@visactor/vmind/cjs/atom/dataInsight/algorithms/outlier/statistics.js

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"use strict";

Object.defineProperty(exports, "__esModule", {
    value: !0
}), exports.StatisticsAlo = exports.getAbnormalByIQR = exports.getAbnormalByZScores = void 0;

const vutils_1 = require("@visactor/vutils"), type_1 = require("../../type"), types_1 = require("../../../../types"), common_1 = require("../../../../utils/common"), statistics_1 = require("../statistics"), utils_1 = require("../../utils");

function getAbnormalByZScores(data, threshold = 3) {
    const {mean: mean, stdDev: stdDev} = (0, statistics_1.getMeanAndstdDev)(data.map((v => v.value)));
    return data.filter((v => Math.abs((v.value - mean) / stdDev) >= threshold)).map((v => v.index));
}

function calculateQuantile(sortedData, quantile) {
    const pos = (sortedData.length - 1) * quantile, base = Math.floor(pos), rest = pos - base;
    return void 0 !== sortedData[base + 1] ? sortedData[base] + rest * (sortedData[base + 1] - sortedData[base]) : sortedData[base];
}

function getAbnormalByIQR(data) {
    const sortedData = data.slice().sort(((a, b) => a.value - b.value)), dataList = sortedData.map((v => v.value)), q1 = calculateQuantile(dataList, .25), q3 = calculateQuantile(dataList, .75), iqr = q3 - q1, lowerBound = q1 - 1.5 * iqr, upperBound = q3 + 1.5 * iqr;
    return sortedData.filter((dataPoint => dataPoint.value < lowerBound || dataPoint.value > upperBound)).map((v => v.index));
}

exports.getAbnormalByZScores = getAbnormalByZScores, exports.getAbnormalByIQR = getAbnormalByIQR;

const zscoreIQRAlgoFunc = (context, options) => {
    const result = [], {threshold: threshold = 3} = options || {}, {seriesDataMap: seriesDataMap, cell: cell, spec: spec} = context, {y: celly} = cell, yField = (0, 
    vutils_1.isArray)(celly) ? celly.flat() : [ celly ];
    return Object.keys(seriesDataMap).forEach((group => {
        const dataset = seriesDataMap[group];
        yField.forEach((field => {
            if ((0, utils_1.isPercenSeries)(spec, field)) return;
            const dataList = dataset.map(((d, index) => ({
                index: index,
                value: Number(d.dataItem[field])
            }))).filter((v => (0, common_1.isValidData)(v.value) && !isNaN(v.value))), zScoreResult = dataList.length >= 30 ? getAbnormalByZScores(dataList, threshold) : null, iqrResult = dataList.length >= 10 ? getAbnormalByIQR(dataList) : null;
            ((zScoreResult ? (0, common_1.getIntersection)(zScoreResult, iqrResult) : iqrResult) || []).forEach((index => {
                const insightDataItem = dataset[index], lofInsight = {
                    type: type_1.InsightType.Outlier,
                    data: [ insightDataItem ],
                    fieldId: field,
                    value: insightDataItem.dataItem[field],
                    significant: 1,
                    seriesName: group
                };
                result.push(lofInsight);
            }));
        }));
    })), result;
};

exports.StatisticsAlo = {
    name: "statistics",
    forceChartType: [ types_1.ChartType.DualAxisChart, types_1.ChartType.LineChart, types_1.ChartType.BarChart, types_1.ChartType.AreaChart, types_1.ChartType.RadarChart, types_1.ChartType.PieChart, types_1.ChartType.RoseChart, types_1.ChartType.WaterFallChart ],
    insightType: type_1.InsightType.Outlier,
    algorithmFunction: zscoreIQRAlgoFunc,
    supportPercent: !1
};
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