scikit-opt | Genetic Algorithm , Particle Swarm Optimization | Machine Learning library
kandi X-RAY | scikit-opt Summary
kandi X-RAY | scikit-opt Summary
Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python).
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Top functions reviewed by kandi - BETA
- Run the optimizer
- Update the V
- Record the recording
- Calculate the y - axis
- Convert the model to a tensor
- Register an operator
- Convert from gray to rv
- Run the iteration
- Convert X to Y
- Generate documentation
- Search for code in py_file
- Decorator to turn a function into a function
- Set the run mode
- Run the algorithm
- Generate new x
- Set run mode
- Read a file and return the long description
- Swap mutation
- Calculate the total distance matrix
- Reverse mutation
- Runs the iteration
- Generate a random TSP
- Generate a high cost function
- Run iteration
- Run the optimizer
- Simple demo function
scikit-opt Key Features
scikit-opt Examples and Code Snippets
Lines of Code : 32License : Permissive (MIT)
Copyimport sys
sys.path.append("..")
from src.BaseSVDD import BaseSVDD, BananaDataset
from sko.PSO import PSO
import matplotlib.pyplot as plt
# Banana-shaped dataset generation and partitioning
X, y = BananaDataset.generate(number=100, display='off')
X def create_model_NN():
#start the model making process and create our first layer
model = Sequential()
model.add(Dense(num_input_nodes, input_shape=(40,), activation=activation
))
#create a loop making a Community Discussions
Trending Discussions on scikit-opt
QUESTION
ANSWER
Answered 2021-Dec-08 at 09:421. Reason
scikit-optimize 0.8.1 has parameter iid which is not accepted by scikit-learn 0.24.2
2. Solution
Downgrade scikit-learn version to 0.22.2 and scikit-optimize to 0.8.1 by:
QUESTION
General question: Using scikit-optimize for a black box optimization. Can't find in the doc what model_queue_size does. I'm doing the ask-tell because I can parallelize the calculation of y as described in the example. So, doing some profiling, it looks like the opt.tell() call runs faster when model_queue_size is set smaller. Is that what model_queue_size does - limits the number of sample used in the opt.tell() call? 2nd question, how can I set kappa when using the Optimizier - ask-tell method?
thanks
...ANSWER
Answered 2021-Dec-01 at 20:04When using the default model_queue_size=None all surrogate models are stored in the optimizer's models attribute. If a number is specified, only model_queue_size models will be remembered. That is in the the docs.
A new model is added each time tell is called and old models will be discarded once model_queue_size is reached. So only the most recent models are remembered. That can be seen by looking at the code.
Not sure why this would affect runtime in your case. I suppose if you run many iterations and models are very large it could be a memory thing.
Kappa can be set by using the acq_func_kwargs parameter of the Optimizer constructor as shown in the exploration vs exploitation example.
QUESTION
The skopt package (https://scikit-optimize.github.io/stable/install.html) was installed on a cluster I use.
When I run the code in python directly in the terminal (i.e., cluster terminal), no problem occurs and the code works as expected.
However when I simply place the command to execute the code in a PBS queue system file (e.g., python3 ./code.py), I cannot load the installed package and I get the following message:
...ANSWER
Answered 2021-Sep-09 at 17:26This has happened to me before but its a pretty simple fix
QUESTION
In Scikit-learn RandomSearchCV and GridSearchCV require the cross validation object for the cv argument, e.g. GroupKFold or any other CV splitter from sklearn.model_selection.
However, how can I use single, static validation set? I have very large training set, large validation set and I only need the interface of CV objects, not whole cross validation.
Specifically, I'm using Scikit-optimize and BayesSearchCV (docs) and it requires the CV object (same interface as regular Scikit-learn SearchCV objects). I want to use my chosen validation set with it, not whole CV.
ANSWER
Answered 2021-May-30 at 17:07The docs of the model selection objects of scikit-learn, e.g. GridSearchCV, are maybe a bit clearer how to achieve this:
cv: int, cross-validation generator or an iterable, default=None
- ...
- An iterable yielding (train, test) splits as arrays of indices.
So you need the arrays of indices for training and test samples as a tuple and then wrap them in an iterable, e.g. a list:
QUESTION
I'm using BayesSearchCV from scikit-optimize to optimise an XGBoost model to fit some data I have. While the model fits fine, I am puzzled by the scores provided in the diagnostic information and am unable to replicate them.
Here's an example script using the Boston house prices dataset to illustrate my point:
...ANSWER
Answered 2021-Mar-24 at 21:44best_estimator_ is the refitted estimator, fitted on the entire training set after choosing the hyperparameters; so scoring it on any portion of the training set will be optimistically biased. To reproduce cv_results_, you would need to refit estimators to each training fold and score the corresponding test fold.
Beyond that, there does appear to be more randomness not covered by the XGBoost random_state. There is another parameter seed; setting that produces consistent results for me. (There are some older posts here (example) reporting similar issues even with seed set, but perhaps those have been resolved by newer versions of xgb.)
QUESTION
I just read about Bayesian optimization and I want to try it.
I installed scikit-optimize and checked the API, and I'm confused:
I read that Bayesian optimization starts with some initialize samples.
- I can't see where I can change this number ? (
BayesSearchCV) n_pointswill change the number of parameter settings to sample in parallel andn_iteris the number of iterations (and if I'm not wrong the iterations can't run in parallel, the algorithm improve the parameters after every iteration)
- I can't see where I can change this number ? (
I read that we can use different acquisition functions. I can't see where I can change the acquisition function in
BayesSearchCV?
ANSWER
Answered 2021-Mar-03 at 15:14Is this something you are looking for?
QUESTION
There's a couple of other questions similar to this, but I couldn't find a solution which seems to fit. I am using LightGBM with Scikit-Optimize BayesSearchCV.
...ANSWER
Answered 2021-Jan-21 at 10:29I think this was due to my hyperparameter limits being wrong, causing one hyperparameter to be set to zero which shouldn't have been, though I'm not sure which one.
QUESTION
I am using sklearn's GPR library, but occasionally run into this annoying warning:
...ANSWER
Answered 2020-Jun-15 at 01:45You want to extend and/or modify the behavior of an existing Python object, which sounds like a good use case for inheritance.
A solution could be to inherit from the scikit-learn implementation, and ensure that the usual optimizer is called with the arguments you'd like. Here's a sketch, but note that this is not tested.
QUESTION
I am using the scikit-learn optimize package to tune the hyperparameters of my model. For performance and readability reasons (I am training several models with the same process), I want to structure the whole hyperparameter-tuning in a class:
...ANSWER
Answered 2020-Nov-06 at 09:39The problem of self not being defined is unrelated to scikit.learn. You cannot use self to define a decorator, because it is only defined inside the method you are decorating. But even if you sidestep this issue (e.g. by providing param_space as a global variable) I expect the next problem will be that self will be passed to the use_named_args decorator, but it expects only arguments to be optimized.
The most obvious solution would be to not use the decorator on the fitness method but to define a decorated function that calls the fitness method, inside the find_best_model method, like this:
QUESTION
I came to know scikit-optimize package , and I am relatively new to Bayesian optimization which I want to use it in my current Convolutional NN. However, I tried to find best hyperparameters of convolutional NN by using Bayesian-optimization but my current attempt is not working properly.
So far, I tried to come up implementation for this purpose but my code is not working properly which I don't know which part of my code remain issues. Can anyone point me out how to make this right? Is there any efficient implementation for using Bayesian optimization on convolutional NN for the sake of finding best hyperparameters? Any possible thoughts?
update
I tried GridSearchCV, RandomSearchCV for my convolutional NN which has really deep layer, and using GridSearchCV took too much time to complete even 2-3 whole days can't finish the optimization. I want to use new optimization framework like bayesian-optimization (i.e, skopt, optuna) for finding best param and hyperparams of convolutional NN. Can anyone provide possible remedy and efficient approach to my current attempt 1 in colab and my attempt 2 in colab ? Any thoughts?
my current attempt:
here is my current attempt where I used scikit-optimize package for Bayesian optimization. here is my attempt in this colab where I ran all my experiment of implementing Bayesian optimization on convolutional NN to find its best hyperparams:
ANSWER
Answered 2020-Aug-16 at 10:39I will suggest you to use the Keras Tuner package for Bayesian Optimization.
Below is just a small example on how you can achieve this.
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