Error running code

[diego-uva]

Hello. Thank you very much for sharing your KAN neural networks example.

Running the following cell of your regression example notebook

results = model.train(calhous_dataset, opt="LBFGS", device=device, metrics=(train_mse, test_mse), loss_fn=torch.nn.MSELoss(), steps=25, lamb=0.01, lamb_entropy=2., save_fig=True, img_folder=image_folder)

I have encountered this error:

{ "name": "TypeError", "message": "Module.train() got an unexpected keyword argument 'opt'", "stack": "--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[6], line 13 10 mse = torch.nn.functional.mse_loss(predictions, calhous_dataset['test_label']) 11 return mse ---> 13 results = model.train(calhous_dataset, opt=\"LBFGS\", device=device, metrics=(train_mse, test_mse), 14 loss_fn=torch.nn.MSELoss(), steps=25, lamb=0.01, lamb_entropy=2., save_fig=True, img_folder=image_folder)

TypeError: Module.train() got an unexpected keyword argument 'opt'" }

Best regards.

[team-daniel]

Hi Diego,

It seems that the PyKAN package has recently been updated. As you can see below, .train() has been replaced with .fit().

Here is an example from the authors of PyKAN:

Try replacing this and let me know if this works. If not, I will quickly throw a fix together :)

Best, Dan

[diego-uva]

Thank you very much Dan.

Now the line

results = model.fit(calhous_dataset, opt="LBFGS", device=device, metrics=(train_mse, test_mse),
                      loss_fn=torch.nn.MSELoss(), steps=25, lamb=0.01, lamb_entropy=2., save_fig=True, img_folder=image_folder)

gives this error:

    "name": "_LinAlgError",
    "message": "torch.linalg.lstsq: (Batch element 6): The least squares solution could not be computed because the input matrix does not have full rank (error code: 9).",
    "stack": "---------------------------------------------------------------------------
_LinAlgError                              Traceback (most recent call last)
Cell In[5], line 16
     11     return mse
     13 #results = model.fit(calhous_dataset, opt=\"LBFGS\", device=device, metrics=(train_mse, test_mse),
     14 #                      loss_fn=torch.nn.MSELoss(), steps=25, lamb=0.01, lamb_entropy=2., save_fig=True, img_folder=image_folder)
---> 16 results = model.fit(calhous_dataset, opt=\"LBFGS\", device=device, metrics=(train_mse, test_mse),
     17                       loss_fn=torch.nn.MSELoss(), steps=25, lamb=0.01, lamb_entropy=2., save_fig=True, img_folder=image_folder)

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\MultKAN.py:807, in MultKAN.fit(self, dataset, opt, steps, log, lamb, lamb_l1, lamb_entropy, lamb_coef, lamb_coefdiff, update_grid, grid_update_num, loss_fn, lr, start_grid_update_step, stop_grid_update_step, batch, small_mag_threshold, small_reg_factor, metrics, save_fig, in_vars, out_vars, beta, save_fig_freq, img_folder, device, singularity_avoiding, y_th, reg_metric)
    804 test_id = np.random.choice(dataset['test_input'].shape[0], batch_size_test, replace=False)
    806 if _ % grid_update_freq == 0 and _ < stop_grid_update_step and update_grid and _ >= start_grid_update_step:
--> 807     self.update_grid_from_samples(dataset['train_input'][train_id].to(device))
    809 if opt == \"LBFGS\":
    810     optimizer.step(closure)

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\MultKAN.py:289, in MultKAN.update_grid_from_samples(self, x)
    287 for l in range(self.depth):
    288     self.forward(x)
--> 289     self.act_fun[l].update_grid_from_samples(self.acts[l])

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\KANLayer.py:232, in KANLayer.update_grid_from_samples(self, x)
    230 grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
    231 self.grid.data = extend_grid(grid, k_extend=self.k)
--> 232 self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k, device=self.device)

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\spline.py:167, in curve2coef(x_eval, y_eval, grid, k, device)
    165 # coef shape: (in_dim, outdim, G+k) 
    166 y_eval = y_eval.permute(1,2,0).unsqueeze(dim=3) # y_eval: (in_dim, out_dim, batch, 1)
--> 167 coef = torch.linalg.lstsq(mat.to(device), y_eval.to(device),
    168                           driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0]
    169 return coef.to(device)

_LinAlgError: torch.linalg.lstsq: (Batch element 6): The least squares solution could not be computed because the input matrix does not have full rank (error code: 9)."

I will try to build a simpler dataset to see if I am able to get the example you provide to work.

Thanks again. Diego.