i can't run the code without QCsamples

[YettaWang]

I got the error:
the settings of training：
task: train
meta_data: ./allearly_MSRT_knn_log.csv
sample_data: ./batch1_6_alldata_order_batch_noqc.csv
train_data: all
save: ./result_noqc
ae_encoder_units: [1000, 1000]
ae_decoder_units: [1000, 1000]
disc_b_units: [250, 250]
disc_o_units: [250, 250]
bottle_num: 500
dropouts: (0.3, 0.1, 0.3, 0.3)
lambda_b: 1.0
lambda_o: 1.0
lr_rec: 0.0002
lr_disc_b: 0.005
lr_disc_o: 0.0005
epoch: (1000, 10, 700)
use_batch_for_order: True
batch_size: 64
load: None
visdom_env: main
visdom_port: 8097
num_workers: 12
use_log: False
use_batch: None
sample_size: None
random_seed: 1234
device: None

Traceback (most recent call last):
File "main.py", line 83, in
main()
File "main.py", line 30, in main
random_seed=opts.random_seed)
File "/storage1/lilab/student/ynwang/meta_data/batch_effact/knn_1499features/NormAE/datasets.py", line 136, in get_metabolic_data
meta_df, y_df = pre_transfer(meta_df, y_df)
File "/storage1/lilab/student/ynwang/meta_data/batch_effact/knn_1499features/NormAE/transfer.py", line 24, in call
x = self.scaler.fit_transform(values)
File "/home/ynwang/miniconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/base.py", line 852, in fit_transform
return self.fit(X, **fit_params).transform(X)
File "/home/ynwang/miniconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/preprocessing/_data.py", line 806, in fit
return self.partial_fit(X, y, sample_weight)
File "/home/ynwang/miniconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/preprocessing/_data.py", line 847, in partial_fit
reset=first_call,
File "/home/ynwang/miniconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/base.py", line 566, in _validate_data
X = check_array(X, **check_params)
File "/home/ynwang/miniconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py", line 817, in check_array
% (n_features, array.shape, ensure_min_features, context)
ValueError: Found array with 0 feature(s) (shape=(598, 0)) while a minimum of 1 is required by StandardScaler.

[S-KD]

I received the same error without QC samples. I narrow down the source of this error to lines 94-97 in datasets.py. Unfortunately, simply commenting these lines leads to new errors because later visual.py expect qc_pca. It appears that in the current form tool can't be used without QC samples.

@luyiyun could you please update the NormAE, so it can be used without QC samples?