"""
InsightfulAI - Random Forest Template with Sync, Async, and OpenTelemetry 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 both sync and async support, retry logic, batch processing, 
             and OpenTelemetry tracing.

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

"""

import numpy as np
import logging
import asyncio
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

# 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:
    """
    Encapsulated Random Forest Template with sync and async batch support, retry logic, 
    customizable parameters, and OpenTelemetry tracing for classification tasks.
    """

    def __init__(self, n_estimators: int = 100, max_depth: int = None, max_retries: int = 3) -> None:
        """
        Initializes the Random Forest Template.

        Parameters:
        - n_estimators (int): The number of trees in the forest. Default is 100.
        - max_depth (int): The maximum depth of each tree. Default is None (unrestricted).
        - max_retries (int): Number of retries for failed operations.
        """
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_retries = max_retries
        self.model = None
        self.scaler = None

    def initialize_model(self):
        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) -> None:
        """
        Synchronously trains the Random Forest model with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("fit"):
            if self.model is None:
                self.initialize_model()
            X_scaled = self.scaler.fit_transform(X)
            self.model.fit(X_scaled, y)
            logging.info("Model training completed successfully.")

    @retry_exponential_backoff
    def predict(self, X: np.ndarray) -> np.ndarray:
        """
        Synchronously predicts class labels for input data with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("predict"):
            if self.model is None:
                self.initialize_model()
            X_scaled = self.scaler.transform(X)
            predictions = self.model.predict(X_scaled)
            logging.info("Prediction completed successfully.")
            return predictions

    @retry_exponential_backoff
    def evaluate(self, X: np.ndarray, y: np.ndarray) -> float:
        """
        Synchronously evaluates the model on input data and true labels with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("evaluate"):
            predictions = self.predict(X)
            from sklearn.metrics import accuracy_score
            accuracy = accuracy_score(y, predictions)
            logging.info(f"Evaluation accuracy: {accuracy:.2f}")
            return accuracy

    # Synchronous Batch Processing
    def fit_batch(self, X_batches: list, y_batches: list) -> None:
        """
        Synchronously trains the model on multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("fit_batch"):
            for X, y in zip(X_batches, y_batches):
                self.fit(X, y)

    def predict_batch(self, X_batches: list) -> list:
        """
        Synchronously predicts for multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("predict_batch"):
            predictions = [self.predict(X) for X in X_batches]
            return predictions

    def evaluate_batch(self, X_batches: list, y_batches: list) -> list:
        """
        Synchronously evaluates the model for multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("evaluate_batch"):
            accuracies = [self.evaluate(X, y) for X, y in zip(X_batches, y_batches)]
            return accuracies

    # Asynchronous Batch Processing
    async def async_fit(self, X_batches: list, y_batches: list) -> None:
        """
        Asynchronously trains the model on multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("async_fit"):
            tasks = [self._async_fit(X, y) for X, y in zip(X_batches, y_batches)]
            await asyncio.gather(*tasks)

    async def async_predict(self, X_batches: list) -> list:
        """
        Asynchronously predicts for multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("async_predict"):
            tasks = [self._async_predict(X) for X in X_batches]
            return await asyncio.gather(*tasks)

    async def async_evaluate_batch(self, X_batches: list, y_batches: list) -> list:
        """
        Asynchronously evaluates the model for multiple data batches with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("async_evaluate_batch"):
            tasks = [self._async_evaluate(X, y) for X, y in zip(X_batches, y_batches)]
            return await asyncio.gather(*tasks)

    # 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 function for asynchronous training with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("_async_fit"):
            if self.model is None:
                self.initialize_model()
            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 function for asynchronous prediction with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("_async_predict"):
            if self.model is None:
                self.initialize_model()
            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 function for asynchronous evaluation with retry logic and OpenTelemetry tracing.
        """
        with tracer.start_as_current_span("_async_evaluate"):
            y_pred = await self._async_predict(X)
            from sklearn.metrics import accuracy_score
            return accuracy_score(y, y_pred)
