def calculate_gap(predictions, actuals, top_k=20):
"""Performs a local (numpy) calculation of the global average precision.
Only the top_k predictions are taken for each of the videos.
Args:
predictions: Matrix containing the outputs of the model. Dimensions are
'batch' x 'num_classes'.
actuals: Matrix containing the ground truth labels. Dimensions are 'batch' x
'num_classes'.
top_k: How many predictions to use per video.
Returns:
float: The global average precision.
"""
gap_calculator = ap_calculator.AveragePrecisionCalculator()
sparse_predictions, sparse_labels, num_positives = top_k_by_class(
predictions, actuals, top_k)
gap_calculator.accumulate(flatten(sparse_predictions),
flatten(sparse_labels), sum(num_positives))
return gap_calculator.peek_ap_at_n()
def __init__(self, num_class, top_k, top_n):
"""Construct an EvaluationMetrics object to store the evaluation metrics.
Args:
num_class: A positive integer specifying the number of classes.
top_k: A positive integer specifying how many predictions are considered
per video.
top_n: A positive Integer specifying the average precision at n, or None
to use all provided data points.
Raises:
ValueError: An error occurred when MeanAveragePrecisionCalculator cannot
not be constructed.
"""
self.sum_hit_at_one = 0.0
self.sum_perr = 0.0
self.map_calculator = map_calculator.MeanAveragePrecisionCalculator(
num_class, top_n=top_n)
self.global_ap_calculator = ap_calculator.AveragePrecisionCalculator()
self.top_k = top_k
self.num_examples = 0
self.num_class = num_class
def __init__(self, num_class, filter_empty_classes=True, top_n=None):
"""Construct a calculator to calculate the (macro) average precision.
Args:
num_class: A positive Integer specifying the number of classes.
filter_empty_classes: whether to filter classes without any positives.
top_n: A positive Integer specifying the average precision at n, or None
to use all provided data points.
Raises:
ValueError: An error occurred when num_class is not a positive integer;
or the top_n_array is not a list of positive integers.
"""
if not isinstance(num_class, int) or num_class <= 1:
raise ValueError("num_class must be a positive integer.")
self._ap_calculators = [] # member of AveragePrecisionCalculator
self._num_class = num_class # total number of classes
self._filter_empty_classes = filter_empty_classes
for _ in range(num_class):
self._ap_calculators.append(
average_precision_calculator.AveragePrecisionCalculator(top_n=top_n))