.. _l-ExtraTreeClassifier-~m-label-default-zipmap:False-o17: ExtraTreeClassifier - ~m-label - default - {'zipmap': False} ============================================================ Fitted on a problem type *~m-label* (see :func:`find_suitable_problem `), method `predict_proba` matches output . Model was converted with additional parameter: ``={'zipmap': False}``. :: ExtraTreeClassifier(random_state=0) +-----------------------+----------+ | index | 0 | +=======================+==========+ | skl_nop | 1 | +-----------------------+----------+ | skl_nnodes | 127 | +-----------------------+----------+ | skl_ntrees | 1 | +-----------------------+----------+ | skl_max_depth | 13 | +-----------------------+----------+ | onx_size | 10411 | +-----------------------+----------+ | onx_nnodes | 23 | +-----------------------+----------+ | onx_ninits | 7 | +-----------------------+----------+ | 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_ai.onnx.ml | 3 | +-----------------------+----------+ | onx_ | 17 | +-----------------------+----------+ | onx_op_Cast | 2 | +-----------------------+----------+ | onx_op_Reshape | 7 | +-----------------------+----------+ | onx_size_optim | 10411 | +-----------------------+----------+ | onx_nnodes_optim | 23 | +-----------------------+----------+ | onx_ninits_optim | 7 | +-----------------------+----------+ | fit_n_classes_.shape | 3 | +-----------------------+----------+ | fit_n_classes_ | [2 2 2] | +-----------------------+----------+ | fit_tree_.node_count | 127 | +-----------------------+----------+ | fit_tree_.leave_count | 64 | +-----------------------+----------+ | fit_tree_.max_depth | 13 | +-----------------------+----------+ .. 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, 3))" fontsize=10]; probabilities [shape=box color=green label="probabilities\nfloat((3, 0, 2))" fontsize=10]; values [shape=box label="values\nfloat32((3, 2, 127))\n[[[69. 8. 8. 7. 0. 0. 0. 0. 0. 0. 0. 0...." fontsize=10]; shape_tensor [shape=box label="shape_tensor\nint64((2,))\n[ 1 -1]" fontsize=10]; k_column [shape=box label="k_column\nint64(())\n0" fontsize=10]; classes [shape=box label="classes\nint64((2,))\n[0 1]" fontsize=10]; shape_tensor2 [shape=box label="shape_tensor2\nint64((2,))\n[-1 1]" fontsize=10]; k_column1 [shape=box label="k_column1\nint64(())\n1" fontsize=10]; k_column2 [shape=box label="k_column2\nint64(())\n2" fontsize=10]; indices [shape=box label="indices" fontsize=10]; dummy_proba [shape=box label="dummy_proba" fontsize=10]; TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nclass_ids=[ 5 7 9 11 13 ...\nclass_nodeids=[ 5 7 9 11 ...\nclass_treeids=[0 0 0 0 0 0 0 0 ...\nclass_weights=[1. 1. 1. 1. 1. 1...\nclasslabels_int64s=[ 0 1 2...\nnodes_falsenodeids=[ 32 23 22...\nnodes_featureids=[0 1 3 2 1 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[5.384941 3.13197...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier; TreeEnsembleClassifier -> indices; TreeEnsembleClassifier -> dummy_proba; reshaped_indices [shape=box label="reshaped_indices" fontsize=10]; Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape)" fontsize=10]; indices -> Reshape; shape_tensor -> Reshape; Reshape -> reshaped_indices; out_indices [shape=box label="out_indices" fontsize=10]; ArrayFeatureExtractor [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor)" fontsize=10]; values -> ArrayFeatureExtractor; reshaped_indices -> ArrayFeatureExtractor; ArrayFeatureExtractor -> out_indices; proba_output [shape=box label="proba_output" fontsize=10]; Transpose [shape=box style="filled,rounded" color=orange label="Transpose\n(Transpose)\nperm=[0 2 1]" fontsize=10]; out_indices -> Transpose; Transpose -> proba_output; transposed_result [shape=box label="transposed_result" fontsize=10]; Transpose1 [shape=box style="filled,rounded" color=orange label="Transpose\n(Transpose1)\nperm=[2 1 0]" fontsize=10]; out_indices -> Transpose1; Transpose1 -> transposed_result; out_k_column2 [shape=box label="out_k_column2" fontsize=10]; ArrayFeatureExtractor5 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor5)" fontsize=10]; transposed_result -> ArrayFeatureExtractor5; k_column2 -> ArrayFeatureExtractor5; ArrayFeatureExtractor5 -> out_k_column2; cast_result [shape=box label="cast_result" fontsize=10]; Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=9" fontsize=10]; proba_output -> Cast; Cast -> cast_result; out_k_column1 [shape=box label="out_k_column1" fontsize=10]; ArrayFeatureExtractor3 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor3)" fontsize=10]; transposed_result -> ArrayFeatureExtractor3; k_column1 -> ArrayFeatureExtractor3; ArrayFeatureExtractor3 -> out_k_column1; out_k_column [shape=box label="out_k_column" fontsize=10]; ArrayFeatureExtractor1 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor1)" fontsize=10]; transposed_result -> ArrayFeatureExtractor1; k_column -> ArrayFeatureExtractor1; ArrayFeatureExtractor1 -> out_k_column; argmax_output2 [shape=box label="argmax_output2" fontsize=10]; ArgMax2 [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax2)\naxis=1" fontsize=10]; out_k_column2 -> ArgMax2; ArgMax2 -> argmax_output2; argmax_output1 [shape=box label="argmax_output1" fontsize=10]; ArgMax1 [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax1)\naxis=1" fontsize=10]; out_k_column1 -> ArgMax1; ArgMax1 -> argmax_output1; Cast1 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast1)\nto=1" fontsize=10]; cast_result -> Cast1; Cast1 -> 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]; out_k_column -> ArgMax; ArgMax -> argmax_output; reshaped_result2 [shape=box label="reshaped_result2" fontsize=10]; Reshape5 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape5)" fontsize=10]; argmax_output2 -> Reshape5; shape_tensor -> Reshape5; Reshape5 -> reshaped_result2; reshaped_result1 [shape=box label="reshaped_result1" fontsize=10]; Reshape3 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape3)" fontsize=10]; argmax_output1 -> Reshape3; shape_tensor -> Reshape3; Reshape3 -> reshaped_result1; reshaped_result [shape=box label="reshaped_result" fontsize=10]; Reshape1 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape1)" fontsize=10]; argmax_output -> Reshape1; shape_tensor -> Reshape1; Reshape1 -> reshaped_result; preds1 [shape=box label="preds1" fontsize=10]; ArrayFeatureExtractor4 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor4)" fontsize=10]; classes -> ArrayFeatureExtractor4; reshaped_result1 -> ArrayFeatureExtractor4; ArrayFeatureExtractor4 -> preds1; preds2 [shape=box label="preds2" fontsize=10]; ArrayFeatureExtractor6 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor6)" fontsize=10]; classes -> ArrayFeatureExtractor6; reshaped_result2 -> ArrayFeatureExtractor6; ArrayFeatureExtractor6 -> preds2; preds [shape=box label="preds" fontsize=10]; ArrayFeatureExtractor2 [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor2)" fontsize=10]; classes -> ArrayFeatureExtractor2; reshaped_result -> ArrayFeatureExtractor2; ArrayFeatureExtractor2 -> preds; reshaped_preds1 [shape=box label="reshaped_preds1" fontsize=10]; Reshape4 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape4)" fontsize=10]; preds1 -> Reshape4; shape_tensor2 -> Reshape4; Reshape4 -> reshaped_preds1; reshaped_preds2 [shape=box label="reshaped_preds2" fontsize=10]; Reshape6 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape6)" fontsize=10]; preds2 -> Reshape6; shape_tensor2 -> Reshape6; Reshape6 -> reshaped_preds2; reshaped_preds [shape=box label="reshaped_preds" fontsize=10]; Reshape2 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape2)" fontsize=10]; preds -> Reshape2; shape_tensor2 -> Reshape2; Reshape2 -> reshaped_preds; Concat [shape=box style="filled,rounded" color=orange label="Concat\n(Concat)\naxis=1" fontsize=10]; reshaped_preds -> Concat; reshaped_preds1 -> Concat; reshaped_preds2 -> Concat; Concat -> label; }