Computation of estimator values pointwise on kNN sets is not parallelized

[JesseCresswell]

I will use ESS as an example, since it is a pretty slow estimator. Since it is a LocalEstimator, when fit is called kNNs are first computed (if not already provided)

scikit-dimension/skdim/_commonfuncs.py

Line 419 in b9e8845

dists, knnidx = get_nn(X, k=self.n_neighbors, n_jobs=n_jobs)

and this in turn calls to the sklearn library which properly parallelizes the computation based on the parameter n_jobs that we can provide.

Second, a call to self.fit in the ESS class performs a simple, single-threaded for loop over the datapoints.

scikit-dimension/skdim/id/_ESS.py

Lines 75 to 78 in b9e8845

	for i in range(len(X)):
	self.dimension_pw_[i], self.essval_[i] = self._essLocalDimEst(
	X[knnidx[i, :]]
	)

The computations are "embarrassingly parallel". I have locally implemented parallelization and seen at least a 6x speedup in ESS computation on datasets of size 5,000 - 50,000.

I can open a PR with these changes as an example if you are willing to add joblib as a dependency.

[j-bac]

Makes sense and sounds like an awesome speedup! ESS is currently one of the slowest estimators but the same strategy might work for others. Please feel free to open a PR, joblib is already a dependency since it is used by sklearn. We can probably replace multiprocessing with joblib everywhere.

[JesseCresswell]

Closed by PR #24