def main(): exp = nli.experiment(train_reader=nli.SNLITrainReader(SNLI_HOME, samp_percentage=1.0), assess_reader=nli.SNLIDevReader(SNLI_HOME, samp_percentage=1.0), phi=sentence_encoding_rnn_phi, train_func=fit_bilstm_attention, random_state=None, vectorize=False) print(exp)
"multinli_1.0_matched_annotations.txt", "multinli_1.0_mismatched_annotations.txt" ]) def test_read_annotated_subset(src_filename): src_filename = os.path.join(annotations_home, src_filename) data = nli.read_annotated_subset(src_filename, multinli_home) assert len(data) == 495 def test_build_dataset(): nli.build_dataset(reader=nli.SNLITrainReader(snli_home, samp_percentage=0.01), phi=lambda x, y: {"$UNK": 1}, vectorizer=None, vectorize=True) @pytest.mark.parametrize("assess_reader", [None, nli.SNLIDevReader(snli_home)]) def test_experiment(assess_reader): def fit_maxent(X, y): mod = LogisticRegression(solver='liblinear', multi_class='auto') mod.fit(X, y) return mod nli.experiment(train_reader=nli.SNLITrainReader(snli_home, samp_percentage=0.01), phi=lambda x, y: {"$UNK": 1}, train_func=fit_maxent, assess_reader=assess_reader, random_state=42)
def test_read_annotated_subset(src_filename): src_filename = os.path.join( annotations_home, src_filename) data = nli.read_annotated_subset(src_filename, multinli_home) assert len(data) == 495 def test_build_dataset(): nli.build_dataset( reader=nli.SNLITrainReader(snli_home, samp_percentage=0.01), phi=lambda x, y: {"$UNK": 1}, vectorizer=None, vectorize=True) @pytest.mark.parametrize("assess_reader", [ None, nli.SNLIDevReader(snli_home) ]) def test_experiment(assess_reader): def fit_maxent(X, y): mod = LogisticRegression(solver='liblinear', multi_class='auto') mod.fit(X, y) return mod nli.experiment( train_reader=nli.SNLITrainReader(snli_home, samp_percentage=0.01), phi=lambda x, y: {"$UNK": 1}, train_func=fit_maxent, assess_reader=assess_reader, random_state=42)