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validate_on_LFW.py
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validate_on_LFW.py
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"""The code was copied from liorshk's 'face_pytorch' repository:
https://github.com/liorshk/facenet_pytorch/blob/master/eval_metrics.py
Which in turn was copied from David Sandberg's 'facenet' repository:
https://github.com/davidsandberg/facenet/blob/master/src/lfw.py#L34
https://github.com/davidsandberg/facenet/blob/master/src/facenet.py#L424
Modified to also compute precision and recall metrics.
"""
import numpy as np
from sklearn.metrics import auc
from sklearn.model_selection import KFold
from scipy import interpolate
def evaluate_lfw(distances, labels, num_folds=10, far_target=1e-3):
"""Evaluates on the Labeled Faces in the Wild dataset using KFold cross validation based on the Euclidean
Args:
distances: numpy array of the pairwise distances calculated from the LFW pairs.
labels: numpy array containing the correct result of the LFW pairs belonging to the same identity or not.
num_folds (int): Number of folds for KFold cross-validation, defaults to 10 folds.
far_target (float): The False Accept Rate to calculate the True Accept Rate (TAR) at,
defaults to 1e-3.
Returns:
true_positive_rate: Mean value of all true positive rates across all cross validation folds for plotting
the Receiver operating characteristic (ROC) curve.
false_positive_rate: Mean value of all false positive rates across all cross validation folds for plotting
the Receiver operating characteristic (ROC) curve.
accuracy: Array of accuracy values per each fold in cross validation set.
precision: Array of precision values per each fold in cross validation set.
recall: Array of recall values per each fold in cross validation set.
roc_auc: Area Under the Receiver operating characteristic (ROC) metric.
best_distances: Array of Euclidean distance values that had the best performing accuracy on the LFW dataset
per each fold in cross validation set.
tar: Array that contains True Accept Rate values per each fold in cross validation set
when far (False Accept Rate) is set to a specific value.
far: Array that contains False accept rate values per each fold in cross validation set.
"""
# Calculate ROC metrics
thresholds_roc = np.arange(min(distances)-2, max(distances)+2, 0.01)
true_positive_rate, false_positive_rate, precision, recall, accuracy, best_distances = \
calculate_roc_values(
thresholds=thresholds_roc, distances=distances, labels=labels, num_folds=num_folds
)
roc_auc = auc(false_positive_rate, true_positive_rate)
# Calculate validation rate
thresholds_val = np.arange(min(distances)-2, max(distances)+2, 0.001)
tar, far = calculate_val(
thresholds_val=thresholds_val, distances=distances, labels=labels, far_target=far_target, num_folds=num_folds
)
return true_positive_rate, false_positive_rate, precision, recall, accuracy, roc_auc, best_distances,\
tar, far
def calculate_roc_values(thresholds, distances, labels, num_folds=10):
num_pairs = min(len(labels), len(distances))
num_thresholds = len(thresholds)
k_fold = KFold(n_splits=num_folds, shuffle=False)
true_positive_rates = np.zeros((num_folds, num_thresholds))
false_positive_rates = np.zeros((num_folds, num_thresholds))
precision = np.zeros(num_folds)
recall = np.zeros(num_folds)
accuracy = np.zeros(num_folds)
best_distances = np.zeros(num_folds)
indices = np.arange(num_pairs)
for fold_index, (train_set, test_set) in enumerate(k_fold.split(indices)):
# Find the best distance threshold for the k-fold cross validation using the train set
accuracies_trainset = np.zeros(num_thresholds)
for threshold_index, threshold in enumerate(thresholds):
_, _, _, _, accuracies_trainset[threshold_index] = calculate_metrics(
threshold=threshold, dist=distances[train_set], actual_issame=labels[train_set]
)
best_threshold_index = np.argmax(accuracies_trainset)
# Test on test set using the best distance threshold
for threshold_index, threshold in enumerate(thresholds):
true_positive_rates[fold_index, threshold_index], false_positive_rates[fold_index, threshold_index], _, _,\
_ = calculate_metrics(
threshold=threshold, dist=distances[test_set], actual_issame=labels[test_set]
)
_, _, precision[fold_index], recall[fold_index], accuracy[fold_index] = calculate_metrics(
threshold=thresholds[best_threshold_index], dist=distances[test_set], actual_issame=labels[test_set]
)
true_positive_rate = np.mean(true_positive_rates, 0)
false_positive_rate = np.mean(false_positive_rates, 0)
best_distances[fold_index] = thresholds[best_threshold_index]
return true_positive_rate, false_positive_rate, precision, recall, accuracy, best_distances
def calculate_metrics(threshold, dist, actual_issame):
# If distance is less than threshold, then prediction is set to True
predict_issame = np.less(dist, threshold)
true_positives = np.sum(np.logical_and(predict_issame, actual_issame))
false_positives = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
true_negatives = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
false_negatives = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
# For dealing with Divide By Zero exception
true_positive_rate = 0 if (true_positives + false_negatives == 0) else \
float(true_positives) / float(true_positives + false_negatives)
false_positive_rate = 0 if (false_positives + true_negatives == 0) else \
float(false_positives) / float(false_positives + true_negatives)
precision = 0 if (true_positives + false_positives) == 0 else\
float(true_positives) / float(true_positives + false_positives)
recall = 0 if (true_positives + false_negatives) == 0 else \
float(true_positives) / float(true_positives + false_negatives)
accuracy = float(true_positives + true_negatives) / dist.size
return true_positive_rate, false_positive_rate, precision, recall, accuracy
def calculate_val(thresholds_val, distances, labels, far_target=1e-3, num_folds=10):
num_pairs = min(len(labels), len(distances))
num_thresholds = len(thresholds_val)
k_fold = KFold(n_splits=num_folds, shuffle=False)
tar = np.zeros(num_folds)
far = np.zeros(num_folds)
indices = np.arange(num_pairs)
for fold_index, (train_set, test_set) in enumerate(k_fold.split(indices)):
# Find the euclidean distance threshold that gives false acceptance rate (far) = far_target
far_train = np.zeros(num_thresholds)
for threshold_index, threshold in enumerate(thresholds_val):
_, far_train[threshold_index] = calculate_val_far(
threshold=threshold, dist=distances[train_set], actual_issame=labels[train_set]
)
if np.max(far_train) >= far_target:
f = interpolate.interp1d(far_train, thresholds_val, kind='slinear')
threshold = f(far_target)
else:
threshold = 0.0
tar[fold_index], far[fold_index] = calculate_val_far(
threshold=threshold, dist=distances[test_set], actual_issame=labels[test_set]
)
return tar, far
def calculate_val_far(threshold, dist, actual_issame):
# If distance is less than threshold, then prediction is set to True
predict_issame = np.less(dist, threshold)
true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
num_same = np.sum(actual_issame)
num_diff = np.sum(np.logical_not(actual_issame))
if num_diff == 0:
num_diff = 1
if num_same == 0:
return 0, 0
tar = float(true_accept) / float(num_same)
far = float(false_accept) / float(num_diff)
return tar, far