from pycobra.cobra import Cobra
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Create a dataset
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize Pycobra's COBRA ensemble
cobra = Cobra()

# Add models to the ensemble
cobra.models = [
    ("RandomForest", RandomForestClassifier(n_estimators=10)),
    ("LogisticRegression", LogisticRegression()),
    ("SVC", SVC(probability=True))
]

# Fit COBRA to the training data
cobra.fit(X_train, y_train)