File size: 5,419 Bytes
ae7c16d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | from __future__ import annotations
import argparse
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Tuple
import joblib
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.metrics import average_precision_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
TARGET = "isFraud"
def _read_joined(raw_dir: Path, max_rows: int | None) -> pd.DataFrame:
tx = pd.read_csv(raw_dir / "train_transaction.csv", nrows=max_rows)
ident_path = raw_dir / "train_identity.csv"
if ident_path.exists():
ident = pd.read_csv(ident_path, nrows=max_rows)
df = tx.merge(ident, on="TransactionID", how="left")
else:
df = tx
return df
def _select_columns(df: pd.DataFrame) -> Tuple[pd.DataFrame, List[str], List[str]]:
# Compact, stable set (keeps model lightweight + portable)
numeric = [
"TransactionAmt",
"dist1",
"dist2",
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
"C7",
"C8",
"C9",
"C10",
"C11",
"C12",
"C13",
"C14",
"D1",
"D2",
"D3",
"D4",
"D5",
"D10",
"D11",
"D15",
]
categorical = [
"ProductCD",
"card1",
"card2",
"card3",
"card5",
"card6",
"addr1",
"addr2",
"P_emaildomain",
"R_emaildomain",
"DeviceType",
]
numeric = [c for c in numeric if c in df.columns]
categorical = [c for c in categorical if c in df.columns]
keep = [TARGET] + numeric + categorical
keep = [c for c in keep if c in df.columns]
out = df[keep].copy()
return out, numeric, categorical
def _freq_encode(train: pd.DataFrame, test: pd.DataFrame, cols: List[str]) -> Tuple[pd.DataFrame, pd.DataFrame, Dict[str, Dict[Any, int]]]:
mappings: Dict[str, Dict[Any, int]] = {}
tr = train.copy()
te = test.copy()
for c in cols:
vc = tr[c].astype("string").fillna("NA").value_counts(dropna=False)
mapping = vc.to_dict()
mappings[c] = mapping
tr[c] = tr[c].astype("string").fillna("NA").map(mapping).fillna(0).astype(float)
te[c] = te[c].astype("string").fillna("NA").map(mapping).fillna(0).astype(float)
return tr, te, mappings
@dataclass
class Artifacts:
model: XGBClassifier
imputer: SimpleImputer
feature_names: List[str]
freq_mappings: Dict[str, Dict[Any, int]]
categorical_cols: List[str]
def train(df: pd.DataFrame, numeric: List[str], categorical: List[str], seed: int) -> Tuple[Artifacts, Dict[str, Any]]:
df = df.dropna(subset=[TARGET]).copy()
y = df[TARGET].astype(int).to_numpy()
X = df.drop(columns=[TARGET])
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=seed, stratify=y
)
X_train_enc, X_test_enc, mappings = _freq_encode(X_train, X_test, categorical)
feature_names = list(X_train_enc.columns)
imputer = SimpleImputer(strategy="median")
Xtr = imputer.fit_transform(X_train_enc)
Xte = imputer.transform(X_test_enc)
model = XGBClassifier(
n_estimators=900,
learning_rate=0.04,
max_depth=5,
subsample=0.9,
colsample_bytree=0.8,
reg_lambda=2.0,
min_child_weight=5,
objective="binary:logistic",
eval_metric="logloss",
n_jobs=4,
random_state=seed,
)
model.fit(Xtr, y_train)
proba = model.predict_proba(Xte)[:, 1]
metrics = {
"roc_auc": float(roc_auc_score(y_test, proba)),
"pr_auc": float(average_precision_score(y_test, proba)),
"n_test": int(len(y_test)),
"seed": int(seed),
}
artifacts = Artifacts(
model=model,
imputer=imputer,
feature_names=feature_names,
freq_mappings=mappings,
categorical_cols=categorical,
)
return artifacts, metrics
def save(artifacts: Artifacts, metrics: Dict[str, Any], out_dir: Path) -> None:
out_dir.mkdir(parents=True, exist_ok=True)
joblib.dump(
{
"model": artifacts.model,
"imputer": artifacts.imputer,
"feature_names": artifacts.feature_names,
"freq_mappings": artifacts.freq_mappings,
"categorical_cols": artifacts.categorical_cols,
},
out_dir / "model.joblib",
)
with (out_dir / "metrics.json").open("w") as f:
json.dump(metrics, f, indent=2, sort_keys=True)
def main() -> None:
p = argparse.ArgumentParser()
p.add_argument("--seed", type=int, default=42)
p.add_argument("--max_rows", type=int, default=200000)
p.add_argument("--raw_dir", type=str, default=str(Path("data/raw")))
p.add_argument("--out_dir", type=str, default=str(Path("artifacts")))
args = p.parse_args()
raw_dir = Path(args.raw_dir)
df = _read_joined(raw_dir, max_rows=args.max_rows if args.max_rows > 0 else None)
df, numeric, categorical = _select_columns(df)
artifacts, metrics = train(df, numeric, categorical, seed=args.seed)
save(artifacts, metrics, Path(args.out_dir))
print(json.dumps(metrics, indent=2))
if __name__ == "__main__":
main()
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