Add in forecasting, lifecycle phases, associated component and script changes

This commit is contained in:
2026-02-13 22:45:18 -05:00
parent f41b5ab0f6
commit 45ded53530
29 changed files with 3643 additions and 376 deletions

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numpy>=1.24
scipy>=1.10
pandas>=2.0
psycopg2-binary>=2.9
statsmodels>=0.14

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#!/usr/bin/env node
/**
* Forecast Pipeline Orchestrator
*
* Spawns the Python forecast engine with database credentials from the
* environment. Can be run manually, via cron, or integrated into the
* existing metrics pipeline.
*
* Usage:
* node run_forecast.js
*
* Environment:
* Reads DB_HOST, DB_USER, DB_PASSWORD, DB_NAME, DB_PORT from
* /var/www/html/inventory/.env (or current process env).
*/
const { spawn } = require('child_process');
const path = require('path');
const fs = require('fs');
// Load .env file if it exists (production path)
const envPaths = [
'/var/www/html/inventory/.env',
path.join(__dirname, '../../.env'),
];
for (const envPath of envPaths) {
if (fs.existsSync(envPath)) {
const envContent = fs.readFileSync(envPath, 'utf-8');
for (const line of envContent.split('\n')) {
const trimmed = line.trim();
if (!trimmed || trimmed.startsWith('#')) continue;
const eqIndex = trimmed.indexOf('=');
if (eqIndex === -1) continue;
const key = trimmed.slice(0, eqIndex);
const value = trimmed.slice(eqIndex + 1);
if (!process.env[key]) {
process.env[key] = value;
}
}
console.log(`Loaded env from ${envPath}`);
break;
}
}
// Verify required env vars
const required = ['DB_HOST', 'DB_USER', 'DB_PASSWORD', 'DB_NAME'];
const missing = required.filter(k => !process.env[k]);
if (missing.length > 0) {
console.error(`Missing required environment variables: ${missing.join(', ')}`);
process.exit(1);
}
const SCRIPT_DIR = __dirname;
const PYTHON_SCRIPT = path.join(SCRIPT_DIR, 'forecast_engine.py');
const VENV_DIR = path.join(SCRIPT_DIR, 'venv');
const REQUIREMENTS = path.join(SCRIPT_DIR, 'requirements.txt');
// Determine python binary (prefer venv if it exists)
function getPythonBin() {
const venvPython = path.join(VENV_DIR, 'bin', 'python');
if (fs.existsSync(venvPython)) return venvPython;
// Fall back to system python
return 'python3';
}
// Ensure venv and dependencies are installed
async function ensureDependencies() {
if (!fs.existsSync(path.join(VENV_DIR, 'bin', 'python'))) {
console.log('Creating virtual environment...');
await runCommand('python3', ['-m', 'venv', VENV_DIR]);
}
// Always run pip install — idempotent, fast when packages already present
console.log('Checking dependencies...');
const python = path.join(VENV_DIR, 'bin', 'python');
await runCommand(python, ['-m', 'pip', 'install', '--quiet', '-r', REQUIREMENTS]);
}
function runCommand(cmd, args, options = {}) {
return new Promise((resolve, reject) => {
const proc = spawn(cmd, args, {
stdio: 'inherit',
...options,
});
proc.on('close', code => {
if (code === 0) resolve();
else reject(new Error(`${cmd} exited with code ${code}`));
});
proc.on('error', reject);
});
}
async function main() {
const startTime = Date.now();
console.log('='.repeat(60));
console.log(`Forecast Pipeline - ${new Date().toISOString()}`);
console.log('='.repeat(60));
try {
await ensureDependencies();
const pythonBin = getPythonBin();
console.log(`Using Python: ${pythonBin}`);
console.log(`Running: ${PYTHON_SCRIPT}`);
console.log('');
await runCommand(pythonBin, [PYTHON_SCRIPT], {
env: {
...process.env,
PYTHONUNBUFFERED: '1', // Real-time output
},
});
const duration = ((Date.now() - startTime) / 1000).toFixed(1);
console.log('');
console.log('='.repeat(60));
console.log(`Forecast pipeline completed in ${duration}s`);
console.log('='.repeat(60));
} catch (err) {
const duration = ((Date.now() - startTime) / 1000).toFixed(1);
console.error(`Forecast pipeline FAILED after ${duration}s:`, err.message);
process.exit(1);
}
}
main();

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-- Forecasting Pipeline Tables
-- Run once to create the schema. Safe to re-run (IF NOT EXISTS).
-- Precomputed reference decay curves per brand (or brand x category at any hierarchy level)
CREATE TABLE IF NOT EXISTS brand_lifecycle_curves (
id SERIAL PRIMARY KEY,
brand TEXT NOT NULL,
root_category TEXT, -- NULL = brand-level fallback curve, else category name
cat_id BIGINT, -- NULL = brand-only; else category_hierarchy.cat_id for precise matching
category_level SMALLINT, -- NULL = brand-only; 0-3 = hierarchy depth
amplitude NUMERIC(10,4), -- A in: sales(t) = A * exp(-λt) + C
decay_rate NUMERIC(10,6), -- λ (higher = faster decay)
baseline NUMERIC(10,4), -- C (long-tail steady-state daily sales)
r_squared NUMERIC(6,4), -- goodness of fit
sample_size INT, -- number of products that informed this curve
median_first_week_sales NUMERIC(10,2), -- for scaling new launches
median_preorder_sales NUMERIC(10,2), -- for scaling pre-order products
median_preorder_days NUMERIC(10,2), -- median pre-order accumulation window (days)
computed_at TIMESTAMP DEFAULT NOW(),
UNIQUE(brand, cat_id)
);
-- Per-product daily forecasts (next 90 days, regenerated each run)
CREATE TABLE IF NOT EXISTS product_forecasts (
pid BIGINT NOT NULL,
forecast_date DATE NOT NULL,
forecast_units NUMERIC(10,2),
forecast_revenue NUMERIC(14,4),
lifecycle_phase TEXT, -- preorder, launch, decay, mature, slow_mover, dormant
forecast_method TEXT, -- lifecycle_curve, exp_smoothing, velocity, zero
confidence_lower NUMERIC(10,2),
confidence_upper NUMERIC(10,2),
generated_at TIMESTAMP DEFAULT NOW(),
PRIMARY KEY (pid, forecast_date)
);
CREATE INDEX IF NOT EXISTS idx_pf_date ON product_forecasts(forecast_date);
CREATE INDEX IF NOT EXISTS idx_pf_phase ON product_forecasts(lifecycle_phase);
-- Forecast run history (for monitoring)
CREATE TABLE IF NOT EXISTS forecast_runs (
id SERIAL PRIMARY KEY,
started_at TIMESTAMP NOT NULL,
finished_at TIMESTAMP,
status TEXT DEFAULT 'running', -- running, completed, failed
products_forecast INT,
phase_counts JSONB, -- {"launch": 50, "decay": 200, ...}
curve_count INT, -- brand curves computed
error_message TEXT,
duration_seconds NUMERIC(10,2)
);