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minjk-bl
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ML > evaluation schema
1 parent 2cb4f93 commit 0a591b8

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Lines changed: 163 additions & 15 deletions

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‎css/root.css‎

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.vp-orange-text {
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color: var(--font-hightlight);
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}
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.vp-gray-text {
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color: var(--gray-color);
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}
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.vp-italic {
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font-style: italic;
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}
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.vp-bold {
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font-weight: bold;
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}

‎html/m_ml/evaluation.html‎

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<body>
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<div class="vp-grid-box">
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<div class="vp-grid-border-box vp-grid-col-p50">
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<label class="vp-orange-text">Import Library</label>
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<button class="vp-button vp-import-btn" id="vp_importLibrary">Import</button>
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</div>
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<div class="vp-grid-border-box vp-grid-col-95">
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<label for="modelType" class="vp-orange-text">Model Type</label>
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<select id="modelType" class="vp-select vp-state">
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<option value="clf">Classfication</option>
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<option value="rgs">Regression</option>
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<!-- <option value="cls">Clustering</option>
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<option value="pca">PCA</option> -->
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</select>
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<label for="predictData" class="vp-orange-text">Predict Data</label>
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<input type="text" class="vp-input vp-state" id="predictData" placeholder="Input predict data" value="pred"/>
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<label for="targetData" class="vp-orange-text">Target Data</label>
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<input type="text" class="vp-input vp-state" id="targetData" placeholder="Input target data" value="y_test"/>
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</div>
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<div class="vp-grid-border-box">
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<label for="featureData" class="vp-orange-text">Evaluation Metrics</label>
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<div class="vp-grid-box vp-eval-box vp-eval-clf">
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<input type="checkbox" id="confusion_matrix" class="vp-state" checked><label for="confusion_matrix">Confusion Matrix</label>
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<input type="checkbox" id="report" class="vp-state" checked><label for="report">Report (Accuracy/Precision/Recall/F1-score)</label>
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<label class="vp-gray-text vp-italic">* You can select individually</label>
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<input type="checkbox" id="accuracy" class="vp-state"><label for="accuracy">Accuracy</label>
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<input type="checkbox" id="precision" class="vp-state"><label for="precision">Precision</label>
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<input type="checkbox" id="recall" class="vp-state"><label for="recall">Recall</label>
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<input type="checkbox" id="f1_score" class="vp-state"><label for="f1_score">F1-score</label>
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</div>
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<div class="vp-grid-box vp-eval-box vp-eval-rgs">
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<input type="checkbox" id="coefficient" class="vp-state"><label for="coefficient">Coefficient</label>
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<input type="checkbox" id="intercept" class="vp-state"><label for="intercept">Intercept</label>
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<input type="checkbox" id="r_squared" class="vp-state" checked><label for="r_squared">R squared</label>
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<input type="checkbox" id="mae" class="vp-state"><label for="mae">Mean Absolute Error</label>
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<input type="checkbox" id="mape" class="vp-state"><label for="mape">Mean Absolute Percentage Error</label>
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<input type="checkbox" id="rmse" class="vp-state" checked><label for="rmse">Root Mean Squared Error</label>
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<input type="checkbox" id="scatter_plot" class="vp-state"><label for="scatter_plot">Scatter plot</label>
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</div>
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</div>
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</div>
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</body>

‎js/m_ml/evaluation.js‎

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// [CLASS] Evaluation
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//============================================================================
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define([
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'text!vp_base/html/m_ml/evaluation.html!strip',
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'vp_base/js/com/com_util',
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'vp_base/js/com/com_Const',
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'vp_base/js/com/com_String',
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'vp_base/js/com/component/PopupComponent'
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], function(com_util, com_Const, com_String, PopupComponent) {
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], function(evalHTML, com_util, com_Const, com_String, PopupComponent) {
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/**
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* Evaluation
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*/
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class Evaluation extends PopupComponent {
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_init() {
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super._init();
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/** Write codes executed before rendering */
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this.config.dataview = false;
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this.state = {
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modelType: 'clf',
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predictData: 'pred',
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targetData: 'y_test',
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// classification
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confusion_matrix: true, report: true,
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accuracy: false, precision: false, recall: false, f1_score: false,
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// regression
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coefficient: false, intercept: false, r_squared: true,
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mae: false, mape: false, rmse: true, scatter_plot: false,
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...this.state
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}
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}
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_bindEvent() {
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super._bindEvent();
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/** Implement binding events */
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var that = this;
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this.$target.on('click', function(evt) {
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var target = evt.target;
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if ($(that.wrapSelector()).find(target).length > 0) {
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// Sample : getDataList from Kernel
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vpKernel.getDataList().then(function(resultObj) {
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vpLog.display(VP_LOG_TYPE.DEVELOP, resultObj);
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}).catch(function(err) {
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vpLog.display(VP_LOG_TYPE.DEVELOP, err);
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});
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}
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// import library
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$(this.wrapSelector('#vp_importLibrary')).on('click', function() {
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com_interface.insertCell('code', 'from sklearn import metrics');
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});
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// model type change
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$(this.wrapSelector('#modelType')).on('change', function() {
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let modelType = $(this).val();
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that.state.modelType = modelType;
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$(page).find('.vp-eval-box').hide();
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$(page).find('.vp-eval-'+modelType).show();
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})
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}
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templateForBody() {
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/** Implement generating template */
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return 'This is sample.';
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let page = $(evalHTML);
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$(page).find('.vp-eval-box').hide();
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$(page).find('.vp-eval-'+this.state.modelType).show();
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return page;
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}
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generateCode() {
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return "print('sample code')";
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let code = new com_String();
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let {
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modelType, predictData, targetData,
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// classification
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confusion_matrix, report, accuracy, precision, recall, f1_score,
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// regression
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coefficient, intercept, r_squared, mae, mape, rmse, scatter_plot
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} = this.state;
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//====================================================================
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// Classfication
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//====================================================================
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if (modelType == 'clf') {
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if (confusion_matrix) {
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code.appendLine("# Confusion Matrix");
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code.appendFormatLine('pd.crosstab({0}, {1}, margins=True)', targetData, predictData);
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}
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if (report) {
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code.appendLine("# Classification report");
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code.appendFormatLine('print(metrics.classification_report({0}, {1}))', targetData, predictData);
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}
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if (accuracy) {
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code.appendLine("# Accuracy");
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code.appendFormatLine('metrics.accuracy_score({0}, {1})', targetData, predictData);
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}
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if (precision) {
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code.appendLine("# Precision");
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code.appendFormatLine("metrics.precision_score({0}, {1}, average='weighted')", targetData, predictData);
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}
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if (recall) {
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code.appendLine("# Recall");
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code.appendFormatLine("metrics.recall_score({0}, {1}, average='weighted')", targetData, predictData);
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}
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if (f1_score) {
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code.appendLine("# F1-score");
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code.appendFormatLine("metrics.f1_score({0}, {1}, average='weighted')", targetData, predictData);
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}
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}
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//====================================================================
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// Regression
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//====================================================================
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if (modelType == 'rgs') {
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if (coefficient) {
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code.appendLine("# Coefficient (scikit-learn only)");
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code.appendFormatLine('model.coef_');
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}
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if (intercept) {
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code.appendLine("# Intercept (scikit-learn only)");
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code.appendFormatLine('model.intercept_');
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}
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if (r_squared) {
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code.appendLine("# R square");
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code.appendFormatLine('metrics.r2_score({0}, {1})', targetData, predictData);
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}
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if (mae) {
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code.appendLine("# MAE(Mean Absolute Error)");
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code.appendFormatLine('metrics.mean_absolute_error({0}, {1})', targetData, predictData);
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}
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if (mape) {
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code.appendLine("# MAPE(Mean Absolute Percentage Error)");
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code.appendLine('def MAPE(y_test, y_pred):');
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code.appendLine(' return np.mean(np.abs((y_test - pred) / y_test)) * 100');
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code.appendLine();
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code.appendFormatLine('MAPE({0}, {1})', targetData, predictData);
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}
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if (rmse) {
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code.appendLine("# RMSE(Root Mean Squared Error)");
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code.appendFormatLine('metrics.mean_squared_error({0}, {1})**0.5', targetData, predictData);
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}
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if (scatter_plot) {
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code.appendLine('# Regression plot');
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code.appendFormatLine('plt.scatter({0}, {1})', targetData, predictData);
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code.appendFormatLine("plt.xlabel('{0}')", targetData);
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code.appendFormatLine("plt.ylabel('{1}')", predictData);
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code.appendLine('plt.show()');
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}
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}
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// FIXME: as seperated cells
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return code.toString();
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}
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}

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