Memory profile target for DistanceMetric32 dtype preservation

target.py

from sklearn.metrics.pairwise import pairwise_distances
from scipy.sparse import csr_matrix
from sklearn.metrics import DistanceMetric
import numpy as np

N_FEATURES = 10
N_SAMPLES = 10_000

def _generate_PWD_data(n_samples_X, n_samples_Y, n_features, n_classes, n_outs=1, random_state=0):
    rng = np.random.RandomState(random_state)
    X = rng.randn(n_samples_X, n_features)
    Y = rng.randn(n_samples_Y, n_features)
    y_shape = (n_samples_X,) if n_outs == 1 else (n_samples_X, n_outs)
    y = rng.randint(n_classes, size=y_shape)

    return X, Y, y

X, Y, y = _generate_PWD_data(n_samples_X=N_SAMPLES, n_samples_Y=N_SAMPLES, n_features=N_FEATURES, n_classes=2, random_state=0)
X = csr_matrix(X, dtype=np.float32)
dst = DistanceMetric.get_metric(metric="manhattan", dtype=np.float32)

for _ in range(5):
    dst.pairwise(X)