Using ONTraC via Command Line

This document showing the step-by-step process of running OnTraC using Stereo-seq mouse midbrain dataset.

Preparation

The hardware environment used for this tutorial

CPU

lscpu

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         INTEL(R) XEON(R) PLATINUM 8568Y+
CPU family:                         6
Model:                              207
Thread(s) per core:                 1
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           2
Frequency boost:                    enabled
CPU(s) scaling MHz:                 100%
CPU max MHz:                        2301.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4600.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
L1d cache:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           600 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47
NUMA node1 CPU(s):                  48-95
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Note

We only use 4 CPUs here.

GPU

nvidia-smi

Thu Jun  5 15:32:39 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.07              Driver Version: 550.90.07      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H100 80GB HBM3          On  |   00000000:3D:00.0 Off |                  Off |
| N/A   26C    P0             66W /  700W |       1MiB /  81559MiB |      0%   E. Process |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

ONTraC Installation

source ~/.bash_profile  # change it to "source ~/.zshrc" if you are using zsh

conda activate ONTraC  # ONTraC is the conda environment name

pip install "ONTraC[analysis]==1.*"

Show output (Click to expand)

Collecting ONTraC==1.* (from ONTraC[analysis]==1.*)
  Downloading ontrac-1.2.0-py3-none-any.whl.metadata (7.0 kB)
Requirement already satisfied: pyyaml==6.0.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (6.0.1)
Requirement already satisfied: pandas==2.2.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.2.1)
Requirement already satisfied: scipy<1.15.0a0,>=1.14.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (1.14.1)
Requirement already satisfied: torch<2.7.0a0,>=2.6.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.6.0)
Requirement already satisfied: torch-geometric<2.7.0a0,>=2.6.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.6.1)
Requirement already satisfied: session-info in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (1.0.0)
Requirement already satisfied: matplotlib<3.9 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC[analysis]==1.*) (3.8.4)
Requirement already satisfied: seaborn in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC[analysis]==1.*) (0.13.2)
Requirement already satisfied: numpy<2,>=1.23.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (1.26.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2024.1)
Requirement already satisfied: contourpy>=1.0.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (1.2.1)
Requirement already satisfied: cycler>=0.10 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (4.51.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (1.4.5)
Requirement already satisfied: packaging>=20.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (24.0)
Requirement already satisfied: pillow>=8 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (10.3.0)
Requirement already satisfied: pyparsing>=2.3.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from matplotlib<3.9->ONTraC[analysis]==1.*) (3.1.2)
Requirement already satisfied: filelock in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.13.4)
Requirement already satisfied: typing-extensions>=4.10.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (4.11.0)
Requirement already satisfied: networkx in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.3)
Requirement already satisfied: jinja2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.1.3)
Requirement already satisfied: fsspec in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2024.3.1)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.127)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.127)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.127)
Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (9.1.0.70)
Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.5.8)
Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (11.2.1.3)
Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (10.3.5.147)
Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (11.6.1.9)
Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.3.1.170)
Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (0.6.2)
Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2.21.5)
Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.127)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (12.4.127)
Requirement already satisfied: triton==3.2.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.2.0)
Requirement already satisfied: sympy==1.13.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (1.13.1)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from sympy==1.13.1->torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (1.3.0)
Requirement already satisfied: aiohttp in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.9.5)
Requirement already satisfied: psutil>=5.8.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (5.9.8)
Requirement already satisfied: requests in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2.31.0)
Requirement already satisfied: tqdm in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (4.66.5)
Requirement already satisfied: stdlib-list in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from session-info->ONTraC==1.*->ONTraC[analysis]==1.*) (0.10.0)
Requirement already satisfied: six>=1.5 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (1.16.0)
Requirement already satisfied: aiosignal>=1.1.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from aiohttp->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (1.3.1)
Requirement already satisfied: attrs>=17.3.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from aiohttp->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (23.2.0)
Requirement already satisfied: frozenlist>=1.1.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from aiohttp->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (1.4.1)
Requirement already satisfied: multidict<7.0,>=4.5 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from aiohttp->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (6.0.5)
Requirement already satisfied: yarl<2.0,>=1.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from aiohttp->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (1.9.4)
Requirement already satisfied: MarkupSafe>=2.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from jinja2->torch<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2.1.5)
Requirement already satisfied: charset-normalizer<4,>=2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from requests->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from requests->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from requests->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2.2.1)
Requirement already satisfied: certifi>=2017.4.17 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from requests->torch-geometric<2.7.0a0,>=2.6.0->ONTraC==1.*->ONTraC[analysis]==1.*) (2024.2.2)
Downloading ontrac-1.2.0-py3-none-any.whl (74 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 74.8/74.8 kB 7.4 MB/s eta 0:00:00
Installing collected packages: ONTraC
Successfully installed ONTraC-1.2.0

Download Dataset

curl -O https://zenodo.org/records/15571644/files/Simulated_data.zip

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 46.7M  100 46.7M    0     0  18.4M      0  0:00:02  0:00:02 --:--:-- 18.5M

unzip Simulated_data.zip

Show output (Click to expand)

Archive:  Simulated_data.zip
   creating: Simulated_data/
   creating: Simulated_data/ONTraC_output/
   creating: Simulated_data/ONTraC_run_log/
   creating: Simulated_data/preprocessing/
   creating: Simulated_data/ONTraC_output/simulated_disconnected_hidden_feats_16_NN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_disconnected_hidden_feats_16_NN  
   creating: Simulated_data/ONTraC_output/simulated_data_n_gcn_layers_5_NN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_data_n_gcn_layers_5_NN  
   creating: Simulated_data/ONTraC_output/simulated_data_hidden_feats_8_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_data_hidden_feats_8_NT  
   creating: Simulated_data/ONTraC_output/simulated_non_linear_k_4_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_non_linear_k_4_NT  
   creating: Simulated_data/ONTraC_output/simulated_disconnected_hidden_feats_8_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_disconnected_hidden_feats_8_NT  
   creating: Simulated_data/ONTraC_output/simulated_disconnected_n_neighbors_10_GNN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_disconnected_n_neighbors_10_GNN  
   creating: Simulated_data/ONTraC_output/simulated_non_linear_k_10_GNN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_non_linear_k_10_GNN  
   creating: Simulated_data/ONTraC_output/simulated_disconnected_n_gcn_layers_5_NN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_disconnected_n_gcn_layers_5_NN  
   creating: Simulated_data/ONTraC_output/simulated_linear_base_GNN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_linear_base_GNN  
   creating: Simulated_data/ONTraC_output/simulated_data_k_4_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_data_k_4_NT  
   creating: Simulated_data/ONTraC_output/simulated_disconnected_n_gcn_layers_1_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_disconnected_n_gcn_layers_1_NT  
   creating: Simulated_data/ONTraC_output/simulated_linear_n_gcn_layers_6_GNN/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_linear_n_gcn_layers_6_GNN  
   creating: Simulated_data/ONTraC_output/simulated_data_n_neighbors_100_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_data_n_neighbors_100_NT  
   creating: Simulated_data/ONTraC_output/simulated_data_n_gcn_layers_1_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_data_n_gcn_layers_1_NT  
   creating: Simulated_data/ONTraC_output/simulated_non_linear_n_gcn_layers_4_NT/
  inflating: __MACOSX/Simulated_data/ONTraC_output/._simulated_non_linear_n_gcn_layers_4_NT  
   creating: Simulated_data/ONTraC_output/simulated_non_linear_n_gcn_layers_4_NN/
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ONTraC Run

ONTraC \
    --meta-input Simulated_data/preprocessing/Simulated_dataset_1/simulated_input.csv \
    --NN-dir output/simulation_NN \
    --GNN-dir output/simulation_GNN \
    --NT-dir output/simulation_NT \
    --device cuda \
    --epochs 1000 \
    --batch-size 5 \
    -s 42 \
    --patience 100 \
    --min-delta 0.001 \
    --min-epochs 50 \
    --lr 0.03 \
    --hidden-feats 4 \
    -k 6 \
    --modularity-loss-weight 0.3 \
    --regularization-loss-weight 0.1 \
    --purity-loss-weight 300 \
    --beta 0.03 2>&1 | tee log/simulation.log

Show output (Click to expand)

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                        version: 1.2.0

##################################################################################
16:10:00 --- INFO: ------------------ RUN params memo ------------------ 
16:10:00 --- INFO:             -------- I/O options -------             
16:10:00 --- INFO: Niche network output directory:  output/simulation_NN
16:10:00 --- INFO: GNN output directory:  output/simulation_GNN
16:10:00 --- INFO: Niche trajectory output directory:  output/simulation_NT
16:10:00 --- INFO: Meta data file:  Simulated_data/preprocessing/Simulated_dataset_1/simulated_input.csv
16:10:00 --- INFO:       -------- niche net constr options -------      
16:10:00 --- INFO: n_cpu:   4
16:10:00 --- INFO: n_neighbors: 50
16:10:00 --- INFO: n_local: 20
16:10:00 --- INFO:            -------- train options -------            
16:10:00 --- INFO: device:  cuda
16:10:00 --- INFO: epochs:  1000
16:10:00 --- INFO: batch_size:  5
16:10:00 --- INFO: patience:  100
16:10:00 --- INFO: min_delta:  0.001
16:10:00 --- INFO: min_epochs:  50
16:10:00 --- INFO: seed:  42
16:10:00 --- INFO: lr:  0.03
16:10:00 --- INFO: hidden_feats:  4
16:10:00 --- INFO: n_gcn_layers:  2
16:10:00 --- INFO: k:  6
16:10:00 --- INFO: modularity_loss_weight:  0.3
16:10:00 --- INFO: purity_loss_weight:  300.0
16:10:00 --- INFO: regularization_loss_weight:  0.1
16:10:00 --- INFO: beta:  0.03
16:10:00 --- INFO: ---------------- Niche trajectory options ----------------
16:10:00 --- INFO: Niche trajectory construction method: BF
16:10:00 --- INFO: --------------- RUN params memo end ----------------- 
16:10:00 --- INFO: ------------- Niche network construct --------------- 
16:10:00 --- INFO: Constructing niche network for sample: Simulation.
16:10:00 --- INFO: Building KNN network for sample: Simulation...
16:10:00 --- INFO: Calculating edge index for sample: Simulation...
16:10:00 --- INFO: Calculating niche weight matrix for sample: Simulation...
16:10:00 --- INFO: Calculating cell type composition for sample: Simulation...
16:10:00 --- INFO: Generating samples.yaml file.
16:10:00 --- INFO: ------------ Niche network construct end ------------ 
16:10:00 --- INFO: ------------------------ GNN ------------------------ 
16:10:00 --- INFO: Loading dataset.
16:10:00 --- INFO: Maximum number of cell in one sample is: 1100.
Processing...
16:10:01 --- INFO: Processing sample 1 of 1: Simulation
Done!
16:10:01 --- INFO: Processing sample 1 of 1: Simulation
16:10:03 --- INFO: epoch: 1, batch: 1, loss: 5.28548002243042, modularity_loss: -3.2737284527684096e-06, purity_loss: 5.216475963592529, regularization_loss: 0.06900753825902939
16:10:03 --- INFO: epoch: 2, batch: 1, loss: 5.285218715667725, modularity_loss: -6.284053142735502e-06, purity_loss: 5.216220855712891, regularization_loss: 0.06900409609079361
16:10:03 --- INFO: epoch: 3, batch: 1, loss: 5.284871578216553, modularity_loss: -1.0322082744096406e-05, purity_loss: 5.215879917144775, regularization_loss: 0.06900212913751602
16:10:03 --- INFO: epoch: 4, batch: 1, loss: 5.2844038009643555, modularity_loss: -1.5791869373060763e-05, purity_loss: 5.215417861938477, regularization_loss: 0.06900154799222946
16:10:03 --- INFO: epoch: 5, batch: 1, loss: 5.283760070800781, modularity_loss: -2.3352840798906982e-05, purity_loss: 5.214781284332275, regularization_loss: 0.06900208443403244
16:10:03 --- INFO: epoch: 6, batch: 1, loss: 5.282847881317139, modularity_loss: -3.4071570553351194e-05, purity_loss: 5.213878631591797, regularization_loss: 0.0690031498670578
16:10:03 --- INFO: epoch: 7, batch: 1, loss: 5.281559467315674, modularity_loss: -4.917501428280957e-05, purity_loss: 5.212604522705078, regularization_loss: 0.06900433450937271
16:10:03 --- INFO: epoch: 8, batch: 1, loss: 5.2797746658325195, modularity_loss: -7.003570499364287e-05, purity_loss: 5.210838794708252, regularization_loss: 0.06900576502084732
16:10:03 --- INFO: epoch: 9, batch: 1, loss: 5.277322292327881, modularity_loss: -9.865784522844478e-05, purity_loss: 5.208413124084473, regularization_loss: 0.0690079852938652
16:10:03 --- INFO: epoch: 10, batch: 1, loss: 5.273987770080566, modularity_loss: -0.00013753112580161542, purity_loss: 5.205113410949707, regularization_loss: 0.06901168823242188
16:10:03 --- INFO: epoch: 11, batch: 1, loss: 5.2695536613464355, modularity_loss: -0.00018925504991784692, purity_loss: 5.200725078582764, regularization_loss: 0.06901773065328598
16:10:03 --- INFO: epoch: 12, batch: 1, loss: 5.263756275177002, modularity_loss: -0.00025721127167344093, purity_loss: 5.194986343383789, regularization_loss: 0.06902701407670975
16:10:03 --- INFO: epoch: 13, batch: 1, loss: 5.256264686584473, modularity_loss: -0.00034563092049211264, purity_loss: 5.187570095062256, regularization_loss: 0.06904002279043198
16:10:03 --- INFO: epoch: 14, batch: 1, loss: 5.246661186218262, modularity_loss: -0.0004597530933097005, purity_loss: 5.178064346313477, regularization_loss: 0.06905638426542282
16:10:03 --- INFO: epoch: 15, batch: 1, loss: 5.234406471252441, modularity_loss: -0.0006063984474167228, purity_loss: 5.165937900543213, regularization_loss: 0.06907480210065842
16:10:03 --- INFO: epoch: 16, batch: 1, loss: 5.218887805938721, modularity_loss: -0.0007936400361359119, purity_loss: 5.150585651397705, regularization_loss: 0.0690956711769104
16:10:03 --- INFO: epoch: 17, batch: 1, loss: 5.199424743652344, modularity_loss: -0.0010309221688658, purity_loss: 5.1313347816467285, regularization_loss: 0.06912105530500412
16:10:03 --- INFO: epoch: 18, batch: 1, loss: 5.175188064575195, modularity_loss: -0.0013304809108376503, purity_loss: 5.107362747192383, regularization_loss: 0.06915583461523056
16:10:03 --- INFO: epoch: 19, batch: 1, loss: 5.145195960998535, modularity_loss: -0.0017075332580134273, purity_loss: 5.077697277069092, regularization_loss: 0.06920632719993591
16:10:03 --- INFO: epoch: 20, batch: 1, loss: 5.108408451080322, modularity_loss: -0.002179180271923542, purity_loss: 5.041309833526611, regularization_loss: 0.06927801668643951
16:10:03 --- INFO: epoch: 21, batch: 1, loss: 5.0638108253479, modularity_loss: -0.002763425000011921, purity_loss: 4.9972004890441895, regularization_loss: 0.06937380880117416
16:10:03 --- INFO: epoch: 22, batch: 1, loss: 5.01038932800293, modularity_loss: -0.0034794749226421118, purity_loss: 4.9443769454956055, regularization_loss: 0.06949187815189362
16:10:03 --- INFO: epoch: 23, batch: 1, loss: 4.947177886962891, modularity_loss: -0.004347739275544882, purity_loss: 4.881899356842041, regularization_loss: 0.06962606310844421
16:10:03 --- INFO: epoch: 24, batch: 1, loss: 4.873462200164795, modularity_loss: -0.00538914930075407, purity_loss: 4.809074878692627, regularization_loss: 0.06977648288011551
16:10:03 --- INFO: epoch: 25, batch: 1, loss: 4.78887414932251, modularity_loss: -0.006624677684158087, purity_loss: 4.725546360015869, regularization_loss: 0.06995254755020142
16:10:03 --- INFO: epoch: 26, batch: 1, loss: 4.693502426147461, modularity_loss: -0.008072642609477043, purity_loss: 4.631413459777832, regularization_loss: 0.07016138732433319
16:10:03 --- INFO: epoch: 27, batch: 1, loss: 4.588165283203125, modularity_loss: -0.009743880480527878, purity_loss: 4.527505397796631, regularization_loss: 0.07040389627218246
16:10:03 --- INFO: epoch: 28, batch: 1, loss: 4.474507808685303, modularity_loss: -0.011639253236353397, purity_loss: 4.4154744148254395, regularization_loss: 0.07067283242940903
16:10:03 --- INFO: epoch: 29, batch: 1, loss: 4.355066299438477, modularity_loss: -0.013746660202741623, purity_loss: 4.297853469848633, regularization_loss: 0.07095951586961746
16:10:03 --- INFO: epoch: 30, batch: 1, loss: 4.233127593994141, modularity_loss: -0.016040191054344177, purity_loss: 4.177913665771484, regularization_loss: 0.07125414162874222
16:10:03 --- INFO: epoch: 31, batch: 1, loss: 4.112618446350098, modularity_loss: -0.018478065729141235, purity_loss: 4.059542655944824, regularization_loss: 0.07155390083789825
16:10:03 --- INFO: epoch: 32, batch: 1, loss: 3.9976160526275635, modularity_loss: -0.021002966910600662, purity_loss: 3.946774482727051, regularization_loss: 0.07184451073408127
16:10:03 --- INFO: epoch: 33, batch: 1, loss: 3.891801357269287, modularity_loss: -0.02354886196553707, purity_loss: 3.843238115310669, regularization_loss: 0.07211221754550934
16:10:03 --- INFO: epoch: 34, batch: 1, loss: 3.7979230880737305, modularity_loss: -0.026050761342048645, purity_loss: 3.751629590988159, regularization_loss: 0.07234418392181396
16:10:03 --- INFO: epoch: 35, batch: 1, loss: 3.7175583839416504, modularity_loss: -0.028448864817619324, purity_loss: 3.673473596572876, regularization_loss: 0.07253360003232956
16:10:03 --- INFO: epoch: 36, batch: 1, loss: 3.6510701179504395, modularity_loss: -0.03069290705025196, purity_loss: 3.6090734004974365, regularization_loss: 0.07268955558538437
16:10:03 --- INFO: epoch: 37, batch: 1, loss: 3.597705125808716, modularity_loss: -0.03274816274642944, purity_loss: 3.557629346847534, regularization_loss: 0.07282402366399765
16:10:03 --- INFO: epoch: 38, batch: 1, loss: 3.555978536605835, modularity_loss: -0.03459617868065834, purity_loss: 3.517627477645874, regularization_loss: 0.07294731587171555
16:10:03 --- INFO: epoch: 39, batch: 1, loss: 3.5240330696105957, modularity_loss: -0.03623472899198532, purity_loss: 3.487198829650879, regularization_loss: 0.07306908071041107
16:10:03 --- INFO: epoch: 40, batch: 1, loss: 3.499948263168335, modularity_loss: -0.03767409548163414, purity_loss: 3.4644253253936768, regularization_loss: 0.0731971487402916
16:10:03 --- INFO: epoch: 41, batch: 1, loss: 3.48195219039917, modularity_loss: -0.038934800773859024, purity_loss: 3.4475505352020264, regularization_loss: 0.07333650439977646
16:10:03 --- INFO: epoch: 42, batch: 1, loss: 3.468569755554199, modularity_loss: -0.04003976657986641, purity_loss: 3.435119390487671, regularization_loss: 0.07349014282226562
16:10:03 --- INFO: epoch: 43, batch: 1, loss: 3.4585952758789062, modularity_loss: -0.041014414280653, purity_loss: 3.4259495735168457, regularization_loss: 0.0736600011587143
16:10:03 --- INFO: epoch: 44, batch: 1, loss: 3.451097011566162, modularity_loss: -0.04188314080238342, purity_loss: 3.419132709503174, regularization_loss: 0.07384748756885529
16:10:03 --- INFO: epoch: 45, batch: 1, loss: 3.4453680515289307, modularity_loss: -0.04266894608736038, purity_loss: 3.413982629776001, regularization_loss: 0.07405431568622589
16:10:04 --- INFO: epoch: 46, batch: 1, loss: 3.4408679008483887, modularity_loss: -0.04339265078306198, purity_loss: 3.409978151321411, regularization_loss: 0.07428240031003952
16:10:04 --- INFO: epoch: 47, batch: 1, loss: 3.4371962547302246, modularity_loss: -0.044073544442653656, purity_loss: 3.406735897064209, regularization_loss: 0.07453381270170212
16:10:04 --- INFO: epoch: 48, batch: 1, loss: 3.434048652648926, modularity_loss: -0.04472927004098892, purity_loss: 3.4039666652679443, regularization_loss: 0.07481134682893753
16:10:04 --- INFO: epoch: 49, batch: 1, loss: 3.431185722351074, modularity_loss: -0.045375436544418335, purity_loss: 3.4014430046081543, regularization_loss: 0.07511819899082184
16:10:04 --- INFO: epoch: 50, batch: 1, loss: 3.428419351577759, modularity_loss: -0.04602745547890663, purity_loss: 3.3989882469177246, regularization_loss: 0.0754585713148117
16:10:04 --- INFO: epoch: 51, batch: 1, loss: 3.4255855083465576, modularity_loss: -0.0466998927295208, purity_loss: 3.3964476585388184, regularization_loss: 0.075837641954422
16:10:04 --- INFO: epoch: 52, batch: 1, loss: 3.4225478172302246, modularity_loss: -0.04740772396326065, purity_loss: 3.393693685531616, regularization_loss: 0.076261967420578
16:10:04 --- INFO: epoch: 53, batch: 1, loss: 3.4191815853118896, modularity_loss: -0.04816614091396332, purity_loss: 3.390608549118042, regularization_loss: 0.07673914730548859
16:10:04 --- INFO: epoch: 54, batch: 1, loss: 3.415365219116211, modularity_loss: -0.048991184681653976, purity_loss: 3.387078285217285, regularization_loss: 0.07727809995412827
16:10:04 --- INFO: epoch: 55, batch: 1, loss: 3.410975933074951, modularity_loss: -0.0499001145362854, purity_loss: 3.3829872608184814, regularization_loss: 0.07788889110088348
16:10:04 --- INFO: epoch: 56, batch: 1, loss: 3.4058806896209717, modularity_loss: -0.050910744816064835, purity_loss: 3.378209114074707, regularization_loss: 0.0785822719335556
16:10:04 --- INFO: epoch: 57, batch: 1, loss: 3.3999404907226562, modularity_loss: -0.05204174667596817, purity_loss: 3.372612714767456, regularization_loss: 0.07936952263116837
16:10:04 --- INFO: epoch: 58, batch: 1, loss: 3.393017292022705, modularity_loss: -0.05330994352698326, purity_loss: 3.3660669326782227, regularization_loss: 0.08026034384965897
16:10:04 --- INFO: epoch: 59, batch: 1, loss: 3.384976625442505, modularity_loss: -0.05472927168011665, purity_loss: 3.3584437370300293, regularization_loss: 0.08126217871904373
16:10:04 --- INFO: epoch: 60, batch: 1, loss: 3.3756861686706543, modularity_loss: -0.056307047605514526, purity_loss: 3.3496153354644775, regularization_loss: 0.0823778584599495
16:10:04 --- INFO: epoch: 61, batch: 1, loss: 3.3650622367858887, modularity_loss: -0.058039724826812744, purity_loss: 3.3395004272460938, regularization_loss: 0.08360160142183304
16:10:04 --- INFO: epoch: 62, batch: 1, loss: 3.3530921936035156, modularity_loss: -0.05990936979651451, purity_loss: 3.3280861377716064, regularization_loss: 0.08491552621126175
16:10:04 --- INFO: epoch: 63, batch: 1, loss: 3.3398354053497314, modularity_loss: -0.061880651861429214, purity_loss: 3.315425395965576, regularization_loss: 0.08629056811332703
16:10:04 --- INFO: epoch: 64, batch: 1, loss: 3.3254451751708984, modularity_loss: -0.06390120834112167, purity_loss: 3.3016655445098877, regularization_loss: 0.087680883705616
16:10:04 --- INFO: epoch: 65, batch: 1, loss: 3.3101494312286377, modularity_loss: -0.0659104734659195, purity_loss: 3.2870290279388428, regularization_loss: 0.08903095871210098
16:10:04 --- INFO: epoch: 66, batch: 1, loss: 3.2942137718200684, modularity_loss: -0.06784937530755997, purity_loss: 3.271775007247925, regularization_loss: 0.09028825908899307
16:10:04 --- INFO: epoch: 67, batch: 1, loss: 3.2778971195220947, modularity_loss: -0.06966306269168854, purity_loss: 3.2561533451080322, regularization_loss: 0.09140689671039581
16:10:04 --- INFO: epoch: 68, batch: 1, loss: 3.261399030685425, modularity_loss: -0.07131224870681763, purity_loss: 3.240354061126709, regularization_loss: 0.09235730767250061
16:10:04 --- INFO: epoch: 69, batch: 1, loss: 3.2448348999023438, modularity_loss: -0.07277137041091919, purity_loss: 3.224482297897339, regularization_loss: 0.09312398731708527
16:10:04 --- INFO: epoch: 70, batch: 1, loss: 3.2282180786132812, modularity_loss: -0.07402641326189041, purity_loss: 3.208543062210083, regularization_loss: 0.09370134770870209
16:10:04 --- INFO: epoch: 71, batch: 1, loss: 3.2113988399505615, modularity_loss: -0.07506338506937027, purity_loss: 3.1923770904541016, regularization_loss: 0.09408514946699142
16:10:04 --- INFO: epoch: 72, batch: 1, loss: 3.1942474842071533, modularity_loss: -0.07587370276451111, purity_loss: 3.1758460998535156, regularization_loss: 0.09427505731582642
16:10:04 --- INFO: epoch: 73, batch: 1, loss: 3.1766467094421387, modularity_loss: -0.076450414955616, purity_loss: 3.158820867538452, regularization_loss: 0.09427613765001297
16:10:04 --- INFO: epoch: 74, batch: 1, loss: 3.1584489345550537, modularity_loss: -0.07678649574518204, purity_loss: 3.141144275665283, regularization_loss: 0.09409104287624359
16:10:04 --- INFO: epoch: 75, batch: 1, loss: 3.139439582824707, modularity_loss: -0.07689649611711502, purity_loss: 3.1225948333740234, regularization_loss: 0.09374135732650757
16:10:04 --- INFO: epoch: 76, batch: 1, loss: 3.1194751262664795, modularity_loss: -0.07682538032531738, purity_loss: 3.1030330657958984, regularization_loss: 0.09326745569705963
16:10:04 --- INFO: epoch: 77, batch: 1, loss: 3.098288059234619, modularity_loss: -0.07660239189863205, purity_loss: 3.0821988582611084, regularization_loss: 0.09269153326749802
16:10:04 --- INFO: epoch: 78, batch: 1, loss: 3.0757675170898438, modularity_loss: -0.07624781876802444, purity_loss: 3.0599911212921143, regularization_loss: 0.09202416986227036
16:10:04 --- INFO: epoch: 79, batch: 1, loss: 3.051815986633301, modularity_loss: -0.07578045129776001, purity_loss: 3.0363197326660156, regularization_loss: 0.09127674996852875
16:10:04 --- INFO: epoch: 80, batch: 1, loss: 3.026252031326294, modularity_loss: -0.07522313296794891, purity_loss: 3.0110132694244385, regularization_loss: 0.09046197682619095
16:10:04 --- INFO: epoch: 81, batch: 1, loss: 2.9988222122192383, modularity_loss: -0.07459431886672974, purity_loss: 2.9838297367095947, regularization_loss: 0.08958683907985687
16:10:04 --- INFO: epoch: 82, batch: 1, loss: 2.9692039489746094, modularity_loss: -0.07390538603067398, purity_loss: 2.9544565677642822, regularization_loss: 0.08865278959274292
16:10:04 --- INFO: epoch: 83, batch: 1, loss: 2.9366180896759033, modularity_loss: -0.07313193380832672, purity_loss: 2.922114372253418, regularization_loss: 0.08763554692268372
16:10:04 --- INFO: epoch: 84, batch: 1, loss: 2.9003798961639404, modularity_loss: -0.07225389778614044, purity_loss: 2.886115074157715, regularization_loss: 0.0865187868475914
16:10:04 --- INFO: epoch: 85, batch: 1, loss: 2.8596553802490234, modularity_loss: -0.0712897852063179, purity_loss: 2.8456289768218994, regularization_loss: 0.08531614392995834
16:10:04 --- INFO: epoch: 86, batch: 1, loss: 2.813706159591675, modularity_loss: -0.07027032226324081, purity_loss: 2.799922466278076, regularization_loss: 0.0840541273355484
16:10:04 --- INFO: epoch: 87, batch: 1, loss: 2.7620697021484375, modularity_loss: -0.06924298405647278, purity_loss: 2.7485363483428955, regularization_loss: 0.08277627825737
16:10:04 --- INFO: epoch: 88, batch: 1, loss: 2.704533338546753, modularity_loss: -0.0682893916964531, purity_loss: 2.691272020339966, regularization_loss: 0.08155069500207901
16:10:04 --- INFO: epoch: 89, batch: 1, loss: 2.6416075229644775, modularity_loss: -0.06755015254020691, purity_loss: 2.628682851791382, regularization_loss: 0.08047474920749664
16:10:04 --- INFO: epoch: 90, batch: 1, loss: 2.574474811553955, modularity_loss: -0.06721139699220657, purity_loss: 2.5620388984680176, regularization_loss: 0.0796472579240799
16:10:04 --- INFO: epoch: 91, batch: 1, loss: 2.5048861503601074, modularity_loss: -0.06742584705352783, purity_loss: 2.493195056915283, regularization_loss: 0.07911687344312668
16:10:04 --- INFO: epoch: 92, batch: 1, loss: 2.4356489181518555, modularity_loss: -0.06825752556324005, purity_loss: 2.4250400066375732, regularization_loss: 0.07886631786823273
16:10:04 --- INFO: epoch: 93, batch: 1, loss: 2.3702807426452637, modularity_loss: -0.06959864497184753, purity_loss: 2.361046552658081, regularization_loss: 0.07883286476135254
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16:10:12 --- INFO: epoch: 963, batch: 1, loss: 0.7133567333221436, modularity_loss: -0.0938306599855423, purity_loss: 0.7363725304603577, regularization_loss: 0.0708148330450058
16:10:12 --- INFO: epoch: 964, batch: 1, loss: 0.7132857441902161, modularity_loss: -0.09383360296487808, purity_loss: 0.7363042235374451, regularization_loss: 0.07081512361764908
16:10:12 --- INFO: epoch: 965, batch: 1, loss: 0.713214635848999, modularity_loss: -0.09383849054574966, purity_loss: 0.7362380623817444, regularization_loss: 0.0708150640130043
16:10:12 --- INFO: epoch: 966, batch: 1, loss: 0.7131443023681641, modularity_loss: -0.09384451806545258, purity_loss: 0.7361741662025452, regularization_loss: 0.07081463932991028
16:10:12 --- INFO: epoch: 967, batch: 1, loss: 0.7130745649337769, modularity_loss: -0.0938485786318779, purity_loss: 0.7361087203025818, regularization_loss: 0.07081441581249237
16:10:12 --- INFO: epoch: 968, batch: 1, loss: 0.7130044102668762, modularity_loss: -0.09385059028863907, purity_loss: 0.7360405325889587, regularization_loss: 0.07081447541713715
16:10:12 --- INFO: epoch: 969, batch: 1, loss: 0.7129347920417786, modularity_loss: -0.09385137259960175, purity_loss: 0.7359713315963745, regularization_loss: 0.07081484794616699
16:10:12 --- INFO: epoch: 970, batch: 1, loss: 0.7128657102584839, modularity_loss: -0.09385407716035843, purity_loss: 0.7359047532081604, regularization_loss: 0.07081501185894012
16:10:12 --- INFO: epoch: 971, batch: 1, loss: 0.7127963900566101, modularity_loss: -0.09385871142148972, purity_loss: 0.7358400821685791, regularization_loss: 0.07081502676010132
16:10:12 --- INFO: epoch: 972, batch: 1, loss: 0.7127273678779602, modularity_loss: -0.09386443346738815, purity_loss: 0.735776960849762, regularization_loss: 0.0708148330450058
16:10:12 --- INFO: epoch: 973, batch: 1, loss: 0.7126587629318237, modularity_loss: -0.09386827051639557, purity_loss: 0.7357120513916016, regularization_loss: 0.07081501185894012
16:10:12 --- INFO: epoch: 974, batch: 1, loss: 0.7125908136367798, modularity_loss: -0.09387015551328659, purity_loss: 0.7356454133987427, regularization_loss: 0.0708155483007431
16:10:12 --- INFO: epoch: 975, batch: 1, loss: 0.7125230431556702, modularity_loss: -0.09387269616127014, purity_loss: 0.7355798482894897, regularization_loss: 0.07081589102745056
16:10:12 --- INFO: epoch: 976, batch: 1, loss: 0.7124553918838501, modularity_loss: -0.09387621283531189, purity_loss: 0.7355155944824219, regularization_loss: 0.07081599533557892
16:10:12 --- INFO: epoch: 977, batch: 1, loss: 0.7123879194259644, modularity_loss: -0.09388050436973572, purity_loss: 0.7354524731636047, regularization_loss: 0.07081592828035355
16:10:12 --- INFO: epoch: 978, batch: 1, loss: 0.7123208045959473, modularity_loss: -0.09388517588376999, purity_loss: 0.7353901863098145, regularization_loss: 0.07081577926874161
16:10:12 --- INFO: epoch: 979, batch: 1, loss: 0.7122541666030884, modularity_loss: -0.09388962388038635, purity_loss: 0.7353280782699585, regularization_loss: 0.07081570476293564
16:10:12 --- INFO: epoch: 980, batch: 1, loss: 0.7121874094009399, modularity_loss: -0.09389147907495499, purity_loss: 0.7352627515792847, regularization_loss: 0.07081616669893265
16:10:12 --- INFO: epoch: 981, batch: 1, loss: 0.7121212482452393, modularity_loss: -0.09389349073171616, purity_loss: 0.7351981997489929, regularization_loss: 0.07081656157970428
16:10:12 --- INFO: epoch: 982, batch: 1, loss: 0.7120553255081177, modularity_loss: -0.09389609843492508, purity_loss: 0.7351347208023071, regularization_loss: 0.07081671059131622
16:10:12 --- INFO: epoch: 983, batch: 1, loss: 0.7119896411895752, modularity_loss: -0.09389951080083847, purity_loss: 0.7350725531578064, regularization_loss: 0.07081657648086548
16:10:12 --- INFO: epoch: 984, batch: 1, loss: 0.7119245529174805, modularity_loss: -0.0939035639166832, purity_loss: 0.735011875629425, regularization_loss: 0.07081623375415802
16:10:12 --- INFO: epoch: 985, batch: 1, loss: 0.7118593454360962, modularity_loss: -0.09390591830015182, purity_loss: 0.7349489331245422, regularization_loss: 0.07081631571054459
16:10:12 --- INFO: epoch: 986, batch: 1, loss: 0.711794912815094, modularity_loss: -0.09390715509653091, purity_loss: 0.7348852753639221, regularization_loss: 0.07081679999828339
16:10:12 --- INFO: epoch: 987, batch: 1, loss: 0.7117305397987366, modularity_loss: -0.09391013532876968, purity_loss: 0.7348236441612244, regularization_loss: 0.07081705331802368
16:10:12 --- INFO: epoch: 988, batch: 1, loss: 0.7116662263870239, modularity_loss: -0.09391487389802933, purity_loss: 0.7347640991210938, regularization_loss: 0.07081697136163712
16:10:12 --- INFO: epoch: 989, batch: 1, loss: 0.7116020917892456, modularity_loss: -0.09392067044973373, purity_loss: 0.7347061634063721, regularization_loss: 0.07081656903028488
16:10:12 --- INFO: epoch: 990, batch: 1, loss: 0.7115387916564941, modularity_loss: -0.09392647445201874, purity_loss: 0.7346492409706116, regularization_loss: 0.0708160400390625
16:10:13 --- INFO: epoch: 991, batch: 1, loss: 0.711475133895874, modularity_loss: -0.09392940253019333, purity_loss: 0.7345883846282959, regularization_loss: 0.07081612199544907
16:10:13 --- INFO: epoch: 992, batch: 1, loss: 0.7114124298095703, modularity_loss: -0.0939299687743187, purity_loss: 0.7345255017280579, regularization_loss: 0.07081688940525055
16:10:13 --- INFO: epoch: 993, batch: 1, loss: 0.7113502621650696, modularity_loss: -0.09393168240785599, purity_loss: 0.734464168548584, regularization_loss: 0.07081775367259979
16:10:13 --- INFO: epoch: 994, batch: 1, loss: 0.7112884521484375, modularity_loss: -0.09393499046564102, purity_loss: 0.7344050407409668, regularization_loss: 0.07081837952136993
16:10:13 --- INFO: epoch: 995, batch: 1, loss: 0.7112263441085815, modularity_loss: -0.09393953531980515, purity_loss: 0.734347403049469, regularization_loss: 0.0708184614777565
16:10:13 --- INFO: epoch: 996, batch: 1, loss: 0.7111647129058838, modularity_loss: -0.09394443780183792, purity_loss: 0.7342911958694458, regularization_loss: 0.07081794738769531
16:10:13 --- INFO: epoch: 997, batch: 1, loss: 0.7111031413078308, modularity_loss: -0.09394896030426025, purity_loss: 0.7342349290847778, regularization_loss: 0.07081715017557144
16:10:13 --- INFO: epoch: 998, batch: 1, loss: 0.7110422253608704, modularity_loss: -0.09395254403352737, purity_loss: 0.7341782450675964, regularization_loss: 0.0708165392279625
16:10:13 --- INFO: epoch: 999, batch: 1, loss: 0.7109813690185547, modularity_loss: -0.09395528584718704, purity_loss: 0.73412024974823, regularization_loss: 0.07081642746925354
16:10:13 --- INFO: epoch: 1000, batch: 1, loss: 0.7109206914901733, modularity_loss: -0.09395747631788254, purity_loss: 0.7340613007545471, regularization_loss: 0.07081687450408936
16:10:13 --- INFO: Training process end.
16:10:13 --- INFO: Evaluating process start.
16:10:13 --- INFO: Evaluation loss, {'modularity_loss': -0.09395743161439896, 'purity_loss': 0.7339998483657837, 'regularization_loss': 0.07081811130046844, 'total_loss': 0.710860550403595}
16:10:13 --- INFO: Evaluating process end.
16:10:13 --- INFO: Predicting process start.
16:10:13 --- INFO: Generating prediction results for Simulation.
16:10:13 --- INFO: Predicting process end.
16:10:13 --- INFO: --------------------- GNN end ---------------------- 
16:10:13 --- INFO: ----------------- Niche trajectory ------------------ 
16:10:13 --- INFO: Calculating NTScore for each niche.
16:10:13 --- INFO: Finding niche trajectory with maximum connectivity using Brute Force.
16:10:13 --- INFO: Calculating NTScore for each niche cluster based on the trajectory path.
16:10:13 --- INFO: Projecting NTScore from niche-level to cell-level.
16:10:13 --- INFO: Output NTScore tables.
16:10:13 --- INFO: --------------- Niche trajectory end ---------------- 

Note

Please refer to the Parameters Explanation for details about parameters used here.

Note

Please refer to the IO Files Explanation for details of input and output files.

Visualization

ONTraC_analysis \
    -o analysis_output/simulation \
    -l log/simulation.log \
    --NN-dir output/simulation_NN \
    --GNN-dir output/simulation_GNN \
    --NT-dir output/simulation_NT \
    -r

##################################################################################

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        ██      ██  █ ▀█▄ █     ██     ██▀ ▀▀ ▀▀ ▄██  ██
        ▀█▄     ██  █   ███     ██     ██     ▄█▀ ██  ▀█▄      ▄
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                        version: 1.2.0

##################################################################################
19:54:56 --- WARNING: The --meta-input option is no longer required for ONTraC analysis. This options will be deprecated from v2.0.
19:54:56 --- INFO: Creating directory: analysis_output/simulation
19:54:56 --- INFO: ------------------ RUN params memo ------------------ 
19:54:56 --- INFO:             -------- I/O options -------             
19:54:56 --- INFO: Niche network output directory:  output/simulation_NN
19:54:56 --- INFO: GNN output directory:  output/simulation_GNN
19:54:56 --- INFO: Niche trajectory output directory:  output/simulation_NT
19:54:56 --- INFO: Output directory:  analysis_output/simulation
19:54:56 --- INFO: Log file:  log/simulation.log
19:54:56 --- INFO: ---------------- Visualization options ----------------
19:54:56 --- INFO: Reverse the NT score during visualization: True
19:54:56 --- INFO: Plot each sample separately: False
19:54:56 --- INFO: Scale factor control the size of spatial-based plots: 1.0
19:54:56 --- INFO: ---------------- Suppress options ----------------
19:54:56 --- INFO: Suppress the cell type composition visualization: False
19:54:56 --- INFO: Suppress the niche cluster loadings visualization: False
19:54:56 --- INFO: Suppress the niche trajectory related visualization: False
19:54:56 --- INFO: --------------- RUN params memo end ----------------- 
19:54:56 --- INFO: -------------- Analysis pipeline start -------------- 
19:54:56 --- INFO: Analysis pipeline step 0: load data.
19:54:56 --- INFO: Analysis pipeline step 1: train loss visualization.
19:54:58 --- INFO: Analysis pipeline step 2: spatial-based visualization.
19:55:00 --- INFO: Analysis pipeline step 3: niche cluster visualization.
/sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages/ONTraC/analysis/niche_cluster.py:1047: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
19:55:01 --- INFO: Analysis pipeline step 4: cell type-based visualization.
19:55:03 --- INFO: --------------- Analysis pipeline end --------------- 

Note

Now you can check the visualization outputs under analysis_output/simulation directory.