Add label array

README.md

@@ -13,7 +13,9 @@ Data downloaded and processed using the following Python script:
 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 = df.melt(['Kingdom', 'ACC', 'Sequence','Membrane'], var_name='Location').query('value == 1.0').drop(labels='value', axis=1).astype({'Membrane': 'int8'})
+df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('int8').values.tolist()
+df['Membrane'] = df['Membrane'].astype('int8')
+df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']]
 
 train = df.sample(frac=0.8)
 df = df.drop(train.index)
@@ -29,6 +31,19 @@ val.to_parquet('deploc-val.parquet', index=False)
 test.to_parquet('deeploc-test.parquet', index=False)
 ```
 
+## Labels
+
+{'Cell membrane': 0,
+ 'Cytoplasm': 1,
+ 'Endoplasmic reticulum': 2,
+ 'Extracellular': 3,
+ 'Golgi apparatus': 4,
+ 'Lysosome/Vacuole': 5,
+ 'Mitochondrion': 6,
+ 'Nucleus': 7,
+ 'Peroxisome': 8,
+ 'Plastid': 9}
+
 ## Citation
 
 **DeepLoc-2.0:**

deeploc-test.parquet

@@ -1,3 +1,3 @@
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-size 2016514
+oid sha256:15a8ad44a8ccf204be9f5e423adec792138360885dead352282ceab007460254
+size 1635356

deeploc-train.parquet

@@ -1,3 +1,3 @@
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-size 16422156
+oid sha256:635c8ca79d28616b449bd91a8a8c0b00916dd4c6b304a14a1870107ff6b9504e
+size 12854801

deploc-val.parquet

@@ -1,3 +1,3 @@
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-oid sha256:3d0a708c9338df6177c58fa5ee70c02f1e4fe1b47f79a5a36ec7ed9ad1ca8d0a
-size 2045512
+oid sha256:bd20e1ce78f568ea3255ebfcb915b275355aaea7c91db737f298ad30f279ce0f
+size 1630148

prep.py

@@ -1,7 +1,9 @@
 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 = df.melt(['Kingdom', 'ACC', 'Sequence','Membrane'], var_name='Location').query('value == 1.0').drop(labels='value', axis=1).astype({'Membrane': 'int8'})
+df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('int8').values.tolist()
+df['Membrane'] = df['Membrane'].astype('int8')
+df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']]
 
 train = df.sample(frac=0.8)
 df = df.drop(train.index)