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LeakyPDB/train/state.json

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MCL1/dataset_dict.json

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MCL1/train/data-00000-of-00001.arrow

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MCL1/train/dataset_info.json

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MCL1/train/state.json

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SYK/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["train"]}

SYK/train/data-00000-of-00001.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:579f267988a351cee19321ab84d6dbcb61de16cd8c5324aec33ed6e2ddeac5fb
+size 16432

SYK/train/dataset_info.json

@@ -0,0 +1,50 @@
+{
+  "builder_name": "csv",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "csv",
+  "dataset_size": 15536,
+  "description": "",
+  "download_checksums": {
+    "/Users/aryopg/Documents/school/BALM-benchmark/./SYK/data.csv": {
+      "num_bytes": 15491,
+      "checksum": null
+    }
+  },
+  "download_size": 15491,
+  "features": {
+    "Index": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "Drug": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "Target": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "Y": {
+      "dtype": "float64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "size_in_bytes": 31027,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 15536,
+      "num_examples": 44,
+      "dataset_name": "csv"
+    }
+  },
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

SYK/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "bfdea1926fb3b980",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}

convert_csv_to_hf_dataset.py

@@ -1,18 +1,31 @@
 import os
+import pandas as pd
 from datasets import load_dataset
 
 def main():
     data_dirs = [
-        "./bindingdb_filtered",
+        "./BindingDB_filtered",
         "./CATS",
-        "./HSP9",
-        "./leakypdb",
+        "./HIF2A",
+        "./HSP90",
+        "./LeakyPDB",
+        "./MCL1",
         "./Mpro",
+        "./SYK",
     ]
 
     for data_dir in data_dirs:
         print(data_dir)
-        dataset = load_dataset("csv", data_files=os.path.join(data_dir, "data.csv"), delimiter=",")
+        filepath = os.path.join(data_dir, "data.csv")
+
+        df = pd.read_csv(filepath)
+        # Add Index column, make sure it's the left most column
+        if "Index" not in df.columns:
+            df["Index"] = df.index
+            df = df[["Index"] + [col for col in df.columns if col != "Index"]]
+            df.to_csv(filepath, index=False)
+
+        dataset = load_dataset("csv", data_files=filepath, delimiter=",")
         print(dataset)
         print("Saving to disk...")
         dataset.save_to_disk(data_dir)