[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:from hyperopt import Trials, STATUS_OK, tpe\nfrom hyperas import optim\nfrom hyperas.distributions import choice, uniform, conditional\ndef data():\nMAX_WORDS = 20000\nEMBEDDING_DIM = 100\ndata = pandas.read_csv(\'train.tsv\',sep=\'\\t\')\ntest = pandas.read_csv(\'test.tsv\',sep=\'\\t\')\ntexts = data[\'Phrase\'].values\ntokenizer = Tokenizer(num_words=MAX_WORDS)\ntokenizer.fit_on_texts(texts)\ntokenizer.fit_on_texts(test[\'Phrase\'].values)\nsequences = tokenizer.texts_to_sequences(texts)\nword_index = tokenizer.word_index\nprint(\'Found %s unique tokens.\' % len(word_index))\nembeddings_index = {}\nf = open(\'glove.6B.\'+str(EMBEDDING_DIM)+\'d.txt\',encoding="utf8")\nfor line in f:\nvalues = line.split()\nword = values[0]\ncoefs = numpy.asarray(values[1:], dtype=\'float32\')\nembeddings_index[word] = coefs\nf.close()\nprint(\'Found %s word vectors.\' % len(embeddings_index))\nembedding_matrix = numpy.zeros((len(word_index) + 1, EMBEDDING_DIM))\nfor word, i in word_index.items():\nembedding_vector = embeddings_index.get(word)\nif embedding_vector is not None:\nembedding_matrix[i] = embedding_vector\nMAX_SEQUENCE_LENGTH = max([len(i) for i in sequences])\nx = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\ny = np_utils.to_categorical(data[\'Sentiment\'].values)\nreturn x, y, word_index, EMBEDDING_DIM, embedding_matrix, MAX_SEQUENCE_LENGTH\ndef model(x, y, word_index, EMBEDDING_DIM, embedding_matrix, MAX_SEQUENCE_LENGTH):\nmodel = Sequential()\nmodel.add(Embedding(len(word_index) + 1,EMBEDDING_DIM,weights=[embedding_matrix],\ninput_length=MAX_SEQUENCE_LENGTH,mask_zero=True))\nmodel.add(LSTM({{choice([128,256,512])}},recurrent_dropout={{choice([0.2, 0.4])}},dropout={{choice([0.2, 0.4])}}))\nmodel.add(Dense({{choice([128,256,512])}},activation=\'relu\'))\nmodel.add(Dropout({{choice([0.2,0.4])}}))\nmodel.add(Dense(5,activation=\'softmax\'))\nmodel.compile(loss=\'categorical_crossentropy\', optimizer={{choice([\'adam\',\'rmsprop\'])}}, metrics=[\'accuracy\'])\nes = EarlyStopping(patience=2)\nhistory = model.fit(x, y, batch_size=128, epochs=5, verbose=2, validation_split=0.2,callbacks=[es])\nacc = max(history.history[\'val_acc\'])\nprint(\'Test accuracy:\', acc)\nreturn {\'loss\': -acc, \'status\': STATUS_OK, \'model\': model}'}, {'role': 'assistant', 'content': 'modelling'}]