def main(c_runtime, c_transformer, c_model, c_trainer, c_log):
with blocktimer('Preprocess'):
train, test = Transformer.run(**c_transformer.__dict__)
X_train, y_train, X_test = split_X_y(train, test)
with blocktimer('Tune & Train'):
modelfactory = ModelFactory()
# tune the model params
model = modelfactory.create(c_model)
optimal_c_model = tune_gbdt_params(model, X_train, y_train,
c_trainer.n_splits)
# train with best params, full data
model = modelfactory.create(optimal_c_model)
model = model.train(X_train, y_train)
with blocktimer('Predict'):
sub = pd.DataFrame(columns=['TransactionID', 'isFraud'])
sub['TransactionID'] = test['TransactionID']
y_test = model.predict(X_test)
sub['isFraud'] = y_test
sub.to_csv(c_runtime.out_sub_path, index=False)
logger.info(f'Saved {c_runtime.out_sub_path}')
def main(c):
with blocktimer('Preprocess'):
train, test = Transformer.run(**c.transformer.__dict__)
X_train, y_train, X_test = split_X_y(train, test)
test = test.sort_values('TransactionDT')
with blocktimer('Tune & Train'):
modelfactory = ModelFactory()
# tune the model params
model = modelfactory.create(c.model)
optimal_c_model = tune_gbdt_params(model, X_train, y_train,
c.trainer.n_splits)
# train with best params, full data
model = modelfactory.create(optimal_c_model)
model = model.train(X_train, y_train)
# save results
model.save(c.model.dir /
f'model_{c.runtime.VERSION}_{c.model.TYPE}.pkl')
importance = pd.DataFrame(model.feature_importance,
index=X_train.columns,
columns=['importance'])
importance_path = c.runtime.ROOTDIR / 'feature/importance' / f'importance_{c.runtime.VERSION}.csv'
importance.to_csv(importance_path)
logger.info(f'Saved {str(importance_path)}')
with blocktimer('Predict'):
sub = pd.DataFrame(columns=['TransactionID', 'isFraud'])
sub['TransactionID'] = test['TransactionID']
y_test = model.predict(X_test)
sub['isFraud'] = y_test
sub.to_csv(c.runtime.out_sub_path, index=False)
logger.debug(f'Saved {c.runtime.out_sub_path}')
def tune_gbdt_params(model, X, y, n_splits) -> dict:
'''
Tune parameters by training
'''
# start tuning train log
create_logger('train', **c.log)
logger_train = getLogger('train')
logger_train.debug('{}\t{}\t{}\t{}'.format('fold', 'iteration',
'train_auc', 'val_auc'))
aucs = list()
feature_importances = pd.DataFrame()
feature_importances['feature'] = X.columns
# split data into train, validation
folds = TimeSeriesSplit(n_splits=n_splits)
for i, (idx_train, idx_val) in enumerate(folds.split(X, y)):
fold = i + 1
with blocktimer(f'Fold {fold}'):
# prepare
logger.info(f'Training on fold {fold}')
X_train = X.iloc[idx_train]
y_train = y.iloc[idx_train]
X_val = X.iloc[idx_val]
y_val = y.iloc[idx_val]
# train
model = model.train_and_validate(X_train, y_train, X_val, y_val,
logger_train, fold)
# record result
feature_importances[f'fold_{fold}'] = model.feature_importance
aucs.append(model.validation_auc)
# TODO: save models at each steps
logger.debug(f'Fold {fold} finished')
logger.info('Training has finished.')
logger.debug(f'Mean AUC: {np.mean(aucs)}')
# TODO: save feature importance and other
# make optimal config from result
optimal_c_model = model.config
if model.best_iteration is not None:
# new param
optimal_c_model.params['num_boost_round'] = model.best_iteration
else:
logger.warn(
'Did not meet early stopping. Try larger num_boost_rounds.')
# no need after optimized num_boost_round
del optimal_c_model.params['early_stopping_rounds']
return optimal_c_model
def tune_gbdt_params(model, X, y, n_splits) -> dict:
'''
Tune parameter num_boost_round
'''
# start tuning train log
create_logger('train', **c.log)
logger_train = getLogger('train')
logger_train.debug('{}\t{}\t{}\t{}'.format('fold', 'iteration',
'train_auc', 'val_auc'))
# aucs = list()
# split data into train, validation
folds = TimeSeriesSplit(n_splits=n_splits)
for i, (idx_train, idx_val) in enumerate(folds.split(X, y)):
fold = i + 1
with blocktimer(f'Training on Fold {fold}'):
X_train = X.iloc[idx_train]
y_train = y.iloc[idx_train]
X_val = X.iloc[idx_val]
y_val = y.iloc[idx_val]
# train
model = model.train_and_validate(X_train, y_train, X_val, y_val,
logger_train, fold)
model.save(
c.model.dir /
f'model_{c.runtime.VERSION}_{c.model.TYPE}_fold{fold}.pkl')
# record result
# aucs.append(model.val_auc)
# logger.info(f'train_auc: {model.train_auc} val_auc: {model.val_auc}')
# logger.info(f'Mean AUC: {np.mean(aucs)}')
# make optimal config from result
optimal_c_model = model.config
if model.best_iteration is not None:
optimal_c_model.params['num_boost_round'] = model.best_iteration
else:
logger.warn(
'Did not meet early stopping. Try larger num_boost_rounds.')
# no need after optimized num_boost_round
del optimal_c_model.params['early_stopping_rounds']
return optimal_c_model
def optimize_num_boost_round(model, X, y, n_splits, dsize, paths,
scores) -> dict:
'''
Estimate best num_boost_round by early stopping
'''
# split data into train, validation
folds = TimeSeriesSplit(n_splits=n_splits)
for i, (idx_train, idx_val) in enumerate(folds.split(X, y)):
fold = i + 1
with blocktimer(f'Training on Fold {fold}', level=INFO):
X_train = X.iloc[idx_train]
y_train = y.iloc[idx_train]
X_val = X.iloc[idx_val]
y_val = y.iloc[idx_val]
# train
model = model.train(
X_train,
y_train,
X_val,
y_val,
num_boost_round=c.train.num_boost_round,
early_stopping_rounds=c.train.early_stopping_rounds,
fold=fold)
model_fold_path = f'data/model/model_{c.runtime.version}_{c.model.type}_fold{fold}{dsize}.pkl'
paths.update({f'model_fold_{fold}_path': model_fold_path})
model.save(paths[f'model_fold_{fold}_path'])
# make optimal config from result
if model.best_iteration > 0:
logger.info(
f'Early stopping. Best iteration is: {model.best_iteration}')
scores.best_iteration = model.best_iteration
return model.best_iteration
else:
logger.warn(
'Did not meet early stopping. Try larger num_boost_rounds.')
scores.best_iteration = None
return c.train.num_boost_round
def main(c, r):
r.scores = {}
with blocktimer('Preprocess', level=INFO):
# unpack feature set list. set[i]={name: cols}
for name, col_list in c.feature.set.items():
in_train_path = f'data/feature/{name}_train.pkl'
in_test_path = f'data/feature/{name}_test.pkl'
cols = col_list
train = pd.read_pickle(in_train_path)
test = pd.read_pickle(in_test_path)
logger.debug(f'Loaded feature {name}')
if c.runtime.use_small_data:
frac = 0.001
train = train.sample(frac=frac, random_state=42)
test = test.sample(frac=frac, random_state=42)
logger.debug(f'train.shape: {train.shape}, test.shape: {test.shape}')
# Split into X, y
X_train = train.drop(c.feature.target, axis=1)
y_train = train[c.feature.target].copy(deep=True)
X_test = test
del train, test
with blocktimer('Tune hyper params', level=INFO):
'''
Run optimization
'''
mlflow.log_param('type', c.model.type)
mlflow.log_param('num_boost_round', c.train.num_boost_round)
mlflow.log_param('early_stopping_rounds',
c.train.early_stopping_rounds)
f = partial(objective,
X_train=X_train,
y_train=y_train,
X_test=X_test,
cols=cols,
c=c)
opt = optuna.create_study(
direction='maximize',
study_name=
f'{experiment_type}_{c.runtime.version}{c.runtime.dsize}',
storage=
f'sqlite:///data/optimization/{experiment_type}_{c.runtime.version}{c.runtime.dsize}.db',
load_if_exists=True)
opt.optimize(f, n_trials=c.optimize.n_trials)
trial = opt.best_trial
r.optimize = {}
r.scores.best_trial = trial.number
r.scores.best_score = trial.value
r.optimize.best_params = trial.params
tuned_params = c.model.params.copy()
tuned_params.update(trial.params)
r.model.tuned_params = tuned_params
logger.debug(f'Best trial: {trial.number}')
logger.debug(f'Best score: {trial.value}')
logger.debug(f'Best params: {trial.params}')
mlflow.log_metric('best_trial', trial.number)
mlflow.log_metric('best_score', trial.value)
mlflow.log_params(trial.params)
return r
def main(c):
dsize = '.small' if c.runtime.use_small_data is True else ''
paths = EasyDict()
scores = EasyDict()
modelfactory = ModelFactory()
with blocktimer('Preprocess', level=INFO):
paths.in_train_path = f'data/feature/{c.features[0]}_train.pkl'
paths.in_test_path = f'data/feature/{c.features[0]}_test.pkl'
train = pd.read_pickle(paths.in_train_path)
test = pd.read_pickle(paths.in_test_path)
logger.debug(f'Loaded feature {c.features[0]}')
if c.runtime.use_small_data:
frac = 0.001
train = train.sample(frac=frac, random_state=42)
test = test.sample(frac=frac, random_state=42)
logger.debug(f'train.shape: {train.shape}, test.shape: {test.shape}')
# Split into X, y
X_train = train.drop('isFraud', axis=1)
X_test = test
y_train = train['isFraud'].copy(deep=True)
del train, test
with blocktimer('Optimize', level=INFO):
if c.train.optimize_num_boost_round is True:
# tune the model params
model = modelfactory.create(c.model)
best_iteration = optimize_num_boost_round(model, X_train[c.cols],
y_train,
c.train.n_splits, dsize,
paths, scores)
else:
logger.debug('Skip optimization')
best_iteration = c.train.num_boost_round
with blocktimer('Train', level=INFO):
logger.debug(f'Now using the following {len(c.cols)} features.')
logger.debug(f'{np.array(c.cols)}')
# CHRIS - TRAIN 75% PREDICT 25%
idxT = X_train.index[:3 * len(X_train) // 4]
idxV = X_train.index[3 * len(X_train) // 4:]
'''
model = modelfactory.create(c.model)
model = model.train(X_train.loc[idxT, :], y_train[idxT],
X_train.loc[idxV, :], y_train[idxV],
num_boost_round=best_iteration)
importance = pd.DataFrame(model.feature_importance,
index=X_train.columns,
columns=['importance'])
# save results
paths.out_model_dir = f'data/model/model_{c.runtime.version}_{c.model.type}{dsize}.pkl'
paths.importance_path = f'feature/importance/importance_{c.runtime.version}{dsize}.csv'
model.save(paths.out_model_dir)
importance.to_csv(paths.importance_path)
'''
from sklearn.model_selection import GroupKFold
from sklearn.metrics import roc_auc_score
oof = np.zeros(len(X_train))
preds = np.zeros(len(X_test))
skf = GroupKFold(n_splits=6)
for i, (idxT, idxV) in enumerate(
skf.split(X_train, y_train, groups=X_train['DT_M'])):
month = X_train.iloc[idxV]['DT_M'].iloc[0]
logger.info(f'Fold {i+1} withholding month {month}')
logger.info(
f'rows of train ={len(idxT)}, rows of holdout ={len(idxV)}')
categorical_features = [
'ProductCD',
'M4',
'card1',
'card2',
'card3',
'card5',
'card6',
'addr1',
'addr2',
'dist1',
'dist2',
'P_emaildomain',
'R_emaildomain',
]
model = modelfactory.create(c.model)
model = model.train(
X_train[c.cols].iloc[idxT],
y_train.iloc[idxT],
X_train[c.cols].iloc[idxV],
y_train.iloc[idxV],
num_boost_round=best_iteration,
early_stopping_rounds=c.train.early_stopping_rounds,
# categorical_features=categorical_features,
fold=i + 1)
oof[idxV] += model.predict(X_train[c.cols].iloc[idxV])
preds += model.predict(X_test[c.cols]) / skf.n_splits
del model
logger.info(f'OOF cv= {roc_auc_score(y_train, oof)}')
paths.importance_path = f'feature/importance/importance_{c.runtime.version}{dsize}.csv'
# model.save(paths.out_model_dir)
'''
importance = pd.DataFrame(model.feature_importance,
index=X_train.columns,
columns=['importance'])
importance.to_csv(paths.importance_path)
'''
with blocktimer('Predict', level=INFO):
# y_test = model.predict(X_test)
sub = pd.DataFrame(columns=['TransactionID', 'isFraud'])
sub['TransactionID'] = X_test.reset_index()['TransactionID']
# sub['isFraud'] = y_test
sub['isFraud'] = preds
paths.out_sub_path = f'data/submission/submission_{c.runtime.version}{dsize}.csv'
sub.to_csv(paths.out_sub_path, index=False)
result = EasyDict()
result.update(c)
result.scores = scores
result.paths = paths
return result
def main(c):
dsize = '.small' if c.runtime.use_small_data is True else ''
paths = EasyDict()
scores = EasyDict()
result = EasyDict()
result.update(c)
modelfactory = ModelFactory()
with blocktimer('Preprocess', level=INFO):
paths.in_train_path = f'data/feature/{c.features[0]}_train.pkl'
paths.in_test_path = f'data/feature/{c.features[0]}_test.pkl'
train = pd.read_pickle(paths.in_train_path)
test = pd.read_pickle(paths.in_test_path)
logger.debug(f'Loaded feature {c.features[0]}')
if c.runtime.use_small_data:
frac = 0.001
train = train.sample(frac=frac, random_state=42)
test = test.sample(frac=frac, random_state=42)
logger.debug(f'train.shape: {train.shape}, test.shape: {test.shape}')
# Split into X, y
X_train = train.drop('isFraud', axis=1)
X_test = test
y_train = train['isFraud'].copy(deep=True)
del train, test
with blocktimer('Optimize num_boost_round', level=INFO):
if c.train.optimize_num_boost_round is True:
# tune the model params
model = modelfactory.create(c.model)
best_iteration = optimize_num_boost_round(model, X_train[c.cols],
y_train,
c.train.n_splits, dsize,
paths, scores)
else:
logger.debug('Skip optimization')
best_iteration = c.train.num_boost_round
with blocktimer('Optimize model params', level=INFO):
if c.train.optimize_model_params is True:
# define objective for optuna
def objectives(trial):
max_depth = trial.suggest_int('max_depth', 3, 12)
params = {
'boosting_type':
'gbdt',
# num_leaves should be smaller than approximately 2^max_depth*0.75
'num_leaves':
2**max_depth * 3 // 4,
'max_depth':
max_depth,
'learning_rate':
0.05,
'objective':
'binary',
'min_child_weight':
trial.suggest_loguniform('min_child_weight', 1e-3,
1e0), # 0.03454472573214212,
'reg_alpha':
trial.suggest_loguniform('reg_alpha', 1e-2,
1e0), # 0.3899927210061127,
'reg_lambda':
trial.suggest_loguniform('reg_lambda', 1e-2,
1e0), # 0.6485237330340494,
'random_state':
42,
'min_data_in_leaf':
trial.suggest_int('min_data_in_leaf', 50, 200), # 106,
'metric':
'auc',
'max_bin':
255
}
c.model.params = params
# Train by 6-fold CV
oof = np.zeros(len(X_train))
preds = np.zeros(len(X_test))
skf = GroupKFold(n_splits=6)
for i, (idxT, idxV) in enumerate(
skf.split(X_train, y_train, groups=X_train['DT_M'])):
fold = i + 1
month = X_train.iloc[idxV]['DT_M'].iloc[0]
model_fold_path = f'data/model/model_{c.runtime.version}_{c.model.type}_opt_fold{fold}{dsize}.pkl'
model = modelfactory.create(c.model)
logger.info(f'Fold {fold} withholding month {month}')
logger.info(
f'rows of train= {len(idxT)}, rows of holdout= {len(idxV)}'
)
model = model.train(
X_train[c.cols].iloc[idxT],
y_train.iloc[idxT],
X_train[c.cols].iloc[idxV],
y_train.iloc[idxV],
num_boost_round=best_iteration,
early_stopping_rounds=c.train.early_stopping_rounds,
# categorical_features=categorical_features,
fold=i + 1)
oof[idxV] = model.predict(X_train[c.cols].iloc[idxV])
preds += model.predict(X_test[c.cols]) / skf.n_splits
paths.update({f'model_fold_{fold}_path': model_fold_path})
model.save(paths[f'model_fold_{fold}_path'])
del model
score = roc_auc_score(y_train, oof)
logger.info(f'Fold {fold} OOF cv= {score}')
return score
# run optimization
opt = optuna.create_study(
direction='maximize',
study_name=f'parameter_study_0016{dsize}',
storage=
f'sqlite:///data/optimization/parameter_study_0016{dsize}.db',
load_if_exists=True)
opt.optimize(objectives, n_trials=20)
trial = opt.best_trial
logger.debug(f'Best trial: {trial.value}')
logger.debug(f'Best params: {trial.params}')
scores.best_trial = trial.value
result.optimize = {}
result.optimize.best_params = trial.params
else:
logger.debug('Skip optimization')
with blocktimer('Train', level=INFO):
if c.train.train_model:
logger.debug(f'Now using the following {len(c.cols)} features.')
logger.debug(f'{np.array(c.cols)}')
oof = np.zeros(len(X_train))
preds = np.zeros(len(X_test))
skf = GroupKFold(n_splits=6)
for i, (idxT, idxV) in enumerate(
skf.split(X_train, y_train, groups=X_train['DT_M'])):
month = X_train.iloc[idxV]['DT_M'].iloc[0]
logger.info(f'Fold {i+1} withholding month {month}')
logger.info(
f'rows of train ={len(idxT)}, rows of holdout ={len(idxV)}'
)
'''
categorical_features = ['ProductCD', 'M4',
'card1', 'card2', 'card3', 'card5', 'card6',
'addr1', 'addr2', 'dist1', 'dist2',
'P_emaildomain', 'R_emaildomain',
]
'''
model = modelfactory.create(c.model)
model = model.train(
X_train[c.cols].iloc[idxT],
y_train.iloc[idxT],
X_train[c.cols].iloc[idxV],
y_train.iloc[idxV],
num_boost_round=best_iteration,
early_stopping_rounds=c.train.early_stopping_rounds,
# categorical_features=categorical_features,
fold=i + 1)
oof[idxV] = model.predict(X_train[c.cols].iloc[idxV])
preds += model.predict(X_test[c.cols]) / skf.n_splits
del model
logger.info(f'OOF cv= {roc_auc_score(y_train, oof)}')
paths.importance_path = f'feature/importance/importance_{c.runtime.version}{dsize}.csv'
# model.save(paths.out_model_dir)
'''
importance = pd.DataFrame(model.feature_importance,
index=X_train.columns,
columns=['importance'])
importance.to_csv(paths.importance_path)
'''
with blocktimer('Predict', level=INFO):
if c.train.predict:
sub = pd.DataFrame(columns=['TransactionID', 'isFraud'])
sub['TransactionID'] = X_test.reset_index()['TransactionID']
sub['isFraud'] = preds
paths.out_sub_path = f'data/submission/submission_{c.runtime.version}{dsize}.csv'
sub.to_csv(paths.out_sub_path, index=False)
result.scores = scores
result.paths = paths
return result