feat: Better visualizability of first convolutional layer with onehot encoding

src/polyA_classifier/polyA_classifier.py

@@ -15,7 +15,7 @@ class Net(Module):
 
         self.cnn_layers = Sequential(
             # Defining a 1D convolution layer
-            Conv1d(1, 4, kernel_size=3, stride=1, padding=1),
+            Conv1d(4, 4, kernel_size=3, stride=1, padding=1),
             BatchNorm1d(4),
             ReLU(inplace=True),
             MaxPool1d(kernel_size=2, stride=2),
@@ -42,11 +42,11 @@ class PolyAClassifier:
     """Classifier object using the state-dict of a pretrained pytorch model."""
 
     enum = {
-        'A': 0.0,
-        'U': 1 / 3,
-        'T': 1 / 3,
-        'G': 2 / 3,
-        'C': 1.0
+        'A': [1, 0, 0, 0],
+        'U': [0, 1, 0, 0],
+        'T': [0, 1, 0, 0],
+        'G': [0, 0, 1, 0],
+        'C': [0, 0, 0, 1]
     }
 
     def __init__(self, model=Net, state_dict_path: str = './models/internal_priming.pth'):
@@ -103,7 +103,7 @@ class PolyAClassifier:
             raise ValueError('Not all sequences of length 200')
 
         test_shape = test.shape
-        test = test.reshape(test_shape[0], 1, test_shape[1])
+        test = test.reshape(test_shape[0], 4, test_shape[1])
 
         if test_shape[1] != 200:
             raise ValueError('Sequences not of length 200')