/*

* Project Name : Visual Python

* Description : GUI-based Python code generator

* File Name : CorrAnalysis.js

* Author : Black Logic

* Note : Correlation Analysis

* License : GNU GPLv3 with Visual Python special exception

* Date : 2023. 05. 24

* Change Date :

*/

//============================================================================

// [CLASS] CorrAnalysis

//============================================================================

define([

__VP_TEXT_LOADER__('vp_base/html/m_stats/corrAnalysis.html'),

'vp_base/js/com/com_util',

'vp_base/js/com/com_Const',

'vp_base/js/com/com_String',

'vp_base/js/com/component/PopupComponent',

'vp_base/js/com/component/DataSelector',

'vp_base/js/com/component/MultiSelector',

'vp_base/js/m_apps/Subset'

], function(eqHTML, com_util, com_Const, com_String, PopupComponent, DataSelector, MultiSelector, Subset) {

/**

* CorrAnalysis

*/

class CorrAnalysis extends PopupComponent {

_init() {

super._init();

/** Write codes executed before rendering */

this.config.sizeLevel = 2;

this.config.checkModules = ['pd'];

this.config.docs = 'https://docs.scipy.org/doc/scipy/reference/';

this.state = {

data: '',

variable: [],

corrType: 'pearson',

corrAnlaysis: true,

corrMatrix: true,

corrHeatmap: false,

scatterMatrix: false,

...this.state

};

this.subsetEditor = null;

this.columnSelector = null;

}

_bindEvent() {

super._bindEvent();

/** Implement binding events */

var that = this;

$(this.wrapSelector('#data')).on('change', function() {

let data = $(this).val();

that.handleVariableChange(data);

});

}

handleVariableChange(data) {

this.state.data = data;

// render variable selector

this.columnSelector = new MultiSelector(this.wrapSelector('#variable'),

{ mode: 'columns', parent: data, showDescription: false }

);

}

templateForBody() {

let page = $(eqHTML);

let that = this;

// generate dataselector

let dataSelector = new DataSelector({

pageThis: this, id: 'data', placeholder: 'Select data', required: true, boxClasses: 'vp-flex-gap5',

allowDataType: ['DataFrame'], withPopup: false,

finish: function(data, type) {

that.state.data = data;

$(that.wrapSelector('#data')).trigger('change');

},

select: function(data, type) {

that.state.data = data;

$(that.wrapSelector('#data')).trigger('change');

}

});

$(page).find('#data').replaceWith(dataSelector.toTagString());

return page;

}

render() {

super.render();

let that = this;

// render Subset

this.subsetEditor = new Subset({

pandasObject: '',

config: { name: 'Subset', category: this.name } },

{

useAsModule: true,

useInputColumns: true,

targetSelector: this.wrapSelector('#data'),

pageThis: this,

finish: function(code) {

$(that.wrapSelector('#data')).val(code);

that.handleVariableChange(code);

}

});

// render variable selector

this.columnSelector = new MultiSelector(this.wrapSelector('#variable'),

{ mode: 'columns', parent: this.state.data, selectedList: this.state.variable?.map(x=>x.code), showDescription: false }

);

}

generateCode() {

let { data, variable, corrType, corrAnlaysis, corrMatrix, corrHeatmap, scatterMatrix } = this.state;

let codeList = [];

let code = new com_String();

// data declaration

code.appendFormat("vp_df = {0}", data);

if (this.columnSelector) {

let columns = this.columnSelector.getDataList();

this.state.variable = columns;

if (columns.length > 0) {

code.appendFormat("[[{0}]]", columns.map(x => x.code).join(', '));

}

}

code.append('.dropna().copy()');

let corrTypeLabel = $(this.wrapSelector('#corrType option:selected')).text();

// Display option : Correlation Analysis

if (corrAnlaysis === true) {

// Inner function : vp_confidence_interval_corr

this.addCheckModules('vp_confidence_interval_corr');

code.appendLine();

code.appendLine();

code.appendLine("# Correlation Analysis");

code.appendLine("from scipy import stats");

code.appendLine("from IPython.display import display, Markdown");

code.appendLine("_dfr = pd.DataFrame()");

code.appendLine("for i, col1 in enumerate(vp_df.columns):");

code.appendLine(" for j, col2 in enumerate(vp_df.columns):");

code.appendLine(" if i >= j: continue");

code.appendLine(" if pd.api.types.is_numeric_dtype(vp_df[col1]) and pd.api.types.is_numeric_dtype(vp_df[col2]):");

code.appendFormatLine(" _res = vp_confidence_interval_corr(vp_df[col1], vp_df[col2], method='{0}')", corrType);

code.appendLine(" _df_t = pd.DataFrame(data={'Variable1':col1,'Variable2':col2,'N':vp_df[col1].size,'Correlation coefficient':_res[0],");

code.appendLine(" 'p-value':_res[1],'Lower(95%)':_res[2],'Upper(95%)':_res[3]}, index=[0])");

code.appendLine(" _dfr = pd.concat([_dfr, _df_t]).reset_index(drop=True)");

code.appendFormatLine("display(Markdown('### Correlation Analysis: {0}'))", corrTypeLabel.replace("'", "\\'"));

code.append("display(_dfr)");

}

// Display option : Correlation Matrix

if (corrMatrix === true) {

code.appendLine();

code.appendLine();

code.appendFormatLine("# Correlation matrix: {0}", corrTypeLabel);

code.appendLine("from IPython.display import display");

code.appendFormat("display(vp_df.corr(method='{0}', numeric_only=True).round(2))", corrType);

}

if (corrHeatmap === true || scatterMatrix === true) {

code.appendLine();

code.appendLine();

code.appendLine("# Chart");

code.appendLine("import seaborn as sns");

code.appendLine("import warnings");

code.appendLine("with warnings.catch_warnings():");

code.append(" warnings.simplefilter(action='ignore', category=Warning)");

// Display option : Correlation Heatmap

if (corrHeatmap === true) {

code.appendLine();

code.appendLine();

code.appendLine(" # Heatmap");

code.appendFormatLine(" sns.heatmap(vp_df.corr(method='{0}', numeric_only=True), annot=True, fmt='.2f', cmap='coolwarm')", corrType);

code.appendFormatLine(" plt.title('Correlation heatmap: {0}')", corrTypeLabel.replace("'", "\\'"));

code.append(" plt.show()");

}

// Display option : Scatter Matrix

if (scatterMatrix === true) {

code.appendLine();

code.appendLine();

code.appendLine(" # Scatter matrix");

code.appendLine(" pd.plotting.scatter_matrix(vp_df)");

code.append(" plt.show()");

}

}

codeList.push(code.toString());

return codeList;

}

}

return CorrAnalysis;

});