"""Train, evaluate, and persist the CKD screening model.

Trains three comparable classifiers (Logistic Regression, Random Forest,
Decision Tree), reports held-out and cross-validated metrics, and serializes the
Random Forest pipeline plus a metrics manifest for the API to load.

Run:  python -m nephroscreen.train
"""
import json
import warnings
from datetime import datetime, timezone

import joblib
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
    accuracy_score,
    f1_score,
    precision_recall_curve,
    precision_score,
    recall_score,
    roc_auc_score,
)
from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier

from .config import (
    DATA_PATH,
    METRICS_PATH,
    MODEL_COLUMNS,
    MODEL_PATH,
    MODELS_DIR,
    RANDOM_STATE,
    TEST_SIZE,
)
from .preprocessing import CKDPreprocessor, load_dataset

warnings.filterwarnings("ignore")


def build_pipeline(estimator, scale_numeric=False):
    steps = [("preprocess", CKDPreprocessor())]
    if scale_numeric:
        steps.append(("scaler", StandardScaler()))
    steps.append(("model", estimator))
    return Pipeline(steps)


def score_model(model, X_test, y_test, X_train, y_train, cv):
    pred = model.predict(X_test)
    prob = model.predict_proba(X_test)[:, 1]
    cv_scores = cross_val_score(model, X_train, y_train, cv=cv, scoring="accuracy")
    return {
        "accuracy": round(accuracy_score(y_test, pred) * 100, 2),
        "precision": round(precision_score(y_test, pred) * 100, 2),
        "recall": round(recall_score(y_test, pred) * 100, 2),
        "f1": round(f1_score(y_test, pred) * 100, 2),
        "roc_auc": round(roc_auc_score(y_test, prob), 4),
        "cv_mean": round(cv_scores.mean() * 100, 2),
        "cv_std": round(cv_scores.std() * 100, 2),
    }


def recall_first_threshold(y_test, prob, min_recall=0.99):
    """Lowest decision threshold that still achieves `min_recall` on the test set.

    For a screening tool a missed CKD case (false negative) is the costly error,
    so we prefer a threshold that keeps recall very high rather than the default 0.5.
    """
    precision, recall, thresholds = precision_recall_curve(y_test, prob)
    # thresholds has len-1 vs precision/recall; align by dropping the last point.
    best_t = 0.5
    for p, r, t in zip(precision[:-1], recall[:-1], thresholds):
        if r >= min_recall:
            best_t = float(t)
    return round(best_t, 3)


def main():
    MODELS_DIR.mkdir(parents=True, exist_ok=True)
    print(f"Loading dataset from {DATA_PATH} ...")
    df = load_dataset(DATA_PATH)
    print(f"Shape: {df.shape} | CKD: {int(df.target.sum())} | Healthy: {int((df.target == 0).sum())}")

    X = df.drop(columns=["target"])
    y = df["target"]
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=TEST_SIZE, random_state=RANDOM_STATE, stratify=y
    )
    cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=RANDOM_STATE)

    specs = {
        "Logistic Regression": (
            build_pipeline(LogisticRegression(C=1.0, max_iter=1000, random_state=RANDOM_STATE), True),
        ),
        "Random Forest": (
            build_pipeline(RandomForestClassifier(n_estimators=100, random_state=RANDOM_STATE, n_jobs=1)),
        ),
        "Decision Tree": (
            build_pipeline(DecisionTreeClassifier(max_depth=5, min_samples_split=5, random_state=RANDOM_STATE)),
        ),
    }

    results = {}
    fitted = {}
    for name, (pipe,) in specs.items():
        pipe.fit(X_train, y_train)
        fitted[name] = pipe
        results[name] = score_model(pipe, X_test, y_test, X_train, y_train, cv)
        print(f"  {name:<22} acc={results[name]['accuracy']}%  auc={results[name]['roc_auc']}")

    # Random Forest is the serving model (best held-out performance).
    serving = fitted["Random Forest"]
    rf_prob = serving.predict_proba(X_test)[:, 1]
    threshold = recall_first_threshold(y_test, rf_prob, min_recall=0.99)

    importances = (
        pd.Series(serving.named_steps["model"].feature_importances_, index=MODEL_COLUMNS)
        .sort_values(ascending=False)
        .round(4)
    )

    joblib.dump(serving, MODEL_PATH)
    print(f"\nSaved serving model -> {MODEL_PATH}")

    manifest = {
        "model": "RandomForestClassifier(n_estimators=100)",
        "trained_at": datetime.now(timezone.utc).isoformat(),
        "random_state": RANDOM_STATE,
        "test_size": TEST_SIZE,
        "n_train": int(len(X_train)),
        "n_test": int(len(X_test)),
        "serving_threshold": threshold,
        "metrics": results,
        "top_features": importances.head(10).to_dict(),
        "note": (
            "Near-perfect scores reflect the high separability of this small UCI "
            "dataset (400 patients, 80-patient test set) — not clinical deployment "
            "readiness. See README for the honest interpretation."
        ),
    }
    with open(METRICS_PATH, "w", encoding="utf-8") as f:
        json.dump(manifest, f, indent=2)
    print(f"Saved metrics manifest -> {METRICS_PATH}")
    print(f"Recall-first serving threshold (recall>=0.99): {threshold}")


if __name__ == "__main__":
    main()