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javaAnchorExplainer | Explains machine learning models | Machine Learning library

 by   viadee Java Version: Current License: BSD-3-Clause

 by   viadee Java Version: Current License: BSD-3-Clause

kandi X-RAY | javaAnchorExplainer Summary

javaAnchorExplainer is a Java library typically used in Artificial Intelligence, Machine Learning applications. javaAnchorExplainer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.
Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018
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kandi-support Support

  • javaAnchorExplainer has a low active ecosystem.
  • It has 17 star(s) with 2 fork(s). There are 11 watchers for this library.
  • It had no major release in the last 6 months.
  • There are 2 open issues and 10 have been closed. On average issues are closed in 11 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of javaAnchorExplainer is current.
javaAnchorExplainer Support
Best in #Machine Learning
Average in #Machine Learning
javaAnchorExplainer Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • javaAnchorExplainer has 0 bugs and 0 code smells.
javaAnchorExplainer Quality
Best in #Machine Learning
Average in #Machine Learning
javaAnchorExplainer Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • javaAnchorExplainer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • javaAnchorExplainer code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
javaAnchorExplainer Security
Best in #Machine Learning
Average in #Machine Learning
javaAnchorExplainer Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • javaAnchorExplainer is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
javaAnchorExplainer License
Best in #Machine Learning
Average in #Machine Learning
javaAnchorExplainer License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • javaAnchorExplainer releases are not available. You will need to build from source code and install.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 2830 lines of code, 248 functions and 48 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
javaAnchorExplainer Reuse
Best in #Machine Learning
Average in #Machine Learning
javaAnchorExplainer Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed javaAnchorExplainer and discovered the below as its top functions. This is intended to give you an instant insight into javaAnchorExplainer implemented functionality, and help decide if they suit your requirements.

  • Perform a beam search .
    • Get an array of Anchors for the specified instances .
      • Gets a greedy selection .
        • Identifies anchor candidates .
          • Identifies a single anchor .
            • Create an anchor builder for a given instance builder .
              • Batch sample sample .
                • Performs a prediction on a candidate .
                  • calculate M
                    • Run a batch of explanations .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      javaAnchorExplainer Key Features

                      Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018

                      javaAnchorExplainer Examples and Code Snippets

                      Community Discussions

                      Trending Discussions on Machine Learning
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                      • How to compare baseline and GridSearchCV results fair?
                      • Getting Error 524 while running jupyter lab in google cloud platform
                      • TypeError: brain.NeuralNetwork is not a constructor
                      • Ordinal Encoding or One-Hot-Encoding
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                      • How to identify what features affect predictions result?
                      Trending Discussions on Machine Learning

                      QUESTION

                      Using RNN Trained Model without pytorch installed

                      Asked 2022-Feb-28 at 20:17

                      I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

                      I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

                      I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

                      import torch
                      import torch.nn as nn
                      import torch.nn.functional as F
                      import torch.optim as optim
                      import random
                      
                      torch.manual_seed(1)
                      random.seed(1)
                      device = torch.device('cpu')
                      
                      class RNN(nn.Module):
                        def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
                          super(RNN, self).__init__()
                          self.input_size = input_size
                          self.hidden_size = hidden_size
                          self.output_size = output_size
                          self.num_layers = num_layers
                          self.batch_size = batch_size
                          self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
                          self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
                          self.hidden2out = nn.Linear(hidden_size, output_size)
                          self.hidden = self.init_hidden()
                        def forward(self, feature_list):
                          feature_list=torch.tensor(feature_list)
                          
                          if self.matching_in_out:
                            lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
                            output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
                            output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
                            return output_scores #output_scores
                          else:
                            for i in range(len(feature_list)):
                              cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
                              cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
                              lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
                              outs=self.hidden2out(lstm_out)
                            return outs
                        def init_hidden(self):
                          #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
                          return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
                                  torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))
                      

                      I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

                      Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

                      • nn.LSTM
                      • nn.Linear
                      • torch.sigmoid

                      I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

                      So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

                      EDIT

                      I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

                      rnn = nn.LSTM(10, 20, 2)
                      input = torch.randn(5, 3, 10)
                      h0 = torch.randn(2, 3, 20)
                      c0 = torch.randn(2, 3, 20)
                      output, (hn, cn) = rnn(input, (h0, c0))
                      

                      and also for linear:

                      m = nn.Linear(20, 30)
                      input = torch.randn(128, 20)
                      output = m(input)
                      

                      ANSWER

                      Answered 2022-Feb-17 at 10:47

                      You should try to export the model using torch.onnx. The page gives you an example that you can start with.

                      An alternative is to use TorchScript, but that requires torch libraries.

                      Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

                      ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

                      A running example

                      Just modifying a little your example to go over the errors I found

                      Notice that via tracing any if/elif/else, for, while will be unrolled

                      import torch
                      import torch.nn as nn
                      import torch.nn.functional as F
                      import torch.optim as optim
                      import random
                      
                      torch.manual_seed(1)
                      random.seed(1)
                      device = torch.device('cpu')
                      
                      class RNN(nn.Module):
                        def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
                          super(RNN, self).__init__()
                          self.input_size = input_size
                          self.hidden_size = hidden_size
                          self.output_size = output_size
                          self.num_layers = num_layers
                          self.batch_size = batch_size
                          self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
                          self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
                          self.hidden2out = nn.Linear(hidden_size, output_size)
                        def forward(self, x, h0, c0):
                          lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
                          outs=self.hidden2out(lstm_out)
                          return outs, (hidden_a, hidden_b)
                        def init_hidden(self):
                          #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
                          return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
                                  torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())
                      
                      # convert the arguments passed during onnx.export call
                      class MWrapper(nn.Module):
                          def __init__(self, model):
                              super(MWrapper, self).__init__()
                              self.model = model;
                          def forward(self, kwargs):
                              return self.model(**kwargs)
                      

                      Run an example

                      rnn = RNN(10, 10, 10, 3)
                      X = torch.randn(3,1,10)
                      h0,c0  = rnn.init_hidden()
                      print(rnn(X, h0, c0)[0])
                      

                      Use the same input to trace the model and export an onnx file

                      
                      torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                                        dynamic_axes={'x':{1:'N'},
                                                     'c0':{1: 'N'},
                                                     'h0':{1: 'N'}
                                                     },
                                        input_names=['x', 'h0', 'c0'],
                                        output_names=['y', 'hn', 'cn']
                                       )
                      

                      Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

                      Next we load the ONNX model and pass the same inputs

                      import onnxruntime
                      ort_model = onnxruntime.InferenceSession('rnn.onnx')
                      print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))
                      

                      Source https://stackoverflow.com/questions/71146140

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install javaAnchorExplainer

                      In order to use the core project, no prerequisites and installation is are required. There are no dependencies and the algorithm may be used by providing the required interfaces.

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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                      Clone
                      • https://github.com/viadee/javaAnchorExplainer.git

                      • gh repo clone viadee/javaAnchorExplainer

                      • git@github.com:viadee/javaAnchorExplainer.git

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