57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import logging
|
|
|
|
# Configure logging
|
|
logging.basicConfig(
|
|
level=logging.INFO,
|
|
format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
|
|
)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
def processing(data: dict) -> dict:
|
|
try:
|
|
hd_score_s1 = (
|
|
min(1225 + (data["cluster_size_users_v2"] * 5), 1390)
|
|
if data["cluster_size_users_v2"] >= 3 and data["hd_score_m1"] >= 1140
|
|
else 0
|
|
)
|
|
logger.info(f"score_s1 calculated: {hd_score_s1}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing score_s1 calculations: {e}")
|
|
return {}
|
|
|
|
try:
|
|
hd_score_s2 = (
|
|
min(1215 + (data["cluster_size_users_v2"] * 5), 1380)
|
|
if data["cluster_size_users_v2"] >= 2 and data["app_dt_day_cnt"] == 1
|
|
else 0
|
|
)
|
|
logger.info(f"score_s2 calculated: {hd_score_s2}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing score_s2 calculations: {e}")
|
|
return {}
|
|
|
|
try:
|
|
target_connected_30_sum = data.get("target_connected_30_sum", 0) or 0 # Handling None case
|
|
hd_score_s3 = (
|
|
min(1250 + (target_connected_30_sum * 5), 1400)
|
|
if target_connected_30_sum >= 1
|
|
else 0
|
|
)
|
|
logger.info(f"score_s3 calculated: {hd_score_s3}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing score_s3 calculations: {e}")
|
|
return {}
|
|
|
|
# Return the final results as a dictionary
|
|
return {
|
|
"hd_score_m1": data["hd_score_m1"],
|
|
"hd_score_g1": data["hd_score_g1"],
|
|
"hd_score_s1": hd_score_s1,
|
|
"hd_score_s2": hd_score_s2,
|
|
"hd_score_s3": hd_score_s3,
|
|
"hd_score_iso_m2": data["hd_score_iso_m2"],
|
|
"hd_score_g2": data["hd_score_g2"]
|
|
}
|
|
|
|
|