blocks-transformer/graph_post_processing.py

import logging
import math
from decimal import Decimal, ROUND_HALF_UP

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
)
logger = logging.getLogger(__name__)


def post_processing_g1(data):
    try:
        prediction = data.get("prediction", 0)
        score_g1 = round(
            min(prediction * 100 + 0.00001, 1) * 89 +
            max(math.log2(prediction * 100 + 0.000001) * 193, 0),
            0
        )
        data["hd_score_g1"] = score_g1
        logger.info("score_g1 calculated: %s", score_g1)
    except Exception as e:
        logger.error("Error processing score_g1 calculations: %s", e)

    return {
        key: data.get(key, None)
        for key in [
            "hd_score_m1", "hd_score_g1", "cluster_size_users_v2",
            "target_connected_30_sum", "email_cnt", "rejected_app_count",
            "app_dt_day_cnt", "hd_score_iso_m2"
        ]
    }


def post_processing_g2(data):
    prediction = data.get("prediction_g2", data.get("prediction"))
    hd_score_g2 = None

    try:
        if prediction is not None:
            prediction_val = float(prediction)
            raw_score = (prediction_val * 100) * 20 + math.log((prediction_val + 0.000001) * 100, 2) * 41.6
            # SQL-like rounding (half up)
            hd_score_g2 = int(Decimal(str(raw_score)).quantize(Decimal("1"), rounding=ROUND_HALF_UP))
            hd_score_g2 = max(hd_score_g2, 0.0)
            logger.info("score_g2 calculated: %s", hd_score_g2)
    except Exception as e:
        logger.error("Error processing score_g2 calculations: %s", e)

    return {"hd_score_g2": hd_score_g2}


# Backward compatibility alias
post_processing = post_processing_g1