toxic-comment-pooled-gru-v3.py

# The script is modified from
# https://www.kaggle.com/prashantkikani/pooled-gru-with-preprocessing/code

import numpy as np

np.random.seed(42)
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

from keras.models import Model
from keras.layers import Input, Dense, Embedding, SpatialDropout1D, concatenate
from keras.layers import GRU, Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D
from keras.preprocessing import text, sequence
from keras.callbacks import Callback
from keras import backend as K
import re
import warnings

warnings.filterwarnings('ignore')

import os

os.environ['OMP_NUM_THREADS'] = '4'


classes = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
submission = pd.read_csv('sample_submission.csv')

# PREPROCESSING PART
repl = {
    "<3": " good ",
    ":d": " good ",
    ":dd": " good ",
    ":p": " good ",
    "8)": " good ",
    ":-)": " good ",
    ":)": " good ",
    ";)": " good ",
    "(-:": " good ",
    "(:": " good ",
    "yay!": " good ",
    "yay": " good ",
    "yaay": " good ",
    "yaaay": " good ",
    "yaaaay": " good ",
    "yaaaaay": " good ",
    ":/": " bad ",
    ":>": " sad ",
    ":')": " sad ",
    ":-(": " bad ",
    ":(": " bad ",
    ":s": " bad ",
    ":-s": " bad ",
    "<3": " heart ",
    ":d": " smile ",
    ":p": " smile ",
    ":dd": " smile ",
    "8)": " smile ",
    ":-)": " smile ",
    ":)": " smile ",
    ";)": " smile ",
    "(-:": " smile ",
    "(:": " smile ",
    ":/": " worry ",
    ":>": " angry ",
    ":')": " sad ",
    ":-(": " sad ",
    ":(": " sad ",
    ":s": " sad ",
    ":-s": " sad ",
    r"\br\b": "are",
    r"\bu\b": "you",
    r"\bhaha\b": "ha",
    r"\bhahaha\b": "ha",
    r"\bdon't\b": "do not",
    r"\bdoesn't\b": "does not",
    r"\bdidn't\b": "did not",
    r"\bhasn't\b": "has not",
    r"\bhaven't\b": "have not",
    r"\bhadn't\b": "had not",
    r"\bwon't\b": "will not",
    r"\bwouldn't\b": "would not",
    r"\bcan't\b": "can not",
    r"\bcannot\b": "can not",
    r"\bi'm\b": "i am",
    "m": "am",
    "r": "are",
    "u": "you",
    "haha": "ha",
    "hahaha": "ha",
    "don't": "do not",
    "doesn't": "does not",
    "didn't": "did not",
    "hasn't": "has not",
    "haven't": "have not",
    "hadn't": "had not",
    "won't": "will not",
    "wouldn't": "would not",
    "can't": "can not",
    "cannot": "can not",
    "i'm": "i am",
    "m": "am",
    "i'll": "i will",
    "its": "it is",
    "it's": "it is",
    "'s": " is",
    "that's": "that is",
    "weren't": "were not",
}

keys = [i for i in repl.keys()]

new_train_data = []
new_test_data = []
ltr = train["comment_text"].tolist()
lte = test["comment_text"].tolist()
for i in ltr:
    arr = str(i).split()
    xx = ""
    for j in arr:
        j = str(j).lower()
        if j[:4] == 'http' or j[:3] == 'www':
            continue
        if j in keys:
            # print("inn")
            j = repl[j]
        xx += j + " "
    new_train_data.append(xx)
for i in lte:
    arr = str(i).split()
    xx = ""
    for j in arr:
        j = str(j).lower()
        if j[:4] == 'http' or j[:3] == 'www':
            continue
        if j in keys:
            # print("inn")
            j = repl[j]
        xx += j + " "
    new_test_data.append(xx)
train["new_comment_text"] = new_train_data
test["new_comment_text"] = new_test_data
print("crap removed")
trate = train["new_comment_text"].tolist()
tete = test["new_comment_text"].tolist()
for i, c in enumerate(trate):
    trate[i] = re.sub('[^a-zA-Z ?!]+', '', str(trate[i]).lower())
for i, c in enumerate(tete):
    tete[i] = re.sub('[^a-zA-Z ?!]+', '', tete[i])
train["comment_text"] = trate
test["comment_text"] = tete
print('only alphabets')

X_train = train["comment_text"].fillna("fillna").values
y_train = train[classes].values
X_test = test["comment_text"].fillna("fillna").values


# EMBEDDING_FILE = 'glove.6B.200d.txt'
EMBEDDING_FILE = 'glove.42B.300d.txt'

max_features = 30000
maxlen = 100
embed_size = 300

tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(list(X_train) + list(X_test))
X_train = tokenizer.texts_to_sequences(X_train)
X_test = tokenizer.texts_to_sequences(X_test)
x_train = sequence.pad_sequences(X_train, maxlen=maxlen)
x_test = sequence.pad_sequences(X_test, maxlen=maxlen)


def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')


embeddings_index = dict(get_coefs(*o.strip().split()) for o in open(EMBEDDING_FILE))

all_embs = np.stack(embeddings_index.values())
emb_mean, emb_std = all_embs.mean(), all_embs.std()

word_index = tokenizer.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))
for word, i in word_index.items():
    if i >= max_features: continue
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None: embedding_matrix[i] = embedding_vector


class RocAucEvaluation(Callback):
    def __init__(self, validation_data=(), interval=1):
        super(Callback, self).__init__()

        self.interval = interval
        self.X_val, self.y_val = validation_data

    def on_epoch_end(self, epoch, logs={}):
        if epoch % self.interval == 0:
            y_pred = self.model.predict(self.X_val, verbose=0)
            score = roc_auc_score(self.y_val, y_pred)
            print("\n ROC-AUC - epoch: %d - score: %.6f \n" % (epoch + 1, score))


def get_model():
    inp = Input(shape=(maxlen,))
    x = Embedding(max_features, embed_size, weights=[embedding_matrix])(inp)
    x = SpatialDropout1D(0.2)(x)
    x = Bidirectional(GRU(80, return_sequences=True))(x)
    avg_pool = GlobalAveragePooling1D()(x)
    max_pool = GlobalMaxPooling1D()(x)
    conc = concatenate([avg_pool, max_pool])
    outp = Dense(6, activation="sigmoid")(conc)

    model = Model(inputs=inp, outputs=outp)
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    return model


model = get_model()

batch_size = 32
epochs = 2

[X_tra, X_val, y_tra, y_val] = train_test_split(x_train, y_train, train_size=0.95, random_state=233)
RocAuc = RocAucEvaluation(validation_data=(X_val, y_val), interval=1)

exp_decay = lambda init, fin, steps: (init / fin) ** (1 / (steps - 1)) - 1
steps = int(len(X_tra) / batch_size) * epochs
lr_init, lr_fin = 0.001, 0.0005
lr_decay = exp_decay(lr_init, lr_fin, steps)
K.set_value(model.optimizer.lr, lr_init)
K.set_value(model.optimizer.decay, lr_decay)

hist = model.fit(X_tra, y_tra, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val),
                 callbacks=[RocAuc], verbose=2)

y_pred = model.predict(x_test, batch_size=1024)
submission[classes] = y_pred
submission.to_csv('submission-pooled-gru-v3.csv', index=False)

print('save submission to submission-pooled-gru-v3.csv')