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Sklearn GaussianProcessRegressor fixing kernel hyperparameters?

By : user2175420
Date : October 15 2020, 08:10 AM
I hope this helps . Its not documented on the Kernels them self. But the hyper parameters can fixed by the following.
code :
ExpSineSquared(periodicity=7, periodicity_bounds='fixed')

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sklearn MDS crashes my kernel?

By : user3536985
Date : March 29 2020, 07:55 AM
This might help you Our current implementation of MDS is based on the smacof method which is too generic. A PCA / SVD might be much faster in many cases. This is planned as a pull request.
In the mean time you can directly use sklearn.decomposition.RandomizedPCA instead of the MDS class.

How to retrieve trained hyperparameters from sklearn GaussianProcessRegressor model

By : Socrates
Date : March 29 2020, 07:55 AM
To fix this issue The model.get_params() as mentioned in documentation will return the parameters passed into the initialization of the GPR.
It optimizes the passed kernel's internal parameters which is available using model.kernel_.

Sklearn: Using pretrained hyperparameters Gaussian Process Regression

By : Khalid Basher
Date : March 29 2020, 07:55 AM
it should still fix some issue You should serialize your GaussianProcessRegressor model using pickle or joblib library.
code :
from sklearn.externals import joblib

if train:
    gp.fit(X, y)
    joblib.dump(gp, 'filename.pkl') 
    gp = joblib.load('filename.pkl') 

Selecting kernel and hyperparameters for kernel PCA reduction

By : user2134233
Date : March 29 2020, 07:55 AM
I hope this helps you . GridSearchCV is capable of doing cross-validation of unsupervised learning (without a y) as can be seen here in documentation:
code :
y : array-like, shape = [n_samples] or [n_samples, n_output], optional 
Target relative to X for classification or regression; 
None for unsupervised learning
neg_mean_squared_error_scorer = make_scorer(mean_squared_error,
from sklearn.metrics import mean_squared_error
def my_scorer(estimator, X, y=None):
    X_reduced = estimator.transform(X)
    X_preimage = estimator.inverse_transform(X_reduced)
    return -1 * mean_squared_error(X, X_preimage)
param_grid = [{
        "gamma": np.linspace(0.03, 0.05, 10),
        "kernel": ["rbf", "sigmoid", "linear", "poly"]

kpca=KernelPCA(fit_inverse_transform=True, n_jobs=-1) 
grid_search = GridSearchCV(kpca, param_grid, cv=3, scoring=my_scorer)

sklearn SVM custom kernel

By : Zachary Zhu
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I need to implement a custom kernel in sklearn. , Your function looks good. Just use
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