update grid extension

.gitignore

@@ -12,3 +12,4 @@ expressiveness
 figures
 molecule
 applications
+experiments

kan/.ipynb_checkpoints/KANLayer-checkpoint.py

@@ -109,7 +109,7 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.5, scale_base_
 
         self.scale_base = torch.nn.Parameter(scale_base_mu * 1 / np.sqrt(in_dim) + \
                          scale_base_sigma * (torch.rand(in_dim, out_dim)*2-1) * 1/np.sqrt(in_dim)).requires_grad_(sb_trainable)
-        self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * self.mask).requires_grad_(sp_trainable)  # make scale trainable
+        self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * 1 / np.sqrt(in_dim) * self.mask).requires_grad_(sp_trainable)  # make scale trainable
         self.base_fun = base_fun
 
 
@@ -197,11 +197,13 @@ def update_grid_from_samples(self, x, mode='sample'):
         def get_grid(num_interval):
             ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1]
             grid_adaptive = x_pos[ids, :].permute(1,0)
-            h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval
-            grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device)
+            margin = 0.00
+            h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]] + 2 * margin)/num_interval
+            grid_uniform = grid_adaptive[:,[0]] - margin + h * torch.arange(num_interval+1,)[None, :].to(x.device)
             grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
             return grid
 
+
         grid = get_grid(num_interval)
 
         if mode == 'grid':
@@ -210,6 +212,8 @@ def get_grid(num_interval):
             y_eval = coef2curve(x_pos, self.grid, self.coef, self.k)
 
         self.grid.data = extend_grid(grid, k_extend=self.k)
+        #print('x_pos 2', x_pos.shape)
+        #print('y_eval 2', y_eval.shape)
         self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k)
 
     def initialize_grid_from_parent(self, parent, x, mode='sample'):
@@ -240,16 +244,40 @@ def initialize_grid_from_parent(self, parent, x, mode='sample'):
 
         batch = x.shape[0]
 
+        # shrink grid
         x_pos = torch.sort(x, dim=0)[0]
         y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k)
         num_interval = self.grid.shape[1] - 1 - 2*self.k
 
+
+        '''
+        # based on samples
         def get_grid(num_interval):
             ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1]
             grid_adaptive = x_pos[ids, :].permute(1,0)
             h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval
             grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device)
             grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
+            return grid'''
+
+        #print('p', parent.grid)
+        # based on interpolating parent grid
+        def get_grid(num_interval):
+            x_pos = parent.grid[:,parent.k:-parent.k]
+            #print('x_pos', x_pos)
+            sp2 = KANLayer(in_dim=1, out_dim=self.in_dim,k=1,num=x_pos.shape[1]-1,scale_base_mu=0.0, scale_base_sigma=0.0).to(x.device)
+
+            #print('sp2_grid', sp2.grid[:,sp2.k:-sp2.k].permute(1,0).expand(-1,self.in_dim))
+            #print('sp2_coef_shape', sp2.coef.shape)
+            sp2_coef = curve2coef(sp2.grid[:,sp2.k:-sp2.k].permute(1,0).expand(-1,self.in_dim), x_pos.permute(1,0).unsqueeze(dim=2), sp2.grid[:,:], k=1).permute(1,0,2)
+            shp = sp2_coef.shape
+            #sp2_coef = torch.cat([torch.zeros(shp[0], shp[1], 1), sp2_coef, torch.zeros(shp[0], shp[1], 1)], dim=2)
+            #print('sp2_coef',sp2_coef)
+            #print(sp2.coef.shape)
+            sp2.coef.data = sp2_coef
+            percentile = torch.linspace(-1,1,self.num+1).to(self.device)
+            grid = sp2(percentile.unsqueeze(dim=1))[0].permute(1,0)
+            #print('c', grid)
             return grid
 
         grid = get_grid(num_interval)

kan/.ipynb_checkpoints/MultKAN-checkpoint.py

@@ -164,9 +164,13 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=0.3, sca
         self.act_fun = []
         self.depth = len(width) - 1
 
+        #print('haha1', width)
         for i in range(len(width)):
-            if type(width[i]) == int:
+            #print(type(width[i]), type(width[i]) == int)
+            if type(width[i]) == int or type(width[i]) == np.int64:
                 width[i] = [width[i],0]
+
+        #print('haha2', width)
 
         self.width = width
 
@@ -196,7 +200,18 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=0.3, sca
 
         for l in range(self.depth):
             # splines
-            sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid, k=k, noise_scale=noise_scale, scale_base_mu=scale_base_mu, scale_base_sigma=scale_base_sigma, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init)
+            if isinstance(grid, list):
+                grid_l = grid[l]
+            else:
+                grid_l = grid
+
+            if isinstance(k, list):
+                k_l = k[l]
+            else:
+                k_l = k
+
+
+            sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid_l, k=k_l, noise_scale=noise_scale, scale_base_mu=scale_base_mu, scale_base_sigma=scale_base_sigma, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init)
             self.act_fun.append(sp_batch)
 
         self.node_bias = []
@@ -951,14 +966,14 @@ def unfix_symbolic(self, l, i, j, log_history=True):
         if log_history:
             self.log_history('unfix_symbolic')
 
-    def unfix_symbolic_all(self):
+    def unfix_symbolic_all(self, log_history=True):
         '''
         unfix all activation functions.
         '''
         for l in range(len(self.width) - 1):
-            for i in range(self.width[l]):
-                for j in range(self.width[l + 1]):
-                    self.unfix_symbolic(l, i, j)
+            for i in range(self.width_in[l]):
+                for j in range(self.width_out[l + 1]):
+                    self.unfix_symbolic(l, i, j, log_history)
 
     def get_range(self, l, i, j, verbose=True):
         '''
@@ -1522,6 +1537,10 @@ def closure():
 
             if _ == steps-1 and old_save_act:
                 self.save_act = True
+
+            if save_fig and _ % save_fig_freq == 0:
+                save_act = self.save_act
+                self.save_act = True
 
             train_id = np.random.choice(dataset['train_input'].shape[0], batch_size, replace=False)
             test_id = np.random.choice(dataset['test_input'].shape[0], batch_size_test, replace=False)
@@ -1579,6 +1598,7 @@ def closure():
                 self.plot(folder=img_folder, in_vars=in_vars, out_vars=out_vars, title="Step {}".format(_), beta=beta)
                 plt.savefig(img_folder + '/' + str(_) + '.jpg', bbox_inches='tight', dpi=200)
                 plt.close()
+                self.save_act = save_act
 
         self.log_history('fit')
         # revert back to original state
@@ -2160,7 +2180,7 @@ def suggest_symbolic(self, l, i, j, a_range=(-10, 10), b_range=(-10, 10), lib=No
 
         return best_name, best_fun, best_r2, best_c;
 
-    def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=1):
+    def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=1, weight_simple = 0.8, r2_threshold=0.0):
         '''
         automatic symbolic regression for all edges
 
@@ -2174,7 +2194,10 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
                 library of candidate symbolic functions
             verbose : int
                 larger verbosity => more verbosity
-            
+            weight_simple : float
+                a weight that prioritizies simplicity (low complexity) over performance (high r2) - set to 0.0 to ignore complexity
+            r2_threshold : float
+                If r2 is below this threshold, the edge will not be fixed with any symbolic function - set to 0.0 to ignore this threshold
         Returns:
         --------
             None
@@ -2191,17 +2214,19 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
         for l in range(len(self.width_in) - 1):
             for i in range(self.width_in[l]):
                 for j in range(self.width_out[l + 1]):
-                    #if self.symbolic_fun[l].mask[j, i] > 0. and self.act_fun[l].mask[i][j] == 0.:
                     if self.symbolic_fun[l].mask[j, i] > 0. and self.act_fun[l].mask[i][j] == 0.:
                         print(f'skipping ({l},{i},{j}) since already symbolic')
                     elif self.symbolic_fun[l].mask[j, i] == 0. and self.act_fun[l].mask[i][j] == 0.:
                         self.fix_symbolic(l, i, j, '0', verbose=verbose > 1, log_history=False)
                         print(f'fixing ({l},{i},{j}) with 0')
                     else:
-                        name, fun, r2, c = self.suggest_symbolic(l, i, j, a_range=a_range, b_range=b_range, lib=lib, verbose=False)
-                        self.fix_symbolic(l, i, j, name, verbose=verbose > 1, log_history=False)
-                        if verbose >= 1:
-                            print(f'fixing ({l},{i},{j}) with {name}, r2={r2}, c={c}')
+                        name, fun, r2, c = self.suggest_symbolic(l, i, j, a_range=a_range, b_range=b_range, lib=lib, verbose=False, weight_simple=weight_simple)
+                        if r2 >= r2_threshold:
+                            self.fix_symbolic(l, i, j, name, verbose=verbose > 1, log_history=False)
+                            if verbose >= 1:
+                                print(f'fixing ({l},{i},{j}) with {name}, r2={r2}, c={c}')
+                        else:
+                            print(f'For ({l},{i},{j}) the best fit was {name}, but r^2 = {r2} and this is lower than {r2_threshold}. This edge was omitted, keep training or try a different threshold.')
 
         self.log_history('auto_symbolic')
 

kan/.ipynb_checkpoints/spline-checkpoint.py

@@ -68,7 +68,7 @@ def coef2curve(x_eval, grid, coef, k, device="cpu"):
     Returns:
     --------
         y_eval : 3D torch.tensor
-            shape (number of samples, in_dim, out_dim)
+            shape (batch, in_dim, out_dim)
         
     '''
 
@@ -78,16 +78,16 @@ def coef2curve(x_eval, grid, coef, k, device="cpu"):
     return y_eval
 
 
-def curve2coef(x_eval, y_eval, grid, k, lamb=1e-8):
+def curve2coef(x_eval, y_eval, grid, k):
     '''
     converting B-spline curves to B-spline coefficients using least squares.
     
     Args:
     -----
         x_eval : 2D torch.tensor
-            shape (in_dim, out_dim, number of samples)
-        y_eval : 2D torch.tensor
-            shape (in_dim, out_dim, number of samples)
+            shape (batch, in_dim)
+        y_eval : 3D torch.tensor
+            shape (batch, in_dim, out_dim)
         grid : 2D torch.tensor
             shape (in_dim, grid+2*k)
         k : int
@@ -100,25 +100,33 @@ def curve2coef(x_eval, y_eval, grid, k, lamb=1e-8):
         coef : 3D torch.tensor
             shape (in_dim, out_dim, G+k)
     '''
+    #print('haha', x_eval.shape, y_eval.shape, grid.shape)
     batch = x_eval.shape[0]
     in_dim = x_eval.shape[1]
     out_dim = y_eval.shape[2]
     n_coef = grid.shape[1] - k - 1
     mat = B_batch(x_eval, grid, k)
     mat = mat.permute(1,0,2)[:,None,:,:].expand(in_dim, out_dim, batch, n_coef)
+    #print('mat', mat.shape)
     y_eval = y_eval.permute(1,2,0).unsqueeze(dim=3)
+    #print('y_eval', y_eval.shape)
     device = mat.device
 
-    #coef = torch.linalg.lstsq(mat, y_eval,
-                             #driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0]
-
+    #coef = torch.linalg.lstsq(mat, y_eval, driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0]
+    try:
+        coef = torch.linalg.lstsq(mat, y_eval).solution[:,:,:,0]
+    except:
+        print('lstsq failed')
+
+    # manual psuedo-inverse
+    '''lamb=1e-8
     XtX = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), mat)
     Xty = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), y_eval)
     n1, n2, n = XtX.shape[0], XtX.shape[1], XtX.shape[2]
     identity = torch.eye(n,n)[None, None, :, :].expand(n1, n2, n, n).to(device)
     A = XtX + lamb * identity
     B = Xty
-    coef = (A.pinverse() @ B)[:,:,:,0]
+    coef = (A.pinverse() @ B)[:,:,:,0]'''
 
     return coef
 

kan/.ipynb_checkpoints/utils-checkpoint.py

@@ -384,7 +384,7 @@ def augment_input(orig_vars, aux_vars, x):
     return x
 
 
-def batch_jacobian(func, x, create_graph=False):
+def batch_jacobian(func, x, create_graph=False, mode='scalar'):
     '''
     jacobian
     
@@ -408,7 +408,10 @@ def batch_jacobian(func, x, create_graph=False):
     # x in shape (Batch, Length)
     def _func_sum(x):
         return func(x).sum(dim=0)
-    return torch.autograd.functional.jacobian(_func_sum, x, create_graph=create_graph)[0]
+    if mode == 'scalar':
+        return torch.autograd.functional.jacobian(_func_sum, x, create_graph=create_graph)[0]
+    elif mode == 'vector':
+        return torch.autograd.functional.jacobian(_func_sum, x, create_graph=create_graph).permute(1,0,2)
 
 def batch_hessian(model, x, create_graph=False):
     '''
@@ -588,4 +591,4 @@ def model2param(model):
     p = torch.tensor([]).to(model.device)
     for params in model.parameters():
         p = torch.cat([p, params.reshape(-1,)], dim=0)
-    return p
+    return p

kan/KANLayer.py

@@ -109,7 +109,7 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.5, scale_base_
 
         self.scale_base = torch.nn.Parameter(scale_base_mu * 1 / np.sqrt(in_dim) + \
                          scale_base_sigma * (torch.rand(in_dim, out_dim)*2-1) * 1/np.sqrt(in_dim)).requires_grad_(sb_trainable)
-        self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * self.mask).requires_grad_(sp_trainable)  # make scale trainable
+        self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * 1 / np.sqrt(in_dim) * self.mask).requires_grad_(sp_trainable)  # make scale trainable
         self.base_fun = base_fun
 
 
@@ -197,11 +197,13 @@ def update_grid_from_samples(self, x, mode='sample'):
         def get_grid(num_interval):
             ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1]
             grid_adaptive = x_pos[ids, :].permute(1,0)
-            h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval
-            grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device)
+            margin = 0.00
+            h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]] + 2 * margin)/num_interval
+            grid_uniform = grid_adaptive[:,[0]] - margin + h * torch.arange(num_interval+1,)[None, :].to(x.device)
             grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
             return grid
 
+
         grid = get_grid(num_interval)
 
         if mode == 'grid':
@@ -210,6 +212,8 @@ def get_grid(num_interval):
             y_eval = coef2curve(x_pos, self.grid, self.coef, self.k)
 
         self.grid.data = extend_grid(grid, k_extend=self.k)
+        #print('x_pos 2', x_pos.shape)
+        #print('y_eval 2', y_eval.shape)
         self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k)
 
     def initialize_grid_from_parent(self, parent, x, mode='sample'):
@@ -240,16 +244,40 @@ def initialize_grid_from_parent(self, parent, x, mode='sample'):
 
         batch = x.shape[0]
 
+        # shrink grid
         x_pos = torch.sort(x, dim=0)[0]
         y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k)
         num_interval = self.grid.shape[1] - 1 - 2*self.k
 
+
+        '''
+        # based on samples
         def get_grid(num_interval):
             ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1]
             grid_adaptive = x_pos[ids, :].permute(1,0)
             h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval
             grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device)
             grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
+            return grid'''
+
+        #print('p', parent.grid)
+        # based on interpolating parent grid
+        def get_grid(num_interval):
+            x_pos = parent.grid[:,parent.k:-parent.k]
+            #print('x_pos', x_pos)
+            sp2 = KANLayer(in_dim=1, out_dim=self.in_dim,k=1,num=x_pos.shape[1]-1,scale_base_mu=0.0, scale_base_sigma=0.0).to(x.device)
+
+            #print('sp2_grid', sp2.grid[:,sp2.k:-sp2.k].permute(1,0).expand(-1,self.in_dim))
+            #print('sp2_coef_shape', sp2.coef.shape)
+            sp2_coef = curve2coef(sp2.grid[:,sp2.k:-sp2.k].permute(1,0).expand(-1,self.in_dim), x_pos.permute(1,0).unsqueeze(dim=2), sp2.grid[:,:], k=1).permute(1,0,2)
+            shp = sp2_coef.shape
+            #sp2_coef = torch.cat([torch.zeros(shp[0], shp[1], 1), sp2_coef, torch.zeros(shp[0], shp[1], 1)], dim=2)
+            #print('sp2_coef',sp2_coef)
+            #print(sp2.coef.shape)
+            sp2.coef.data = sp2_coef
+            percentile = torch.linspace(-1,1,self.num+1).to(self.device)
+            grid = sp2(percentile.unsqueeze(dim=1))[0].permute(1,0)
+            #print('c', grid)
             return grid
 
         grid = get_grid(num_interval)