--- title: NumPy Evaluation Metrics keywords: fastai sidebar: home_sidebar summary: "Metrics for evaluation." description: "Metrics for evaluation." nb_path: "nbs/losses__numpy.ipynb" ---
y = np.random.random(size=(100, 7))
y_q = np.random.random(size=(100, 7, 4))
weights = np.ones_like(y_q)
quantiles = np.array([0.1, 0.2, 0.3, 0.4])
mqloss(y, y_q, quantiles, weights=weights, axis=(1, 2))
mqloss(y, y_q, quantiles)
y = np.array([1,1,1,0,0,0,0,0,1, np.nan])
y_mask = np.array([1,1,1,1,1,1,1,1,2,0])
y_hat = np.array([1,2,3,-4,-5,-6,-7,-8,-9,-10])
print(mae(y=y, y_hat=y_hat, weights=y_mask))
print(mae(y=y, y_hat=y_hat))
print(mae(y=y, y_hat=y_hat, weights=y_mask))
print(mae(y=y, y_hat=y_hat))
len(y)