revert back to 1-hot encoded labels

deeploc-test.parquet

@@ -1,3 +1,3 @@
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+size 1608942

deeploc-train.parquet

@@ -1,3 +1,3 @@
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+size 12856595

deploc-val.parquet

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-size 1609584
+oid sha256:88e50248e25b3900459ad6f43e9052c55206aedefc4fb76ebf4c6db6be7ab632
+size 1696313

prep.py

@@ -1,9 +1,7 @@
 import pandas as pd
 
 df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1)
-df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('float32').values.tolist()
 df['Membrane'] = df['Membrane'].astype('float32')
-df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']]
 
 train = df.sample(frac=0.8)
 df = df.drop(train.index)