"""Deprecated score metrics."""
import numpy as np
import keras.backend as K
def get_confusion(a, b, n):
"""Compute the confusion matrix given two vectors and number of classes."""
k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(int) + b[k],
minlength=n**2).reshape(n, n)
def compute_error_matrix(y_true, y_pred):
"""Compute Confusion matrix (a.k.a. error matrix).
a predicted
c 0 1 2
t 0 [[ 5, 3, 0],
u 1 [ 2, 3, 1],
a 2 [ 0, 2, 11]]
l
Note true positves are in diagonal
"""
# Find channel axis given backend
if K.image_data_format() == 'channels_last':
ax_chn = 3
else:
ax_chn = 1
classes = y_true.shape[ax_chn]
confusion = get_confusion(K.argmax(y_true, axis=ax_chn).flatten(),
K.argmax(y_pred, axis=ax_chn).flatten(),
classes)
return confusion
def accuracy(y_true, y_pred):
"""Compute accuracy."""
confusion = compute_error_matrix(y_true, y_pred)
# per-class accuracy
acc = np.diag(confusion).sum() / float(confusion.sum())
return acc
def mean_accuracy(y_true, y_pred):
"""Compute mean accuracy."""
confusion = compute_error_matrix(y_true, y_pred)
# per-class accuracy
acc = np.diag(confusion) / confusion.sum(1)
return np.nanmean(acc)
def mean_IU(y_true, y_pred):
"""Compute mean IoU."""
confusion = compute_error_matrix(y_true, y_pred)
# per-class IU
iu = np.diag(confusion) / (confusion.sum(1) + confusion.sum(0)
- np.diag(confusion))
return np.nanmean(iu)
def freq_weighted_IU(y_true, y_pred):
"""Compute frequent weighted IoU."""
confusion = compute_error_matrix(y_true, y_pred)
freq = confusion.sum(1) / float(confusion.sum())
# per-class IU
iu = np.diag(confusion) / (confusion.sum(1) + confusion.sum(0)
- np.diag(confusion))
return (freq[freq > 0] * iu[freq > 0]).sum()
