Development

.gitignore

@@ -209,5 +209,6 @@ lib/mgltools_x86_64Linux2_1.5.7p1.tar.gz
 log_test/
 slurm_tests/
 slurm_out_DDP/
+slurm_out*/
 /*.sh
 results/model_checkpoints/ours/*.model*

docs/requirements.txt

@@ -1,29 +1,31 @@
-numpy==1.23.5
-pandas==1.5.3
-tqdm==4.65.0
-rdkit==2023.3.1
-scipy==1.10.1
+numpy
+pandas
+tqdm
+rdkit
+scipy
 
 # for generating figures:
-matplotlib==3.7.1
-seaborn==0.11.2
-statannotations==0.6.0
+matplotlib
+seaborn
+statannotations
 
-lifelines==0.27.7 # used for concordance index calc
-#biopython # used for cmap
+lifelines
 
 # model building
-torch==2.0.1
-torch-geometric==2.3.1
-transformers==4.31.0 # huggingface needed for esm
+torch
+torch-geometric
+transformers
 
 # optional:
-torchsummary==1.5.1
-tabulate==0.9.0 # for torch_geometric.nn.summary
-ipykernel==6.23.1
-plotly==5.14.1
-requests==2.31.0
-#ray[tune]
+torchsummary
+tabulate
+ipykernel
+plotly
+requests
+ray[tune]
 
-submitit==1.4.5
-ProDy==2.4.1
+submitit
+ProDy
+
+# for chemgpt
+selfies

docs/requirements_versions.txt

@@ -0,0 +1,32 @@
+numpy==1.23.5
+pandas==1.5.3
+tqdm==4.65.0
+rdkit==2023.3.1
+scipy==1.10.1
+
+# for generating figures:
+matplotlib==3.7.1
+seaborn==0.11.2
+statannotations==0.6.0
+
+lifelines==0.27.7 # used for concordance index calc
+#biopython # used for cmap
+
+# model building
+torch==2.0.1
+torch-geometric==2.3.1
+transformers==4.31.0 # huggingface needed for esm
+
+# optional:
+torchsummary==1.5.1
+tabulate==0.9.0 # for torch_geometric.nn.summary
+ipykernel==6.23.1
+plotly==5.14.1
+requests==2.31.0
+#ray[tune]
+
+submitit==1.4.5
+ProDy==2.4.1
+
+# for chemgpt
+selfies==1.0.4

playground.py

@@ -1,19 +1,21 @@
-# %%
-from src.data_analysis.figures import prepare_df, fig3_edge_feat
-from src.utils import config
+#%%
+from src.data_analysis.stratify_protein import check_davis_names, kinbase_to_df
 
-from transformers import AutoTokenizer, AutoModel
+df = kinbase_to_df()
+# %%
+import json
+prot_dict = json.load(open('/home/jyaacoub/projects/data/davis/proteins.txt', 'r'))
+# %%
+# returns a dictionary of davis protein names (keys) and a truple of the protein name, main family, and subgroup (values)
+prots = check_davis_names(prot_dict, df)
 
+# %% plot histogram of main families and their counts
+import seaborn as sns
+import pandas as pd
 
-df = prepare_df('results/model_media/model_stats.csv')
+main_families = [v[1] for v in prots.values()]
+main_families = pd.Series(main_families)
+sns.histplot(main_families)
 
-# %%
-fig3_edge_feat(df, show=True, exclude=[])
 
 # %%
-print('test')
-
-#### ChemGPT ####
-
-tokenizer = AutoTokenizer.from_pretrained("ncfrey/ChemGPT-4.7M")
-model = AutoModel.from_pretrained("ncfrey/ChemGPT-4.7M")

results/model_media/davis/train_log/CDM_davisD_nomsaF_binaryE_64B_0.0001LR_0.4D_2000E.json

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