Metadata-Version: 2.1
Name: surropt
Version: 0.0.4
Summary: Surrogate optimization toolbox for time consuming models
Home-page: https://github.com/feslima/surropt
Author: Felipe Souza Lima
Author-email: feslima93@gmail.com
License: Apache License 2.0
Description: # Surropt
        Surrogate optimization toolbox for time consuming models
        
        # Installation
        To install the module in develop moode, first you need to setup an environment with the following packages:
        
        - SciPy >= 1.2.0
        - Numpy >= 1.15.0
        - pyDOE2 >= 1.2
        - pydace >= 0.1.1
        
        Having these installed, open a terminal window, navigate to the folder where the setup.py file is located and execute the following command:
        ```
        $python setup.py develop
        ```
        
        After this you are ready to use the package via python command line.
        
        # Usage
        
        ## Optimization server
        ### Server environment installation
        Make sure WSL Ubuntu is installed (**NOT UBUNTU LTS, IT HAS TO BE PURE UBUNTU**) in your system.
        
        Make sure that Anaconda is installed in your WSL system.
        
        Open a WSL terminal and navigate to folder **tests_/resources/ipopt_server/**.
        
        Install the server by executing the following line in the WSL terminal:
        
        ```
        conda env create -f ipopt_server.yaml
        ```
        
        ### Starting the server
        Each time you are going to perform a optimization through Caballero's algorithm using the `DockerNLPOptions` as NLP solver, you have to start the server manually. To do so, execute the following steps:
        
        1. Open a WSL terminal and navigate to folder **tests_/resources/ipopt_server/**
        2. Activate the `ipopt_server` conda environment
        3. Start the server by typing in the WSL terminal: ```$python server.py```
        4. If everything is fine, you should see that a flask server is initialized
        5. To make sure that the server is good to go, open a browser window and type `localhost:5000`. You should see the following message on your browser: "*Hey! I'm running from Flask in a Docker container!*". If so, you can close the browser tab (**do not close the WSL terminal while performing the optimization!**) and proceed normally.
        
        ## Optimization procedure
        1. Start the optimization server.
        
        2. See file *test_evap.py* in folder **tests_/surropt/caballero/**. You can run it to see how a simple example of usage the Caballero procedure is done.
Keywords: surrogate optimization,infill criteria optimization,blackbox optimization
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.5
Description-Content-Type: text/markdown
