Add allennlp example.

README.md

@@ -89,6 +89,7 @@ study.optimize(objective, n_trials=100)  # Invoke optimization of the objective
 * [PyTorch Ignite](./examples/pytorch_ignite_simple.py)
 * [PyTorch Lightning](./examples/pytorch_lightning_simple.py)
 * [FastAI](./examples/fastai_simple.py)
+* [AllenNLP](./examples/allennlp_simple.py)
 
 ## Installation
 

examples/allennlp_simple.py

@@ -0,0 +1,127 @@
+import uuid
+
+import allennlp
+import allennlp.data
+import allennlp.models
+import allennlp.modules
+import optuna
+import torch
+
+
+DEVICE = -1  # If you want to use GPU, use DEVICE = 0
+
+# Run tuning with small portion of data
+# to reduce computational time.
+# https://github.com/optuna/optuna/pull/949#pullrequestreview-364110499
+MAX_DATA_SIZE = 3000
+
+GLOVE_FILE_PATH = (
+    'https://s3-us-west-2.amazonaws.com/'
+    'allennlp/datasets/glove/glove.6B.50d.txt.gz'
+)
+
+
+def prepare_data():
+    glove_indexer = allennlp.data.token_indexers.SingleIdTokenIndexer(
+        lowercase_tokens=True
+    )
+    tokenizer = allennlp.data.tokenizers.WordTokenizer(
+        word_splitter=allennlp.data.tokenizers.word_splitter.JustSpacesWordSplitter(),
+    )
+
+    reader = allennlp.data.dataset_readers.TextClassificationJsonReader(
+        token_indexers={'tokens': glove_indexer},
+        tokenizer=tokenizer,
+    )
+    train_dataset = reader.read(
+        'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/train.jsonl'
+    )
+    train_dataset = train_dataset[:MAX_DATA_SIZE]
+
+    valid_dataset = reader.read(
+        'https://s3-us-west-2.amazonaws.com/allennlp/datasets/imdb/dev.jsonl'
+    )
+    valid_dataset = valid_dataset[:MAX_DATA_SIZE]
+
+    vocab = allennlp.data.Vocabulary.from_instances(train_dataset)
+    return train_dataset, valid_dataset, vocab
+
+
+def create_model(vocab, trial: optuna.Trial):
+    embedding = allennlp.modules.Embedding(
+        embedding_dim=50,
+        trainable=True,
+        pretrained_file=GLOVE_FILE_PATH,
+        num_embeddings=vocab.get_vocab_size('tokens'),
+    )
+
+    embedder = allennlp.modules.text_field_embedders.BasicTextFieldEmbedder(
+        {'tokens': embedding}
+    )
+
+    output_dim = trial.suggest_int('output_dim', 80, 120)
+    max_filter_size = trial.suggest_int('max_filter_size', 3, 6)
+    num_filters = trial.suggest_int('num_filters', 64, 256)
+    encoder = allennlp.modules.seq2vec_encoders.CnnEncoder(
+        ngram_filter_sizes=range(1, max_filter_size),
+        num_filters=num_filters,
+        embedding_dim=50,
+        output_dim=output_dim,
+    )
+
+    dropout = trial.suggest_uniform('dropout', 0, 0.5)
+    model = allennlp.models.BasicClassifier(
+        text_field_embedder=embedder,
+        seq2vec_encoder=encoder,
+        dropout=dropout,
+        vocab=vocab,
+    )
+
+    print(output_dim, max_filter_size, num_filters, dropout)
+    return model
+
+
+def objective(trial: optuna.Trial):
+    model = create_model(vocab, trial)
+
+    if DEVICE > -1:
+        print(f'send model to GPU #{DEVICE}')
+        model.cuda(DEVICE)
+
+    lr = trial.suggest_loguniform('lr', 1e-1, 1e0)
+    optimizer = torch.optim.SGD(model.parameters(), lr=lr)
+
+    iterator = allennlp.data.iterators.BasicIterator(
+        batch_size=10,
+    )
+    iterator.index_with(vocab)
+
+    trainer = allennlp.training.Trainer(
+        model=model,
+        optimizer=optimizer,
+        iterator=iterator,
+        train_dataset=train_dataset,
+        validation_dataset=valid_dataset,
+        patience=3,
+        num_epochs=6,
+        cuda_device=DEVICE,
+        serialization_dir=f'/tmp/xx/{uuid.uuid1()}',
+    )
+    metrics = trainer.train()
+    return metrics['best_validation_accuracy']
+
+
+if __name__ == '__main__':
+    train_dataset, valid_dataset, vocab = prepare_data()
+
+    study = optuna.create_study(direction='maximize')
+    study.optimize(objective, n_trials=2)
+
+    print('Number of finished trials: ', len(study.trials))
+    print('Best trial:')
+    trial = study.best_trial
+
+    print('  Value: ', trial.value)
+    print('  Params: ')
+    for key, value in trial.params.items():
+        print('    {}: {}'.format(key, value))

setup.py

@@ -73,6 +73,7 @@ def get_extras_require():
             'sphinx_rtd_theme',
         ],
         'example': [
+            'allennlp',
             'catboost',
             'chainer',
             'lightgbm',