On 11/7/18 4:01 AM, William Heymann wrote:
Hello,
I am trying to tune the bandwidth for my KernelDensity. I need to find
out what optimization goal to use.
I started with
from sklearn.grid_search import GridSearchCV
grid = GridSearchCV(KernelDensity(),
{'bandwidth': np.linspace(0.1, 1.0, 30)},
cv=20) # 20-fold cross-validation
grid.fit(x[:, None])
print grid.best_params_
From
https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/#Bandwidth-Cross-Validation-in-Scikit-Learn
I have also used RandomizedSearchCV to optimize the parameters.
The problem I have is that neither refines the answer so if I don't
sample at high enough density I don't get a good answer. What I would
like to do is use the same goal but put it into a different global
optimizer.
I have looked through the code for GridSearchCV and RandomizedSearchCV
and I have not been able to figure out yet what is the actual
optimization goal.
Originally I thought the system was using something like
kde_bw = KernelDensity(kernel='gaussian', bandwidth=bw)
score = max(cross_val_score(kde_bw, data, cv=3))
That's basically what it's doing. It's maximizing the "score" method of
KernelDensity.
you could look at scikit-optimize for a more elaborate optimizer (or try
using any of the scipy ones)
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