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Copy pathModelEditor.js
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1167 lines (1114 loc) · 68.3 KB
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define([
__VP_CSS_LOADER__('vp_base/css/component/instanceEditor'), // INTEGRATION: unified version of css loader
'vp_base/js/com/com_String',
'vp_base/js/com/com_util',
'vp_base/js/com/com_generatorV2',
'vp_base/js/com/component/Component',
'vp_base/js/com/component/SuggestInput'
], function(insCss, com_String, com_util, com_generator, Component, SuggestInput) {
// temporary const
const VP_INS_BOX = 'vp-ins-box';
const VP_INS_SELECT_CONTAINER = 'vp-ins-select-container';
const VP_INS_SELECT_TITLE = 'vp-ins-select-title';
const VP_INS_SEARCH = 'vp-ins-search';
const VP_INS_TYPE = 'vp-ins-type';
const VP_INS_SELECT_BOX = 'vp-ins-select-box';
const VP_INS_SELECT_LIST = 'vp-ins-select-list';
const VP_INS_SELECT_ITEM = 'vp-ins-select-item';
const VP_INS_PARAMETER_BOX = 'vp-ins-parameter-box';
const VP_INS_PARAMETER = 'vp-ins-parameter';
class ModelEditor extends Component {
constructor(pageThis, targetId, containerId) {
super(null, { pageThis: pageThis, targetId: targetId, containerId: containerId });
}
_init() {
super._init();
this.pageThis = this.state.pageThis;
this.targetId = this.state.targetId;
this.containerId = this.state.containerId;
let modelEditorType = '';
let modelEditorName = '';
if (this.pageThis.state['modelEditorType'] == undefined) {
modelEditorType = '';
}
if (this.pageThis.state['modelEditorName'] == undefined) {
modelEditorName = '';
}
this.state = {
modelEditorType: modelEditorType,
modelEditorName: modelEditorName,
action: {},
info: {},
config: {},
...this.state
}
this.loaded = false;
}
render() {
;
}
getModelCategory(modelType) {
let mlDict = vpConfig.getMLDataDict();
let keys = Object.keys(mlDict);
let modelCategory = '';
for (let i = 0; i < keys.length; i++) {
let key = keys[i];
if (mlDict[key].includes(modelType)) {
modelCategory = key;
break;
}
}
return modelCategory;
}
getAction(modelType) {
let category = this.getModelCategory(modelType);
let defaultActions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Perform modeling from features, or distance matrix.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Predict the closest target data X belongs to.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_test' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'predict_proba': {
name: 'predict_proba',
label: 'Predict probability',
code: '${pred_prob_allocate} = ${model}.predict_proba(${pred_prob_featureData})',
description: 'Predict class probabilities for X.',
options: [
{ name: 'pred_prob_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_test' },
{ name: 'pred_prob_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Apply dimensionality reduction to X.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
};
let actions = {};
switch (category) {
case 'Data Preparation':
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Fit Encoder/Scaler to X.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Fit Encoder/Scaler to X, then transform X.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'transform': {
...defaultActions['transform'],
description: 'Transform labels to normalized encoding.'
}
}
if (modelType != 'ColumnTransformer') {
actions = {
...actions,
'inverse_transform': {
name: 'inverse_transform',
label: 'Inverse transform',
code: '${inverse_allocate} = ${model}.inverse_transform(${inverse_featureData})',
description: 'Transform binary labels back to multi-class labels.',
options: [
{ name: 'inverse_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
}
}
}
if (modelType === 'SMOTE') {
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Check inputs and statistics of the sampler.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' }
]
},
'fit_resample': {
name: 'fit_resample',
label: 'Fit and resample',
code: '${fit_res_allocateX}, ${fit_res_allocatey} = ${model}.fit_resample(${fit_res_featureData}, ${fit_res_targetData})',
description: 'Resample the dataset.',
options: [
{ name: 'fit_res_allocateX', label: 'Allocate feature', component: ['input'], placeholder: 'New variable', value: 'X_res' },
{ name: 'fit_res_allocatey', label: 'Allocate target', component: ['input'], placeholder: 'New variable', value: 'y_res' },
{ name: 'fit_res_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'fit_res_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' }
]
}
}
}
break;
case 'Regression':
actions = {
'fit': defaultActions['fit'],
'predict': defaultActions['predict']
}
break;
case 'Classification':
actions = {
'fit': defaultActions['fit'],
'predict': defaultActions['predict'],
'predict_proba': defaultActions['predict_proba'],
}
if (['LogisticRegression', 'SVC', 'GradientBoostingClassifier'].includes(modelType)) {
actions = {
...actions,
'decision_function': {
name: 'decision_function',
label: 'Decision function',
code: '${dec_allocate} = ${model}.decision_function(${dec_featureData})',
description: 'Compute the decision function of X.',
options: [
{ name: 'dec_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'dec_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable' }
]
}
}
}
break;
case 'Auto ML':
actions = {
'fit': defaultActions['fit'],
'predict': defaultActions['predict'],
'fit_predict': {
name: 'fit_predict',
label: 'Fit and predict',
code: '${fit_pred_allocate} = ${model}.fit_predict(${fit_pred_featureData})',
description: 'Fit and predict.',
options: [
{ name: 'fit_pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'predict_proba': defaultActions['predict_proba']
}
break;
case 'Clustering':
if (modelType == 'AgglomerativeClustering'
|| modelType == 'DBSCAN') {
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Perform clustering from features, or distance matrix.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
]
},
'fit_predict': {
name: 'fit_predict',
label: 'Fit and predict',
code: '${fit_pred_allocate} = ${model}.fit_predict(${fit_pred_featureData})',
description: 'Compute clusters from a data or distance matrix and predict labels.',
options: [
{ name: 'fit_pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
}
}
break;
}
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Compute clustering.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Predict the closest target data X belongs to.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'fit_predict': {
name: 'fit_predict',
label: 'Fit and predict',
code: '${fit_pred_allocate} = ${model}.fit_predict(${fit_pred_featureData})',
description: 'Compute cluster centers and predict cluster index for each sample.',
options: [
{ name: 'fit_pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
}
}
if (modelType == 'KMeans') {
actions = {
...actions,
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Compute clustering and transform X to cluster-distance space.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Transform X to a cluster-distance space.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
}
break;
case 'Dimension Reduction':
if (modelType == 'TSNE') {
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Fit X into an embedded space.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Fit X into an embedded space and return that transformed output.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
}
if (modelType == 'LinearDiscriminantAnalysis') { // LDA
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Fit the Linear Discriminant Analysis model.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}${fit_trans_targetData})',
description: 'Fit to data, then transform it.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_trans_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Predict class labels for samples in X.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Project data to maximize class separation.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
}
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Fit X into an embedded space.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Fit the model with X and apply the dimensionality reduction on X.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'inverse_transform': {
name: 'inverse_transform',
label: 'Inverse transform',
code: '${inverse_allocate} = ${model}.inverse_transform(${inverse_featureData})',
description: 'Transform data back to its original space.',
options: [
{ name: 'inverse_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Apply dimensionality reduction to X.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
case 'ETC':
if (modelType === 'GridSearchCV') {
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData}${fit_targetData})',
description: 'Run fit with all sets of parameters.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train', usePair: true, pairKey: 'y' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Call predict on the estimator with the best found parameters.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_test' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'inverse_transform': {
name: 'inverse_transform',
label: 'Inverse transform',
code: '${inverse_allocate} = ${model}.inverse_transform(${inverse_featureData})',
description: 'Call inverse_transform on the estimator with the best found params.',
options: [
{ name: 'inverse_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Call transform on the estimator with the best found parameters.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
}
break;
}
return actions;
}
getInfo(modelType) {
let category = this.getModelCategory(modelType);
let infos = {};
let defaultInfos = {
'score': {
name: 'score',
label: 'Score',
code: '${score_allocate} = ${model}.score(${score_featureData}, ${score_targetData})',
description: '',
options: [
{ name: 'score_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'score_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
{ name: 'score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
},
'get_params': {
name: 'get_params',
label: 'Get parameters',
code: '${param_allocate} = ${model}.get_params(${deep})',
description: 'Get parameters for this estimator.',
options: [
{ name: 'deep', component: ['bool_select'], default: 'True', usePair: true },
{ name: 'param_allocate', label: 'Allocate to', component: ['input'], value: 'params' }
]
},
'permutation_importance': {
name: 'permutation_importance',
label: 'Permutation importance',
import: 'from sklearn.inspection import permutation_importance',
code: '${importance_allocate} = vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
description: 'Permutation importance for feature evaluation.',
options: [
{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
{ name: 'scoring', component: ['input'], usePair: true },
{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
{ name: 'importance_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'importances' }
]
},
'plot_permutation_importance': {
name: 'plot_permutation_importance',
label: 'Plot permutation importance',
import: 'from sklearn.inspection import permutation_importance',
code: 'vp_plot_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort}${top_count})',
description: 'Permutation importance for feature evaluation.',
options: [
{ name: 'importance_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'importance_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_train' },
{ name: 'scoring', component: ['input'], usePair: true },
{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
{ name: 'top_count', label: 'Top count', component: ['input_number'], min: 0, usePair: true }
]
},
'feature_importances': {
name: 'feature_importances',
label: 'Feature importances',
code: "${fi_allocate} = vp_create_feature_importances(${model}, ${fi_featureData}${sort})",
description: 'Allocate feature_importances_',
options: [
{ name: 'fi_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'fi_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'df_i' },
{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true }
]
},
'plot_feature_importances': {
name: 'plot_feature_importances',
label: 'Plot feature importances',
code: "vp_plot_feature_importances(${model}, ${fi_featureData}${sort}${top_count})",
description: 'Draw feature_importances_',
options: [
{ name: 'fi_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_train' },
{ name: 'sort', label: 'Sort data', component: ['bool_checkbox'], value: true, usePair: true },
{ name: 'top_count', label: 'Top count', component: ['input_number'], min: 0, usePair: true },
]
}
}
switch (category) {
case 'Data Preparation':
if (modelType == 'OneHotEncoder') {
infos = {
'categories_': { // TODO:
name: 'categories_',
label: 'Categories',
code: '${categories_allocate} = ${model}.categories_',
description: 'The categories of each feature determined during fitting',
options: [
{ name: 'categories_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'categories' }
]
},
'get_feature_names_out': {
name: 'get_feature_names_out',
label: 'Get feature names',
code: '${feature_names_allocate} = ${model}.get_feature_names_out()',
description: 'Get output feature names.',
options: [
{ name: 'feature_names_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'features' }
]
}
}
}
if (modelType == 'LabelEncoder') {
infos = {
'classes_': {
name: 'classes_',
label: 'Classes',
code: '${classes_allocate} = ${model}.classes_',
description: 'Holds the label for each class.',
options: [
{ name: 'classes_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'classes' }
]
}
}
}
if (modelType == 'KBinsDiscretizer') {
infos = {
'bin_edges': { // TODO:
name: 'bin_edges',
label: 'Bin edges',
code: '${bin_edges_allocate} = ${model}.bin_edges_',
description: 'The edges of each bin. Contain arrays of varying shapes',
options: [
{ name: 'bin_edges_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'bin_edges' }
]
}
}
}
if (modelType == 'ColumnTransformer') {
infos = {
'transformers_': {
name: 'transformers_',
label: 'Transformers_',
code: '${transformers_allocate} = ${model}.transformers_',
description: 'The collection of fitted transformers as tuples of (name, fitted_transformer, column).',
options: [
{ name: 'transformers_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'classes' }
]
},
'get_feature_names_out': {
name: 'get_feature_names_out',
label: 'Get feature names',
code: '${feature_names_allocate} = ${model}.get_feature_names_out()',
description: 'Get output feature names.',
options: [
{ name: 'feature_names_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'features' }
]
}
}
}
if (modelType === 'SMOTE') {
infos = {
'get_feature_names_out': {
name: 'get_feature_names_out',
label: 'Get feature names',
code: '${feature_names_allocate} = ${model}.get_feature_names_out()',
description: 'Get output feature names for transformation.',
options: [
{ name: 'feature_names_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'features' }
]
}
}
}
infos = {
...infos,
'get_params': defaultInfos['get_params']
}
break;
case 'Regression':
infos = {
'score': {
...defaultInfos['score'],
description: 'Return the coefficient of determination of the prediction.'
},
'cross_val_score': {
name: 'cross_val_score',
label: 'Cross validation score',
import: 'from sklearn.model_selection import cross_val_score',
code: '${cvs_allocate} = cross_val_score(${model}, ${cvs_featureData}, ${cvs_targetData}${scoring}${cv})',
description: 'Evaluate a score by cross-validation.',
options: [
{ name: 'cvs_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'cvs_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'scoring', component: ['option_select'], usePair: true, type: 'text',
options: [
'',
'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_root_mean_squared_error',
'neg_mean_squared_log_error', 'neg_median_absolute_error', 'r2', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance',
'neg_mean_absolute_percentage_error'
] },
{ name: 'cv', label: 'Cross Validation', component: ['input_number'], placeholder: '1 ~ 10', default: 5, usePair: true },
{ name: 'cvs_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
},
'permutation_importance': defaultInfos['permutation_importance'],
'Coefficient': {
name: 'coef_',
label: 'Coefficient',
code: '${coef_allocate} = ${model}.coef_',
description: 'Weights assigned to the features.',
options: [
{ name: 'coef_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'coef' }
]
},
'Intercept': {
name: 'intercept_',
label: 'Intercept',
code: '${intercept_allocate} = ${model}.intercept_',
description: 'Constants in decision function.',
options: [
{ name: 'intercept_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'intercepts' }
]
}
}
let svcList = [
'DecisionTreeRegressor',
'RandomForestRegressor',
'GradientBoostingRegressor',
'XGBRegressor', 'LGBMRegressor', 'CatBoostRegressor'
];
if (svcList.includes(modelType)) {
infos = {
...infos,
'feature_importances': defaultInfos['feature_importances'],
'plot_feature_importances': defaultInfos['plot_feature_importances']
}
}
break;
case 'Classification':
infos = {
'score': {
...defaultInfos['score'],
description: 'Return the mean accuracy on the given test data and labels.'
},
'cross_val_score': {
name: 'cross_val_score',
label: 'Cross validation score',
import: 'from sklearn.model_selection import cross_val_score',
code: '${cvs_allocate} = cross_val_score(${model}, ${cvs_featureData}, ${cvs_targetData}${scoring}${cv})',
description: 'Evaluate a score by cross-validation.',
options: [
{ name: 'cvs_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'cvs_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'scoring', component: ['option_select'], usePair: true, type: 'text',
options: [
'',
'accuracy', 'balanced_accuracy', 'top_k_accuracy', 'average_precision', 'neg_brier_score',
'f1', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'neg_log_loss', 'precision', 'recall', 'jaccard',
'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'
] },
{ name: 'cv', label: 'Cross Validation', component: ['input_number'], placeholder: '1 ~ 10', default: 5, usePair: true },
{ name: 'cvs_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
},
'roc_curve': {
name: 'roc_curve',
label: 'ROC Curve',
import: 'from sklearn import metrics',
code: "fpr, tpr, thresholds = metrics.roc_curve(${roc_targetData}, ${model}.predict_proba(${roc_featureData})[:, 1])\
\nplt.plot(fpr, tpr, label='ROC Curve')\
\nplt.xlabel('Sensitivity')\
\nplt.ylabel('Specificity')\
\nplt.show()",
description: '',
options: [
{ name: 'roc_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_test' },
{ name: 'roc_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_test' }
]
},
'auc': {
name: 'auc',
label: 'AUC',
import: 'from sklearn import metrics',
code: 'metrics.roc_auc_score(${auc_targetData}, ${model}.predict_proba(${auc_featureData})[:, 1])',
description: '',
options: [
{ name: 'auc_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X_test' },
{ name: 'auc_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y_test' }
]
},
'permutation_importance': defaultInfos['permutation_importance']
}
// feature importances
let clfList = [
'DecisionTreeClassifier',
'RandomForestClassifier',
'GradientBoostingClassifier',
'XGBClassifier',
'LGBMClassifier',
'CatBoostClassifier',
]
if (clfList.includes(modelType)) {
infos = {
...infos,
'feature_importances': defaultInfos['feature_importances'],
'plot_feature_importances': defaultInfos['plot_feature_importances']
}
}
// use decision_function on ROC, AUC
let decisionFunctionTypes = [
'LogisticRegression', 'SVC', 'GradientBoostingClassifier'
];
if (decisionFunctionTypes.includes(modelType)) {
infos = {
...infos,
'roc_curve': {
...infos['roc_curve'],
code: "fpr, tpr, thresholds = metrics.roc_curve(${roc_targetData}, ${model}.decision_function(${roc_featureData}))\
\nplt.plot(fpr, tpr, label='ROC Curve')\
\nplt.xlabel('Sensitivity')\
\nplt.ylabel('Specificity')\
\nplt.show()"
},
'auc': {
...infos['auc'],
code: 'metrics.roc_auc_score(${auc_targetData}, ${model}.decision_function(${auc_featureData}))',
}
}
}
break;
case 'Auto ML':
infos = {
'score': {
...defaultInfos['score'],
description: 'Return the mean accuracy on the given test data and labels.'
},
'get_params': {
...defaultInfos['get_params']
}
}
break;
case 'Clustering':
infos = {
'get_params': {
...defaultInfos['get_params']
}
}
if (modelType == 'KMeans') {
infos = {
...infos,
'score': {
...defaultInfos['score'],
description: 'Return the mean accuracy on the given test data and labels.'
},
'cluster_centers_': {
name: 'cluster_centers',
label: 'Cluster centers',
code: '${centers_allocate} = ${model}.cluster_centers_',
description: 'Coordinates of cluster centers.',
options: [
{ name: 'centers_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'cluster_centers' }
]
}
}
}
if (modelType == 'AgglomerativeClustering') {
infos = {
...infos,
'Dendrogram': { // FIXME:
name: 'dendrogram',
label: 'Dendrogram',
code: "# import\nfrom scipy.cluster.hierarchy import dendrogram, ward\n\nlinkage_array = ward(${dendro_data})\ndendrogram(linkage_array, p=3, truncate_mode='level', no_labels=True)\nplt.show()",
description: 'Draw a dendrogram',
options: [
{ name: 'dendro_data', label: 'Data', component: ['data_select'], var_type: ['DataFrame'] }
]
}
}
}
if (modelType == 'GaussianMixture') {
infos = {
...infos,
'score': {
...defaultInfos['score'],
description: 'Compute the per-sample average log-likelihood of the given data X.'
}
}
}
break;
case 'Dimension Reduction':
if (modelType == 'LDA') {
infos = {
'score': {
name: 'score',
label: 'Score',
code: '${score_allocate} = ${model}.score(${score_featureData}, ${score_targetData})',
description: 'Return the average log-likelihood of all samples.',
options: [
{ name: 'score_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'score_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
}
}
break;
}
if (modelType == 'PCA') {
infos = {
'explained_variance_ratio_': {
name: 'explained_variance_ratio_',
label: 'Explained variance ratio',
code: '${ratio_allocate} = ${model}.explained_variance_ratio_',
description: 'Percentage of variance explained by each of the selected components.',
options: [
{ name: 'ratio_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'ratio' }
]
}
}
}
infos = {
...infos,
'score': {
name: 'score',
label: 'Score',
code: '${score_allocate} = ${model}.score(${score_featureData})',
description: 'Return the average log-likelihood of all samples.',
options: [
{ name: 'score_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
{ name: 'score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
}
}
break;
case 'ETC':
if (modelType === 'GridSearchCV') {
infos = {
'best_estimator_': {
name: 'best_estimator_',
label: 'Best estimator',
code: '${best_estimator_allocate} = ${model}.best_estimator_',
description: 'Estimator that was chosen by the search.',
options: [
{ name: 'best_estimator_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'best_estimator' }
]
},
'best_score_': {
name: 'best_score_',
label: 'Best score',
code: '${best_score_allocate} = ${model}.best_score_',
description: 'Mean cross-validated score of the best_estimator.',
options: [
{ name: 'best_score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'best_score' }
]
},
'best_params_': {
name: 'best_params_',
label: 'Best params',
code: '${best_params_allocate} = ${model}.best_params_',
description: 'Parameter setting that gave the best results on the hold out data.',
options: [
{ name: 'best_params_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'best_params' }
]
}
}
}
break;
}
return infos;
}
renderPage() {
var tag = new com_String();
tag.appendFormatLine('<div class="{0} {1}">', VP_INS_BOX, this.uuid); // vp-select-base
// Model Editor State (Saved state)
tag.appendFormatLine('<input type="hidden" id="{0}" class="vp-state" value="{1}"/>', 'modelEditorType', this.state.modelEditorType);
tag.appendFormatLine('<input type="hidden" id="{0}" class="vp-state" value="{1}"/>', 'modelEditorName', this.state.modelEditorName);
tag.appendFormatLine('<div class="{0} {1}">', VP_INS_SELECT_CONTAINER, 'action');
tag.appendFormatLine('<div class="vp-multilang {0}">Action</div>', VP_INS_SELECT_TITLE);
tag.appendFormatLine('<div style="{0}">', 'position: relative;');
tag.appendFormatLine('<input class="vp-input {0} {1}" type="text" placeholder="Search Action"/>', VP_INS_SEARCH, 'attr');
tag.appendFormatLine('<input class="{0} {1}" type="hidden"/>', VP_INS_TYPE, 'action');
tag.appendFormatLine('<i class="{0} {1}"></i>', 'fa fa-search', 'vp-ins-search-icon');
tag.appendLine('</div>');
tag.appendFormatLine('<div class="{0} {1}">', VP_INS_SELECT_BOX, 'action');
tag.appendFormatLine('<ul class="{0} {1}" style="height:80px">', VP_INS_SELECT_LIST, 'action');
tag.appendLine('</ul>');
tag.appendLine('</div>'); // VP_INS_SELECT_BOX
tag.appendLine('</div>'); // VP_INS_SELECT_CONTAINER
tag.appendFormatLine('<div class="{0} {1}">', VP_INS_SELECT_CONTAINER, 'info');
tag.appendFormatLine('<div class="vp-multilang {0}">Information</div>', VP_INS_SELECT_TITLE);
tag.appendFormatLine('<div style="{0}">', 'position: relative;');
tag.appendFormatLine('<input class="vp-input {0} {1}" type="text" placeholder="Search Info"/>', VP_INS_SEARCH, 'method');
tag.appendFormatLine('<input class="{0} {1}" type="hidden"/>', VP_INS_TYPE, 'info');
tag.appendFormatLine('<i class="{0} {1}"></i>', 'fa fa-search', 'vp-ins-search-icon');
tag.appendLine('</div>');
tag.appendFormatLine('<div class="{0} {1}">', VP_INS_SELECT_BOX, 'info');
tag.appendFormatLine('<ul class="{0} {1}" style="height:80px">', VP_INS_SELECT_LIST, 'info');
tag.appendLine('</ul>');
tag.appendLine('</div>'); // VP_INS_SELECT_BOX
tag.appendLine('</div>'); // VP_INS_SELECT_CONTAINER
tag.appendFormatLine('<div class="vp-multilang {0}">Options</div>', VP_INS_SELECT_TITLE);
tag.appendFormatLine('<div class="{0} vp-grid-col-95"></div>', VP_INS_PARAMETER_BOX);
tag.appendLine('</div>'); // VP_INS_BOX END
$(this.pageThis.wrapSelector('#' + this.containerId)).html(tag.toString());
return tag.toString();
}
reload() {
this.renderPage();
let targetTag = $(this.pageThis.wrapSelector('#' + this.targetId));
let model = $(targetTag).val();
let modelType = $(targetTag).find('option:selected').data('type');
let actions = this.getAction(modelType);
let infos = this.getInfo(modelType);
this.state.action = { ...actions };
this.state.info = { ...infos };