(X, y, n_queries)
| 38 | |
| 39 | @timeit() |
| 40 | def libact_uncertainty(X, y, n_queries): |
| 41 | y_train = np.array([None for _ in range(len(y))]) |
| 42 | y_train[0], y_train[50], y_train[100] = 0, 1, 2 |
| 43 | libact_train_dataset = Dataset(X, y_train) |
| 44 | libact_full_dataset = Dataset(X, y) |
| 45 | libact_learner = LogisticRegressionLibact(solver='liblinear', n_jobs=1, multi_class='ovr') #SVM(gamma='auto', probability=True) |
| 46 | libact_qs = UncertaintySampling(libact_train_dataset, model=libact_learner, method='lc') |
| 47 | libact_labeler = IdealLabeler(libact_full_dataset) |
| 48 | libact_learner.train(libact_train_dataset) |
| 49 | |
| 50 | for _ in range(n_queries): |
| 51 | query_idx = libact_qs.make_query() |
| 52 | query_label = libact_labeler.label(X[query_idx]) |
| 53 | libact_train_dataset.update(query_idx, query_label) |
| 54 | libact_learner.train(libact_train_dataset) |
| 55 | |
| 56 | |
| 57 | @timeit() |
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