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| import json | |
| from pathlib import Path | |
| import joblib | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| from sklearn.metrics import ( | |
| accuracy_score, | |
| f1_score, | |
| classification_report, | |
| confusion_matrix, | |
| ) | |
| # ========================= | |
| # CONFIG | |
| # ========================= | |
| DATASET_REPO_ID = "harikrishna1985/Engine_data" | |
| MODEL_REPO_ID = "harikrishna1985/predictive-maintenance-model" | |
| TEST_FILENAME = "processed/test.csv" | |
| MODEL_FILENAME = "best_model.pkl" | |
| MODEL_INFO_FILENAME = "best_model_info.json" | |
| TARGET_COLUMN = "engine_condition" | |
| LOCAL_EVAL_DIR = Path("artifacts") | |
| LOCAL_EVAL_DIR.mkdir(parents=True, exist_ok=True) | |
| EVAL_SUMMARY_FILE = LOCAL_EVAL_DIR / "evaluation_summary.json" | |
| CLASSIFICATION_REPORT_FILE = LOCAL_EVAL_DIR / "classification_report.csv" | |
| CONFUSION_MATRIX_FILE = LOCAL_EVAL_DIR / "confusion_matrix.csv" | |
| def load_test_data(): | |
| test_path = hf_hub_download( | |
| repo_id=DATASET_REPO_ID, | |
| filename=TEST_FILENAME, | |
| repo_type="dataset", | |
| ) | |
| test_df = pd.read_csv(test_path) | |
| test_df.columns = [c.strip().lower().replace(" ", "_") for c in test_df.columns] | |
| return test_df | |
| def load_model_and_info(): | |
| model_path = hf_hub_download( | |
| repo_id=MODEL_REPO_ID, | |
| filename=MODEL_FILENAME, | |
| repo_type="model", | |
| ) | |
| info_path = hf_hub_download( | |
| repo_id=MODEL_REPO_ID, | |
| filename=MODEL_INFO_FILENAME, | |
| repo_type="model", | |
| ) | |
| model = joblib.load(model_path) | |
| with open(info_path, "r", encoding="utf-8") as f: | |
| model_info = json.load(f) | |
| return model, model_info | |
| def prepare_test_features(test_df: pd.DataFrame, feature_columns: list[str]): | |
| target_col_clean = TARGET_COLUMN.strip().lower().replace(" ", "_") | |
| if target_col_clean not in test_df.columns: | |
| raise ValueError(f"Target column '{target_col_clean}' missing in test data.") | |
| X_test = test_df.drop(columns=[target_col_clean]) | |
| y_test = test_df[target_col_clean] | |
| X_test = pd.get_dummies(X_test, drop_first=False) | |
| # align to training features | |
| X_test = X_test.reindex(columns=feature_columns, fill_value=0) | |
| return X_test, y_test | |
| def evaluate(): | |
| test_df = load_test_data() | |
| model, model_info = load_model_and_info() | |
| feature_columns = model_info["feature_columns"] | |
| X_test, y_test = prepare_test_features(test_df, feature_columns) | |
| preds = model.predict(X_test) | |
| acc = accuracy_score(y_test, preds) | |
| f1 = f1_score(y_test, preds, average="weighted") | |
| report = classification_report(y_test, preds, output_dict=True) | |
| report_df = pd.DataFrame(report).transpose() | |
| labels = sorted(y_test.astype(str).unique().tolist()) | |
| cm = confusion_matrix(y_test.astype(str), pd.Series(preds).astype(str), labels=labels) | |
| cm_df = pd.DataFrame(cm, index=labels, columns=labels) | |
| summary = { | |
| "model_name": model_info.get("model_name"), | |
| "params": model_info.get("params"), | |
| "accuracy": acc, | |
| "f1_weighted": f1, | |
| } | |
| with open(EVAL_SUMMARY_FILE, "w", encoding="utf-8") as f: | |
| json.dump(summary, f, indent=2) | |
| report_df.to_csv(CLASSIFICATION_REPORT_FILE, index=True) | |
| cm_df.to_csv(CONFUSION_MATRIX_FILE, index=True) | |
| print("Evaluation completed.") | |
| print(json.dumps(summary, indent=2)) | |
| print(f"Saved: {EVAL_SUMMARY_FILE}") | |
| print(f"Saved: {CLASSIFICATION_REPORT_FILE}") | |
| print(f"Saved: {CONFUSION_MATRIX_FILE}") | |
| if __name__ == "__main__": | |
| evaluate() | |