Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<save_to_csv>
%%HTML
<style type="text/css">
div.h1 {
background-color:
color: white;
padding: 8px;
padding-right: 300px;
font-size: 35px;
max-width: 1500px;
margin: auto;
margin-top: 50px;
}
div.h2 {
background-color:
color: white;
padding: 8px;
padding-right: 300px;
font-size: 35px;
max-width: 1500px;
margin: auto;
margin-top: 50px;
}
</style>
model = RandomForestClassifier(n_estimators=110, max_depth=5, random_state=1 ).fit(X, y)
 predictions = model.predict(X_unknown)
 output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
 output.to_csv('my_submission.csv', index=False )<import_modules>
warnings.filterwarnings('ignore' )
warnings.filterwarnings("ignore")
<load_from_csv>
def seed_everything(seed=1903):
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 )
train=pd.read_csv('.. /input/titanic/train.csv')
 test=pd.read_csv('.. /input/titanic/test.csv')
 y_test=pd.read_csv('.. /input/titanic/gender_submission.csv' )<count_values>
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')
 sample_submission = pd.read_csv('.. /input/lish-moa/sample_submission.csv' )
p=train.loc[train['Survived']==1 ]
print(len(p))
male=p.loc[p['Sex']=='male']
female=p.loc[p['Sex']=='female']
print(male['Pclass'].value_counts())
 print(female['Pclass'].value_counts() )<count_values>
GENES = [col for col in train_features.columns if col.startswith('g-')]
CELLS = [col for col in train_features.columns if col.startswith('c-')]
p=train.loc[train['Survived']==0 ]
male=p.loc[p['Sex']=='male']
female=p.loc[p['Sex']=='female']
print(male['Pclass'].value_counts())
 print(female['Pclass'].value_counts() )<count_values>
n_comp = 28
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])
 data2 =(PCA(n_components=n_comp, random_state=1903 ).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=1903 ).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 )
feature_mix=[]
for i in range(0,len(train)) :
marks=0
if(train['Sex'].iloc[i]=='female'):
marks=marks+10
if(train['Pclass'].iloc[i]!=1):
marks=marks+5
else:
marks=marks+2
if(train['Age'].iloc[i]<35 and train['Age'].iloc[i]>20):
marks=marks+4
else:
marks=marks+2
feature_mix.append(marks)
 train['feature_mix']=feature_mix
train['feature_mix'].value_counts()<count_values>
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
feature_mix=[]
for i in range(0,len(test)) :
marks=0
if(test['Sex'].iloc[i]=='female'):
marks=marks+10
if(test['Pclass'].iloc[i]!=1):
marks=marks+5
else:
marks=marks+2
if(test['Age'].iloc[i]<35 and test['Age'].iloc[i]>20):
marks=marks+4
feature_mix.append(marks)
 test['feature_mix']=feature_mix
test['feature_mix'].value_counts()<drop_column>
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 )
y_test.head()
PassengerId=y_test['PassengerId']
y_test=y_test.drop(['PassengerId'],axis=1 )<data_type_conversions>
target_cols = target.drop('sig_id', axis=1 ).columns.values.tolist()
train_m=(max(train['Age'])+min(train['Age'])) /2
values={'Cabin':'nocabin','Age':train_m,'Embarked':'notknown'}
train=train.fillna(value=values)
 test_m=(max(test['Age'])+min(test['Age'])) /2
print(test_m)
 values={'Cabin':'nocabin','Age':test_m,'Embarked':'notknown',"Fare":max(test['Fare'])}
test=test.fillna(value=values)
<prepare_x_and_y>
folds = train.copy()
mskf = MultilabelStratifiedKFold(n_splits=7)
 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 )
y= train["Survived"]
train=train.drop(["Survived"],axis=1 )<split>
def process_data(df):
df['time_dose'] = df['cp_time'].astype(str)+df['cp_dose']
df = pd.get_dummies(df, columns=['cp_time','cp_dose','time_dose'])
 features_g = list(df.columns[4:776])
 features_c = list(df.columns[776:876])
 df['g_sum'] = df[features_g].sum(axis = 1)
 df['g_mean'] = df[features_g].mean(axis = 1)
 df['g_std'] = df[features_g].std(axis = 1)
 df['g_kurt'] = df[features_g].kurtosis(axis = 1)
 df['g_skew'] = df[features_g].skew(axis = 1)
 df['c_sum'] = df[features_c].sum(axis = 1)
 df['c_mean'] = df[features_c].mean(axis = 1)
 df['c_std'] = df[features_c].std(axis = 1)
 df['c_kurt'] = df[features_c].kurtosis(axis = 1)
 df['c_skew'] = df[features_c].skew(axis = 1)
 df['gc_sum'] = df[features_g + features_c].sum(axis = 1)
 df['gc_mean'] = df[features_g + features_c].mean(axis = 1)
 df['gc_std'] = df[features_g + features_c].std(axis = 1)
 df['gc_kurt'] = df[features_g + features_c].kurtosis(axis = 1)
 df['gc_skew'] = df[features_g + features_c].skew(axis = 1)
 features_c = list(df.columns[776:876])
 for feature in features_c:
df[f'{feature}_squared'] = df[feature] ** 2
return df
X_train, X_cv, y_train, y_cv = train_test_split(train, y, stratify=y, test_size=0.2,random_state=42 )<feature_engineering>
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 )
vectorizer = CountVectorizer()
X_tr_emb =vectorizer.fit_transform(X_train['Embarked'])
 X_cv_emb =vectorizer.transform(X_cv['Embarked'])
 X_te_emb =vectorizer.transform(test['Embarked'] )<categorify>
class MoADataset:
def __init__(self, features, targets):
self.features = features
self.targets = targets
def __len__(self):
return(self.features.shape[0])
 def __getitem__(self, idx):
dct = {
'x' : torch.tensor(self.features[idx, :], dtype=torch.float),
'y' : torch.tensor(self.targets[idx, :], dtype=torch.float)
 }
return dct
class TestDataset:
def __init__(self, features):
self.features = features
def __len__(self):
return(self.features.shape[0])
 def __getitem__(self, idx):
dct = {
'x' : torch.tensor(self.features[idx, :], dtype=torch.float)
 }
return dct
enc = OneHotEncoder(handle_unknown='ignore')
 X_tr_age =enc.fit_transform(np.array(X_train['Age'] ).reshape(-1,1))
X_cv_age =enc.transform(np.array(X_cv['Age'] ).reshape(-1,1))
X_te_age =enc.transform(np.array(test['Age'] ).reshape(-1,1))<categorify>
def train_fn(model, optimizer, scheduler, loss_fn, dataloader, device):
model.train()
final_loss = 0
for data in dataloader:
optimizer.zero_grad()
inputs, targets = data['x'].to(device), data['y'].to(device)
 outputs = model(inputs)
 loss = loss_fn(outputs, targets)
 loss.backward()
optimizer.step()
scheduler.step()
final_loss += loss.item()
final_loss /= len(dataloader)
 return final_loss
def valid_fn(model, loss_fn, dataloader, device):
model.eval()
final_loss = 0
valid_preds = []
for data in dataloader:
inputs, targets = data['x'].to(device), data['y'].to(device)
 outputs = model(inputs)
 loss = loss_fn(outputs, targets)
 final_loss += loss.item()
valid_preds.append(outputs.sigmoid().detach().cpu().numpy())
 final_loss /= len(dataloader)
 valid_preds = np.concatenate(valid_preds)
 return final_loss, valid_preds
def inference_fn(model, dataloader, device):
model.eval()
preds = []
for data in dataloader:
inputs = data['x'].to(device)
 with torch.no_grad() :
outputs = model(inputs)
 preds.append(outputs.sigmoid().detach().cpu().numpy())
 preds = np.concatenate(preds)
 return preds
X_tr_fare =enc.fit_transform(np.array(X_train['Fare'] ).reshape(-1,1))
X_cv_fare =enc.transform(np.array(X_cv['Fare'] ).reshape(-1,1))
X_te_fare =enc.transform(np.array(test['Fare'] ).reshape(-1,1))<categorify>
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(0.2)
 self.dense2 = nn.utils.weight_norm(nn.Linear(hidden_size, hidden_size))
self.batch_norm3 = nn.BatchNorm1d(hidden_size)
 self.dropout3 = nn.Dropout(0.2)
 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.leaky_relu(self.dense1(x), 1e-3)
 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
X_tr_Sbp =enc.fit_transform(np.array(X_train['SibSp'] ).reshape(-1,1))
X_cv_Sbp =enc.transform(np.array(X_cv['SibSp'] ).reshape(-1,1))
X_te_Sbp =enc.transform(np.array(test['SibSp'] ).reshape(-1,1))<categorify>
DEVICE =('cuda' if torch.cuda.is_available() else 'cpu')
 EPOCHS = 26
BATCH_SIZE = 256
LEARNING_RATE = 6e-4
WEIGHT_DECAY = 1e-5
NFOLDS = 7
EARLY_STOPPING_STEPS = 10
EARLY_STOP = True
num_features=len(feature_cols)
 num_targets=len(target_cols)
 hidden_size=2048
X_tr_par =enc.fit_transform(np.array(X_train['Parch'] ).reshape(-1,1))
X_cv_par =enc.transform(np.array(X_cv['Parch'] ).reshape(-1,1))
X_te_par=enc.transform(np.array(test['Parch'] ).reshape(-1,1))<categorify>
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
print(f"FOLD: {fold}")
 for epoch in range(EPOCHS):
train_loss = train_fn(model, optimizer,scheduler, loss_fn, trainloader, DEVICE)
 valid_loss, valid_preds = valid_fn(model, loss_fn, validloader, DEVICE)
 print(f"EPOCH: {epoch}, train_loss: {train_loss}, 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
X_tr_pclass =enc.fit_transform(np.array(X_train['Pclass'] ).reshape(-1,1))
X_cv_pclass =enc.transform(np.array(X_cv['Pclass'] ).reshape(-1,1))
X_te_pclass =enc.transform(np.array(test['Pclass'] ).reshape(-1,1))<feature_engineering>
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
X_tr_sex =vectorizer.fit_transform(X_train['Sex'])
 X_cv_sex =vectorizer.transform(X_cv['Sex'])
 X_te_sex =vectorizer.transform(test['Sex'] )<feature_engineering>
SEED = [1903, 1881]
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 for seed in SEED:
print(f"SEED: {seed}")
 oof_, predictions_ = run_k_fold(NFOLDS, seed)
 oof += oof_ / len(SEED)
 predictions += predictions_ / len(SEED)
 train[target_cols] = oof
test[target_cols] = predictions
X_tr_cabin =vectorizer.fit_transform(X_train['Cabin'])
 X_cv_cabin =vectorizer.transform(X_cv['Cabin'])
 X_te_cabin =vectorizer.transform(test['Cabin'] )<feature_engineering>
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 )
X_tr_tkt =vectorizer.fit_transform(X_train['Ticket'])
 X_cv_tkt =vectorizer.transform(X_cv['Ticket'])
 X_te_tkt =vectorizer.transform(test['Ticket'] )<categorify>
sub = sample_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 )