Add daal4py benchmarks and update native, python benchmarks

[bibikar]

@oleksandr-pavlyk

This PR contains both refactoring and adding new benchmarks since the last PR.

• Universally read from .npy files as described in numpy's NEP-0001. note: The current reader npyfile.h used in native code does not really understand dtypes and will simply read the entire array into memory, relying on the caller's interpretation for this data.
• Factor out all dataset generation into make_datasets.py
• Move sklearn benches to sklearn/ for clarity
• Add daal4py benches for correlation/cosine distances, linear/ridge regression, kmeans, SVM
• Add native/sklearn benchmarks for logistic regression with L_BFGS solver and RandomForest regression and classification
• Move kmeans.predict benchmarks into kmeans bench files
• Update thread setting to use daal4py API in sklearn/daal4py benches
• Update license headers

[oleksandr-pavlyk]

n_jobs is not controlling the number of threads used by DAAL, if we are running this in IDP. What does?

[bibikar]

on line 30, the following code

num_threads, daal_version = bench.prepare_benchmark(args)

runs both daal4py.daalinit(nthreads=args.num_threads) and disables finiteness checking for sklearn.

Do we want to remove the n_jobs argument from KMeans?

[oleksandr-pavlyk]

We can keep it, to ensure that upstream KMeans is run in parallel as well.