6. Metrics¶
6.1. Distances¶
6.1.1. Wasserstein¶
In mathematics, the Wasserstein distance or Kantorovich–Rubinstein metric is a distance function defined between probability distributions on a given metric space M.
from scipy.stats import wasserstein_distance, beta
wasserstein_distance([1,2,3,4],[1,2,3,4,4])
x = np.linspace(0, 1, 100)
dist1 = stats.beta.pdf(x,5,5)
dist2 = stats.beta.pdf(x,8,5)
ws_distance = wasserstein_distance(dist1,dist2)
6.1.2. Sklearn Metrics¶
To import or get the short name of all metrics in sklearn.
from sklearn.metrics import SCORERS
SCORERS.keys()