Kaggle competition: Jigsaw Unintended Bias in Toxicity Classification
( keys,values)=np.unique(train_data[train_data.Survived==1].title,return_counts=True )<data_type_conversions>
import pickle
from nltk.tokenize.treebank import TreebankWordTokenizer
from keras.preprocessing import sequence
from keras.models import model_from_json
train_data.Age.fillna(train_data.Age.mean() ,inplace=True )<correct_missing_values>
KERAS_PATH = "saved-keras-rnn"
TEXT_COLUMN = 'comment_text'
MAX_LEN = 220
test_data.Age.fillna(test_data.Age.mean() ,inplace=True )<count_missing_values>
test_df = pd.read_csv('.. /input/jigsaw-unintended-bias-in-toxicity-classification/test.csv' )
train_data.isnull().sum()<count_missing_values>
symbols_to_isolate = '.,?!-;*"…:—() %
symbols_to_delete = '
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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_delete}
test_data.isnull().sum()<count_values>
def handle_punctuation(x):
x = x.translate(remove_dict)
 x = x.translate(isolate_dict)
 return x
def handle_contractions(x):
x = TreebankWordTokenizer().tokenize(x)
 return x
def fix_quote(x):
x = [x_[1:] if x_.startswith("'")else x_ for x_ in x]
x = ' '.join(x)
 return x
def preprocess(x):
x = handle_punctuation(x)
 x = handle_contractions(x)
 x = fix_quote(x)
 return x
train_data.fillna(train_data.Embarked.value_counts().idxmax() ,inplace=True )<correct_missing_values>
with open(f".. /input/{KERAS_PATH}/saved_tokenizer.pickle", 'rb')as handle:
tokenizer = pickle.load(handle )
test_data.fillna(test_data.Fare.mean() ,inplace=True )<filter>
test_df[TEXT_COLUMN] = test_df[TEXT_COLUMN].apply(lambda x:preprocess(x))
x_test = test_df[TEXT_COLUMN].astype(str)
 x_test = tokenizer.texts_to_sequences(x_test)
 x_test = sequence.pad_sequences(x_test, maxlen=MAX_LEN )
survived_train_data=train_data[train_data.Survived==1]<count_unique_values>
json_file = open(f".. /input/{KERAS_PATH}/saved_model.json", 'r')
 model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
 model.load_weights(f".. /input/{KERAS_PATH}/saved_model.h5" )
( keys,values)=np.unique(survived_train_data.Embarked,return_counts=True )<categorify>
model.compile(loss='binary_crossentropy', optimizer='adam')
 prediction = model.predict(x_test, batch_size=2048)[0].flatten()
dummies=pd.get_dummies(train_data.Embarked )<categorify>
submission = pd.DataFrame.from_dict({
'id': test_df.id,
'prediction': prediction
})
 submission.to_csv('submission.csv', index=False )
<concatenate><EOS> 
validation = pd.DataFrame.from_dict({
'id': test_df.id,
'comment_text': test_df.comment_text,
'prediction': prediction
} )