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1e11bce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | from __future__ import annotations
from pathlib import Path
from typing import Iterable
import numpy as np
import pandas as pd
NUMERIC_FEATURES = [
"hour_of_day",
"day_of_week",
"is_weekend",
"is_festival",
"inventory_level",
"inventory_days_cover",
"competitor_price",
"click_through_rate",
"conversion_rate",
"units_sold_last_5m",
"units_sold_last_1h",
"base_cost",
"current_price",
"demand_index",
"inventory_pressure",
"competitor_gap",
]
CATEGORICAL_FEATURES = ["category", "brand", "customer_segment"]
TARGET_COLUMN = "optimal_price"
KAGGLE_RETAIL_NUMERIC_FEATURES = [
"qty",
"freight_price",
"product_name_lenght",
"product_description_lenght",
"product_photos_qty",
"product_weight_g",
"product_score",
"customers",
"weekday",
"weekend",
"holiday",
"volume",
"comp_1",
"ps1",
"fp1",
"comp_2",
"ps2",
"fp2",
"comp_3",
"ps3",
"fp3",
"lag_price",
"month",
"year",
]
KAGGLE_RETAIL_CATEGORICAL_FEATURES = ["product_id", "product_category_name"]
KAGGLE_RETAIL_TARGET_COLUMN = "unit_price"
def add_derived_features(frame: pd.DataFrame) -> pd.DataFrame:
enriched = frame.copy()
enriched["demand_index"] = (
enriched["units_sold_last_1h"] * 0.55
+ enriched["units_sold_last_5m"] * 0.35
+ enriched["conversion_rate"] * 100 * 0.10
)
enriched["inventory_pressure"] = np.where(
enriched["inventory_level"] <= 20,
1.25,
np.where(enriched["inventory_level"] <= 60, 1.05, 0.92),
)
enriched["competitor_gap"] = (
enriched["current_price"] - enriched["competitor_price"]
) / enriched["competitor_price"].clip(lower=1.0)
return enriched
def load_training_data(path: Path) -> pd.DataFrame:
frame = pd.read_csv(path)
return add_derived_features(frame)
def load_kaggle_retail_training_data(path: Path) -> pd.DataFrame:
frame = pd.read_csv(path)
if "month_year" in frame.columns:
parsed_month_year = pd.to_datetime(frame["month_year"], dayfirst=True, errors="coerce")
if "month" not in frame.columns:
frame["month"] = parsed_month_year.dt.month
if "year" not in frame.columns:
frame["year"] = parsed_month_year.dt.year
numeric_defaults = [
"comp_1",
"ps1",
"fp1",
"comp_2",
"ps2",
"fp2",
"comp_3",
"ps3",
"fp3",
"lag_price",
]
for column in numeric_defaults:
if column not in frame.columns:
frame[column] = np.nan
if "volume" not in frame.columns:
frame["volume"] = (
frame.get("product_name_lenght", 0).fillna(0)
* frame.get("product_description_lenght", 0).fillna(0)
* frame.get("product_photos_qty", 0).fillna(0).clip(lower=1)
)
if "weekday" not in frame.columns:
frame["weekday"] = 0
if "weekend" not in frame.columns:
frame["weekend"] = 0
if "holiday" not in frame.columns:
frame["holiday"] = 0
ensure_columns(
frame,
KAGGLE_RETAIL_NUMERIC_FEATURES
+ KAGGLE_RETAIL_CATEGORICAL_FEATURES
+ [KAGGLE_RETAIL_TARGET_COLUMN],
)
return frame
def split_xy(
frame: pd.DataFrame,
numeric_features: list[str] | None = None,
categorical_features: list[str] | None = None,
target_column: str = TARGET_COLUMN,
) -> tuple[pd.DataFrame, pd.Series]:
selected_numeric = numeric_features or NUMERIC_FEATURES
selected_categorical = categorical_features or CATEGORICAL_FEATURES
features = frame[selected_numeric + selected_categorical].copy()
target = frame[target_column].copy()
return features, target
def ensure_columns(frame: pd.DataFrame, required_columns: Iterable[str]) -> None:
missing = [column for column in required_columns if column not in frame.columns]
if missing:
raise ValueError(f"Missing required columns: {missing}")
|