"""
DataCompressionOptimizer
Generated by Eden via recursive self-improvement
2025-11-01 12:11:07.398939
"""

import os
from sklearn.model_selection import ParameterGrid
from sklearn.utils import resample
import pandas as pd
import numpy as np

class DataCompressionOptimizer:
    """
    Class for optimizing data compression algorithms.
    
    Parameters:
        - dataset: Pandas DataFrame containing the data to be compressed.
        - algorithm_list: List of compression algorithms to test (e.g., 'gzip', 'bzip2').
        - param_grid: Dictionary specifying parameters for each algorithm to test.
        - sample_size: Number of samples to use for testing. Default is 10% of dataset.
        - random_state: Random seed for reproducibility. Default is None.

    Methods:
        - optimize_compression: Finds the best compression algorithm and its parameters.
        - decompress_and_test_speed: Tests decompression speed for a given set of parameters.
    """
    
    def __init__(self, dataset, algorithm_list, param_grid, sample_size=0.1, random_state=None):
        self.dataset = dataset
        self.algorithm_list = algorithm_list
        self.param_grid = param_grid
        self.sample_size = sample_size
        self.random_state = random_state

    def optimize_compression(self):
        """
        Optimize the compression of a given dataset by iteratively testing different algorithms and parameters.
        
        Returns:
            - best_algorithm: The name of the best algorithm found.
            - best_params: Parameters that yield the highest compression ratio with acceptable decompression speed.
        """
        compressed_data = {}
        decompressed_data = {}

        # Sample data for faster computation
        sample_data = resample(self.dataset, replace=False, n_samples=int(len(self.dataset) * self.sample_size), random_state=self.random_state)

        # Test each algorithm and its parameters
        for algorithm in self.algorithm_list:
            for params in ParameterGrid(self.param_grid[algorithm]):
                print(f"Testing {algorithm} with parameters: {params}")
                
                # Compress the sample data
                compressed_sample = self.compress_data(sample_data, algorithm=algorithm, **params)
                compressed_size = os.path.getsize(compressed_sample)

                # Decompress and test decompression speed
                decompressed_sample = self.decompress_and_test_speed(compressed_sample, algorithm=algorithm, **params)
                if len(decompressed_sample) == 0:
                    continue

                # Save results
                compressed_data[algorithm] = (compressed_size, params)
                decompressed_data[algorithm] = decompressed_sample

        # Find the best algorithm and parameters based on compression ratio and decompression speed
        best_algorithm, _ = max(compressed_data.items(), key=lambda x: (x[1][0], -x[1][1]['decompression_speed']))

        return best_algorithm, compressed_data[best_algorithm]

    def compress_data(self, data, algorithm='gzip', **params):
        """
        Compress a given dataset using the specified algorithm and parameters.
        
        Parameters:
            - data: Pandas DataFrame to be compressed.
            - algorithm: Compression algorithm name (default is 'gzip').
            - params: Parameters for the compression algorithm.

        Returns:
            - Path to the compressed file.
        """
        # Placeholder function to simulate actual compression
        return f"/path/to/{algorithm}_{params}.gz"

    def decompress_and_test_speed(self, compressed_data_path, algorithm='gzip', **params):
        """
        Decompress a given dataset and test its decompression speed.

        Parameters:
            - compressed_data_path: Path to the compressed file.
            - algorithm: Compression algorithm name (default is 'gzip').
            - params: Parameters for the compression algorithm.

        Returns:
            - Decompressed data as Pandas DataFrame, or an empty list if decompression fails.
        """
        # Placeholder function to simulate actual decompression and speed test
        return pd.read_csv(compressed_data_path)  # Assuming compressed data is stored in CSV format

# Example Usage
if __name__ == "__main__":
    # Create a sample dataset
    np.random.seed(42)
    df = pd.DataFrame(np.random.randint(0, 100, size=(1000, 5)), columns=['A', 'B', 'C', 'D', 'E'])
    
    optimizer = DataCompressionOptimizer(df, algorithm_list=['gzip', 'bzip2'], param_grid={'gzip': {'compression_level': [1, 9]}, 'bzip2': {'block_size': [64 * 1024, 32 * 1024]}})
    best_algorithm, best_params = optimizer.optimize_compression()
    
    print(f"Best algorithm: {best_algorithm}")
    print(f"Best parameters: {best_params}")

    # Test the optimized compression
    compressed_file_path = optimizer.compress_data(df, **best_params)
    decompressed_df = optimizer.decompress_and_test_speed(compressed_file_path, algorithm=best_algorithm, **best_params)

    if not decompressed_df.empty:
        print("Decompression successful.")
        print(decompressed_df.head())