; probability_tensor5 -> Normalizer2; Normalizer2 -> probability_tensor2; probability_tensor2_castio [shape=box label="probability_tensor2_castio" fontsize=10]; Cast2 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast2)\nto=1" fontsize=10]; probability_tensor2 -> Cast2; Cast2 -> probability_tensor2_castio; OpProb [shape=box style="filled,rounded" color=orange label="Identity\n(OpProb)" fontsize=10]; probability_tensor2_castio -> OpProb; OpProb -> probabilities; argmax_output [shape=box label="argmax_output" fontsize=10]; ArgMax [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax)\naxis=1" fontsize=10]; probability_tensor2_castio -> ArgMax; ArgMax -> argmax_output; array_feature_extractor_result [shape=box label="array_feature_extractor_result" fontsize=10]; ArrayFeatureExtractor2 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor2)" fontsize=10]; classes -> ArrayFeatureExtractor2; argmax_output -> ArrayFeatureExtractor2; ArrayFeatureExtractor2 -> array_feature_extractor_result; reshaped_result [shape=box label="reshaped_result" fontsize=10]; Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape)" fontsize=10]; array_feature_extractor_result -> Reshape; shape_tensor -> Reshape; Reshape -> reshaped_result; Cast3 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast3)\nto=7" fontsize=10]; reshaped_result -> Cast3; Cast3 -> label; }