Kaggle competition: Natural Language Processing with Disaster Tweets
<SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<normalization>
!pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
scaler = StandardScaler()
X = scaler.fit_transform(train.values)
 x_tt = scaler.transform(test_features.values )<data_type_conversions>
torch.__version__
train = train.astype('float')
 test = test.astype('float' )<define_variables>
!pip install fastai==2.0.13
p_min = 0.0015
p_max = 0.9985<choose_model_class>
torch.__version__, fastai.__version__
N_STARTS = [8,9]
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 N_STARTS:
for n,(tr, te)in enumerate(MultilabelStratifiedKFold(n_splits=12, 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>
import fastai
from fastai.text.all import *
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)}' )<save_to_csv>
train_df = pd.read_csv(path/'train.csv')
 train_df.head(5 )
ss_NN.to_csv('submission.csv', index = False )<set_options>
test_df = pd.read_csv(path/'test.csv')
 test_df.head(5 )
warnings.filterwarnings("ignore")
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )<feature_engineering>
op = pd.concat([train_df['text'], test_df['text']])
 op = pd.DataFrame(op)
 len(op), len(train_df)+len(test_df )
def get_logger(filename='log'):
logger = getLogger(__name__)
 logger.setLevel(INFO)
 handler1 = StreamHandler()
handler1.setFormatter(Formatter("%(message)s"))
handler2 = FileHandler(filename=f"{filename}.log")
 handler2.setFormatter(Formatter("%(message)s"))
logger.addHandler(handler1)
 logger.addHandler(handler2)
 return logger
logger = get_logger()
def seed_everything(seed=42):
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=42 )<load_from_csv>
dls_lm = TextDataLoaders.from_df(op, path=path, text_col='text', is_lm=True, valid_col=None)
 dls_lm.show_batch(max_n=3 )
train_features = pd.read_csv('.. /input/lish-moa/train_features.csv')
 train_targets_scored = pd.read_csv('.. /input/lish-moa/train_targets_scored.csv')
 train_targets_nonscored = pd.read_csv('.. /input/lish-moa/train_targets_nonscored.csv')
 test_features = pd.read_csv('.. /input/lish-moa/test_features.csv')
 submission = pd.read_csv('.. /input/lish-moa/sample_submission.csv' )<merge>
learn = language_model_learner(dls_lm, AWD_LSTM, metrics=[accuracy, Perplexity() ], path='/kaggle/working/', wd=0.1 ).to_fp16()
train = train_features.merge(train_targets_scored, on='sig_id')
 target_cols = [c for c in train_targets_scored.columns if c not in ['sig_id']]
cols = target_cols + ['cp_type']
train[cols].groupby('cp_type' ).sum().sum(1 )<categorify>
torch.cuda.is_available()
folds = train.copy()
Fold = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)
 for n,(train_index, val_index)in enumerate(Fold.split(folds, folds[target_cols])) :
folds.loc[val_index, 'fold'] = int(n)
 folds['fold'] = folds['fold'].astype(int)
 print(folds.shape )<prepare_x_and_y>
learn.fit_one_cycle(1, 1e-2 )
class TrainDataset(Dataset):
def __init__(self, df, num_features, cat_features, labels):
self.cont_values = df[num_features].values
self.cate_values = df[cat_features].values
self.labels = labels
def __len__(self):
return len(self.cont_values)
 def __getitem__(self, idx):
cont_x = torch.FloatTensor(self.cont_values[idx])
 cate_x = torch.LongTensor(self.cate_values[idx])
 label = torch.tensor(self.labels[idx] ).float()
return cont_x, cate_x, label
class TestDataset(Dataset):
def __init__(self, df, num_features, cat_features):
self.cont_values = df[num_features].values
self.cate_values = df[cat_features].values
def __len__(self):
return len(self.cont_values)
 def __getitem__(self, idx):
cont_x = torch.FloatTensor(self.cont_values[idx])
 cate_x = torch.LongTensor(self.cate_values[idx])
 return cont_x, cate_x<categorify>
learn.save('1epoch' )
cat_features = ['cp_time', 'cp_dose']
num_features = [c for c in train.columns if train.dtypes[c] != 'object']
num_features = [c for c in num_features if c not in cat_features]
num_features = [c for c in num_features if c not in target_cols]
target = train[target_cols].values
def cate2num(df):
df['cp_time'] = df['cp_time'].map({24: 0, 48: 1, 72: 2})
 df['cp_dose'] = df['cp_dose'].map({'D1': 3, 'D2': 4})
 return df
train = cate2num(train)
 test = cate2num(test )<init_hyperparams>
learn = learn.load('1epoch' )
class CFG:
max_grad_norm=1000
gradient_accumulation_steps=1
hidden_size=512
dropout=0.5
lr=1e-2
weight_decay=1e-6
batch_size=32
epochs=20
num_features=num_features
cat_features=cat_features
target_cols=target_cols<choose_model_class>
learn.unfreeze()
learn.fit_one_cycle(10, 1e-3 )
class TabularNN(nn.Module):
def __init__(self, cfg, num_units = 5):
super().__init__()

 self.mlp = nn.Sequential(
 nn.Linear(len(cfg.num_features), cfg.hidden_size),
nn.BatchNorm1d(cfg.hidden_size),
nn.Dropout(cfg.dropout),
nn.PReLU() ,
nn.Linear(cfg.hidden_size, cfg.hidden_size),
nn.BatchNorm1d(cfg.hidden_size),
nn.Dropout(cfg.dropout),
nn.PReLU())
 self.fc1 = nn.ModuleList([nn.Linear(cfg.hidden_size,len(cfg.target_cols)) for i in range(num_units)])
 def maxout(self, x, layer_list):
max_output = layer_list[0](x)
 for _, layer in enumerate(layer_list, start=1):
max_output = torch.max(max_output, layer(x))
return max_output
def forward(self, cont_x, cate_x):
x = self.mlp(cont_x)
 x = self.maxout(x, self.fc1)
 return x<train_on_grid>
learn.save_encoder('finetuned' )
def train_fn(train_loader, model, optimizer, epoch, scheduler, device):
losses = AverageMeter()
model.train()
for step,(cont_x, cate_x, y)in enumerate(train_loader):
cont_x, cate_x, y = cont_x.to(device), cate_x.to(device), y.to(device)
 batch_size = cont_x.size(0)
 pred = model(cont_x, cate_x)
 loss = nn.BCEWithLogitsLoss()(pred, y)
 losses.update(loss.item() , batch_size)
 if CFG.gradient_accumulation_steps > 1:
loss = loss / CFG.gradient_accumulation_steps
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters() , CFG.max_grad_norm)
 if(step + 1)% CFG.gradient_accumulation_steps == 0:
scheduler.step()
optimizer.step()
optimizer.zero_grad()
return losses.avg
def validate_fn(valid_loader, model, device):
losses = AverageMeter()
model.eval()
val_preds = []
for step,(cont_x, cate_x, y)in enumerate(valid_loader):
cont_x, cate_x, y = cont_x.to(device), cate_x.to(device), y.to(device)
 batch_size = cont_x.size(0)
 with torch.no_grad() :
pred = model(cont_x, cate_x)
 loss = nn.BCEWithLogitsLoss()(pred, y)
 losses.update(loss.item() , batch_size)
 val_preds.append(pred.sigmoid().detach().cpu().numpy())
 if CFG.gradient_accumulation_steps > 1:
loss = loss / CFG.gradient_accumulation_steps
val_preds = np.concatenate(val_preds)
 return losses.avg, val_preds
def inference_fn(test_loader, model, device):
model.eval()
preds = []
for step,(cont_x, cate_x)in enumerate(test_loader):
cont_x, cate_x = cont_x.to(device), cate_x.to(device)
 with torch.no_grad() :
pred = model(cont_x, cate_x)
 preds.append(pred.sigmoid().detach().cpu().numpy())
 preds = np.concatenate(preds)
 return preds
class AverageMeter(object):

 def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count<create_dataframe>
!cp.. /input/nlp-getting-started/train.csv /kaggle/working
def run_single_nn(cfg, train, test, folds, num_features, cat_features, target, device, fold_num=0, seed=42):
logger.info(f'Set seed {seed}')
 seed_everything(seed=seed)
 trn_idx = folds[folds['fold'] != fold_num].index
val_idx = folds[folds['fold'] == fold_num].index
train_folds = train.loc[trn_idx].reset_index(drop=True)
 valid_folds = train.loc[val_idx].reset_index(drop=True)
 train_target = target[trn_idx]
valid_target = target[val_idx]
train_dataset = TrainDataset(train_folds, num_features, cat_features, train_target)
 valid_dataset = TrainDataset(valid_folds, num_features, cat_features, valid_target)
 train_loader = DataLoader(train_dataset, batch_size=cfg.batch_size, shuffle=True,
num_workers=4, pin_memory=True, drop_last=True)
 valid_loader = DataLoader(valid_dataset, batch_size=cfg.batch_size, shuffle=False,
num_workers=4, pin_memory=True, drop_last=False)
 model = TabularNN(cfg)
 model.to(device)
 optimizer = optim.Adam(model.parameters() , lr=cfg.lr, weight_decay=cfg.weight_decay)
 scheduler = optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.1, div_factor=1e3,
max_lr=1e-2, epochs=cfg.epochs, steps_per_epoch=len(train_loader))
log_df = pd.DataFrame(columns=(['EPOCH']+['TRAIN_LOSS']+['VALID_LOSS']))
best_loss = np.inf
for epoch in range(cfg.epochs):
train_loss = train_fn(train_loader, model, optimizer, epoch, scheduler, device)
 valid_loss, val_preds = validate_fn(valid_loader, model, device)
 log_row = {'EPOCH': epoch,
'TRAIN_LOSS': train_loss,
'VALID_LOSS': valid_loss,
}
log_df = log_df.append(pd.DataFrame(log_row, index=[0]), sort=False)
 if valid_loss < best_loss:
logger.info(f'epoch{epoch} save best model...{valid_loss}')
 best_loss = valid_loss
oof = np.zeros(( len(train), len(cfg.target_cols)))
 oof[val_idx] = val_preds
torch.save(model.state_dict() , f"fold{fold_num}_seed{seed}.pth")
 test_dataset = TestDataset(test, num_features, cat_features)
 test_loader = DataLoader(test_dataset, batch_size=cfg.batch_size, shuffle=False,
num_workers=4, pin_memory=True)
 model = TabularNN(cfg)
 model.load_state_dict(torch.load(f"fold{fold_num}_seed{seed}.pth"))
model.to(device)
 predictions = inference_fn(test_loader, model, device)
 torch.cuda.empty_cache()
return oof, predictions
def run_kfold_nn(cfg, train, test, folds, num_features, cat_features, target, device, n_fold=5, seed=42):
oof = np.zeros(( len(train), len(cfg.target_cols)))
 predictions = np.zeros(( len(test), len(cfg.target_cols)))
 for _fold in range(n_fold):
logger.info("Fold {}".format(_fold))
_oof, _predictions = run_single_nn(cfg,
train,
test,
folds,
num_features,
cat_features,
target,
device,
fold_num=_fold,
seed=seed)
 oof += _oof
predictions += _predictions / n_fold
score = 0
for i in range(target.shape[1]):
_score = log_loss(target[:,i], oof[:,i])
 score += _score / target.shape[1]
logger.info(f"CV score: {score}")
 return oof, predictions<categorify>
dls = TextDataLoaders.from_csv(path='/kaggle/working/', csv_fname='train.csv', text_col='text', label_col='target', bs = 8, text_vocab=dls_lm.vocab)
 dls.show_batch(max_n=8 )
oof = np.zeros(( len(train), len(CFG.target_cols)))
 predictions = np.zeros(( len(test), len(CFG.target_cols)))
 SEED = [0, 1, 2]
for seed in SEED:
_oof, _predictions = run_kfold_nn(CFG,
train, test, folds,
num_features, cat_features, target,
device,
n_fold=5, seed=seed)
 oof += _oof / len(SEED)
 predictions += _predictions / len(SEED)
 score = 0
for i in range(target.shape[1]):
_score = log_loss(target[:,i], oof[:,i])
 score += _score / target.shape[1]
logger.info(f"Seed Averaged CV score: {score}" )<save_to_csv>
learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics = accuracy ).to_fp16()
train[target_cols] = oof
train[['sig_id']+target_cols].to_csv('oof.csv', index=False)
 test[target_cols] = predictions
test[['sig_id']+target_cols].to_csv('pred.csv', index=False )<compute_test_metric>
learn = learn.load_encoder('finetuned' )
result = 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 = result[target_cols].values
score = 0
for i in range(y_true.shape[1]):
_score = log_loss(y_true[:,i], y_pred[:,i])
 score += _score / y_true.shape[1]
logger.info(f"Final result: {score}" )<save_to_csv>
learn.fit_one_cycle(4, 2e-2 )
sub = submission.drop(columns=target_cols ).merge(test[['sig_id']+target_cols], on='sig_id', how='left' ).fillna(0)
 sub.to_csv('submission.csv', index=False)
 sub.head()<load_from_csv>
learn.freeze_to(-2)
 learn.fit_one_cycle(4, slice(1e-2/(2.6**4),1e-2))
data_dir = '.. /input/lish-moa/'
train_set = pd.read_csv('.. /input/lish-moa/train_features.csv')
 train_target = pd.read_csv('.. /input/lish-moa/train_targets_scored.csv')
 test_set = pd.read_csv('.. /input/lish-moa/test_features.csv')
 ss = pd.read_csv('.. /input/lish-moa/sample_submission.csv')
 ss.loc[:, train_target.columns[1:]] = 0<define_variables>
id_ , target_ = [], []
for i, text in zip(test_df.iloc[:,0], test_df.iloc[:,3]):
id_.append(i)
 target_.append(int(learn.predict(text)[0]))
id_, target_
GENES = [col for col in train_set.columns if col.startswith('g-')]
CELLS = [col for col in train_set.columns if col.startswith('c-')]
drop_index = train_set[train_set.cp_type == 'ctl_vehicle'].index
add_index = test_set[test_set.cp_type == 'ctl_vehicle'].index<categorify>
submission = pd.DataFrame(list(zip(id_, target_)) ,
columns =['id', 'target'] )
<normalization><EOS> 
submission.to_csv('/kaggle/working/submission.csv', index = False )