{"cells":[{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\n\nimport os\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom tqdm import tqdm\nimport math\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"21f85dfd5b9bf3d8b27ba29149d52253e5d64049"},"cell_type":"markdown","source":"# Setup"},{"metadata":{"trusted":true,"_uuid":"78578eab64a477d0a5ad6b1c917ae154868a44df"},"cell_type":"code","source":"train_df = pd.read_csv(\"../input/train.csv\")\ntrain_df, val_df = train_test_split(train_df, test_size=0.1, random_state=28)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"92ffbf2ef35d2dc5863ee27c61eadd1869ca5440"},"cell_type":"code","source":"# embdedding setup\n# Source https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html\nembeddings_index = {}\nf = open('../input/embeddings/glove.840B.300d/glove.840B.300d.txt')\nfor line in tqdm(f):\n    values = line.split(\" \")\n    word = values[0]\n    coefs = np.asarray(values[1:], dtype='float32')\n    embeddings_index[word] = coefs\nf.close()\n\nprint('Found %s word vectors.' % len(embeddings_index))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9cc8b0bcd285225ce651d024f506615d4656b9f7"},"cell_type":"code","source":"# Convert values to embeddings\ndef text_to_array(text):\n    empyt_emb = np.zeros(300)\n    text = text[:-1].split()[:30]\n    embeds = [embeddings_index.get(x, empyt_emb) for x in text]\n    embeds+= [empyt_emb] * (30 - len(embeds))\n    return np.array(embeds)\n\n# train_vects = [text_to_array(X_text) for X_text in tqdm(train_df[\"question_text\"])]\nval_vects = np.array([text_to_array(X_text) for X_text in tqdm(val_df[\"question_text\"][:3000])])\nval_y = np.array(val_df[\"target\"][:3000])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0c950448e0717eaebb920d93cfdc6b4561e21853"},"cell_type":"code","source":"# Data providers\nbatch_size = 128\n\ndef batch_gen(train_df):\n    n_batches = math.ceil(len(train_df) / batch_size)\n    while True: \n        train_df = train_df.sample(frac=1.)  # Shuffle the data.\n        for i in range(n_batches):\n            texts = train_df.iloc[i*batch_size:(i+1)*batch_size, 1]\n            text_arr = np.array([text_to_array(text) for text in texts])\n            yield text_arr, np.array(train_df[\"target\"][i*batch_size:(i+1)*batch_size])\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"706c0224b6112e5a8f00ad25f35e90fbb9519a5f"},"cell_type":"markdown","source":"# Training"},{"metadata":{"trusted":true,"_uuid":"798c303ec834fb530a60a1e590cfbd9a86f93fde"},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import CuDNNLSTM, Dense, Bidirectional, Dropout\nfrom keras.callbacks import Callback\nfrom sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d9cc1694fd174537c14346d647bdf67141e915cd"},"cell_type":"code","source":"from keras import backend as K\n\ndef f1(y_true, y_pred):\n    def recall(y_true, y_pred):\n        \"\"\"Recall metric.\n\n        Only computes a batch-wise average of recall.\n\n        Computes the recall, a metric for multi-label classification of\n        how many relevant items are selected.\n        \"\"\"\n        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n        recall = true_positives / (possible_positives + K.epsilon())\n        return recall\n\n    def precision(y_true, y_pred):\n        \"\"\"Precision metric.\n\n        Only computes a batch-wise average of precision.\n\n        Computes the precision, a metric for multi-label classification of\n        how many selected items are relevant.\n        \"\"\"\n        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n        precision = true_positives / (predicted_positives + K.epsilon())\n        return precision\n    precision = precision(y_true, y_pred)\n    recall = recall(y_true, y_pred)\n    return 2*((precision*recall)/(precision+recall+K.epsilon()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9ad418c95b31ab5691d49b72c7c9622ef9ea42cf"},"cell_type":"code","source":"model = Sequential()\nmodel.add(Bidirectional(CuDNNLSTM(64, return_sequences=True),\n                        input_shape=(30, 300)))\nmodel.add(Bidirectional(CuDNNLSTM(64)))\n\n# model.add(Dropout(0.1))\n\nmodel.add(Dense(1, activation=\"sigmoid\"))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy', f1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bc17dc0c7755d48c03c5b327491cec2c463d9141"},"cell_type":"code","source":"# use the Keras F1 Metric is enough\n# class Metrics(Callback):\n#     def on_train_begin(self, logs={}):\n#         self.val_f1s = []\n#         self.val_recalls = []\n#         self.val_precisions = []\n\n#     def on_epoch_end(self, epoch, logs={}):\n#         val_predict = (np.asarray(self.model.predict(self.model.validation_data[0]))).round()\n#         val_targ = self.model.validation_data[1]\n#         _val_f1 = f1_score(val_targ, val_predict)\n#         _val_recall = recall_score(val_targ, val_predict)\n#         _val_precision = precision_score(val_targ, val_predict)\n#         self.val_f1s.append(_val_f1)\n#         self.val_recalls.append(_val_recall)\n#         self.val_precisions.append(_val_precision)\n#         print(\" — val_f1: %f — val_precision: %f — val_recall %f\" %(_val_f1, _val_precision, _val_recall))\n#         return \n \n# my_metrics = Metrics()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3f0b703ded691293450beb4ddf2d903d0e08c737","scrolled":false},"cell_type":"code","source":"from keras.callbacks import EarlyStopping, ModelCheckpoint\ncheck_point = ModelCheckpoint('model.hdf5', monitor=\"val_f1\", mode=\"max\",\n                              verbose=True, save_best_only=True)\nearly_stop = EarlyStopping(monitor=\"val_f1\", mode=\"max\", patience=8,verbose=True)\nmg = batch_gen(train_df)\nmodel.fit_generator(mg, epochs=30,\n                    steps_per_epoch=1000,\n                    validation_data=(val_vects, val_y),\n                    verbose=True,\n                    callbacks=[early_stop,check_point])","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d6cdda0e301be0b84e826e29f0f3a85c41aa6da9"},"cell_type":"markdown","source":"# Inference"},{"metadata":{"trusted":true,"_uuid":"0e54e8eeb530debbb4d240ef30f266d7ac4f938c"},"cell_type":"code","source":"model.load_weights('model.hdf5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b58cd95254f41e5002a17de0c3feab54a5fc3c67"},"cell_type":"code","source":"# prediction part\nbatch_size = 256\ndef batch_gen(test_df):\n    n_batches = math.ceil(len(test_df) / batch_size)\n    for i in range(n_batches):\n        texts = test_df.iloc[i*batch_size:(i+1)*batch_size, 1]\n        text_arr = np.array([text_to_array(text) for text in texts])\n        yield text_arr\n\ntest_df = pd.read_csv(\"../input/test.csv\")\n\nall_preds = []\nfor x in tqdm(batch_gen(test_df)):\n    all_preds.extend(model.predict(x).flatten())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3e6ed54def110c881f401c6f8a844752baedcfbd"},"cell_type":"code","source":"y_te = (np.array(all_preds) > 0.5).astype(np.int)\n\nsubmit_df = pd.DataFrame({\"qid\": test_df[\"qid\"], \"prediction\": y_te})\nsubmit_df.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}