tweak script

README.md

@@ -2,13 +2,16 @@
 
 ### Use the already preprocessed data
 
-The file `data/all.parquet` contains the preprocessed data. Load the dataset using
+Load the dataset using
 
 ```
 from datasets import load_dataset
 dataset = load_dataset("jglaser/binding_affinity")
 ```
 
+The file `data/all.parquet` contains the preprocessed data. To extract it,
+you need download and install [git LFS support] https://git-lfs.github.com/].
+
 ### Pre-process yourself
 
 To manually perform the preprocessing, fownload the data sets from
@@ -16,14 +19,14 @@ To manually perform the preprocessing, fownload the data sets from
 1. BindingDB
 
 In `bindingdb`, download the database as tab separated values
-[https://bindingdb.org] > Download > BindingDB_All_2021m4.tsv.zip
+<https://bindingdb.org> > Download > BindingDB_All_2021m4.tsv.zip
 and extract the zip archive into `bindingdb/data`
 
 Run the steps in `bindingdb.ipynb`
 
 2. PDBBind-cn
 
-Register for an account at [https://www.pdbbind.org.cn/], confirm the validation
+Register for an account at <https://www.pdbbind.org.cn/>, confirm the validation
 email, then login and download 
 
 - the Index files (1)
@@ -39,7 +42,7 @@ Perform the steps in the notebook `pdbbind.ipynb`
 
 3. BindingMOAD
 
-Go to [https://bindingmoad.org] and download the files `every.csv`
+Go to <https://bindingmoad.org> and download the files `every.csv`
 (All of Binding MOAD, Binding Data) and the non-redundant biounits
 (`nr_bind.zip`). Place and extract those files into `binding_moad`.
 
@@ -50,7 +53,7 @@ Perform the steps in the notebook `moad.ipynb`
 
 4. BioLIP
 
-Download from [https://zhanglab.ccmb.med.umich.edu/BioLiP/] the files
+Download from <https://zhanglab.ccmb.med.umich.edu/BioLiP/> the files
 - receptor_nr1.tar.bz2 (Receptor1, Non-redudant set)
 - ligand_nr.tar.bz2 (Ligands)
 - BioLiP_nr.tar.bz2 (Annotations)

binding_affinity.py

@@ -120,7 +120,8 @@ class BindingAffinity(datasets.ArrowBasedBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         my_urls = _URLs[self.config.name]
-        data_dir = dl_manager.download_and_extract(my_urls)
+        files = dl_manager.download_and_extract(my_urls)
+        data_dir = os.path.dirname(files[0])+'/'
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,

requirements.txt

@@ -2,3 +2,5 @@ mpi4py
 rdkit
 openbabel
 pyarrow
+huggingface_hub
+datasets