"""
src/evaluation/evaluator.py

Evaluación estandarizada de modelos.
Genera métricas, visualizaciones e informes JSON.

Uso:
    evaluator = Evaluator(output_dir="reports/pipeline/lr")
    metrics = evaluator.evaluate_and_report(
        model, X_test, y_test, model_name="LR",
        summary_path="reports/summary.csv",
    )
"""

import json
import re
from collections import Counter

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from pathlib import Path
from sklearn.metrics import (
    f1_score, precision_score, recall_score,
    roc_auc_score, accuracy_score,
    confusion_matrix, classification_report,
    RocCurveDisplay,
)
from src.utils.logger import get_logger

logger = get_logger(__name__)

DEFAULT_SUMMARY_PATH = Path("reports/summary.csv")
_TOKEN_RE = re.compile(r"[a-záéíóúñ'][a-záéíóúñ]{2,}")


class Evaluator:
    """
    Evaluador estandarizado de modelos de clasificación binaria.

    Genera:
    - Métricas completas (F1, Precision, Recall, ROC-AUC)
    - Ambas métricas de gap (train-test y CV-test)
    - Matriz de confusión (PNG)
    - Curva ROC (PNG)
    - Análisis de errores (FP y FN más comunes)
    - Informe JSON por experimento
    - CSV resumen de todos los experimentos
    """

    def __init__(self, output_dir: str | Path = "reports/pipeline"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

    # ── Evaluación principal ─────────────────────────────────────────────────
    def evaluate(
        self,
        model,
        X_test,
        y_test,
        model_name: str,
        X_train=None,
        y_train=None,
        cv_results: dict = None,
    ) -> dict:
        """
        Evalúa un modelo sobre el test set.

        Args:
            model: objeto con método predict() y predict_proba()
            X_test, y_test: datos de test
            model_name: nombre para los reports
            X_train, y_train: opcional — para calcular train_test_gap
            cv_results: opcional — dict con cv_f1_mean para calcular cv_test_gap

        Returns:
            Dict con todas las métricas.
        """
        logger.info(f"Evaluando: {model_name}")

        y_pred  = model.predict(X_test)
        y_proba = model.predict_proba(X_test)[:, 1]
        y_test_arr = np.array(y_test)

        # ── Métricas test ────────────────────────────────────────────────────
        metrics = {
            "model"      : model_name,
            "timestamp"  : datetime.now().isoformat(),
            "f1_weighted": round(f1_score(y_test_arr, y_pred, average="weighted"), 4),
            "f1_toxic"   : round(f1_score(y_test_arr, y_pred, pos_label=1), 4),
            "precision"  : round(precision_score(y_test_arr, y_pred, average="weighted"), 4),
            "recall"     : round(recall_score(y_test_arr, y_pred, average="weighted"), 4),
            "accuracy"   : round(accuracy_score(y_test_arr, y_pred), 4),
            "roc_auc"    : round(roc_auc_score(y_test_arr, y_proba), 4),
            "fp"         : int(((y_test_arr == 0) & (y_pred == 1)).sum()),
            "fn"         : int(((y_test_arr == 1) & (y_pred == 0)).sum()),
            "n_test"     : len(y_test_arr),
        }

        # ── Train-test gap (in-sample vs OOS) ────────────────────────────────
        if X_train is not None and y_train is not None:
            y_train_pred = model.predict(X_train)
            f1_train = f1_score(np.array(y_train), y_train_pred, average="weighted")
            metrics["f1_train"]         = round(f1_train, 4)
            metrics["train_test_gap_pp"]= round((f1_train - metrics["f1_weighted"]) * 100, 2)

        # ── CV-test gap (OOS vs OOS — métrica correcta para la rúbrica) ──────
        if cv_results and "cv_f1_mean" in cv_results:
            cv_mean = cv_results["cv_f1_mean"]
            metrics["cv_f1_mean"]    = round(cv_mean, 4)
            metrics["cv_f1_std"]     = round(cv_results.get("cv_f1_std", 0), 4)
            metrics["cv_test_gap_pp"]= round(abs(cv_mean - metrics["f1_weighted"]) * 100, 2)

        self._print_summary(metrics)
        return metrics

    def evaluate_and_report(
        self,
        model,
        X_test,
        y_test,
        model_name: str,
        X_train=None,
        y_train=None,
        cv_results: dict = None,
        summary_path: str | Path | None = None,
        n_error_examples: int = 5,
        show_plots: bool = False,
    ) -> dict:
        """
        Evaluación completa: métricas, gráficos, análisis de errores y summary.csv.

        Usado por run_pipeline; actualiza reports/summary.csv por defecto del proyecto.
        """
        metrics = self.evaluate(
            model, X_test, y_test, model_name,
            X_train=X_train, y_train=y_train, cv_results=cv_results,
        )

        y_pred = model.predict(X_test)
        y_proba = model.predict_proba(X_test)[:, 1]

        cm_path = self.plot_confusion_matrix(
            y_test, y_pred, model_name, save=True, show=show_plots,
        )
        roc_path = self.plot_roc_curve(
            y_test, y_proba, model_name, save=True, show=show_plots,
        )
        errors = self.error_analysis(
            X_test, y_test, y_pred, y_proba,
            model_name=model_name, n_examples=n_error_examples,
        )

        metrics["cm_plot"] = str(cm_path) if cm_path else ""
        metrics["roc_plot"] = str(roc_path) if roc_path else ""
        metrics["top_fp_terms"] = ", ".join(
            f"{t}({c})" for t, c in errors.get("top_fp_terms", [])
        )
        metrics["top_fn_terms"] = ", ".join(
            f"{t}({c})" for t, c in errors.get("top_fn_terms", [])
        )

        out = Path(summary_path or DEFAULT_SUMMARY_PATH)
        self.save_summary([metrics], path=out)
        return metrics

    # ── Visualizaciones ──────────────────────────────────────────────────────
    def plot_confusion_matrix(
        self,
        y_test,
        y_pred,
        model_name: str,
        save: bool = True,
        show: bool = False,
    ) -> Path | None:
        """Genera y guarda la matriz de confusión."""
        cm = confusion_matrix(y_test, y_pred)
        fig, ax = plt.subplots(figsize=(5, 4))
        sns.heatmap(
            cm, annot=True, fmt="d", cmap="Blues", ax=ax,
            xticklabels=["No tóxico", "Tóxico"],
            yticklabels=["No tóxico", "Tóxico"],
            linewidths=0.5,
        )
        ax.set_title(f"{model_name} — Matriz de confusión", fontweight="bold")
        ax.set_xlabel("Predicción")
        ax.set_ylabel("Real")
        plt.tight_layout()

        safe = model_name.lower().replace(" ", "_").replace("/", "_")
        path = self.output_dir / f"cm_{safe}.png"
        if save:
            fig.savefig(path, dpi=150, bbox_inches="tight")
            logger.info(f"Matriz de confusión guardada: {path}")
        if show:
            plt.show()
        else:
            plt.close(fig)
        return path if save else None

    def plot_roc_curve(
        self,
        y_test,
        y_proba,
        model_name: str,
        save: bool = True,
        show: bool = False,
    ) -> Path | None:
        """Genera y guarda la curva ROC."""
        fig, ax = plt.subplots(figsize=(6, 5))
        RocCurveDisplay.from_predictions(
            y_test, y_proba, ax=ax, name=model_name, color="#7F77DD"
        )
        ax.plot([0, 1], [0, 1], "--", color="gray", alpha=0.5, label="Azar")
        ax.set_title(f"{model_name} — Curva ROC", fontweight="bold")
        ax.legend()
        plt.tight_layout()

        safe = model_name.lower().replace(" ", "_").replace("/", "_")
        path = self.output_dir / f"roc_{safe}.png"
        if save:
            fig.savefig(path, dpi=150, bbox_inches="tight")
            logger.info(f"Curva ROC guardada: {path}")
        if show:
            plt.show()
        else:
            plt.close(fig)
        return path if save else None

    # ── Análisis de errores ──────────────────────────────────────────────────
    def error_analysis(
        self,
        X_test,
        y_test,
        y_pred,
        y_proba,
        model_name: str = "modelo",
        n_examples: int = 5,
    ) -> dict:
        """
        Analiza los falsos positivos y falsos negativos más relevantes.

        FP → comentarios OK que el modelo censura (peor UX)
        FN → hate speech que se escapa (peor para el objetivo del proyecto)
        """
        texts = np.array(X_test) if not isinstance(X_test, np.ndarray) else X_test
        y_arr = np.array(y_test)

        error_df = pd.DataFrame({
            "text"      : texts,
            "real"      : y_arr,
            "pred"      : y_pred,
            "prob_toxic": y_proba,
        })

        fp = error_df[(error_df["real"] == 0) & (error_df["pred"] == 1)]
        fn = error_df[(error_df["real"] == 1) & (error_df["pred"] == 0)]

        top_fp_terms = self._most_common_terms(fp["text"].tolist())
        top_fn_terms = self._most_common_terms(fn["text"].tolist())

        logger.info(f"Errores {model_name}: FP={len(fp)} | FN={len(fn)}")

        print(f"\n{'='*65}")
        print(f"FALSOS NEGATIVOS — tóxico no detectado ({len(fn)} total)")
        if top_fn_terms:
            print("  Términos más frecuentes:", ", ".join(f"{w}({c})" for w, c in top_fn_terms[:8]))
        print(f"{'='*65}")
        for _, row in fn.nsmallest(n_examples, "prob_toxic").iterrows():
            print(f"  Prob: {row['prob_toxic']:.3f} | {str(row['text'])[:110]}")
            print()

        print(f"{'='*65}")
        print(f"FALSOS POSITIVOS — seguro marcado como tóxico ({len(fp)} total)")
        if top_fp_terms:
            print("  Términos más frecuentes:", ", ".join(f"{w}({c})" for w, c in top_fp_terms[:8]))
        print(f"{'='*65}")
        for _, row in fp.nlargest(n_examples, "prob_toxic").iterrows():
            print(f"  Prob: {row['prob_toxic']:.3f} | {str(row['text'])[:110]}")
            print()

        safe = model_name.lower().replace(" ", "_").replace("/", "_")
        errors_path = self.output_dir / f"errors_{safe}.csv"
        pd.concat([
            fp.assign(tipo_error="falso_positivo"),
            fn.assign(tipo_error="falso_negativo"),
        ], ignore_index=True).to_csv(errors_path, index=False)
        logger.info(f"Errores guardados: {errors_path}")

        return {
            "top_fp_terms": top_fp_terms,
            "top_fn_terms": top_fn_terms,
            "fp_examples": fp.head(n_examples).to_dict("records"),
            "fn_examples": fn.head(n_examples).to_dict("records"),
            "errors_csv": str(errors_path),
        }

    # ── Reports ──────────────────────────────────────────────────────────────
    def save_report(self, metrics: dict, experiment_id: str) -> Path:
        """Guarda las métricas de un experimento en JSON."""
        path = self.output_dir / f"{experiment_id}.json"
        with open(path, "w") as f:
            json.dump(metrics, f, indent=2)
        logger.info(f"Report guardado: {path}")
        return path

    def save_summary(self, all_metrics: list[dict], path: str | Path = None) -> Path:
        """
        Guarda un CSV acumulativo con todos los experimentos.
        Si summary.csv ya existe, agrega nuevas filas.
        """

        path = Path(path or DEFAULT_SUMMARY_PATH)
        path.parent.mkdir(parents=True, exist_ok=True)

        # Nuevo dataframe
        new_df = pd.DataFrame(all_metrics)

        # Si ya existe un summary anterior → cargarlo
        if path.exists():
            old_df = pd.read_csv(path)

            # Concatenar viejo + nuevo
            df = pd.concat([old_df, new_df], ignore_index=True)

            # Evitar duplicados por run_id si existe
            if "run_id" in df.columns:
                df = df.drop_duplicates(subset=["run_id"], keep="last")
            elif "model" in df.columns and "timestamp" in df.columns:
                df = df.drop_duplicates(subset=["model", "timestamp"], keep="last")

        else:
            df = new_df

        # Ordenar por F1 descendente
        if "f1_weighted" in df.columns:
            df = df.sort_values("f1_weighted", ascending=False, na_position="last")

        # Guardar actualizado
        df.to_csv(path, index=False)

        logger.info(f"Summary actualizado: {path}")

        cols = [c for c in ["model", "f1_weighted", "roc_auc", "fp", "fn"] if c in df.columns]
        print(df[cols].to_string(index=False))

        return path

    @staticmethod
    def _most_common_terms(texts: list, top_n: int = 10) -> list[tuple[str, int]]:
        counter: Counter[str] = Counter()
        for text in texts:
            counter.update(_TOKEN_RE.findall(str(text).lower()))
        return counter.most_common(top_n)

    # ── Interno ──────────────────────────────────────────────────────────────
    def _print_summary(self, metrics: dict) -> None:
        gap_str = ""
        if "cv_test_gap_pp" in metrics:
            ok  = "✅" if metrics["cv_test_gap_pp"] < 5 else "⚠️"
            gap_str = f"CV-test gap: {metrics['cv_test_gap_pp']:.2f}pp {ok}"
        elif "train_test_gap_pp" in metrics:
            ok  = "✅" if metrics["train_test_gap_pp"] < 5 else "⚠️"
            gap_str = f"Train-test gap: {metrics['train_test_gap_pp']:.2f}pp {ok}"

        print(f"\n{'='*55}")
        print(f"RESULTADOS — {metrics['model']}")
        print(f"{'='*55}")
        print(f"  F1 weighted : {metrics['f1_weighted']:.4f}")
        print(f"  ROC-AUC     : {metrics['roc_auc']:.4f}")
        print(f"  FP / FN     : {metrics['fp']} / {metrics['fn']}")
        if gap_str:
            print(f"  {gap_str}")
        print(f"{'='*55}")