| |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix |
|
|
|
|
| DATA_DIR = Path("data") |
| TRAIN_PATH = DATA_DIR / "train.csv" |
| TEST_PATH = DATA_DIR / "tester.csv" |
|
|
|
|
| def find_label_column(df: pd.DataFrame) -> str: |
| label_cols = [c for c in df.columns if c.startswith("label_")] |
| if not label_cols: |
| raise ValueError("No label column found. Expected a column like label_<target_name>.") |
| return sorted(label_cols)[0] |
|
|
|
|
| def to_int_labels(y: pd.Series) -> np.ndarray: |
| if y.dtype == bool: |
| return y.astype(int).to_numpy() |
| if np.issubdtype(y.dtype, np.number): |
| return y.astype(int).to_numpy() |
|
|
| y_str = y.astype(str).str.strip().str.lower() |
| mapping = { |
| "0": 0, "1": 1, |
| "false": 0, "true": 1, |
| "no": 0, "yes": 1, |
| "neg": 0, "pos": 1, |
| "negative": 0, "positive": 1, |
| "green": 0, "red": 1, |
| "amber": 1, |
| } |
| if not y_str.isin(mapping.keys()).all(): |
| unknown = sorted(set(y_str.unique()) - set(mapping.keys())) |
| raise ValueError(f"Unknown label values: {unknown}") |
| return y_str.map(mapping).astype(int).to_numpy() |
|
|
|
|
| def main() -> None: |
| if not TRAIN_PATH.exists(): |
| raise FileNotFoundError(f"Missing {TRAIN_PATH}") |
| if not TEST_PATH.exists(): |
| raise FileNotFoundError(f"Missing {TEST_PATH}") |
|
|
| train = pd.read_csv(TRAIN_PATH) |
| test = pd.read_csv(TEST_PATH) |
|
|
| label_col = find_label_column(train) |
| if label_col not in test.columns: |
| raise ValueError(f"Label column {label_col} missing from tester.csv") |
|
|
| feature_cols = [c for c in train.columns if c != label_col] |
|
|
| X_train = train[feature_cols].to_numpy(dtype=float) |
| y_train = to_int_labels(train[label_col]) |
|
|
| X_test = test[feature_cols].to_numpy(dtype=float) |
| y_test = to_int_labels(test[label_col]) |
|
|
| model = LogisticRegression(max_iter=2000, solver="lbfgs") |
| model.fit(X_train, y_train) |
|
|
| y_pred = model.predict(X_test) |
|
|
| metrics = { |
| "label_column": label_col, |
| "n_train": int(len(train)), |
| "n_test": int(len(test)), |
| "accuracy": float(accuracy_score(y_test, y_pred)), |
| "precision": float(precision_score(y_test, y_pred, zero_division=0)), |
| "recall": float(recall_score(y_test, y_pred, zero_division=0)), |
| "f1": float(f1_score(y_test, y_pred, zero_division=0)), |
| "confusion_matrix": confusion_matrix(y_test, y_pred).tolist(), |
| } |
|
|
| print(json.dumps(metrics, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
