import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import roc_curve, auc

# Generate synthetic data for binary classification
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10,
        n_redundant=10, random_state=42)

# Standardize the data using StandardScaler
scaler = StandardScaler()
X = scaler.fit_transform(X)

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
                                    random_state=42)

# Fit the train set with classifiers
rf_classifier = RandomForestClassifier(random_state=42)
knn_classifier = KNeighborsClassifier()
svm_classifier = SVC(probability=True, random_state=42)

rf_classifier.fit(X_train, y_train)
knn_classifier.fit(X_train, y_train)
svm_classifier.fit(X_train, y_train)

# Generate predictions for the test set
rf_probs = rf_classifier.predict_proba(X_test)[:, 1]
knn_probs = knn_classifier.predict_proba(X_test)[:, 1]
svm_probs = svm_classifier.predict_proba(X_test)[:, 1]

# Calculate false positive rate, true positive rate,
# and area under the curve for ROC curve
rf_fpr, rf_tpr, _ = roc_curve(y_test, rf_probs)
rf_auc = auc(rf_fpr, rf_tpr)

knn_fpr, knn_tpr, _ = roc_curve(y_test, knn_probs)
knn_auc = auc(knn_fpr, knn_tpr)

svm_fpr, svm_tpr, _ = roc_curve(y_test, svm_probs)
svm_auc = auc(svm_fpr, svm_tpr)

# Plot the ROC curve
plt.figure()
plt.plot(rf_fpr, rf_tpr, label=f'Random Forest (AUC = {rf_auc:.2f})')
plt.plot(knn_fpr, knn_tpr, label=f'KNN (AUC = {knn_auc:.2f})')
plt.plot(svm_fpr, svm_tpr, label=f'SVM (AUC = {svm_auc:.2f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc='lower right')
plt.show()