script

scripts/training_reg_class.py

@@ -11,6 +11,8 @@ import torch.utils.data as utils_data
 import torch.nn.functional as F
 import datetime
 
+from sklearn.metrics import confusion_matrix, matthews_corrcoef
+
 torch.manual_seed(21894)
 np.random.seed(21894)
 
@@ -150,7 +152,13 @@ np.random.shuffle(ixs_valid_test)
 ixs_valid = ixs_valid_test[::2]
 ixs_test = ixs_valid_test[1::2]
 
-dst_min = data_out[:last_train].min(axis=1).values.flatten()
+ixs_tr1 = np.where((mm_scaler_out.inverse_transform(data_out_scaled[:last_train].clone()).min(axis=1)[0]).numpy()<-20)[0]
+ixs_tr2 = np.where((mm_scaler_out.inverse_transform(data_out_scaled[:last_train].clone()).min(axis=1)[0]).numpy()>=-20)[0]
+np.random.shuffle(ixs_tr2)
+ixs_tr2 = ixs_tr2[:10000]
+ixs_tr = list(ixs_tr1) + list(ixs_tr2)
+
+dst_min = data_out[ixs_tr].min(axis=1).values.flatten()
 
 bins = [dst_min.min() - 10] + list(np.arange(-300, dst_min.max() + 10, 10))
 h, b = np.histogram(dst_min, bins=bins)
@@ -174,7 +182,7 @@ sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, num_samples= l
 
 BATCH_SIZE=256
 
-dataset_tr = Dataset(data_in_scaled[:last_train], data_out_scaled[:last_train], data_out_c[:last_train], weights)
+dataset_tr = Dataset(data_in_scaled[ixs_tr], data_out_scaled[ixs_tr], data_out_c[ixs_tr], weights)
 # data_loader_tr = utils_data.DataLoader(dataset_tr, batch_size=BATCH_SIZE, num_workers = 4, shuffle=False, sampler = sampler)
 data_loader_tr = utils_data.DataLoader(dataset_tr, batch_size=BATCH_SIZE, num_workers = 4, shuffle=True)
 
@@ -200,7 +208,7 @@ class DSTnet(nn.Module):
         self.linear_o_4_c = nn.Linear(self.nhidden_o // 2, self.after * 4)
         
         self.linear_o_4_r = nn.Linear(4, 16)
-        self.linear_o_4b_r = nn.Linear(4, 4)
+        self.linear_o_4b_r = nn.Linear(16, 16)
         self.linear_o_5_r = nn.Linear(16, 1)
         
         
@@ -226,39 +234,43 @@ class DSTnet(nn.Module):
         x = F.dropout(x, 0.2, training=self.training)
         x1 = self.linear_o_4_c(x)
         x2 = F.relu(self.linear_o_4_r(x1.view(-1, 4)))
-#         x2 = F.relu(self.linear_o_4b_r(x2))
+        x2 = F.relu(self.linear_o_4b_r(x2))
         x2 = self.linear_o_5_r(x2)
         x2 = x2.reshape(x0.size(0), self.after)
         x1 = x1.reshape(x0.size(0) * self.after, 4)
         
         return x2, x1
     
-aa = data_out_c[:last_train]
-weights_c = torch.tensor([len(aa[aa==0])/len(aa[aa==0]), len(aa[aa==1])/len(aa[aa==1]), len(aa[aa==0])/len(aa[aa==2]), len(aa[aa==0])/len(aa[aa==3])]).to(device).sqrt()
+aa = data_out_c[ixs_tr]
+weights_c = torch.tensor([len(aa[aa==1])/len(aa[aa==0]), len(aa[aa==1])/len(aa[aa==1]), len(aa[aa==0])/len(aa[aa==2]), len(aa[aa==0])/len(aa[aa==3])]).to(device)#.sqrt()
 
 loss_f = nn.L1Loss()
 loss_mse = nn.MSELoss(reduction='none')
 
-loss_fc= nn.CrossEntropyLoss()
-# loss_fc= nn.CrossEntropyLoss(weight = weights_c)
+#loss_fc= nn.CrossEntropyLoss()
+loss_fc= nn.CrossEntropyLoss(weight = weights_c)
 
 nhidden_i = 2
+#nhidden_o = 96
+#n_out_i = 8
 nhidden_o = 96
-n_out_i = 8
+n_out_i = 12
 before = BEFORE
 nvars = data_in_scaled.shape[-1]
 
 dst_net = DSTnet(nvars, nhidden_i, nhidden_o, n_out_i, before, AFTER).to(device)
 print(dst_net)
 
-num_epochs = 2000
-lr = 1e-4
-optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr)#, weight_decay=1e-5)
+num_epochs = 10000
+lr = 1e-5
+optimizer = torch.optim.Adam(dst_net.parameters(), lr=lr, weight_decay=1e-5)
 
 history_tr = np.zeros((num_epochs, 3))
 history_valid = np.zeros((num_epochs, 3))
 history_ts = np.zeros((num_epochs, 3))
 
+np.set_printoptions(suppress=True, precision=3)
+
 for epoch in range(num_epochs):
 
     start_time = time.time()
@@ -304,10 +316,18 @@ for epoch in range(num_epochs):
     history_ts[epoch] = [loss_ts, loss_mae_ts, loss_c_ts]
     epoch_time = time.time() - start_time
 
-    if (epoch % 10 == 0):
+    if (epoch % 100 == 0):
         print('Epoch %d time = %.2f, tr_rmse = %0.5f, val_rmse = %0.5f, ts_rmse = %0.5f, tr_c = %.5f, val_c = %.5f, ts_c = %.5f' % 
               (epoch, epoch_time, loss_tr, loss_val, loss_ts, loss_c_tr, loss_c_val, loss_c_ts))        
-      
+
+    if (epoch % 100 == 0):
+        out_r, out_c = dst_net(data_in_scaled[last_train:].to(device).float())
+        tp = 11
+        out_cc = F.softmax(out_c, dim=1)
+        out_cc = out_cc.detach().cpu().numpy()
+        print(confusion_matrix(data_out_c[last_train:][:,tp].cpu().detach().numpy(), out_cc.argmax(axis=1).reshape(-1,12)[:,tp])/(confusion_matrix(data_out_c[last_train:][:,tp].cpu().detach().numpy(), out_cc.argmax(axis=1).reshape(-1,12)[:,tp]).sum(axis=1)[:, None]))
+
+
 
 torch.save(dst_net.state_dict(), os.path.join('/home/mcristofo/DST/models','dst_regr_class_nsc.pth'))    
 
@@ -319,18 +339,13 @@ dst_net.eval()
 
 out_r, out_c = dst_net(data_in_scaled[last_train:].to(device).float())
 
-out_cc = F.softmax(out_c, dim=1)
-out_cc = out_cc.detach().cpu().numpy()
-
-from sklearn.metrics import confusion_matrix, matthews_corrcoef
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 
-np.set_printoptions(suppress=True, precision=3)
-
-tp = 11
+out_cc = F.softmax(out_c, dim=1)
+out_cc = out_cc.detach().cpu().numpy()
 print(confusion_matrix(data_out_c[last_train:][:,tp].cpu().detach().numpy(), out_cc.argmax(axis=1).reshape(-1,12)[:,tp])/(confusion_matrix(data_out_c[last_train:][:,tp].cpu().detach().numpy(), out_cc.argmax(axis=1).reshape(-1,12)[:,tp]).sum(axis=1)[:, None]))
 
-dst_levels = [-20,-50,-100]
+
 
 truth = data_out[last_train:].cpu().detach().numpy().copy()
 out = mm_scaler_out.inverse_transform(out_r.cpu().clone()).detach().numpy()