"""Ocena punktacji feedu Edge na cechach z scripts/edge_replay_features.py.

Trening na 70% najstarszych wyswietlen, test na reszcie. Glowna miara: AUC W OBREBIE
URZADZENIA (czy wyzszy wynik u tego samego urzadzenia znaczy lepsza reakcje), bo globalne
AUC miesza aktywnych z nowymi. Dwie etykiety: lajk albo >= 10 s ogladania (odporna na
dlugosc klipu) oraz dokonczenie albo lajk. F5 to formula wdrozona 2026-10-02.
Wymaga pandas i scikit-learn (lokalnie, nie w kontenerze).
"""
import sys

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

df = pd.read_csv(sys.argv[1], parse_dates=["ts"])
A = 10.0
P_LIKE, P_SKIP = 0.03, 0.5
df["like_rate"] = (df.c_like + A * P_LIKE) / (df.c_seen + A)
df["skip_rate"] = (df.c_skip + A * P_SKIP) / (df.c_seen + A)
df["popul"] = df["pop"]
slog = lambda x: np.sign(x) * np.log1p(np.abs(x))  # noqa: E731
for c in ["tag_sum", "tag_idf_sum", "creator_w", "niche_sum"]:
    df["s_" + c] = slog(df[c])


def within(d, col):
    num = den = 0.0
    for _, g in d.groupby("dev"):
        if len(g) < 30 or g.y.nunique() < 2 or g[col].nunique() < 2:
            continue
        num += roc_auc_score(g.y, g[col]) * len(g)
        den += len(g)
    return num / den if den else float("nan")


def labelled(kind):
    if kind == "dwell":
        good = (df.liked == 1) | (df.dwell >= 10000)
    else:
        good = (df.kind == "complete") | (df.liked == 1)
    bad = df.kind == "skip"
    d = df[good | bad].copy()
    d["y"] = good[good | bad].astype(int)
    return d.sort_values("ts")


def cur(d):
    return d.tag_sum + 2 * d.niche_sum + 3 * d.creator_w + 0.3 * d.popul + 0.5 * d.fresh


def formula(d, w):
    return (w["tag"] * d.s_tag_idf_sum + w["creator"] * d.s_creator_w + w["pop"] * d.popul
            + w["like"] * (d.like_rate - P_LIKE) - w["skip"] * (d.skip_rate - P_SKIP))


for kind in ("dwell", "complete"):
    d = labelled(kind)
    cut = int(len(d) * 0.7)
    tr, te = d.iloc[:cut].copy(), d.iloc[cut:].copy()
    print(f"\n===== etykieta {kind}: trening {len(tr)}, test {len(te)}")
    print("  AUC w obrebie urzadzenia (test):")
    for f in ["s_tag_sum", "s_tag_idf_sum", "tag_idf_pos", "s_creator_w", "s_niche_sum", "popul", "like_rate"]:
        print(f"    {f:14s} {within(te, f):.3f}")
    cols = ["s_tag_idf_sum", "s_creator_w", "s_niche_sum", "popul", "like_rate", "skip_rate"]
    m = LogisticRegression(max_iter=3000).fit(tr[cols], tr.y)
    coef = dict(zip(cols, m.coef_[0]))
    base = coef["s_tag_idf_sum"]
    print("  wspolczynniki w jednostkach tagu:", {c: round(v / base, 2) for c, v in coef.items()})
    te["cur"] = cur(te)
    te["model"] = m.decision_function(te[cols])
    for name, w in {
        "F1": {"tag": 1.0, "creator": 1.0, "pop": 0.3, "like": 20.0, "skip": 2.0},
        "F2": {"tag": 1.0, "creator": 1.5, "pop": 0.5, "like": 30.0, "skip": 3.0},
        "F3": {"tag": 1.0, "creator": 1.0, "pop": 0.5, "like": 40.0, "skip": 4.0},
        "F4": {"tag": 1.0, "creator": 2.5, "pop": 0.8, "like": 120.0, "skip": 15.0},
    }.items():
        te[name] = formula(te, w)
    print("  porownanie (test): AUC globalnie / w obrebie urzadzenia")
    te["F5"] = formula(te, {"tag": 1.0, "creator": 2.5, "pop": 0.8, "like": 120.0, "skip": 15.0}) + 0.25 * te.s_niche_sum + 0.3 * te.fresh
    for f in ["cur", "model", "F1", "F2", "F3", "F4", "F5"]:
        print(f"    {f:6s} {roc_auc_score(te.y, te[f]):.3f} / {within(te, f):.3f}")