def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}', filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='train')
valid = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='valid')
train = train.sample(15000000, random_state=SEED)
# qs = pd.read_csv('./data/output/question_lsi50.csv')
# qs = qs[['question_id','tags_lsi']]
# qs = qs.rename(columns={'question_id':'content_id'})
# train = pd.merge(train,qs,on='content_id',how='left')
# valid = pd.merge(valid,qs,on='content_id',how='left')
# train = data_util.reduce_mem_usage(train)
# valid = data_util.reduce_mem_usage(valid)
LOG.info(
f'train_size:{train[USE_COLS].shape} valid_size:{valid[USE_COLS].shape}'
)
model, fi, valid['pred'] = run_lgb(train=train, valid=valid, LOG=LOG)
data_util.seve_model(model, fi, file_name)
valid[['row_id', 'pred']].to_feather(f'./data/oof/{file_name}.feather')
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}', filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='train')
valid = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='valid')
train = train.sample(15000000, random_state=SEED)
# train = data_util.reduce_mem_usage(train)
# valid = data_util.reduce_mem_usage(valid)
LOG.info(
f'train_size:{train[USE_COLS].shape} valid_size:{valid[USE_COLS].shape}'
)
model, fi, valid['pred'] = run_lgb(train=train, valid=valid, LOG=LOG)
data_util.seve_model(model, fi, file_name)
valid[['row_id', 'pred']].to_feather(f'./data/oof/{file_name}.feather')
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}', filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train_df = data_util.load_features(feature_list)
train_df = train_df.sample(frac=0.10, random_state=127)
valid_df = train_df.sample(frac=0.02, random_state=127)
valid_id = valid_df['row_id']
train_df = train_df[~train_df['row_id'].isin(valid_id)]
train_df = train_df.reset_index(drop=True)
valid_df = valid_df.reset_index(drop=True)
LOG.info(f'train shape : {train_df.shape}')
LOG.info(f'valid shape : {valid_df.shape}')
train_x = train_df[USE_COLS]
train_y = train_df[TARGET]
valid_x = valid_df[USE_COLS]
valid_y = valid_df[TARGET]
lgb_model, fi, valid_df['pred'] = run_lgb(train_x=train_x,
train_y=train_y,
valid_x=valid_x,
valid_y=valid_y,
LOG=LOG)
data_util.seve_model(lgb_model, fi, file_name)
valid_df[['user_id', 'pred']]
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}', filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='train')
valid = data_util.load_features(FEATURES_LIST,
path=f'features/{path}',
train_valid='valid')
qs = pd.read_csv('./data/input/question_cmnts.csv')
qs.columns = ['content_id', 'community']
train = pd.merge(train, qs, on='content_id', how='left')
valid = pd.merge(valid, qs, on='content_id', how='left')
# train = data_util.reduce_mem_usage(train)
# valid = data_util.reduce_mem_usage(valid)
LOG.info(
f'train_size:{train[USE_COLS].shape} valid_size:{valid[USE_COLS].shape}'
)
model, fi, valid['pred'] = run_lgb(train=train, valid=valid, LOG=LOG)
data_util.seve_model(model, fi, file_name)
valid[['row_id', 'pred']].to_feather(f'./data/oof/{file_name}.feather')
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}',filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train = data_util.load_features(FEATURES_LIST,path='features/mini_data',train_valid='train')
valid = data_util.load_features(FEATURES_LIST,path='features/mini_data',train_valid='valid')
LOG.info(f'train_size:{train.shape} valid_size:{valid.shape}')
model,fi,valid_pred = run_lgb(train=train,valid=valid,LOG=LOG)
data_util.seve_model(model,fi,file_name)
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}',filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train = data_util.load_features(FEATURES_LIST,path=f'features/{path}',train_valid='train')
valid = data_util.load_features(FEATURES_LIST,path=f'features/{path}',train_valid='valid')
train = train.sample(15000000, random_state = SEED)
questions = pd.read_csv('./data/input/questions.csv')
lst = []
for tags in questions["tags"]:
ohe = np.zeros(188)
if str(tags) != "nan":
for tag in tags.split():
ohe += np.eye(188)[int(tag)]
lst.append(ohe)
tags_df = pd.DataFrame(lst, columns=[f"tag_{i}" for i in range(188)]).astype(int)
questions = pd.concat([questions,tags_df],axis=1)
questions = questions.rename(columns={'question_id':'content_id'})
questions = questions[QS+['content_id']]
train = pd.merge(train,questions,on='content_id',how='left')
valid = pd.merge(valid,questions,on='content_id',how='left')
# qs = pd.read_csv('./data/output/question_lsi50.csv')
# qs = qs[['question_id','tags_lsi']]
# qs = qs.rename(columns={'question_id':'content_id'})
# train = pd.merge(train,qs,on='content_id',how='left')
# valid = pd.merge(valid,qs,on='content_id',how='left')
# train = data_util.reduce_mem_usage(train)
# valid = data_util.reduce_mem_usage(valid)
LOG.info(f'train_size:{train[USE_COLS].shape} valid_size:{valid[USE_COLS].shape}')
model,fi,valid['pred'] = run_lgb(train=train,valid=valid,LOG=LOG)
data_util.seve_model(model,fi,file_name)
valid[['row_id','pred']].to_feather(f'./data/oof/{file_name}.feather')
def main():
file_name = os.path.basename(__file__)[:-3]
LOG = logger.Logger(name=f'{file_name}', filename=file_name)
LOG.info('base line')
LOG.info(f'{USE_COLS}')
train_df = data_util.load_features(feature_list)
train_index = pd.read_feather(f'./data/train_valid/cv1_train.feather')
valid_index = pd.read_feather(f'./data/train_valid/cv1_valid.feather')
train = train_df[train_df['row_id'].isin(train_index['row_id'])]
valid = train_df[train_df['row_id'].isin(valid_index['row_id'])]
del train_df
gc.collect()
LOG.info(f'train_size:{train.shape} valid_size:{valid.shape}')
model, fi, valid_pred = run_lgb(train=train, valid=valid, LOG=LOG)
data_util.seve_model(model, fi, file_name)
