Yivlad/pretrain mlm

analysis.sh

@@ -8,8 +8,8 @@ bash pretraining.sh "$DIR"/pretraining
 
 # Then generate negative decoys
 # This step is very CPU and RAM intensive
-bash negative_decoys.sh "$DIR"/negative_decoys "$CPU"
+# bash negative_decoys.sh "$DIR"/negative_decoys "$CPU"
 
 # Finally perform supervised training and evaluate the model
 # This step is faster with a GPU
-bash train_and_evaluate.sh "$DIR"/negative_decoys/datasets "$DIR"/pretraining/model "$DIR"/training
+# bash train_and_evaluate.sh "$DIR"/negative_decoys/datasets "$DIR"/pretraining/model "$DIR"/training

bertrand-job.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+#SBATCH --mail-type=ALL          # Powiadomienia mailowe. Opcje: NONE, BEGIN, END, FAIL, ALL
+#SBATCH [email protected]     # adres e-mail
+#SBATCH --ntasks=4                    # Uruchomienie na jednym procesorze
+#SBATCH --mem=32gb
+#SBATCH --gpus=a100:1
+#SBATCH --time=72:00:00               # maksymalny limit czasu DD-HH:MM:SS
+#SBATCH --partition=long
+
+pwd; hostname; date
+
+source /home2/sfglab/yvladyslav/anaconda3/etc/profile.d/conda.sh
+cd bertrand
+conda activate bertrand
+./analysis.sh "/home2/sfglab/yvladyslav/pretrain-mlm/bertrand_results" 4
+
+date

bertrand/pretraining/peptide_tcr_repertoire.py

@@ -80,11 +80,10 @@ def read_peptides(fn: str) -> pd.DataFrame:
     logging.info(f"{len(presented_peptides)} peptides read")
     presented_unique = (
         presented_peptides.reset_index()
-        .groupby("Peptide2")
+        .groupby("peptide_seq")
         .agg(
             {
                 "HLA_type": lambda x: "|".join(sorted(x)),
-                "index": lambda x: "|".join(sorted(x)),
             }
         )
         .reset_index()
@@ -114,7 +113,7 @@ def sample_peptide_tcr_repertoire(
     )
 
     peptides_sampled.loc[:, "CDR3b"] = synthetic_tcrs.values
-    peptide_tcr_repertoire = peptides_sampled.rename(columns={"Peptide2": "Peptide"})
+    peptide_tcr_repertoire = peptides_sampled.rename(columns={"peptide_seq": "Peptide"})
     return peptide_tcr_repertoire
 
 

bertrand/training/evaluate.py

@@ -1,7 +1,6 @@
 import os
 import shutil
 from copy import deepcopy
-from functools import partial
 from glob import glob
 from typing import Union, List, Tuple, Dict
 

env.yml

@@ -1,27 +1,135 @@
 name: bertrand
 channels:
+  - nvidia
   - pytorch
   - defaults
 dependencies:
-  - biopython=1.78=py38h7b6447c_0
-  - h5py=2.10.0=py38hd6299e0_1
-  - hdf5=1.10.6=hb1b8bf9_0
-  - joblib=1.1.0=pyhd3eb1b0_0
-  - matplotlib=3.3.4=py38h06a4308_0
-  - numpy=1.21.2=py38h20f2e39_0
-  - pandas=1.4.1=py38h295c915_0
-  - pip=21.2.4=py38h06a4308_0
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - biopython=1.78=py38h7f8727e_0
+  - blas=1.0=mkl
+  - bottleneck=1.3.5=py38h7deecbd_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
+  - ca-certificates=2023.01.10=h06a4308_0
+  - contourpy=1.0.5=py38hdb19cb5_0
+  - cudatoolkit=11.5.1=hcf5317a_9
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
+  - expat=2.4.9=h6a678d5_0
+  - fftw=3.3.9=h27cfd23_1
+  - fontconfig=2.14.1=h52c9d5c_1
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.12.1=h4a9f257_0
+  - giflib=5.2.1=h5eee18b_3
+  - glib=2.63.1=h5a9c865_0
+  - gst-plugins-base=1.14.0=hbbd80ab_1
+  - gstreamer=1.14.0=hb453b48_1
+  - h5py=3.7.0=py38h737f45e_0
+  - hdf5=1.10.6=h3ffc7dd_1
+  - icu=58.2=he6710b0_3
+  - importlib_resources=5.2.0=pyhd3eb1b0_1
+  - intel-openmp=2021.4.0=h06a4308_3561
+  - joblib=1.2.0=py38h06a4308_0
+  - jpeg=9e=h5eee18b_1
+  - kiwisolver=1.4.4=py38h6a678d5_0
+  - lcms2=2.12=h3be6417_0
+  - lerc=3.0=h295c915_0
+  - libbrotlicommon=1.0.9=h5eee18b_7
+  - libbrotlidec=1.0.9=h5eee18b_7
+  - libbrotlienc=1.0.9=h5eee18b_7
+  - libdeflate=1.17=h5eee18b_0
+  - libedit=3.1.20221030=h5eee18b_0
+  - libffi=3.2.1=hf484d3e_1007
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgfortran-ng=11.2.0=h00389a5_1
+  - libgfortran5=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libpng=1.6.39=h5eee18b_0
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtiff=4.5.0=h6a678d5_2
+  - libuuid=1.41.5=h5eee18b_0
+  - libuv=1.44.2=h5eee18b_0
+  - libwebp=1.2.4=h11a3e52_1
+  - libwebp-base=1.2.4=h5eee18b_1
+  - libxcb=1.15=h7f8727e_0
+  - libxml2=2.9.14=h74e7548_0
+  - lz4-c=1.9.4=h6a678d5_0
+  - matplotlib=3.7.1=py38h06a4308_1
+  - matplotlib-base=3.7.1=py38h417a72b_1
+  - mkl=2021.4.0=h06a4308_640
+  - mkl-service=2.4.0=py38h7f8727e_0
+  - mkl_fft=1.3.1=py38hd3c417c_0
+  - mkl_random=1.2.2=py38h51133e4_0
+  - munkres=1.1.4=py_0
+  - ncurses=6.4=h6a678d5_0
+  - numexpr=2.8.4=py38he184ba9_0
+  - numpy=1.22.3=py38he7a7128_0
+  - numpy-base=1.22.3=py38hf524024_0
+  - openssl=1.1.1t=h7f8727e_0
+  - packaging=23.0=py38h06a4308_0
+  - pandas=1.5.3=py38h417a72b_0
+  - pcre=8.45=h295c915_0
+  - pillow=9.4.0=py38h6a678d5_0
+  - pip=23.0.1=py38h06a4308_0
+  - pyparsing=3.0.9=py38h06a4308_0
+  - pyqt=5.9.2=py38h05f1152_4
   - python=3.8.0=h0371630_2
-  - pytorch=1.11.0=py3.8_cuda10.2_cudnn7.6.5_0
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - pytorch=1.11.0=py3.8_cuda11.5_cudnn8.3.2_0
+  - pytorch-mutex=1.0=cuda
+  - pytz=2022.7=py38h06a4308_0
+  - qt=5.9.7=h5867ecd_1
+  - readline=7.0=h7b6447c_5
   - scikit-learn=0.24.2=py38ha9443f7_0
-  - scipy=1.7.3=py38hc147768_0
-  - seaborn=0.11.1=pyhd3eb1b0_0
-  - tokenizers=0.10.3=py38hb317417_1
-  - tqdm=4.62.3=pyhd3eb1b0_1
+  - scipy=1.7.3=py38h6c91a56_2
+  - seaborn=0.12.2=py38h06a4308_0
+  - setuptools=66.0.0=py38h06a4308_0
+  - sip=4.19.13=py38h295c915_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.33.0=h62c20be_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tk=8.6.12=h1ccaba5_0
+  - tokenizers=0.11.4=py38h3dcd8bd_1
+  - tornado=6.2=py38h5eee18b_0
+  - tqdm=4.65.0=py38hb070fc8_0
+  - typing_extensions=4.5.0=py38h06a4308_0
+  - wheel=0.38.4=py38h06a4308_0
+  - xz=5.4.2=h5eee18b_0
+  - zipp=3.11.0=py38h06a4308_0
+  - zlib=1.2.13=h5eee18b_0
+  - zstd=1.5.5=hc292b87_0
   - pip:
-    - datasets==1.18.3
-    - fastcluster==1.2.4
-    - leven==1.0.4
-    - pytorch-lightning==0.7.1
-    - transformers==4.16.2
-prefix: /home/ardigen/miniconda3/envs/bertrand
+      - aiohttp==3.8.4
+      - aiosignal==1.3.1
+      - async-timeout==4.0.2
+      - attrs==23.1.0
+      - certifi==2023.5.7
+      - charset-normalizer==3.1.0
+      - click==8.1.3
+      - datasets==2.12.0
+      - dill==0.3.6
+      - fastcluster==1.2.6
+      - filelock==3.12.0
+      - frozenlist==1.3.3
+      - fsspec==2023.5.0
+      - huggingface-hub==0.14.1
+      - idna==3.4
+      - leven==1.0.4
+      - lightning-utilities==0.8.0
+      - multidict==6.0.4
+      - multiprocess==0.70.14
+      - nose==1.3.7
+      - pyarrow==12.0.0
+      - pytorch-lightning==2.0.2
+      - pyyaml==6.0
+      - regex==2023.5.5
+      - requests==2.31.0
+      - responses==0.18.0
+      - sacremoses==0.0.53
+      - torchmetrics==0.11.4
+      - transformers==4.16.2
+      - urllib3==2.0.2
+      - xxhash==3.2.0
+      - yarl==1.9.2
+prefix: /home2/sfglab/yvladyslav/anaconda3/envs/bertrand

train_and_evaluate.sh

@@ -1,4 +1,4 @@
-set -x
+set -ex
 DATA_DIR=$1
 MODEL_DIR=$2
 OUT_DIR=$3
@@ -9,9 +9,9 @@ python -m bertrand.training.train \
   --input-dir=$DATA_DIR \
   --model-ckpt=$MODEL_DIR \
   --output-dir=$OUT_DIR \
-  --n-splits=21
+  --n-splits=1
 
 python -m bertrand.training.evaluate \
   --datasets-dir=$DATA_DIR \
   --results-dir=$OUT_DIR \
-  --out=$OUT_DIR/results.csv
+  --out=$OUT_DIR/results.csv