diff --git a/visualpython/data/m_library/pandasLibrary.js b/visualpython/data/m_library/pandasLibrary.js
index 08ef7889..1478aa9e 100644
--- a/visualpython/data/m_library/pandasLibrary.js
+++ b/visualpython/data/m_library/pandasLibrary.js
@@ -206,7 +206,7 @@ define([
},
{
"name": "sep",
- "label": "Seperator",
+ "label": "Separator",
"type": "text",
"usePair": true
},
@@ -304,7 +304,7 @@ define([
},
{
"name": "sep",
- "label": "Seperator",
+ "label": "Separator",
"type": "text",
"usePair": true
},
@@ -4342,7 +4342,7 @@ define([
},
{
"name": "prefix_sep",
- "label": "Header Seperator",
+ "label": "Header Separator",
"default": "_",
"usePair": true
},
diff --git a/visualpython/data/m_library/pandasLibrary_v1.js b/visualpython/data/m_library/pandasLibrary_v1.js
index d1f38172..f97c6546 100644
--- a/visualpython/data/m_library/pandasLibrary_v1.js
+++ b/visualpython/data/m_library/pandasLibrary_v1.js
@@ -163,7 +163,7 @@ define([
{
name: 'sep',
type: 'text',
- label: 'Seperator'
+ label: 'Separator'
},
{
name: 'names',
@@ -243,7 +243,7 @@ define([
{
name: 'sep',
type: 'text',
- label: 'Seperator'
+ label: 'Separator'
},
{
name: 'na_rep',
@@ -3514,7 +3514,7 @@ define([
{
name: 'prefix_sep',
type: ['text'],
- label: 'Header Seperator',
+ label: 'Header Separator',
default: '_'
},
{
diff --git a/visualpython/data/m_ml/mlLibrary.js b/visualpython/data/m_ml/mlLibrary.js
index 5f574e33..11fc11a8 100644
--- a/visualpython/data/m_ml/mlLibrary.js
+++ b/visualpython/data/m_ml/mlLibrary.js
@@ -128,10 +128,10 @@ define([
'prep-onehot': {
name: 'OneHotEncoder',
import: 'from sklearn.preprocessing import OneHotEncoder',
- code: 'OneHotEncoder(${sparse}${handle_unknown}${etc})',
+ code: 'OneHotEncoder(${sparse_output}${handle_unknown}${etc})',
returnType: 'OneHotEncoder',
options: [
- { name: 'sparse', component: ['bool_select'], default: 'False', usePair: true },
+ { name: 'sparse_output', component: ['bool_select'], default: 'True', value: 'False', usePair: true },
{ name: 'handle_unknown', component: ['option_suggest'], usePair: true,
options: ['error', 'ignore'], default: 'error' },
]
@@ -490,8 +490,8 @@ define([
code: 'DecisionTreeClassifier(${criterion}${max_depth}${min_samples_split}${random_state}${etc})',
returnType: 'DecisionTreeClassifier',
options: [
- { name: 'criterion', component: ['option_select'], type: 'text', default: 'squared_error', type:'text',
- options: ['squared_error', 'friedman_mse', 'absolute_error', 'poisson'], usePair: true },
+ { name: 'criterion', component: ['option_select'], type: 'text', default: 'gini', type:'text',
+ options: ['gini','entropy','log_loss'], usePair: true },
{ name: 'max_depth', component: ['input_number'], placeholder: 'None', usePair: true },
{ name: 'min_samples_split', component: ['input_number'], default: 2, usePair: true },
{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true }
@@ -505,7 +505,7 @@ define([
options: [
{ name: 'n_estimators', component: ['input_number'], default: 100, usePair: true },
{ name: 'criterion', component: ['option_select'], type: 'text', default: 'gini', type:'text', usePair: true,
- options: ['gini', 'entropy'] },
+ options: ['gini', 'entropy', 'log_loss'] },
{ name: 'max_depth', component: ['input_number'], placeholder: 'None', usePair: true },
{ name: 'min_samples_split', component: ['input_number'], default: 2, usePair: true },
{ name: 'n_jobs', component: ['input_number'], placeholder: 'None', usePair: true },
@@ -518,12 +518,12 @@ define([
code: 'GradientBoostingClassifier(${loss}${learning_rate}${n_estimators}${criterion}${random_state}${etc})',
returnType: 'GradientBoostingClassifier',
options: [
- { name: 'loss', component: ['option_select'], type: 'text', default: 'deviance', type: 'text', usePair: true,
- options: ['deviance', 'exponential'] },
+ { name: 'loss', component: ['option_select'], type: 'text', default: 'log_loss', type: 'text', usePair: true,
+ options: ['log_loss', 'exponential'] },
{ name: 'learning_rate', component: ['input_number'], default: 0.1, usePair: true },
{ name: 'n_estimators', component: ['input_number'], default: 100, usePair: true },
{ name: 'criterion', component: ['option_select'], type: 'text', default: 'friedman_mse', type:'text', usePair: true,
- options: ['friedman_mse', 'squared_error', 'mse', 'mae'] },
+ options: ['friedman_mse', 'squared_error'] },
{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true }
]
},
diff --git a/visualpython/html/m_stats/regression.html b/visualpython/html/m_stats/regression.html
index 124f7066..93ac63fd 100644
--- a/visualpython/html/m_stats/regression.html
+++ b/visualpython/html/m_stats/regression.html
@@ -76,6 +76,7 @@
+
diff --git a/visualpython/js/com/com_generatorV2.js b/visualpython/js/com/com_generatorV2.js
index f1762c36..42e6c760 100644
--- a/visualpython/js/com/com_generatorV2.js
+++ b/visualpython/js/com/com_generatorV2.js
@@ -376,6 +376,8 @@ define([
allowDataType: obj.var_type,
placeholder: obj.placeholder || 'Select data',
value: value,
+ columnSelection: obj.columnSelection || 'multiple', // single / multiple
+ returnFrameType: obj.returnFrameType || '', // '' / DataFrame / Series
required: obj.required === true
});
content = $(dataSelector.toTagString());
@@ -890,7 +892,7 @@ define([
suggestInputX.addClass('vp-input vp-state');
suggestInputX.setNormalFilter(false);
suggestInputX.setValue(defaultValue);
- $(selector + ' #' + columnInputId).replaceWith(function() {
+ $(pageThis.wrapSelector('#' + columnInputId)).replaceWith(function() {
return suggestInputX.toTagString();
});
} else {
@@ -899,7 +901,7 @@ define([
'id': columnInputId,
'class': 'vp-select vp-state'
});
- $(selector + ' #' + columnInputId).replaceWith(function() {
+ $(pageThis.wrapSelector('#' + columnInputId)).replaceWith(function() {
return $(tag);
});
}
@@ -955,7 +957,7 @@ define([
suggestInputX.setSuggestList(function() { return list; }); //FIXME:
suggestInputX.setNormalFilter(false);
suggestInputX.setValue(defaultValue);
- $(selector + ' #' + columnInputId).replaceWith(function() {
+ $(pageThis.wrapSelector('#' + columnInputId)).replaceWith(function() {
return suggestInputX.toTagString();
});
} else {
diff --git a/visualpython/js/com/component/DataSelector.js b/visualpython/js/com/component/DataSelector.js
index 770770ba..1b64b9ee 100644
--- a/visualpython/js/com/component/DataSelector.js
+++ b/visualpython/js/com/component/DataSelector.js
@@ -59,6 +59,11 @@ define([
select: null, // callback after selection from suggestInput (value, dtype)
allowDataType: null, // list of allowed data types
dataCategory: null, // list of data category (use it for ml categories)
+ columnSelection: 'multiple', // single/multi : allowed column selection
+ returnFrameType: '', // DataFrame/Series : required data type for DataFrame -> Series/DataFrame operation
+ // if Series, only one column selection is allowed and returns Series
+ // if DataFrame, always returns DataFrame
+ // if Empty(=== ''), if one column selected, returns Series / others, returns DataFrame
// additional options
boxClasses: '',
classes: '',
@@ -87,6 +92,9 @@ define([
this.prop.dataCategory = this.prop.allowDataType;
}
}
+ if (this.prop.returnFrameType === 'Series') {
+ this.prop.columnSelection = 'single'; // only single selection allowed
+ }
this.state = {
filterType: 'All',
@@ -98,6 +106,7 @@ define([
slicingEnd1: '',
slicingStart2: '',
slicingEnd2: '',
+ singleColumn: '',
ndRowType: 'slicing',
ndColType: 'slicing',
useIndex: false,
@@ -409,6 +418,19 @@ define([
}
}
+ templateForSingleSelector() {
+ return `
+
+
+
+
+
+
+
@@ -599,22 +621,93 @@ define([
switch (dataType) {
case 'DataFrame':
- // render option page
- $(this.wrapSelector('.vp-ds-option-inner-box')).html(this.templateForMultiSelector());
// column selecting
- this._columnSelector = new MultiSelector(this.wrapSelector('.vp-ds-df-multiselector'),
- { mode: 'columns', parent: [data], selectedList: this.state.indexing, allowAdd: true }
- );
-
+ if (this.prop.columnSelection === 'single') {
+ // render option page
+ $(this.wrapSelector('.vp-ds-option-inner-box')).html(this.templateForSingleSelector());
+ // bind column source
+ // com_generator.vp_bindColumnSource(this, 'data', ['singleColumn'], 'select', false, false);
+ // vp_bindColumnSource(pageThis, targetId, columnInputIdList, tagType="input", columnWithEmpty=false, columnWithIndex=false)
+ const columnInputIdList = ['singleColumn'];
+ if (data === '') {
+ // reset with no source
+ columnInputIdList && columnInputIdList.forEach(columnInputId => {
+ let defaultValue = that.state[columnInputId];
+ if (defaultValue === null || defaultValue === undefined) {
+ defaultValue = '';
+ }
+ // option tags
+ var tag = $('').attr({
+ 'id': columnInputId,
+ 'class': 'vp-select vp-state'
+ });
+ $(that.wrapSelector('#' + columnInputId)).replaceWith(function() {
+ return $(tag);
+ });
+ });
+ return ;
+ }
+ // get result and show on detail box
+ vpKernel.getColumnList(data).then(function(resultObj) {
+ try {
+ let { result, type, msg } = resultObj;
+ var { list } = JSON.parse(result);
+
+ // columns using suggestInput
+ columnInputIdList && columnInputIdList.forEach((columnInputId, idx) => {
+ let defaultValue = that.state[columnInputId];
+ if (defaultValue === null || defaultValue === undefined) {
+ defaultValue = '';
+ }
+ // create tag
+ var tag = $('').attr({
+ 'id': columnInputId,
+ 'class': 'vp-select vp-state w150'
+ });
+ // make tag
+ list.forEach((listVar, idx) => {
+ var option = document.createElement('option');
+ $(option).attr({
+ 'value':listVar.value,
+ 'text':listVar.label,
+ 'data-type':listVar.dtype
+ });
+ // cell metadata test : defaultValue as selected
+ if (listVar.value === defaultValue) {
+ $(option).prop('selected', true);
+ }
+ option.append(document.createTextNode(listVar.label));
+ $(tag).append(option);
+ });
+ $(that.wrapSelector('#' + columnInputId)).replaceWith(function() {
+ return $(tag);
+ });
+ $(that.wrapSelector('#' + columnInputId)).trigger('change');
+ }).catch(function(err) {
+ vpLog.display(VP_LOG_TYPE.ERROR, 'com_generator - bindColumnSource error ', err)
+ });
+ } catch (e) {
+ vpLog.display(VP_LOG_TYPE.ERROR, 'com_generator - bindColumnSource: not supported data type. ', e);
+ }
+ });
+ } else {
+ // render option page
+ $(this.wrapSelector('.vp-ds-option-inner-box')).html(this.templateForMultiSelector());
+ this._columnSelector = new MultiSelector(this.wrapSelector('.vp-ds-df-multiselector'),
+ { mode: 'columns', parent: [data], selectedList: this.state.indexing, allowAdd: true }
+ );
+ }
// bind event
$(this.wrapSelector('#useIndex')).on('change', function() {
let checked = $(this).prop('checked');
that.state.useIndex = checked;
if (checked === true) {
$(that.wrapSelector('.vp-ds-df-multiselector')).hide();
+ $(that.wrapSelector('.vp-ds-df-singleselector')).hide();
$(that.wrapSelector('.vp-ds-df-index-box')).show();
} else {
$(that.wrapSelector('.vp-ds-df-multiselector')).show();
+ $(that.wrapSelector('.vp-ds-df-singleselector')).show();
$(that.wrapSelector('.vp-ds-df-index-box')).hide();
}
});
@@ -728,6 +821,7 @@ define([
let {
data, dataType,
useIndex,
+ singleColumn,
slicingStart1, slicingEnd1,
slicingStart2, slicingEnd2,
ndRowType, ndColType
@@ -742,21 +836,39 @@ define([
code.append('.index');
} else {
// use column selector
- if (this._columnSelector != null) {
- let result = this._columnSelector.getDataList();
- this.state.indexing = result.map(obj => obj.code); // save state
- let columnList = [];
- result && result.forEach(obj => {
- columnList.push(obj.code);
- });
- if (columnList.length > 0) {
- if (columnList.length == 1) {
- // return as Series
- code.appendFormat('[{0}]', columnList.join(', '));
- // change datatype to Series
- this.state.returnDataType = 'Series';
- } else {
- code.appendFormat('[[{0}]]', columnList.join(', '));
+ if (this.prop.columnSelection === 'single') {
+ // single selector
+ if (this.prop.returnFrameType === 'DataFrame') {
+ // return as DataFrame
+ code.appendFormat('[[{0}]]', singleColumn);
+ } else {
+ // return as Series
+ code.appendFormat('[{0}]', singleColumn);
+ this.state.returnDataType = 'Series';
+ }
+ } else {
+ // multiple selector
+ if (this._columnSelector != null) {
+ let result = this._columnSelector.getDataList();
+ this.state.indexing = result.map(obj => obj.code); // save state
+ let columnList = [];
+ result && result.forEach(obj => {
+ columnList.push(obj.code);
+ });
+ if (columnList.length > 0) {
+ if (columnList.length == 1) {
+ if (this.prop.returnFrameType === 'DataFrame') {
+ // return as DataFrame
+ code.appendFormat('[[{0}]]', columnList.join(', '));
+ } else {
+ // return as Series
+ code.appendFormat('[{0}]', columnList.join(', '));
+ // change datatype to Series
+ this.state.returnDataType = 'Series';
+ }
+ } else {
+ code.appendFormat('[[{0}]]', columnList.join(', '));
+ }
}
}
}
diff --git a/visualpython/js/com/component/ModelEditor.js b/visualpython/js/com/component/ModelEditor.js
index da24a5d0..c2fab057 100644
--- a/visualpython/js/com/component/ModelEditor.js
+++ b/visualpython/js/com/component/ModelEditor.js
@@ -125,7 +125,7 @@ define([
code: '${model}.fit(${fit_featureData})',
description: 'Fit Encoder/Scaler to X.',
options: [
- { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -134,13 +134,19 @@ define([
code: '${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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'transform': {
- ...defaultActions['transform'],
- description: 'Transform labels to normalized encoding.'
+ name: 'transform',
+ label: 'Transform',
+ code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
+ description: 'Transform labels to normalized encoding.',
+ options: [
+ { name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
+ { name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
+ ]
}
}
if (modelType != 'ColumnTransformer') {
@@ -152,12 +158,49 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
}
}
}
+ if (modelType == 'LabelEncoder') {
+ actions = {
+ ...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',
+ columnSelection: 'single', returnFrameType: 'Series' }
+ ]
+ },
+ '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',
+ columnSelection: 'single', returnFrameType: 'Series' },
+ { 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 labels to normalized encoding.',
+ options: [
+ { name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X',
+ columnSelection: 'single', returnFrameType: 'Series' },
+ { name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
+ ]
+ }
+ }
+ }
if (modelType === 'SMOTE') {
actions = {
'fit': {
@@ -322,7 +365,7 @@ define([
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' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -331,7 +374,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
@@ -346,17 +389,17 @@ define([
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Fit the Linear Discriminant Analysis model.',
options: [
- { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
- code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}${fit_trans_targetData})',
+ code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}, ${fit_trans_targetData})',
description: 'Fit to data, then transform it.',
options: [
- { name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
+ { name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
@@ -367,7 +410,7 @@ define([
code: '${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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
@@ -377,7 +420,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
@@ -391,7 +434,7 @@ define([
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' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -400,7 +443,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
@@ -410,7 +453,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
},
@@ -420,7 +463,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
diff --git a/visualpython/js/m_apps/File.js b/visualpython/js/m_apps/File.js
index 6a04a91c..2a3e8741 100644
--- a/visualpython/js/m_apps/File.js
+++ b/visualpython/js/m_apps/File.js
@@ -499,15 +499,15 @@ define([
return suggestInput.toTagString();
});
- // seperator suggest input
+ // separator suggest input
$(this.wrapSelector('#sep')).replaceWith(function() {
- // seperator list :
+ // separator list :
var sepList = [',', '|', '\\t', '\\n', ':', ';', '-', '_', '&', '/', '\\'];
var suggestInput = new SuggestInput();
suggestInput.setComponentID('sep');
suggestInput.addClass('vp-input vp-state');
suggestInput.setSuggestList(function() { return sepList; });
- suggestInput.setPlaceholder('Input seperator');
+ suggestInput.setPlaceholder('Input separator');
return suggestInput.toTagString();
});
}
diff --git a/visualpython/js/m_apps/Frame.js b/visualpython/js/m_apps/Frame.js
index fd786ad0..4e91083d 100644
--- a/visualpython/js/m_apps/Frame.js
+++ b/visualpython/js/m_apps/Frame.js
@@ -3620,7 +3620,12 @@ define([
code.appendFormat(", limit={0}", content['limit']);
}
} else {
- code.appendFormat("{0}.{1}()", subsetObjStr, content['method']);
+ if (content['method'] === 'mode') {
+ // get mode()'s first element (mode returns Series)
+ code.appendFormat("{0}.{1}()[0]", subsetObjStr, content['method']);
+ } else {
+ code.appendFormat("{0}.{1}()", subsetObjStr, content['method']);
+ }
}
code.append(')');
break;
diff --git a/visualpython/js/m_ml/Classification.js b/visualpython/js/m_ml/Classification.js
index f487be39..f327b611 100644
--- a/visualpython/js/m_ml/Classification.js
+++ b/visualpython/js/m_ml/Classification.js
@@ -50,7 +50,7 @@ define([
this.modelConfig = ML_LIBRARIES;
this.modelTypeList = {
- 'Classfication': ['lg-rgs', 'bern-nb', 'mulnom-nb', 'gaus-nb', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf'],
+ 'Classification': ['lg-rgs', 'bern-nb', 'mulnom-nb', 'gaus-nb', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf'],
}
diff --git a/visualpython/js/m_ml/Clustering.js b/visualpython/js/m_ml/Clustering.js
index c3781201..95ee735c 100644
--- a/visualpython/js/m_ml/Clustering.js
+++ b/visualpython/js/m_ml/Clustering.js
@@ -51,7 +51,7 @@ define([
this.modelTypeList = {
// 'Regression': ['ln-rgs', 'sv-rgs', 'dt-rgs', 'rf-rgs', 'gbm-rgs', 'xgb-rgs', 'lgbm-rgs', 'cb-rgs'],
- // 'Classfication': ['lg-rgs', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf'],
+ // 'Classification': ['lg-rgs', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf'],
// 'Auto ML': ['tpot-rgs', 'tpot-clf'],
'Clustering': ['k-means', 'agg-cls', 'gaus-mix', 'dbscan'],
// 'Dimension Reduction': ['pca', 'lda', 'svd', 'nmf']
diff --git a/visualpython/js/m_ml/FitPredict.js b/visualpython/js/m_ml/FitPredict.js
index cdd979b9..d48c20c5 100644
--- a/visualpython/js/m_ml/FitPredict.js
+++ b/visualpython/js/m_ml/FitPredict.js
@@ -41,6 +41,8 @@ define([
method: '',
action: {},
optionConfig: {},
+ modelEditorType: '',
+ modelEditorName: '',
userOption: '',
...this.state
}
@@ -315,7 +317,7 @@ define([
}
generateCode() {
- let { model } = this.state;
+ let { model, modelType, modelEditorName } = this.state;
let code = new com_String();
let replaceDict = {'${model}': model};
@@ -336,6 +338,31 @@ define([
code.appendLine();
code.append(allocateCode);
}
+ // Data Preparation > Scaling
+ const scalingTypeList = ['StandardScaler', 'RobustScaler', 'MinMaxScaler', 'Normalizer'];
+ // Dimension Reduction
+ const dimensionTypeList = ['PCA', 'NMF'];
+ if (scalingTypeList.includes(modelType) || dimensionTypeList.includes(modelType)) {
+ // fit_transform, transform returns df_trans also
+ switch (modelEditorName) {
+ case 'fit_transform':
+ const allocatedFitTrans = this.state.fit_trans_allocate || 'trans';
+ code.appendLine();
+ code.appendLine();
+ code.appendFormatLine("df_{0} = pd.DataFrame({1}, columns=[{2}])", allocatedFitTrans, allocatedFitTrans, this.state.fit_trans_featureData);
+ code.append("df_" + allocatedFitTrans);
+ break;
+ case 'transform':
+ const allocatedTrans = this.state.trans_allocate || 'trans';
+ code.appendLine();
+ code.appendLine();
+ code.appendFormatLine("df_{0} = pd.DataFrame({1}, columns=[{2}])", allocatedTrans, allocatedTrans, this.state.trans_featureData);
+ code.append("df_" + allocatedTrans);
+ break;
+ default:
+ break;
+ }
+ }
}
return code.toString();
@@ -409,7 +436,7 @@ define([
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' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -418,13 +445,19 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'transform': {
- ...defaultActions['transform'],
- description: 'Transform labels to normalized encoding.'
+ name: 'transform',
+ label: 'Transform',
+ code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
+ description: 'Transform labels to normalized encoding.',
+ options: [
+ { name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
+ { name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
+ ]
}
}
if (modelType != 'ColumnTransformer') {
@@ -436,12 +469,49 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
}
}
}
+ if (modelType == 'LabelEncoder') {
+ actions = {
+ ...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',
+ columnSelection: 'single', returnFrameType: 'Series' }
+ ]
+ },
+ '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',
+ columnSelection: 'single', returnFrameType: 'Series' },
+ { 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 labels to normalized encoding.',
+ options: [
+ { name: 'trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X',
+ columnSelection: 'single', returnFrameType: 'Series' },
+ { name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
+ ]
+ }
+ }
+ }
if (modelType === 'SMOTE') {
actions = {
'fit': {
@@ -606,7 +676,7 @@ define([
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' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -615,7 +685,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
@@ -630,7 +700,7 @@ define([
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Fit the Linear Discriminant Analysis model.',
options: [
- { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' }
]
},
@@ -640,7 +710,7 @@ define([
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}${fit_trans_targetData})',
description: 'Fit to data, then transform it.',
options: [
- { name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X' },
+ { name: 'fit_trans_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_targetData', label: 'Target Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'y' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
@@ -651,7 +721,7 @@ define([
code: '${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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
@@ -661,7 +731,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
@@ -675,7 +745,7 @@ define([
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' }
+ { name: 'fit_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' }
]
},
'fit_transform': {
@@ -684,7 +754,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
@@ -694,7 +764,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
},
@@ -704,7 +774,7 @@ define([
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_featureData', label: 'Feature Data', component: ['data_select'], var_type: ['DataFrame', 'Series', 'ndarray', 'list', 'dict'], value: 'X', returnFrameType: 'DataFrame' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
diff --git a/visualpython/js/m_ml/GridSearch.js b/visualpython/js/m_ml/GridSearch.js
index 32dbc967..1dfdbaa4 100644
--- a/visualpython/js/m_ml/GridSearch.js
+++ b/visualpython/js/m_ml/GridSearch.js
@@ -49,7 +49,7 @@ define([
this.modelTypeList = {
'Regression': ['ln-rgs', 'ridge', 'lasso', 'elasticnet', 'sv-rgs', 'dt-rgs', 'rf-rgs', 'gbm-rgs', 'xgb-rgs', 'lgbm-rgs', 'cb-rgs'],
- 'Classfication': ['lg-rgs', 'bern-nb', 'mulnom-nb', 'gaus-nb', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf']
+ 'Classification': ['lg-rgs', 'bern-nb', 'mulnom-nb', 'gaus-nb', 'sv-clf', 'dt-clf', 'rf-clf', 'gbm-clf', 'xgb-clf', 'lgbm-clf', 'cb-clf']
}
diff --git a/visualpython/js/m_ml/Pipeline.js b/visualpython/js/m_ml/Pipeline.js
index 6dfc1989..856c33db 100644
--- a/visualpython/js/m_ml/Pipeline.js
+++ b/visualpython/js/m_ml/Pipeline.js
@@ -210,6 +210,7 @@ define([
$(that.wrapSelector(`.vp-pp-step-page:not([data-name="${name}"])`)).hide();
$(that.wrapSelector(`.vp-pp-step-page[data-name="${name}"]`)).show();
if (ppObj.useApp === true) {
+ ppObj.app && that.handleAppView(name, ppObj.app);
ppObj.app && ppObj.app.open($(that.wrapSelector(`.vp-pp-step-page[data-name="${name}"]`)));
} else {
that.renderApp(name);
@@ -372,7 +373,7 @@ define([
// click next button
$(this.wrapSelector('.vp-pp-step-next:not(.disabled)')).on('click', function() {
let selectedTag = $(that.wrapSelector('.vp-pp-item.selected'));
- let nextTagList = $(selectedTag).nextAll('.vp-pp-item[data-flag="enabled"]:visible')
+ let nextTagList = $(selectedTag).nextAll('.vp-pp-item[data-flag="enabled"]:visible');
$(nextTagList[0]).trigger('click');
});
}
@@ -562,6 +563,32 @@ define([
case 'ml_dataSplit':
$(mlApp.wrapSelector('#inputData')).parent().hide();
break;
+ case 'ml_evaluation':
+ // for pipeline
+ $(mlApp.wrapSelector('.vp-upper-box')).hide();
+ $(mlApp.wrapSelector('.vp-upper-box.' + mlApp.state.modelType)).show();
+
+ $(mlApp.wrapSelector('.vp-eval-box')).hide();
+ $(mlApp.wrapSelector('.vp-eval-' + mlApp.state.modelType)).show();
+
+ if (mlApp.state.modelType == 'rgs') {
+ // Regression
+
+ } else if (mlApp.state.modelType == 'clf') {
+ // Classification
+ // if (this.state.roc_curve == false && this.state.auc == false) {
+ // $(page).find('.vp-ev-model.roc-auc').prop('disabled', true);
+ // }
+ } else {
+ // Clustering
+ if (mlApp.state.silhouetteScore == false) {
+ $(mlApp.wrapSelector('.vp-ev-model.silhouette')).prop('disabled', true);
+ }
+ if (mlApp.state.ari == false && mlApp.state.nmi == false) {
+ $(mlApp.wrapSelector('.vp-ev-model.ari-nmi')).prop('disabled', true);
+ }
+ }
+ break;
}
}
diff --git a/visualpython/js/m_ml/evaluation.js b/visualpython/js/m_ml/evaluation.js
index 1cf1552d..ed63120c 100644
--- a/visualpython/js/m_ml/evaluation.js
+++ b/visualpython/js/m_ml/evaluation.js
@@ -158,6 +158,9 @@ define([
$(page).find('.vp-upper-box').hide();
$(page).find('.vp-upper-box.' + this.state.modelType).show();
+
+ $(page).find('.vp-eval-box').hide();
+ $(page).find('.vp-eval-' + this.state.modelType).show();
if (this.state.modelType == 'rgs') {
// Regression
@@ -204,7 +207,7 @@ define([
let needMarkdown = false;
//====================================================================
- // Classfication
+ // Classification
//====================================================================
if (modelType == 'clf') {
if (confusion_matrix) {
@@ -351,7 +354,7 @@ define([
...codeCells
];
}
- // return as seperated cells
+ // return as separated cells
return codeCells;
}
diff --git a/visualpython/js/m_stats/Regression.js b/visualpython/js/m_stats/Regression.js
index 89b0738d..a38c811b 100644
--- a/visualpython/js/m_stats/Regression.js
+++ b/visualpython/js/m_stats/Regression.js
@@ -58,6 +58,7 @@ define([
normTest: true,
histogram: true,
scatterplot: true,
+ rmse: false,
...this.state
};
@@ -259,7 +260,7 @@ define([
// Multi-collinearity
multiCollinearity,
// Residual option
- statistics, normTest, histogram, scatterplot
+ statistics, normTest, histogram, scatterplot, rmse,
} = this.state;
let codeList = [];
let code = new com_String();
@@ -697,7 +698,7 @@ define([
}
// Residual option
- if (statistics === true || normTest === true || histogram === true || scatterplot === true) {
+ if (statistics === true || normTest === true || histogram === true || scatterplot === true || rmse === true) {
let residualTitle = 'Residual'
if (lastModelNum > 0) {
residualTitle += ' - Model ' + lastModelNum;
@@ -768,6 +769,14 @@ define([
code.appendLine(" plt.tight_layout()");
code.append(" plt.show()");
}
+ if (rmse === true) {
+ code.appendLine();
+ code.appendLine();
+ code.appendLine("# RMSE (Root Mean Squared Error)");
+ code.appendLine("_rmse = np.sqrt(_result.mse_resid)");
+ code.appendLine("display(Markdown('### RMSE (Root Mean Squared Error)'))");
+ code.append("display(Markdown(f'RMSE: {_rmse}'))");
+ }
}
codeList.push(code.toString());