/*

* Project Name : Visual Python

* Description : GUI-based Python code generator

* File Name : StudentstTest.js

* Author : Black Logic

* Note : Student's t-test

* License : GNU GPLv3 with Visual Python special exception

* Date : 2023. 05. 09

* Change Date :

*/

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

// [CLASS] StudentstTest

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

define([

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

'vp_base/js/com/com_util',

'vp_base/js/com/com_Const',

'vp_base/js/com/com_String',

'vp_base/js/com/com_generatorV2',

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

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

'vp_base/js/com/component/SuggestInput',

'vp_base/js/m_apps/Subset'

], function(stHTML, com_util, com_Const, com_String, com_generator, PopupComponent, DataSelector, SuggestInput, Subset) {

/**

* StudentstTest

* - confidence_interval is available on upper 1.10.0 version of scipy

*/

class StudentstTest 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 = {

testType: 'one-sample',

inputType: 'long-data',

data: '',

dataType: '',

testVariable: '',

testVariable1: '',

testVariable2: '',

groupingVariable: '',

group1: '',

group2: '',

group1_istext: true,

group2_istext: true,

pairedVariable1: '',

pairedVariable2: '',

testValue: '',

alterHypo: 'two-sided',

confInt: '95',

...this.state

};

this.columnBindDict = {

'one-sample': ['testVariable'],

'two-sample': ['testVariable', 'testVariable1', 'testVariable2', 'groupingVariable'],

'paired-sample': ['pairedVariable1', 'pairedVariable2']

};

this.subsetEditor = null;

}

_unbindEvent() {

super._unbindEvent();

var that = this;

$(document).off(this.wrapSelector('#testVariable'), 'change');

$(document).off(this.wrapSelector('#groupingVariable'), 'change');

}

_bindEvent() {

super._bindEvent();

/** Implement binding events */

var that = this;

// change test type

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

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

that.state.testType = testType;

that.handleVariableChange(that.state.data);

$(that.wrapSelector('.vp-st-option')).hide();

$(that.wrapSelector('.vp-st-option.' + testType)).show();

if (testType === 'two-sample') {

$(that.wrapSelector('.vp-st-option.two-sample-' + that.state.inputType)).show();

}

});

// change input type

$(this.wrapSelector('input[name="inputType"]:radio')).on('change', function() {

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

that.state.inputType = inputType;

$(that.wrapSelector('.vp-st-option.two-sample-long-data')).hide();

$(that.wrapSelector('.vp-st-option.two-sample-wide-data')).hide();

$(that.wrapSelector('.vp-st-option.two-sample-' + inputType)).show();

});

// data change event

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

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

that.handleVariableChange(data);

});

// change test variable

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

if (that.state.testType === 'one-sample') {

// get mean of data and show on placeholder

$(that.wrapSelector('#testValue')).prop('placeholder', '');

vpKernel.execute(com_util.formatString("int({0}[{1}].mean())", that.state.data, that.state.testVariable)).then(function(resultObj) {

let { result } = resultObj;

$(that.wrapSelector('#testValue')).prop('placeholder', result);

});

}

});

// change grouping variable

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

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

var colName = $(this).find('option:selected').text();

var colDtype = $(this).find('option:selected').attr('data-type');

that.state.groupingVariable = colCode;

// get result and load column list

vpKernel.getColumnCategory(that.state.data, colCode).then(function(resultObj) {

let { result } = resultObj;

$(that.wrapSelector('#group1')).val('');

$(that.wrapSelector('#group2')).val('');

that.state.group1 = '';

that.state.group2 = '';

that.state.group1_istext = true;

that.state.group2_istext = true;

try {

var category = JSON.parse(result);

// if (category && category.length > 0 && colDtype == 'object') {

// // if it's categorical column and its dtype is object, check 'Text' as default

// category.forEach(obj => {

// let selected1 = obj.value === that.state.group1;

// let selected2 = obj.value === that.state.group2;

// $(that.wrapSelector('#group1')).append(`<option value="${obj.value}" ${selected1?'selected':''}>${obj.label}</option>`);

// $(that.wrapSelector('#group2')).append(`<option value="${obj.value}" ${selected2?'selected':''}>${obj.label}</option>`);

// });

// }

var groupSuggest1 = new SuggestInput();

groupSuggest1.setComponentID('group1');

groupSuggest1.addClass('vp-input vp-state');

groupSuggest1.setSuggestList(function() { return category; });

groupSuggest1.setNormalFilter(true);

groupSuggest1.setPlaceholder('Select value');

$(that.wrapSelector('#group1')).replaceWith(groupSuggest1.toTagString());

var groupSuggest2 = new SuggestInput();

groupSuggest2.setComponentID('group2');

groupSuggest2.addClass('vp-input vp-state');

groupSuggest2.setSuggestList(function() { return category; });

groupSuggest2.setNormalFilter(true);

groupSuggest2.setPlaceholder('Select value');

$(that.wrapSelector('#group2')).replaceWith(groupSuggest2.toTagString());

if (category && category.length > 0) {

that.state.group1 = category[0].value;

that.state.group2 = category[0].value;

}

if (colDtype == 'object') {

// check as default

$(that.wrapSelector('#group1_istext')).prop('checked', true);

$(that.wrapSelector('#group2_istext')).prop('checked', true);

that.state.group1_istext = true;

that.state.group2_istext = true;

} else {

$(that.wrapSelector('#group1_istext')).prop('checked', false);

$(that.wrapSelector('#group2_istext')).prop('checked', false);

that.state.group1_istext = false;

that.state.group2_istext = false;

}

} catch {

$(that.wrapSelector('#group1')).val('');

$(that.wrapSelector('#group2')).val('');

}

});

});

}

handleVariableChange(data) {

let that = this;

this.state.data = data;

let columnBindList = this.columnBindDict[this.state.testType];

if (this.state.dataType === 'DataFrame') {

// DataFrame

columnBindList.forEach(col => {

$(that.wrapSelector('#' + col)).prop('disabled', false);

});

com_generator.vp_bindColumnSource(that, 'data', columnBindList, 'select', false, false);

} else {

// Series

columnBindList.forEach(col => {

$(that.wrapSelector('#' + col)).html('');

$(that.wrapSelector('#' + col)).prop('disabled', true);

});

}

}

templateForBody() {

let page = $(stHTML);

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.state.dataType = type;

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

},

select: function(data, type) {

that.state.data = data;

that.state.dataType = type;

$(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' } },

{

useAsModule: true,

useInputColumns: true,

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

pageThis: this,

finish: function(code, state) {

that.state.data = code;

that.state.dataType = state.returnType;

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

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

}

});

if (this.state.data !== '') {

let columnBindList = this.columnBindDict[this.state.testType];

com_generator.vp_bindColumnSource(this, 'data', columnBindList, 'select', false, false);

}

// control display option

$(this.wrapSelector('.vp-st-option')).hide();

$(this.wrapSelector('.vp-st-option.' + this.state.testType)).show();

if (this.state.testType === 'two-sample') {

$(this.wrapSelector('.vp-st-option.two-sample-' + this.state.inputType)).show();

}

}

generateCode() {

let {

testType, inputType, data,

testVariable, testVariable1, testVariable2, groupingVariable,

pairedVariable1, pairedVariable2,

group1, group2, group1_istext, group2_istext,

testValue, alterHypo, confInt

} = this.state;

let codeList = [];

let code = new com_String();

// 95% -> 0.95

confInt = confInt/100;

switch (testType) {

case 'one-sample':

code.appendLine("# One-sample t-test");

// variable declaration

code.appendFormatLine("vp_df = {0}.dropna().copy()", data);

code.appendLine("");

// 1. Normality test

code.appendLine("# Normality test (Shapiro-Wilk)");

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

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

code.appendFormatLine("_res = stats.shapiro(vp_df[{0}])", testVariable);

code.appendLine("display(Markdown('### Normality test (Shapiro-Wilk)'))");

code.appendLine("display(pd.DataFrame(data={'Statistic':_res.statistic,'p-value':_res.pvalue},index=['Normality test (Shapiro-Wilk)']))");

code.appendLine("");

// 2. One-sample Statistics

code.appendLine("# Statistics");

code.appendLine("display(Markdown('### Statistics'))");

code.appendFormatLine("display(pd.DataFrame(data={'N':vp_df[{0}].size,'Mean':vp_df[{1}].mean(),", testVariable, testVariable);

code.appendFormatLine(" 'Std. Deviation':vp_df[{0}].std(),", testVariable);

code.appendFormatLine(" 'Std. Error Mean':vp_df[{0}].std()/np.sqrt(vp_df[{1}].size)},", testVariable, testVariable);

code.appendLine(" index=['Statistics']))");

code.appendLine("");

// 3. One-sample t-test

code.appendLine("# One-sample t-test");

code.appendFormatLine("_res = stats.ttest_1samp(vp_df[{0}], popmean={1}, alternative='{2}')", testVariable, testValue, alterHypo);

code.appendFormatLine("_lower, _upper = _res.confidence_interval(confidence_level={0})", confInt);

code.appendLine("display(Markdown('### One-sample t-test'))");

code.appendFormatLine("display(pd.DataFrame(data={'Statistic':_res.statistic,'dof':_res.df,'Alternative':'{0}',", alterHypo);

code.appendFormatLine(" 'p-value':_res.pvalue,'Test Value':{0},'Mean difference':vp_df[{1}].mean()-{2},", testValue, testVariable, testValue);

code.appendFormatLine(" 'Confidence interval':{0},'Lower':_lower,'Upper':_upper},", confInt);

code.append(" index=['One-sample t-test']))");

break;

case 'two-sample':

code.appendLine("# Independent two-sample t-test");

// variable declaration

if (inputType === 'long-data') {

code.appendFormatLine("vp_df1 = {0}[({1}[{2}] == {3})][{4}].dropna().copy()", data, data, groupingVariable, com_util.convertToStr(group1, group1_istext), testVariable);

code.appendFormatLine("vp_df2 = {0}[({1}[{2}] == {3})][{4}].dropna().copy()", data, data, groupingVariable, com_util.convertToStr(group2, group2_istext), testVariable);

} else if (inputType === 'wide-data') {

code.appendFormatLine("vp_df1 = {0}[{1}].dropna().copy()", data, testVariable1);

code.appendFormatLine("vp_df2 = {0}[{1}].dropna().copy()", data, testVariable2);

}

code.appendLine("");

// 1. Normality test

code.appendLine("# Normality test (Shapiro-Wilk)");

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

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

code.appendLine("_res1 = stats.shapiro(vp_df1)");

code.appendLine("_res2 = stats.shapiro(vp_df2)");

code.appendLine("display(Markdown('### Normality test (Shapiro-Wilk)'))");

code.appendLine("display(pd.DataFrame(data={'Statistic':[_res1.statistic,_res2.statistic],'p-value':[_res1.pvalue,_res2.pvalue]},");

code.appendLine(" index=[['Normality test (Shapiro-Wilk)' for i in range(2)],['Variable1','Variable2']]))");

code.appendLine("");

// 2. Equal Variance test

code.appendLine("# Equal Variance test (Levene)");

code.appendLine("display(Markdown('### Equal Variance test (Levene)'))");

code.appendLine("_res = stats.levene(vp_df1, vp_df2, center='mean')");

code.appendLine("display(pd.DataFrame(data={'Statistic':_res.statistic,'p-value':_res.pvalue}, index=['Equal Variance test (Levene)']))");

code.appendLine("");

// 3. Independent two-sample Statistics

code.appendLine("# Statistics");

code.appendLine("display(Markdown('### Statistics'))");

code.appendLine("display(pd.DataFrame(data={'N':[vp_df1.size,vp_df2.size],");

code.appendLine(" 'Mean':[vp_df1.mean(),vp_df2.mean()],");

code.appendLine(" 'Std. Deviation':[vp_df1.std(),vp_df2.std()],");

code.appendLine(" 'Std. Error mean':[vp_df1.std()/np.sqrt(vp_df1.size),");

code.appendLine(" vp_df2.std()/np.sqrt(vp_df2.size )]},");

code.appendLine(" index=[['Statistics' for i in range(2)],['Variable1','Variable2']]))");

code.appendLine("");

// 4. Independent two-sample t-test

code.appendLine("# Independent two-sample t-test");

code.appendFormatLine("_res1 = stats.ttest_ind(vp_df1, vp_df2, equal_var=True, alternative='{0}')", alterHypo);

code.appendFormatLine("_res2 = stats.ttest_ind(vp_df1, vp_df2, equal_var=False, alternative='{0}')", alterHypo);

code.appendLine("display(Markdown('### Independent two-sample t-test'))");

code.appendFormatLine("display(pd.DataFrame(data={'Statistic':[_res1.statistic,_res2.statistic],'Alternative':['{0}' for i in range(2)],", alterHypo);

code.appendLine(" 'p-value':[_res1.pvalue,_res2.pvalue],");

code.appendLine(" 'Mean difference':[vp_df1.mean()-vp_df2.mean() for i in range(2)]},");

code.appendLine(" index=[['Independent two-sample t-test' for i in range(2)],['Equal variance' for i in range(2)],[True,False]]))");

code.append("display(Markdown('If equal_var is False, perform Welch\\\'s t-test, which does not assume equal population variance'))");

break;

case 'paired-sample':

// variable declaration

code.appendLine("# Paired samples t-test");

code.appendFormatLine("vp_df = {0}.dropna().copy()", data);

code.appendLine("");

code.appendFormatLine("try: vp_df[{0}].reset_index(drop=True, inplace=True)", pairedVariable1);

code.appendLine("except: pass");

code.appendFormatLine("try: vp_df[{0}].reset_index(drop=True, inplace=True)", pairedVariable2);

code.appendLine("except: pass");

code.appendLine("");

// 1. Normality test

code.appendLine("# Normality test (Shapiro-Wilk)");

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

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

code.appendFormatLine("_res = stats.shapiro(vp_df[{0}]-vp_df[{1}])", pairedVariable1, pairedVariable2);

code.appendLine("display(Markdown('### Normality test (Shapiro-Wilk)'))");

code.appendLine("display(pd.DataFrame(data={'Statistic':_res.statistic,'p-value':_res.pvalue},");

code.appendLine(" index=['Normality test (Shapiro-Wilk): Paired differences']))");

code.appendLine("");

// 2. Paired samples Statistics

code.appendLine("# Statistics");

code.appendLine("display(Markdown('### Statistics'))");

code.appendFormatLine("display(pd.DataFrame(data={'N':[vp_df[{0}].size,vp_df[{1}].size,vp_df[{2}].size],", pairedVariable1, pairedVariable2, pairedVariable1);

code.appendFormatLine(" 'Mean':[vp_df[{0}].mean(),vp_df[{1}].mean(),(vp_df[{2}]-vp_df[{3}]).mean()],", pairedVariable1, pairedVariable2, pairedVariable1, pairedVariable2);

code.appendFormatLine(" 'Std. Deviation':[vp_df[{0}].std(),vp_df[{1}].std(),(vp_df[{2}]-vp_df[{3}]).std()],", pairedVariable1, pairedVariable2, pairedVariable1, pairedVariable2);

code.appendFormatLine(" 'Std. Error mean':[vp_df[{0}].std()/np.sqrt(vp_df[{1}].size),", pairedVariable1, pairedVariable1);

code.appendFormatLine(" vp_df[{0}].std()/np.sqrt(vp_df[{1}].size),", pairedVariable2, pairedVariable2);

code.appendFormatLine(" (vp_df[{0}]-vp_df[{1}]).std()/np.sqrt(vp_df[{2}].size)]},", pairedVariable1, pairedVariable2, pairedVariable1);

code.appendLine(" index=[['Statistics' for i in range(3)],['Variable1','Variable2','Paired differences']]))");

code.appendLine("");

// 3. Paired samples t-test

code.appendLine("# Paired samples t-test");

code.appendFormatLine("_res = stats.ttest_rel(vp_df[{0}], vp_df[{1}], alternative='{2}')", pairedVariable1, pairedVariable2, alterHypo);

code.appendFormatLine("_lower, _upper = _res.confidence_interval(confidence_level={0})", confInt);

code.appendLine("display(Markdown('### Paired samples t-test'))");

code.appendFormatLine("display(pd.DataFrame(data={'Statistic':_res.statistic,'dof':_res.df,'Alternative':'{0}',", alterHypo);

code.appendFormatLine(" 'p-value':_res.pvalue,'Mean difference':(vp_df[{0}]-vp_df[{1}]).mean(),", pairedVariable1, pairedVariable2);

code.appendFormatLine(" 'Confidence interval':{0},'Lower':_lower,'Upper':_upper},", confInt);

code.append(" index=['Paired samples t-test']))");

break;

}

codeList.push(code.toString());

return codeList;

}

}

return StudentstTest;

});