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minjk-bl
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Edit permutation importances and add plot
1 parent 8001e7c commit 112792c

3 files changed

Lines changed: 67 additions & 4 deletions

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‎visualpython/js/com/component/ModelEditor.js‎

Lines changed: 16 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -479,16 +479,30 @@ define([
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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: '${importance_allocate} = permutation_importance(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${random_state}${etc})',
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code: '${importance_allocate} = vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['input'], usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
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]
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},
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'plot_permutation_importance': {
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name: 'plot_permutation_importance',
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label: 'Plot permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: 'vp_plot_permutation_importances(${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: ['input'], usePair: true },
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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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'feature_importances': {
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name: 'feature_importances',
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label: 'Feature importances',

‎visualpython/js/m_ml/ModelInfo.js‎

Lines changed: 16 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -409,16 +409,30 @@ define([
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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: '${importance_allocate} = permutation_importance(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${random_state}${etc})',
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code: '${importance_allocate} = vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description: 'Permutation importance for feature evaluation.',
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options: [
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{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
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{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
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{ name: 'scoring', component: ['input'], usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true },
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{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
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{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
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]
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},
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'plot_permutation_importance': {
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name: 'plot_permutation_importance',
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label: 'Plot permutation importance',
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import: 'from sklearn.inspection import permutation_importance',
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code: 'vp_plot_permutation_importances(${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: ['input'], usePair: true },
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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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'feature_importances': {
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name: 'feature_importances',
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label: 'Feature importances',

‎visualpython/python/userCommand.py‎

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Original file line numberDiff line numberDiff line change
@@ -123,6 +123,41 @@ def vp_plot_feature_importances(model, X_train=None, sort=False, top_count=0):
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_vp_plt.show()
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######
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# Visual Python: Machine Learning > Model Info
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######
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def vp_create_permutation_importances(model, X_train, y_train, scoring=None, sort=False):
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from sklearn.inspection import permutation_importance
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if isinstance(X_train, _vp_pd.core.frame.DataFrame):
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feature_names = X_train.columns
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else:
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feature_names = [ 'X{}'.format(i) for i in range(len(model.feature_importances_)) ]
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imp = permutation_importance(model, X_train, y_train, scoring)
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df_i = _vp_pd.DataFrame(imp['importances_mean'], index=feature_names, columns=['Feature_importance'])
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df_i['Percentage'] = 100 * df_i['Feature_importance']
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if sort: df_i.sort_values(by='Feature_importance', ascending=False, inplace=True)
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df_i = df_i.round(2)
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return df_i
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######
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# Visual Python: Machine Learning > Model Info
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######
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def vp_plot_permutation_importances(model, X_train, y_train, scoring=None, sort=False, top_count=0):
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df_i = vp_create_permutation_importances(model, X_train, y_train, scoring, sort)
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if sort:
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if top_count > 0:
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df_i['Percentage'].sort_values().tail(top_count).plot(kind='barh')
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else:
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df_i['Percentage'].sort_values().plot(kind='barh')
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else:
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df_i['Percentage'].plot(kind='barh')
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_vp_plt.xlabel('Feature importance Percentage')
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_vp_plt.ylabel('Features')
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_vp_plt.show()
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######
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# Visual Python: Visualization > Seaborn
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######
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def vp_seaborn_show_values(axs, precision=1, space=0.01):

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