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import argparse
import json
import os
import dagshub
import joblib
from loguru import logger
import mlflow
from mlflow.models.signature import infer_signature
from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score
from predicting_outcomes_in_heart_failure.config import (
DATASET_NAME,
EXPERIMENT_NAME,
MODELS_DIR,
PROCESSED_DATA_DIR,
REPO_NAME,
REPO_OWNER,
TARGET_COL,
TEST_METRICS_DIR,
VALID_MODELS,
VALID_VARIANTS,
)
from predicting_outcomes_in_heart_failure.modeling.train import load_split
def compute_metrics(model, X_test, y_test) -> dict:
"""Compute evaluation metrics (F1, recall, accuracy, ROC-AUC)."""
y_pred = model.predict(X_test)
results = {
"test_f1": f1_score(y_test, y_pred, zero_division=0),
"test_recall": recall_score(y_test, y_pred, zero_division=0),
"test_accuracy": accuracy_score(y_test, y_pred),
}
if hasattr(model, "predict_proba"):
try:
y_prob = model.predict_proba(X_test)[:, 1]
results["test_roc_auc"] = roc_auc_score(y_test, y_prob)
except Exception as e:
logger.warning(f"ROC AUC not computed: {e}")
return results, y_pred
def evaluate_variant(variant: str, model_name: str | None = None):
"""Evaluate trained models for a given variant, optionally by model."""
logger.info(f"=== Evaluation started (variant={variant}, model={model_name or 'ALL'}) ===")
test_path = PROCESSED_DATA_DIR / variant / "test.csv"
test_df = load_split(test_path)
X_test = test_df.drop(columns=[TARGET_COL])
y_test = test_df[TARGET_COL].astype(int)
models_dir_variant = MODELS_DIR / variant
if not models_dir_variant.exists():
logger.warning(
f"[{variant}] Models directory does not exist: {models_dir_variant} — skipping."
)
return
experiment_name = f"{EXPERIMENT_NAME}_{variant}"
experiment = mlflow.get_experiment_by_name(experiment_name)
if experiment is None:
logger.error(f"Experiment '{experiment_name}' not found.")
return
model_files = []
if model_name is not None:
model_files = [f"{model_name}.joblib"]
else:
model_files = [f for f in os.listdir(models_dir_variant) if f.endswith(".joblib")]
for file in model_files:
if not file.endswith(".joblib"):
continue
current_model_name = file.split(".joblib")[0]
run_name = f"{current_model_name}_{variant}"
logger.info(
f"[{variant} | {current_model_name}] Looking for training run '{run_name}' in MLflow."
)
runs = mlflow.search_runs(
experiment_ids=[experiment.experiment_id],
filter_string=f"tags.mlflow.runName = '{run_name}'",
order_by=["start_time DESC"],
max_results=1,
)
if runs.empty:
logger.warning(
f"[{variant} | {current_model_name}]No matching MLflow run found — skipping."
)
continue
tracked_id = runs.loc[0, "run_id"]
with mlflow.start_run(run_id=tracked_id):
rawdata = mlflow.data.from_pandas(test_df, name=f"{DATASET_NAME}_{variant}_test")
mlflow.log_input(rawdata, context="testing")
model_path = models_dir_variant / file
model = joblib.load(model_path)
metrics, _ = compute_metrics(model, X_test, y_test)
mlflow.log_metrics(metrics)
logger.info(f"[{variant} | {current_model_name}] Test set metrics:")
for k in ["test_f1", "test_recall", "test_accuracy", "test_roc_auc"]:
if k in metrics:
logger.info(f" - {k}: {metrics[k]:.4f}")
metrics_dir = TEST_METRICS_DIR / variant
metrics_dir.mkdir(parents=True, exist_ok=True)
metrics_path = metrics_dir / f"{current_model_name}.json"
to_save = {
"variant": variant,
"model_name": current_model_name,
"metrics": metrics,
}
with open(metrics_path, "w", encoding="utf-8") as f:
json.dump(to_save, f, indent=4)
logger.info(
f"[{variant} | {current_model_name}] Saved test metrics locally → {metrics_path}"
)
if (
metrics.get("test_f1", 0.0) >= 0.80
and metrics.get("test_recall", 0.0) >= 0.80
and metrics.get("test_accuracy", 0.0) >= 0.80
and metrics.get("test_roc_auc", 0.0) >= 0.85
):
signature = infer_signature(X_test, model.predict(X_test))
registered_name = f"{current_model_name}_{variant}"
mlflow.sklearn.log_model(
sk_model=model,
artifact_path="Model_Info",
signature=signature,
input_example=X_test,
registered_model_name=registered_name,
)
logger.success(
f"[{variant} | {current_model_name}] "
f"Model promoted and registered as '{registered_name}'."
)
logger.success(
f"=== Evaluation completed (variant={variant}, model={model_name or 'ALL'}) ==="
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--variant",
type=str,
choices=VALID_VARIANTS,
required=True,
help="Data variant to use: all, female, male, or nosex.",
)
parser.add_argument(
"--model",
type=str,
choices=VALID_MODELS,
required=False,
help=(
"Specific model to evaluate (logreg, random_forest, decision_tree)."
" If omitted, evaluate all models."
),
)
args = parser.parse_args()
dagshub.init(repo_owner=REPO_OWNER, repo_name=REPO_NAME, mlflow=True)
evaluate_variant(args.variant, args.model)
if __name__ == "__main__":
main()
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