Add README for finetune

finetune_pinnacle/README.md

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+# Finetuning PINNACLE
+
+You can finetune PINNACLE on your own datasets by using our provided model checkpoints or contextualized representations (i.e., no re-training needed).
+
+## Step-by-Step Instructions
+
+We provide detailed instructions for fine-tuning PINNACLE on the pretrained contextualized protein representations.
+
+### Step 1: Curate fine-tuning data
+
+You may use `prepare_txdata.py` as an example.
+
+The required outputs of your script are:
+- Positive proteins (dict)
+    - Filename: `positive_proteins_<task_name>.json`
+    - Data structure: `{"<celltype name>": ["<protein name>"]}`
+- Negative proteins (dict)
+    - Filename: `negative_proteins_<task_name>.json`
+    - Data structure: `{"<celltype name>": ["<protein name>"]}`
+- Raw data (list)
+    - Filename: `raw_data_<task_name>.json`
+    - Data structure: `["<protein name>"]`
+
+### Step 2: Split and format data
+
+With the three files created in Step 1, run `data_prep.py`. The outputs of this script are:
+- Data split indices (dict)
+    - Filename: `data_split_<task_name>.json`
+    - Data Structure: `{"pos_train_indices": [...], "neg_train_indices": [...], "pos_test_indices": [...], "neg_test_indices": [...]}`
+- Data split name (dict)
+    - Filename: `data_split_<task_name>_name.json`
+    - Data Structure: `{"pos_train_names": [...], "neg_train_names": [...], "pos_test_names": [...], "neg_test_names": [...]}`
+
+Example command:
+```
+python data_prep.py \
+    --embeddings_dir ../data/pinnacle_embeds/ \
+    --embed pinnacle \
+    --celltype_ppi ../data/networks/ \
+    --positive_proteins_prefix ../data/therapeutic_target_task/positive_proteins_EFO_0000685 \
+    --negative_proteins_prefix ../data/therapeutic_target_task/negative_proteins_EFO_0000685 \
+    --raw_data_prefix ../data/therapeutic_target_task/raw_targets_EFO_0000685 \
+    --data_split_path ../data/therapeutic_target_task/data_split_EFO_0000685
+```
+
+### Step 3: Finetune
+
+With the following files, run `train.py`:
+- `positive_proteins_<task_name>.json`
+- `negative_proteins_<task_name>.json`
+- `data_split_<task_name>.json`
+
+Example command:
+```
+python train.py \
+    --task_name EFO_0000685 \
+    --embeddings_dir ../data/pinnacle_embeds/ \
+    --embed pinnacle \
+    --positive_proteins_prefix ../data/therapeutic_target_task/positive_proteins_EFO_0000685 \
+    --negative_proteins_prefix ../data/therapeutic_target_task/negative_proteins_EFO_0000685 \
+    --data_split_path ../data/therapeutic_target_task/data_split_EFO_0000685
+```