Knn Imputer Class and dependency functionalities

[SreekiranprasadV]

Merge PR : #4797 before merging this one. The functionalities required for this are in #4797

Created a draft PR and Added KNN Imputer class and dependency functionalities for imputation of missing values.

Supported Inputs: Numpy arrays, Pandas DataFrame, Cupy arrays, Cudf DataFrame

Tested on: Tesla T4 Single GPU

Time Latency:

Tested on numpy arrays with 25% of the data is masked, averaged the distance metric and set the column size to 100.
Data Points Cuml Sklearn
100000 0.513s 0.383s
1M 10.5s 36.1s
10M 105s 373s

Tested on numpy arrays with 1% of the data is masked, averaged the distance metric and set the column size to 100.
Data Points Cuml Sklearn
100000 0.217s 0.208s
1M 2.86s 7.73s
10M 10.2s 122s

Profiling on 1 million records:

    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
      100    0.491    0.005    0.570    0.006 {method 'argpartition' of 'cupy._core.core.ndarray' objects}
     3561    0.197    0.000    0.213    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/rmm/rmm.py:212(rmm_cupy_allocator)
        1    0.149    0.149    1.078    1.078 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cuml/_thirdparty/sklearn/preprocessing/_imputation.py:951(transform)
      2/1    0.087    0.044    0.161    0.161 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cuml/internals/api_decorators.py:453(inner_with_getters)
        3    0.056    0.019    0.064    0.021 {method 'dot' of 'cupy._core.core.ndarray' objects}
      201    0.024    0.000    0.039    0.000 {method 'nonzero' of 'cupy._core.core.ndarray' objects}
      100    0.014    0.000    0.621    0.006 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cuml/_thirdparty/sklearn/preprocessing/_imputation.py:863(_calc_impute)
      200    0.005    0.000    0.009    0.000 {built-in method cupy._core._routines_math._nansum}
     3562    0.005    0.000    0.010    0.000 cuda/cudart.pyx:10521(cudaGetDevice)
      101    0.004    0.000    0.009    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cupy/_creation/ranges.py:9(arange)
     3562    0.004    0.000    0.014    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/rmm/_cuda/gpu.py:53(getDevice)
      200    0.004    0.000    0.008    0.000 {method 'take' of 'cupy._core.core.ndarray' objects}
      100    0.003    0.000    0.006    0.000 {method 'all' of 'cupy._core.core.ndarray' objects}
     3562    0.003    0.000    0.005    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/enum.py:358(__call__)
      103    0.003    0.000    0.005    0.000 {method 'any' of 'cupy._core.core.ndarray' objects}
      616    0.002    0.000    0.014    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cupy/_creation/basic.py:7(empty)
     3561    0.002    0.000    0.002    0.000 {built-in method cupy.cuda.stream.get_current_stream}
     3562    0.002    0.000    0.002    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/enum.py:670(__new__)
     2107    0.002    0.000    0.004    0.000 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/numpy/core/numeric.py:1858(isscalar)
      7/1    0.002    0.000    1.080    1.080 /nvme/1/svadaga/miniconda3/envs/cuml_dev/lib/python3.9/site-packages/cuml/internals/api_decorators.py:357(inner)

Cupy in built functionalities are costing more time.

[SreekiranprasadV]

rerun tests

[SreekiranprasadV]

rerun tests

[SreekiranprasadV]

rerun tests

[codecov-commenter]

Codecov Report

Base: 77.62% // Head: 78.24% // Increases project coverage by +0.61% 🎉

Coverage data is based on head (e629e77) compared to base (dc77d6b).
Patch coverage: 81.81% of modified lines in pull request are covered.

Additional details and impacted files

@@               Coverage Diff                @@
##           branch-22.10    #4820      +/-   ##
================================================
+ Coverage         77.62%   78.24%   +0.61%     
================================================
  Files               180      181       +1     
  Lines             11384    11610     +226     
================================================
+ Hits               8837     9084     +247     
+ Misses             2547     2526      -21     

Flag	Coverage Δ
dask	46.27% <14.39%> (+0.75%)	⬆️
non-dask	67.70% <81.81%> (+0.43%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
python/cuml/_thirdparty/sklearn/neighbors/_base.py	66.66% <66.66%> (ø)
...l/_thirdparty/sklearn/preprocessing/_imputation.py	85.71% <84.90%> (-0.62%)	⬇️
...cuml/_thirdparty/sklearn/preprocessing/__init__.py	100.00% <100.00%> (ø)
python/cuml/metrics/__init__.py	100.00% <100.00%> (ø)
python/cuml/common/array.py	97.21% <0.00%> (-0.78%)	⬇️
python/cuml/cluster/__init__.py	100.00% <0.00%> (ø)
python/cuml/feature_extraction/_vectorizers.py	89.93% <0.00%> (+0.37%)	⬆️
python/cuml/common/import_utils.py	59.82% <0.00%> (+0.85%)	⬆️
python/cuml/thirdparty_adapters/adapters.py	92.99% <0.00%> (+1.50%)	⬆️
.../dask/extended/linear_model/logistic_regression.py	92.00% <0.00%> (+57.33%)	⬆️

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