Kaggle competition: Jigsaw Unintended Bias in Toxicity Classification
submission.to_csv('submission.csv', index=False )<set_options>
warnings.filterwarnings(action='once')
 device = torch.device('cuda')
 def convert_lines(example, max_seq_length,tokenizer):
max_seq_length -=2
all_tokens = []
longer = 0
for text in tqdm(example):
tokens_a = tokenizer.tokenize(text)
 if len(tokens_a)>max_seq_length:
tokens_a = tokens_a[:max_seq_length]
longer += 1
one_token = tokenizer.convert_tokens_to_ids(["[CLS]"]+tokens_a+["[SEP]"])+[0] *(max_seq_length - len(tokens_a))
all_tokens.append(one_token)
 return np.array(all_tokens)
 MAX_SEQUENCE_LENGTH = 400
SEED = 1234
BATCH_SIZE = 32
BERT_MODEL_PATH = '.. /input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
np.random.seed(SEED)
 torch.manual_seed(SEED)
 torch.cuda.manual_seed(SEED)
 torch.backends.cudnn.deterministic = True
bert_config = BertConfig('.. /input/bertconfig/bert_config.json')
 tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None,do_lower_case=True)
 train_df = pd.read_csv(".. /input/jigsaw-unintended-bias-in-toxicity-classification/train.csv")
 test_df = pd.read_csv(".. /input/jigsaw-unintended-bias-in-toxicity-classification/test.csv")
 test_df['comment_text'] = test_df['comment_text'].astype(str)
 X_test = convert_lines(test_df["comment_text"].fillna("DUMMY_VALUE"), MAX_SEQUENCE_LENGTH, tokenizer)
 model = BertForSequenceClassification(bert_config, num_labels=1)
 model.load_state_dict(torch.load(".. /input/bertconfig/bert_pytorch.bin"))
model.to(device)
 for param in model.parameters() :
param.requires_grad = False
model.eval()
test_preds = np.zeros(( len(X_test)))
 test = torch.utils.data.TensorDataset(torch.tensor(X_test, dtype=torch.long))
test_loader = torch.utils.data.DataLoader(test, batch_size=32, shuffle=False)
 tk0 = tqdm(test_loader)
 for i,(x_batch,)in enumerate(tk0):
pred = model(x_batch.to(device), attention_mask=(x_batch > 0 ).to(device), labels=None)
 test_preds[i * 32:(i + 1)* 32] = pred[:, 0].detach().cpu().squeeze().numpy()
test_pred = torch.sigmoid(torch.tensor(test_preds)).numpy().ravel()
%matplotlib inline
warnings.filterwarnings("ignore" )<load_from_csv>
bert_submission = pd.DataFrame.from_dict({
'id': test_df['id'],
'prediction': test_pred
})
train_df = pd.read_csv('.. /input/titanic/train.csv',index_col='PassengerId')
 test_df = pd.read_csv('.. /input/titanic/test.csv',index_col='PassengerId' )<create_dataframe>
EMBEDDING_PATHS = ['.. /input/pickled-word-embedding/crawl-300d-2M.pkl',
'.. /input/pickled-word-embedding/glove.840B.300d.pkl']
NUM_MODELS = 2
BATCH_SIZE = 512
LSTM_UNITS = 148
DENSE_HIDDEN_UNITS = 4*LSTM_UNITS
EPOCHS = 4
MAX_LEN = 300
IDENTITY_COLUMNS = [
'transgender', 'female', 'homosexual_gay_or_lesbian', 'muslim', 'hindu',
'white', 'black', 'psychiatric_or_mental_illness', 'jewish'
]
AUX_COLUMNS = ['target', 'severe_toxicity','obscene','identity_attack','insult','threat']
TEXT_COLUMN = 'comment_text'
TARGET_COLUMN = 'target'
df1 = train_df.copy()<correct_missing_values>
SYMBOLS_TO_ISOLATE = '.,?!-;*"…:—() %
SYMBOLS_TO_REMOVE = '
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ISOLATE_DICT = {ord(c):f' {c} ' for c in SYMBOLS_TO_ISOLATE}
REMOVE_DICT = {ord(c):f'' for c in SYMBOLS_TO_REMOVE}
CHARS_TO_REMOVE = '!"
“”’'∞θ÷α•à−β∅³π‘₹´°£€\×™√²—'
CONTRACTION_MAPPING = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is", "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will", "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have" }
def handle_punctuation(text):
text = text.translate(REMOVE_DICT)
 text = text.translate(ISOLATE_DICT)
 return text
def clean_contractions(text, mapping=CONTRACTION_MAPPING):

 specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
 text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
 return text
def preprocess(x):
x = handle_punctuation(x)
 return x
def get_coefs(word, *arr):

 return word, np.asarray(arr,dtype='float32')
 def load_embeddings(path):
if path.split('.')[-1] in ['txt','vec']:
with open(path,'rb')as f:
return dict(get_coefs(*line.strip().split(' ')) for line in f)
 if path.split('.')[-1] =='pkl':
with open(path,'rb')as f:
return pickle.load(f)
 def build_matrix(word_index, path):

 embedding_index = load_embeddings(path)
 embedding_matrix = np.zeros(( len(word_index)+1, 300))
for word, i in word_index.items() :
try:
embedding_matrix[i] = embedding_index[word]
except KeyError:
pass
return embedding_matrix
df2 = df1.dropna(subset=['Embarked'] )<drop_column>
class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
 self.W_regularizer = regularizers.get(W_regularizer)
 self.b_regularizer = regularizers.get(b_regularizer)
 self.W_constraint = constraints.get(W_constraint)
 self.b_constraint = constraints.get(b_constraint)
 self.bias = bias
self.step_dim = step_dim
self.features_dim = 0
super(Attention, self ).__init__(**kwargs)
 def build(self, input_shape):
assert len(input_shape)== 3
self.W = self.add_weight(( input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
 self.features_dim = input_shape[-1]
if self.bias:
self.b = self.add_weight(( input_shape[1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
 else:
self.b = None
self.built = True
def compute_mask(self, input, input_mask=None):
return None
def call(self, x, mask=None):
features_dim = self.features_dim
step_dim = self.step_dim
eij = K.reshape(K.dot(K.reshape(x,(-1, features_dim)) ,
K.reshape(self.W,(features_dim, 1))),(-1, step_dim))
if self.bias:
eij += self.b
eij = K.tanh(eij)
 a = K.exp(eij)
 if mask is not None:
a *= K.cast(mask, K.floatx())
 a /= K.cast(K.sum(a, axis=1, keepdims=True)+ K.epsilon() , K.floatx())
 a = K.expand_dims(a)
 weighted_input = x * a
return K.sum(weighted_input, axis=1)
 def compute_output_shape(self, input_shape):
return input_shape[0], self.features_dim
def build_model(embedding_matrix, num_aux_targets):

 words = Input(shape=(MAX_LEN,))
x = Embedding(*embedding_matrix.shape, weights = [embedding_matrix], trainable=False )(words)
 x = SpatialDropout1D(0.2 )(x)
 x = Bidirectional(CuDNNLSTM(LSTM_UNITS, return_sequences=True))(x)
 x = Bidirectional(CuDNNLSTM(LSTM_UNITS, return_sequences=True))(x)
 hidden = concatenate([
GlobalMaxPooling1D()(x),
GlobalAveragePooling1D()(x)
 ])
 hidden = add([hidden, Dense(DENSE_HIDDEN_UNITS, activation='relu' )(hidden)])
 hidden = add([hidden, Dense(DENSE_HIDDEN_UNITS, activation='relu' )(hidden)])
 result = Dense(1, activation='sigmoid' )(hidden)
 aux_result =Dense(num_aux_targets, activation='sigmoid' )(hidden)
 model = Model(inputs =words, outputs =[result, aux_result])
 model.compile(loss='binary_crossentropy', optimizer='adam')
 return model
df3 = df2.drop('Cabin',axis=1 )<feature_engineering>
train_df['comment_text'] = train_df['comment_text'].progress_apply(lambda x:preprocess(x))
test_df['comment_text'] = test_df['comment_text'].progress_apply(lambda x:preprocess(x))
gc.collect()
df3['Title'] = df3['Name'].str.replace(r' (.*,)|(\.. *)','' )<drop_column>
x_train = train_df[TEXT_COLUMN].astype(str)
 y_train = train_df[TARGET_COLUMN].values
y_aux_train = train_df[AUX_COLUMNS].values
x_test = test_df[TEXT_COLUMN].astype(str)
 for column in IDENTITY_COLUMNS + [TARGET_COLUMN]:
train_df[column] = np.where(train_df[column] >=0.5, True, False )
df4 = df3.drop(['Name','Ticket'],axis=1 )<define_variables>
tokenizer = text.Tokenizer(filters = CHARS_TO_REMOVE)
 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=MAX_LEN)
 x_test= sequence.pad_sequences(x_test, maxlen=MAX_LEN )
cat_col = ['Pclass','Sex','Embarked','Title']
num_col = ['Age','SibSp','Parch','Fare']<feature_engineering>
sample_weights = np.ones(len(x_train), dtype=np.float32)
 sample_weights += train_df[IDENTITY_COLUMNS].sum(axis=1)
 sample_weights += train_df[TARGET_COLUMN]*(~train_df[IDENTITY_COLUMNS] ).sum(axis=1)
 sample_weights +=(~train_df[TARGET_COLUMN])*train_df[IDENTITY_COLUMNS].sum(axis=1)
 sample_weights/=sample_weights.mean()
df5 = df4.copy()
df5['Title'] = df5['Title'].apply(lambda x: 'others' if x not in ['Mr','Mrs','Miss','Master'] else x)
<feature_engineering>
embedding_matrix = np.concatenate([build_matrix(tokenizer.word_index,f)for f in EMBEDDING_PATHS], axis =-1)
 print("Embedding matrix shape:", embedding_matrix.shape)
 del train_df, tokenizer
gc.collect()
df6 = df5.copy()
df6['SibSp'] = df6['SibSp'].apply(lambda x: 3 if x>2 else x)
 df6['Parch'] = df6['Parch'].apply(lambda x: 3 if x>2 else x )<groupby>
checkpoint_predictions = []
weights = []
NUM_MODELS = 1
for model_idx in range(NUM_MODELS):
model = build_model(embedding_matrix, y_aux_train.shape[-1])
 for global_epoch in range(EPOCHS):
model.fit(
 x_train,
[y_train, y_aux_train],
batch_size=BATCH_SIZE,
epochs=1,
verbose=1,
sample_weight=[sample_weights.values, np.ones_like(sample_weights)],
callbacks = [
LearningRateScheduler(lambda _: 1e-3*(0.55**global_epoch))
] 
)
 checkpoint_predictions.append(model.predict(x_test, batch_size=2048)[0].flatten())
 weights.append(2 ** global_epoch)
 del model
gc.collect()
for col in cat_col:
print(df6.groupby(col)['Survived'].mean() )<groupby>
predictions = np.average(checkpoint_predictions, weights=weights, axis=0)
 lstm_submission = pd.DataFrame.from_dict({
'id': test_df.id,
'prediction': predictions
})
<feature_engineering><EOS> 
submission = pd.DataFrame.from_dict({
'id': test_df['id'],
'prediction': lstm_submission['prediction'].rank(pct=True)*0.337 + bert_submission['prediction'].rank(pct=True)*0.663})
 submission.to_csv('submission.csv', index=False)