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- import numpy as np
- import openml
-
-
- # Function to load and preprocess the dataset
- def load_and_preprocess_dataset(dataset_id):
- dataset = openml.datasets.get_dataset(dataset_id, download_data=True, download_qualities=False, download_features_meta_data=False)
- X, y, _, attribute_names = dataset.get_data(target=dataset.default_target_attribute)
- # Convert data types
- for col in X.select_dtypes(include='bool').columns:
- X[col] = X[col].astype(int)
- y = y.cat.codes.astype(int)
- X, y = X.to_numpy(), y.to_numpy()
- return X, y
-
- # Function to split data (one shot)
- def split_dataset(X, y, test_size = 0.3):
- # For every class: 1 : (1-test_size)*(len-1) : test_size*(len-1)
- label_indices, unlabel_indices, test_indices = [], [], []
- for class_label in np.unique(y):
- idxs = np.where(y == class_label)[0]
- np.random.shuffle(idxs)
- n_train_unlabel = int((1-test_size)*(len(idxs)-1))
- label_indices.append(idxs[0])
- unlabel_indices.extend(idxs[1:1+n_train_unlabel])
- test_indices.extend(idxs[1+n_train_unlabel:])
- X_label, y_label = X[label_indices], y[label_indices]
- X_unlabel, y_unlabel = X[unlabel_indices], y[unlabel_indices]
- X_test, y_test = X[test_indices], y[test_indices]
- return X_label, y_label, X_unlabel, y_unlabel, X_test, y_test
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