Package: disagree
disagree - Assessing Annotator Disagreements in PythonThis library aims to address annotation disagreements in manually labelled data.I started it as a project to develop some understanding of Python packaging and workflow. (This is
the primary reason for the messy release history and commit logs, for which I apologise.) But I hope this will be useful for a wider audience as well.InstallTo install, setup a virtualenv and do:$ python3 -m pip install --index-url https://pypi.org/project/ disagreeor$ pip3 install disagreeTo update to the latest version do:$ pip3 install --upgrade disagreeBackgroundWhilst working in NLP, I've been repeatedly working with datasets that have been manually labelled, and have thus had to evaluate the quality of the agreements between the annotators. In my (limited) experience of doing this, I have encountered a number of ways of it that have been helpful. In this library, I aim to group all of those things together for people to use.Please suggest any additions/functionalities, and I will try my best to add them.Summary of featuresVisualisationsAbility to visualise bidisagreements between annotatorsAbility to visualise agreement statisticsRetrieve summaries of numbers of disagreements and their extentAnnotation statistics:Joint probabilityCohens kappaFleiss kappaPearson, Spearman, Kendall correlationsKrippendorff's alphaPython examplesWorked examples are provided in the Jupyter notebooks directory.Documentationdisagree.BiDisagreements(df)BiDisagreementsclass is primarily there for you to visualise the disagreements in the form of a matrix, but has some other small functionalities.df: Pandas DataFrame containing annotator labelsRows: Instances of the data that is labelledColumns: AnnotatorsElement [i, j] is annotator j's label for data instance i.Entries must be integers, floats, strings, or pandas nan valuesAttributes:agreements_summary()This will print out statistics on the number of instances with no disagreements, the number of bidisagreements, the number of tridisagreements, and the number of instances with worse cases (i.e. 3+ disagreements).agreements_matrix()This will return a matrix of bidisagreements. Do with this what you will! The intention is that
you use something like matplotlib to visualise them properly.Element $(i, j)$ is the number of times there is a bidisagreement involving label $i$ and label $j$.labels_to_index()Returns a dictionary mapping label names to indexes used in theagreements_matrix().disagree.metrics.Metrics(df)This module gives you access to a number of metrics typically used for annotation disagreement statistics.Attributes:joint_probability(ann1, ann2)Parameter: ann1, string, name of one of the annotators from the DataFrame columnsParameter: ann2, string, name of one of the annotators from the DataFrame columnsThis gives the join probability of agreement between ann1 and ann2. You should probably not use this measure for academic purposes, but is here for completion.cohens_kappa(ann1, ann2):Parameter: ann1, string, name of one of the annotators from the DataFrame columnsParameter: ann2, string, name of one of the annotators from the DataFrame columnsfliess_kappa()No argscorrelation(ann1, ann2, measure="pearson")Parameter: ann1, string, name of one of the annotators from the DataFrame columnsParameter: ann2, string, name of one of the annotators from the DataFrame columnsParamater: measure, string, optionalOptions: (pearson (default), kendall, spearman)This gives you either pearson , kendall, or spearman correlation statistics between two annotatorsmetric_matrix(func)Returns a matrix of size (num_annotators x num_annotators). Element $(i, j)$ is the statistic value for agreements between annotator $i$ and annotator $j$.Parameter: func, name of function for the metric you want to visualise.Options: (metrics.Metrics.cohens_kappa, metrics.Metrics.joint_probability)disagree.metrics.Krippendorff(df)Attributesalpha(data_type="nominal")In this library, Krippendorff's alpha can handle four data types, one of which must be specified:nominal (default)ordinalintervalratio