def save_features(df, data_type='train'):
save_path = Path('../configs/feature/all.yml')
dh = DataHandler()
if not save_path.exists():
save_path.touch()
feature_dict = {'features': []}
else:
feature_dict = dh.load(save_path)
new_feature = sorted(set(feature_dict['features'] + df.columns.tolist()))
feature_dict['features'] = new_feature
dh.save(save_path, feature_dict)
for col in df.columns:
df[[col]].reset_index(drop=True).to_feather(f'../features/{col}_{data_type}.feather')
class Trainer:
def __init__(self, cfg):
self.cfg = cfg
self.cat_features = self.cfg.data.features.cat_features
self.oof = None
self.raw_preds = None
self.weights = []
self.models = []
self.scores = []
self.feature_importance_df = pd.DataFrame(
columns=['feature', 'importance'])
self.dh = DataHandler()
def train(self, train_df: pd.DataFrame, target_df: pd.DataFrame,
fold_df: pd.DataFrame):
self.oof = np.zeros(len(train_df))
for fold_, col in enumerate(fold_df.columns):
print(
f'\n========================== FOLD {fold_} ... ==========================\n'
)
logging.debug(
f'\n========================== FOLD {fold_} ... ==========================\n'
)
self._train_fold(train_df, target_df, fold_df[col])
print('\n\n===================================\n')
print(f'CV: {np.mean(self.scores):.4f}')
print('\n===================================\n\n')
logging.debug('\n\n===================================\n')
logging.debug(f'CV: {np.mean(self.scores):.4f}')
logging.debug('\n===================================\n\n')
return np.mean(self.scores)
def _train_fold(self, train_df, target_df, fold):
tr_x, va_x = train_df[fold == 0], train_df[fold > 0]
tr_y, va_y = target_df[fold == 0], target_df[fold > 0]
weight = fold.max()
self.weights.append(weight)
model = factory.get_model(self.cfg.model)
model.fit(tr_x, tr_y, va_x, va_y, self.cat_features)
va_pred = model.predict(va_x, self.cat_features)
if self.cfg.data.target.reconvert_type:
va_y = getattr(np, self.cfg.data.target.reconvert_type)(va_y)
va_pred = getattr(np, self.cfg.data.target.reconvert_type)(va_pred)
va_pred = np.where(va_pred >= 0, va_pred, 0)
self.models.append(model)
self.oof[va_x.index] = va_pred.copy()
score = factory.get_metrics(self.cfg.common.metrics.name)(va_y,
va_pred)
self.scores.append(score)
if self.cfg.model.name in ['lightgbm', 'catboost', 'xgboost']:
importance_fold_df = pd.DataFrame()
fold_importance = model.extract_importances()
importance_fold_df['feature'] = train_df.columns
importance_fold_df['importance'] = fold_importance
self.feature_importance_df = pd.concat(
[self.feature_importance_df, importance_fold_df], axis=0)
def predict(self, test_df):
preds = np.zeros(len(test_df))
for fold_, model in enumerate(self.models):
pred = model.predict(test_df, self.cat_features)
if self.cfg.data.target.reconvert_type:
pred = getattr(np, self.cfg.data.target.reconvert_type)(pred)
pred = np.where(pred >= 0, pred, 0)
preds += pred.copy() * self.weights[fold_]
self.raw_preds = preds.copy()
return preds
def save(self, run_name):
log_dir = Path(f'../logs/{run_name}')
self.dh.save(log_dir / 'oof.npy', self.oof)
self.dh.save(log_dir / 'raw_preds.npy', self.raw_preds)
self.dh.save(log_dir / 'importance.csv', self.feature_importance_df)
self.dh.save(log_dir / 'model_weight.pkl', self.models)
