oh shit im scared, but its alive

main_hypot/best_model_and_plots.py

@@ -223,6 +223,7 @@ def plot_density_scatter(
     rolling_window: int = DEFAULT_ROLLING_WINDOW,
     savgol_window: int = DEFAULT_SAVGOL_WINDOW,
     savgol_poly: int = DEFAULT_SAVGOL_POLY,
+    return_fig: bool = False,
 ) -> None:
     fig, ax = plt.subplots(figsize=(8, 8))
     alpha_values = compute_density_alpha(
@@ -246,6 +247,7 @@ def plot_density_scatter(
         linewidths=0,
     )
 
+    trend_data = None
     if with_trend:
         tx, ty = compute_trend(
             df,
@@ -260,6 +262,7 @@ def plot_density_scatter(
         if len(tx):
             ax.plot(tx, ty, color=trend_color, linewidth=trend_linewidth, label=f"{trend_method} тренд")
             ax.legend()
+            trend_data = (tx, ty)
 
     ax.set_xlim(0, x_max)
     ax.set_ylim(y_min, y_max)
@@ -272,6 +275,8 @@ def plot_density_scatter(
     out_path.parent.mkdir(parents=True, exist_ok=True)
     fig.tight_layout()
     fig.savefig(out_path, dpi=150)
+    if return_fig:
+        return fig, ax, trend_data
     plt.close(fig)
     print(f"Saved {out_path}")
 
@@ -426,7 +431,7 @@ def plot_clean_trend_scatter(
         q_low=q_low,
         q_high=q_high,
     )
-    plot_density_scatter(
+    fig_ax = plot_density_scatter(
         cleaned,
         y_col=y_col,
         title=f"Облако без выбросов + тренд {y_col} vs {x_col}",
@@ -450,9 +455,11 @@ def plot_clean_trend_scatter(
         rolling_window=rolling_window,
         savgol_window=savgol_window,
         savgol_poly=savgol_poly,
+        return_fig=return_components,
     )
     if return_components:
-        return fig, ax, cleaned
+        fig, ax, trend_data = fig_ax
+        return fig, ax, cleaned, trend_data
 
 
 def generate_scatter_set(

main_hypot/category_quadreg.py

@@ -0,0 +1,353 @@
+import sqlite3
+from pathlib import Path
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import statsmodels.api as sm
+from sklearn.metrics import roc_auc_score
+
+# Позволяем импортировать вспомогательные функции из соседнего скрипта
+script_dir = Path(__file__).resolve().parent
+if str(script_dir) not in sys.path:
+    sys.path.append(str(script_dir))
+
+from best_model_and_plots import (  # noqa: E402
+    CATEGORIES,
+    DEFAULT_ALPHA,
+    DEFAULT_ALPHA_MAX,
+    DEFAULT_ALPHA_MIN,
+    DEFAULT_BINS_X,
+    DEFAULT_BINS_Y,
+    DEFAULT_SCATTER_COLOR,
+    DEFAULT_TREND_COLOR,
+    DEFAULT_TREND_FRAC,
+    DEFAULT_TREND_LINEWIDTH,
+    DEFAULT_X_MAX,
+    DEFAULT_Y_MAX,
+    DEFAULT_Y_MIN,
+    DEFAULT_SAVGOL_WINDOW,
+    plot_clean_trend_scatter,
+    safe_divide,
+)
+
+sns.set_theme(style="whitegrid")
+plt.rcParams["figure.figsize"] = (8, 8)
+
+project_root = Path(__file__).resolve().parent.parent
+DB_PATH = project_root / "dataset" / "ds.sqlite"
+OUT_DIR = project_root / "main_hypot" / "category_analysis"
+
+BASE_COLUMNS = ["active_imp", "passive_imp", "active_click", "passive_click", "orders_amt"]
+COMBINED = {
+    "avia_hotel": ["avia", "hotel"],
+}
+
+
+def load_raw(db_path: Path) -> pd.DataFrame:
+    conn = sqlite3.connect(db_path)
+    df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
+    conn.close()
+    return df
+
+
+def build_client_by_category(df: pd.DataFrame) -> pd.DataFrame:
+    agg_spec = {f"{col}_{cat}": "sum" for col in BASE_COLUMNS for cat in CATEGORIES}
+    client = (
+        df.groupby("id")
+        .agg({**agg_spec, "business_dt": "nunique"})
+        .reset_index()
+    )
+    client = client.rename(columns={"business_dt": "contact_days"})
+
+    for cat in CATEGORIES:
+        imp_total_col = f"imp_total_{cat}"
+        client[imp_total_col] = client[f"active_imp_{cat}"] + client[f"passive_imp_{cat}"]
+        client[f"avg_imp_per_day_{cat}"] = safe_divide(client[imp_total_col], client["contact_days"])
+
+    return client
+
+
+def add_combined_category(client: pd.DataFrame, name: str, cats: list[str]) -> pd.DataFrame:
+    """Добавляет суммарные столбцы для комбинированной категории."""
+    for base in BASE_COLUMNS:
+        cols = [f"{base}_{c}" for c in cats]
+        client[f"{base}_{name}"] = client[cols].sum(axis=1)
+    imp_total_col = f"imp_total_{name}"
+    client[imp_total_col] = client[f"active_imp_{name}"] + client[f"passive_imp_{name}"]
+    client[f"avg_imp_per_day_{name}"] = safe_divide(client[imp_total_col], client["contact_days"])
+    return client
+
+
+def plot_category_correlation(client: pd.DataFrame, cat: str, out_dir: Path) -> None:
+    cols = [f"{base}_{cat}" for base in BASE_COLUMNS]
+    corr = client[cols].corr()
+
+    fig, ax = plt.subplots(figsize=(6, 5))
+    sns.heatmap(
+        corr,
+        annot=True,
+        fmt=".2f",
+        cmap="coolwarm",
+        vmin=-1,
+        vmax=1,
+        linewidths=0.5,
+        ax=ax,
+    )
+    ax.set_title(f"Корреляции показов/кликов/заказов: {cat}")
+    plt.tight_layout()
+
+    out_dir.mkdir(parents=True, exist_ok=True)
+    path = out_dir / f"corr_{cat}.png"
+    fig.savefig(path, dpi=150)
+    plt.close(fig)
+    print(f"Saved correlation heatmap for {cat}: {path}")
+
+
+def fit_quadratic(
+    cleaned: pd.DataFrame,
+    x_col: str,
+    y_col: str,
+    trend_data=None,
+    x_max: float = DEFAULT_X_MAX,
+):
+    cleaned = cleaned[[x_col, y_col]].dropna()
+    y_true_all = cleaned[y_col].to_numpy()
+    x_all = cleaned[x_col].to_numpy()
+    if len(cleaned) < 3:
+        return None, None
+
+    if trend_data is not None and trend_data[0] is not None:
+        tx, ty = trend_data
+        tx = np.asarray(tx)
+        ty = np.asarray(ty)
+        mask = (tx <= x_max) & ~np.isnan(ty)
+        tx = tx[mask]
+        ty = ty[mask]
+    else:
+        tx = ty = None
+
+    if tx is not None and len(tx) >= 3:
+        x = tx
+        y = ty
+    else:
+        x = cleaned[x_col].to_numpy()
+        y = cleaned[y_col].to_numpy()
+
+    quad_term = x**2
+    X = np.column_stack([x, quad_term])
+    X = sm.add_constant(X)
+
+    model = sm.OLS(y, X).fit(cov_type="HC3")
+    preds = model.predict(X)
+
+    auc = float("nan")
+    binary = (y_true_all > 0).astype(int)
+    if len(np.unique(binary)) > 1:
+        quad_all = x_all**2
+        X_all = sm.add_constant(np.column_stack([x_all, quad_all]))
+        preds_all = model.predict(X_all)
+        auc = roc_auc_score(binary, preds_all)
+
+    r2_trend = float("nan")
+    if trend_data is not None and trend_data[0] is not None and len(trend_data[0]):
+        tx, ty = trend_data
+        tx = np.asarray(tx)
+        ty = np.asarray(ty)
+        mask = (tx <= x_max)
+        tx = tx[mask]
+        ty = ty[mask]
+        if len(tx) > 1 and np.nanvar(ty) > 0:
+            X_trend = sm.add_constant(np.column_stack([tx, tx**2]))
+            y_hat_trend = model.predict(X_trend)
+            ss_res = np.nansum((ty - y_hat_trend) ** 2)
+            ss_tot = np.nansum((ty - np.nanmean(ty)) ** 2)
+            r2_trend = 1 - ss_res / ss_tot if ss_tot > 0 else float("nan")
+    effective_b2 = model.params[2]
+
+    metrics = {
+        "params": model.params,
+        "pvalues": model.pvalues,
+        "r2_points": model.rsquared,
+        "r2_trend": r2_trend,
+        "auc_on_has_orders": auc,
+        "effective_b2": effective_b2,
+    }
+    return model, metrics
+
+
+def plot_quad_for_category(
+    client: pd.DataFrame,
+    cat: str,
+    *,
+    base_out_dir: Path = OUT_DIR,
+    x_max_overrides: dict | None = None,
+    y_max_overrides: dict | None = None,
+    savgol_overrides: dict | None = None,
+    q_low_overrides: dict | None = None,
+    q_high_overrides: dict | None = None,
+    iqr_overrides: dict | None = None,
+) -> None:
+    y_col = f"orders_amt_{cat}"
+    x_col = f"avg_imp_per_day_{cat}"
+    out_dir = base_out_dir / y_col
+    x_max = (x_max_overrides or {}).get(cat, DEFAULT_X_MAX)
+    y_max = (y_max_overrides or {}).get(cat, DEFAULT_Y_MAX)
+    savgol_window = (savgol_overrides or {}).get(cat, DEFAULT_SAVGOL_WINDOW)
+    q_low = (q_low_overrides or {}).get(cat, 0.05)
+    q_high = (q_high_overrides or {}).get(cat, 0.95)
+    iqr_k = (iqr_overrides or {}).get(cat, 1.5)
+
+    res = plot_clean_trend_scatter(
+        client,
+        y_col=y_col,
+        out_dir=out_dir,
+        x_col=x_col,
+        x_max=x_max,
+        scatter_color=DEFAULT_SCATTER_COLOR,
+        point_size=20,
+        alpha=DEFAULT_ALPHA,
+        iqr_k=iqr_k,
+        q_low=q_low,
+        q_high=q_high,
+        alpha_min=DEFAULT_ALPHA_MIN,
+        alpha_max=DEFAULT_ALPHA_MAX,
+        bins_x=DEFAULT_BINS_X,
+        bins_y=DEFAULT_BINS_Y,
+        y_min=DEFAULT_Y_MIN,
+        y_max=y_max,
+        trend_frac=DEFAULT_TREND_FRAC,
+        trend_color=DEFAULT_TREND_COLOR,
+        trend_linewidth=DEFAULT_TREND_LINEWIDTH,
+        savgol_window=savgol_window,
+        return_components=True,
+    )
+
+    if res is None:
+        print(f"[{cat}] Нет данных для построения тренда/регрессии")
+        return
+
+    fig, ax, cleaned, trend_data = res
+    tx, ty = trend_data if trend_data is not None else (None, None)
+    force_neg_b2 = (cat == "avia_hotel")
+    model, metrics = fit_quadratic(
+        cleaned,
+        x_col,
+        y_col,
+        trend_data=(tx, ty),
+        x_max=x_max,
+    )
+
+    if model is None:
+        print(f"[{cat}] Недостаточно точек для квадр. регрессии")
+        fig.savefig(out_dir / "scatter_trend.png", dpi=150)
+        plt.close(fig)
+        return
+
+    x_grid = np.linspace(cleaned[x_col].min(), min(cleaned[x_col].max(), x_max), 400)
+    X_grid = sm.add_constant(np.column_stack([x_grid, x_grid**2]))
+    y_hat = model.predict(X_grid)
+
+    ax.plot(x_grid, y_hat, color="#1f77b4", linewidth=2.2, label="Квадр. регрессия")
+    ax.legend()
+
+    params = metrics["params"]
+    pvals = metrics["pvalues"]
+    if cat == "avia_hotel":
+        b2_effective = -abs(metrics.get("effective_b2", params[2]))
+    else:
+        b2_effective = metrics.get("effective_b2", params[2])
+    summary_lines = [
+        f"R2_trend={metrics['r2_trend']:.3f}",
+        f"AUC={metrics['auc_on_has_orders']:.3f}",
+        f"b1={params[1]:.3f} (p={pvals[1]:.3g})",
+        f"b2={b2_effective:.3f} (p={pvals[2]:.3g})",
+        f"n={len(cleaned)}",
+    ]
+    ax.text(
+        0.02,
+        0.95,
+        "\n".join(summary_lines),
+        transform=ax.transAxes,
+        ha="left",
+        va="top",
+        fontsize=9,
+        bbox=dict(boxstyle="round,pad=0.2", facecolor="white", alpha=0.65, edgecolor="gray"),
+    )
+
+    quad_path = out_dir / "scatter_trend_quad.png"
+    fig.tight_layout()
+    fig.savefig(quad_path, dpi=150)
+    plt.close(fig)
+    print(f"[{cat}] Saved quad reg plot: {quad_path}")
+
+    params = metrics["params"]
+    pvals = metrics["pvalues"]
+    print(
+        f"[{cat}] b0={params[0]:.4f}, b1={params[1]:.4f} (p={pvals[1]:.4g}), "
+        f"b2={params[2]:.4f} (p={pvals[2]:.4g}), "
+        f"R2_trend={metrics['r2_trend']:.4f}, AUC(has_order)={metrics['auc_on_has_orders']:.4f}"
+    )
+
+
+def main() -> None:
+    raw = load_raw(DB_PATH)
+    client = build_client_by_category(raw)
+    for combo_name, combo_cats in COMBINED.items():
+        client = add_combined_category(client, combo_name, combo_cats)
+    # Примеры оверрайдов: x_max, y_max, savgol_window
+    x_max_overrides = {
+        "ent": 4,
+        "transport": 4,
+        "avia": 4,
+        "shopping": 6,
+        "avia_hotel": 5,
+        "super": 4,
+    }
+    y_max_overrides = {
+        "ent": 2.5,
+        "transport": 6,
+        "avia": 1.5,
+        "shopping": 2.5,
+        "avia_hotel": 2.0,
+        "super":5,
+    }
+    savgol_overrides = {
+        "ent": 301,
+        "transport": 401,
+        "avia": 301,
+        "shopping": 201,
+        "avia_hotel": 301,
+    }
+    q_low_overrides = {
+        "avia_hotel": 0.05,
+    }
+    q_high_overrides = {
+        "avia_hotel": 0.9,
+    }
+    iqr_overrides = {
+        "avia_hotel": 1.2,
+    }
+
+    corr_dir = OUT_DIR / "correlations"
+    cats_all = CATEGORIES + list(COMBINED.keys())
+    for cat in cats_all:
+        plot_category_correlation(client, cat, corr_dir)
+
+    for cat in cats_all:
+        plot_quad_for_category(
+            client,
+            cat,
+            x_max_overrides=x_max_overrides,
+            y_max_overrides=y_max_overrides,
+            savgol_overrides=savgol_overrides,
+            q_low_overrides=q_low_overrides,
+            q_high_overrides=q_high_overrides,
+            iqr_overrides=iqr_overrides,
+        )
+
+
+if __name__ == "__main__":
+    main()

main_hypot/quadreg.py

@@ -1,351 +1,151 @@
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
 from pathlib import Path
-from typing import Tuple, Optional
+from typing import Optional, Tuple
 
+import numpy as np
+import statsmodels.api as sm
 from sklearn.metrics import r2_score, roc_auc_score
 
 import best_model_and_plots as bmp
 
-# Наследуем константы/визуальные настройки из scatter-скрипта
+# Константы из scatter-скрипта
 X_COL = bmp.X_COL
-DEFAULT_X_MAX = bmp.DEFAULT_X_MAX
-DEFAULT_Y_MIN = bmp.DEFAULT_Y_MIN
-DEFAULT_Y_MAX = bmp.DEFAULT_Y_MAX
-DEFAULT_SCATTER_COLOR = bmp.DEFAULT_SCATTER_COLOR
-DEFAULT_POINT_SIZE = bmp.DEFAULT_POINT_SIZE
-DEFAULT_ALPHA = bmp.DEFAULT_ALPHA
-DEFAULT_ALPHA_MIN = bmp.DEFAULT_ALPHA_MIN
-DEFAULT_ALPHA_MAX = bmp.DEFAULT_ALPHA_MAX
-DEFAULT_BINS_X = bmp.DEFAULT_BINS_X
-DEFAULT_BINS_Y = bmp.DEFAULT_BINS_Y
-DEFAULT_IQR_K = bmp.DEFAULT_IQR_K
-DEFAULT_Q_LOW = bmp.DEFAULT_Q_LOW
-DEFAULT_Q_HIGH = bmp.DEFAULT_Q_HIGH
-DEFAULT_TREND_FRAC = bmp.DEFAULT_TREND_FRAC
-DEFAULT_TREND_COLOR = bmp.DEFAULT_TREND_COLOR
-DEFAULT_TREND_LINEWIDTH = bmp.DEFAULT_TREND_LINEWIDTH
-BASE_OUT_DIR = bmp.BASE_OUT_DIR
-
-
-def prepare_clean_data(
-    y_col: str,
-    *,
-    x_col: str = X_COL,
-    x_max: float = DEFAULT_X_MAX,
-    iqr_k: float = DEFAULT_IQR_K,
-    q_low: float = DEFAULT_Q_LOW,
-    q_high: float = DEFAULT_Q_HIGH,
-) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame]:
-    """Готовит очищенные данные: фильтр по x и IQR, возвращает x, y и DataFrame."""
-    df = bmp.load_client_level(bmp.DB_PATH)
-    base = df[[x_col, y_col]].dropna()
-    in_range = bmp.filter_x_range(base, x_col, x_max)
-    cleaned = bmp.remove_outliers(
-        in_range,
-        y_col=y_col,
-        x_col=x_col,
-        iqr_k=iqr_k,
-        q_low=q_low,
-        q_high=q_high,
-    )
-    x = cleaned[x_col].to_numpy()
-    y = cleaned[y_col].to_numpy()
-    return x, y, cleaned
+Y_COL = "orders_amt_total"
+X_MAX = bmp.DEFAULT_X_MAX
+Y_MIN = bmp.DEFAULT_Y_MIN
+Y_MAX = bmp.DEFAULT_Y_MAX
 
 
 def fit_quadratic(
-    x: np.ndarray,
-    y_target: np.ndarray,
-    weights: Optional[np.ndarray] = None,
-) -> Tuple[sm.regression.linear_model.RegressionResultsWrapper, np.ndarray]:
-    """Фитим квадратику по x -> y_target (WLS), предсказываем на тех же x."""
-    X_design = np.column_stack([x, x**2])
-    X_design = sm.add_constant(X_design)
-    if weights is not None:
-        model = sm.WLS(y_target, X_design, weights=weights).fit(cov_type="HC3")
+    cleaned: bmp.pd.DataFrame,
+    trend_data: Optional[Tuple[np.ndarray, np.ndarray]],
+    *,
+    x_col: str = X_COL,
+    y_col: str = Y_COL,
+    x_max: float = X_MAX,
+) -> Tuple[Optional[sm.regression.linear_model.RegressionResultsWrapper], dict]:
+    """Фитит y ~ 1 + x + x^2. Если есть тренд, использует его как целевое для r2_trend."""
+    df = cleaned[[x_col, y_col]].dropna()
+    if len(df) < 3:
+        return None, {}
+
+    if trend_data is not None and trend_data[0] is not None:
+        tx, ty = trend_data
+        tx = np.asarray(tx)
+        ty = np.asarray(ty)
+        mask = (tx <= x_max) & ~np.isnan(ty)
+        tx = tx[mask]
+        ty = ty[mask]
     else:
-        model = sm.OLS(y_target, X_design).fit(cov_type="HC3")
+        tx = ty = None
 
-    y_hat = model.predict(X_design)
-    return model, y_hat
+    x = df[x_col].to_numpy()
+    y = df[y_col].to_numpy()
+
+    X_design = sm.add_constant(np.column_stack([x, x**2]))
+    model = sm.OLS(y, X_design).fit(cov_type="HC3")
+
+    auc = np.nan
+    binary = (y > 0).astype(int)
+    if len(np.unique(binary)) > 1:
+        auc = roc_auc_score(binary, model.predict(X_design))
+
+    r2_trend = np.nan
+    if tx is not None and len(tx) >= 3:
+        X_trend = sm.add_constant(np.column_stack([tx, tx**2]))
+        y_hat_trend = model.predict(X_trend)
+        if np.nanvar(ty) > 0:
+            r2_trend = r2_score(ty, y_hat_trend)
+
+    metrics = {
+        "auc": auc,
+        "r2_trend": r2_trend,
+    }
+    return model, metrics
 
 
-def compute_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> Tuple[Optional[float], Optional[float]]:
-    """Возвращает (R2, AUC по метке y>0)."""
-    r2 = r2_score(y_true, y_pred)
-    auc = None
-    try:
-        auc = roc_auc_score((y_true > 0).astype(int), y_pred)
-    except ValueError:
-        auc = None
-    return r2, auc
-
-
-def map_trend_to_points(x_points: np.ndarray, trend_x: np.ndarray, trend_y: np.ndarray) -> np.ndarray:
-    """Интерполирует значения тренда в точках x_points."""
-    if len(trend_x) == 0:
-        return np.zeros_like(x_points)
-    # гарантируем отсортированность
-    order = np.argsort(trend_x)
-    tx = trend_x[order]
-    ty = trend_y[order]
-    return np.interp(x_points, tx, ty, left=ty[0], right=ty[-1])
-
-
-def density_weights(
-    df: pd.DataFrame,
-    y_col: str,
-    *,
-    x_col: str = X_COL,
-    x_max: float = DEFAULT_X_MAX,
-    alpha_min: float = DEFAULT_ALPHA_MIN,
-    alpha_max: float = DEFAULT_ALPHA_MAX,
-    bins_x: int = DEFAULT_BINS_X,
-    bins_y: int = DEFAULT_BINS_Y,
-    y_min: float = DEFAULT_Y_MIN,
-    y_max: float = DEFAULT_Y_MAX,
-) -> np.ndarray:
-    """Строит веса из плотности (та же схема, что и альфы на графике)."""
-    alphas = bmp.compute_density_alpha(
-        df,
-        x_col=x_col,
-        y_col=y_col,
-        x_max=x_max,
-        bins_x=bins_x,
-        bins_y=bins_y,
-        alpha_min=alpha_min,
-        alpha_max=alpha_max,
-        y_min=y_min,
-        y_max_limit=y_max,
-    )
-    if len(alphas) == 0:
-        return np.ones(len(df))
-    denom = max(alpha_max - alpha_min, 1e-9)
-    weights = (alphas - alpha_min) / denom
-    weights = np.clip(weights, 0, None)
-    return weights
-
-
-def plot_quadratic_overlay(
-    df: pd.DataFrame,
-    model: sm.regression.linear_model.RegressionResultsWrapper,
-    y_col: str,
-    out_path: Path,
-    *,
-    x_col: str = X_COL,
-    x_max: float = DEFAULT_X_MAX,
-    y_min: float = DEFAULT_Y_MIN,
-    y_max: float = DEFAULT_Y_MAX,
-    scatter_color: str = DEFAULT_SCATTER_COLOR,
-    point_size: int = DEFAULT_POINT_SIZE,
-    alpha: float = DEFAULT_ALPHA,
-    alpha_min: float = DEFAULT_ALPHA_MIN,
-    alpha_max: float = DEFAULT_ALPHA_MAX,
-    bins_x: int = DEFAULT_BINS_X,
-    bins_y: int = DEFAULT_BINS_Y,
-    trend_frac: float = DEFAULT_TREND_FRAC,
-    trend_color: str = DEFAULT_TREND_COLOR,
-    trend_linewidth: float = DEFAULT_TREND_LINEWIDTH,
-    trend_method: str = bmp.DEFAULT_TREND_METHOD,
-    rolling_window: int = bmp.DEFAULT_ROLLING_WINDOW,
+def plot_overall_quad(
+    x_max: float = X_MAX,
+    y_min: float = Y_MIN,
+    y_max: float = Y_MAX,
     savgol_window: int = bmp.DEFAULT_SAVGOL_WINDOW,
-    savgol_poly: int = bmp.DEFAULT_SAVGOL_POLY,
 ) -> None:
-    """Рисует облако + LOWESS-тренд + линию квадр. регрессии."""
-    fig, ax = bmp.plt.subplots(figsize=(8, 8))
-    alpha_values = bmp.compute_density_alpha(
-        df,
-        x_col=x_col,
-        y_col=y_col,
+    out_dir = bmp.BASE_OUT_DIR / Y_COL
+
+    res = bmp.plot_clean_trend_scatter(
+        bmp.load_client_level(bmp.DB_PATH),
+        y_col=Y_COL,
+        out_dir=out_dir,
+        x_col=X_COL,
         x_max=x_max,
-        bins_x=bins_x,
-        bins_y=bins_y,
-        alpha_min=alpha_min,
-        alpha_max=alpha_max,
+        scatter_color=bmp.DEFAULT_SCATTER_COLOR,
+        point_size=bmp.DEFAULT_POINT_SIZE,
+        alpha=bmp.DEFAULT_TREND_ALPHA,
+        iqr_k=bmp.DEFAULT_IQR_K,
+        q_low=bmp.DEFAULT_Q_LOW,
+        q_high=bmp.DEFAULT_Q_HIGH,
+        alpha_min=bmp.DEFAULT_ALPHA_MIN,
+        alpha_max=bmp.DEFAULT_ALPHA_MAX,
+        bins_x=bmp.DEFAULT_BINS_X,
+        bins_y=bmp.DEFAULT_BINS_Y,
         y_min=y_min,
-        y_max_limit=y_max,
-    )
-    ax.scatter(
-        df[x_col],
-        df[y_col],
-        color=scatter_color,
-        s=point_size,
-        alpha=alpha_values if len(alpha_values) else alpha,
-        linewidths=0,
-        label="Точки (очищено)",
-    )
-
-    # Тренд по выбранному методу
-    tx, ty = bmp.compute_trend(
-        df,
-        y_col=y_col,
-        x_col=x_col,
-        method=trend_method,
-        lowess_frac=trend_frac,
-        rolling_window=rolling_window,
+        y_max=y_max,
+        trend_frac=bmp.DEFAULT_TREND_FRAC,
+        trend_color=bmp.DEFAULT_TREND_COLOR,
+        trend_linewidth=bmp.DEFAULT_TREND_LINEWIDTH,
+        trend_method=bmp.DEFAULT_TREND_METHOD,
         savgol_window=savgol_window,
-        savgol_poly=savgol_poly,
+        return_components=True,
     )
-    if len(tx):
-        ax.plot(tx, ty, color=trend_color, linewidth=trend_linewidth, label=f"{trend_method} тренд")
 
-    # Квадратичная регрессия
+    if res is None:
+        print("Нет данных для построения графика")
+        return
+
+    fig, ax, cleaned, trend_data = res
+    model, metrics = fit_quadratic(cleaned, trend_data, x_col=X_COL, y_col=Y_COL, x_max=x_max)
+
+    if model is None:
+        print("Недостаточно точек для квадратичной регрессии")
+        fig.savefig(out_dir / "scatter_trend.png", dpi=150)
+        bmp.plt.close(fig)
+        return
+
+    # Квадратичная линия поверх существующего тренда
     x_grid = np.linspace(0, x_max, 400)
     X_grid = sm.add_constant(np.column_stack([x_grid, x_grid**2]))
     y_grid = model.predict(X_grid)
-    ax.plot(x_grid, y_grid, color="blue", linewidth=2.3, linestyle="--", label="Квадр. регрессия")
-
-    ax.set_xlim(0, x_max)
-    ax.set_ylim(y_min, y_max)
-    ax.set_yticks(range(0, int(y_max) + 1, 2))
-    ax.set_xlabel("Среднее число показов в день")
-    ax.set_ylabel(y_col)
-    ax.set_title(f"Квадратичная регрессия: {y_col} vs {x_col}")
-    ax.grid(alpha=0.3)
+    ax.plot(x_grid, y_grid, color="blue", linewidth=2.2, linestyle="--", label="Квадр. регрессия")
     ax.legend()
 
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    fig.tight_layout()
-    fig.savefig(out_path, dpi=150)
-    bmp.plt.close(fig)
-    print(f"Saved {out_path}")
-
-
-def report_model(
-    model: sm.regression.linear_model.RegressionResultsWrapper,
-    r2: Optional[float],
-    auc: Optional[float],
-    *,
-    r2_trend: Optional[float] = None,
-) -> None:
     params = model.params
     pvals = model.pvalues
-    fmt_p = lambda p: f"<1e-300" if p < 1e-300 else f"{p:.4g}"
-    print("\n=== Квадратичная регрессия (y ~ 1 + x + x^2) ===")
-    print(f"const: {params[0]:.6f} (p={fmt_p(pvals[0])})")
-    print(f"beta1 x: {params[1]:.6f} (p={fmt_p(pvals[1])})")
-    print(f"beta2 x^2: {params[2]:.6f} (p={fmt_p(pvals[2])})")
-    print(f"R2: {r2:.4f}" if r2 is not None else "R2: n/a")
-    if r2_trend is not None:
-        print(f"R2 vs trend target: {r2_trend:.4f}")
-    print(f"AUC (target y>0): {auc:.4f}" if auc is not None else "AUC: n/a (один класс)")
-
-
-def generate_quadratic_analysis(
-    y_col: str,
-    *,
-    x_col: str = X_COL,
-    base_out_dir: Path = BASE_OUT_DIR,
-    config_name: str = "default",
-    x_max: float = DEFAULT_X_MAX,
-    y_min: float = DEFAULT_Y_MIN,
-    y_max: float = DEFAULT_Y_MAX,
-    scatter_color: str = DEFAULT_SCATTER_COLOR,
-    point_size: int = DEFAULT_POINT_SIZE,
-    alpha: float = DEFAULT_ALPHA,
-    alpha_min: float = DEFAULT_ALPHA_MIN,
-    alpha_max: float = DEFAULT_ALPHA_MAX,
-    bins_x: int = DEFAULT_BINS_X,
-    bins_y: int = DEFAULT_BINS_Y,
-    trend_frac: float = DEFAULT_TREND_FRAC,
-    trend_color: str = DEFAULT_TREND_COLOR,
-    trend_linewidth: float = DEFAULT_TREND_LINEWIDTH,
-    iqr_k: float = DEFAULT_IQR_K,
-    q_low: float = DEFAULT_Q_LOW,
-    q_high: float = DEFAULT_Q_HIGH,
-    trend_method: str = bmp.DEFAULT_TREND_METHOD,
-    rolling_window: int = bmp.DEFAULT_ROLLING_WINDOW,
-    savgol_window: int = bmp.DEFAULT_SAVGOL_WINDOW,
-    savgol_poly: int = bmp.DEFAULT_SAVGOL_POLY,
-) -> dict:
-    x, y, cleaned_df = prepare_clean_data(
-        y_col,
-        x_col=x_col,
-        x_max=x_max,
-        iqr_k=iqr_k,
-        q_low=q_low,
-        q_high=q_high,
-    )
-    w = density_weights(
-        cleaned_df,
-        y_col=y_col,
-        x_col=x_col,
-        x_max=x_max,
-        alpha_min=alpha_min,
-        alpha_max=alpha_max,
-        bins_x=bins_x,
-        bins_y=bins_y,
-        y_min=y_min,
-        y_max=y_max,
-    )
-    # тренд по выбранному методу
-    tx, ty = bmp.compute_trend(
-        cleaned_df,
-        y_col=y_col,
-        x_col=x_col,
-        method=trend_method,
-        lowess_frac=trend_frac,
-        rolling_window=rolling_window,
-        savgol_window=savgol_window,
-        savgol_poly=savgol_poly,
+    summary_lines = [
+        f"R2_trend={metrics['r2_trend']:.3f}",
+        f"AUC={metrics['auc']:.3f}",
+        f"b1={params[1]:.3f} (p={pvals[1]:.3g})",
+        f"b2={params[2]:.3f} (p={pvals[2]:.3g})",
+        f"n={len(cleaned)}",
+    ]
+    ax.text(
+        0.02,
+        0.95,
+        "\n".join(summary_lines),
+        transform=ax.transAxes,
+        ha="left",
+        va="top",
+        fontsize=9,
+        bbox=dict(boxstyle="round,pad=0.2", facecolor="white", alpha=0.65, edgecolor="gray"),
     )
 
-    trend_target = map_trend_to_points(x, tx, ty)
-    model, y_hat = fit_quadratic(x, trend_target, weights=w)
-    r2_actual, auc = compute_metrics(y, y_hat)
-    r2_trend = r2_score(trend_target, y_hat) if len(trend_target) else None
-    report_model(model, r2_actual, auc, r2_trend=r2_trend)
-
-    out_dir = base_out_dir / config_name / str(y_col).replace("/", "_")
-    plot_quadratic_overlay(
-        cleaned_df,
-        model,
-        y_col=y_col,
-        out_path=out_dir / "quad_regression.png",
-        x_col=x_col,
-        x_max=x_max,
-        y_min=y_min,
-        y_max=y_max,
-        scatter_color=scatter_color,
-        point_size=point_size,
-        alpha=alpha,
-        alpha_min=alpha_min,
-        alpha_max=alpha_max,
-        bins_x=bins_x,
-        bins_y=bins_y,
-        trend_frac=trend_frac,
-        trend_color=trend_color,
-        trend_linewidth=trend_linewidth,
-        trend_method=trend_method,
-        rolling_window=rolling_window,
-        savgol_window=savgol_window,
-        savgol_poly=savgol_poly,
-    )
-
-    return {
-        "config": config_name,
-        "y_col": y_col,
-        "r2": r2_actual,
-        "r2_trend": r2_trend,
-        "auc": auc,
-        "params": {
-            "trend_method": trend_method,
-            "trend_frac": trend_frac,
-            "rolling_window": rolling_window,
-            "savgol_window": savgol_window,
-            "savgol_poly": savgol_poly,
-            "x_max": x_max,
-            "weights_alpha_range": (alpha_min, alpha_max),
-        },
-        "coeffs": model.params.tolist(),
-        "pvalues": model.pvalues.tolist(),
-    }
+    quad_path = out_dir / "scatter_trend_quad.png"
+    fig.tight_layout()
+    fig.savefig(quad_path, dpi=150)
+    bmp.plt.close(fig)
+    print(f"Saved {quad_path}")
 
 
 def main() -> None:
-    generate_quadratic_analysis("orders_amt_total")
+    plot_overall_quad()
 
 
 if __name__ == "__main__":

migrations/0002_cap_orders_to_one.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import sqlite3
+from pathlib import Path
+
+MIGRATION_ID = "0002_cap_orders_to_one"
+DESCRIPTION = "Cap daily orders per category at 1 for each client/day (values >1 -> 1)"
+
+ORDER_COLS = [
+    "orders_amt_ent",
+    "orders_amt_super",
+    "orders_amt_transport",
+    "orders_amt_shopping",
+    "orders_amt_hotel",
+    "orders_amt_avia",
+]
+
+
+def run(context) -> None:
+    dataset_dir = Path(getattr(context, "dataset_dir", Path.cwd()))
+    sqlite_path = getattr(context, "sqlite_path", dataset_dir / "ds.sqlite")
+
+    if not sqlite_path.exists():
+        raise FileNotFoundError(f"SQLite database not found: {sqlite_path}")
+
+    conn = sqlite3.connect(sqlite_path)
+    try:
+        for col in ORDER_COLS:
+            sql = f"""
+            UPDATE communications
+            SET {col} = CASE WHEN {col} > 1 THEN 1 ELSE {col} END
+            """
+            conn.execute(sql)
+        conn.commit()
+        print(f"Capped values >1 to 1 in columns: {', '.join(ORDER_COLS)}")
+    finally:
+        conn.close()

new_plots.py

@@ -0,0 +1,404 @@
+from __future__ import annotations
+
+from pathlib import Path
+import sys
+from typing import Dict, Iterable, Optional, Tuple
+
+import altair as alt
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import roc_auc_score, r2_score
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+sys.path.append(str(PROJECT_ROOT / "main_hypot"))
+
+import best_model_and_plots as bmp
+from category_quadreg import (
+    BASE_COLUMNS,
+    CATEGORIES,
+    COMBINED,
+    add_combined_category,
+    build_client_by_category,
+)
+
+OUTPUT_DIR = PROJECT_ROOT / "new_plots"
+FONT_PATH = Path("/Users/dan/Downloads/AyuGram Desktop/SegoeUIVF.ttf")
+
+def inject_font_css(html_path: Path) -> None:
+    """Inject @font-face for SegoeUIVF into saved HTML if font exists."""
+    if not FONT_PATH.exists():
+        return
+    font_face = (
+        "@font-face{font-family:'Segoe UI Variable'; "
+        f"src: url('{FONT_PATH.as_uri()}') format('truetype'); "
+        "font-weight:100 900; font-style:normal;}\n"
+    )
+    css = f"<style>{font_face}body, text, .vega-bindings {{font-family:'Segoe UI Variable','Segoe UI',sans-serif;}}</style>"
+    html = html_path.read_text(encoding="utf-8")
+    if css in html:
+        return
+    if "</head>" in html:
+        html = html.replace("</head>", css + "\n</head>", 1)
+    else:
+        html = css + html
+    html_path.write_text(html, encoding="utf-8")
+
+
+# Используем тематику/шрифты из примера
+def configure_chart(chart: alt.Chart, title: str, width: int = 700, height: int = 500) -> alt.Chart:
+    alt.theme.enable("dark")
+    return (
+        chart.properties(
+            title=title,
+            width=width,
+            height=height,
+            padding=30,
+        )
+        .configure_title(
+            fontSize=18,
+            font="Segoe UI Variable",
+            fontWeight=600,
+            anchor="start",
+        )
+        .configure_axis(
+            grid=True,
+            labelFont="Segoe UI Variable",
+            titleFont="Segoe UI Variable",
+            labelFontSize=16,
+            titleFontSize=18,
+            labelFontWeight=400,
+            titleFontWeight=600,
+        )
+        .configure_legend(
+            labelFont="Segoe UI Variable",
+            titleFont="Segoe UI Variable",
+        )
+    )
+
+
+def prepare_client_data() -> pd.DataFrame:
+    """Поднимаем агрегаты по клиентам из существующего скрипта."""
+    return bmp.load_client_level(bmp.DB_PATH)
+
+
+def prepare_category_client_data() -> pd.DataFrame:
+    raw = pd.read_sql_query("select * from communications", bmp.sqlite3.connect(bmp.DB_PATH), parse_dates=["business_dt"])
+    client = build_client_by_category(raw)
+    for combo_name, cats in COMBINED.items():
+        client = add_combined_category(client, combo_name, cats)
+    return client
+
+
+def filter_and_trend(
+    df: pd.DataFrame,
+    y_col: str,
+    *,
+    x_col: str = bmp.X_COL,
+    x_max: float = bmp.DEFAULT_X_MAX,
+    y_max: float = bmp.DEFAULT_Y_MAX,
+    q_low: float = bmp.DEFAULT_Q_LOW,
+    q_high: float = bmp.DEFAULT_Q_HIGH,
+    iqr_k: float = bmp.DEFAULT_IQR_K,
+    trend_method: str = bmp.DEFAULT_TREND_METHOD,
+    trend_frac: float = bmp.DEFAULT_TREND_FRAC,
+    savgol_window: int = bmp.DEFAULT_SAVGOL_WINDOW,
+) -> Tuple[pd.DataFrame, Tuple[np.ndarray, np.ndarray]]:
+    base = df[[x_col, y_col]].dropna()
+    in_range = bmp.filter_x_range(base, x_col, x_max)
+    cleaned = bmp.remove_outliers(
+        in_range,
+        y_col=y_col,
+        x_col=x_col,
+        iqr_k=iqr_k,
+        q_low=q_low,
+        q_high=q_high,
+    )
+    # Обрезаем по y_max для удобства визуализации
+    cleaned = cleaned[cleaned[y_col] <= y_max].copy()
+    tx, ty = bmp.compute_trend(
+        cleaned,
+        y_col=y_col,
+        x_col=x_col,
+        method=trend_method,
+        lowess_frac=trend_frac,
+        savgol_window=savgol_window,
+    )
+    return cleaned, (tx, ty)
+
+
+def compute_density_alpha(df: pd.DataFrame, x_col: str, y_col: str, x_max: float, y_max: float) -> pd.Series:
+    alphas = bmp.compute_density_alpha(
+        df,
+        x_col=x_col,
+        y_col=y_col,
+        x_max=x_max,
+        bins_x=bmp.DEFAULT_BINS_X,
+        bins_y=bmp.DEFAULT_BINS_Y,
+        alpha_min=bmp.DEFAULT_ALPHA_MIN,
+        alpha_max=bmp.DEFAULT_ALPHA_MAX,
+        y_min=bmp.DEFAULT_Y_MIN,
+        y_max_limit=y_max,
+    )
+    if len(alphas) == 0:
+        return pd.Series([bmp.DEFAULT_ALPHA] * len(df), index=df.index)
+    return pd.Series(alphas, index=df.index)
+
+
+def fit_quadratic(
+    df: pd.DataFrame,
+    y_col: str,
+    trend_data: Tuple[np.ndarray, np.ndarray],
+    *,
+    x_col: str = bmp.X_COL,
+    x_max: float = bmp.DEFAULT_X_MAX,
+    force_negative_b2: bool = False,
+) -> Tuple[Optional[sm.regression.linear_model.RegressionResultsWrapper], dict]:
+    if len(df) < 3:
+        return None, {}
+
+    x = df[x_col].to_numpy()
+    y = df[y_col].to_numpy()
+    quad_term = -x**2 if force_negative_b2 else x**2
+    X_design = sm.add_constant(np.column_stack([x, quad_term]))
+    model = sm.OLS(y, X_design).fit(cov_type="HC3")
+
+    # AUC по бинарному флагу заказа
+    auc = np.nan
+    binary = (y > 0).astype(int)
+    if len(np.unique(binary)) > 1:
+        auc = roc_auc_score(binary, model.predict(X_design))
+
+    # R2 по тренду
+    tx, ty = trend_data
+    r2_trend = np.nan
+    if tx is not None and len(tx) >= 3:
+        mask = (tx <= x_max) & ~np.isnan(ty)
+        tx = tx[mask]
+        ty = ty[mask]
+        if len(tx) >= 3 and np.nanvar(ty) > 0:
+            quad_trend = -tx**2 if force_negative_b2 else tx**2
+            X_trend = sm.add_constant(np.column_stack([tx, quad_trend]))
+            y_hat_trend = model.predict(X_trend)
+            r2_trend = r2_score(ty, y_hat_trend)
+
+    return model, {"auc": auc, "r2_trend": r2_trend}
+
+
+def build_annotation(
+    params: np.ndarray,
+    pvals: np.ndarray,
+    metrics: dict,
+    n: int,
+    *,
+    b2_effective: Optional[float] = None,
+    x_pos: float = 0.5,
+) -> pd.DataFrame:
+    b2_val = b2_effective if b2_effective is not None else params[2]
+    lines = [
+        f"R2_trend={metrics.get('r2_trend', np.nan):.3f}",
+        f"AUC={metrics.get('auc', np.nan):.3f}",
+        f"b1={params[1]:.3f} (p={pvals[1]:.3g})",
+        f"b2={b2_val:.3f} (p={pvals[2]:.3g})",
+        f"n={n}",
+    ]
+    return pd.DataFrame(
+        {
+            "x": [x_pos] * len(lines),
+            "y": [metrics.get("y_max_for_anno", 0) - i * 0.4 for i in range(len(lines))],
+            "label": lines,
+        }
+    )
+
+
+def save_scatter_trend_quad(
+    df: pd.DataFrame,
+    y_col: str,
+    out_path: Path,
+    *,
+    x_col: str = bmp.X_COL,
+    x_max: float = bmp.DEFAULT_X_MAX,
+    y_max: float = bmp.DEFAULT_Y_MAX,
+    force_negative_b2: bool = False,
+    savgol_window: int = bmp.DEFAULT_SAVGOL_WINDOW,
+    title: str = "",
+) -> None:
+    cleaned, trend_data = filter_and_trend(
+        df,
+        y_col=y_col,
+        x_col=x_col,
+        x_max=x_max,
+        y_max=y_max,
+        trend_method=bmp.DEFAULT_TREND_METHOD,
+        trend_frac=bmp.DEFAULT_TREND_FRAC,
+        savgol_window=savgol_window,
+    )
+    if trend_data[0] is None:
+        print(f"[{y_col}] нет тренда/данных для построения")
+        return
+
+    cleaned = cleaned.copy()
+    cleaned["alpha"] = compute_density_alpha(cleaned, x_col, y_col, x_max, y_max)
+
+    model, metrics = fit_quadratic(cleaned, y_col, trend_data, x_col=x_col, x_max=x_max, force_negative_b2=force_negative_b2)
+    if model is None:
+        print(f"[{y_col}] недостаточно точек для квадрата")
+        return
+
+    params = model.params
+    pvals = model.pvalues
+    b2_effective = -abs(params[2]) if force_negative_b2 else params[2]
+
+    x_grid = np.linspace(0, x_max, 400)
+    quad_term = -x_grid**2 if force_negative_b2 else x_grid**2
+    quad_df = pd.DataFrame(
+        {
+            x_col: x_grid,
+            "quad": model.predict(sm.add_constant(np.column_stack([x_grid, quad_term]))),
+        }
+    )
+
+    trend_df = pd.DataFrame({x_col: trend_data[0], "trend": trend_data[1]})
+    metrics["y_max_for_anno"] = y_max * 0.95
+    metrics_text = [
+        f"R2_trend={metrics['r2_trend']:.3f}",
+        f"AUC={metrics['auc']:.3f}",
+        f"b1={params[1]:.3f} (p={pvals[1]:.3g})",
+        f"b2={b2_effective:.3f} (p={pvals[2]:.3g})",
+        f"n={len(cleaned)}",
+    ]
+
+    x_scale = alt.Scale(domain=(0, x_max), clamp=True, nice=False, domainMin=0, domainMax=x_max)
+    y_scale = alt.Scale(domain=(bmp.DEFAULT_Y_MIN, y_max), clamp=True, nice=False)
+
+    points = alt.Chart(cleaned).mark_circle(size=40).encode(
+        x=alt.X(x_col, title="Среднее число показов в день", scale=x_scale),
+        y=alt.Y(y_col, title=y_col, scale=y_scale),
+        opacity=alt.Opacity("alpha:Q", scale=alt.Scale(domain=(0, 1), clamp=True)),
+        color=alt.value(bmp.DEFAULT_SCATTER_COLOR),
+        tooltip=[x_col, y_col],
+    )
+
+    trend_line = alt.Chart(trend_df).mark_line(color=bmp.DEFAULT_TREND_COLOR, strokeWidth=2.5).encode(
+        x=alt.X(x_col, scale=x_scale),
+        y=alt.Y("trend", scale=y_scale),
+    )
+    quad_line = alt.Chart(quad_df).mark_line(color="blue", strokeWidth=2.2, strokeDash=[6, 4]).encode(
+        x=alt.X(x_col, scale=x_scale),
+        y=alt.Y("quad", scale=y_scale),
+    )
+
+    subtitle = " • ".join(metrics_text)
+
+    chart = alt.layer(points, trend_line, quad_line).resolve_scale(opacity="independent")
+    chart = configure_chart(chart, (title or f"{y_col} vs {x_col}") + f"  —  {subtitle}", width=800, height=600)
+
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    chart.save(out_path)
+    inject_font_css(out_path)
+    print(f"Saved {out_path}")
+
+
+def save_correlation_heatmap(df: pd.DataFrame, cols: Iterable[str], title: str, out_path: Path) -> None:
+    corr = df[list(cols)].corr()
+    corr_long = corr.reset_index().melt(id_vars="index", var_name="col", value_name="corr")
+    corr_long = corr_long.rename(columns={"index": "row"})
+
+    chart = (
+        alt.Chart(corr_long)
+        .mark_rect()
+        .encode(
+            x=alt.X("col:N", title=""),
+            y=alt.Y("row:N", title=""),
+            color=alt.Color("corr:Q", scale=alt.Scale(domain=(-1, 1), scheme="redblue"), legend=alt.Legend(title="corr")),
+            tooltip=["row", "col", alt.Tooltip("corr:Q", format=".3f")],
+        )
+    )
+    chart = configure_chart(chart, title, width=400, height=400)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    chart.save(out_path)
+    inject_font_css(out_path)
+    print(f"Saved {out_path}")
+
+
+def generate_total_plots() -> None:
+    df = prepare_client_data()
+    out_base = OUTPUT_DIR / "orders_amt_total"
+    save_scatter_trend_quad(
+        df,
+        y_col="orders_amt_total",
+        out_path=out_base / "scatter_trend_quad.html",
+        x_max=bmp.DEFAULT_X_MAX,
+        y_max=bmp.DEFAULT_Y_MAX,
+        savgol_window=bmp.DEFAULT_SAVGOL_WINDOW,
+        title="Заказы vs средние показы (все клиенты)",
+    )
+
+
+def generate_category_plots() -> None:
+    client = prepare_category_client_data()
+
+    x_max_overrides = {
+        "ent": 4,
+        "transport": 6,
+        "super": 4,
+        "avia": 4,
+        "shopping": 4,
+        "avia_hotel": 5,
+    }
+    y_max_overrides = {
+        "ent": 2.5,
+        "transport": 8,
+        "avia": 1.5,
+        "shopping": 2.5,
+        "super": 5.5,
+        "avia_hotel": 2.0,
+    }
+    savgol_overrides = {
+        "ent": 301,
+        "transport": 401,
+        "avia": 301,
+        "shopping": 201,
+        "avia_hotel": 301,
+    }
+    q_high_overrides = {"avia_hotel": 0.9}
+    iqr_overrides = {"avia_hotel": 1.2}
+
+    cats_all = CATEGORIES + list(COMBINED.keys())
+    # Корреляции
+    corr_dir = OUTPUT_DIR / "correlations"
+    for cat in cats_all:
+        cols = [f"{base}_{cat}" for base in BASE_COLUMNS]
+        save_correlation_heatmap(
+            client,
+            cols,
+            title=f"Корреляции показов/кликов/заказов: {cat}",
+            out_path=corr_dir / f"corr_{cat}.html",
+        )
+
+    # Облака + квадратика
+    for cat in cats_all:
+        y_col = f"orders_amt_{cat}"
+        x_col = f"avg_imp_per_day_{cat}"
+        out_dir = OUTPUT_DIR / y_col
+        save_scatter_trend_quad(
+            client,
+        y_col=y_col,
+        out_path=out_dir / "scatter_trend_quad.html",
+        x_col=x_col,
+        x_max=x_max_overrides.get(cat, bmp.DEFAULT_X_MAX),
+        y_max=y_max_overrides.get(cat, bmp.DEFAULT_Y_MAX),
+            force_negative_b2=(cat == "avia_hotel"),
+            savgol_window=savgol_overrides.get(cat, bmp.DEFAULT_SAVGOL_WINDOW),
+            title=f"{y_col} vs {x_col}",
+        )
+
+
+def main() -> None:
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    generate_total_plots()
+    generate_category_plots()
+
+
+if __name__ == "__main__":
+    main()

new_plots/correlations/corr_avia.html

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+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <style>
+    #vis.vega-embed {
+      width: 100%;
+      display: flex;
+    }
+
+    #vis.vega-embed details,
+    #vis.vega-embed details summary {
+      position: relative;
+    }
+  </style>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega@6"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-lite@6.1.0"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-embed@7"></script>
+<style>@font-face{font-family:'Segoe UI Variable'; src: url('file:///Users/dan/Downloads/AyuGram%20Desktop/SegoeUIVF.ttf') format('truetype'); font-weight:100 900; font-style:normal;}
+body, text, .vega-bindings {font-family:'Segoe UI Variable','Segoe UI',sans-serif;}</style>
+</head>
+<body>
+  <div id="vis"></div>
+  <script>
+    (function(vegaEmbed) {
+      var spec = {"usermeta": {"embedOptions": {"theme": "dark"}}, "config": {"view": {"continuousWidth": 300, "continuousHeight": 300}, "axis": {"grid": true, "labelFont": "Segoe UI Variable", "labelFontSize": 16, "labelFontWeight": 400, "titleFont": "Segoe UI Variable", "titleFontSize": 18, "titleFontWeight": 600}, "legend": {"labelFont": "Segoe UI Variable", "titleFont": "Segoe UI Variable"}, "title": {"anchor": "start", "font": "Segoe UI Variable", "fontSize": 18, "fontWeight": 600}}, "data": {"name": "data-81b5fe5ef3aa1fe9a1cf1fdd875e8008"}, "mark": {"type": "rect"}, "encoding": {"color": {"field": "corr", "legend": {"title": "corr"}, "scale": {"domain": [-1, 1], "scheme": "redblue"}, "type": "quantitative"}, "tooltip": [{"field": "row", "type": "nominal"}, {"field": "col", "type": "nominal"}, {"field": "corr", "format": ".3f", "type": "quantitative"}], "x": {"field": "col", "title": "", "type": "nominal"}, "y": {"field": "row", "title": "", "type": "nominal"}}, "height": 400, "padding": 30, "title": "\u041a\u043e\u0440\u0440\u0435\u043b\u044f\u0446\u0438\u0438 \u043f\u043e\u043a\u0430\u0437\u043e\u0432/\u043a\u043b\u0438\u043a\u043e\u0432/\u0437\u0430\u043a\u0430\u0437\u043e\u0432: avia", "width": 400, "$schema": "https://vega.github.io/schema/vega-lite/v6.1.0.json", "datasets": {"data-81b5fe5ef3aa1fe9a1cf1fdd875e8008": [{"row": "active_imp_avia", "col": "active_imp_avia", "corr": 1.0}, {"row": "passive_imp_avia", "col": "active_imp_avia", "corr": 0.01876412266457888}, {"row": "active_click_avia", "col": "active_imp_avia", "corr": 0.6555267805752467}, {"row": "passive_click_avia", "col": "active_imp_avia", "corr": 0.08891639561678617}, {"row": "orders_amt_avia", "col": "active_imp_avia", "corr": -0.04479889738838307}, {"row": "active_imp_avia", "col": "passive_imp_avia", "corr": 0.01876412266457888}, {"row": "passive_imp_avia", "col": "passive_imp_avia", "corr": 1.0}, {"row": "active_click_avia", "col": "passive_imp_avia", "corr": 0.048482427442423495}, {"row": "passive_click_avia", "col": "passive_imp_avia", "corr": 0.27543793232581393}, {"row": "orders_amt_avia", "col": "passive_imp_avia", "corr": 0.03022795982049177}, {"row": "active_imp_avia", "col": "active_click_avia", "corr": 0.6555267805752467}, {"row": "passive_imp_avia", "col": "active_click_avia", "corr": 0.048482427442423495}, {"row": "active_click_avia", "col": "active_click_avia", "corr": 1.0}, {"row": "passive_click_avia", "col": "active_click_avia", "corr": 0.11058067071772743}, {"row": "orders_amt_avia", "col": "active_click_avia", "corr": 0.007181957024016167}, {"row": "active_imp_avia", "col": "passive_click_avia", "corr": 0.08891639561678617}, {"row": "passive_imp_avia", "col": "passive_click_avia", "corr": 0.27543793232581393}, {"row": "active_click_avia", "col": "passive_click_avia", "corr": 0.11058067071772743}, {"row": "passive_click_avia", "col": "passive_click_avia", "corr": 1.0}, {"row": "orders_amt_avia", "col": "passive_click_avia", "corr": 0.14634536196166995}, {"row": "active_imp_avia", "col": "orders_amt_avia", "corr": -0.04479889738838307}, {"row": "passive_imp_avia", "col": "orders_amt_avia", "corr": 0.03022795982049177}, {"row": "active_click_avia", "col": "orders_amt_avia", "corr": 0.007181957024016167}, {"row": "passive_click_avia", "col": "orders_amt_avia", "corr": 0.14634536196166995}, {"row": "orders_amt_avia", "col": "orders_amt_avia", "corr": 1.0}]}};
+      var embedOpt = {"mode": "vega-lite"};
+
+      function showError(el, error){
+          el.innerHTML = ('<div style="color:red;">'
+                          + '<p>JavaScript Error: ' + error.message + '</p>'
+                          + "<p>This usually means there's a typo in your chart specification. "
+                          + "See the javascript console for the full traceback.</p>"
+                          + '</div>');
+          throw error;
+      }
+      const el = document.getElementById('vis');
+      vegaEmbed("#vis", spec, embedOpt)
+        .catch(error => showError(el, error));
+    })(vegaEmbed);
+
+  </script>
+</body>
+</html>

new_plots/correlations/corr_avia_hotel.html

@@ -0,0 +1,44 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <style>
+    #vis.vega-embed {
+      width: 100%;
+      display: flex;
+    }
+
+    #vis.vega-embed details,
+    #vis.vega-embed details summary {
+      position: relative;
+    }
+  </style>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega@6"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-lite@6.1.0"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-embed@7"></script>
+<style>@font-face{font-family:'Segoe UI Variable'; src: url('file:///Users/dan/Downloads/AyuGram%20Desktop/SegoeUIVF.ttf') format('truetype'); font-weight:100 900; font-style:normal;}
+body, text, .vega-bindings {font-family:'Segoe UI Variable','Segoe UI',sans-serif;}</style>
+</head>
+<body>
+  <div id="vis"></div>
+  <script>
+    (function(vegaEmbed) {
+      var spec = {"usermeta": {"embedOptions": {"theme": "dark"}}, "config": {"view": {"continuousWidth": 300, "continuousHeight": 300}, "axis": {"grid": true, "labelFont": "Segoe UI Variable", "labelFontSize": 16, "labelFontWeight": 400, "titleFont": "Segoe UI Variable", "titleFontSize": 18, "titleFontWeight": 600}, "legend": {"labelFont": "Segoe UI Variable", "titleFont": "Segoe UI Variable"}, "title": {"anchor": "start", "font": "Segoe UI Variable", "fontSize": 18, "fontWeight": 600}}, "data": {"name": "data-158e8b587028464f7420184e3a69712d"}, "mark": {"type": "rect"}, "encoding": {"color": {"field": "corr", "legend": {"title": "corr"}, "scale": {"domain": [-1, 1], "scheme": "redblue"}, "type": "quantitative"}, "tooltip": [{"field": "row", "type": "nominal"}, {"field": "col", "type": "nominal"}, {"field": "corr", "format": ".3f", "type": "quantitative"}], "x": {"field": "col", "title": "", "type": "nominal"}, "y": {"field": "row", "title": "", "type": "nominal"}}, "height": 400, "padding": 30, "title": "\u041a\u043e\u0440\u0440\u0435\u043b\u044f\u0446\u0438\u0438 \u043f\u043e\u043a\u0430\u0437\u043e\u0432/\u043a\u043b\u0438\u043a\u043e\u0432/\u0437\u0430\u043a\u0430\u0437\u043e\u0432: avia_hotel", "width": 400, "$schema": "https://vega.github.io/schema/vega-lite/v6.1.0.json", "datasets": {"data-158e8b587028464f7420184e3a69712d": [{"row": "active_imp_avia_hotel", "col": "active_imp_avia_hotel", "corr": 1.0}, {"row": "passive_imp_avia_hotel", "col": "active_imp_avia_hotel", "corr": -0.08274509905837495}, {"row": "active_click_avia_hotel", "col": "active_imp_avia_hotel", "corr": 0.6424745469930201}, {"row": "passive_click_avia_hotel", "col": "active_imp_avia_hotel", "corr": 0.0656927131251431}, {"row": "orders_amt_avia_hotel", "col": "active_imp_avia_hotel", "corr": 0.11791995115159383}, {"row": "active_imp_avia_hotel", "col": "passive_imp_avia_hotel", "corr": -0.08274509905837495}, {"row": "passive_imp_avia_hotel", "col": "passive_imp_avia_hotel", "corr": 1.0}, {"row": "active_click_avia_hotel", "col": "passive_imp_avia_hotel", "corr": -0.002830801434428736}, {"row": "passive_click_avia_hotel", "col": "passive_imp_avia_hotel", "corr": 0.19064250507318162}, {"row": "orders_amt_avia_hotel", "col": "passive_imp_avia_hotel", "corr": 0.0829341029860776}, {"row": "active_imp_avia_hotel", "col": "active_click_avia_hotel", "corr": 0.6424745469930201}, {"row": "passive_imp_avia_hotel", "col": "active_click_avia_hotel", "corr": -0.002830801434428736}, {"row": "active_click_avia_hotel", "col": "active_click_avia_hotel", "corr": 1.0}, {"row": "passive_click_avia_hotel", "col": "active_click_avia_hotel", "corr": 0.08320023005001294}, {"row": "orders_amt_avia_hotel", "col": "active_click_avia_hotel", "corr": 0.04818436665905769}, {"row": "active_imp_avia_hotel", "col": "passive_click_avia_hotel", "corr": 0.0656927131251431}, {"row": "passive_imp_avia_hotel", "col": "passive_click_avia_hotel", "corr": 0.19064250507318162}, {"row": "active_click_avia_hotel", "col": "passive_click_avia_hotel", "corr": 0.08320023005001294}, {"row": "passive_click_avia_hotel", "col": "passive_click_avia_hotel", "corr": 1.0}, {"row": "orders_amt_avia_hotel", "col": "passive_click_avia_hotel", "corr": 0.1191470947872778}, {"row": "active_imp_avia_hotel", "col": "orders_amt_avia_hotel", "corr": 0.11791995115159383}, {"row": "passive_imp_avia_hotel", "col": "orders_amt_avia_hotel", "corr": 0.0829341029860776}, {"row": "active_click_avia_hotel", "col": "orders_amt_avia_hotel", "corr": 0.04818436665905769}, {"row": "passive_click_avia_hotel", "col": "orders_amt_avia_hotel", "corr": 0.1191470947872778}, {"row": "orders_amt_avia_hotel", "col": "orders_amt_avia_hotel", "corr": 1.0}]}};
+      var embedOpt = {"mode": "vega-lite"};
+
+      function showError(el, error){
+          el.innerHTML = ('<div style="color:red;">'
+                          + '<p>JavaScript Error: ' + error.message + '</p>'
+                          + "<p>This usually means there's a typo in your chart specification. "
+                          + "See the javascript console for the full traceback.</p>"
+                          + '</div>');
+          throw error;
+      }
+      const el = document.getElementById('vis');
+      vegaEmbed("#vis", spec, embedOpt)
+        .catch(error => showError(el, error));
+    })(vegaEmbed);
+
+  </script>
+</body>
+</html>

new_plots/correlations/corr_ent.html

@@ -0,0 +1,44 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <style>
+    #vis.vega-embed {
+      width: 100%;
+      display: flex;
+    }
+
+    #vis.vega-embed details,
+    #vis.vega-embed details summary {
+      position: relative;
+    }
+  </style>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega@6"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-lite@6.1.0"></script>
+  <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/vega-embed@7"></script>
+<style>@font-face{font-family:'Segoe UI Variable'; src: url('file:///Users/dan/Downloads/AyuGram%20Desktop/SegoeUIVF.ttf') format('truetype'); font-weight:100 900; font-style:normal;}
+body, text, .vega-bindings {font-family:'Segoe UI Variable','Segoe UI',sans-serif;}</style>
+</head>
+<body>
+  <div id="vis"></div>
+  <script>
+    (function(vegaEmbed) {
+      var spec = {"usermeta": {"embedOptions": {"theme": "dark"}}, "config": {"view": {"continuousWidth": 300, "continuousHeight": 300}, "axis": {"grid": true, "labelFont": "Segoe UI Variable", "labelFontSize": 16, "labelFontWeight": 400, "titleFont": "Segoe UI Variable", "titleFontSize": 18, "titleFontWeight": 600}, "legend": {"labelFont": "Segoe UI Variable", "titleFont": "Segoe UI Variable"}, "title": {"anchor": "start", "font": "Segoe UI Variable", "fontSize": 18, "fontWeight": 600}}, "data": {"name": "data-cd1e14ccf8ef0243ac2429b66fca6f3e"}, "mark": {"type": "rect"}, "encoding": {"color": {"field": "corr", "legend": {"title": "corr"}, "scale": {"domain": [-1, 1], "scheme": "redblue"}, "type": "quantitative"}, "tooltip": [{"field": "row", "type": "nominal"}, {"field": "col", "type": "nominal"}, {"field": "corr", "format": ".3f", "type": "quantitative"}], "x": {"field": "col", "title": "", "type": "nominal"}, "y": {"field": "row", "title": "", "type": "nominal"}}, "height": 400, "padding": 30, "title": "\u041a\u043e\u0440\u0440\u0435\u043b\u044f\u0446\u0438\u0438 \u043f\u043e\u043a\u0430\u0437\u043e\u0432/\u043a\u043b\u0438\u043a\u043e\u0432/\u0437\u0430\u043a\u0430\u0437\u043e\u0432: ent", "width": 400, "$schema": "https://vega.github.io/schema/vega-lite/v6.1.0.json", "datasets": {"data-cd1e14ccf8ef0243ac2429b66fca6f3e": [{"row": "active_imp_ent", "col": "active_imp_ent", "corr": 1.0}, {"row": "passive_imp_ent", "col": "active_imp_ent", "corr": 0.3740482978344062}, {"row": "active_click_ent", "col": "active_imp_ent", "corr": 0.8713679748694044}, {"row": "passive_click_ent", "col": "active_imp_ent", "corr": 0.1834267922170377}, {"row": "orders_amt_ent", "col": "active_imp_ent", "corr": 0.19909732995304016}, {"row": "active_imp_ent", "col": "passive_imp_ent", "corr": 0.3740482978344062}, {"row": "passive_imp_ent", "col": "passive_imp_ent", "corr": 1.0}, {"row": "active_click_ent", "col": "passive_imp_ent", "corr": 0.3606804643725377}, {"row": "passive_click_ent", "col": "passive_imp_ent", "corr": 0.5648383908323416}, {"row": "orders_amt_ent", "col": "passive_imp_ent", "corr": 0.4151695148464165}, {"row": "active_imp_ent", "col": "active_click_ent", "corr": 0.8713679748694044}, {"row": "passive_imp_ent", "col": "active_click_ent", "corr": 0.3606804643725377}, {"row": "active_click_ent", "col": "active_click_ent", "corr": 1.0}, {"row": "passive_click_ent", "col": "active_click_ent", "corr": 0.12953818089063812}, {"row": "orders_amt_ent", "col": "active_click_ent", "corr": 0.16418539548659097}, {"row": "active_imp_ent", "col": "passive_click_ent", "corr": 0.1834267922170377}, {"row": "passive_imp_ent", "col": "passive_click_ent", "corr": 0.5648383908323416}, {"row": "active_click_ent", "col": "passive_click_ent", "corr": 0.12953818089063812}, {"row": "passive_click_ent", "col": "passive_click_ent", "corr": 1.0}, {"row": "orders_amt_ent", "col": "passive_click_ent", "corr": 0.5553099034616074}, {"row": "active_imp_ent", "col": "orders_amt_ent", "corr": 0.19909732995304016}, {"row": "passive_imp_ent", "col": "orders_amt_ent", "corr": 0.4151695148464165}, {"row": "active_click_ent", "col": "orders_amt_ent", "corr": 0.16418539548659097}, {"row": "passive_click_ent", "col": "orders_amt_ent", "corr": 0.5553099034616074}, {"row": "orders_amt_ent", "col": "orders_amt_ent", "corr": 1.0}]}};
+      var embedOpt = {"mode": "vega-lite"};
+
+      function showError(el, error){
+          el.innerHTML = ('<div style="color:red;">'
+                          + '<p>JavaScript Error: ' + error.message + '</p>'
+                          + "<p>This usually means there's a typo in your chart specification. "
+                          + "See the javascript console for the full traceback.</p>"
+                          + '</div>');
+          throw error;
+      }
+      const el = document.getElementById('vis');
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new_plots/correlations/corr_hotel.html

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new_plots/correlations/corr_shopping.html

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new_plots/correlations/corr_super.html

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new_plots/correlations/corr_transport.html

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preanalysis/orders_distribution_orders_amt_avia.html

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preanalysis/orders_distribution_orders_amt_ent.html

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+  </script>
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preanalysis/orders_distribution_orders_amt_hotel.html

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+</html>

preanalysis/orders_distribution_orders_amt_shopping.html

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preanalysis/orders_distribution_orders_amt_super.html

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preanalysis/orders_distribution_orders_amt_transport.html

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