Kaggle competition: Jigsaw Multilingual Toxic Comment Classification
<SOS> metric: AUC Kaggle data source: jigsaw-multilingual-toxic-comment-classification<feature_engineering>
data = {k.split('/')[-1][:-4]:k for k in glob.glob('/kaggle/input/**/**.csv')}
train = pd.read_csv(data['jigsaw-toxic-comment-train'], usecols=['id', 'comment_text', 'toxic'])
 val = pd.read_csv(data['validation'], usecols=['comment_text', 'toxic'])
 test = pd.read_csv(data['test'], usecols=['id', 'content'])
 test.columns = ['id', 'comment_text']
test['toxic'] = 0.5
sub2 = pd.read_csv('.. /input/ensemble/submission.csv' )
train, test = train_test_apply_func(train, test, convert_OHE2LE )<categorify>
%%time
def f_experience(c, s):
it = {'memory':10,
'influence':0.5,
'inference':0.5,
'interest':0.9,
'sentiment':1e-10,
'harmony':0.5}
exp = {}
for i in range(len(c)) :
words = set([w for w in str(c[i] ).lower().split(' ')])
 for w in words:
try:
exp[w]['influence'] = exp[w]['influence'][1:] + [s[i]]
exp[w]['inference'] += 1
exp[w]['interest'] = exp[w]['interest'][1:] + [(exp[w]['interest'][it['memory']-1] +(s[i] * it['interest'])) /2]
exp[w]['sentiment'] += s[i]
except:
m = [0.for m_ in range(it['memory'])]
exp[w] = {}
exp[w]['influence'] = m[1:] + [s[i]]
exp[w]['inference'] = 1
exp[w]['interest'] = m[1:] + [s[i] * it['interest'] / 2]
exp[w]['sentiment'] = s[i]
for w in exp:
exp[w]['sentiment'] /= exp[w]['inference'] + it['sentiment']
exp[w]['inference'] /= len(c)* it['inference']
return exp
exp = f_experience(train['comment_text'].values, train['toxic'].values )
cols_2_ohe = ['eviv_LE', 'etecho_LE', 'epared_LE', 'elimbasu_LE',
'energcocinar_LE', 'sanitario_LE', 'manual_elec_LE',
'pared_LE']
cols_nums = ['age', 'meaneduc', 'dependency',
'hogar_nin', 'hogar_adul', 'hogar_mayor', 'hogar_total',
'bedrooms', 'overcrowding']
def convert_geo2aggs(df_):
tmp_df = pd.concat([df_[(['lugar_LE', 'idhogar']+cols_nums)],
pd.get_dummies(df_[cols_2_ohe],
columns=cols_2_ohe)],axis=1)
 geo_agg = tmp_df.groupby(['lugar_LE','idhogar'] ).mean().groupby('lugar_LE' ).mean().astype(np.float32)
 geo_agg.columns = pd.Index(['geo_' + e for e in geo_agg.columns.tolist() ])
 del tmp_df
return df_.join(geo_agg, how='left', on='lugar_LE')
 train, test = train_test_apply_func(train, test, convert_geo2aggs )<categorify>
%%time
def features(df):
df['len'] = df['comment_text'].map(len)
 df['wlen'] = df['comment_text'].map(lambda x: len(str(x ).split(' ')))
 df['influence_sum'] = df['comment_text'].map(lambda x: np.sum([np.mean(exp[w]['influence'])if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['influence_mean'] = df['comment_text'].map(lambda x: np.mean([np.mean(exp[w]['influence'])if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['inference_sum'] = df['comment_text'].map(lambda x: np.sum([exp[w]['inference'] if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['inference_mean'] = df['comment_text'].map(lambda x: np.mean([exp[w]['inference'] if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['interest_sum'] = df['comment_text'].map(lambda x: np.sum([np.mean(exp[w]['interest'])if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['interest_mean'] = df['comment_text'].map(lambda x: np.mean([np.mean(exp[w]['interest'])if w in exp else 0 for w in str(x ).lower().split(' ')]))
df['sentiment_sum'] = df['comment_text'].map(lambda x: np.sum([exp[w]['sentiment'] if w in exp else 0.5 for w in str(x ).lower().split(' ')]))
df['sentiment_mean'] = df['comment_text'].map(lambda x: np.mean([exp[w]['sentiment'] if w in exp else 0.5 for w in str(x ).lower().split(' ')]))
return df
val = features(val)
 test= features(test )
train['num_over_18'] = 0
train['num_over_18'] = train[train.age >= 18].groupby('idhogar' ).transform("count")
 train['num_over_18'] = train.groupby("idhogar")["num_over_18"].transform("max")
 train['num_over_18'] = train['num_over_18'].fillna(0)
 test['num_over_18'] = 0
test['num_over_18'] = test[test.age >= 18].groupby('idhogar' ).transform("count")
 test['num_over_18'] = test.groupby("idhogar")["num_over_18"].transform("max")
 test['num_over_18'] = test['num_over_18'].fillna(0)
 def extract_features(df):
df['bedrooms_to_rooms'] = df['bedrooms']/df['rooms']
df['rent_to_rooms'] = df['v2a1']/df['rooms']
df['tamhog_to_rooms'] = df['tamhog']/df['rooms']
df['r4t3_to_tamhog'] = df['r4t3']/df['tamhog']
df['r4t3_to_rooms'] = df['r4t3']/df['rooms']
df['v2a1_to_r4t3'] = df['v2a1']/df['r4t3']
df['v2a1_to_r4t3'] = df['v2a1']/(df['r4t3'] - df['r4t1'])
 df['hhsize_to_rooms'] = df['hhsize']/df['rooms']
df['rent_to_hhsize'] = df['v2a1']/df['hhsize']
df['rent_to_over_18'] = df['v2a1']/df['num_over_18']
df.loc[df.num_over_18 == 0, "rent_to_over_18"] = df[df.num_over_18 == 0].v2a1
df['dependency_yes'] = df['dependency'].apply(lambda x: 1 if x == 'yes' else 0)
 df['dependency_no'] = df['dependency'].apply(lambda x: 1 if x == 'no' else 0)
 extract_features(train)
 extract_features(test )<drop_column>
col = [c for c in val if c not in ['id', 'comment_text', 'toxic']]
x1, x2, y1, y2 = model_selection.train_test_split(val[col], val['toxic'], test_size=0.3, random_state=20)
 model = ensemble.ExtraTreesClassifier(n_estimators=1000, max_depth=7, n_jobs=-1, random_state=20)
 model.fit(x1, y1)
 print(metrics.roc_auc_score(y2, model.predict_proba(x2)[:,1].clip(0.,1.)))
model.fit(val[col], val['toxic'])
 test['toxic'] = model.predict_proba(test[col])[:,1].clip(0.,1.)
sub1 = test[['id', 'toxic']]
needless_cols = ['r4t3', 'tamhog', 'tamviv', 'hhsize', 'v18q', 'v14a', 'agesq',
'mobilephone', 'female', ]
instlevel_cols = [s for s in train.columns.tolist() if 'instlevel' in s]
needless_cols.extend(instlevel_cols)
 train = train.drop(needless_cols, axis=1)
 test = test.drop(needless_cols, axis=1 )<split>
sub1.rename(columns={'toxic':'toxic1'}, inplace=True)
 sub2.rename(columns={'toxic':'toxic2'}, inplace=True)
 sub3 = pd.merge(sub1, sub2, how='left', on='id')
 sub3['toxic'] =(sub3['toxic1'] * 0.1)+(sub3['toxic2'] * 0.9)
 sub3['toxic'] =(sub3['toxic2'] * 0.51)+(sub3['toxic'] * 0.49)
 sub3[['id', 'toxic']].to_csv('submission.csv', index=False )
<split><EOS> 
test = pd.DataFrame(['Howling with Wolf on Lügenpresse'], columns=['comment_text'])
 test['id'] = test.index
test= features(test)
 test['toxic'] = model.predict_proba(test[col])[:,1].clip(0.,1.)
test[['id', 'comment_text', 'toxic']].head()