def LogME(f: tf.Tensor, y: tf.Tensor, regression=False):
f = f.numpy().astype(np.float64)
y = y.numpy()
if regression:
y = y.numpy().astype(np.float64)
fh = f
f = f.transpose()
D, N = f.shape
v, s, vh = np.linalg.svd(f @ fh, full_matrices=True)
evidences = []
if regression:
K = y.shape[1]
for i in range(K):
y_ = y[:, i]
evidence = each_evidence(y_, f, fh, v, s, vh, N, D)
evidences.append(evidence)
else:
K = int(y.max() + 1)
for i in range(K):
y_ = (y == i).astype(np.float64)
evidence = each_evidence(y_, f, fh, v, s, vh, N, D)
evidences.append(evidence)
return np.mean(evidences)
def predictions_to_classes(self,
predictions: tf.Tensor,
best=True) -> pd.DataFrame:
if best:
return pd.DataFrame({
"class_id": predictions.argmax(-1),
"score": predictions.max(-1)
}).assign(
label=lambda df: df.class_id.map(self.id_to_label.__getitem__))
return pd.DataFrame(predictions, columns=self.id_to_label)
