def t003_lgb_train(args, script_name, configs, logger):
'''
policy
------------
* use original functions only if there's no pre-coded functions
in useful libraries such as sklearn.
todos
------------
* load features
* train the model
* save the followings
* logs
* oofs
* importances
* trained models
* submissions (if test mode)
'''
# -- Prepare for training
exp_time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
# -- Load train data
sel_log('loading training data ...', None)
trn_ids = pd.read_pickle(NES_DIR + 'trn_ID_code_w_fakes.pkl.gz',
compression='gzip')
tst_ids = pd.read_pickle(NES_DIR + 'tst_ID_code.pkl.gz',
compression='gzip')
target = pd.read_pickle(NES_DIR + 'target_w_fakes.pkl.gz',
compression='gzip')
if args.debug:
sample_idxes = trn_ids.reset_index(drop=True).sample(random_state=71,
frac=0.05).index
target = target.iloc[sample_idxes].reset_index(drop=True)
trn_ids = trn_ids.iloc[sample_idxes].reset_index(drop=True)
# load features
if configs['train']['all_features']:
_features = get_all_features(FEATURE_DIR)
else:
_features = configs['features']
trn_tst_df = load_features(_features, FEATURE_DIR, logger=logger)\
.set_index('ID_code')
# feature selection if needed
if configs['train']['feature_selection']:
trn_tst_df = select_features(trn_tst_df,
configs['train']['feature_select_path'],
configs['train']['metric'],
configs['train']['feature_topk'])
features = trn_tst_df.columns
# split train and test
sel_log(f'now splitting the df to train and test ones ...', None)
features_df = trn_tst_df.loc[trn_ids].reset_index(drop=True)
test_features_df = trn_tst_df.loc[tst_ids].reset_index(drop=True)
# -- Split using group k-fold w/ shuffling
# NOTE: this is not stratified, I wanna implement it in the future
if configs['train']['fold_type'] == 'skf':
skf = StratifiedKFold(configs['train']['fold_num'], random_state=71)
folds = skf.split(features_df, target)
configs['train']['single_model'] = False
else:
print(f"ERROR: wrong fold_type, {configs['train']['fold_type']}")
folds, pred_folds = tee(folds)
# -- Make training dataset
# print shape
sel_log(f'used features are {features_df.columns.tolist()}', logger)
sel_log(f'the shape features_df is {features_df.shape}', logger)
# -- CV
# Set params
PARAMS = configs['lgbm_params']
PARAMS['nthread'] = os.cpu_count()
sel_log('start training ...', None)
oofs = []
y_trues = []
val_idxes = []
scores = []
fold_importance_dict = {}
cv_model = []
for i, idxes in tqdm(list(enumerate(folds))):
trn_idx, val_idx = idxes
# -- Data resampling
# Stock original data for validation
fold_features_df, fold_target = value_resampling(
features_df.iloc[trn_idx],
target.iloc[trn_idx],
configs['train']['sampling_type'],
configs['train']['sampling_random_state'],
configs['train']['os_lim'],
configs['train']['pos_t'],
configs['train']['neg_t'],
logger=logger)
# make lgbm dataset
train_set = lightgbm.Dataset(fold_features_df, fold_target)
valid_set = lightgbm.Dataset(
features_df.iloc[val_idx],
target[val_idx],
)
# train
booster = lightgbm.train(
params=PARAMS.copy(),
train_set=train_set,
num_boost_round=100000,
valid_sets=[valid_set, train_set],
verbose_eval=1000,
early_stopping_rounds=2000,
callbacks=[log_evaluation(logger, period=1000)],
)
# predict using trained model
y_pred = booster.predict(features_df.values[val_idx],
num_iteration=None)
y_true = target.values[val_idx]
oofs.append(y_pred)
y_trues.append(y_true)
val_idxes.append(val_idx)
# Calc AUC
auc = roc_auc_score(y_true, y_pred)
sel_log(f'fold AUC: {auc}', logger=logger)
scores.append(auc)
# Save importance info
fold_importance_df = pd.DataFrame()
fold_importance_df['split'] = booster.feature_importance('split')
fold_importance_df['gain'] = booster.feature_importance('gain')
fold_importance_dict[i] = fold_importance_df
# save model
cv_model.append(booster)
auc_mean, auc_std = np.mean(scores), np.std(scores)
sel_log(f'AUC_mean: {auc_mean:.5f}, AUC_std: {auc_std:.5f}', logger)
# -- Post processings
filename_base = f'{script_name}_{exp_time}_{auc_mean:.5}'
# Save oofs
with open('./mnt/oofs/' + filename_base + '_oofs.pkl', 'wb') as fout:
pickle.dump([val_idxes, oofs], fout)
# Save importances
# save_importance(configs['features'], fold_importance_dict,
save_importance(features,
fold_importance_dict,
'./mnt/importances/' + filename_base + '_importances',
topk=100,
main_metric='gain')
# Save trained models
with open('./mnt/trained_models/' + filename_base + '_models.pkl',
'wb') as fout:
pickle.dump(cv_model, fout)
# --- Make submission file
if args.test:
if configs['train']['single_model']:
# train single model
best_iter = np.mean(
[booster.best_iteration for booster in cv_model])
single_train_set = lightgbm.Dataset(features_df, target.values)
single_booster = lightgbm.train(
params=PARAMS,
num_boost_round=int(best_iter * 1.3),
train_set=single_train_set,
verbose_eval=1000,
callbacks=[log_evaluation(logger, period=1000)],
)
# re-save model for prediction
# cv_model.append(single_booster)
# -- Prediction
sel_log('predicting for test ...', None)
preds = []
# for booster in tqdm(cv_model.boosters):
for booster in tqdm(cv_model):
pred = booster.predict(test_features_df.values, num_iteration=None)
pred = pd.Series(pred)
preds.append(pred.rank() / pred.shape)
if len(cv_model) > 1:
target_values = np.mean(preds, axis=0)
else:
target_values = preds[0]
# blend single model
if configs['train']['single_model']:
pred = single_booster.predict(test_features_df.values,
num_iteration=None)
pred = pd.Series(pred)
target_values = (target_values + (pred.rank() / pred.shape)) / 2
# -- Make submission file
sel_log(f'loading sample submission file ...', None)
sub_df = pd.read_csv('./mnt/inputs/origin/sample_submission.csv.zip',
compression='zip')
sub_df.target = target_values
# print stats
submission_filename = f'./mnt/submissions/{filename_base}_sub.csv.gz'
sel_log(f'saving submission file to {submission_filename}', logger)
sub_df.to_csv(submission_filename, compression='gzip', index=False)
if args.submit:
os.system(f'kaggle competitions submit '
f'santander-customer-transaction-prediction '
f'-f {submission_filename} -m "{args.message}"')
return auc_mean, auc_std
def t007_lgb_train(args, script_name, configs, logger):
'''
policy
------------
* use original functions only if there's no pre-coded functions
in useful libraries such as sklearn.
todos
------------
* load features
* train the model
* save the followings
* logs
* oofs
* importances
* trained models
* submissions (if test mode)
'''
# -- Load train data
sel_log('loading training data ...', None)
trn_ids = pd.read_pickle(NES_DIR + 'trn_ID_code.pkl.gz',
compression='gzip')
tst_ids = pd.read_pickle(NES_DIR + 'tst_ID_code.pkl.gz',
compression='gzip')
target = pd.read_pickle(NES_DIR + 'target.pkl.gz', compression='gzip')
if args.debug:
sample_idxes = trn_ids.reset_index(drop=True).sample(random_state=71,
frac=0.05).index
target = target.iloc[sample_idxes].reset_index(drop=True)
trn_ids = trn_ids.iloc[sample_idxes].reset_index(drop=True)
# load features
if configs['train']['all_features']:
_features = get_all_features(FEATURE_DIR)
else:
_features = configs['features']
trn_tst_df = load_features(_features, FEATURE_DIR, logger=logger)\
.set_index('ID_code')
# feature selection if needed
if configs['train']['feature_selection']:
trn_tst_df = select_features(trn_tst_df,
configs['train']['feature_select_path'],
configs['train']['metric'],
configs['train']['feature_topk'])
# split train and test
sel_log(f'now splitting the df to train and test ones ...', None)
features_df = trn_tst_df.loc[trn_ids].reset_index(drop=True)
test_features_df = trn_tst_df.loc[tst_ids].reset_index(drop=True)
# -- Split using group k-fold w/ shuffling
# NOTE: this is not stratified, I wanna implement it in the future
if configs['train']['fold_type'] == 'skf':
skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=4221)
folds = skf.split(features_df, target)
configs['train']['single_model'] = False
else:
print(f"ERROR: wrong fold_type, {configs['train']['fold_type']}")
folds, pred_folds = tee(folds)
# -- Make training dataset
# print shape
sel_log(f'used features are {features_df.columns.tolist()}', logger)
sel_log(f'the shape features_df is {features_df.shape}', logger)
# -- CV
# Set params
PARAMS = configs['lgbm_params']
PARAMS['nthread'] = os.cpu_count()
sel_log('start training ...', None)
with open(
'./mnt/trained_models/e071_e036_t006_w_pair_augmentation_7_7_2019-04-05-17-35-24_0.92357_models.pkl',
'rb') as fin:
cv_model = pickle.load(fin)
# --- Make submission file
if args.test:
# -- Prediction
sel_log('predicting for test ...', None)
preds = []
preds_no_rank = []
reals = np.load('./mnt/inputs/nes_info/real_samples_indexes.npz.npy')
# for booster in tqdm(cv_model.boosters):
for booster in tqdm(cv_model):
pred = booster.predict(test_features_df.values, num_iteration=None)
pred = pd.Series(pred)
# rank avg only using real part
preds_no_rank.append(pred.copy())
pred.iloc[reals] = pred.iloc[reals].rank() / reals.shape
preds.append(pred)
if len(cv_model) > 1:
target_values_no_rank = np.mean(preds_no_rank, axis=0)
else:
target_values_no_rank = preds[0]
# -- Make submission file
sel_log(f'loading sample submission file ...', None)
sub_df_no_rank = pd.read_csv(
'./mnt/inputs/origin/sample_submission.csv.zip', compression='zip')
sub_df_no_rank.target = target_values_no_rank
# print stats
submission_filename_no_rank = f'./mnt/submissions/temp_sub_no_rank.csv'
sub_df_no_rank.to_csv(submission_filename_no_rank, index=False)
return None, None, None
