(X, y, n_queries)
| 74 | |
| 75 | @timeit() |
| 76 | def libact_QBC(X, y, n_queries): |
| 77 | y_train = np.array([None for _ in range(len(y))]) |
| 78 | y_train[0], y_train[50], y_train[100] = 0, 1, 2 |
| 79 | libact_train_dataset = Dataset(X, y_train) |
| 80 | libact_full_dataset = Dataset(X, y) |
| 81 | libact_learner_list = [LogisticRegressionLibact(solver='liblinear', n_jobs=1, multi_class='ovr'), |
| 82 | LogisticRegressionLibact(solver='liblinear', n_jobs=1, multi_class='ovr')] |
| 83 | libact_qs = QueryByCommittee(libact_train_dataset, models=libact_learner_list, |
| 84 | method='lc') |
| 85 | libact_labeler = IdealLabeler(libact_full_dataset) |
| 86 | for libact_learner in libact_learner_list: |
| 87 | libact_learner.train(libact_train_dataset) |
| 88 | |
| 89 | for _ in range(n_queries): |
| 90 | query_idx = libact_qs.make_query() |
| 91 | query_label = libact_labeler.label(X[query_idx]) |
| 92 | libact_train_dataset.update(query_idx, query_label) |
| 93 | for libact_learner in libact_learner_list: |
| 94 | libact_learner.train(libact_train_dataset) |
| 95 | |
| 96 | |
| 97 | @timeit() |
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