{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # Algèbre Linéaire\nimport pandas as pd # Analyse des données, CSV I/O \nimport math\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\n\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation, Conv1D #LSTM = (Long-Short Term Memory)\nfrom tensorflow.compat.v1.keras.layers import CuDNNGRU # Gated Recurrent Unit\nfrom keras.layers import Bidirectional, GlobalMaxPool1D\nfrom keras.models import Model\nfrom keras import initializers, regularizers, constraints, optimizers, layers","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cd /kaggle/input/quora-insincere-questions-classification","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('train.csv')\ntest_df = pd.read_csv('test.csv')\nprint('Forme Train : ',train_df.shape)\nprint('Forme Test : ',test_df.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## split to train and val\ntrain_df, val_df = train_test_split(train_df, test_size=0.1, random_state=2018)\n\n## some config values \nembed_size = 300 # how big is each word vector\nmax_features = 50000 # how many unique words to use (i.e num rows in embedding vector)\nmaxlen = 100 # max number of words in a question to use\n\n## fill up the missing values\ntrain_X = train_df[\"question_text\"].fillna(\"_na_\").values\nval_X = val_df[\"question_text\"].fillna(\"_na_\").values\ntest_X = test_df[\"question_text\"].fillna(\"_na_\").values\n\n## Tokenize the sentences\ntokenizer = Tokenizer(num_words=max_features)\ntokenizer.fit_on_texts(list(train_X))\ntrain_X = tokenizer.texts_to_sequences(train_X)\nval_X = tokenizer.texts_to_sequences(val_X)\ntest_X = tokenizer.texts_to_sequences(test_X)\n\n## Pad the sentences \ntrain_X = pad_sequences(train_X, maxlen=maxlen)\nval_X = pad_sequences(val_X, maxlen=maxlen)\ntest_X = pad_sequences(test_X, maxlen=maxlen)\n\n## Get the target values\ntrain_y = train_df['target'].values\nval_y = val_df['target'].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"inp = Input(shape=(maxlen,))\nx = Embedding(max_features, embed_size)(inp)\nx = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)\nx = GlobalMaxPool1D()(x)\nx = Dense(16, activation=\"relu\")(x)\nx = Dropout(0.1)(x)\nx = Dense(1, activation=\"sigmoid\")(x)\nmodel = Model(inputs=inp, outputs=x)\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\nprint(model.summary())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Entrainement du modèle \nmodel.fit(train_X, train_y, batch_size=512, epochs=2, validation_data=(val_X, val_y))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_X = test_df[\"question_text\"].fillna(\"_na_\").values\ntest_X = tokenizer.texts_to_sequences(test_X)\ntest_X = pad_sequences(test_X, maxlen=maxlen)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_glove_test_y = model.predict([test_X], batch_size=1024, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_fasttext_test_y = model.predict([test_X], batch_size=1024, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_paragram_test_y = model.predict([test_X], batch_size=1024, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cd ../../..","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cd ./kaggle/working/","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_test_y = 0.33*pred_glove_test_y + 0.33*pred_fasttext_test_y + 0.34*pred_paragram_test_y\npred_test_y = (pred_test_y>0.35).astype(int)\nout_df = pd.DataFrame({\"qid\":test_df[\"qid\"].values})\nout_df['prediction'] = pred_test_y\nout_df.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}