fully working spam hypot

alternative/category_mix_uplift/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

alternative/contact_frequency_orders/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

alternative/device_orders/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

alternative/ent_passive_ctr_uplift/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

alternative/passive_share_orders/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

alternative/saturation_effect/eda_utils.py

@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df

spam_hypot/01_stat_analysis.ipynb

@@ -1,188 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4d7d3347",
-   "metadata": {},
-   "source": [
-    "# Спам-гипотеза: плотность показов vs CTR/CR\n",
-    "\n",
-    "Цель: проверить, что высокая плотность показов на контактный день снижает CTR и CR (спам-эффект)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7acbd1c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sqlite3\n",
-    "from pathlib import Path\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import stats\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
-    "\n",
-    "project_root = Path.cwd().resolve()\n",
-    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
-    "    project_root = project_root.parent\n",
-    "sys.path.append(str(project_root / \"preanalysis\"))\n",
-    "import eda_utils as eda\n",
-    "\n",
-    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
-    "conn = sqlite3.connect(db_path)\n",
-    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
-    "conn.close()\n",
-    "\n",
-    "for cols, name in [\n",
-    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
-    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
-    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
-    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
-    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
-    "]:\n",
-    "    df[name] = df[cols].sum(axis=1)\n",
-    "\n",
-    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
-    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
-    "\n",
-    "contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
-    "client = df.groupby(\"id\").agg(\n",
-    "    {\n",
-    "        \"imp_total\": \"sum\",\n",
-    "        \"click_total\": \"sum\",\n",
-    "        \"orders_amt_total\": \"sum\",\n",
-    "        \"age\": \"median\",\n",
-    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
-    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
-    "    }\n",
-    ").merge(contact_days, on=\"id\", how=\"left\").reset_index()\n",
-    "\n",
-    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
-    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
-    "client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
-    "client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
-    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94eb2d26",
-   "metadata": {},
-   "source": [
-    "## Базовые статистики"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "287a09b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary = client[[\"imp_total\", \"click_total\", \"orders_amt_total\", \"contact_days\", \"avg_imp_per_day\", \"ctr_all\", \"cr_click2order\"]].describe().T\n",
-    "missing = client.isna().mean().sort_values(ascending=False)\n",
-    "summary, missing.head(10)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10cd44b7",
-   "metadata": {},
-   "source": [
-    "## Корреляции и тесты\n",
-    "Спирмен между плотностью и CTR/CR, а также Mann–Whitney между Q1 и Q4 по плотности."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "88714a03",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corr_ctr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"ctr_all\"])\n",
-    "corr_cr = stats.spearmanr(client[\"avg_imp_per_day\"], client[\"cr_click2order\"])\n",
-    "q1 = client[\"avg_imp_per_day\"].quantile(0.25)\n",
-    "q4 = client[\"avg_imp_per_day\"].quantile(0.75)\n",
-    "low = client.loc[client[\"avg_imp_per_day\"] <= q1, \"ctr_all\"].dropna()\n",
-    "high = client.loc[client[\"avg_imp_per_day\"] >= q4, \"ctr_all\"].dropna()\n",
-    "wu = stats.mannwhitneyu(low, high, alternative=\"greater\")\n",
-    "{ \"spearman_ctr\": corr_ctr, \"spearman_cr\": corr_cr, \"mw_low_gt_high\": wu }\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20d492fa",
-   "metadata": {},
-   "source": [
-    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
-    "stats_bin = client.groupby(bins, observed=False).agg(\n",
-    "    ctr_all=(\"ctr_all\", \"median\"),\n",
-    "    cr_click2order=(\"cr_click2order\", \"median\"),\n",
-    "    avg_imp_per_day=(\"avg_imp_per_day\", \"median\"),\n",
-    ").reset_index()\n",
-    "stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
-    "fig, ax1 = plt.subplots(figsize=(12, 5))\n",
-    "ax2 = ax1.twinx()\n",
-    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
-    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
-    "ax1.set_ylabel(\"CTR\")\n",
-    "ax2.set_ylabel(\"CR click→order\")\n",
-    "plt.xticks(rotation=35)\n",
-    "ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
-    "fig.tight_layout()\n",
-    "plt.show()\n",
-    "stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "943f0d4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
-    "stats_bin = client.groupby(bins).agg({\"ctr_all\": \"median\", \"cr_click2order\": \"median\", \"avg_imp_per_day\": \"median\"}).reset_index()\n",
-    "stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
-    "fig, ax1 = plt.subplots(figsize=(12, 5))\n",
-    "ax2 = ax1.twinx()\n",
-    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"ctr_all\", marker=\"o\", ax=ax1, color=\"#4c72b0\", label=\"CTR\")\n",
-    "sns.lineplot(data=stats_bin, x=\"bin_label\", y=\"cr_click2order\", marker=\"o\", ax=ax2, color=\"#c44e52\", label=\"CR\")\n",
-    "ax1.set_ylabel(\"CTR\")\n",
-    "ax2.set_ylabel(\"CR click→order\")\n",
-    "plt.xticks(rotation=35)\n",
-    "ax1.set_title(\"CTR и CR по децилям avg_imp_per_day\")\n",
-    "fig.tight_layout()\n",
-    "plt.show()\n",
-    "stats_bin[[\"bin_label\", \"ctr_all\", \"cr_click2order\"]]\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

spam_hypot/02_models.ipynb

@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7254b4c1",
-   "metadata": {},
-   "source": [
-    "# Спам-гипотеза: сравнение моделей\n",
-    "\n",
-    "Target: `high_ctr` (верхний квартиль CTR)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7f54168",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sqlite3\n",
-    "from pathlib import Path\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import stats\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
-    "\n",
-    "project_root = Path.cwd().resolve()\n",
-    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
-    "    project_root = project_root.parent\n",
-    "sys.path.append(str(project_root / \"preanalysis\"))\n",
-    "import eda_utils as eda\n",
-    "\n",
-    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
-    "conn = sqlite3.connect(db_path)\n",
-    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
-    "conn.close()\n",
-    "\n",
-    "for cols, name in [\n",
-    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
-    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
-    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
-    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
-    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
-    "]:\n",
-    "    df[name] = df[cols].sum(axis=1)\n",
-    "\n",
-    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
-    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
-    "\n",
-    "contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
-    "client = df.groupby(\"id\").agg(\n",
-    "    {\n",
-    "        \"imp_total\": \"sum\",\n",
-    "        \"click_total\": \"sum\",\n",
-    "        \"orders_amt_total\": \"sum\",\n",
-    "        \"age\": \"median\",\n",
-    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
-    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
-    "    }\n",
-    ").merge(contact_days, on=\"id\", how=\"left\").reset_index()\n",
-    "\n",
-    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
-    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
-    "client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
-    "client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
-    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21786c63",
-   "metadata": {},
-   "source": [
-    "## Модели: Logistic Regression vs GradientBoosting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc8dbc94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
-    "X = X.copy()\n",
-    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
-    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
-    "y = client[\"high_ctr\"]\n",
-    "\n",
-    "num_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
-    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
-    "pre = ColumnTransformer([\n",
-    "    (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]), num_cols),\n",
-    "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
-    "])\n",
-    "\n",
-    "log_reg = Pipeline([(\"pre\", pre), (\"clf\", LogisticRegression(max_iter=1000))])\n",
-    "gb = Pipeline([(\"pre\", pre), (\"clf\", GradientBoostingClassifier(random_state=42))])\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
-    "res = {}\n",
-    "for name, model in [(\"log_reg\", log_reg), (\"gb\", gb)]:\n",
-    "    model.fit(X_train, y_train)\n",
-    "    proba = model.predict_proba(X_test)[:, 1]\n",
-    "    res[name] = roc_auc_score(y_test, proba)\n",
-    "res\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "203acf70",
-   "metadata": {},
-   "source": [
-    "## Важности признаков (GradientBoosting)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3eac9e17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gb_model = gb\n",
-    "feat_names = gb_model.named_steps[\"pre\"].get_feature_names_out()\n",
-    "importances = gb_model.named_steps[\"clf\"].feature_importances_\n",
-    "imp_df = pd.DataFrame({\"feature\": feat_names, \"importance\": importances}).sort_values(\"importance\", ascending=False)\n",
-    "plt.figure(figsize=(8, 5))\n",
-    "sns.barplot(data=imp_df.head(15), x=\"importance\", y=\"feature\", palette=\"viridis\")\n",
-    "plt.title(\"Top-15 feature importances (GB)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "imp_df.head(15)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

spam_hypot/03_best_model.ipynb

@@ -1,206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d88bf2d8",
-   "metadata": {},
-   "source": [
-    "# Спам-гипотеза: лучшая модель и визуализации\n",
-    "\n",
-    "Используем GradientBoostingClassifier (лучше логрега по AUC) для подтверждения гипотезы."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87f3f728",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sqlite3\n",
-    "from pathlib import Path\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import stats\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
-    "\n",
-    "project_root = Path.cwd().resolve()\n",
-    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
-    "    project_root = project_root.parent\n",
-    "sys.path.append(str(project_root / \"preanalysis\"))\n",
-    "import eda_utils as eda\n",
-    "\n",
-    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
-    "conn = sqlite3.connect(db_path)\n",
-    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
-    "conn.close()\n",
-    "\n",
-    "for cols, name in [\n",
-    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
-    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
-    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
-    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
-    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
-    "]:\n",
-    "    df[name] = df[cols].sum(axis=1)\n",
-    "\n",
-    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
-    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
-    "\n",
-    "contact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\n",
-    "client = df.groupby(\"id\").agg(\n",
-    "    {\n",
-    "        \"imp_total\": \"sum\",\n",
-    "        \"click_total\": \"sum\",\n",
-    "        \"orders_amt_total\": \"sum\",\n",
-    "        \"age\": \"median\",\n",
-    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
-    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
-    "    }\n",
-    ").merge(contact_days, on=\"id\", how=\"left\").reset_index()\n",
-    "\n",
-    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
-    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
-    "client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
-    "client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
-    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17da010c",
-   "metadata": {},
-   "source": [
-    "## Обучение лучшей модели"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81433d7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
-    "X = X.copy()\n",
-    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
-    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
-    "y = client[\"high_ctr\"]\n",
-    "\n",
-    "num_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
-    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
-    "pre = ColumnTransformer([\n",
-    "    (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]), num_cols),\n",
-    "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
-    "])\n",
-    "\n",
-    "best = Pipeline([(\"pre\", pre), (\"clf\", GradientBoostingClassifier(random_state=42))])\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
-    "best.fit(X_train, y_train)\n",
-    "proba = best.predict_proba(X_test)[:, 1]\n",
-    "auc = roc_auc_score(y_test, proba)\n",
-    "auc\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63f4db9b",
-   "metadata": {},
-   "source": [
-    "## Прогноз vs плотность показов"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f48584b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid = pd.DataFrame({\"avg_imp_per_day\": np.linspace(client[\"avg_imp_per_day\"].min(), client[\"avg_imp_per_day\"].max(), 50)})\n",
-    "base = client.median(numeric_only=True)\n",
-    "base_gender = client[\"gender_cd\"].mode().iat[0]\n",
-    "base_device = client[\"device_platform_cd\"].mode().iat[0]\n",
-    "grid[\"imp_total\"] = base[\"imp_total\"]\n",
-    "grid[\"click_total\"] = base[\"click_total\"]\n",
-    "grid[\"age\"] = base[\"age\"]\n",
-    "grid[\"gender_cd\"] = base_gender\n",
-    "grid[\"device_platform_cd\"] = base_device\n",
-    "proba_grid = best.predict_proba(grid)[:, 1]\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "plt.plot(grid[\"avg_imp_per_day\"], proba_grid, marker=\"o\")\n",
-    "plt.xlabel(\"avg_imp_per_day\")\n",
-    "plt.ylabel(\"P(high CTR)\")\n",
-    "plt.title(\"Предсказанная вероятность высокого CTR vs плотность показов\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32f73b44",
-   "metadata": {},
-   "source": [
-    "## График CTR и CR по тонким бинам (две оси)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb4d0190",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bins = pd.qcut(client[\"avg_imp_per_day\"], 15, duplicates=\"drop\")\n",
-    "stats_bin = client.groupby(bins).agg({\"ctr_all\": \"median\", \"cr_click2order\": \"median\", \"avg_imp_per_day\": \"median\"}).reset_index()\n",
-    "stats_bin[\"bin_label\"] = stats_bin[\"avg_imp_per_day\"].round(2).astype(str)\n",
-    "fig, ax1 = plt.subplots(figsize=(12, 5))\n",
-    "ax2 = ax1.twinx()\n",
-    "ax1.plot(stats_bin[\"bin_label\"], stats_bin[\"ctr_all\"], marker=\"o\", color=\"#4c72b0\", label=\"CTR\")\n",
-    "ax2.plot(stats_bin[\"bin_label\"], stats_bin[\"cr_click2order\"], marker=\"s\", color=\"#c44e52\", label=\"CR\")\n",
-    "ax1.set_ylabel(\"CTR\")\n",
-    "ax2.set_ylabel(\"CR click→order\")\n",
-    "ax1.set_xlabel(\"avg_imp_per_day bins\")\n",
-    "plt.xticks(rotation=35)\n",
-    "ax1.set_title(\"CTR и CR по 15 бинам avg_imp_per_day\")\n",
-    "fig.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebb2ca5e",
-   "metadata": {},
-   "source": [
-    "## Вывод\n",
-    "- AUC модели GradientBoosting > логрега; `avg_imp_per_day` ключевой драйвер: рост плотности снижает шанс попасть в верхний квартиль CTR.\n",
-    "- Биновые графики показывают монотонное падение CTR и CR при росте avg_imp_per_day.\n",
-    "- Гипотеза о спам-эффекте подтверждается как статистически, так и по ML-модели."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

spam_hypot/model_compare.py

@@ -46,32 +46,62 @@ client = (
     .merge(contact_days, on="id", how="left")
     .reset_index()
 )
+# ... всё как у тебя до расчёта client["ctr_all"] включительно
+
 client["ctr_all"] = eda.safe_divide(client["click_total"], client["imp_total"])
 client["avg_imp_per_day"] = eda.safe_divide(client["imp_total"], client["contact_days"])
-client["high_ctr"] = (client["ctr_all"] >= client["ctr_all"].quantile(0.75)).astype(int)
 
-X = client[["avg_imp_per_day", "imp_total", "click_total", "age", "gender_cd", "device_platform_cd"]]
+# --- SPLIT СНАЧАЛА, ТАРГЕТ ПОТОМ ---
-X = X.copy()
+train_idx, test_idx = train_test_split(
-X["gender_cd"] = eda.normalize_gender(X["gender_cd"])
+    client.index, test_size=0.2, random_state=42
-X["device_platform_cd"] = eda.normalize_device(X["device_platform_cd"])
+)
-y = client["high_ctr"]
 
-num_cols = ["avg_imp_per_day", "imp_total", "click_total", "age"]
+train = client.loc[train_idx].copy()
+test = client.loc[test_idx].copy()
+
+thr = train["ctr_all"].quantile(0.75)   # порог только по train
+train["high_ctr"] = (train["ctr_all"] >= thr).astype(int)
+test["high_ctr"]  = (test["ctr_all"]  >= thr).astype(int)
+
+# --- ФИЧИ БЕЗ click_total (иначе это чит) ---
+X_train = train[[
+    "avg_imp_per_day", "imp_total", "contact_days",  # можно оставить
+    "age", "gender_cd", "device_platform_cd"
+]].copy()
+X_test = test[[
+    "avg_imp_per_day", "imp_total", "contact_days",
+    "age", "gender_cd", "device_platform_cd"
+]].copy()
+
+X_train["gender_cd"] = eda.normalize_gender(X_train["gender_cd"])
+X_train["device_platform_cd"] = eda.normalize_device(X_train["device_platform_cd"])
+X_test["gender_cd"] = eda.normalize_gender(X_test["gender_cd"])
+X_test["device_platform_cd"] = eda.normalize_device(X_test["device_platform_cd"])
+
+y_train = train["high_ctr"]
+y_test = test["high_ctr"]
+
+num_cols = ["avg_imp_per_day", "imp_total", "contact_days", "age"]
 cat_cols = ["gender_cd", "device_platform_cd"]
+
 pre = ColumnTransformer([
-    ("num", Pipeline([("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]), num_cols),
+    ("num", Pipeline([
+        ("imputer", SimpleImputer(strategy="median")),
+        ("scaler", StandardScaler())
+    ]), num_cols),
     ("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
 ])
 
 log_reg = Pipeline([("pre", pre), ("clf", LogisticRegression(max_iter=1000))])
 gb = Pipeline([("pre", pre), ("clf", GradientBoostingClassifier(random_state=42))])
 
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
 results = {}
 for name, model in [("log_reg", log_reg), ("gb", gb)]:
     model.fit(X_train, y_train)
     proba = model.predict_proba(X_test)[:, 1]
     results[name] = roc_auc_score(y_test, proba)
+
+print("CTR threshold (train 0.75q):", thr)
 print("AUC results:", results)
 
 imp = gb.named_steps["clf"].feature_importances_