diff --git a/_modules/icenet/deep/autogradxgb.html b/_modules/icenet/deep/autogradxgb.html index 0b38f258..aa460013 100644 --- a/_modules/icenet/deep/autogradxgb.html +++ b/_modules/icenet/deep/autogradxgb.html @@ -529,17 +529,20 @@

Source code for icenet.deep.autogradxgb

 [docs]
 class XgboostObjective():
     def __init__(self, loss_func: Callable[[Tensor, Tensor], Tensor], mode='train', loss_sign=1, 
-                 flatten_grad=False, skip_hessian=False, hessian_const=1.0, device='cpu'):
+                 flatten_grad=False, hessian_mode='constant', hessian_const=1.0, device='cpu'):
 
         self.mode          = mode
         self.loss_func     = loss_func
         self.loss_sign     = loss_sign
         self.device        = device
-        self.skip_hessian  = skip_hessian
+        self.hessian_mode  = hessian_mode
         self.hessian_const = hessian_const
         self.flatten_grad  = flatten_grad
 
-        print(__name__ + f'.__init__: Using device: {self.device} | skip_hessian = {self.skip_hessian} | hessian_const = {self.hessian_const}')
+        if self.hessian_mode == 'constant':
+            print(__name__ + f': Using device: {self.device} | hessian_mode = {self.hessian_mode} | hessian_const = {self.hessian_const}')
+        else:
+            print(__name__ + f': Using device: {self.device} | hessian_mode = {self.hessian_mode}')
         
     def __call__(self, preds: np.ndarray, targets: xgboost.DMatrix):
 
@@ -574,19 +577,34 @@ 
Source code for icenet.deep.autogradxgb
 

 [docs]
     def derivatives(self, loss: Tensor, preds: Tensor):
-
-        # Gradient
+        """ Gradient and Hessian diagonal
+        """
+        
+        ## Gradient
         grad1 = torch.autograd.grad(loss, preds, create_graph=True)[0]
         
-        # Diagonal elements of the Hessian matrix
-        grad2 = self.hessian_const * torch.ones_like(grad1)
+        ## Diagonal elements of the Hessian matrix
+        
+        # Constant
+        if   self.hessian_mode == 'constant':
+            grad2 = self.hessian_const * torch.ones_like(grad1)
+        
+        # Squared derivative based approximation
+        elif self.hessian_mode == 'squared_approx':
+            grad2 = grad1 * grad1
         
-        if not self.skip_hessian:
-            print('Computing Hessian ...')
+        # Exact autograd
+        elif self.hessian_mode == 'exact':
+            
+            print(__name__ + f'.derivatives: Computing Hessian diagonal with exact autograd ...')
+            
             for i in tqdm(range(len(preds))): # Can be very slow
                 grad2_i  = torch.autograd.grad(grad1[i], preds, retain_graph=True)[0]
                 grad2[i] = grad2_i[i]
         
+        else:
+            raise Exception(__name__ + f'.derivatives: Unknown "hessian_mode" {self.hessian_mode}')
+        
         grad1, grad2 = grad1.detach().cpu().numpy(), grad2.detach().cpu().numpy()
         
         if self.flatten_grad:
@@ -595,7 +613,6 @@ 
Source code for icenet.deep.autogradxgb
         return grad1, grad2

 

 
-
diff --git a/_modules/icenet/deep/iceboost.html b/_modules/icenet/deep/iceboost.html index b24844cd..caaddcde 100644 --- a/_modules/icenet/deep/iceboost.html +++ b/_modules/icenet/deep/iceboost.html @@ -1145,30 +1145,30 @@

Source code for icenet.deep.iceboost

             x         = copy.deepcopy(data_trn.x)
             MI_x      = copy.deepcopy(data_trn_MI)
             
+            # Custom loss string of type 'custom_loss:loss_name:hessian:hessian_mode'
+            
             if strs[1] == 'binary_cross_entropy':
                 
-                if len(strs) == 3 and 'hessian' in strs[2]:
-                    print('Using Hessian with custom loss')
-                    skip_hessian = False
+                if 'hessian' in strs:
+                    hessian_mode = strs[strs.index('hessian')+1]
                 else:
-                    skip_hessian = True
+                    hessian_mode = 'constant'
                 
-                a['obj'] = autogradxgb.XgboostObjective(loss_func=_binary_cross_entropy, skip_hessian=skip_hessian, device=device)
+                a['obj'] = autogradxgb.XgboostObjective(loss_func=_binary_cross_entropy, hessian_mode=hessian_mode, device=device)
                 a['params']['disable_default_eval_metric'] = 1
             
             elif strs[1] == 'sliced_wasserstein':
                 
-                if len(strs) == 3 and 'hessian' in strs[2]:
-                    print('Using Hessian with custom loss')
-                    skip_hessian = False
+                if 'hessian' in strs:
+                    hessian_mode = strs[strs.index('hessian')+1]
                 else:
-                    skip_hessian = True
+                    hessian_mode = 'constant'
                 
-                a['obj'] = autogradxgb.XgboostObjective(loss_func=_sliced_wasserstein, skip_hessian=skip_hessian, device=device)
+                a['obj'] = autogradxgb.XgboostObjective(loss_func=_sliced_wasserstein, hessian_mode=hessian_mode, device=device)
                 a['params']['disable_default_eval_metric'] = 1
             
             else:
-                raise Exception(__name__ + f'.train_xgb: Unknown custom loss {strs[1]}')
+                raise Exception(__name__ + f'.train_xgb: Unknown custom loss {strs[1]} (check syntax)')
             
             #!
             del a['params']['eval_metric']
diff --git a/modules/icenet.html b/modules/icenet.html
index ea282d19..b49944f4 100644
--- a/modules/icenet.html
+++ b/modules/icenet.html
@@ -757,11 +757,12 @@ 
icenet.deep
icenet.deep.autogradxgb

 

 

-class XgboostObjective(loss_func: Callable[[Tensor, Tensor], Tensor], mode='train', loss_sign=1, flatten_grad=False, skip_hessian=False, hessian_const=1.0, device='cpu')[source]

+class XgboostObjective(loss_func: Callable[[Tensor, Tensor], Tensor], mode='train', loss_sign=1, flatten_grad=False, hessian_mode='constant', hessian_const=1.0, device='cpu')[source]
 

 

 derivatives(loss: Tensor, preds: Tensor)[source]

-

+
Gradient and Hessian diagonal

+

 
 

 

diff --git a/searchindex.js b/searchindex.js
index d31e60f8..a3946a64 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"AI-algorithms and models": [[15, "ai-algorithms-and-models"]], "Advanced ML-training technology": [[15, "advanced-ml-training-technology"]], "Automated selectors and combinatorics for distributions": [[15, "automated-selectors-and-combinatorics-for-distributions"]], "Automated setup": [[14, "automated-setup"]], "Basic design principles": [[15, "basic-design-principles"]], "C-library versions": [[14, "c-library-versions"]], "CMSSW setup": [[13, "cmssw-setup"]], "Conda virtual environment commands": [[14, "conda-virtual-environment-commands"]], "Contents": [[0, "contents"]], "First steps": [[0, "first-steps"]], "Folder structure": [[15, "folder-structure"]], "GPU-support commands": [[14, "gpu-support-commands"]], "HTCondor GPU job submission": [[14, "htcondor-gpu-job-submission"]], "Indices and Tables": [[0, "indices-and-tables"]], "Initialize the environment": [[14, "initialize-the-environment"]], "Installation": [[14, "installation"]], "Introduction": 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\ No newline at end of file
+Search.setIndex({"alltitles": {"AI-algorithms and models": [[15, "ai-algorithms-and-models"]], "Advanced ML-training technology": [[15, "advanced-ml-training-technology"]], "Automated selectors and combinatorics for distributions": [[15, "automated-selectors-and-combinatorics-for-distributions"]], "Automated setup": [[14, "automated-setup"]], "Basic design principles": [[15, "basic-design-principles"]], "C-library versions": [[14, "c-library-versions"]], "CMSSW setup": [[13, "cmssw-setup"]], "Conda virtual environment commands": [[14, "conda-virtual-environment-commands"]], "Contents": [[0, "contents"]], "First steps": [[0, "first-steps"]], "Folder structure": [[15, "folder-structure"]], "GPU-support commands": [[14, "gpu-support-commands"]], "HTCondor GPU job submission": [[14, "htcondor-gpu-job-submission"]], "Indices and Tables": [[0, "indices-and-tables"]], "Initialize the environment": [[14, "initialize-the-environment"]], "Installation": [[14, "installation"]], "Introduction": [[15, "introduction"]], "Markup": [[16, "markup"]], "Notes": [[0, null]], "Package Reference": [[0, null]], "Possible problems": [[14, "possible-problems"]], "Pre-installed CUDA paths (OBSOLETE)": [[14, "pre-installed-cuda-paths-obsolete"]], "Preliminaries: CMSSW release setup": [[13, "preliminaries-cmssw-release-setup"]], "Preliminaries: Conda installation": [[14, "preliminaries-conda-installation"]], "Preliminaries: SSH public key to github": [[13, "preliminaries-ssh-public-key-to-github"]], "References": [[0, "references"]], "Show installation paths of binaries": [[14, "show-installation-paths-of-binaries"]], "Sun Grid Engine (SGE) / HTCondor execution": [[15, "sun-grid-engine-sge-htcondor-execution"]], "YAML-configuration files": [[15, "yaml-configuration-files"]], "icebrem": [[1, "icebrem"]], "icebrem.common": [[1, "module-icebrem.common"]], "icebrk": [[2, "icebrk"]], "icebrk.PDG": [[2, "module-icebrk.PDG"]], "icebrk.common": [[2, "icebrk-common"]], "icebrk.cutstats": [[2, 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