Kaggle competition: Toxic Comment Classification Challenge
<SOS> metric: MCAUC Kaggle data source: toxic-comment-classification-challenge<normalization>
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
from sklearn.linear_model import LogisticRegression
from sklearn import linear_model
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score
from scipy.sparse import hstack
class Model(nn.Module):
def __init__(self, num_features, num_targets, hidden_size):
super(Model, self ).__init__()
self.batch_norm1 = nn.BatchNorm1d(num_features)
 self.dropout1 = nn.Dropout(0.2)
 self.dense1 = nn.utils.weight_norm(nn.Linear(num_features, hidden_size))
self.batch_norm2 = nn.BatchNorm1d(hidden_size)
 self.dropout2 = nn.Dropout(0.3)
 self.dense2 = nn.utils.weight_norm(nn.Linear(hidden_size, hidden_size))
self.batch_norm3 = nn.BatchNorm1d(hidden_size)
 self.dropout3 = nn.Dropout(0.25)
 self.dense3 = nn.utils.weight_norm(nn.Linear(hidden_size, num_targets))
def forward(self, x):
x = self.batch_norm1(x)
 x = self.dropout1(x)
 x = F.relu(self.dense1(x))
x = self.batch_norm2(x)
 x = self.dropout2(x)
 x = F.relu(self.dense2(x))
x = self.batch_norm3(x)
 x = self.dropout3(x)
 x = self.dense3(x)
 return x<feature_engineering>
stopwords = nltk.corpus.stopwords.words('english' )
def process_data(data):
data = pd.get_dummies(data, columns=['cp_time','cp_dose'])
 return data<define_variables>
sns.set_style("dark" )
feature_cols = [c for c in process_data(folds ).columns if c not in target_cols]
feature_cols = [c for c in feature_cols if c not in ['kfold','sig_id']]
len(feature_cols )<define_variables>
train = pd.read_csv('.. /input/train.csv', error_bad_lines=False ).fillna(' ')
 test = pd.read_csv('.. /input/test.csv', error_bad_lines=False ).fillna(' ')
 subm = pd.read_csv('.. /input/sample_submission.csv' )
DEVICE =('cuda' if torch.cuda.is_available() else 'cpu')
 EPOCHS = 25
BATCH_SIZE = 128
LEARNING_RATE = 1e-3
WEIGHT_DECAY = 1e-5
NFOLDS = 5
EARLY_STOPPING_STEPS = 25
EARLY_STOP = True
num_features=len(feature_cols)
 num_targets=len(target_cols)
 hidden_size=2048<prepare_x_and_y>
vectorizer = TfidfVectorizer(ngram_range=(1, 2),
max_df=0.5,
min_df=4,
max_features=1000)
 vector_space_model = vectorizer.fit_transform(train['comment_text'].values.astype('U' ).tolist())
 n_comments = vector_space_model.shape[0]
print('%d Total Comments' % n_comments )
def run_training(fold, seed):
seed_everything(seed)
 train = process_data(folds)
 test_ = process_data(test)
 trn_idx = train[train['kfold'] != fold].index
val_idx = train[train['kfold'] == fold].index
train_df = train[train['kfold'] != fold].reset_index(drop=True)
 valid_df = train[train['kfold'] == fold].reset_index(drop=True)
 x_train, y_train = train_df[feature_cols].values, train_df[target_cols].values
x_valid, y_valid = valid_df[feature_cols].values, valid_df[target_cols].values
train_dataset = MoADataset(x_train, y_train)
 valid_dataset = MoADataset(x_valid, y_valid)
 trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
 validloader = torch.utils.data.DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)
 model = Model(
 num_features=num_features,
num_targets=num_targets,
hidden_size=hidden_size, 
)
 model.to(DEVICE)
 optimizer = torch.optim.Adam(model.parameters() , lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)
 scheduler = optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.1, div_factor=1e3,
max_lr=1e-2, epochs=EPOCHS, steps_per_epoch=len(trainloader))
loss_fn = nn.BCEWithLogitsLoss()
early_stopping_steps = EARLY_STOPPING_STEPS
early_step = 0
oof = np.zeros(( len(train), target.iloc[:, 1:].shape[1]))
best_loss = np.inf
for epoch in range(EPOCHS):
train_loss = train_fn(model, optimizer,scheduler, loss_fn, trainloader, DEVICE)
 print(f"FOLD: {fold}, EPOCH: {epoch}, train_loss: {train_loss}")
 valid_loss, valid_preds = valid_fn(model, loss_fn, validloader, DEVICE)
 print(f"FOLD: {fold}, EPOCH: {epoch}, valid_loss: {valid_loss}")
 if valid_loss < best_loss:
best_loss = valid_loss
oof[val_idx] = valid_preds
torch.save(model.state_dict() , f"FOLD{fold}_.pth")
 elif(EARLY_STOP == True):
early_step += 1
if(early_step >= early_stopping_steps):
break
x_test = test_[feature_cols].values
testdataset = TestDataset(x_test)
 testloader = torch.utils.data.DataLoader(testdataset, batch_size=BATCH_SIZE, shuffle=False)
 model = Model(
 num_features=num_features,
num_targets=num_targets,
hidden_size=hidden_size, 
)
 model.load_state_dict(torch.load(f"FOLD{fold}_.pth"))
model.to(DEVICE)
 predictions = np.zeros(( len(test_), target.iloc[:, 1:].shape[1]))
predictions = inference_fn(model, testloader, DEVICE)
 return oof, predictions<categorify>
training_set_size = int(n_comments * 0.33)
 X = vector_space_model[:training_set_size,:]
Z = vector_space_model[training_set_size:vector_space_model.shape[0]-1,:]
print('%d comments for the estimation of the parameters and %d for the evaluation' % 
(X.shape[0], Z.shape[0]))
def run_k_fold(NFOLDS, seed):
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 for fold in range(NFOLDS):
oof_, pred_ = run_training(fold, seed)
 predictions += pred_ / NFOLDS
oof += oof_
return oof, predictions<feature_engineering>
X = X.toarray()
Y = train['toxic'][:training_set_size]
model = linear_model.BayesianRidge(verbose=True)
 model.fit(X, Y )
SEED = [456]
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 for seed in SEED:
oof_, predictions_ = run_k_fold(NFOLDS, seed)
 oof += oof_ / len(SEED)
 predictions += predictions_ / len(SEED)
 train[target_cols] = oof
test[target_cols] = predictions<merge>
ground_truth = train['toxic'][training_set_size:vector_space_model.shape[0]-1]
prediction = model.predict(Z)
 prediction = binarize(prediction.reshape(-1, 1), 0.5 )
valid_results = train_targets_scored.drop(columns=target_cols ).merge(train[['sig_id']+target_cols], on='sig_id', how='left' ).fillna(0)
 y_true = train_targets_scored[target_cols].values
y_pred = valid_results[target_cols].values
score = 0
for i in range(len(target_cols)) :
score_ = log_loss(y_true[:, i], y_pred[:, i])
 score += score_ / target.shape[1]
print("CV log_loss: ", score )<merge>
toxic_ids = [i for i, c in enumerate(prediction)if c == 1]
toxic_ids
sub= sample_submission.drop(columns=target_cols ).merge(test[['sig_id']+target_cols], on='sig_id', how='left' ).fillna(0 )<import_modules>
comment_id = toxic_ids[0]
print('Content of the comment:
%s
' % train['comment_text'][training_set_size+comment_id])
 print('Is this comment "toxic" according to the model?
%s' % str(model.predict(Z[comment_id,:])>0.5))
import os
import gc
import pickle
import datetime
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import log_loss
from sklearn.decomposition import PCA
from tqdm.notebook import tqdm
from time import time<import_modules>
comment_id = toxic_ids[1]
print('Content of the comment:
%s
' % train['comment_text'][training_set_size+comment_id])
 print('Is this comment "toxic" according to the model?
%s' % str(model.predict(Z[comment_id,:])>0.5))
import tensorflow as tf
import tensorflow.keras.backend as K
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
import tensorflow_addons as tfa
from sklearn.model_selection import KFold<load_from_csv>
comment_id = toxic_ids[2]
print('Content of the comment:
%s
' % train['comment_text'][training_set_size+comment_id])
 print('Is this comment "toxic" according to the model?
%s' % str(model.predict(Z[comment_id,:])>0.5))
train_features = pd.read_csv('.. /input/lish-moa/train_features.csv')
 train_targets = pd.read_csv('.. /input/lish-moa/train_targets_scored.csv')
 test_features = pd.read_csv('.. /input/lish-moa/test_features.csv')
 ss_svc = pd.read_csv('.. /input/lish-moa/sample_submission.csv')
 ss_lr = ss_svc.copy()
ss_rf = ss_svc.copy()
ss = ss_svc.copy()
cols = [c for c in ss_svc.columns.values if c != 'sig_id']<compute_test_metric>
comment_text_list = train.apply(lambda row : nltk.word_tokenize(row['comment_text']),axis=1 )
def preprocess(df):
df.loc[:, 'cp_type'] = df.loc[:, 'cp_type'].map({'trt_cp': 0, 'ctl_vehicle': 1})
 df.loc[:, 'cp_dose'] = df.loc[:, 'cp_dose'].map({'D1': 0, 'D2': 1})
 del df['sig_id']
return df
def log_loss_metric(y_true, y_pred):
metrics = []
for _target in train_targets.columns:
metrics.append(log_loss(y_true.loc[:, _target], y_pred.loc[:, _target].astype(float), labels = [0,1]))
return np.mean(metrics)
 train = preprocess(train_features)
 test = preprocess(test_features)
 del train_targets['sig_id']<choose_model_class>
rate_punctuation=0.7
rate_capital=0.7
def odd_comment(comment):
punctuation_count=0
capital_letter_count=0
total_letter_count=0
for token in comment:
if token in list(string.punctuation):
punctuation_count+=1
capital_letter_count+=sum(1 for c in token if c.isupper())
 total_letter_count+=len(token)
 return(( punctuation_count/len(comment)) >=rate_punctuation or 
(capital_letter_count/total_letter_count)>rate_capital)
 odd=comment_text_list.apply(odd_comment )
def create_model1(num_columns):
model = tf.keras.Sequential([
tf.keras.layers.Input(num_columns),
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.3),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(512, activation="elu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.3),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1024, activation="elu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.3),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(512, activation="elu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.4),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(2048, activation="elu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.3),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(206, activation="sigmoid"))
])
 model.compile(optimizer=tfa.optimizers.Lookahead(tf.optimizers.Adam() , sync_period=10),
loss='binary_crossentropy', 
)
 return model<choose_model_class>
odd_ones=odd[odd==True]
odd_comments=train.loc[list(odd_ones.index)]
odd_comments[odd_comments.clean==False].count() /len(odd_comments )
def create_model2(num_columns):
model = tf.keras.Sequential([
tf.keras.layers.Input(num_columns),
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.2),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(2048, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.2),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1500, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.2),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1048, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.5),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(500, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.2),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(206, activation="sigmoid"))
])
 model.compile(optimizer=tfa.optimizers.Lookahead(tf.optimizers.Adam() , sync_period=10),
loss='binary_crossentropy', 
)
 return model<define_variables>
empty_comments=train[train.comment_text==""]
empty_comments
top_feats = [ 1, 2, 3, 4, 5, 6, 7, 9, 11, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31,
32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46,
47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88,
89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,
115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128,
129, 130, 131, 132, 133, 136, 137, 138, 139, 140, 141, 142, 143,
144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 156, 157,
158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,
171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,
184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197,
198, 199, 200, 202, 203, 204, 205, 206, 208, 209, 210, 211, 212,
213, 214, 215, 216, 217, 218, 219, 220, 221, 223, 224, 225, 226,
227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
240, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253,
254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266,
267, 268, 269, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280,
281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294,
295, 296, 298, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309,
310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323,
324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336,
337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349,
350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362,
363, 364, 365, 366, 367, 368, 369, 370, 371, 374, 375, 376, 377,
378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 390, 391,
392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404,
405, 406, 407, 408, 409, 411, 412, 413, 414, 415, 416, 417, 418,
419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,
432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 445, 446,
447, 448, 449, 450, 453, 454, 456, 457, 458, 459, 460, 461, 462,
463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475,
476, 477, 478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489,
490, 491, 492, 493, 494, 495, 496, 498, 500, 501, 502, 503, 505,
506, 507, 509, 510, 511, 512, 513, 514, 515, 518, 519, 520, 521,
522, 523, 524, 525, 526, 527, 528, 530, 531, 532, 534, 535, 536,
538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 549, 550, 551,
552, 554, 557, 559, 560, 561, 562, 565, 566, 567, 568, 569, 570,
571, 572, 573, 574, 575, 577, 578, 580, 581, 582, 583, 584, 585,
586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 599,
600, 601, 602, 606, 607, 608, 609, 611, 612, 613, 615, 616, 617,
618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630,
631, 632, 633, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644,
645, 646, 647, 648, 649, 650, 651, 652, 654, 655, 656, 658, 659,
660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672,
673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685,
686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 699, 700,
701, 702, 704, 705, 707, 708, 709, 710, 711, 713, 714, 716, 717,
718, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732,
733, 734, 735, 737, 738, 739, 740, 742, 743, 744, 745, 746, 747,
748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 759, 760, 761,
762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774,
775, 776, 777, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788,
789, 790, 792, 793, 794, 795, 796, 797, 798, 800, 801, 802, 803,
804, 805, 806, 808, 809, 811, 813, 814, 815, 816, 817, 818, 819,
821, 822, 823, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835,
837, 838, 839, 840, 841, 842, 845, 846, 847, 848, 850, 851, 852,
854, 855, 856, 858, 859, 860, 861, 862, 864, 866, 867, 868, 869,
870, 871, 872, 873, 874]
print(len(top_feats))<init_hyperparams>
duplicate=train.comment_text.duplicated()
duplicate[duplicate==True]
N_STARTS = 2
tf.random.set_seed(42)
 ss_NN = ss_svc.copy()
res_NN = train_targets.copy()
ss_NN.loc[:, train_targets.columns] = 0
res_NN.loc[:, train_targets.columns] = 0
for seed in range(N_STARTS):
for n,(tr, te)in enumerate(MultilabelStratifiedKFold(n_splits=4, random_state=seed, shuffle=True ).split(train_targets, train_targets)) :
print(f'Fold {n}')
 if seed%2 == 0:
model = create_model1(len(top_feats))
else :
model = create_model2(len(top_feats))
checkpoint_path = f'repeat:{seed}_Fold:{n}.hdf5'
reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, epsilon=1e-4, mode='min')
 cb_checkpt = ModelCheckpoint(checkpoint_path, monitor = 'val_loss', verbose = 0, save_best_only = True,
save_weights_only = True, mode = 'min')
 model.fit(train.values[tr][:, top_feats],
train_targets.values[tr],
validation_data=(train.values[te][:, top_feats], train_targets.values[te]),
epochs=35, batch_size=128,
callbacks=[reduce_lr_loss, cb_checkpt], verbose=2 
)
 model.load_weights(checkpoint_path)
 test_predict = model.predict(test.values[:, top_feats])
 val_predict = model.predict(train.values[te][:, top_feats])
 ss_NN.loc[:, train_targets.columns] += test_predict
res_NN.loc[te, train_targets.columns] += val_predict
print('')
 ss_NN.loc[:, train_targets.columns] /=(( n+1)* N_STARTS)
 res_NN.loc[:, train_targets.columns] /= N_STARTS<compute_test_metric>
toxic=train[train.toxic==1]['comment_text'].values
severe_toxic=train[train.severe_toxic==1]['comment_text'].values
obscene=train[train.obscene==1]['comment_text'].values
threat=train[train.threat==1]['comment_text'].values
insult=train[train.insult==1]['comment_text'].values
identity_hate=train[train.identity_hate==1]['comment_text'].values
print(f'NN OOF Metric: {log_loss_metric(train_targets, res_NN)}')
 res_NN.loc[train['cp_type'] == 1, train_targets.columns] = 0
ss_NN.loc[test['cp_type'] == 1, train_targets.columns] = 0
print(f'NN OOF Metric with postprocessing: {log_loss_metric(train_targets, res_NN)}' )<define_variables>
replacement_patterns = [ 
(r'won't', 'will not'), 
(r'can't', 'cannot'), 
(r'i'm', 'i am'), 
(r'ain't', 'is not'), 
(r'(\w+)'ll', '\g<1> will'), 
(r'(\w+)n't', '\g<1> not'), 
(r'(\w+)'ve', '\g<1> have'), 
(r'(\w+)'s', '\g<1> is'), 
(r'(\w+)'re', '\g<1> are'), 
(r'(\w+)'d', '\g<1> would')
 ]
class RegexpReplacer(object):
def __init__(self, patterns=replacement_patterns):
self.patterns = [(re.compile(regex), repl)for(regex, repl)in
patterns]
def replace(self, text):
s = text
for(pattern, repl)in self.patterns:
s = re.sub(pattern, repl, s)
 return s
old_list = [4, 5, 17, 20, 21, 23, 25, 26, 28, 29, 30, 31, 33, 40, 41, 42, 44, 49, 52, 55, 56, 60, 64, 66, 70, 72, 74, 76, 77, 79, 83, 84, 87, 91, 92, 93, 97, 98, 100, 106, 107, 108, 115, 124, 125, 128, 131, 132, 134, 135, 139, 140, 143, 144, 147, 148, 152, 155, 160, 162, 165, 173, 174, 176, 177, 179, 181, 183, 186, 190, 195, 196, 198, 199, 203, 205]<compute_test_metric>
lemmer = WordNetLemmatizer()
stopwords = nltk.corpus.stopwords.words('english')
 replacer = RegexpReplacer()
tokenizer=TweetTokenizer()
def comment_process(category):
category_processed=[]
for i in range(category.shape[0]):
comment_list=tokenizer.tokenize(replacer.replace(category[i]))
comment_list_cleaned= [word for word in comment_list if(word.lower() not in stopwords
and word.lower() not in list(string.punctuation)) ]
comment_list_lemmed=[lemmer.lemmatize(word, 'v')for word in comment_list_cleaned]
category_processed.extend(list(comment_list_lemmed))
return category_processed
avg_list = []
new_list = []
for tar in range(206):
score1 = log_loss(train_targets.loc[:, train_targets.columns[tar]], valid_results.loc[:, train_targets.columns[tar]])
 score2 = log_loss(train_targets.loc[:, train_targets.columns[tar]], res_NN.loc[:, train_targets.columns[tar]])
 if score2 >= score1:
ss_NN.loc[:, train_targets.columns[tar]] = sub.loc[:, train_targets.columns[tar]]
res_NN.loc[:, train_targets.columns[tar]] = valid_results.loc[:, train_targets.columns[tar]]
else :
if tar in old_list:
avg_list.append(tar)
 else:
new_list.append(tar)
<compute_test_metric>
warnings.filterwarnings('ignore' )
<feature_engineering><EOS> 
class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
train_text = train['comment_text']
test_text = test['comment_text']
all_text = pd.concat([train_text, test_text])
 word_vectorizer = TfidfVectorizer(
 sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
ngram_range=(1, 1),
max_features=20000)
 word_vectorizer.fit(all_text)
 train_word_features = word_vectorizer.transform(train_text)
 test_word_features = word_vectorizer.transform(test_text)
 char_vectorizer = TfidfVectorizer(
 sublinear_tf=True,
strip_accents='unicode',
analyzer='char',
ngram_range=(1, 4),
max_features=20000)
 char_vectorizer.fit(all_text)
 train_char_features = char_vectorizer.transform(train_text)
 test_char_features = char_vectorizer.transform(test_text)
 train_features = hstack([train_char_features, train_word_features])
 test_features = hstack([test_char_features, test_word_features])
 scores = []
submission = pd.DataFrame.from_dict({'id': test['id']})
 for class_name in class_names:
train_target = train[class_name]
classifier = LogisticRegression(solver='sag')
 cv_score = np.mean(cross_val_score(classifier, train_features, train_target, cv=3, scoring='roc_auc'))
scores.append(cv_score)
 print('CV score for class {} is {}'.format(class_name, cv_score))
classifier.fit(train_features, train_target)
 submission[class_name] = classifier.predict_proba(test_features)[:, 1]
print('Total CV score is {}'.format(np.mean(scores)))
 submission.to_csv('submission.csv', index=False )