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fd78077 79a730b fd78077 79a730b fd78077 79a730b fd78077 79a730b fd78077 79a730b fd78077 79a730b fd78077 79a730b fd78077 | 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 | from __future__ import annotations
import pandas as pd
import numpy as np
from . import storage
try:
from prophet import Prophet
except Exception:
Prophet = None
try:
from neuralprophet import NeuralProphet
except Exception:
NeuralProphet = None
class SeasonalityModel:
def __init__(self, campaign_id: str):
self.campaign_id = campaign_id
self.model = None
self.model_type = "none"
self.global_mean = 0.05
def fit(self):
with storage.get_conn() as con:
df = pd.read_sql_query(
"SELECT ts, event_type FROM events WHERE campaign_id=?",
con,
params=(self.campaign_id,),
)
if df.empty:
self.model_type = "none"
return
df["ts"] = pd.to_datetime(df["ts"], errors="coerce")
df = df.dropna(subset=["ts"])
df["hour"] = df["ts"].dt.floor("h")
agg = df.pivot_table(index="hour", columns="event_type", values="ts", aggfunc="count").fillna(0)
if "impression" not in agg: agg["impression"] = 0
if "click" not in agg: agg["click"] = 0
ctr = np.where(agg["impression"] > 0, agg["click"] / agg["impression"], np.nan)
if np.all(np.isnan(ctr)):
self.model_type = "none"
return
self.global_mean = float(np.nanmean(ctr))
ds = agg.index.to_series().reset_index(drop=True)
train = pd.DataFrame({"ds": ds, "y": pd.Series(ctr).fillna(self.global_mean).values})
try:
if Prophet is not None:
m = Prophet(weekly_seasonality=True, daily_seasonality=True)
m.fit(train)
self.model, self.model_type = m, "prophet"
elif NeuralProphet is not None:
m = NeuralProphet(weekly_seasonality=True, daily_seasonality=True)
m.fit(train, freq="H")
self.model, self.model_type = m, "neuralprophet"
else:
self.model_type = "none"
except Exception:
self.model_type = "none"
def expected_ctr(self, context: dict) -> float:
hour = int(context.get("hour", 12))
if self.model_type in {None, "none"}:
base = self.global_mean
if 11 <= hour <= 13: return min(0.99, base * 1.1)
if 20 <= hour <= 23: return min(0.99, base * 1.15)
return max(0.01, base)
now_ds = pd.Timestamp.utcnow().floor("D") + pd.Timedelta(hours=hour)
if self.model_type == "prophet":
yhat = float(self.model.predict(pd.DataFrame({"ds": [now_ds]}))["yhat"].iloc[0])
else:
yhat = float(self.model.predict(pd.DataFrame({"ds": [now_ds]}))["yhat1"].iloc[0])
return max(0.01, min(0.99, yhat))
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