def test_hook_from_json_config_for_losses(tmpdir, monkeypatch, params):
out_dir = tmpdir.join("test_hook_from_json_config_for_losses")
config_file = tmpdir.join("config.json")
config_file.write(get_json_config_for_losses(str(out_dir)))
monkeypatch.setenv(CONFIG_FILE_PATH_ENV_STR, str(config_file))
hook = Hook.create_from_json_file()
assert has_training_ended(out_dir) is False
run_xgboost_model(hook=hook, params=params)
trial = create_trial(str(out_dir))
eval_metric = params["eval_metric"]
test_metric = f"test-{eval_metric}"
train_metric = f"train-{eval_metric}"
if eval_metric == "rmse":
assert train_metric in trial.tensor_names(
collection=CollectionKeys.METRICS)
assert train_metric in trial.tensor_names(
collection=CollectionKeys.LOSSES)
assert test_metric in trial.tensor_names(
collection=CollectionKeys.METRICS)
assert test_metric in trial.tensor_names(
collection=CollectionKeys.LOSSES)
if eval_metric == "auc" or eval_metric == "map":
assert train_metric in trial.tensor_names(
collection=CollectionKeys.METRICS)
assert train_metric not in trial.tensor_names(
collection=CollectionKeys.LOSSES)
assert test_metric in trial.tensor_names(
collection=CollectionKeys.METRICS)
assert test_metric not in trial.tensor_names(
collection=CollectionKeys.LOSSES)
def test_hook_from_json_config_full(tmpdir, monkeypatch):
out_dir = tmpdir.join("test_hook_from_json_config_full")
config_file = tmpdir.join("config.json")
config_file.write(get_json_config_full(str(out_dir)))
monkeypatch.setenv(CONFIG_FILE_PATH_ENV_STR, str(config_file))
hook = Hook.create_from_json_file()
assert has_training_ended(out_dir) is False
run_xgboost_model(hook=hook)
def main():
args = parse_args()
train_files_path, validation_files_path = args.train, args.validation
train_features_path = os.path.join(args.train, 'train_features.csv')
train_labels_path = os.path.join(args.train, 'train_labels.csv')
val_features_path = os.path.join(args.validation, 'val_features.csv')
val_labels_path = os.path.join(args.validation, 'val_labels.csv')
print('Loading training dataframes...')
df_train_features = pd.read_csv(train_features_path, header=None)
df_train_labels = pd.read_csv(train_labels_path, header=None)
print('Loading validation dataframes...')
df_val_features = pd.read_csv(val_features_path, header=None)
df_val_labels = pd.read_csv(val_labels_path, header=None)
X = df_train_features.values
y = df_train_labels.values.reshape(-1)
val_X = df_val_features.values
val_y = df_val_labels.values.reshape(-1)
print('Train features shape: {}'.format(X.shape))
print('Train labels shape: {}'.format(y.shape))
print('Validation features shape: {}'.format(val_X.shape))
print('Validation labels shape: {}'.format(val_y.shape))
dtrain = xgboost.DMatrix(X, label=y)
dval = xgboost.DMatrix(val_X, label=val_y)
hook = Hook.create_from_json_file()
hook.train_data = dtrain
hook.validation_data = dval
watchlist = [(dtrain, "train"), (dval, "validation")]
params = {
"max_depth": args.max_depth,
"eta": args.eta,
"gamma": args.gamma,
"min_child_weight": args.min_child_weight,
"silent": args.silent,
"objective": args.objective,
"eval_metric": args.eval_metric
}
bst = xgboost.train(params=params,
dtrain=dtrain,
evals=watchlist,
num_boost_round=args.num_round,
callbacks=[hook])
model_dir = os.environ.get('SM_MODEL_DIR')
pkl.dump(bst, open(model_dir + '/model.bin', 'wb'))
def main():
args = parse_args()
train_files_path, validation_files_path = args.train, args.validation
train_files_list = glob.glob(train_files_path + '/*.*')
print(train_files_list)
val_files_list = glob.glob(validation_files_path + '/*.*')
print(val_files_list)
print('Loading training data...')
df_train = pd.concat(map(pd.read_csv, train_files_list))
print('Loading validation data...')
df_val = pd.concat(map(pd.read_csv, val_files_list))
print('Data loading completed.')
y = df_train.Target.values
X = df_train.drop(['Target'], axis=1).values
val_y = df_val.Target.values
val_X = df_val.drop(['Target'], axis=1).values
dtrain = xgboost.DMatrix(X, label=y)
dval = xgboost.DMatrix(val_X, label=val_y)
params = {
"max_depth": args.max_depth,
"eta": args.eta,
"gamma": args.gamma,
"min_child_weight": args.min_child_weight,
"silent": args.silent,
"objective": args.objective,
"num_class": args.num_class
}
hook = Hook.create_from_json_file()
hook.train_data = dtrain
hook.validation_data = dval
watchlist = [(dtrain, "train"), (dval, "validation")]
bst = xgboost.train(params=params,
dtrain=dtrain,
evals=watchlist,
num_boost_round=args.num_round,
callbacks=[hook])
model_dir = os.environ.get('SM_MODEL_DIR')
pkl.dump(bst, open(model_dir + '/model.bin', 'wb'))