Kaggle competition: Jigsaw Unintended Bias in Toxicity Classification
<SOS> metric: JigsawBiasAUC Kaggle data source: jigsaw-unintended-bias-in-toxicity-classification<feature_engineering>
package_dir = ".. /input/ppbert/pytorch-pretrained-bert/pytorch-pretrained-BERT"
sys.path.append(package_dir )
donnees['Pont'] = donnees.Cabin.str.extract('([A-Za-z])', expand=False)
 donnees.Pont = donnees.Pont.fillna('Na')
 pd.crosstab(donnees.Pont, np.ones(donnees.shape[0]))<categorify>
%reload_ext autoreload
%autoreload 2
%matplotlib inline
donnees['TicketNum'] = donnees.Ticket.replace(regex=r'([^0-9]+)',value='')
 donnees.Ticket = donnees.Ticket.replace(regex=r'([^a-zA-Z]+)',value='')
 donnees.Ticket = donnees.Ticket.replace({r'^(CASOTON|SOTONO|STONO|STONOQ)$':'SOTONOQ',
r'^(SC|SCParis)$':'SCPARIS',
r'^FCC$':'FC',
r'^$':'Vide'}, regex=True )<rename_columns>
def convert_lines(example, max_seq_length,tokenizer):
max_seq_length -=2
all_tokens = []
longer = 0
for text in tqdm(example):
tokens_a = tokenizer.tokenize(text)
 if len(tokens_a)>max_seq_length:
tokens_a = tokens_a[:max_seq_length]
longer += 1
one_token = tokenizer.convert_tokens_to_ids(["[CLS]"]+tokens_a+["[SEP]"])+[0] *(max_seq_length - len(tokens_a))
all_tokens.append(one_token)
 return np.array(all_tokens)
 def is_interactive() :
return 'SHLVL' not in os.environ
def seed_everything(seed=123):
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
def get_coefs(word, *arr):
return word, np.asarray(arr, dtype='float32')
 def load_embeddings(path):
emb_arr = KeyedVectors.load(path)
 return emb_arr
def build_matrix(word_index, path):
embedding_index = load_embeddings(path)
 embedding_matrix = np.zeros(( max_features + 1, 300))
unknown_words = []
for word, i in word_index.items() :
if i <= max_features:
try:
embedding_matrix[i] = embedding_index[word]
except KeyError:
try:
embedding_matrix[i] = embedding_index[word.lower() ]
except KeyError:
try:
embedding_matrix[i] = embedding_index[word.title() ]
except KeyError:
unknown_words.append(word)
 return embedding_matrix, unknown_words
def sigmoid(x):
return 1 /(1 + np.exp(-x))
class SpatialDropout(nn.Dropout2d):
def forward(self, x):
x = x.unsqueeze(2)
 x = x.permute(0, 3, 2, 1)
 x = super(SpatialDropout, self ).forward(x)
 x = x.permute(0, 3, 2, 1)
 x = x.squeeze(2)
 return x
def train_model(learn,test,output_dim,lr=0.001,
batch_size=512, n_epochs=4,
enable_checkpoint_ensemble=True):
all_test_preds = []
checkpoint_weights = [2 ** epoch for epoch in range(n_epochs)]
test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
 n = len(learn.data.train_dl)
 phases = [(TrainingPhase(n ).schedule_hp('lr', lr *(0.6**(i)))) for i in range(n_epochs)]
sched = GeneralScheduler(learn, phases)
 learn.callbacks.append(sched)
 for epoch in range(n_epochs):
learn.fit(1)
 test_preds = np.zeros(( len(test), output_dim))
for i, x_batch in enumerate(test_loader):
X = x_batch[0].cuda()
y_pred = sigmoid(learn.model(X ).detach().cpu().numpy())
 test_preds[i * batch_size:(i+1)* batch_size, :] = y_pred
all_test_preds.append(test_preds)
 if enable_checkpoint_ensemble:
test_preds = np.average(all_test_preds, weights=checkpoint_weights, axis=0)
 else:
test_preds = all_test_preds[-1]
return test_preds
def handle_punctuation(x):
x = x.translate(remove_dict)
 x = x.translate(isolate_dict)
 return x
def handle_contractions(x):
x = tokenizer.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
class SequenceBucketCollator() :
def __init__(self, choose_length, sequence_index, length_index, label_index=None):
self.choose_length = choose_length
self.sequence_index = sequence_index
self.length_index = length_index
self.label_index = label_index
def __call__(self, batch):
batch = [torch.stack(x)for x in list(zip(*batch)) ]
sequences = batch[self.sequence_index]
lengths = batch[self.length_index]
length = self.choose_length(lengths)
 mask = torch.arange(start=maxlen, end=0, step=-1)< length
padded_sequences = sequences[:, mask]
batch[self.sequence_index] = padded_sequences
if self.label_index is not None:
return [x for i, x in enumerate(batch)if i != self.label_index], batch[self.label_index]
return batch
class NeuralNet(nn.Module):
def __init__(self, embedding_matrix, num_aux_targets):
super(NeuralNet, self ).__init__()
embed_size = embedding_matrix.shape[1]
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = SpatialDropout(0.3)
 self.lstm1 = nn.LSTM(embed_size, LSTM_UNITS, bidirectional=True, batch_first=True)
 self.lstm2 = nn.LSTM(LSTM_UNITS * 2, LSTM_UNITS, bidirectional=True, batch_first=True)
 self.linear1 = nn.Linear(DENSE_HIDDEN_UNITS, DENSE_HIDDEN_UNITS)
 self.linear2 = nn.Linear(DENSE_HIDDEN_UNITS, DENSE_HIDDEN_UNITS)
 self.linear_out = nn.Linear(DENSE_HIDDEN_UNITS, 1)
 self.linear_aux_out = nn.Linear(DENSE_HIDDEN_UNITS, num_aux_targets)
 def forward(self, x, lengths=None):
h_embedding = self.embedding(x.long())
 h_embedding = self.embedding_dropout(h_embedding)
 h_lstm1, _ = self.lstm1(h_embedding)
 h_lstm2, _ = self.lstm2(h_lstm1)
 avg_pool = torch.mean(h_lstm2, 1)
 max_pool, _ = torch.max(h_lstm2, 1)
 h_conc = torch.cat(( max_pool, avg_pool), 1)
 h_conc_linear1 = F.relu(self.linear1(h_conc))
h_conc_linear2 = F.relu(self.linear2(h_conc))
hidden = h_conc + h_conc_linear1 + h_conc_linear2
result = self.linear_out(hidden)
 aux_result = self.linear_aux_out(hidden)
 out = torch.cat([result, aux_result], 1)
 return out
def custom_loss(data, targets):
bce_loss_1 = nn.BCEWithLogitsLoss(weight=targets[:,1:2] )(data[:,:1],targets[:,:1])
 bce_loss_2 = nn.BCEWithLogitsLoss()(data[:,1:],targets[:,2:])
 return(bce_loss_1 * loss_weight)+ bce_loss_2
def reduce_mem_usage(df):
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
for col in df.columns:
col_type = df[col].dtype
if col_type != object:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8 ).min and c_max < np.iinfo(np.int8 ).max:
df[col] = df[col].astype(np.int8)
 elif c_min > np.iinfo(np.int16 ).min and c_max < np.iinfo(np.int16 ).max:
df[col] = df[col].astype(np.int16)
 elif c_min > np.iinfo(np.int32 ).min and c_max < np.iinfo(np.int32 ).max:
df[col] = df[col].astype(np.int32)
 elif c_min > np.iinfo(np.int64 ).min and c_max < np.iinfo(np.int64 ).max:
df[col] = df[col].astype(np.int64)
 else:
if c_min > np.finfo(np.float16 ).min and c_max < np.finfo(np.float16 ).max:
df[col] = df[col].astype(np.float16)
 elif c_min > np.finfo(np.float32 ).min and c_max < np.finfo(np.float32 ).max:
df[col] = df[col].astype(np.float32)
 else:
df[col] = df[col].astype(np.float64)
 else:
df[col] = df[col].astype('category')
 end_mem = df.memory_usage().sum() / 1024**2
print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
print('Decreased by {:.1f}%'.format(100 *(start_mem - end_mem)/ start_mem))
return df
def ensemble_predictions(predictions, weights, type_="linear"):
assert np.isclose(np.sum(weights), 1.0)
 if type_ == "linear":
res = np.average(predictions, weights=weights, axis=0)
 elif type_ == "harmonic":
res = np.average([1 / p for p in predictions], weights=weights, axis=0)
 return 1 / res
elif type_ == "geometric":
numerator = np.average(
 [np.log(p)for p in predictions], weights=weights, axis=0 
)
 res = np.exp(numerator / sum(weights))
return res
elif type_ == "rank":
res = np.average([rankdata(p)for p in predictions], weights=weights, axis=0)
 return res /(len(res)+ 1)
 return res
donnees.rename(columns={"Pclass": "Classe",
"Embarked": "Port",
"Cabin": "Cabine",
"SibSp": "ConjointsOuFratrie",
"Parch": "EnfantsOuParents",
},
inplace=True )<categorify>
warnings.filterwarnings(action='once')
 device = torch.device('cuda')
 MAX_SEQUENCE_LENGTH = 300
SEED = 1234
BATCH_SIZE = 512
BERT_MODEL_PATH = '.. /input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
np.random.seed(SEED)
 torch.manual_seed(SEED)
 torch.cuda.manual_seed(SEED)
 torch.backends.cudnn.deterministic = True
bert_config = BertConfig('.. /input/bert-inference/bert/bert_config.json')
 tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None,do_lower_case=True)
 tqdm.pandas()
CRAWL_EMBEDDING_PATH = '.. /input/gensim-embeddings-dataset/crawl-300d-2M.gensim'
GLOVE_EMBEDDING_PATH = '.. /input/gensim-embeddings-dataset/glove.840B.300d.gensim'
NUM_MODELS = 2
LSTM_UNITS = 128
DENSE_HIDDEN_UNITS = 4 * LSTM_UNITS
MAX_LEN = 220
if not is_interactive() :
def nop(it, *a, **k):
return it
tqdm = nop
fastprogress.fastprogress.NO_BAR = True
master_bar, progress_bar = force_console_behavior()
fastai.basic_train.master_bar, fastai.basic_train.progress_bar = master_bar, progress_bar
seed_everything()
donnees.Port = donnees.Port.fillna('Pas')
 donnees.Cabine = donnees.Cabine.apply(lambda x: 0 if type(x)== float else 1)
 donnees.head()<count_missing_values>
test_df = pd.read_csv(".. /input/jigsaw-unintended-bias-in-toxicity-classification/test.csv")
 test_df['comment_text'] = test_df['comment_text'].astype(str)
 X_test = convert_lines(test_df["comment_text"].fillna("DUMMY_VALUE"), MAX_SEQUENCE_LENGTH, tokenizer )
donnees.isnull().sum()<filter>
model = BertForSequenceClassification(bert_config, num_labels=1)
 model.load_state_dict(torch.load(".. /input/bert-inference/bert/bert_pytorch.bin"))
model.to(device)
 for param in model.parameters() :
param.requires_grad = False
model.eval()
donnees[donnees['Fare'].isnull() ]<filter>
test_preds = np.zeros(( len(X_test)))
 test = torch.utils.data.TensorDataset(torch.tensor(X_test, dtype=torch.long))
test_loader = torch.utils.data.DataLoader(test, batch_size=512, shuffle=False)
 tk0 = tqdm(test_loader)
 for i,(x_batch,)in enumerate(tk0):
pred = model(x_batch.to(device), attention_mask=(x_batch > 0 ).to(device), labels=None)
 test_preds[i * 512:(i + 1)* 512] = pred[:, 0].detach().cpu().squeeze().numpy()
test_pred = torch.sigmoid(torch.tensor(test_preds)).numpy().ravel()
donnees[(donnees['Port'] == 'S')&(donnees['Classe'] == 3)].Fare.median()<merge>
submission_bert = pd.DataFrame.from_dict({
'id': test_df['id'],
'prediction': test_pred
} )
coefficient = 1.2
ageCalc = donnees[~donnees.Age.isna() ].groupby(['Sex','Classe'] ).agg({'Age':['mean','std']})
 ageCalc.columns = ['_'.join(col ).rstrip('_')for col in ageCalc.columns]
ageCalc.reset_index(inplace=True)
 ageCalc['borneMin'] = ageCalc.Age_mean - ageCalc.Age_std*coefficient
ageCalc['borneMax'] = ageCalc.Age_mean + ageCalc.Age_std*coefficient
ageCalc.drop(columns=['Age_mean','Age_std'],inplace=True)
 ageCalcCount = donnees[donnees.Age.isna() ].groupby(['Sex','Classe'] ).agg({'Title':['count']} ).reset_index()
ageCalcCount.columns = ['Sex','Classe','nb']
ageCalc = ageCalc.merge(ageCalcCount,on=['Sex','Classe'])
 ageCalc<create_dataframe>
train_df = reduce_mem_usage(pd.read_csv('.. /input/jigsaw-unintended-bias-in-toxicity-classification/train.csv'))
ageRand = pd.DataFrame(columns=['Sex','Classe','Age'])
 for i in [(row.Sex,row.Classe,np.random.randint(round(row.borneMin),round(row.borneMax),size=row.nb))
for indx, row in ageCalc.iterrows() ]:
calc = pd.DataFrame(columns=['Sex','Classe','Age'])
 calc.Age = i[2]
calc.Sex = i[0]
calc.Classe = i[1]
ageRand = pd.concat([ageRand,calc] )<count_missing_values>
symbols_to_isolate = '.,?!-;*"…:—() %
symbols_to_delete = '
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donnees.Age01.isna().sum()<drop_column>
tokenizer = TreebankWordTokenizer()
isolate_dict = {ord(c):f' {c} ' for c in symbols_to_isolate}
remove_dict = {ord(c):f'' for c in symbols_to_delete}
donnees.Age = donnees.Age01.values
donnees.drop(columns=['AgeOld','Age01'],inplace=True )<drop_column>
x_train = train_df['comment_text'].progress_apply(lambda x:preprocess(x))
y_aux_train = train_df[['target', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat']]
x_test = test_df['comment_text'].progress_apply(lambda x:preprocess(x))
identity_columns = [
'male', 'female', 'homosexual_gay_or_lesbian', 'christian', 'jewish',
'muslim', 'black', 'white', 'psychiatric_or_mental_illness']
weights = np.ones(( len(x_train),)) / 4
weights +=(train_df[identity_columns].fillna(0 ).values>=0.5 ).sum(axis=1 ).astype(bool ).astype(np.int)/ 4
weights +=(((train_df['target'].values>=0.5 ).astype(bool ).astype(np.int)+ 
(train_df[identity_columns].fillna(0 ).values<0.5 ).sum(axis=1 ).astype(bool ).astype(np.int)) > 1 ).astype(bool ).astype(np.int)/ 4
weights +=(((train_df['target'].values<0.5 ).astype(bool ).astype(np.int)+ 
(train_df[identity_columns].fillna(0 ).values>=0.5 ).sum(axis=1 ).astype(bool ).astype(np.int)) > 1 ).astype(bool ).astype(np.int)/ 4
loss_weight = 1.0 / weights.mean()
y_train = np.vstack([(train_df['target'].values>=0.5 ).astype(np.int),weights] ).T
max_features = 410047
listeVariblesInitiales = donnees.drop(columns=['Name'] ).columns
donnees = donnees.set_index('PassengerId' ).sort_index()<feature_engineering>
tokenizer = text.Tokenizer(num_words = max_features, filters='',lower=False )
donnees['TitreFamille'] = donnees.apply(lambda ligne : 'Homme' if ligne['Title'] == 'Mr'
else 'Femme' if ligne['Sex'] == 'Femme'
else 'Garçon' if ligne['Title'] == 'Master'
else 'Homme' , axis=1)
 plt.figure(figsize=(14,12))
plt.title('La distribution du titre calculé pour le groupe famille',size=20)
 sns.countplot(x='TitreFamille',hue='Survived',data=donnees);<feature_engineering>
tokenizer.fit_on_texts(list(x_train)+ list(x_test))
crawl_matrix, unknown_words_crawl = build_matrix(tokenizer.word_index, CRAWL_EMBEDDING_PATH)
 print('n unknown words(crawl): ', len(unknown_words_crawl))
glove_matrix, unknown_words_glove = build_matrix(tokenizer.word_index, GLOVE_EMBEDDING_PATH)
 print('n unknown words(glove): ', len(unknown_words_glove))
max_features = max_features or len(tokenizer.word_index)+ 1
max_features
embedding_matrix = np.concatenate([crawl_matrix, glove_matrix], axis=-1)
 embedding_matrix.shape
del crawl_matrix
del glove_matrix
gc.collect()
y_train_torch = torch.tensor(np.hstack([y_train, y_aux_train]), dtype=torch.float32 )
donnees['GroupFamille'] = donnees.Name+'-'+\
donnees.Classe.apply(lambda x: '%1d' % x)+'-'+\
donnees.Fare.apply(lambda x: '%.3f' % x)+'-'+\
donnees.Port+'-'+donnees.TicketNum
donnees.GroupFamille.unique() [:6]<feature_engineering>
x_train = tokenizer.texts_to_sequences(x_train)
 x_test = tokenizer.texts_to_sequences(x_test )
donnees['GroupTicket'] = donnees.Classe.apply(lambda x: '%1d' % x)+'-'+\
donnees.Fare.apply(lambda x: '%.3f' % x)+'-'+\
donnees.Port+'-'+donnees.TicketNum
donnees.GroupTicket.unique() [:6]<drop_column>
lengths = torch.from_numpy(np.array([len(x)for x in x_train]))
maxlen = 300
x_train_padded = torch.from_numpy(sequence.pad_sequences(x_train, maxlen=maxlen))
donnees.drop(columns=['Name','TitreFamille','GroupFamille'], inplace=True)
 listeVariblesAvecGroups = donnees.columns<feature_engineering>
test_lengths = torch.from_numpy(np.array([len(x)for x in x_test]))
x_test_padded = torch.from_numpy(sequence.pad_sequences(x_test, maxlen=maxlen))
def conversionVariableCategorielle(donnees,variable):
valeurs = list(donnees[variable].sort_values().unique())
 dicoVar = {nom:indx for indx,nom in enumerate(valeurs)}
dicoVarRev = {indx:nom for indx,nom in enumerate(valeurs)}
donnees[variable] = donnees[variable].apply(lambda x : dicoVar[x])
 return dicoVar,dicoVarRev
for col in donnees.dtypes[donnees.dtypes == 'object'].reset_index() ['index']:
_,_ = conversionVariableCategorielle(donnees,col)
 donnees.head(10 )<prepare_x_and_y>
batch_size = 512
test_dataset = data.TensorDataset(x_test_padded, test_lengths)
 train_dataset = data.TensorDataset(x_train_padded, lengths, y_train_torch)
 valid_dataset = data.Subset(train_dataset, indices=[0, 1])
 train_collator = SequenceBucketCollator(lambda lenghts: lenghts.max() ,
sequence_index=0,
length_index=1,
label_index=2)
 test_collator = SequenceBucketCollator(lambda lenghts: lenghts.max() , sequence_index=0, length_index=1)
 train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=train_collator)
 valid_loader = data.DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, collate_fn=train_collator)
 test_loader = data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=test_collator)
 databunch = DataBunch(train_dl=train_loader, valid_dl=valid_loader, collate_fn=train_collator )
apprentissage = donnees[~donnees.Survived.isnull() ]
apprentissage.Survived = apprentissage.Survived.astype('int8')
 X = apprentissage.drop(columns='Survived')
 y = apprentissage.Survived
apprentissage.head()<filter>
all_test_preds = []
for model_idx in range(NUM_MODELS):
print('Model ', model_idx)
 seed_everything(1 + model_idx)
 model = NeuralNet(embedding_matrix, y_aux_train.shape[-1])
 learn = Learner(databunch, model, loss_func=custom_loss)
 test_preds = train_model(learn,test_dataset,output_dim=7)
 all_test_preds.append(test_preds )
test = donnees[donnees.Survived.isnull() ]
test.reset_index().head()<train_model>
submission_lstm = pd.DataFrame.from_dict({
'id': test_df['id'],
'prediction': np.mean(all_test_preds, axis=0)[:, 0]
} )
X = apprentissage.drop(columns='Survived')
 y = apprentissage.Survived
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.112, stratify = y, random_state = 101)
 print(X_train.shape, y_train.shape)
 print(X_test.shape, y_test.shape)
 plt.figure(figsize=(10,6))
plt.hist(y_train,label='apprentissage');
plt.hist(y_test,label='validation');
plt.legend() ;<compute_test_metric>
submission = pd.read_csv(
 ".. /input/jigsaw-unintended-bias-in-toxicity-classification/sample_submission.csv" 
)
 weights = [0.333, 0.667]
submission["prediction"] = ensemble_predictions(
 [submission_bert.prediction.values, submission_lstm.prediction.values],
weights,
type_="rank", 
)
 submission.to_csv("submission.csv", index=False)