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https://github.com/christianvidalwolf-prog/CrazeAnalytix.git
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feat: remove MKT and Experiments tabs and all related code
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Christian Vidal Wolf
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
9f9c0c2975
commit
76d901fcde
@@ -1,541 +0,0 @@
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import {
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Experiment,
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CombinedKPIs,
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DifferenceInDifferencesResult,
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DiDMetricResult,
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ExperimentVerdict,
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} from '../types';
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import { getExperimentAsins } from './experiments';
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// ============ Math Helpers ============
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function erf(x: number): number {
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const a1 = 0.254829592, a2 = -0.284496736, a3 = 1.421413741;
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const a4 = -1.453152027, a5 = 1.061405429, p = 0.3275911;
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const sign = x < 0 ? -1 : 1;
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const abs = Math.abs(x);
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const t = 1.0 / (1.0 + p * abs);
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const y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.exp(-abs * abs);
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return sign * y;
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}
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function normalCDF(x: number): number {
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return 0.5 * (1 + erf(x / Math.sqrt(2)));
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}
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// ============ Weekly Metric Aggregation ============
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interface WeeklyMetrics {
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week: string; // "2025-W05"
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timestamp: number;
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units: number;
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revenue: number;
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adRevenue: number;
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sessions: number; // glanceViews
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cost: number;
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clicks: number;
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impressions: number;
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detail_bsr: number;
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bsrCount: number;
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}
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interface ComputedWeeklyMetrics extends WeeklyMetrics {
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cvr: number;
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ctr: number;
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roas: number;
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}
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function getWeekStartSunday(year: number, week: number): number {
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const jan1 = new Date(year, 0, 1);
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const day = jan1.getDay(); // 0 = Sunday, 1 = Monday...
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const startYear = new Date(year, 0, 1 - day); // Sunday of the week containing Jan 1
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return startYear.getTime() + (week - 1) * 7 * 86400000;
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}
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/**
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* Get ISO week number for a given date
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* ISO weeks start on Monday, week 1 contains the first Thursday of the year
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*/
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function getISOWeek(date: Date): { year: number; week: number } {
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const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
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const dayNum = d.getUTCDay() || 7; // Convert Sunday (0) to 7
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d.setUTCDate(d.getUTCDate() + 4 - dayNum); // Set to nearest Thursday
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const yearStart = new Date(Date.UTC(d.getUTCFullYear(), 0, 1));
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const weekNum = Math.ceil((((d.getTime() - yearStart.getTime()) / 86400000) + 1) / 7);
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return { year: d.getUTCFullYear(), week: weekNum };
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}
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/**
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* Get the Monday of an ISO week
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*/
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function getISOWeekMonday(year: number, week: number): number {
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const jan4 = new Date(Date.UTC(year, 0, 4)); // Jan 4 is always in week 1
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const dayOfWeek = jan4.getUTCDay() || 7; // Get day of week (1-7, Monday=1)
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const week1Monday = new Date(Date.UTC(year, 0, 4 - (dayOfWeek - 1)));
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return week1Monday.getTime() + (week - 1) * 7 * 86400000;
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}
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/**
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* Calculate the number of distinct ISO weeks between two dates
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* and return the expanded date range (first Monday to last Sunday)
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*/
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function calculateISOWeekRange(startDate: Date, endDate: Date): {
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numWeeks: number;
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expandedStartTs: number;
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expandedEndTs: number;
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weekKeys: string[];
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} {
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const startISO = getISOWeek(startDate);
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const endISO = getISOWeek(endDate);
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const weekKeys: string[] = [];
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let currentYear = startISO.year;
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let currentWeek = startISO.week;
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// Collect all week keys between start and end
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while (true) {
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const weekKey = `${currentYear}-W${String(currentWeek).padStart(2, '0')}`;
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weekKeys.push(weekKey);
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if (currentYear === endISO.year && currentWeek === endISO.week) {
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break;
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}
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// Move to next week
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currentWeek++;
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const weeksInYear = getWeeksInYear(currentYear);
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if (currentWeek > weeksInYear) {
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currentWeek = 1;
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currentYear++;
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}
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}
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// Get expanded range: from Monday of first week to Sunday of last week
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const expandedStartTs = getISOWeekMonday(startISO.year, startISO.week);
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const expandedEndTs = getISOWeekMonday(endISO.year, endISO.week) + 7 * 86400000;
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return {
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numWeeks: weekKeys.length,
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expandedStartTs,
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expandedEndTs,
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weekKeys,
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};
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}
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/**
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* Get number of weeks in an ISO year (52 or 53)
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*/
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function getWeeksInYear(year: number): number {
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const dec28 = new Date(Date.UTC(year, 11, 28)); // Dec 28 is always in last week
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const iso = getISOWeek(dec28);
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return iso.week;
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}
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function aggregateWeeklyMetrics(
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asinSet: Set<string>,
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salesData: CombinedKPIs[],
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marketplace: string
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): ComputedWeeklyMetrics[] {
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const weeklyMap = new Map<string, WeeklyMetrics>();
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for (const r of salesData) {
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const asin = (r.asin || '').trim().toUpperCase();
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if (!asinSet.has(asin)) continue;
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const mkt = (r.marketplace || (r as any).customer || '').toLowerCase();
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if (marketplace && marketplace !== 'All' && !mkt.includes(marketplace.toLowerCase())) continue;
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const weekNum = r.week || 1;
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const year = r.year || new Date().getFullYear();
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const key = `${year}-W${String(weekNum).padStart(2, '0')}`;
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if (!weeklyMap.has(key)) {
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// Use ISO week start (Monday) instead of Sunday for consistency
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weeklyMap.set(key, {
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week: key,
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timestamp: getISOWeekMonday(year, weekNum),
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units: 0,
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revenue: 0,
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adRevenue: 0,
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sessions: 0,
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cost: 0,
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clicks: 0,
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impressions: 0,
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detail_bsr: 0,
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bsrCount: 0,
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});
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}
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const w = weeklyMap.get(key)!;
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w.units += r.unitsTotal || (r as any).units || 0;
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w.revenue += r.salesTotal || (r as any).sellOut || 0;
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w.adRevenue += r.salesAds || 0;
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w.sessions += r.glanceViews || 0;
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w.cost += Number(r.cost) || 0;
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w.clicks += r.clicks || 0;
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w.impressions += r.impressions || 0;
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if (r.detailLevelBSR != null) {
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w.detail_bsr += r.detailLevelBSR;
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w.bsrCount += 1;
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}
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}
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// Debug ad map
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console.log(`[aggregateWeeklyMetrics] ASIN match for ${marketplace}:`, asinSet);
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const debugArr = Array.from(weeklyMap.values());
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const hasAds = debugArr.some(w => w.cost > 0 || w.adRevenue > 0);
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if (!hasAds) {
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console.log(`[aggregateWeeklyMetrics WARNING] 0 ad data grouped for mkt ${marketplace}!`, debugArr);
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} else {
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console.log(`[aggregateWeeklyMetrics SUCCESS] Found ad usage:`, debugArr.filter(w => w.cost > 0));
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}
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return debugArr
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.sort((a, b) => a.timestamp - b.timestamp)
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.map(w => ({
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...w,
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cvr: w.sessions > 0 ? (w.units / w.sessions) * 100 : 0,
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ctr: w.impressions > 0 ? (w.clicks / w.impressions) * 100 : 0,
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roas: w.cost > 0 ? w.adRevenue / w.cost : 0,
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detail_bsr: w.bsrCount > 0 ? w.detail_bsr / w.bsrCount : 0,
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}));
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}
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function getMetricValue(w: ComputedWeeklyMetrics, metric: string): number {
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switch (metric) {
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case 'units': return w.units;
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case 'sessions': return w.sessions;
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case 'cvr': return w.cvr;
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case 'ctr': return w.ctr;
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case 'roas': return w.roas;
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case 'revenue': return w.revenue;
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case 'acos': return w.cost > 0 && w.adRevenue > 0 ? (w.cost / w.adRevenue) * 100 : 0;
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case 'detail_bsr': return w.detail_bsr;
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default: return w.units;
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}
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}
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// ============ DiD Computation ============
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function parseLocalDate(dateStr?: string): Date {
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if (!dateStr) return new Date();
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const [y, m, d] = dateStr.split('T')[0].split('-');
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return new Date(Number(y), Number(m) - 1, Number(d));
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}
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function splitPeriods(
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data: ComputedWeeklyMetrics[],
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startTs: number,
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endTs: number,
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beforeStartTs: number,
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beforeEndTs: number
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): { before: ComputedWeeklyMetrics[]; after: ComputedWeeklyMetrics[] } {
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const before: ComputedWeeklyMetrics[] = [];
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const after: ComputedWeeklyMetrics[] = [];
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for (const w of data) {
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const weekStartTs = w.timestamp;
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const weekEndTs = w.timestamp + 6 * 86400000 + 86399999;
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const overlapsAfter = weekEndTs >= startTs && weekStartTs < endTs;
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const overlapsBefore = weekEndTs >= beforeStartTs && weekStartTs < beforeEndTs;
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if (overlapsAfter) {
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after.push(w);
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} else if (overlapsBefore) {
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before.push(w);
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}
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}
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return { before, after };
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}
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function avgMetric(data: ComputedWeeklyMetrics[], metric: string, durationWeeks: number): number {
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if (data.length === 0) return 0;
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// For rates and ratios, we must sum the raw absolute components across all included weeks
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// and THEN calculate the ratio, otherwise averaging percentages yields mathematically incorrect results.
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if (metric === 'cvr') {
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const totalUnits = data.reduce((s, w) => s + w.units, 0);
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const totalSessions = data.reduce((s, w) => s + w.sessions, 0);
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return totalSessions > 0 ? (totalUnits / totalSessions) * 100 : 0;
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}
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if (metric === 'ctr') {
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const totalClicks = data.reduce((s, w) => s + w.clicks, 0);
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const totalImpressions = data.reduce((s, w) => s + w.impressions, 0);
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return totalImpressions > 0 ? (totalClicks / totalImpressions) * 100 : 0;
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}
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if (metric === 'roas') {
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const totalAdRevenue = data.reduce((s, w) => s + w.adRevenue, 0);
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const totalCost = data.reduce((s, w) => s + w.cost, 0);
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return totalCost > 0 ? totalAdRevenue / totalCost : 0;
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}
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if (metric === 'acos') {
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const totalAdRevenue = data.reduce((s, w) => s + w.adRevenue, 0);
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const totalCost = data.reduce((s, w) => s + w.cost, 0);
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return totalAdRevenue > 0 && totalCost > 0 ? (totalCost / totalAdRevenue) * 100 : 0;
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}
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if (metric === 'detail_bsr') {
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// For detail_bsr, we want the average of the non-zero weeks
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const validWeeks = data.filter(w => w.detail_bsr > 0);
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if (validWeeks.length === 0) return 0;
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const sum = validWeeks.reduce((s, w) => s + w.detail_bsr, 0);
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return sum / validWeeks.length;
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}
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// For absolute quantities (units, revenue, sessions), we sum them and divide by the number of weeks
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// Use actual data.length instead of theoretical durationWeeks for accuracy
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const sum = data.reduce((s, w) => s + getMetricValue(w, metric), 0);
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return sum / data.length;
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}
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const METRICS = ['units', 'sessions', 'cvr', 'ctr', 'roas', 'revenue', 'acos', 'detail_bsr'];
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const LOWER_IS_BETTER = new Set(['acos', 'detail_bsr']);
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function computeMetricDiD(
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treatmentBefore: ComputedWeeklyMetrics[],
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treatmentAfter: ComputedWeeklyMetrics[],
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controlBefore: ComputedWeeklyMetrics[],
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controlAfter: ComputedWeeklyMetrics[],
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metric: string,
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hasControlGroup: boolean,
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treatmentDurationWeeks: number,
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baselineDurationWeeks: number
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): DiDMetricResult {
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const tBefore = avgMetric(treatmentBefore, metric, baselineDurationWeeks);
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const tAfter = avgMetric(treatmentAfter, metric, treatmentDurationWeeks);
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const cBefore = hasControlGroup ? avgMetric(controlBefore, metric, baselineDurationWeeks) : 0;
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const cAfter = hasControlGroup ? avgMetric(controlAfter, metric, treatmentDurationWeeks) : 0;
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let didEstimate: number;
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if (hasControlGroup) {
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didEstimate = (tAfter - tBefore) - (cAfter - cBefore);
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} else {
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didEstimate = tAfter - tBefore;
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}
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// For ACOS, lower is better — invert the estimate
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if (LOWER_IS_BETTER.has(metric)) {
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didEstimate = -didEstimate;
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}
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const liftPercent = tBefore !== 0 ? (didEstimate / Math.abs(tBefore)) * 100 : 0;
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// Bayesian posterior probability
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const weeklyDiffs: number[] = [];
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const minLen = Math.min(treatmentAfter.length, hasControlGroup ? controlAfter.length : treatmentAfter.length);
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for (let i = 0; i < minLen; i++) {
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const tVal = getMetricValue(treatmentAfter[i], metric);
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let diff: number;
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if (hasControlGroup && controlAfter[i]) {
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const cVal = getMetricValue(controlAfter[i], metric);
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diff = (tVal - tBefore) - (cVal - cBefore);
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} else {
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diff = tVal - tBefore;
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}
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if (LOWER_IS_BETTER.has(metric)) diff = -diff;
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weeklyDiffs.push(diff);
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}
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let posteriorProb = 0.5;
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if (weeklyDiffs.length >= 3) {
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const mean = weeklyDiffs.reduce((s, v) => s + v, 0) / weeklyDiffs.length;
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const variance = weeklyDiffs.reduce((s, v) => s + (v - mean) ** 2, 0) / (weeklyDiffs.length - 1);
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const se = Math.sqrt(variance / weeklyDiffs.length);
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if (se > 0) {
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posteriorProb = normalCDF(mean / se);
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} else {
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posteriorProb = mean > 0 ? 1 : mean < 0 ? 0 : 0.5;
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}
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}
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return {
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treatment_before: Math.round(tBefore * 100) / 100,
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treatment_after: Math.round(tAfter * 100) / 100,
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control_before: Math.round(cBefore * 100) / 100,
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control_after: Math.round(cAfter * 100) / 100,
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did_estimate: Math.round(didEstimate * 100) / 100,
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lift_percent: Math.round(liftPercent * 10) / 10,
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posterior_prob_positive: Math.round(posteriorProb * 1000) / 1000,
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};
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}
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export function computeDiD(
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experiment: Experiment,
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salesData: CombinedKPIs[]
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): DifferenceInDifferencesResult {
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const startDate = parseLocalDate(experiment.start_date);
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const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date(new Date().setHours(0, 0, 0, 0));
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// Calculate ISO week range: expands to full weeks (Monday to Sunday)
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const isoRange = calculateISOWeekRange(startDate, endDate);
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const startTs = isoRange.expandedStartTs;
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const endTs = isoRange.expandedEndTs;
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const treatmentDurationWeeks = isoRange.numWeeks;
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let beforeStartTs: number;
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let beforeEndTs: number;
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if (experiment.baseline_start_date && experiment.baseline_end_date) {
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// Use custom baseline dates - also expand to full ISO weeks
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const baselineStart = parseLocalDate(experiment.baseline_start_date);
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const baselineEnd = parseLocalDate(experiment.baseline_end_date);
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const baselineIsoRange = calculateISOWeekRange(baselineStart, baselineEnd);
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beforeStartTs = baselineIsoRange.expandedStartTs;
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beforeEndTs = baselineIsoRange.expandedEndTs;
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} else {
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// Take the same number of complete weeks immediately before the experiment
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beforeEndTs = startTs; // Baseline ends right when experiment starts (Monday)
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beforeStartTs = beforeEndTs - (treatmentDurationWeeks * 7 * 86400000);
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}
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const treatmentAsinSet = getExperimentAsins(experiment.asins || [], salesData);
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const treatmentWeekly = aggregateWeeklyMetrics(treatmentAsinSet, salesData, experiment.marketplace);
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const hasControlGroup = (experiment.control_asins || []).length > 0;
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let controlWeekly: ComputedWeeklyMetrics[] = [];
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if (hasControlGroup) {
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const controlAsinSet = getExperimentAsins(experiment.control_asins, salesData);
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controlWeekly = aggregateWeeklyMetrics(controlAsinSet, salesData, experiment.marketplace);
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}
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const tSplit = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
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const cSplit = hasControlGroup
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? splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs)
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: { before: [] as ComputedWeeklyMetrics[], after: [] as ComputedWeeklyMetrics[] };
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const baselineDurationWeeks = Math.max(1, cSplit.before.length || tSplit.before.length);
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// Debug: log data flow for ACOS diagnosis
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const afterTotalCost = tSplit.after.reduce((s, w) => s + w.cost, 0);
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const afterTotalAdRev = tSplit.after.reduce((s, w) => s + w.adRevenue, 0);
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const beforeTotalCost = tSplit.before.reduce((s, w) => s + w.cost, 0);
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const beforeTotalAdRev = tSplit.before.reduce((s, w) => s + w.adRevenue, 0);
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console.log(`[computeDiD DEBUG] Experiment: ${experiment.name} | Mkt: ${experiment.marketplace}`);
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console.log(` Treatment ASINs: ${treatmentAsinSet.size} | Weekly buckets: ${treatmentWeekly.length}`);
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console.log(` Period: ${new Date(startTs).toISOString().split('T')[0]} → ${new Date(endTs).toISOString().split('T')[0]}`);
|
||||
console.log(` Baseline: ${new Date(beforeStartTs).toISOString().split('T')[0]} → ${new Date(beforeEndTs).toISOString().split('T')[0]}`);
|
||||
console.log(` After split: ${tSplit.after.length} weeks [cost=${afterTotalCost.toFixed(2)}, adRev=${afterTotalAdRev.toFixed(2)}]`);
|
||||
console.log(` Before split: ${tSplit.before.length} weeks [cost=${beforeTotalCost.toFixed(2)}, adRev=${beforeTotalAdRev.toFixed(2)}]`);
|
||||
if (tSplit.after.length > 0) {
|
||||
console.log(` After weeks: ${tSplit.after.map(w => w.week).join(', ')}`);
|
||||
}
|
||||
if (tSplit.before.length > 0) {
|
||||
console.log(` Before weeks: ${tSplit.before.map(w => w.week).join(', ')}`);
|
||||
}
|
||||
|
||||
const metrics: Record<string, DiDMetricResult> = {};
|
||||
for (const metric of METRICS) {
|
||||
metrics[metric] = computeMetricDiD(
|
||||
tSplit.before, tSplit.after,
|
||||
cSplit.before, cSplit.after,
|
||||
metric, hasControlGroup,
|
||||
treatmentDurationWeeks, baselineDurationWeeks
|
||||
);
|
||||
}
|
||||
|
||||
return {
|
||||
metrics,
|
||||
computed_at: new Date().toISOString(),
|
||||
};
|
||||
}
|
||||
|
||||
// ============ Verdict ============
|
||||
|
||||
export function computeVerdict(
|
||||
didResult: DifferenceInDifferencesResult,
|
||||
primaryMetric: string
|
||||
): { verdict: ExperimentVerdict; probability: number } {
|
||||
const result = didResult.metrics[primaryMetric];
|
||||
if (!result) return { verdict: 'inconclusive', probability: 0.5 };
|
||||
|
||||
const prob = result.posterior_prob_positive;
|
||||
|
||||
if (prob > 0.50) return { verdict: 'winner', probability: prob };
|
||||
return { verdict: 'loser', probability: prob };
|
||||
}
|
||||
|
||||
// ============ Counterfactual Time Series ============
|
||||
|
||||
export interface TrendDataPoint {
|
||||
week: string;
|
||||
timestamp: number;
|
||||
actual: number;
|
||||
counterfactual: number;
|
||||
}
|
||||
|
||||
export function buildCounterfactualSeries(
|
||||
experiment: Experiment,
|
||||
salesData: CombinedKPIs[],
|
||||
metric: string
|
||||
): TrendDataPoint[] {
|
||||
const startDate = parseLocalDate(experiment.start_date);
|
||||
const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date(new Date().setHours(0, 0, 0, 0));
|
||||
|
||||
const startTs = startDate.getTime();
|
||||
const endTs = endDate.getTime() + 86400000; // Add 24h
|
||||
|
||||
let beforeStartTs: number;
|
||||
let beforeEndTs: number;
|
||||
|
||||
if (experiment.baseline_start_date && experiment.baseline_end_date) {
|
||||
beforeStartTs = parseLocalDate(experiment.baseline_start_date).getTime();
|
||||
beforeEndTs = parseLocalDate(experiment.baseline_end_date).getTime() + 86400000;
|
||||
} else {
|
||||
const durationMs = endTs - startTs;
|
||||
beforeEndTs = startTs;
|
||||
beforeStartTs = beforeEndTs - durationMs;
|
||||
}
|
||||
|
||||
const treatmentAsinSet = getExperimentAsins(experiment.asins || [], salesData);
|
||||
const treatmentWeekly = aggregateWeeklyMetrics(treatmentAsinSet, salesData, experiment.marketplace);
|
||||
|
||||
const hasControlGroup = (experiment.control_asins || []).length > 0;
|
||||
|
||||
if (!hasControlGroup) {
|
||||
const { before: beforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
|
||||
const baselineDurationWeeks = Math.max(1, Math.round((beforeEndTs - beforeStartTs) / (7 * 86400000)));
|
||||
const preAvg = avgMetric(beforeData, metric, baselineDurationWeeks);
|
||||
|
||||
return treatmentWeekly.map(w => ({
|
||||
week: w.week,
|
||||
timestamp: w.timestamp,
|
||||
actual: Math.round(getMetricValue(w, metric) * 100) / 100,
|
||||
counterfactual: Math.round(preAvg * 100) / 100,
|
||||
}));
|
||||
}
|
||||
|
||||
// Calculate variances for Bayesian update
|
||||
const baselineDurationWeeks = Math.max(1, Math.round((beforeEndTs - beforeStartTs) / (7 * 86400000)));
|
||||
const treatmentDurationWeeks = Math.max(1, Math.round((endTs - startTs) / (7 * 86400000)));
|
||||
|
||||
const controlAsinSet = getExperimentAsins(experiment.control_asins, salesData);
|
||||
const controlWeekly = aggregateWeeklyMetrics(controlAsinSet, salesData, experiment.marketplace);
|
||||
|
||||
const { before: tBeforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
|
||||
const { before: cBeforeData } = splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
|
||||
|
||||
const tPreAvg = avgMetric(tBeforeData, metric, baselineDurationWeeks);
|
||||
const cPreAvg = avgMetric(cBeforeData, metric, baselineDurationWeeks);
|
||||
|
||||
// Build a map of control weekly values
|
||||
const controlMap = new Map<string, number>();
|
||||
for (const w of controlWeekly) {
|
||||
controlMap.set(w.week, getMetricValue(w, metric));
|
||||
}
|
||||
|
||||
return treatmentWeekly.map(w => {
|
||||
const actual = getMetricValue(w, metric);
|
||||
const controlVal = controlMap.get(w.week) ?? cPreAvg;
|
||||
// Counterfactual: treatment pre-avg + (control current - control pre-avg)
|
||||
const counterfactual = tPreAvg + (controlVal - cPreAvg);
|
||||
|
||||
return {
|
||||
week: w.week,
|
||||
timestamp: w.timestamp,
|
||||
actual: Math.round(actual * 100) / 100,
|
||||
counterfactual: Math.round(counterfactual * 100) / 100,
|
||||
};
|
||||
});
|
||||
}
|
||||
@@ -1,299 +0,0 @@
|
||||
import {
|
||||
Experiment,
|
||||
ExperimentCreateInput,
|
||||
ExperimentListItem,
|
||||
ActiveExperiment,
|
||||
ExperimentType,
|
||||
ExperimentStatus,
|
||||
ExperimentVerdict,
|
||||
CombinedKPIs,
|
||||
} from '../types';
|
||||
|
||||
const API_BASE = '/api/experiments';
|
||||
|
||||
// ============ ASIN Resolution ============
|
||||
|
||||
export const getExperimentAsins = (experimentAsins: string[], salesData: CombinedKPIs[]): Set<string> => {
|
||||
const explicitAsins = new Set<string>();
|
||||
const lines = new Set<string>();
|
||||
|
||||
(experimentAsins || []).forEach(a => {
|
||||
const val = (a || '').trim().toUpperCase();
|
||||
if (val.startsWith('LINE:')) {
|
||||
lines.add(val.substring(5).trim());
|
||||
} else if (val) {
|
||||
explicitAsins.add(val);
|
||||
}
|
||||
});
|
||||
|
||||
const asinSet = new Set<string>(explicitAsins);
|
||||
if (lines.size > 0 && salesData) {
|
||||
salesData.forEach(r => {
|
||||
const line = (r.line || '').trim().toUpperCase();
|
||||
if (line && lines.has(line)) {
|
||||
if (r.asin) asinSet.add(r.asin.toUpperCase());
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
return asinSet;
|
||||
};
|
||||
|
||||
// ============ CRUD Operations ============
|
||||
|
||||
export const createExperiment = async (input: ExperimentCreateInput): Promise<Experiment> => {
|
||||
const response = await fetch(API_BASE, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(input),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const error = await response.json();
|
||||
throw new Error(error.error || 'Failed to create experiment');
|
||||
}
|
||||
|
||||
return response.json();
|
||||
};
|
||||
|
||||
export const updateExperiment = async (
|
||||
id: string,
|
||||
updates: Partial<Experiment>
|
||||
): Promise<Experiment> => {
|
||||
const response = await fetch(`${API_BASE}?id=${id}`, {
|
||||
method: 'PUT',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(updates),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
let errText = await response.text();
|
||||
try {
|
||||
const errJson = JSON.parse(errText);
|
||||
errText = errJson.error || errText;
|
||||
} catch (e) { }
|
||||
throw new Error(errText);
|
||||
}
|
||||
|
||||
return response.json();
|
||||
};
|
||||
|
||||
export const deleteExperiment = async (id: string): Promise<void> => {
|
||||
const response = await fetch(`${API_BASE}?id=${id}`, {
|
||||
method: 'DELETE',
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const error = await response.json();
|
||||
throw new Error(error.error || 'Failed to delete experiment');
|
||||
}
|
||||
};
|
||||
|
||||
export const getExperiment = async (id: string): Promise<Experiment | null> => {
|
||||
const response = await fetch(`${API_BASE}?id=${id}`);
|
||||
if (!response.ok) return null;
|
||||
return response.json();
|
||||
};
|
||||
|
||||
export const listExperiments = async (
|
||||
filters?: {
|
||||
status?: ExperimentStatus[];
|
||||
type?: ExperimentType[];
|
||||
marketplace?: string[];
|
||||
asin?: string;
|
||||
dateFrom?: string;
|
||||
dateTo?: string;
|
||||
}
|
||||
): Promise<ExperimentListItem[]> => {
|
||||
const params = new URLSearchParams();
|
||||
|
||||
if (filters?.status?.length) {
|
||||
filters.status.forEach(s => params.append('status', s));
|
||||
}
|
||||
if (filters?.type?.length) {
|
||||
filters.type.forEach(t => params.append('type', t));
|
||||
}
|
||||
if (filters?.marketplace?.length) {
|
||||
filters.marketplace.forEach(m => params.append('marketplace', m));
|
||||
}
|
||||
if (filters?.asin) {
|
||||
params.append('asin', filters.asin);
|
||||
}
|
||||
if (filters?.dateFrom) {
|
||||
params.append('dateFrom', filters.dateFrom);
|
||||
}
|
||||
if (filters?.dateTo) {
|
||||
params.append('dateTo', filters.dateTo);
|
||||
}
|
||||
|
||||
const response = await fetch(`${API_BASE}?${params.toString()}`);
|
||||
|
||||
if (!response.ok) {
|
||||
const error = await response.json();
|
||||
throw new Error(error.error || 'Failed to list experiments');
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
|
||||
return data.map((exp: any) => {
|
||||
const today = new Date();
|
||||
const startDate = new Date(exp.start_date);
|
||||
const endDate = exp.end_date ? new Date(exp.end_date) : null;
|
||||
|
||||
let progressPercent = 0;
|
||||
if (endDate) {
|
||||
const totalDays = endDate.getTime() - startDate.getTime();
|
||||
const elapsedDays = today.getTime() - startDate.getTime();
|
||||
progressPercent = Math.min(100, Math.max(0, (elapsedDays / totalDays) * 100));
|
||||
}
|
||||
|
||||
return {
|
||||
id: exp.id,
|
||||
name: exp.name,
|
||||
type: exp.type,
|
||||
status: exp.status,
|
||||
asin_count: exp.asins?.length || 0,
|
||||
asins: exp.asins || [],
|
||||
control_asin_count: exp.control_asins?.length || 0,
|
||||
marketplace: exp.marketplace,
|
||||
start_date: exp.start_date,
|
||||
end_date: exp.end_date,
|
||||
progress_percent: Math.round(progressPercent),
|
||||
primary_metric: exp.primary_metric,
|
||||
verdict: exp.verdict,
|
||||
verdict_probability: exp.verdict_probability,
|
||||
actual_lift_percent: exp.actual_lift_percent,
|
||||
owner: exp.owner,
|
||||
};
|
||||
});
|
||||
};
|
||||
|
||||
export const getActiveExperiments = async (
|
||||
salesData: CombinedKPIs[]
|
||||
): Promise<Map<string, ActiveExperiment[]>> => {
|
||||
const response = await fetch(`${API_BASE}?status=active`);
|
||||
|
||||
if (!response.ok) {
|
||||
const error = await response.json();
|
||||
throw new Error(error.error || 'Failed to fetch active experiments');
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const today = new Date().toISOString().split('T')[0];
|
||||
|
||||
const activeExperiments = data.filter((exp: any) =>
|
||||
exp.status === 'active' &&
|
||||
exp.start_date <= today &&
|
||||
(!exp.end_date || exp.end_date >= today)
|
||||
);
|
||||
|
||||
const map = new Map<string, ActiveExperiment[]>();
|
||||
|
||||
for (const exp of activeExperiments) {
|
||||
const expAsins = getExperimentAsins(exp.asins || [], salesData);
|
||||
|
||||
const endDate = exp.end_date ? new Date(exp.end_date) : null;
|
||||
const daysRemaining = endDate
|
||||
? Math.ceil((endDate.getTime() - new Date().getTime()) / (1000 * 60 * 60 * 24))
|
||||
: undefined;
|
||||
|
||||
const activeExp: ActiveExperiment = {
|
||||
asin: '',
|
||||
experiment_id: exp.id,
|
||||
experiment_name: exp.name,
|
||||
type: exp.type,
|
||||
status: exp.status,
|
||||
start_date: exp.start_date,
|
||||
end_date: exp.end_date,
|
||||
days_remaining: daysRemaining,
|
||||
};
|
||||
|
||||
expAsins.forEach(asin => {
|
||||
const asinList = map.get(asin) || [];
|
||||
asinList.push({ ...activeExp, asin });
|
||||
map.set(asin, asinList);
|
||||
});
|
||||
}
|
||||
|
||||
return map;
|
||||
};
|
||||
|
||||
// ============ Display Helpers ============
|
||||
|
||||
export const getExperimentStatusColor = (status: ExperimentStatus): string => {
|
||||
switch (status) {
|
||||
case 'active': return 'bg-emerald-500/20 text-emerald-400 border-emerald-500/30';
|
||||
case 'planned': return 'bg-amber-500/20 text-amber-400 border-amber-500/30';
|
||||
case 'completed': return 'bg-slate-500/20 text-slate-400 border-slate-500/30';
|
||||
case 'paused': return 'bg-red-500/20 text-red-400 border-red-500/30';
|
||||
default: return 'bg-slate-500/20 text-slate-400';
|
||||
}
|
||||
};
|
||||
|
||||
export const getExperimentTypeColor = (type: ExperimentType): string => {
|
||||
switch (type) {
|
||||
case 'pricing': return 'bg-blue-500/20 text-blue-400 border-blue-500/30';
|
||||
case 'advertising': return 'bg-purple-500/20 text-purple-400 border-purple-500/30';
|
||||
case 'content': return 'bg-pink-500/20 text-pink-400 border-pink-500/30';
|
||||
case 'promotion': return 'bg-orange-500/20 text-orange-400 border-orange-500/30';
|
||||
case 'seo': return 'bg-cyan-500/20 text-cyan-400 border-cyan-500/30';
|
||||
default: return 'bg-slate-500/20 text-slate-400';
|
||||
}
|
||||
};
|
||||
|
||||
export const getExperimentIcon = (type: ExperimentType): string => {
|
||||
switch (type) {
|
||||
case 'pricing': return '💰';
|
||||
case 'advertising': return '📢';
|
||||
case 'content': return '📝';
|
||||
case 'promotion': return '🏷️';
|
||||
case 'seo': return '🔍';
|
||||
default: return '🧪';
|
||||
}
|
||||
};
|
||||
|
||||
export const getVerdictColor = (verdict?: ExperimentVerdict): string => {
|
||||
switch (verdict) {
|
||||
case 'winner': return 'bg-emerald-500/15 text-emerald-400 border-emerald-500/30';
|
||||
case 'loser': return 'bg-red-500/15 text-red-400 border-red-500/30';
|
||||
case 'inconclusive': return 'bg-slate-500/15 text-slate-400 border-slate-500/30';
|
||||
default: return 'bg-slate-800/50 text-slate-500 border-slate-700';
|
||||
}
|
||||
};
|
||||
|
||||
export const getVerdictLabel = (verdict?: ExperimentVerdict): string => {
|
||||
switch (verdict) {
|
||||
case 'winner': return 'Winner';
|
||||
case 'loser': return 'Unsuccessful';
|
||||
case 'inconclusive': return 'Unsuccessful';
|
||||
default: return 'Pending';
|
||||
}
|
||||
};
|
||||
|
||||
export const METRIC_LABELS: Record<string, string> = {
|
||||
units: 'Units Sold',
|
||||
sessions: 'Sessions',
|
||||
cvr: 'Conversion Rate',
|
||||
ctr: 'Click-Through Rate',
|
||||
roas: 'ROAS',
|
||||
revenue: 'Revenue',
|
||||
acos: 'ACOS',
|
||||
detail_bsr: 'Detail Level BSR',
|
||||
};
|
||||
|
||||
export const formatMetricValue = (value: number, metric: string): string => {
|
||||
switch (metric) {
|
||||
case 'cvr':
|
||||
case 'ctr':
|
||||
case 'acos':
|
||||
return `${value.toFixed(1)}%`;
|
||||
case 'roas':
|
||||
return `${value.toFixed(2)}x`;
|
||||
case 'revenue':
|
||||
return `€${value.toLocaleString('de-DE', { minimumFractionDigits: 0, maximumFractionDigits: 0 })}`;
|
||||
case 'detail_bsr':
|
||||
return `#${value.toLocaleString('de-DE', { minimumFractionDigits: 0, maximumFractionDigits: 0 })}`;
|
||||
default:
|
||||
return value.toLocaleString('de-DE', { minimumFractionDigits: 0, maximumFractionDigits: 2 });
|
||||
}
|
||||
};
|
||||
Reference in New Issue
Block a user