Kaggle competition: Toxic Comment Classification Challenge
<SOS> metric: MCAUC Kaggle data source: toxic-comment-classification-challenge<define_variables>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from scipy.sparse import hstack
from scipy.special import logit, expit
GENES = [col for col in train_features.columns if col.startswith('g-')]
CELLS = [col for col in train_features.columns if col.startswith('c-')]<set_options>
train = pd.read_csv('.. /input/train.csv' ).fillna(' ')
 test = pd.read_csv('.. /input/test.csv' ).fillna(' ' )
def seed_everything(seed=321):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything(seed=321 )<concatenate>
class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
n_comp = 29
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])
 data2 =(PCA(n_components=n_comp, random_state=321 ).fit_transform(data[GENES]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_G-{i}' for i in range(n_comp)])
 test2 = pd.DataFrame(test2, columns=[f'pca_G-{i}' for i in range(n_comp)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1)
 n_comp = 5
data = pd.concat([pd.DataFrame(train_features[CELLS]), pd.DataFrame(test_features[CELLS])])
 data2 =(PCA(n_components=n_comp, random_state=321 ).fit_transform(data[CELLS]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_C-{i}' for i in range(n_comp)])
 test2 = pd.DataFrame(test2, columns=[f'pca_C-{i}' for i in range(n_comp)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1 )<create_dataframe>
train_text = train['comment_text']
test_text = test['comment_text']
all_text = pd.concat([train_text, test_text] )
var_thresh = VarianceThreshold(threshold=0.4)
 data = train_features.append(test_features)
 data_transformed = var_thresh.fit_transform(data.iloc[:, 4:])
 train_features_transformed = data_transformed[ : train_features.shape[0]]
test_features_transformed = data_transformed[-test_features.shape[0] : ]
train_features = pd.DataFrame(train_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 train_features = pd.concat([train_features, pd.DataFrame(train_features_transformed)], axis=1)
 test_features = pd.DataFrame(test_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 test_features = pd.concat([test_features, pd.DataFrame(test_features_transformed)], axis=1)
 train_features<merge>
word_vectorizer = TfidfVectorizer(
 sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
ngram_range=(1, 1),
max_features=10000)
 word_vectorizer.fit(all_text)
 train_word_features = word_vectorizer.transform(train_text)
 test_word_features = word_vectorizer.transform(test_text )
train = train_features.merge(train_targets_scored, on='sig_id')
 train = train[train['cp_type']!='ctl_vehicle'].reset_index(drop=True)
 test = test_features[test_features['cp_type']!='ctl_vehicle'].reset_index(drop=True)
 target = train[train_targets_scored.columns]
train = train.drop('cp_type', axis=1)
 test = test.drop('cp_type', axis=1)
<drop_column>
char_vectorizer = TfidfVectorizer(
 sublinear_tf=True,
strip_accents='unicode',
analyzer='char',
ngram_range=(1, 5),
max_features=30000)
 char_vectorizer.fit(all_text)
 train_char_features = char_vectorizer.transform(train_text)
 test_char_features = char_vectorizer.transform(test_text )
target_cols = target.drop('sig_id', axis=1 ).columns.values.tolist()<split>
train_features = hstack([train_char_features, train_word_features])
 test_features = hstack([test_char_features, test_word_features])
folds = train.copy()
mskf = MultilabelStratifiedKFold(n_splits=5)
 for f,(t_idx, v_idx)in enumerate(mskf.split(X=train, y=target)) :
folds.loc[v_idx, 'kfold'] = int(f)
 folds['kfold'] = folds['kfold'].astype(int )<categorify>
losses = []
predictions = {'id': test['id']}
for class_name in class_names:
train_target = train[class_name]
classifier = LogisticRegression(solver='sag')
 cv_loss = np.mean(cross_val_score(classifier, train_features, train_target, cv=3, scoring='roc_auc'))
losses.append(cv_loss)
 print('CV score for class {} is {}'.format(class_name, cv_loss))
classifier.fit(train_features, train_target)
 predictions[class_name] = classifier.predict_proba(test_features)[:, 1]
print('Total CV score is {}'.format(np.mean(losses)) )
<train_model><EOS> 
submission = pd.DataFrame.from_dict(predictions)
 submission.to_csv('word_submission.csv', index=False )