FFS algorithm and machine learning model training code.py

import numpy as np
from sklearn.metrics import mean_squared_error, f1_score
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import Matern
from sklearn.pipeline import Pipeline

# Implement the FFS algorithm
def fractal_feature_selection(X, y, num_blocks=5):
    """
    Fractal Feature Selection (FFS) algorithm.

    Parameters:
    X (array): Feature matrix
    y (array): Target variable
    num_blocks (int): Number of blocks to divide features into

    Returns:
    importance (array): Feature importance scores
    """
    # Divide features into blocks
    blocks = np.array_split(X, num_blocks, axis=1)
    
    # Measure similarity between blocks using RMSE
    similarities = []
    for i in range(len(blocks)):
        for j in range(i+1, len(blocks)):
            mse = mean_squared_error(blocks[i], blocks[j])
            similarities.append((i, j, mse))
    
    # Determine feature importance based on low RMSE
    importance = np.zeros(X.shape[1])
    median_mse = np.median([s[2] for s in similarities])
    for i, j, mse in similarities:
        if mse < median_mse:
            importance[i*2:(i+1)*2] += 1
            importance[j*2:(j+1)*2] += 1
    
    return importance

# Train a machine learning model using the FFS algorithm
def train_model(X, y):
    importance = fractal_feature_selection(X, y)
    X_selected = X[:, importance > 0]
    X_scaled = StandardScaler().fit_transform(X_selected)
    
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
    
    # Train a Logistic Regression model
    lr_pipeline = Pipeline([
        ('model', LogisticRegression())
    ])
    
    lr_param_grid = {
        'model__C': [0.1, 1, 10]
    }
    
    lr_grid_search = GridSearchCV(lr_pipeline, lr_param_grid, cv=5, scoring='f1_macro')
    lr_grid_search.fit(X_train, y_train)
    
    lr_best_model = lr_grid_search.best_estimator_
    lr_y_pred = lr_best_model.predict(X_test)
    print("F1 score (LR):", f1_score(y_test, lr_y_pred, average='macro'))
    
    # Train a Gaussian Process Classifier
    gp_classifier = GaussianProcessClassifier(kernel=Matern(), random_state=42)
    gp_classifier.fit(X_train, y_train)
    gp_y_pred = gp_classifier.predict(X_test)
    print("F1 score (GP):", f1_score(y_test, gp_y_pred, average='macro'))
    
    # Train a Random Forest Classifier
    rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
    rf_classifier.fit(X_train, y_train)
    rf_y_pred = rf_classifier.predict(X_test)
    print("F1 score (RF):", f1_score(y_test, rf_y_pred, average='macro'))
    
    # Train a Support Vector Machine Classifier
    svm_classifier = SVC(kernel='rbf', C=1, random_state=42)
    svm_classifier.fit(X_train, y_train)
    svm_y_pred = svm_classifier.predict(X_test)
    print("F1 score (SVM):", f1_score(y_test, svm_y_pred, average='macro'))
    
    # Train a Gradient Boosting Classifier
    gb_classifier = GradientBoostingClassifier(n_estimators=100, random_state=42)
    gb_classifier.fit(X_train, y_train)
    gb_y_pred = gb_classifier.predict(X_test)
    print("F1 score (GB):", f1_score(y_test, gb_y_pred, average='macro'))

# Example usage

# Generate some sample data
X = np.random.rand(100, 10)
y = np.random.randint(0, 2, 100)

# Train a machine learning model using the FFS algorithm
train_model(X, y)