Kaggle competition: Quora Insincere Questions Classification
data.isnull().sum()<filter>
embed_size = 300
max_features = 95000
maxlen = 70
train[['Fare','Name','Ticket','SibSp','Parch']].iloc[train.index[(train['Pclass']==3)&(train['Fare']>60)]]<feature_engineering>
import os
import time
import numpy as np
import pandas as pd
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, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import Bidirectional, GlobalMaxPool1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from keras.layers import Input, Embedding, Dense, Conv2D, MaxPool2D, concatenate
from keras.layers import Reshape, Flatten, Concatenate, Dropout, SpatialDropout1D
from keras.optimizers import Adam
from keras.models import Model
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints, optimizers, layers
train['FareCorr'] = train['Fare'].copy()
m=0
for grp, grp_df in train[['Ticket', 'Name', 'Pclass', 'Fare', 'PassengerId']].groupby(['Ticket']):
if(len(grp_df)!= 1):
if m==0:
print(grp_df)
 m=1
for ind, row in grp_df.iterrows() :
passID = row['PassengerId']
train.loc[train['PassengerId'] == passID, 'FareCorr'] = train['Fare'][train['PassengerId'] == passID]/len(grp_df)
<feature_engineering>
def load_and_prec() :
train_df = pd.read_csv(".. /input/train.csv")
 test_df = pd.read_csv(".. /input/test.csv")
 print("Train shape : ",train_df.shape)
 print("Test shape : ",test_df.shape)
 train_df, val_df = train_test_split(train_df, test_size=0.08, random_state=2018)
 train_X = train_df["question_text"].fillna("_
val_X = val_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
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)
 train_X = pad_sequences(train_X, maxlen=maxlen)
 val_X = pad_sequences(val_X, maxlen=maxlen)
 test_X = pad_sequences(test_X, maxlen=maxlen)
 train_y = train_df['target'].values
val_y = val_df['target'].values
np.random.seed(2018)
 trn_idx = np.random.permutation(len(train_X))
val_idx = np.random.permutation(len(val_X))
train_X = train_X[trn_idx]
val_X = val_X[val_idx]
train_y = train_y[trn_idx]
val_y = val_y[val_idx]
return train_X, val_X, test_X, train_y, val_y, tokenizer.word_index
train['FareCat']=pd.cut(train['FareCorr'], bins=[0,15,65,max(train["FareCorr"]+1)], labels=['low','mid','high'])
 train['FareCat'].value_counts()<count_values>
def load_glove(word_index):
EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
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
return embedding_matrix
def load_fasttext(word_index):
EMBEDDING_FILE = '.. /input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE)if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
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
return embedding_matrix
def load_para(word_index):
EMBEDDING_FILE = '.. /input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore')if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
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
return embedding_matrix
for name_string in data['Name']:
data['Title'] = data['Name'].str.extract('([A-Za-z]+)\.', expand=True)
 data['Title']=data['Title'].replace({'Ms':'Miss','Mlle':'Miss','Mme':'Mrs'})
 data['Title'].value_counts()<count_values>
def model_cnn(embedding_matrix):
filter_sizes = [1,2,3,5]
num_filters = 36
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix] )(inp)
 x = Reshape(( maxlen, embed_size, 1))(x)
 maxpool_pool = []
for i in range(len(filter_sizes)) :
conv = Conv2D(num_filters, kernel_size=(filter_sizes[i], embed_size),
kernel_initializer='he_normal', activation='elu' )(x)
 maxpool_pool.append(MaxPool2D(pool_size=(maxlen - filter_sizes[i] + 1, 1))(conv))
z = Concatenate(axis=1 )(maxpool_pool)
 z = Flatten()(z)
 z = Dropout(0.1 )(z)
 outp = Dense(1, activation="sigmoid" )(z)
 model = Model(inputs=inp, outputs=outp)
 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 return model
data['Title']=data['Title'].replace(['Sir','Don','Dona','Jonkheer','Lady','Countess'], 'Noble')
 data['Title']=data['Title'].replace(['Dr', 'Rev','Col','Major','Capt'], 'Others')
 data['Title'].value_counts()<count_values>
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
data['Parch'][(data['Age']<15)&(data['Title']=='Miss')].value_counts()<count_values>
def model_lstm_atten(embedding_matrix):
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False )(inp)
 x = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x)
 x = Bidirectional(CuDNNLSTM(64, return_sequences=True))(x)
 x = Attention(maxlen )(x)
 x = Dense(64, activation="relu" )(x)
 x = Dense(1, activation="sigmoid" )(x)
 model = Model(inputs=inp, outputs=x)
 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 return model
data['Parch'][(data['Age']>=15)&(data['Age']<25)&(data['Title']=='Miss')].value_counts()<feature_engineering>
def model_gru_srk_atten(embedding_matrix):
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix] )(inp)
 x = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)
 x = Attention(maxlen )(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'])
 return model
title_list=data.groupby('Title')['Age'].median().index.to_list()
for title in title_list:
if title=='Miss':
data.loc[(data['Age'].isnull())&(data['Title'] == title)&(data['Parch'] == 0), 'Age'] \
= data['Age'][(data['Title']== title)&(data['Age']>=15)].median()
data.loc[(data['Age'].isnull())&(data['Title'] == title)&(data['Parch'] > 0), 'Age'] \
= data['Age'][(data['Title']== title)&(data['Age']<15)].median()
data.loc[(data['Age']<15)&(data['Title'] == title), 'Title'] \
= 'youngMiss'
else:
data.loc[(data['Age'].isnull())&(data['Title'] == title), 'Age'] \
= data['Age'][(data['Title']== title)].median()<count_values>
def model_lstm_du(embedding_matrix):
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix] )(inp)
 x = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)
 avg_pool = GlobalAveragePooling1D()(x)
 max_pool = GlobalMaxPooling1D()(x)
 conc = concatenate([avg_pool, max_pool])
 conc = Dense(64, activation="relu" )(conc)
 conc = Dropout(0.1 )(conc)
 outp = Dense(1, activation="sigmoid" )(conc)
 model = Model(inputs=inp, outputs=outp)
 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 return model
data['Title'].value_counts()<feature_engineering>
def model_gru_atten_3(embedding_matrix):
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False )(inp)
 x = Bidirectional(CuDNNGRU(128, return_sequences=True))(x)
 x = Bidirectional(CuDNNGRU(100, return_sequences=True))(x)
 x = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)
 x = Attention(maxlen )(x)
 x = Dense(1, activation="sigmoid" )(x)
 model = Model(inputs=inp, outputs=x)
 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 return model
data.loc[data['SibSp'] + data['Parch'] + 1 == 1, 'FamilySize'] = 'Single'
data.loc[data['SibSp'] + data['Parch'] + 1 > 1 , 'FamilySize'] = 'Small'
data.loc[data['SibSp'] + data['Parch'] + 1 > 4 , 'FamilySize'] = 'Big'<count_values>
def train_pred(model, epochs=2):
for e in range(epochs):
model.fit(train_X, train_y, batch_size=512, epochs=1, validation_data=(val_X, val_y))
pred_val_y = model.predict([val_X], batch_size=1024, verbose=0)
 best_thresh = 0.5
best_score = 0.0
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
 score = metrics.f1_score(val_y,(pred_val_y > thresh ).astype(int))
if score > best_score:
best_thresh = thresh
best_score = score
print("Val F1 Score: {:.4f}".format(best_score))
pred_test_y = model.predict([test_X], batch_size=1024, verbose=0)
 return pred_val_y, pred_test_y, best_score
data.FamilySize.value_counts()<feature_engineering>
train_X, val_X, test_X, train_y, val_y, word_index = load_and_prec()
embedding_matrix_1 = load_glove(word_index)
 embedding_matrix_3 = load_para(word_index )
data['Last_Name'] = data['Name'].str.extract('([A-Za-z]+),', expand=True)
 data['Last_Name'].value_counts() 
<feature_engineering>
outputs = []
data['Last_Name'] = data['Name'].apply(lambda x: str.split(x, ",")[0])
 default_value = 0.5
data['FamilySurvival'] = default_value
for grp, grp_df in data[['Survived', 'Last_Name', 'Ticket', 'PassengerId']].groupby(['Last_Name']):
if(len(grp_df)!= 1):
for ind, row in grp_df.iterrows() :
smax = grp_df.drop(ind)['Survived'].max()
smin = grp_df.drop(ind)['Survived'].min()
passID = row['PassengerId']
if(smax == 1.0):
data.loc[data['PassengerId'] == passID, 'FamilySurvival'] = 1
elif(smin == 0.0):
data.loc[data['PassengerId'] == passID, 'FamilySurvival'] = 0
for _, grp_df in data.groupby('Ticket'):
if(len(grp_df)!= 1):
for ind, row in grp_df.iterrows() :
if(row['FamilySurvival'] == 0)|(row['FamilySurvival']== 0.5):
smax = grp_df.drop(ind)['Survived'].max()
smin = grp_df.drop(ind)['Survived'].min()
passID = row['PassengerId']
if(smax == 1.0):
data.loc[data['PassengerId'] == passID, 'FamilySurvival'] = 1
elif(smin == 0.0):
data.loc[data['PassengerId'] == passID, 'FamilySurvival'] = 0
data['FamilySurvival'].value_counts()<feature_engineering>
pred_val_y, pred_test_y, best_score = train_pred(model_gru_atten_3(embedding_matrix), epochs = 3)
 outputs.append([pred_val_y, pred_test_y, best_score, '3 GRU w/ atten'] )
data['FareCorr'] = data['Fare'].copy()
for grp, grp_df in data[['Ticket','Name', 'Pclass', 'Fare', 'PassengerId']].groupby(['Ticket']):
if(len(grp_df)!= 1):
for ind, row in grp_df.iterrows() :
passID = row['PassengerId']
data.loc[data['PassengerId'] == passID, 'FareCorr'] = data['Fare'][data['PassengerId'] == passID]/len(grp_df )<count_values>
pred_val_y, pred_test_y, best_score = train_pred(model_gru_srk_atten(embedding_matrix), epochs = 2)
 outputs.append([pred_val_y, pred_test_y, best_score, 'gru atten srk'] )
data['FareCat']=pd.qcut(data['FareCorr'], 7, labels=['1','2','3','4','5','6','7'])
 data['FareCat'].value_counts()<filter>
pred_val_y, pred_test_y, best_score = train_pred(model_cnn(embedding_matrix), epochs = 2)
 outputs.append([pred_val_y, pred_test_y, best_score, '2d CNN'] )
data[data['Embarked'].isnull() ]<data_type_conversions>
pred_val_y, pred_test_y, best_score = train_pred(model_cnn(embedding_matrix_1), epochs = 2)
 outputs.append([pred_val_y, pred_test_y, best_score, '2d CNN GloVe'] )
data['Embarked'].fillna('C', inplace = True )<drop_column>
pred_val_y, pred_test_y, best_score = train_pred(model_lstm_du(embedding_matrix), epochs = 2)
 outputs.append([pred_val_y, pred_test_y, best_score, 'LSTM DU'] )
data_pre = data.drop(['Embarked','Survived','PassengerId','Name','Age','Parch','SibSp','Ticket','Fare','Cabin','Last_Name','FareCorr'],axis=1 )<drop_column>
pred_val_y, pred_test_y, best_score = train_pred(model_lstm_atten(embedding_matrix), epochs = 3)
 outputs.append([pred_val_y, pred_test_y, best_score, '2 LSTM w/ attention'] )
num_attribs = []
cat_attribs = list(data_pre.drop(labels=num_attribs, axis=1 ).columns )<choose_model_class>
pred_val_y, pred_test_y, best_score = train_pred(model_lstm_atten(embedding_matrix_1), epochs = 3)
 outputs.append([pred_val_y, pred_test_y, best_score, '2 LSTM w/ attention GloVe'] )
num_pipeline = Pipeline([ 
('imputer', SimpleImputer(strategy="median")) , 
('std_scaler', StandardScaler()),
] )<categorify>
pred_val_y, pred_test_y, best_score = train_pred(model_lstm_atten(embedding_matrix_3), epochs = 3)
 outputs.append([pred_val_y, pred_test_y, best_score, '2 LSTM w/ attention Para'] )
cat_pipeline = Pipeline([ 
('imputer', SimpleImputer(strategy="most_frequent")) , 
('cat', OneHotEncoder()),
] )<categorify>
outputs.sort(key=lambda x: x[2])
 weights = [i for i in range(1, len(outputs)+ 1)]
weights = [float(i)/ sum(weights)for i in weights]
print(weights )
full_pipeline = ColumnTransformer([ 
('cat', cat_pipeline, cat_attribs),
])
 data_post = full_pipeline.fit_transform(data_pre )<categorify>
pred_val_y = np.mean([outputs[i][0] for i in range(len(outputs)) ], axis = 0)
 thresholds = []
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
 res = metrics.f1_score(val_y,(pred_val_y > thresh ).astype(int))
thresholds.append([thresh, res])
 print("F1 score at threshold {0} is {1}".format(thresh, res))
thresholds.sort(key=lambda x: x[1], reverse=True)
 best_thresh = thresholds[0][0]
print("Best threshold: ", best_thresh )
<prepare_x_and_y><EOS> 
pred_test_y = np.mean([outputs[i][1] for i in range(len(outputs)) ], axis = 0)
 pred_test_y =(pred_test_y > best_thresh ).astype(int)
 test_df = pd.read_csv(".. /input/test.csv", usecols=["qid"])
 out_df = pd.DataFrame({"qid":test_df["qid"].values})
 out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False )