"""
Pipeline module for AutoML in Neurenix.

This module provides tools for automated pipeline construction,
including feature selection and data preprocessing.
"""

import numpy as np
import random
from typing import Dict, List, Callable, Any, Optional, Union, Tuple, Type
import time
import logging
import copy

import neurenix as nx
from neurenix.nn import Module


class FeatureSelection:
    """Base class for feature selection algorithms."""
    
    def __init__(self, n_features_to_select: Optional[int] = None):
        """
        Initialize feature selection.
        
        Args:
            n_features_to_select: Number of features to select
        """
        self.n_features_to_select = n_features_to_select
        self.selected_features = None
        
    def fit(self, X, y):
        """
        Fit the feature selector to the data.
        
        Args:
            X: Features
            y: Labels
        
        Returns:
            self
        """
        raise NotImplementedError("Subclasses must implement fit method")
    
    def transform(self, X):
        """
        Transform the data by selecting features.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        if self.selected_features is None:
            raise ValueError("Feature selector has not been fitted")
        
        return X[:, self.selected_features]
    
    def fit_transform(self, X, y):
        """
        Fit the feature selector to the data and transform it.
        
        Args:
            X: Features
            y: Labels
        
        Returns:
            Transformed features
        """
        self.fit(X, y)
        return self.transform(X)


class VarianceThreshold(FeatureSelection):
    """Feature selector that removes low-variance features."""
    
    def __init__(self, threshold: float = 0.0):
        """
        Initialize variance threshold feature selection.
        
        Args:
            threshold: Features with a variance lower than this threshold will be removed
        """
        super().__init__(None)
        self.threshold = threshold
        
    def fit(self, X, y):
        """
        Fit the feature selector to the data.
        
        Args:
            X: Features
            y: Labels
        
        Returns:
            self
        """
        variances = np.var(X, axis=0)
        
        self.selected_features = np.where(variances > self.threshold)[0]
        
        return self


class SelectKBest(FeatureSelection):
    """Feature selector that selects the k best features."""
    
    def __init__(self, score_func: Callable[[np.ndarray, np.ndarray], np.ndarray], 
                 k: int = 10):
        """
        Initialize k-best feature selection.
        
        Args:
            score_func: Function that takes features and labels and returns scores
            k: Number of features to select
        """
        super().__init__(k)
        self.score_func = score_func
        self.scores = None
        
    def fit(self, X, y):
        """
        Fit the feature selector to the data.
        
        Args:
            X: Features
            y: Labels
        
        Returns:
            self
        """
        self.scores = self.score_func(X, y)
        
        if self.n_features_to_select is None:
            self.n_features_to_select = X.shape[1] // 2
        
        self.selected_features = np.argsort(self.scores)[-self.n_features_to_select:]
        
        return self


class DataPreprocessing:
    """Base class for data preprocessing."""
    
    def fit(self, X):
        """
        Fit the preprocessor to the data.
        
        Args:
            X: Features
        
        Returns:
            self
        """
        raise NotImplementedError("Subclasses must implement fit method")
    
    def transform(self, X):
        """
        Transform the data.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        raise NotImplementedError("Subclasses must implement transform method")
    
    def fit_transform(self, X):
        """
        Fit the preprocessor to the data and transform it.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        self.fit(X)
        return self.transform(X)


class StandardScaler(DataPreprocessing):
    """Standardize features by removing the mean and scaling to unit variance."""
    
    def __init__(self):
        """Initialize standard scaler."""
        self.mean = None
        self.std = None
        
    def fit(self, X):
        """
        Fit the scaler to the data.
        
        Args:
            X: Features
        
        Returns:
            self
        """
        self.mean = np.mean(X, axis=0)
        self.std = np.std(X, axis=0)
        
        self.std = np.where(self.std == 0, 1.0, self.std)
        
        return self
    
    def transform(self, X):
        """
        Transform the data.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        if self.mean is None or self.std is None:
            raise ValueError("Scaler has not been fitted")
        
        return (X - self.mean) / self.std


class MinMaxScaler(DataPreprocessing):
    """Scale features to a given range."""
    
    def __init__(self, feature_range: Tuple[float, float] = (0, 1)):
        """
        Initialize min-max scaler.
        
        Args:
            feature_range: Desired range of transformed data
        """
        self.feature_range = feature_range
        self.min = None
        self.max = None
        self.scale = None
        
    def fit(self, X):
        """
        Fit the scaler to the data.
        
        Args:
            X: Features
        
        Returns:
            self
        """
        self.min = np.min(X, axis=0)
        self.max = np.max(X, axis=0)
        
        self.max = np.where(self.max == self.min, self.min + 1.0, self.max)
        
        data_range = self.max - self.min
        self.scale = (self.feature_range[1] - self.feature_range[0]) / data_range
        
        return self
    
    def transform(self, X):
        """
        Transform the data.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        if self.min is None or self.max is None or self.scale is None:
            raise ValueError("Scaler has not been fitted")
        
        X_scaled = (X - self.min) * self.scale
        X_scaled = X_scaled + self.feature_range[0]
        
        return X_scaled


class AutoPipeline:
    """Automated pipeline construction for machine learning."""
    
    def __init__(self, steps: List[Tuple[str, Any]] = None):
        """
        Initialize auto pipeline.
        
        Args:
            steps: List of (name, transform) tuples that are chained together in order
        """
        self.steps = steps or []
        
    def add_step(self, name: str, transform: Any):
        """
        Add a step to the pipeline.
        
        Args:
            name: Name of the step
            transform: Transformer object
        
        Returns:
            self
        """
        self.steps.append((name, transform))
        return self
    
    def fit(self, X, y=None):
        """
        Fit the pipeline to the data.
        
        Args:
            X: Features
            y: Labels (optional)
        
        Returns:
            self
        """
        X_transformed = X
        
        for name, transform in self.steps:
            if hasattr(transform, 'fit'):
                if y is not None and hasattr(transform, 'fit_transform') and 'y' in transform.fit.__code__.co_varnames:
                    transform.fit(X_transformed, y)
                else:
                    transform.fit(X_transformed)
            
            if hasattr(transform, 'transform'):
                X_transformed = transform.transform(X_transformed)
        
        return self
    
    def transform(self, X):
        """
        Transform the data through the pipeline.
        
        Args:
            X: Features
        
        Returns:
            Transformed features
        """
        X_transformed = X
        
        for name, transform in self.steps:
            if hasattr(transform, 'transform'):
                X_transformed = transform.transform(X_transformed)
        
        return X_transformed
    
    def fit_transform(self, X, y=None):
        """
        Fit the pipeline to the data and transform it.
        
        Args:
            X: Features
            y: Labels (optional)
        
        Returns:
            Transformed features
        """
        X_transformed = X
        
        for name, transform in self.steps:
            if hasattr(transform, 'fit_transform') and y is not None and 'y' in transform.fit.__code__.co_varnames:
                X_transformed = transform.fit_transform(X_transformed, y)
            elif hasattr(transform, 'fit_transform'):
                X_transformed = transform.fit_transform(X_transformed)
            else:
                if hasattr(transform, 'fit'):
                    if y is not None and 'y' in transform.fit.__code__.co_varnames:
                        transform.fit(X_transformed, y)
                    else:
                        transform.fit(X_transformed)
                
                if hasattr(transform, 'transform'):
                    X_transformed = transform.transform(X_transformed)
        
        return X_transformed
