#!/usr/bin/env python3
"""Steps 2-4 only: build_data + train_ranker. Features already generated."""
import os, sys, gc, warnings, subprocess, time
from pathlib import Path
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd

DS = "cedwyh/jinjing-shared-data"
hf_token = os.environ.get("HF_TOKEN")
from huggingface_hub import HfApi, hf_hub_download
api = HfApi()

def _download_and_patch(src, dst, patches):
    p = hf_hub_download(repo_id=DS, filename=src, repo_type="dataset")
    with open(p) as f:
        c = f.read()
    for old, new in patches:
        c = c.replace(old, new)
    with open(dst, "w") as f:
        f.write(c)
    return dst

print("=" * 60)
print("Steps 2-4: build_data + train_ranker")
print(f"Features: chan_engine_features_v5.5.1.parquet (new engine, beichi fix)")
print("=" * 60)

# Step 2: build_data
print("\n[2/4] Building ranking training data...")
bd_patched = _download_and_patch("build_data.py", "/tmp/bd_patched.py", [
    ('"chan_engine_features.parquet"', '"chan_engine_features_v5.5.1.parquet"'),
])
bd_result = subprocess.run(
    [sys.executable, bd_patched, "--output", "/tmp/ranking_train_v8.parquet",
     "--dataset", DS, "--no-use-priors"],
    capture_output=True, text=True, timeout=7200
)
print(bd_result.stdout[-800:])
if bd_result.returncode != 0:
    err = (bd_result.stderr or "")[-500:]
    print(f"❌ Build data failed (code {bd_result.returncode}): {err}")
    sys.exit(1)

df = pd.read_parquet("/tmp/ranking_train_v8.parquet")
print(f"\n✅ Build data: {len(df):,} rows x {len(df.columns)} cols, dates {df['date'].min()}..{df['date'].max()}")
n_dates = df["date"].nunique()
if n_dates < 200:
    print(f"  ❌ RED LINE: only {n_dates} unique dates — merge silently dropped rows!")
    sys.exit(1)
del df; gc.collect()

api.upload_file(path_or_fileobj="/tmp/ranking_train_v8.parquet",
                path_in_repo="ranking_train_v8.parquet",
                repo_id=DS, repo_type="dataset")
print("  ✅ Uploaded ranking_train_v8.parquet")

# Step 3: train_ranker
print("\n[3/4] Training LGBMRanker...")
tr_patched = _download_and_patch("scripts/train_ranker.py", "/tmp/tr_patched.py",
    [('TARGET_COL = "label"', 'TARGET_COL = "label_rank"')])
output_dir = "/tmp/v10_ranker"
Path(output_dir).mkdir(exist_ok=True)

tr_result = subprocess.run(
    [sys.executable, tr_patched, "--data", "/tmp/ranking_train_v8.parquet",
     "--output", output_dir],
    capture_output=True, text=True, timeout=14400
)
out = tr_result.stdout
# Show last 40 lines of training output
lines = out.split('\n')
print('\n'.join(lines[-40:]))
if tr_result.returncode != 0:
    err = (tr_result.stderr or "")[-500:]
    print(f"❌ Ranker training failed: {err}")
    sys.exit(1)

# Upload models
for f in sorted(Path(output_dir).glob("*.txt")):
    api.upload_file(path_or_fileobj=str(f),
                    path_in_repo=f"models/v10_{f.name}",
                    repo_id=DS, repo_type="dataset")
    print(f"  ✅ models/v10_{f.name}")

pred_file = Path(output_dir) / "ranker_predictions.parquet"
if pred_file.exists():
    api.upload_file(path_or_fileobj=str(pred_file),
                    path_in_repo="models/ranker_v10_predictions.parquet",
                    repo_id=DS, repo_type="dataset")
    print("  ✅ Uploaded predictions")

print("\n" + "=" * 60)
print("✅ COMPLETE")
print("   Data: ranking_train_v8.parquet")
print("   Models: models/v10_lgbm_w*.txt")
print("=" * 60)