| 1099 | confusion_matrix(pred, np.random.randint(0, 2, size=(10,))) |
| 1100 | |
| 1101 | def test_sparse_matrices(self): |
| 1102 | query_strategies = [ |
| 1103 | modAL.uncertainty.uncertainty_sampling, |
| 1104 | modAL.uncertainty.entropy_sampling, |
| 1105 | modAL.uncertainty.margin_sampling |
| 1106 | ] |
| 1107 | formats = ['lil', 'csc', 'csr'] |
| 1108 | sample_count = range(10, 20) |
| 1109 | feature_count = range(1, 5) |
| 1110 | |
| 1111 | for query_strategy, format, n_samples, n_features in product(query_strategies, formats, sample_count, feature_count): |
| 1112 | X_pool = sp.random(n_samples, n_features, format=format) |
| 1113 | y_pool = np.random.randint(0, 2, size=(n_samples, )) |
| 1114 | initial_idx = np.random.choice( |
| 1115 | range(n_samples), size=5, replace=False) |
| 1116 | |
| 1117 | learner = modAL.models.learners.ActiveLearner( |
| 1118 | estimator=RandomForestClassifier(n_estimators=10), query_strategy=query_strategy, |
| 1119 | X_training=X_pool[initial_idx], y_training=y_pool[initial_idx] |
| 1120 | ) |
| 1121 | query_idx, query_inst = learner.query(X_pool) |
| 1122 | learner.teach(X_pool[query_idx], y_pool[query_idx]) |
| 1123 | |
| 1124 | def test_on_transformed(self): |
| 1125 | n_samples = 10 |