@formant/ava/lib/insight/narrative/strategy/timeSeriesOutlier.js

A framework for automated visual analytics.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
/* eslint-disable no-template-curly-in-string */
var lodash_1 = require("lodash");
var ntv_1 = require("../../../ntv");
var base_1 = require("./base");
var helpers_1 = require("./helpers");
var little_date_1 = require("little-date");
var date_fns_1 = require("date-fns");
var variableMetaMap = {
    dateRange: {
        varType: 'time_desc',
    },
    measure: {
        varType: 'metric_name',
    },
    max: {
        varType: 'metric_value',
    },
    min: {
        varType: 'metric_value',
    },
    total: {
        varType: 'metric_value',
    },
    '.x': {
        varType: 'dim_value',
    },
    '.y': {
        varType: 'metric_value',
    },
    '.base': {
        varType: 'metric_value',
    },
    '.diff': {
        varType: 'delta_value',
    },
};
var TimeSeriesOutlierNarrativeStrategy = /** @class */ (function (_super) {
    tslib_1.__extends(TimeSeriesOutlierNarrativeStrategy, _super);
    function TimeSeriesOutlierNarrativeStrategy() {
        return _super !== null && _super.apply(this, arguments) || this;
    }
    TimeSeriesOutlierNarrativeStrategy.prototype.generateTextSpec = function (insightInfo, lang) {
        var patterns = insightInfo.patterns, data = insightInfo.data;
        var _a = patterns[0], measure = _a.measure, dimension = _a.dimension;
        var spec = (0, ntv_1.generateTextSpec)({
            structures: TimeSeriesOutlierNarrativeStrategy.structures[lang],
            variable: {
                dateRange: "".concat((0, little_date_1.formatDateRange)(new Date((0, lodash_1.first)(data)[dimension]), new Date((0, lodash_1.last)(data)[dimension]), { includeTime: false })),
                total: patterns.length,
                measure: measure,
                max: (0, lodash_1.maxBy)(data, measure)[measure],
                min: (0, lodash_1.minBy)(data, measure)[measure],
                outliers: patterns.map(function (point) {
                    var base = point.baselines[point.index];
                    var diff = point.y - base;
                    return tslib_1.__assign(tslib_1.__assign({}, point), { base: base, diffDesc: (0, helpers_1.getDiffDesc)(diff, lang), x: (0, date_fns_1.format)(new Date(point.x), 'MMM d, yyyy'), diff: diff });
                }),
            },
        });
        return spec.sections[0].paragraphs;
    };
    TimeSeriesOutlierNarrativeStrategy.insightType = 'time_series_outlier';
    TimeSeriesOutlierNarrativeStrategy.structures = {
        'zh-CN': [
            {
                template: '${dateRange}，${measure} 波动范围为最大值 ${max}, 最小值 ${min}，有 ${total} 个异常点，按超过基线大小排序如下：',
                variableMetaMap: variableMetaMap,
            },
            {
                template: '${.x}，${measure} 为 ${.y}, 相比基线（${.base}）${.diffDesc} ${.diff}。',
                displayType: 'bullet',
                bulletOrder: true,
                useVariable: 'outliers',
                variableMetaMap: variableMetaMap,
            },
        ],
        'en-US': [
            {
                template: '${total} outliers detected for ${measure} during ${dateRange}',
                displayType: 'paragraph',
                variableMetaMap: variableMetaMap,
            },
            {
                template: '${.x}: ${measure} at ${.y} (${.diffDesc} ${.diff} baseline)',
                displayType: 'bullet',
                bulletOrder: true,
                useVariable: 'outliers',
                variableMetaMap: variableMetaMap,
            },
        ],
    };
    return TimeSeriesOutlierNarrativeStrategy;
}(base_1.InsightNarrativeStrategy));
exports.default = TimeSeriesOutlierNarrativeStrategy;