(X, y, n_queries)
| 175 | |
| 176 | @timeit() |
| 177 | def alp_QBC(X, y, n_queries): |
| 178 | X_labeled, y_labeled = X[[0, 50, 100]], y[[0, 50, 100]] |
| 179 | estimators = [LogisticRegression(solver='liblinear', n_jobs=1, multi_class='ovr'), |
| 180 | LogisticRegression(solver='liblinear', n_jobs=1, multi_class='ovr')] |
| 181 | |
| 182 | for estimator in estimators: |
| 183 | estimator.fit(X_labeled, y_labeled) |
| 184 | |
| 185 | learner = ActiveLearnerALP(strategy='vote_entropy') |
| 186 | |
| 187 | for _ in range(n_queries): |
| 188 | query_idx = learner.rank(estimators, X, num_queries=1) |
| 189 | X_labeled = np.concatenate((X_labeled, X[query_idx]), axis=0) |
| 190 | y_labeled = np.concatenate((y_labeled, y[query_idx]), axis=0) |
| 191 | for estimator in estimators: |
| 192 | estimator.fit(X_labeled, y_labeled) |
| 193 | |
| 194 | |
| 195 | def comparisons(n_queries=10): |
no test coverage detected
searching dependent graphs…