backend/train_model.py

import os
import json
import warnings
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
import joblib
import matplotlib.pyplot as plt
from datasets import load_dataset
from scipy import stats

from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import (
    accuracy_score, f1_score, precision_score, recall_score, classification_report, log_loss
)
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, BaggingClassifier

from .utils import ensure_dirs, save_json, plot_cm, plot_roc, barplot_metric, lineplot_curves

warnings.filterwarnings("ignore")

# ------------------------------
# Base paths
# ------------------------------
BASE_DIR = os.getcwd()  # repo folder in Hugging Face Spaces
MODEL_DIR = os.path.join(BASE_DIR, "models")
REPORTS_DIR = os.path.join(BASE_DIR, "reports")
PLOTS_DIR = os.path.join(REPORTS_DIR, "plots")

# Ensure folders exist
os.makedirs(MODEL_DIR, exist_ok=True)
os.makedirs(REPORTS_DIR, exist_ok=True)
os.makedirs(PLOTS_DIR, exist_ok=True)


def train_model():
    # ------------------------------
    # Load dataset
    # ------------------------------
    ds = load_dataset("jonathansuru/diabetes")
    df = ds["train"].to_pandas()
    X = df.drop("Outcome", axis=1)
    Y = df["Outcome"].astype(int)
    print(f"[INFO] Loaded dataset: {df.shape[0]} rows, {df.shape[1]} cols")

    # ------------------------------
    # Outlier removal
    # ------------------------------
    z = np.abs(stats.zscore(X))
    mask = (z < 3).all(axis=1)
    X_clean, Y_clean = X[mask], Y[mask]
    print(f"[INFO] Outliers removed: {len(X) - len(X_clean)} | Clean size: {len(X_clean)}")

    # Save variance comparison plot
    var_df = pd.DataFrame({"Before": X.var(), "After": X_clean.var()})
    var_df.to_csv(os.path.join(REPORTS_DIR, "variance_before_after.csv"))
    var_df.plot(kind="bar", figsize=(10, 5))
    plt.title("Feature Variance: Before vs After Outlier Removal")
    plt.ylabel("Variance")
    plt.xticks(rotation=45, ha="right")
    plt.tight_layout()
    plt.savefig(os.path.join(PLOTS_DIR, "variance_comparison.png"), bbox_inches="tight")
    plt.close()

    # ------------------------------
    # Train/test split
    # ------------------------------
    X_train, X_test, y_train, y_test = train_test_split(
        X_clean, Y_clean, test_size=0.2, random_state=42, stratify=Y_clean
    )

    # ------------------------------
    # Models and hyperparameters
    # ------------------------------
    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
    models = {
        "LogReg_L1": Pipeline([
            ("scaler", StandardScaler()),
            ("clf", LogisticRegression(penalty="l1", solver="liblinear"))
        ]),
        "LogReg_L2": Pipeline([
            ("scaler", StandardScaler()),
            ("clf", LogisticRegression(penalty="l2", solver="lbfgs"))
        ]),
        "DecisionTree": DecisionTreeClassifier(random_state=42),
        "RandomForest": RandomForestClassifier(random_state=42),
        "BaggedDecisionTree": BaggingClassifier(
            DecisionTreeClassifier(random_state=42), n_estimators=50, random_state=42
        ),
        "XGBoost": XGBClassifier(
            use_label_encoder=False,
            eval_metric="logloss",
            random_state=42
        )
    }

    param_grids = {
        "LogReg_L1": {"clf__C": [0.01, 0.1, 1, 10]},
        "LogReg_L2": {"clf__C": [0.01, 0.1, 1, 10]},
        "DecisionTree": {"max_depth": [3, 5, 7, None], "min_samples_split": [2, 5, 10]},
        "RandomForest": {"n_estimators": [100, 200], "max_depth": [None, 5, 10], "min_samples_split": [2, 5]},
        "BaggedDecisionTree": {"n_estimators": [30, 50, 100]},
        "XGBoost": {
            "n_estimators": [100, 200],
            "max_depth": [3, 5, 7],
            "learning_rate": [0.01, 0.1, 0.2],
            "subsample": [0.8, 1.0]
        }
    }

    # ------------------------------
    # Grid search + evaluation
    # ------------------------------
    rows = []
    best_name, best_estimator, best_f1 = None, None, -1
    for name, model in models.items():
        print(f"[GRID] Tuning {name} …")
        gs = GridSearchCV(model, param_grids[name], scoring="f1", cv=cv, n_jobs=-1)
        gs.fit(X_train, y_train)
        y_pred = gs.best_estimator_.predict(X_test)

        acc = accuracy_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        prec = precision_score(y_test, y_pred)
        rec = recall_score(y_test, y_pred)

        print(f"[GRID] {name} | best_params={gs.best_params_} | ACC={acc:.4f} F1={f1:.4f} P={prec:.4f} R={rec:.4f}")
        rows.append({
            "Model": name,
            "BestParams": gs.best_params_,
            "Accuracy": acc,
            "F1": f1,
            "Precision": prec,
            "Recall": rec
        })

        if f1 > best_f1:
            best_f1, best_estimator, best_name = f1, gs.best_estimator_, name

    # Save model comparison
    results_df = pd.DataFrame(rows).sort_values(by="F1", ascending=False)
    results_df.to_csv(os.path.join(REPORTS_DIR, "model_comparison.csv"), index=False)
    with open(os.path.join(REPORTS_DIR, "model_comparison.json"), "w") as f:
        json.dump(rows, f, indent=4)

    # Plot Accuracy and F1 barplots
    barplot_metric(results_df, "Accuracy", os.path.join(PLOTS_DIR, "model_accuracy.png"), "Model Accuracy (tuned)")
    barplot_metric(results_df, "F1", os.path.join(PLOTS_DIR, "model_f1.png"), "Model F1 (tuned)")

    # ------------------------------
    # Best model diagnostics
    # ------------------------------
    y_best = best_estimator.predict(X_test)
    plot_cm(y_test, y_best, f"Confusion Matrix – {best_name}", os.path.join(PLOTS_DIR, "confusion_matrix.png"))

    if hasattr(best_estimator, "predict_proba"):
        y_prob = best_estimator.predict_proba(X_test)[:, 1]
        plot_roc(y_test, y_prob, f"ROC – {best_name}", os.path.join(PLOTS_DIR, "roc_curve.png"))

    # Save best model
    joblib.dump(best_estimator, os.path.join(MODEL_DIR, "best_model.pkl"))
    print(f"[OK] Best model ({best_name}) saved with F1={best_f1:.4f}")

    # ------------------------------
    # Logistic Regression loss/accuracy curves
    # ------------------------------
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X_clean)
    X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(
        X_scaled, Y_clean, test_size=0.2, random_state=42, stratify=Y_clean
    )

    def track_training(penalty, max_iter=50):
        clf = LogisticRegression(
            penalty=penalty,
            solver="saga",
            warm_start=True,
            max_iter=1,
            random_state=42
        )

        losses, accs = [], []
        for _ in range(max_iter):
            clf.fit(X_train_g, y_train_g)
            y_pred = clf.predict_proba(X_train_g)
            losses.append(log_loss(y_train_g, y_pred))
            accs.append(accuracy_score(y_train_g, np.argmax(y_pred, axis=1)))
        return losses, accs

    loss_curves, acc_curves = {}, {}
    loss_curves["L2"], acc_curves["L2"] = track_training("l2", max_iter=50)
    loss_curves["L1"], acc_curves["L1"] = track_training("l1", max_iter=50)

    lineplot_curves(
        loss_curves,
        ylabel="Log Loss",
        title="Logistic Regression – Loss vs Iterations",
        save_path=os.path.join(PLOTS_DIR, "logreg_loss_curves.png")
    )
    lineplot_curves(
        acc_curves,
        ylabel="Training Accuracy",
        title="Logistic Regression – Accuracy vs Iterations",
        save_path=os.path.join(PLOTS_DIR, "logreg_accuracy_curves.png")
    )

    print(f"[OK] All plots saved -> {PLOTS_DIR}")
    print(f"[OK] Reports saved -> {REPORTS_DIR}")

    return best_estimator