import sqlite3
from pathlib import Path
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid")
plt.rcParams["figure.figsize"] = (10, 5)

project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(project_root / "preanalysis"))
import eda_utils as eda  # noqa: E402

db_path = project_root / "dataset" / "ds.sqlite"
conn = sqlite3.connect(db_path)
df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
conn.close()

for cols, name in [
    (eda.ACTIVE_IMP_COLS, "active_imp_total"),
    (eda.PASSIVE_IMP_COLS, "passive_imp_total"),
    (eda.ACTIVE_CLICK_COLS, "active_click_total"),
    (eda.PASSIVE_CLICK_COLS, "passive_click_total"),
    (eda.ORDER_COLS, "orders_amt_total"),
]:
    df[name] = df[cols].sum(axis=1)

df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
df["click_total"] = df["active_click_total"] + df["passive_click_total"]
contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
client = (
    df.groupby("id")
    .agg(
        imp_total=("imp_total", "sum"),
        click_total=("click_total", "sum"),
        orders_amt_total=("orders_amt_total", "sum"),
        age=("age", "median"),
        gender_cd=("gender_cd", lambda s: s.mode().iat[0]),
        device_platform_cd=("device_platform_cd", lambda s: s.mode().iat[0]),
    )
    .merge(contact_days, on="id", how="left")
    .reset_index()
)
client["order_rate"] = eda.safe_divide(client["orders_amt_total"], client["imp_total"])  # orders / impressions
client["order_rate_pct"] = 100 * client["order_rate"]  # чтобы шкала была человеческая
client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])

# Mean absolute orders for each exact avg_imp_per_day (no bins), sorted ascending
stats_imp = (
    client.groupby("avg_imp_per_day", as_index=False)
    .agg(
        orders_mean=("orders_amt_total", "mean"),
        n_clients=("id", "count"),
    )
    .sort_values("avg_imp_per_day")
)

K_MULT = 2  # "в разы" -> 5x. Поменяй на 3/10 если хочешь
ABS_DY_MIN = 1
X_MAX = 16

stats_imp = stats_imp.sort_values("avg_imp_per_day").reset_index(drop=True)

# 1) cut by x
stats_f = stats_imp[stats_imp["avg_imp_per_day"] <= X_MAX].copy().reset_index(drop=True)

# 2) detect vertical outliers by dy logic
before = len(stats_f)
y = stats_f["orders_mean"]
abs_dy = y.diff().abs()

prev3_mean = abs_dy.shift(1).rolling(window=3, min_periods=3).mean()
ratio = abs_dy / (prev3_mean.replace(0, np.nan))  # avoid inf when prev mean == 0

is_outlier = (abs_dy >= ABS_DY_MIN) & (ratio >= K_MULT) | (y > 5)
# первые точки не могут нормально иметь "3 предыдущих дельты"
is_outlier = is_outlier.fillna(False)

stats_f = stats_f.loc[~is_outlier].copy().reset_index(drop=True)
after = len(stats_f)
cleaned = before - after

print(f"{before} - {after}, cleaned: {cleaned}")

# --- smoothing (rolling mean on remaining points) ---
w = max(7, int(len(stats_f) * 0.05))
if w % 2 == 0:
    w += 1

stats_f["orders_smooth"] = (
    stats_f["orders_mean"]
    .rolling(window=w, center=True, min_periods=1)
    .mean()
)
# --- cost line (linear expenses) ---
# нормируем так, чтобы масштаб был сопоставим с заказами
c = stats_f["orders_smooth"].max() / stats_f["avg_imp_per_day"].max()
stats_f["cost_line"] = c * stats_f["avg_imp_per_day"]

# plot
plt.figure(figsize=(10, 8))

plt.plot(
    stats_f["avg_imp_per_day"],
    stats_f["orders_mean"],
    marker="o",
    linewidth=1,
    alpha=0.3,
    label="Среднее число заказов"
)

plt.plot(
    stats_f["avg_imp_per_day"],
    stats_f["orders_smooth"],
    color="red",
    linewidth=2.5,
    label="Сглаженный тренд заказов"
)

plt.plot(
    stats_f["avg_imp_per_day"],
    stats_f["cost_line"],
    color="black",
    linestyle="--",
    linewidth=2,
    label="Линейные расходы на показы"
)

plt.xlabel("Среднее число показов в день")
plt.ylabel("Среднее число заказов")
plt.title("Зависимость заказов от интенсивности коммуникаций")

plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()

plt.savefig(
    project_root / "spam_hypot" / "orders_vs_avg_imp_with_costs.png",
    dpi=150
)

print("Saved orders_vs_avg_imp_with_costs.png")