v2.1.3

utils

Module containing helper functions for calculating agreement measures.

class human_protocol_sdk.agreement.utils.NormalDistribution(location=0.0, scale=1.0)

Bases: object

Continuous Normal Distribution.

See: https://en.wikipedia.org/wiki/Normal_distribution

__init__(location=0.0, scale=1.0)

Creates a NormalDistribution from the given parameters.

  • Parameters:

    • location (float) – Location of the distribution.

    • scale (float) – Scale of the distribution. Must be positive.

cdf(x)

Cumulative Distribution Function of the Normal Distribution. Returns the probability that a random sample will be less than the given point.

  • Parameters: x (float) – Point within the distribution’s domain.

  • Return type: float

pdf(x)

Probability Density Function of the Normal Distribution. Returns the probability for observing the given sample in the distribution.

  • Parameters: x (float) – Point within the distribution’s domain.

  • Return type: float

ppf(p)

Probability Point function of the Normal Distribution. Returns the maximum point to which cumulated probabilities equal the given probability. Also called quantile. Inverse of the cdf.

  • Parameters: p (float) – Percentile of the distribution to be covered by the ppf.

  • Return type: float

human_protocol_sdk.agreement.utils.confusion_matrix(annotations, labels=None, return_labels=False)

Generate an N X N confusion matrix from the given sequence of values a and b, where N is the number of unique labels.

  • Parameters:

    • annotations (ndarray) – Annotation data to be converted into confusion matrix. Must be a N x 2 Matrix, where N is the number of items and 2 is the number of annotators.

    • labels (Optional[Sequence]) – Sequence of labels to be counted. Entries not found in the list are omitted. No labels are provided, the list of labels is inferred from the given annotations.

    • return_labels – Whether to return labels with the counts.

  • Returns: A confusion matrix. Rows represent labels assigned by b, columns represent labels assigned by a.

  • Example:

    from human_protocol_sdk.agreement.utils import confusion_matrix
import numpy as np

annotations = np.asarray([
    ["a", "a"],
    ["b", "a"],
    ["c", "c"]
])

# infer labels automatically
cm = confusion_matrix(annotations, return_labels=False)
print(cm)
# [[1 0 0]
#  [1 0 0]
#  [0 0 1]]

human_protocol_sdk.agreement.utils.label_counts(annotations, labels=None, return_labels=False)

Converts the given sequence of item annotations to an array of label counts per item.

  • Parameters:

    • annotations (Sequence) – A two-dimensional sequence. Rows represent items, columns represent annotators.

    • labels – List of labels to be counted. Entries not found in the list are omitted. If omitted, all labels in the annotations are counted.

    • nan_values – Values in the records to be counted as invalid.

    • return_labels – Whether to return labels with the counts. Automatically set to true if labels are inferred.

  • Returns: A two-dimensional array of integers. Rows represent items, columns represent labels.

  • Example:

    from human_protocol_sdk.agreement.utils import label_counts

annotations = [
    ["white", "black", "white"],
    ["white", "white", "white"],
    ["black", "black", "black"],
    ["white",   "nan", "black"],
]

# infer labels automatically
counts, labels = label_counts(annotations, return_labels=True)
print(counts)
# [[1 2]
#  [0 3]
#  [3 0]
#  [1 1]]

# labels are inferred and sorted automatically
print(labels)
# ['black' 'white']
    # labels are provided, label order is preserved
counts, labels = label_counts(
    annotations,
    labels=['white', 'black'],
    return_labels=True
)
print(counts)
# [[2 1]
#  [3 0]
#  [0 3]
#  [1 1]]

print(labels)
# ['white' 'black']
    # can be achieved using nan values
counts, labels = label_counts(
    annotations,
    nan_values=[''],
    return_labels=True
)

print(counts)
# [[1 2]
#  [0 3]
#  [3 0]
#  [1 1]]

print(labels)
# ['black' 'white']

human_protocol_sdk.agreement.utils.observed_and_expected_differences(annotations, distance_function)

Returns observed and expected differences for given annotations (item-value pairs), as used in Krippendorff’s alpha agreement measure and the Sigma agreement measure.

  • Parameters:

    • annotations – Annotation data. Must be a N x M Matrix, where N is the number of items and M is the number of annotators.

    • distance_function – Function to calculate distance between two values. Calling distance_fn(annotations[i, j], annotations[p, q]) must return a number. Can also be one of ‘nominal’, ‘ordinal’, ‘interval’ or ‘ratio’ for default functions pertaining to the level of measurement of the data.

  • Returns: A tuple consisting of numpy ndarrays, containing the observed and expected differences in annotations.

human_protocol_sdk.agreement.utils.records_from_annotations(annotations, annotators=None, items=None, labels=None)

Turns given annotations into sequences of records.

  • Parameters:

    • annotations (ndarray) – Annotation matrix (2d array) to convert. Columns represent

    • annotators – List of annotator ids. Must be the same length as columns in annotations.

    • items – List of item ids. Must be the same length as rows in annotations.

    • labels – The to be included in the matrix.

  • Return type: Tuple[ndarray, ndarray, ndarray]

  • Returns: Tuple containing arrays of item value ids, item ids and annotator ids

  • Example:

    from human_protocol_sdk.agreement.utils import records_from_annotations
import numpy as np

annotations = np.asarray([
    ["cat", "not", "cat"],
    ["cat", "cat", "cat"],
    ["not", "not", "not"],
    ["cat", np.nan, "not"],
])

# nan values are automatically filtered
values, items, annotators = records_from_annotations(annotations)
print(values)
# ['cat' 'not' 'cat' 'cat' 'cat' 'cat' 'not' 'not' 'not' 'cat' 'not']
print(items)
# [0 0 0 1 1 1 2 2 2 3 3]
print(annotators)
# [0 1 2 0 1 2 0 1 2 0 2]
    annotators = np.asarray(["bob", "alice", "charlie"])
items = np.asarray(["item_1", "item_2", "item_3", "item_4"])

values, items, annotators = records_from_annotations(
    annotations,
    annotators,
    items
)
print(values)
# ['cat' 'not' 'cat' 'cat' 'cat' 'cat' 'not' 'not' 'not' 'cat' 'not']
print(items)
# ['item_1' 'item_1' 'item_1' 'item_2' 'item_2' 'item_2' 'item_3' 'item_3' 'item_3' 'item_4' 'item_4']
print(annotators)
# ['bob' 'alice' 'charlie' 'bob' 'alice' 'charlie' 'bob' 'alice' 'charlie' 'bob' 'charlie']

Last updated