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swarmops | Heuristic Optimization for Python | Machine Learning library

 by   Hvass-Labs Python Version: 1.0.0 License: Non-SPDX

 by   Hvass-Labs Python Version: 1.0.0 License: Non-SPDX

kandi X-RAY | swarmops Summary

swarmops is a Python library typically used in Artificial Intelligence, Machine Learning applications. swarmops has no vulnerabilities, it has build file available and it has low support. However swarmops has 2 bugs and it has a Non-SPDX License. You can install using 'pip install swarmops' or download it from GitHub, PyPI.
SwarmOps for Python implements the following heuristic optimizers which do not use the gradient of the problem being optimized:. All these optimizers perform minimization. If you need to maximize a problem then you simply return the negated value in your fitness function (aka. cost- or error-function). The PS and LUS optimizers only require few iterations and are therefore useful for problems that are very time-consuming to compute. PSO, MOL and DE usually require many more iterations but can also sometimes find better solutions. Multiple optimization runs can be performed using the MultiRun-class, which also supports printing various statistics, plotting a trace of the fitness, and refining the best-found solution using the [L-BFGS-B] optimizer from SciPy.
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kandi-support Support

  • swarmops has a low active ecosystem.
  • It has 66 star(s) with 17 fork(s). There are 5 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of swarmops is 1.0.0
swarmops Support
Best in #Machine Learning
Average in #Machine Learning
swarmops Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • swarmops has 2 bugs (0 blocker, 0 critical, 2 major, 0 minor) and 30 code smells.
swarmops Quality
Best in #Machine Learning
Average in #Machine Learning
swarmops Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • swarmops has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • swarmops code analysis shows 0 unresolved vulnerabilities.
  • There are 7 security hotspots that need review.
swarmops Security
Best in #Machine Learning
Average in #Machine Learning
swarmops Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • swarmops has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
swarmops License
Best in #Machine Learning
Average in #Machine Learning
swarmops License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • swarmops releases are not available. You will need to build from source code and install.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 1008 lines of code, 100 functions and 16 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
swarmops Reuse
Best in #Machine Learning
Average in #Machine Learning
swarmops Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Optimizes the search - space
    • Trace the given fitness
    • Update the best fitness
    • Print the status of the iteration
  • Perform optimization
    • Return fitness for agent i
    • Calculate the fitness for each agent
    • Create new agents
  • Run optimizer
    • Return the fitness for a particle
    • Update the fitness for each particle
  • Calculate the fitness of the objective function
    • Update the best solution
    • Stop the timer
  • Optimizes the optimizer
    • Optimizes the problem
  • Sample uniformly from the search space
    • Random array
  • Calculate fitness
    • Calculate the penalty
  • Function to refine the best solution
    • Plot fitness trace
      • Optimizes the optimization
        • Prints the fitness statistics
          • Returns the fitness function
            • Stops the timer

              Get all kandi verified functions for this library.

              Get all kandi verified functions for this library.

              swarmops Key Features

              1.0 - First release (April 12, 2016)

              swarmops Examples and Code Snippets

              Community Discussions

              Trending Discussions on Machine Learning
              • Using RNN Trained Model without pytorch installed
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              • How can I check a confusion_matrix after fine-tuning with custom datasets?
              • CUDA OOM - But the numbers don't add upp?
              • 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
              • How to increase dimension-vector size of BERT sentence-transformers embedding
              • 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 swarmops

              The easiest way to install SwarmOps and use it in your own project, is to use a virtual environment. You write the following in a Linux terminal:.

              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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              Install
              • pip install swarmops

              Clone
              • https://github.com/Hvass-Labs/swarmops.git

              • gh repo clone Hvass-Labs/swarmops

              • git@github.com:Hvass-Labs/swarmops.git

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