em_keras.py

import os
import time
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
from sklearn import metrics

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import Bidirectional, GlobalMaxPool1D
from keras.models import Model
from keras import initializers, regularizers, constraints, optimizers, layers

train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
print("Train shape : ",train_df.shape)
print("Test shape : ",test_df.shape)

## split to train and val
train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=2019)

## some config values
embed_size = 300 # how big is each word vector
max_features = 50000 # how many unique words to use (i.e num rows in embedding vector)
maxlen = 50 # max number of words in a question to use

## fill up the missing values
train_X = train_df["question_text"].fillna("").values
val_X = val_df["question_text"].fillna("").values
test_X = test_df["question_text"].fillna("").values

## Tokenize the sentences
tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(list(train_X))
train_X = tokenizer.texts_to_sequences(train_X)
val_X = tokenizer.texts_to_sequences(val_X)
test_X = tokenizer.texts_to_sequences(test_X)

## Pad the sentences
train_X = pad_sequences(train_X, maxlen=maxlen)
val_X = pad_sequences(val_X, maxlen=maxlen)
test_X = pad_sequences(test_X, maxlen=maxlen)

## Get the target values
train_y = train_df['target'].values
val_y = val_df['target'].values


## without Pretrainted Embeddings
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size)(inp)
x = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)
x = GlobalMaxPool1D()(x)
x = Dense(16, activation="relu")(x)
x = Dropout(0.1)(x)
x = Dense(1, activation="sigmoid")(x)
model = Model(inputs=inp, outputs=x)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

print(model.summary())

## Train the model
model.fit(train_X, train_y, batch_size=512, epochs=2, validation_data=(val_X, val_y))

## Predict
pred_noemb_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
    thresh = np.round(thresh, 2)
    print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y, (pred_noemb_val_y>thresh).astype(int))))


model.save('my_keras_embed_model.h5')

pred_keras_test_y = model.predict([test_X], batch_size=1024, verbose=1)
pred_test_y = pred_keras_test_y
pred_test_y = (pred_test_y>0.35).astype(int)
out_df = pd.DataFrame({"qid":test_df["qid"].values})
out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False)