Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<save_to_csv>
warnings.filterwarnings('ignore')
predictions = pd.concat([prediction1,prediction2],axis=0)
 predictions.to_csv("submissionstoreanddeptwise.csv",index=False )<load_from_csv>
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
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
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
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(0.25)
 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.relu(self.dense1(x))
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
def process_data(data):
data = pd.get_dummies(data, columns=['cp_time','cp_dose'])
 return data
seed_everything(seed=42 )
!gzip submissionstoreanddeptwise.csv<init_hyperparams>
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' )
<set_options>
for col in(GENES + CELLS):
normalize = Normalizer()
vec_len = len(train_features[col].values)
 vec_len_test = len(test_features[col].values)
 raw_vec = train_features[col].values.reshape(vec_len, 1)
 train_features[col] = normalize.fit_transform(raw_vec ).reshape(1, vec_len)[0]
test_features[col] = normalize.fit_transform(test_features[col].values.reshape(vec_len_test,1)).reshape(1, vec_len_test)[0]
pd.set_option('display.max_colwidth', None)
 pd.set_option('display.max_columns', 500)
 pd.set_option('display.max_rows', 4000 )<define_variables>
n_comp = 100
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])
 data2 =(PCA(n_components=n_comp, random_state=42 ).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 )
seed = 2020
random.seed(seed)
 np.random.seed(seed )<set_options>
n_comp = 20
data = pd.concat([pd.DataFrame(train_features[CELLS]), pd.DataFrame(test_features[CELLS])])
 data2 =(PCA(n_components=n_comp, random_state=42 ).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 )
sns.set()
DEFAULT_FIG_WIDTH = 20
sns.set_context("paper", font_scale=1.2 )<import_modules>
var_thresh = VarianceThreshold(threshold=0.8)
 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
print('Python : ' + sys.version.split('
')[0])
 print('Numpy : ' + np.__version__)
 print('Pandas : ' + pd.__version__)
<import_modules>
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 )
print('Prophet : ' + fbprophet.__version__ )<define_variables>
train['cp_time'] = train['cp_time'].map({24:0, 48:0.5, 72:1})
 train['cp_dose'] = train['cp_dose'].map({'D1':0,'D2':1})
 test['cp_time'] = test['cp_time'].map({24:0, 48:0.5, 72:1})
 test['cp_dose'] = test['cp_dose'].map({'D1':0,'D2':1} )
HOME = "./"
DATA_HOME = "/kaggle/input/m5-forecasting-accuracy/"
TRAIN_DATA_HOME = DATA_HOME
CALENDAR = DATA_HOME + "calendar.csv"
SALES = DATA_HOME + "sales_train_validation.csv"
PRICES = DATA_HOME + "sell_prices.csv"
MODELS_DIR = "models"
if not os.path.exists(MODELS_DIR):
os.makedirs(MODELS_DIR)
 NUM_SERIES = 30490
NUM_TRAINING = 1913
NUM_TEST = NUM_TRAINING + 2 * 28
MAX_LEVEL = 9<define_variables>
target_cols = target.drop('sig_id', axis=1 ).columns.values.tolist()
target_cols
series_ids = np.empty(NUM_SERIES, dtype=object)
 item_ids = np.empty(NUM_SERIES, dtype=object)
 dept_ids = np.empty(NUM_SERIES, dtype=object)
 cat_ids = np.empty(NUM_SERIES, dtype=object)
 store_ids = np.empty(NUM_SERIES, dtype=object)
 state_ids = np.empty(NUM_SERIES, dtype=object)
 qties = np.zeros(( NUM_TRAINING, NUM_SERIES), dtype=float)
 sell_prices = np.zeros(( NUM_TEST, NUM_SERIES), dtype=float )<load_from_csv>
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 )
id_idx = {}
with open(SALES, "r", newline='')as f:
is_header = True
i = 0
for row in csv.reader(f):
if is_header:
is_header = False
continue
series_id, item_id, dept_id, cat_id, store_id, state_id = row[0:6]
series_id = f"{item_id}_{store_id}"
qty = np.array(row[6:], dtype=float)
 series_ids[i] = series_id
item_ids[i] = item_id
dept_ids[i] = dept_id
cat_ids[i] = cat_id
store_ids[i] = store_id
state_ids[i] = state_id
qties[:, i] = qty
id_idx[series_id] = i
i += 1<load_from_csv>
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 )
wm_yr_wk_idx = defaultdict(list)
 with open(CALENDAR, "r", newline='')as f:
for row in csv.DictReader(f):
d = int(row['d'][2:])
 wm_yr_wk_idx[row['wm_yr_wk']].append(d )<load_from_csv>
DEVICE =('cuda' if torch.cuda.is_available() else 'cpu')
 EPOCHS = 25
BATCH_SIZE = 128
LEARNING_RATE = 1e-3
WEIGHT_DECAY = 1e-5
NFOLDS = 7
EARLY_STOPPING_STEPS = 10
EARLY_STOP = False
num_features=len(feature_cols)
 num_targets=len(target_cols)
 hidden_size=1024
with open(PRICES, "r", newline='')as f:
is_header = True
for row in csv.reader(f):
if is_header:
is_header = False
continue
store_id, item_id, wm_yr_wk, sell_price = row
series_id = f"{item_id}_{store_id}"
series_idx = id_idx[series_id]
for d in wm_yr_wk_idx[wm_yr_wk]:
sell_prices[d - 1, series_idx] = float(sell_price )<create_dataframe>
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=0.05, div_factor=1.5e3,
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
for epoch in range(EPOCHS):
train_loss = train_fn(model, optimizer,scheduler, loss_fn, trainloader, DEVICE)
 print(f"FOLD: {fold}, EPOCH: {epoch}, train_loss: {train_loss}")
 valid_loss, valid_preds = valid_fn(model, loss_fn, validloader, DEVICE)
 print(f"FOLD: {fold}, EPOCH: {epoch}, 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
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
qty_ts = pd.DataFrame(qties,
index=range(1, NUM_TRAINING + 1),
columns=[state_ids, store_ids,
cat_ids, dept_ids, item_ids])
 qty_ts.index.names = ['d']
qty_ts.columns.names = ['state_id', 'store_id',
'cat_id', 'dept_id', 'item_id']
price_ts = pd.DataFrame(sell_prices,
index=range(1, NUM_TEST + 1),
columns=[state_ids, store_ids,
cat_ids, dept_ids, item_ids])
 price_ts.index.names = ['d']
price_ts.columns.names = ['state_id', 'store_id',
'cat_id', 'dept_id', 'item_id']<define_variables>
SEED = [0,1,2,3,4,5]
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 for seed in SEED:
oof_, predictions_ = run_k_fold(NFOLDS, seed)
 oof += oof_ / len(SEED)
 predictions += predictions_ / len(SEED)
 train[target_cols] = oof
test[target_cols] = predictions
LEVELS = {
1: [],
2: ['state_id'],
3: ['store_id'],
4: ['cat_id'],
5: ['dept_id'],
6: ['state_id', 'cat_id'],
7: ['state_id', 'dept_id'],
8: ['store_id', 'cat_id'],
9: ['store_id', 'dept_id'],
10: ['item_id'],
11: ['state_id', 'item_id'],
12: ['item_id', 'store_id']
}
COARSER = {
'state_id': [],
'store_id': ['state_id'],
'cat_id': [],
'dept_id': ['cat_id'],
'item_id': ['cat_id', 'dept_id']
}<groupby>
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 )
<feature_engineering><EOS> 
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 )