testing kta

src/main.py

@@ -9,7 +9,6 @@ import torch
 from model.transformations import CNNProjection
 import sys
 
-
 hidden_size=32
 channels_out=128
 output_size=1024
@@ -18,12 +17,19 @@ pad_length=3000
 batch_size=50
 n_epochs=256
 bigrams=False
+n_authors=-1
+docs_by_author=-1
 
-#hidden_size=16
-#output_size=32
-#pad_length=100
-#batch_size=10
-#n_epochs=20
+debug=False
+if debug:
+    print(('*'*20)+' DEBUG MODE ' + ('*'*20))
+    hidden_size=16
+    output_size=32
+    pad_length=100
+    batch_size=10
+    n_epochs=20
+    n_authors = 5
+    docs_by_author = 10
 
 if torch.cuda.is_available():
     device = torch.device('cuda')
@@ -32,7 +38,7 @@ else:
 print(f'running on {device}')
 
 #dataset = Victorian(data_path='../../authorship_analysis/data/victoria', n_authors=5, docs_by_author=25)
-dataset = Imdb62(data_path='../../authorship_analysis/data/imdb62/imdb62.txt', n_authors=-1, docs_by_author=-1)
+dataset = Imdb62(data_path='../../authorship_analysis/data/imdb62/imdb62.txt', n_authors=n_authors, docs_by_author=docs_by_author)
 Xtr, ytr = dataset.train.data, dataset.train.target
 Xte, yte = dataset.test.data, dataset.test.target
 A = np.unique(ytr)

src/model/classifiers.py

@@ -18,8 +18,8 @@ class AuthorshipAttributionClassifier(nn.Module):
         self.device = device
 
     def fit(self, X, y, batch_size, epochs, lr=0.001, val_prop=0.1, log='../log/tmp.csv'):
-        #batcher = Batch(batch_size=batch_size, n_epochs=epochs)
-        batcher = TwoClassBatch(batch_size=batch_size, n_epochs=epochs, steps_per_epoch=X.shape[0]//batch_size)
+        batcher = Batch(batch_size=batch_size, n_epochs=epochs)
+        #batcher = TwoClassBatch(batch_size=batch_size, n_epochs=epochs, steps_per_epoch=X.shape[0]//batch_size)
         batcher_val = Batch(batch_size=batch_size, n_epochs=epochs, shuffle=False)
         criterion = torch.nn.CrossEntropyLoss().to(self.device)
         optim = torch.optim.Adam(self.parameters(), lr=lr)
@@ -33,17 +33,32 @@ class AuthorshipAttributionClassifier(nn.Module):
             for epoch in pbar:
                 # training
                 self.train()
-                losses = []
+                losses, attr_losses, sav_losses = [], [], []
                 for xi, yi in batcher.epoch(X, y):
                     optim.zero_grad()
                     xi = self.padder.transform(xi)
-                    logits = self.forward(xi)
-                    loss = criterion(logits, torch.as_tensor(yi).to(self.device))
+                    phi = self.projector(xi)
+
+                    logits = self.ff(phi)
+                    loss_attr = criterion(logits, torch.as_tensor(yi).to(self.device))
+
+                    kernel = torch.matmul(phi, phi.T)
+                    ideal_kernel = torch.as_tensor(1 * (np.outer(1 + yi, 1 / (yi + 1)) == 1)).to(self.device)
+                    loss_sav = KernelAlignmentLoss(kernel, ideal_kernel)
+
+                    loss = loss_attr + loss_sav
+
                     loss.backward()
                     optim.step()
+
+                    attr_losses.append(loss_attr.item())
+                    sav_losses.append(loss_sav.item())
                     losses.append(loss.item())
                     tr_loss = np.mean(losses)
-                    pbar.set_description(f'training epoch={epoch} loss={tr_loss:.5f} val_loss={val_loss:.5f}')
+                    pbar.set_description(f'training epoch={epoch} '
+                                         f'loss={tr_loss:.5f} '
+                                         f'attr-loss={np.mean(attr_losses):.5f} '
+                                         f'sav-loss={np.mean(sav_losses):.5f} val_loss={val_loss:.5f}')
 
                 # validation
                 self.eval()