Tried to run Luis' code. Encountered error again. Going to add checkp…

…oint to save progress.

BML_project/active_learning/ss_active_learning.py

@@ -55,7 +55,8 @@ def run_minibatch_kmeans(data_loader, n_clusters, device, batch_size=100):
     # Iterate through data_loader and fit MiniBatchKMeans
     for batch in data_loader:
         data = batch['data'].view(batch['data'].size(0), -1).to(device).cpu().numpy()
-        minibatch_kmeans.partial_fit(data)
+        # minibatch_kmeans.partial_fit(data)
+        minibatch_kmeans.fit(data) # Dong, 01/22/2024: Debug
 
     return minibatch_kmeans
 

BML_project/cassey_CS330_torch.yml

@@ -0,0 +1,272 @@
+name: CS330_torch
+channels:
+  - pytorch
+  - nvidia
+  - anaconda
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - abseil-cpp=20211102.0=h27087fc_1
+  - absl-py=2.0.0=pyhd8ed1ab_0
+  - aiohttp=3.8.5=py311h5eee18b_0
+  - aiosignal=1.3.1=pyhd8ed1ab_0
+  - asttokens=2.4.0=pyhd8ed1ab_0
+  - async-timeout=4.0.2=py311h06a4308_0
+  - attrs=23.1.0=pyh71513ae_1
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - backports=1.0=pyhd8ed1ab_3
+  - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0
+  - blas=1.1=openblas
+  - blinker=1.6.3=pyhd8ed1ab_0
+  - bottleneck=1.3.5=py311hbed6279_0
+  - brotli=1.0.9=h9c3ff4c_4
+  - brotlipy=0.7.0=py311h5eee18b_1002
+  - bzip2=1.0.8=h7b6447c_0
+  - c-ares=1.19.1=h5eee18b_0
+  - ca-certificates=2023.12.12=h06a4308_0
+  - cachetools=5.3.1=pyhd8ed1ab_0
+  - cairo=1.16.0=hb05425b_5
+  - certifi=2023.11.17=py311h06a4308_0
+  - cffi=1.15.1=py311h5eee18b_3
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - click=8.1.7=unix_pyh707e725_0
+  - cloudpickle=2.2.1=pyhd8ed1ab_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - comm=0.1.4=pyhd8ed1ab_0
+  - contourpy=1.0.5=py311hdb19cb5_0
+  - cryptography=41.0.3=py311hdda0065_0
+  - cuda-cudart=11.8.89=0
+  - cuda-cupti=11.8.87=0
+  - cuda-libraries=11.8.0=0
+  - cuda-nvrtc=11.8.89=0
+  - cuda-nvtx=11.8.86=0
+  - cuda-runtime=11.8.0=0
+  - cycler=0.12.1=pyhd8ed1ab_0
+  - cyrus-sasl=2.1.28=h52b45da_1
+  - dbus=1.13.18=hb2f20db_0
+  - debugpy=1.6.7=py311h6a678d5_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - eigen=3.4.0=h4bd325d_0
+  - exceptiongroup=1.1.3=pyhd8ed1ab_0
+  - executing=1.2.0=pyhd8ed1ab_0
+  - expat=2.5.0=h6a678d5_0
+  - ffmpeg=4.2.2=h20bf706_0
+  - filelock=3.9.0=py311h06a4308_0
+  - fontconfig=2.14.1=h4c34cd2_2
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.12.1=h4a9f257_0
+  - frozenlist=1.3.3=py311h5eee18b_0
+  - fsspec=2023.10.0=pyhca7485f_0
+  - giflib=5.2.1=h5eee18b_3
+  - glib=2.69.1=he621ea3_2
+  - gmp=6.2.1=h295c915_3
+  - gmpy2=2.1.2=py311hc9b5ff0_0
+  - gnutls=3.6.15=he1e5248_0
+  - google-auth=2.23.2=pyhca7485f_0
+  - google-auth-oauthlib=1.0.0=pyhd8ed1ab_1
+  - googledrivedownloader=0.4=pyhd3deb0d_1
+  - gpytorch=1.11=pyhd8ed1ab_0
+  - graphite2=1.3.14=h295c915_1
+  - grpc-cpp=1.48.2=he1ff14a_1
+  - grpcio=1.48.2=py311he1ff14a_1
+  - gst-plugins-base=1.14.1=h6a678d5_1
+  - gstreamer=1.14.1=h5eee18b_1
+  - h5py=3.9.0=py311hdd6beaf_0
+  - harfbuzz=4.3.0=hf52aaf7_1
+  - hdf5=1.12.1=h2b7332f_3
+  - icu=58.2=hf484d3e_1000
+  - idna=3.4=py311h06a4308_0
+  - imageio=2.31.5=pyh8c1a49c_0
+  - importlib-metadata=6.8.0=pyha770c72_0
+  - importlib_metadata=6.8.0=hd8ed1ab_0
+  - iniconfig=1.1.1=pyhd3eb1b0_0
+  - intel-openmp=2023.1.0=hdb19cb5_46305
+  - ipykernel=6.25.2=pyh2140261_0
+  - ipython=8.16.1=pyh0d859eb_0
+  - jaxtyping=0.2.25=pyhd8ed1ab_0
+  - jedi=0.19.1=pyhd8ed1ab_0
+  - jinja2=3.1.2=py311h06a4308_0
+  - joblib=1.2.0=py311h06a4308_0
+  - jpeg=9e=h5eee18b_1
+  - jupyter_client=8.3.1=pyhd8ed1ab_0
+  - jupyter_core=4.12.0=py311h38be061_0
+  - kiwisolver=1.4.4=py311h6a678d5_0
+  - krb5=1.20.1=h143b758_1
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libclang=14.0.6=default_hc6dbbc7_1
+  - libclang13=14.0.6=default_he11475f_1
+  - libcublas=11.11.3.6=0
+  - libcufft=10.9.0.58=0
+  - libcufile=1.7.2.10=0
+  - libcups=2.4.2=h2d74bed_1
+  - libcurand=10.3.3.141=0
+  - libcurl=7.88.1=h251f7ec_2
+  - libcusolver=11.4.1.48=0
+  - libcusparse=11.7.5.86=0
+  - libdeflate=1.17=h5eee18b_1
+  - libedit=3.1.20221030=h5eee18b_0
+  - libev=4.33=h7f8727e_1
+  - libevent=2.1.12=hdbd6064_1
+  - libffi=3.4.4=h6a678d5_0
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgfortran=3.0.0=1
+  - libgfortran-ng=11.2.0=h00389a5_1
+  - libgfortran5=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libiconv=1.16=h7f8727e_2
+  - libidn2=2.3.4=h5eee18b_0
+  - libjpeg-turbo=2.0.0=h9bf148f_0
+  - libllvm14=14.0.6=hdb19cb5_3
+  - libnghttp2=1.52.0=h2d74bed_1
+  - libnpp=11.8.0.86=0
+  - libnvjpeg=11.9.0.86=0
+  - libopenblas=0.3.21=h043d6bf_0
+  - libopus=1.3.1=h7f98852_1
+  - libpng=1.6.39=h5eee18b_0
+  - libpq=12.15=hdbd6064_1
+  - libprotobuf=3.20.3=he621ea3_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libssh2=1.10.0=hdbd6064_2
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtasn1=4.19.0=h5eee18b_0
+  - libtiff=4.5.1=h6a678d5_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libuuid=1.41.5=h5eee18b_0
+  - libvpx=1.7.0=h439df22_0
+  - libwebp=1.3.2=h11a3e52_0
+  - libwebp-base=1.3.2=h5eee18b_0
+  - libxcb=1.15=h7f8727e_0
+  - libxkbcommon=1.0.1=h5eee18b_1
+  - libxml2=2.10.4=hcbfbd50_0
+  - libxslt=1.1.37=h2085143_0
+  - linear_operator=0.5.2=pyhd8ed1ab_0
+  - llvm-openmp=14.0.6=h9e868ea_0
+  - lockfile=0.12.2=py311h06a4308_0
+  - lz4-c=1.9.4=h6a678d5_0
+  - markdown=3.5=pyhd8ed1ab_0
+  - markupsafe=2.1.1=py311h5eee18b_0
+  - matplotlib=3.7.2=py311h06a4308_0
+  - matplotlib-base=3.7.2=py311ha02d727_0
+  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
+  - mkl=2023.1.0=h213fc3f_46343
+  - mkl-service=2.4.0=py311h5eee18b_1
+  - mpc=1.1.0=h10f8cd9_1
+  - mpfr=4.0.2=hb69a4c5_1
+  - mpmath=1.3.0=py311h06a4308_0
+  - multidict=6.0.2=py311h5eee18b_0
+  - munkres=1.1.4=pyh9f0ad1d_0
+  - mysql=5.7.24=h721c034_2
+  - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.5.6=pyhd8ed1ab_0
+  - nettle=3.7.3=hbbd107a_1
+  - networkx=3.1=py311h06a4308_0
+  - ninja=1.10.2=h06a4308_5
+  - ninja-base=1.10.2=hd09550d_5
+  - numexpr=2.8.7=py311h812550d_0
+  - numpy=1.26.0=py311h24aa872_0
+  - numpy-base=1.26.0=py311hbfb1bba_0
+  - oauthlib=3.2.2=pyhd8ed1ab_0
+  - openblas=0.3.3=ha44fe06_1
+  - opencv=4.6.0=py311h10ae9b0_5
+  - openh264=2.1.1=h4ff587b_0
+  - openjpeg=2.4.0=h3ad879b_0
+  - openssl=3.0.12=h7f8727e_0
+  - opt_einsum=3.3.0=pyhc1e730c_2
+  - packaging=23.2=pyhd8ed1ab_0
+  - pandas=2.0.3=py311ha02d727_0
+  - parso=0.8.3=pyhd8ed1ab_0
+  - pcre=8.45=h9c3ff4c_0
+  - pexpect=4.8.0=pyh1a96a4e_2
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pillow=10.0.1=py311ha6cbd5a_0
+  - pip=23.2.1=py311h06a4308_0
+  - pixman=0.40.0=h7f8727e_1
+  - pluggy=1.0.0=py311h06a4308_1
+  - ply=3.11=py_1
+  - pretty_errors=1.2.25=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.39=pyha770c72_0
+  - prompt_toolkit=3.0.39=hd8ed1ab_0
+  - protobuf=3.20.3=py311h6a678d5_0
+  - psutil=5.9.0=py311h5eee18b_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pyasn1=0.5.0=pyhd8ed1ab_0
+  - pyasn1-modules=0.3.0=pyhd8ed1ab_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pygments=2.16.1=pyhd8ed1ab_0
+  - pyjwt=2.8.0=pyhd8ed1ab_0
+  - pyopenssl=23.2.0=py311h06a4308_0
+  - pyparsing=3.0.9=pyhd8ed1ab_0
+  - pyqt=5.15.7=py311h6a678d5_0
+  - pyqt5-sip=12.11.0=py311h6a678d5_0
+  - pysocks=1.7.1=py311h06a4308_0
+  - pytest=7.4.0=py311h06a4308_0
+  - python=3.11.5=h955ad1f_0
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-tzdata=2023.3=pyhd3eb1b0_0
+  - python_abi=3.11=2_cp311
+  - pytorch=2.1.0=cpu_py311h53e38e9_0
+  - pytorch-cuda=11.8=h7e8668a_5
+  - pytorch-model-summary=0.1.1=py_0
+  - pytorch-mutex=1.0=cuda
+  - pytz=2023.3.post1=py311h06a4308_0
+  - pyu2f=0.1.5=pyhd8ed1ab_0
+  - pyyaml=6.0=py311h5eee18b_1
+  - pyzmq=25.1.0=py311h6a678d5_0
+  - qt-main=5.15.2=h7358343_9
+  - qt-webengine=5.15.9=hbbf29b9_6
+  - qtwebkit=5.212=h3fafdc1_5
+  - re2=2022.04.01=h27087fc_0
+  - readline=8.2=h5eee18b_0
+  - requests=2.31.0=py311h06a4308_0
+  - requests-oauthlib=1.3.1=pyhd8ed1ab_0
+  - rsa=4.9=pyhd8ed1ab_0
+  - scikit-learn=1.2.2=py311h6a678d5_1
+  - scipy=1.11.3=py311h24aa872_0
+  - seaborn=0.12.2=py311h06a4308_0
+  - setuptools=68.0.0=py311h06a4308_0
+  - sip=6.6.2=py311h6a678d5_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sqlite=3.41.2=h5eee18b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - sympy=1.11.1=py311h06a4308_0
+  - tbb=2021.8.0=hdb19cb5_0
+  - tensorboard=2.14.1=pyhd8ed1ab_0
+  - tensorboard-data-server=0.7.0=py311h52d8a92_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tk=8.6.12=h1ccaba5_0
+  - toml=0.10.2=pyhd8ed1ab_0
+  - torchaudio=2.1.0=py311_cu118
+  - torchinfo=1.8.0=pyhd8ed1ab_0
+  - torchtriton=2.1.0=py311
+  - torchvision=0.15.2=cpu_py311h6e929fa_0
+  - tornado=6.3.3=py311h5eee18b_0
+  - tqdm=4.66.1=pyhd8ed1ab_0
+  - traitlets=5.11.2=pyhd8ed1ab_0
+  - typeguard=2.13.3=py311h06a4308_0
+  - typing-extensions=4.7.1=py311h06a4308_0
+  - typing_extensions=4.7.1=py311h06a4308_0
+  - tzdata=2023c=h04d1e81_0
+  - urllib3=1.26.16=py311h06a4308_0
+  - wcwidth=0.2.8=pyhd8ed1ab_0
+  - werkzeug=3.0.0=pyhd8ed1ab_0
+  - wheel=0.41.2=py311h06a4308_0
+  - x264=1!157.20191217=h7b6447c_0
+  - xz=5.4.2=h5eee18b_0
+  - yaml=0.2.5=h7b6447c_0
+  - yarl=1.8.1=py311h5eee18b_0
+  - zeromq=4.3.4=h9c3ff4c_1
+  - zipp=3.17.0=pyhd8ed1ab_0
+  - zlib=1.2.13=h5eee18b_0
+  - zstd=1.5.5=hc292b87_0
+  - pip:
+    - beautifulsoup4==4.12.2
+    - gdown==4.7.1
+    - soupsieve==2.5
+    - torchsummary==1.5.1
+prefix: /home/doh16101/anaconda3/envs/CS330_torch

BML_project/models/ss_gp_model.py

@@ -20,7 +20,8 @@
 class MultitaskGPModel(gpytorch.models.ApproximateGP):
     def __init__(self):
         # Let's use a different set of inducing points for each latent function
-        inducing_points = torch.rand(num_latents, num_inducing_points, 127 * 128)  # Assuming flattened 128x128 images
+        inducing_points = torch.rand(num_latents, num_inducing_points, 128 * 128)  # Assuming flattened 128x128 images
+        # Dong, 01/22/2024: I will use 128 * 128.
 
         # We have to mark the CholeskyVariationalDistribution as batch
         # so that we learn a variational distribution for each task

BML_project/ss_main.py

@@ -4,13 +4,13 @@
 
 @author: lrm22005
 """
-import tqdm
+from tqdm import tqdm
 import torch
-from utils.data_loader import preprocess_data, split_uids, update_train_loader_with_uncertain_samples
+from utils_gp.data_loader import preprocess_data, split_uids, update_train_loader_with_uncertain_samples
 from models.ss_gp_model import MultitaskGPModel, train_gp_model
 from utils_gp.ss_evaluation import stochastic_evaluation, evaluate_model_on_all_data
 from active_learning.ss_active_learning import stochastic_uncertainty_sampling, run_minibatch_kmeans, stochastic_compare_kmeans_gp_predictions
-from utils.visualization import plot_comparative_results, plot_training_performance, plot_results
+from utils_gp.visualization import plot_comparative_results, plot_training_performance, plot_results
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -22,6 +22,7 @@ def main():
     data_format = 'pt'
     # Preprocess data
     train_loader, val_loader, test_loader = preprocess_data(data_format, clinical_trial_train, clinical_trial_test, clinical_trial_unlabeled, batch_size)
+    print('Debug: len(train_loader)',len(train_loader))
 
     kmeans_model = run_minibatch_kmeans(train_loader, n_clusters=n_classes, device=device)
 

BML_project/utils_gp/data_loader.py

@@ -12,10 +12,20 @@
 from torch.utils.data import Dataset, DataLoader
 from sklearn.preprocessing import StandardScaler
 from torchvision.transforms import ToTensor
+import socket
 
 def split_uids():
     # ====== Load the per subject arrythmia summary ======
-    df_summary = pd.read_csv(r'\\grove.ad.uconn.edu\research\ENGR_Chon\NIH_Pulsewatch_Database\Adjudication_UConn\final_attemp_4_1_Dong_Ohm_summary_20231025.csv')
+    your_computer_name = socket.gethostname()
+    if your_computer_name == 'localhost.localdomain':
+        # Dong, 12/09/2023: I am so sick of changing the path every time on different computer.
+        # This is Cassey's Luis server name.
+        df_summary = pd.read_csv(r'/mnt/r/ENGR_Chon/NIH_Pulsewatch_Database/Adjudication_UConn/final_attemp_4_1_Dong_Ohm_summary_20231025.csv')
+    elif your_computer_name == 'Darren_computer_name':
+        # Darren, you can put your computer name in the elif condition to separate it from Luis's computer.
+        df_summary = pd.read_csv(r'R:\ENGR_Chon\NIH_Pulsewatch_Database\Adjudication_UConn\final_attemp_4_1_Dong_Ohm_summary_20231025.csv')
+    elif your_computer_name == 'Luis_computer_name':
+        df_summary = pd.read_csv(r'\\grove.ad.uconn.edu\research\ENGR_Chon\NIH_Pulsewatch_Database\Adjudication_UConn\final_attemp_4_1_Dong_Ohm_summary_20231025.csv')
     df_summary['UID'] = df_summary['UID'].astype(str).str.zfill(3)
 
     df_summary['sample_nonAF'] = df_summary['NSR'] + df_summary['PACPVC'] + df_summary['SVT']
@@ -130,7 +140,6 @@ def __getitem__(self, idx):
         else:
             # Load data on-the-fly based on the segment_name
             time_freq_tensor = self.load_data(segment_name)
-
         return {'data': time_freq_tensor, 'label': label, 'segment_name': segment_name}
 
     def add_data_label_pair(self, data, label):
@@ -155,6 +164,7 @@ def extract_segment_names_and_labels(self):
         for UID in self.UIDs:
             label_file = os.path.join(self.labels_path, UID + "_final_attemp_4_1_Dong.csv")
             if os.path.exists(label_file):
+                print('Debug: this file exists',label_file)
                 label_data = pd.read_csv(label_file, sep=',', header=0, names=['segment', 'label'])
                 label_segment_names = label_data['segment'].apply(lambda x: x.split('.')[0])
                 for idx, segment_name in enumerate(label_segment_names):
@@ -207,20 +217,26 @@ def load_data_split_batched(data_path, labels_path, UIDs, batch_size, standardiz
     dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=drop_last, num_workers=num_workers, prefetch_factor=2)
     return dataloader
 
-def get_data_paths(data_format, is_linux=False, is_hpc=False):
-    if is_linux:
+def get_data_paths(data_format):
+    your_computer_name = socket.gethostname()
+    print('Debug: your_computer_name',your_computer_name)
+    if your_computer_name == 'localhost.localdomain':
         base_path = "/mnt/r/ENGR_Chon/Dong/MATLAB_generate_results/NIH_PulseWatch"
         labels_base_path = "/mnt/r/ENGR_Chon/NIH_Pulsewatch_Database/Adjudication_UConn"
         saving_base_path = "/mnt/r/ENGR_Chon/Luis/Research/Casseys_case/Project_1_analysis"
-    elif is_hpc:
+    elif your_computer_name == 'HPC_computer_name':
         base_path = "/gpfs/scratchfs1/kic14002/doh16101"
         labels_base_path = "/gpfs/scratchfs1/hfp14002/lrm22005"
         saving_base_path = "/gpfs/scratchfs1/hfp14002/lrm22005/Casseys_case/Project_1_analysis"
-    else:
+    elif your_computer_name == 'Darren_computer_name':
         # R:\ENGR_Chon\Dong\MATLAB_generate_results\NIH_PulseWatch
         base_path = "R:\ENGR_Chon\Dong\MATLAB_generate_results\\NIH_PulseWatch"
         labels_base_path = "R:\ENGR_Chon\\NIH_Pulsewatch_Database\Adjudication_UConn"
         saving_base_path = r"\\grove.ad.uconn.edu\research\ENGR_Chon\Luis\Research\Casseys_case"
+    else:
+        print('ERROR! YOUR DID NOT GET THE PATH.')
+        raise ValueError
+
     if data_format == 'csv':
         data_path = os.path.join(base_path, "TFS_csv")
         labels_path = os.path.join(labels_base_path, "final_attemp_4_1_Dong_Ohm")