import warnings
from sklearn.datasets import load_digits
from sklearn.exceptions import ConvergenceWarning
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Ignore the convergence warning
warnings.filterwarnings("ignore", category=ConvergenceWarning)

# Load the digits dataset
digits = load_digits()

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=0)

# Train a logistic regression model on the training data
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

# Evaluate the model's performance on the testing data
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy on original test set:", accuracy)