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13bb27f | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import joblib
import numpy as np
from sklearn.exceptions import ConvergenceWarning
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from taxi_fare import (
FEATURE_COLUMNS,
format_metrics,
load_dataset,
prepare_training_frame,
)
import warnings
warnings.filterwarnings("ignore", category=ConvergenceWarning)
def build_model(random_state: int = 42) -> Pipeline:
return Pipeline(
steps=[
("scaler", StandardScaler()),
(
"ann",
MLPRegressor(
hidden_layer_sizes=(128, 64, 32),
activation="relu",
solver="adam",
alpha=0.0001,
batch_size=1024,
learning_rate_init=0.001,
max_iter=120,
early_stopping=True,
validation_fraction=0.15,
n_iter_no_change=12,
random_state=random_state,
verbose=False,
),
),
]
)
def main() -> None:
parser = argparse.ArgumentParser(description="Train an ANN fare prediction model on NYC Taxi Fare Prediction data.")
parser.add_argument("--data", type=str, default="data/nyc_taxi_fare.csv", help="Path to the dataset CSV.")
parser.add_argument("--sample-size", type=int, default=200000, help="Optional training sample size.")
parser.add_argument("--output-dir", type=str, default="artifacts", help="Directory for model artifacts.")
parser.add_argument("--random-state", type=int, default=42, help="Random seed.")
args = parser.parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
raw = load_dataset(args.data, sample_size=args.sample_size, random_state=args.random_state)
features, target, stats = prepare_training_frame(raw)
x_train, x_test, y_train, y_test = train_test_split(
features,
target,
test_size=0.2,
random_state=args.random_state,
)
model = build_model(random_state=args.random_state)
model.fit(x_train, y_train)
predictions = model.predict(x_test)
mse = mean_squared_error(y_test, predictions)
metrics = {
"mae": mean_absolute_error(y_test, predictions),
"rmse": float(np.sqrt(mse)),
"r2": r2_score(y_test, predictions),
}
joblib.dump(model, output_dir / "taxi_fare_ann_model.joblib")
(output_dir / "metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8")
(output_dir / "training_summary.txt").write_text(
"\n".join(
[
f"feature_columns: {FEATURE_COLUMNS}",
f"rows_before_cleaning: {stats.rows_before}",
f"rows_after_cleaning: {stats.rows_after}",
f"rows_dropped: {stats.rows_dropped}",
format_metrics(metrics),
]
),
encoding="utf-8",
)
print("Training complete")
print(format_metrics(metrics))
print(f"Saved model to {output_dir / 'taxi_fare_ann_model.joblib'}")
if __name__ == "__main__":
main()
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