def train_h2o(X: pd.DataFrame, y: pd.Series, config: Config):
h2o.init()
X["target"] = y
train = h2o.H2OFrame(X)
train_x = train.columns
train_y = "target"
train_x.remove(train_y)
if config["mode"] == "classification":
train[train_y] = train[train_y].asfactor()
aml = H2OAutoML(max_runtime_secs=int(config.time_left() * 0.9),
max_models=20,
nfolds=3,
exclude_algos=["GBM", "DeepLearning", "DRF"],
seed=42)
aml.train(x=train_x, y=train_y, training_frame=train)
config['params']['pipeline'][config["stage"]]["model"] = h2o.save_model(
model=aml.leader, path=config.model_dir + "/h2o.model", force=True)
if config.verbose: print(aml.leaderboard)
X.drop("target", axis=1, inplace=True)
def train_lightgbm(X: pd.DataFrame, y: pd.Series, config: Config):
params = {
"objective": "regression" if config.is_regression() else "binary",
"metric": "rmse" if config.is_regression() else "auc",
"verbosity": -1,
"seed": 1,
}
X_sample, y_sample = data_sample(X, y, config, nrows=20000)
hyperparams = hyperopt_lightgbm(X_sample, y_sample, params, config)
X_train, X_val, y_train, y_val = data_split(X, y, config)
config["model"] = lgb.train(
{**params, **hyperparams},
lgb.Dataset(X_train, label=y_train),
5000,
lgb.Dataset(X_val, label=y_val),
early_stopping_rounds=100,
verbose_eval=100,
)
config.save()
try:
with time_limit(config.time_left() - 10):
config["model"] = lgb.train(
{**params, **hyperparams},
lgb.Dataset(X, label=y),
int(1.2 * config["model"].best_iteration),
)
except TimeoutException:
Log.print("Timed out!")
def train_lightgbm(X: pd.DataFrame, y: pd.Series, config: Config):
params = {
"objective":
"regression" if config["mode"] == "regression" else "binary",
"metric": "rmse" if config["mode"] == "regression" else "auc",
"verbosity": -1,
"seed": 1,
}
X_sample, y_sample = data_sample(X, y)
hyperparams = hyperopt_lightgbm(X_sample, y_sample, params, config)
for i in range(1):
print(
'################################################################## cv '
+ str(i))
t1_bagging = time.time()
params['seed'] = i + 1
# cv
nfold = 5
if config["mode"] == 'classification':
skf = StratifiedKFold(n_splits=nfold,
shuffle=True,
random_state=777)
else:
skf = KFold(n_splits=nfold, shuffle=True, random_state=777)
skf_split = skf.split(X, y)
log('####################################################################### begin cv'
)
log('####### cur time = ' +
str(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S")))
score_list = []
config["model"] = []
for fid, (train_idx, valid_idx) in enumerate(skf_split):
t1_cv = time.time()
print("FoldID:{}".format(fid))
X_train, y_train = X.iloc[train_idx], y.iloc[train_idx]
X_valid, y_valid = X.iloc[valid_idx], y.iloc[valid_idx]
dtrain = lgb.Dataset(X_train, label=y_train)
dvalid = lgb.Dataset(X_valid, label=y_valid, reference=dtrain)
cur_model = lgb.train({
**params,
**hyperparams
},
dtrain,
3000,
dvalid,
early_stopping_rounds=50,
verbose_eval=100)
config["model"].append(cur_model)
score_list.append(cur_model.best_score)
# gc.collect()
sys.stdout.flush()
t2_cv = time.time()
time_left = config.time_left()
print('######### cv' + str(time_left))
if (t2_cv - t1_cv) * (nfold - fid + 1) >= time_left:
pass
#break
log('######################################################################### end cv'
)
log('####### cur time = ' +
str(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S")))
valid_auc = np.array(
[i['valid_0'][params['metric']] for i in score_list])
print('valid', valid_auc, np.mean(valid_auc))
cv_score = pd.DataFrame(
{'cv': np.hstack([valid_auc, np.mean(valid_auc)])})
path = config['path_pred']
print(path)
cv_score.to_csv(path + '/cv_score_' + str(i) + '.csv', index=False)
t2_bagging = time.time()
time_left = config.time_left()
print('#########bagging' + str(time_left))
if (t2_bagging - t1_bagging) * 1.5 >= time_left:
#break
pass
