def _score_one_vs_rest(self, Kyx, cy):
""" Score each class against the rest 15. I don't have a NULL class for
the testing set at the moment.
"""
cy = np.array(cy)
ap_scores = np.zeros(self.nr_classes - 1)
print
for ii in xrange(self.nr_classes - 1):
# Scenario 1. Each class vs rest classes
# good_idxs = cy != self.null_class_idx
# Scenario 2. Each class vs NULL
good_idxs = (cy == ii) | (cy == self.null_class_idx)
K_good_idxs = np.ix_(good_idxs, self.cx_idxs[ii])
# Get a +1, -1 vector of labels.
cy_ = map(lambda label: +1 if label == ii else -1, cy[good_idxs])
# Predict.
confidence_values = self.clf[ii].predict_proba(Kyx[K_good_idxs])[:,
1]
ap_scores[ii] = rff.get_ap(confidence_values, cy_)
print "Score for class %d as positive is %2.4f MAP." % (
ii, ap_scores[ii])
return mean(ap_scores) * 100
def _crossvalidate_C_one_vs_rest(self, K, cc, idx_clf):
# TODO Try to avoid duplication of some of this code.
# 1. Split Gram matrix and labels into a training set and a validation
# set.
pp = 0.3 # Proportion of examples used for cross-validation.
M, N = K.shape
assert M == N, 'K is not Gram matrix.'
classes = list(set(cc))
nr_classes = len(classes)
assert nr_classes == 2, 'Number of classes is not two.'
# Randomly pick a subset of the data for cross-validation, but enforce
# to get a proportion of pp points from each of the two classes.
idxs_0 = [ii for ii, ci in enumerate(cc) if ci == classes[0]]
idxs_1 = [ii for ii, ci in enumerate(cc) if ci == classes[1]]
rand_idxs_0 = np.random.permutation(idxs_0)
rand_idxs_1 = np.random.permutation(idxs_1)
P0 = ceil(pp * len(rand_idxs_0))
P1 = ceil(pp * len(rand_idxs_1))
cv_idxs = np.hstack((rand_idxs_0[:P0], rand_idxs_1[:P1]))
tr_idxs = np.hstack((rand_idxs_0[P0:], rand_idxs_1[P1:]))
# Get indices in numpy format.
cv_ix_ = np.ix_(cv_idxs, tr_idxs)
tr_ix_ = np.ix_(tr_idxs, tr_idxs)
# Slice Gram matrix K.
cv_K = K[cv_ix_]
tr_K = K[tr_ix_]
# Get corresponding labels.
cc = array(cc)
cv_cc = cc[cv_idxs]
tr_cc = cc[tr_idxs]
# 2. Try different values for the regularization term C and pick the
# one that yields the best score on the cross-validation set.
log3cs = arange(-2, 8) # Vary C on an exponantional scale.
best_score = -Inf
best_C = 0
for log3c in log3cs:
self.clf[idx_clf].C = 3**log3c
weight = np.ones(len(tr_cc))
weight[tr_cc == +1] *= len(tr_cc[tr_cc == -1])
self.clf[idx_clf].fit(tr_K, tr_cc, sample_weight=weight)
confidence_values = self.clf[idx_clf].predict_proba(cv_K)[:, 1]
score = rff.get_ap(confidence_values, cv_cc)
if score >= best_score:
best_score = score
best_C = self.clf[idx_clf].C
print "Best score for class %d as positive is %2.4f MAP." % (
idx_clf, best_score)
return best_C
def _crossvalidate_C_one_vs_rest(self, K, cc, idx_clf):
# TODO Try to avoid duplication of some of this code.
# 1. Split Gram matrix and labels into a training set and a validation
# set.
pp = 0.3 # Proportion of examples used for cross-validation.
M, N = K.shape
assert M == N, "K is not Gram matrix."
classes = list(set(cc))
nr_classes = len(classes)
assert nr_classes == 2, "Number of classes is not two."
# Randomly pick a subset of the data for cross-validation, but enforce
# to get a proportion of pp points from each of the two classes.
idxs_0 = [ii for ii, ci in enumerate(cc) if ci == classes[0]]
idxs_1 = [ii for ii, ci in enumerate(cc) if ci == classes[1]]
rand_idxs_0 = np.random.permutation(idxs_0)
rand_idxs_1 = np.random.permutation(idxs_1)
P0 = ceil(pp * len(rand_idxs_0))
P1 = ceil(pp * len(rand_idxs_1))
cv_idxs = np.hstack((rand_idxs_0[:P0], rand_idxs_1[:P1]))
tr_idxs = np.hstack((rand_idxs_0[P0:], rand_idxs_1[P1:]))
# Get indices in numpy format.
cv_ix_ = np.ix_(cv_idxs, tr_idxs)
tr_ix_ = np.ix_(tr_idxs, tr_idxs)
# Slice Gram matrix K.
cv_K = K[cv_ix_]
tr_K = K[tr_ix_]
# Get corresponding labels.
cc = array(cc)
cv_cc = cc[cv_idxs]
tr_cc = cc[tr_idxs]
# 2. Try different values for the regularization term C and pick the
# one that yields the best score on the cross-validation set.
log3cs = arange(-2, 8) # Vary C on an exponantional scale.
best_score = -Inf
best_C = 0
for log3c in log3cs:
self.clf[idx_clf].C = 3 ** log3c
weight = np.ones(len(tr_cc))
weight[tr_cc == +1] *= len(tr_cc[tr_cc == -1])
self.clf[idx_clf].fit(tr_K, tr_cc, sample_weight=weight)
confidence_values = self.clf[idx_clf].predict_proba(cv_K)[:, 1]
score = rff.get_ap(confidence_values, cv_cc)
if score >= best_score:
best_score = score
best_C = self.clf[idx_clf].C
print "Best score for class %d as positive is %2.4f MAP." % (idx_clf, best_score)
return best_C
def _score_one_vs_rest(self, Kyx, cy):
""" Score each class against the rest 15. I don't have a NULL class for
the testing set at the moment.
"""
cy = np.array(cy)
ap_scores = np.zeros(self.nr_classes - 1)
print
for ii in xrange(self.nr_classes - 1):
# Scenario 1. Each class vs rest classes
# good_idxs = cy != self.null_class_idx
# Scenario 2. Each class vs NULL
good_idxs = (cy == ii) | (cy == self.null_class_idx)
K_good_idxs = np.ix_(good_idxs, self.cx_idxs[ii])
# Get a +1, -1 vector of labels.
cy_ = map(lambda label: +1 if label == ii else -1, cy[good_idxs])
# Predict.
confidence_values = self.clf[ii].predict_proba(Kyx[K_good_idxs])[:, 1]
ap_scores[ii] = rff.get_ap(confidence_values, cy_)
print "Score for class %d as positive is %2.4f MAP." % (ii, ap_scores[ii])
return mean(ap_scores) * 100
def average_precision(y_true, y_pred):
""" Swaps arguments for Adrien's function, so it is compatible with
sklearn.
"""
return result_file_functions.get_ap(y_pred, y_true)
def average_precision(y_true, y_pred):
""" Swaps arguments for Adrien's function, so it is compatible with
sklearn.
"""
return result_file_functions.get_ap(y_pred, y_true)
