[FEA] Model precision with precision_score

I'd like to be able to use cuML to calculate the precision score of an estimator in Python. I didn't see a C++/CUDA implementation in the metrics/ header files, so this may require both C++/CUDA and Python/Cython bindings eventually. Didn't see an existing issue but will close if I find one.

Currently, this requires going to the CPU if we want to use an existing API (relying on NEP-18 for dispatch doesn't work here). With about three million rows, sklearn's precision_score can take about 1.5 seconds. A stopgap GPU implementation for binary precision using CuPy can be 500x+ faster.

def cupy_precision_score(y, y_pred):
    
    pos_pred_ix = cupy.where(y_pred == 1)
    tp_sum = (y_pred[pos_pred_ix] == y[pos_pred_ix]).sum()
    fp_sum = (y_pred[pos_pred_ix] != y[pos_pred_ix]).sum()
​
    return (tp_sum / (tp_sum + fp_sum)).item()
​
%time print(cupy_precision_score(y, yp.values))
%time print(precision_score(y_host, y_pred))
0.9998934583422118
CPU times: user 2.59 ms, sys: 0 ns, total: 2.59 ms
Wall time: 2.05 ms
0.9998934583422118
CPU times: user 1.47 s, sys: 59.5 ms, total: 1.53 s
Wall time: 1.5 s

Cross-listing to the tracking issue #608