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minjk-bl
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Fix plot_permutation_importance for Regression and Classification
1 parent 46df174 commit 825a80a

3 files changed

Lines changed: 194 additions & 10 deletions

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‎visualpython/js/com/component/ModelEditor.js‎

Lines changed: 94 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -510,7 +510,15 @@ define([
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['input'], usePair: true },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_root_mean_squared_error',
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'neg_mean_squared_log_error', 'neg_median_absolute_error', 'r2', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance',
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'neg_mean_absolute_percentage_error',
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'accuracy', 'balanced_accuracy', 'top_k_accuracy', 'average_precision', 'neg_brier_score',
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'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', 'jaccard',
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'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
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]
@@ -524,7 +532,15 @@ define([
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['input'], usePair: true },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_root_mean_squared_error',
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'neg_mean_squared_log_error', 'neg_median_absolute_error', 'r2', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance',
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'neg_mean_absolute_percentage_error',
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'accuracy', 'balanced_accuracy', 'top_k_accuracy', 'average_precision', 'neg_brier_score',
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'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', 'jaccard',
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'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'top_count', label: 'Top count', component: ['input_number'], min: 0, usePair: true }
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]
@@ -668,7 +684,44 @@ define([
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{ name: 'cvs_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
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]
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},
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'permutation_importance': defaultInfos['permutation_importance'],
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'permutation_importance': {
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name: 'permutation_importance',
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label: 'Permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: '${importance_allocate} = vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_root_mean_squared_error',
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'neg_mean_squared_log_error', 'neg_median_absolute_error', 'r2', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance',
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'neg_mean_absolute_percentage_error'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
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]
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},
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'plot_permutation_importance': {
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name: 'plot_permutation_importance',
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label: 'Plot permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: 'vp_plot_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort}${top_count})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_root_mean_squared_error',
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'neg_mean_squared_log_error', 'neg_median_absolute_error', 'r2', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance',
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'neg_mean_absolute_percentage_error'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'top_count', label: 'Top count', component: ['input_number'], min: 0, usePair: true }
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]
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},
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'Coefficient': {
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name: 'coef_',
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label: 'Coefficient',
@@ -754,7 +807,44 @@ define([
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{ name: 'auc_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_test' }
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]
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},
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'permutation_importance': defaultInfos['permutation_importance']
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'permutation_importance': {
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name: 'permutation_importance',
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label: 'Permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: '${importance_allocate} = vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'accuracy', 'balanced_accuracy', 'top_k_accuracy', 'average_precision', 'neg_brier_score',
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'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', 'jaccard',
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'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
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]
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},
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'plot_permutation_importance': {
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name: 'plot_permutation_importance',
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label: 'Plot permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: 'vp_plot_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort}${top_count})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['option_suggest'], usePair: true, type: 'text',
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options: [
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'accuracy', 'balanced_accuracy', 'top_k_accuracy', 'average_precision', 'neg_brier_score',
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'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', 'jaccard',
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'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'
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] },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'top_count', label: 'Top count', component: ['input_number'], min: 0, usePair: true }
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]
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},
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}
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// feature importances

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