mirror of
https://github.com/hanzoai/insights.git
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zeekay
1b367e2b62
The tree was a debranded snapshot of upstream taken around 2026-02-20, and upstream has moved about five months since. This replaces it with upstream master, mechanically re-derived: bin/debrand does the rename, and the pipeline and squashed migrations are restored from the shipped tree. Not a merge. The previous import replaced the tree wholesale in a single commit, so there is no shared content history to merge against. Reproducing the rename on the new tree is what actually converges. What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md, and insights/migrations. The squashed initial migration is load-bearing — the live database records it by name, so upstream's ~918 individual migrations cannot come back. Nine products are dropped as unreferenced. Twenty-one more that we do not run are kept anyway because the core app still imports them; unpicking those is a refactor and does not belong in a rebase. This base does not build yet, and the reasons are not in the rename: upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against our 4.2, and the dependency manifests still name upstream packages that we republish under our own names. Those are decisions, not renames.
128 lines
5.8 KiB
JSON
128 lines
5.8 KiB
JSON
{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"description": "Inputs for estimating the recommended sample size and running time of an experiment.",
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"properties": {
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"baseline_stats": {
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"anyOf": [
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{
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"description": "Raw control-group statistics the calculator uses to derive a baseline value and variance.\n\nSupply this when you want the server to compute the baseline value and (for ratio/retention)\nthe delta-method variance, instead of passing `baseline_value`/`variance` directly.",
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"properties": {
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"denominator_sum": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Sum of the denominator values. Required for ratio/retention metrics."
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},
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"denominator_sum_squares": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Sum of squared denominator values (ratio/retention variance)."
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},
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"number_of_samples": {
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"description": "Number of control-group samples (users/units) observed.",
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"minimum": 0,
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"type": "number"
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},
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"numerator_denominator_sum_product": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Sum of numerator×denominator products, used for the delta-method covariance term."
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},
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"step_counts": {
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"description": "Per-step counts for funnel metrics; the last entry is the final-step count.",
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"items": {
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"type": "number"
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},
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"type": "array"
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},
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"sum": {
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"description": "Sum of the metric values across the control group (for funnels, the numerator/conversions).",
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"type": "number"
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},
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"sum_squares": {
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"default": 0,
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"description": "Sum of squared metric values. Required for ratio/retention variance.",
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"type": "number"
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}
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},
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"required": ["number_of_samples", "sum"],
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"type": "object"
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},
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{
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"type": "null"
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}
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],
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"description": "Raw control-group statistics. When provided, the server derives baseline_value and variance."
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},
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"baseline_value": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Baseline metric value: conversion rate as a fraction 0-1 (funnel), average per user (mean), or the ratio (ratio/retention). Provide this or baseline_stats."
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},
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"exposure_rate_per_day": {
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"anyOf": [
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{
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"minimum": 0,
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Expected exposures per day. When provided, the response includes the recommended running time."
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},
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"metric_type": {
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"description": "Metric type to size for. 'funnel' for conversion rates, 'mean_count' for event counts per user, 'mean_sum_or_avg' for summed property values per user, 'ratio' and 'retention' for ratio-style metrics (both require baseline_stats or an explicit variance).\n\n* `funnel` - funnel\n* `mean_count` - mean_count\n* `mean_sum_or_avg` - mean_sum_or_avg\n* `ratio` - ratio\n* `retention` - retention",
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"enum": ["funnel", "mean_count", "mean_sum_or_avg", "ratio", "retention"],
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"type": "string"
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},
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"minimum_detectable_effect": {
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"description": "Smallest relative change to detect, as a percentage (e.g. 5 means a 5% lift). Must be > 0.",
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"minimum": 0,
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"type": "number"
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},
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"number_of_variants": {
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"default": 2,
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"description": "Total number of variants including control (default 2).",
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"minimum": 2,
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"type": "number"
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},
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"variance": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"description": "Pre-computed variance for ratio/retention metrics. Provide this or baseline_stats when metric_type is ratio/retention and baseline_value is given directly."
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}
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},
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"required": ["metric_type", "minimum_detectable_effect"],
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"type": "object"
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}
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