"""
InsightfulAI - Random Forest Template with Sync, Async, OpenTelemetry, and ROP Support
======================================================================================

Project: InsightfulAI
Repository: https://github.com/CraftedWithIntent/InsightfulAI
Author: Philip Thomas
Date: 2024-11-13
Description: This module provides a customizable Random Forest template for binary and multi-class 
             classification tasks, with sync and async support, retry logic, batch processing, 
             OpenTelemetry tracing, and Railway Oriented Programming (ROP) principles.

Dependencies:
- scikit-learn
- numpy
- asyncio
- opentelemetry-api
- opentelemetry-sdk
- opentelemetry-instrumentation
"""

import numpy as np
import logging
import asyncio
from typing import List
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
from retry.retry_decorator import retry_exponential_backoff
from operation_result import OperationResult

# Configure logging
logging.basicConfig(level=logging.INFO)

# Set up OpenTelemetry
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
span_processor = SimpleSpanProcessor(ConsoleSpanExporter())
trace.get_tracer_provider().add_span_processor(span_processor)

class RandomForestTemplate:
    def __init__(self, n_estimators: int = 100, max_depth: int = None) -> None:
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.model = None
        self.scaler = None
        self._initialize_model()

    def _initialize_model(self):
        """Initialize the RandomForestClassifier and scaler."""
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.preprocessing import StandardScaler
        self.model = RandomForestClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth)
        self.scaler = StandardScaler()

    @retry_exponential_backoff
    def fit(self, X: np.ndarray, y: np.ndarray) -> OperationResult[None]:
        """Synchronously trains the Random Forest model with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.fit") as span:
            try:
                X_scaled = self.scaler.fit_transform(X)
                self.model.fit(X_scaled, y)
                logging.info("Model training completed successfully.")
                return OperationResult.success(None, span)
            except Exception as e:
                logging.error(f"Model training failed: {e}")
                return OperationResult.failure(e, span)

    @retry_exponential_backoff
    def predict(self, X: np.ndarray) -> OperationResult[np.ndarray]:
        """Synchronously predicts class labels for input data with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.predict") as span:
            try:
                X_scaled = self.scaler.transform(X)
                predictions = self.model.predict(X_scaled)
                logging.info("Prediction completed successfully.")
                return OperationResult.success(predictions, span)
            except Exception as e:
                logging.error(f"Prediction failed: {e}")
                return OperationResult.failure(e, span)

    @retry_exponential_backoff
    def evaluate(self, X: np.ndarray, y: np.ndarray) -> OperationResult[float]:
        """Synchronously evaluates the model on input data with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.evaluate") as span:
            try:
                predictions_result = self.predict(X)
                if not predictions_result.success:
                    return predictions_result  # Propagate failure in prediction

                from sklearn.metrics import accuracy_score
                accuracy = accuracy_score(y, predictions_result.result)
                logging.info(f"Evaluation accuracy: {accuracy:.2f}")
                return OperationResult.success(accuracy, span)
            except Exception as e:
                logging.error(f"Evaluation failed: {e}")
                return OperationResult.failure(e, span)

    # Synchronous Batch Processing
    def fit_batch(self, X_batches: List[np.ndarray], y_batches: List[np.ndarray]) -> OperationResult[None]:
        """Synchronously trains the model on multiple batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.fit_batch") as span:
            try:
                for X, y in zip(X_batches, y_batches):
                    result = self.fit(X, y)
                    if not result.success:
                        return result  # Propagate failure
                logging.info("Batch training completed successfully.")
                return OperationResult.success(None, span)
            except Exception as e:
                logging.error(f"Batch training failed: {e}")
                return OperationResult.failure(e, span)

    def predict_batch(self, X_batches: List[np.ndarray]) -> OperationResult[List[np.ndarray]]:
        """Synchronously predicts for multiple data batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.predict_batch") as span:
            try:
                predictions = [self.predict(X).result for X in X_batches]
                return OperationResult.success(predictions, span)
            except Exception as e:
                logging.error(f"Batch prediction failed: {e}")
                return OperationResult.failure(e, span)

    def evaluate_batch(self, X_batches: List[np.ndarray], y_batches: List[np.ndarray]) -> OperationResult[List[float]]:
        """Synchronously evaluates the model on multiple data batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.evaluate_batch") as span:
            try:
                accuracies = [self.evaluate(X, y).result for X, y in zip(X_batches, y_batches)]
                return OperationResult.success(accuracies, span)
            except Exception as e:
                logging.error(f"Batch evaluation failed: {e}")
                return OperationResult.failure(e, span)

    # Asynchronous Batch Processing
    async def async_fit(self, X_batches: List[np.ndarray], y_batches: List[np.ndarray]) -> OperationResult[None]:
        """Asynchronously trains the model on multiple data batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.async_fit") as span:
            try:
                tasks = [self._async_fit(X, y) for X, y in zip(X_batches, y_batches)]
                await asyncio.gather(*tasks)
                logging.info("Async batch training completed successfully.")
                return OperationResult.success(None, span)
            except Exception as e:
                logging.error(f"Async batch training failed: {e}")
                return OperationResult.failure(e, span)

    async def async_predict(self, X_batches: List[np.ndarray]) -> OperationResult[List[np.ndarray]]:
        """Asynchronously predicts for multiple data batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.async_predict") as span:
            try:
                tasks = [self._async_predict(X) for X in X_batches]
                predictions = await asyncio.gather(*tasks)
                return OperationResult.success(predictions, span)
            except Exception as e:
                logging.error(f"Async batch prediction failed: {e}")
                return OperationResult.failure(e, span)

    async def async_evaluate_batch(self, X_batches: List[np.ndarray], y_batches: List[np.ndarray]) -> OperationResult[List[float]]:
        """Asynchronously evaluates the model on multiple data batches with ROP and OpenTelemetry."""
        with tracer.start_as_current_span("RandomForestTemplate.async_evaluate_batch") as span:
            try:
                tasks = [self._async_evaluate(X, y) for X, y in zip(X_batches, y_batches)]
                accuracies = await asyncio.gather(*tasks)
                return OperationResult.success(accuracies, span)
            except Exception as e:
                logging.error(f"Async batch evaluation failed: {e}")
                return OperationResult.failure(e, span)

    # Helper methods for async operations with retry and OpenTelemetry tracing
    @retry_exponential_backoff
    async def _async_fit(self, X: np.ndarray, y: np.ndarray) -> None:
        """Helper async method for training with retry logic and OpenTelemetry tracing."""
        with tracer.start_as_current_span("RandomForestTemplate._async_fit"):
            X_scaled = self.scaler.fit_transform(X)
            self.model.fit(X_scaled, y)

    @retry_exponential_backoff
    async def _async_predict(self, X: np.ndarray) -> np.ndarray:
        """Helper async method for prediction with retry logic and OpenTelemetry tracing."""
        with tracer.start_as_current_span("RandomForestTemplate._async_predict"):
            X_scaled = self.scaler.transform(X)
            return self.model.predict(X_scaled)

    @retry_exponential_backoff
    async def _async_evaluate(self, X: np.ndarray, y: np.ndarray) -> float:
        """Helper async method for evaluation with retry logic and OpenTelemetry tracing."""
        with tracer.start_as_current_span("RandomForestTemplate._async_evaluate"):
            predictions = await self._async_predict(X)
            from sklearn.metrics import accuracy_score
            return accuracy_score(y, predictions)