Spaces:
Build error
Build error
File size: 3,112 Bytes
ea6f215 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import os
import sys
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
# Add project root to path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.pipeline import RossmannPipeline
from src.core import setup_logger
logger = setup_logger(__name__)
def verify_retraining():
"""
Verification script for the Drift-Triggered Auto-Retraining logic.
"""
train_csv = os.path.abspath("data/raw/train.csv")
if not os.path.exists(train_csv):
print(f"Error: {train_csv} not found.")
return
logger.info("Starting Auto-Retraining Verification...")
pipeline = RossmannPipeline(train_csv)
# 1. Warm start: Train initial model on 2013 data
logger.info("--- Phase 1: Initial Training ---")
df_full = pipeline.ingestor.ingest(train_csv)
df_full['Date'] = pd.to_datetime(df_full['Date'])
# Force a small sample for fast verification
warmup_df = df_full[df_full['Date'] < '2014-01-01'].head(100000)
feat_warmup = pipeline.run_feature_engineering(warmup_df)
feature_cols = [
'DayOfWeek', 'Promo', 'StateHoliday', 'SchoolHoliday',
'Year', 'Month', 'Day', 'IsWeekend', 'DayOfMonth',
'CompetitionDistance', 'CompetitionOpenTime'
] + [c for c in feat_warmup.columns if 'fourier' in c or 'easter' in c]
pipeline.train(feat_warmup[feature_cols].fillna(0), feat_warmup['target'])
# 2. Simulate Normal Streaming (No Drift)
logger.info("--- Phase 2: Normal Streaming (No Drift) ---")
pipeline.simulate_streaming(start_date='2014-01-01', end_date='2014-01-15', interval_days=7)
# 3. Injected Drift & Auto-Retrain
logger.info("--- Phase 3: Injected Drift & Auto-Retrain ---")
drifted_csv = "data/raw/train_drifted.csv"
# Original train.csv columns to avoid duplication on merge
original_cols = ['Store', 'DayOfWeek', 'Date', 'Sales', 'Customers', 'Open', 'Promo', 'StateHoliday', 'SchoolHoliday']
# We use df_full which was already ingested and merged, so we must slice it
# to only the original columns before saving to simulate a raw file.
train_cols_present = [c for c in original_cols if c in df_full.columns]
df_drifted = df_full[train_cols_present].copy()
# Mask: Force Promo=1 for 2014-02-01 onwards to trigger drift
df_drifted.loc[df_drifted['Date'] >= '2014-02-01', 'Promo'] = 1
df_drifted.to_csv(drifted_csv, index=False)
try:
pipeline_drift = RossmannPipeline(os.path.abspath(drifted_csv))
pipeline_drift.model = pipeline.model # Start with same model
# This should trigger drift around 2014-02-01
pipeline_drift.simulate_streaming(
start_date='2014-01-15',
end_date='2014-02-15',
interval_days=7,
drift_threshold=0.1 # Lower threshold for easy trigger
)
finally:
if os.path.exists(drifted_csv):
os.remove(drifted_csv)
logger.info("Verification Complete.")
if __name__ == "__main__":
verify_retraining()
|