import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import check_array, check_X_y, check_random_state

class AdaptiveAbstractionTree(BaseEstimator, ClassifierMixin):
    def __init__(self, max_depth=None, min_samples_split=2, min_samples_leaf=1, random_state=None):
        self.max_depth = max_depth
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.random_state = random_state
        self.tree = None
        self.abstraction_level = None

    def fit(self, X, y):
        X, y = check_X_y(X, y)
        self.abstraction_level = self._determine_abstraction_level(X)
        
        adjusted_max_depth = self.max_depth
        if self.abstraction_level == 'high':
            adjusted_max_depth = min(3, self.max_depth) if self.max_depth else 3
        elif self.abstraction_level == 'medium':
            adjusted_max_depth = min(5, self.max_depth) if self.max_depth else 5
        
        self.tree = DecisionTreeClassifier(
            max_depth=adjusted_max_depth,
            min_samples_split=self.min_samples_split,
            min_samples_leaf=self.min_samples_leaf,
            random_state=self.random_state
        )
        self.tree.fit(X, y)
        return self

    def predict(self, X):
        check_array(X)
        return self.tree.predict(X)

    def _determine_abstraction_level(self, X):
        n_features, n_samples = X.shape
        if n_features * n_samples < 1000:
            return 'high'
        elif n_features * n_samples < 10000:
            return 'medium'
        else:
            return 'low'

class AdaptiveAbstractionForest(BaseEstimator, ClassifierMixin):
    def __init__(self, n_estimators=100, max_depth=None, min_samples_split=2, min_samples_leaf=1, random_state=None):
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.random_state = random_state
        self.trees = []

    def fit(self, X, y):
        X, y = check_X_y(X, y)
        self.classes_ = np.unique(y)
        self.n_classes_ = len(self.classes_)
        
        rng = check_random_state(self.random_state)
        self.trees = []
        
        for i in range(self.n_estimators):
            tree = AdaptiveAbstractionTree(
                max_depth=self.max_depth,
                min_samples_split=self.min_samples_split,
                min_samples_leaf=self.min_samples_leaf,
                random_state=rng.randint(np.iinfo(np.int32).max)
            )
            X_sample, y_sample = self._bootstrap_sample(X, y, rng)
            tree.fit(X_sample, y_sample)
            self.trees.append(tree)
        
        return self

    def predict(self, X):
        X = check_array(X)
        predictions = np.array([tree.predict(X) for tree in self.trees])
        return np.apply_along_axis(lambda x: np.argmax(np.bincount(x)), axis=0, arr=predictions)

    def _bootstrap_sample(self, X, y, random_state):
        n_samples = X.shape[0]
        indices = random_state.randint(0, n_samples, n_samples)
        return X[indices], y[indices]

# Example usage:
if __name__ == "__main__":
    from sklearn.datasets import make_classification
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    
    # Generate a random dataset
    X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=2, random_state=42)
    
    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Create and train the Adaptive Abstraction Forest
    aaf = AdaptiveAbstractionForest(n_estimators=100, max_depth=10, random_state=42)
    aaf.fit(X_train, y_train)
    
    # Make predictions
    y_pred = aaf.predict(X_test)
    
    # Calculate accuracy
    accuracy = accuracy_score(y_test, y_pred)
    print(f"Accuracy: {accuracy:.4f}")