from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split

# Load the iris dataset
X, y = load_iris(return_X_y=True)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Fit the model to the training data
model = LogisticRegression(), y_train)

# Make predictions on the testing data
y_pred = model.predict(X_test)

# Calculate the confusion matrix
cm = confusion_matrix(y_test, y_pred)

# Calculate the accuracy, precision, recall, and F1 score
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='macro')
recall = recall_score(y_test, y_pred, average='macro')
f1 = f1_score(y_test, y_pred, average='macro')

# Print the results
print("Confusion matrix:\n", cm)
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 score:", f1)