Update labels to float

README.md

@@ -13,8 +13,8 @@ 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['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('float16').values.tolist()
-df['Membrane'] = df['Membrane'].astype('int8')
+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)

deeploc-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d48167a2babeb7074f6e2396cc637e026a0e65586efb8c71cfc8e9e2115f23fb
-size 1631338
+oid sha256:858112657d33dc6562c45f39bd5fa25fb49b358c054dbc3ca737c75b1ef5c0ba
+size 1562923

deeploc-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d27b7603157aaba2ac9b2d93d0a5264826f744177c5838f9395d1e8b80c32814
-size 12865785
+oid sha256:2c6df4b9224e0b5fea8a68393e7b16662a6ceb8387381e1f463fa4c8ce45b28e
+size 12945272

deploc-val.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9817999ed6508ee98621dd70791356c1f681b9e1380ea1a814371babbffdec57
-size 1633138
+oid sha256:3fe952befd3353eae1824197d2434e5818014296e83605efc613139ab30e9cc1
+size 1609584

prep.py

@@ -1,8 +1,8 @@
 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('float16').values.tolist()
-df['Membrane'] = df['Membrane'].astype('int8')
+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)