[ { "path": ".coveragerc", "content": "[report]\nomit =\n */python?.?/*\n */site-packages/nose/*" }, { "path": ".gitignore", "content": "*.py[co]\n\n# Packages\n*.egg\n*.egg-info\ndist\nbuild\neggs\nparts\nbin\nvar\nsdist\ndevelop-eggs\n.installed.cfg\n\n# Installer logs\npip-log.txt\n\n# Unit test / coverage reports\n.coverage\n.tox\n\n#Translations\n*.mo\n\n#Mr Developer\n.mr.developer.cfg\n\n# OS generated files #\n.DS_Store\n.DS_Store?\n._*\n.Spotlight-V100\n.Trashes\nehthumbs.db\nThumbs.db\n*~\n\n\n# Vim\n\n# PyCharm\n.idea\n\n# cover\ntests/cover/*\ntests/_dev/*\n.coverage\n.coverage.*\n\n# Sphinx\n#docs/generated/\n\ntests/dcase2016/*\n\n.nfs*\ntests/*.zip\ntests/DCASE2016-baseline-system-python*\ntests/dev/\ndocumentation/source/generated/\ndocumentation/build/" }, { "path": ".travis.yml", "content": "language:\n - python\n\npython:\n - \"2.7\"\n - \"3.5\"\n - \"3.6\"\n\ncache:\n pip: true\n bundler: true\n\nnotifications:\n email: false\n\nbefore_install:\n - \"sudo apt-get install -qq python-scipy\"\n\n# command to install dependencies\ninstall:\n - \"pip install -r requirements.txt\"\n - \"pip install python-coveralls\"\n - \"pip install coveralls\"\n - \"pip install nose\"\n - \"pip install scikit-learn\"\n - \"pip install 'joblib==0.11' --force-reinstall\"\n - \"python setup.py develop\"\n\n# command to run tests\nscript:\n - \"nosetests --with-coverage --cover-erase --cover-package=sed_eval -v -w tests/\"\n\nafter_success:\n - \"coveralls\"\n - \"python setup.py develop --uninstall\"\n\nafter_failure:\n - \"python setup.py develop --uninstall\"" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at toni.heittola@tut.fi. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n" }, { "path": "CONTRIBUTING.md", "content": "Contributing\n============\n\nHow to contribute\n-----------------\n\nThe preferred way to contribute to `sed_eval` is to fork the \n[main repository](https://github.com/TUT-ARG/sed_eval) on\nGitHub:\n\n1. Fork the [project repository](https://github.com/TUT-ARG/sed_eval):\n click on the 'Fork' button near the top of the page. This creates\n a copy of the code under your account on the GitHub server.\n\n2. Clone this copy to your local disk:\n\n git clone git@github.com:[YOUR_LOGIN]/sed_eval.git\n cd sed_eval \n\n3. Create a branch to hold your changes:\n\n git checkout -b my-new-feature\n\n and start making changes. You should never work in the ``master`` branch directly. \n\n4. Work on this copy on your computer using Git to do the version\n control. When you're done editing, do:\n\n git add [MODIFIED FILES]\n git commit\n\n to record your changes in Git, then push them to GitHub with:\n\n git push -u origin my-new-feature\n\nFinally, go to the web page of the your fork of the sed_eval repo,\nand click 'Pull request' to send your changes to the maintainers for\nreview. This will send an email to the committers.\n\nMore information about this kind of process can be found in \n[Git documentation](http://git-scm.com/documentation).\n\nYou should check that your contribution complies with the\nfollowing rules before submitting a pull request:\n\n- All public methods should have informative docstrings\n- Code should be sufficiently commented\n- For major new features there should be also an unittest \n\nYou should check for common programming errors with the following\ntools:\n\n- Check unittests:\n\n pip install nose coverage\n cd tests/\n nosetests -v --with-coverage --cover-erase --cover-html --cover-package=sed_eval --nocapture\n\n- There should be no major pyflakes warnings, check with:\n\n pip install pyflakes\n pyflakes path/to/module.py\n\n- There should be no major PEP8 warnings, check with:\n\n pip install pep8\n pep8 path/to/module.py\n\nCreating an issue\n-----------------\n\nWe use Github issues to track all bugs and feature requests; feel free to\nopen an issue if you have found a bug or wish to see a new feature implemented.\n\nIt is recommended to check that your issue complies with the\nfollowing rules before submitting:\n\n- Verify that your issue is not being currently addressed by other\n [issues](https://github.com/TUT-ARG/sed_eval/issues?q=)\n or [pull requests](https://github.com/TUT-ARG/sed_eval/pulls?q=).\n\n- Please ensure all code snippets and error messages are formatted in\n appropriate code blocks.\n See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).\n\n- Please include your operating system type and version number, as well\n as your Python, numpy, and dcase_util versions. You can get this\n information with following code:\n\n import platform; print(platform.platform())\n import sys; print(\"Python\", sys.version)\n import numpy; print(\"NumPy\", numpy.__version__)\n \n\nDocumentation\n-------------\n\nYou can edit the documentation using any text editor and then generate\nthe HTML output by typing ``make html`` from the docs/ directory.\nThe resulting HTML files will be placed in docs/ and are viewable \nin a web browser. See the README file in the documentation/ directory for more information.\n\nNote\n----\nThis document is based on contribution instructions for [LibROSA](https://github.com/librosa/librosa).\n" }, { "path": "LICENSE.txt", "content": "The MIT License (MIT)\n\nCopyright (c) 2016 Tampere University of Technology\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE." }, { "path": "README.rst", "content": "sed_eval - Evaluation toolbox for Sound Event Detection\n=======================================================\n\n.. image:: https://travis-ci.org/TUT-ARG/sed_eval.svg?branch=master\n :target: https://travis-ci.org/TUT-ARG/sed_eval\n\n.. image:: https://coveralls.io/repos/github/TUT-ARG/sed_eval/badge.svg?branch=master \n :target: https://coveralls.io/github/TUT-ARG/sed_eval?branch=master\n\n.. image:: https://img.shields.io/pypi/v/sed_eval.svg\n :target: https://pypi.python.org/pypi/sed_eval\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n :target: https://opensource.org/licenses/MIT\n\nsed_eval is an open source Python toolbox which provides a standardized, \nand transparent way to evaluate sound event detection systems. \n\nAuthors\n\n- Toni Heittola (toni.heittola@tut.fi, `GitHub `_, ``_)\n- Annamaria Mesaros (annamaria.mesaros@tut.fi, ``_)\n\nDocumentation\n=============\n\nSee http://tut-arg.github.io/sed_eval for manual and tutorials.\n\nInstallation instructions\n=========================\n\nThe latest stable release is available on PyPI, and you can install with pip::\n\n pip install sed_eval\n\nAlternatively you can download or clone toolbox and use ``pip`` to handle dependencies::\n\n unzip sed_eval.zip\n pip install -e sed_eval\n\n\nor::\n\n git clone https://github.com/TUT-ARG/sed_eval.git\n pip install -e sed_eval\n\n\nDependencies\n------------\n\nThe toolbox is tested to work with Python 2.7 and Python 3.6.\n\n- numpy >= 1.7.0\n- dcase_util >= 0.2.4\n\nCiting\n======\n\nIf you use sed_eval in a research project, please cite the following paper:\n\nAnnamaria Mesaros, Toni Heittola, and Tuomas Virtanen, \"Metrics for polyphonic sound event detection\", Applied Sciences, 6(6):162, 2016 [`HTML `_][`PDF `_]\n\nLicense\n=======\n\nCode released under `the MIT license `_.\n" }, { "path": "docs/.buildinfo", "content": "# Sphinx build info version 1\n# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.\nconfig: 950eb7edc6fd5559b7dbbc2ae8b0df21\ntags: 645f666f9bcd5a90fca523b33c5a78b7\n" }, { "path": "docs/.nojekyll", "content": "" }, { "path": "docs/_modules/index.html", "content": "\n\n\n\n \n\n \n \n \n \n Overview: module code — sed_eval 0.1 documentation\n \n\n \n \n \n \n\n \n\n \n \n \n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n \n\n\n\n\n\n \n
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Source code for sed_eval.audio_tag

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nMetrics\n-------\n\nMain functions:\n\n* :func:`sed_eval.audio_tag.AudioTaggingMetrics.evaluate`: Calculate intermediate values for evaluation and accumulate them.\n* :func:`sed_eval.audio_tag.AudioTaggingMetrics.results`: Calculate and return all metrics.\n* :func:`sed_eval.audio_tag.AudioTaggingMetrics.results_overall_metrics`: Calculate and return overall metrics (micro-averaged).\n* :func:`sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_metrics`: Calculate and return class-wise metrics.\n* :func:`sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_average_metrics`: Calculate and return class-wise average metrics (macro-averaged).\n\nFunction :func:`sed_eval.audio_tag.AudioTaggingMetrics.evaluate` takes as a parameter tag lists,\n(use :func:`dcase_util.containers.MetaDataContainer` to read them from a file), and probability lists (use :func:`dcase_util.containers.ProbabilityContainer` to read them from a file).\n\nUsage example:\n\n.. code-block:: python\n    :linenos:\n\n    import sed_eval\n    import dcase_util\n\n    reference_tag_list = dcase_util.containers.MetaDataContainer([\n        {\n            'filename': 'test1.wav',\n            'tags': 'cat,dog'\n        },\n        {\n            'filename': 'test2.wav',\n            'tags': 'dog'\n        },\n        {\n            'filename': 'test3.wav',\n            'tags': 'bird,cat'\n        },\n        {\n            'filename': 'test4.wav',\n            'tags': 'cat'\n        },\n        {\n            'filename': 'test5.wav',\n            'tags': 'bird,speech'\n        },\n        {\n            'filename': 'test6.wav',\n            'tags': 'dog,speech'\n        },\n        {\n            'filename': 'test7.wav',\n            'tags': 'speech'\n        },\n    ])\n\n    estimated_tag_probabilities = dcase_util.containers.ProbabilityContainer([\n        {\n            'filename': 'test1.wav',\n            'label': 'bird',\n            'probability': 0.2\n        },\n        {\n            'filename': 'test1.wav',\n            'label': 'cat',\n            'probability': 0.99\n        },\n        {\n            'filename': 'test1.wav',\n            'label': 'dog',\n            'probability': 0.88\n        },\n        {\n            'filename': 'test1.wav',\n            'label': 'speech',\n            'probability': 0.01\n        },\n\n        {\n            'filename': 'test2.wav',\n            'label': 'bird',\n            'probability': 0.1\n        },\n        {\n            'filename': 'test2.wav',\n            'label': 'cat',\n            'probability': 0.3\n        },\n        {\n            'filename': 'test2.wav',\n            'label': 'dog',\n            'probability': 0.8\n        },\n        {\n            'filename': 'test2.wav',\n            'label': 'speech',\n            'probability': 0.1\n        },\n\n\n        {\n            'filename': 'test3.wav',\n            'label': 'bird',\n            'probability': 0.7\n        },\n        {\n            'filename': 'test3.wav',\n            'label': 'cat',\n            'probability': 0.6\n        },\n        {\n            'filename': 'test3.wav',\n            'label': 'dog',\n            'probability': 0.4\n        },\n        {\n            'filename': 'test3.wav',\n            'label': 'speech',\n            'probability': 0.3\n        },\n\n        {\n            'filename': 'test4.wav',\n            'label': 'bird',\n            'probability': 0.323\n        },\n        {\n            'filename': 'test4.wav',\n            'label': 'cat',\n            'probability': 0.6\n        },\n        {\n            'filename': 'test4.wav',\n            'label': 'dog',\n            'probability': 0.56\n        },\n        {\n            'filename': 'test4.wav',\n            'label': 'speech',\n            'probability': 0.4\n        },\n\n\n        {\n            'filename': 'test5.wav',\n            'label': 'bird',\n            'probability': 0.8\n        },\n        {\n            'filename': 'test5.wav',\n            'label': 'cat',\n            'probability': 0.7\n        },\n        {\n            'filename': 'test5.wav',\n            'label': 'dog',\n            'probability': 0.45\n        },\n        {\n            'filename': 'test5.wav',\n            'label': 'speech',\n            'probability': 0.43\n        },\n\n\n        {\n            'filename': 'test6.wav',\n            'label': 'bird',\n            'probability': 0.9\n        },\n        {\n            'filename': 'test6.wav',\n            'label': 'cat',\n            'probability': 0.53\n        },\n        {\n            'filename': 'test6.wav',\n            'label': 'dog',\n            'probability': 0.83\n        },\n        {\n            'filename': 'test6.wav',\n            'label': 'speech',\n            'probability': 0.95\n        },\n\n\n        {\n            'filename': 'test7.wav',\n            'label': 'bird',\n            'probability': 0.2\n        },\n        {\n            'filename': 'test7.wav',\n            'label': 'cat',\n            'probability': 0.2\n        },\n        {\n            'filename': 'test7.wav',\n            'label': 'dog',\n            'probability': 0.89\n        },\n        {\n            'filename': 'test7.wav',\n            'label': 'speech',\n            'probability': 0.45\n        },\n    ])\n\n    estimated_tag_list = dcase_util.containers.MetaDataContainer()\n    for file in estimated_tag_probabilities.unique_files:\n        k = estimated_tag_probabilities.filter(filename=file)\n        tags = []\n        for item in k:\n            if item.probability > 0.5:\n                tags.append(item.label)\n\n        estimated_tag_list.append(\n            {\n                'filename': file,\n                'tags': tags\n            }\n        )\n\n    tag_evaluator = sed_eval.audio_tag.AudioTaggingMetrics(\n        tags=reference_tag_list.unique_tags\n    )\n\n    tag_evaluator.evaluate(\n        reference_tag_list=reference_tag_list,\n        estimated_tag_list=estimated_tag_list,\n        estimated_tag_probabilities=estimated_tag_probabilities\n    )\n    print(tag_evaluator)\n\n\n.. autosummary::\n    :toctree: generated/\n\n    AudioTaggingMetrics\n    AudioTaggingMetrics.evaluate\n    AudioTaggingMetrics.results\n    AudioTaggingMetrics.results_overall_metrics\n    AudioTaggingMetrics.results_class_wise_metrics\n    AudioTaggingMetrics.results_class_wise_average_metrics\n    AudioTaggingMetrics.result_report_parameters\n    AudioTaggingMetrics.result_report_class_wise\n    AudioTaggingMetrics.result_report_class_wise_average\n    AudioTaggingMetrics.reset\n\n"""\n\nfrom __future__ import absolute_import\nimport numpy\nfrom . import metric\nimport dcase_util\n\n\n
[docs]class AudioTaggingMetrics:\n
[docs]    def __init__(self, tags=None):\n        self.tag_label_list = tags\n\n        self.overall = {\n            'Ncorr': 0.0,\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'Ntp': 0.0,\n            'Ntn': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n        }\n\n        self.tag_wise = {}\n        self.y_true = {}\n        self.y_pred = {}\n        self.y_pred_score = {}\n\n        for label in self.tag_label_list:\n            self.tag_wise[label] = {\n                'Nref': 0.0,\n                'Nsys': 0.0,\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n            }\n            self.y_true[label] = []\n            self.y_pred[label] = []\n            self.y_pred_score[label] = []\n\n        self.ui = dcase_util.ui.FancyStringifier()
\n\n    def __str__(self):\n        """Print result reports"""\n\n        output = self.ui.section_header('Audio tagging metrics') + '\\n'\n        output += self.result_report_parameters() + '\\n'\n        output += self.result_report_overall() + '\\n'\n        output += self.result_report_class_wise_average() + '\\n'\n        output += self.result_report_class_wise() + '\\n'\n\n        return output\n\n
[docs]    def evaluate(self, reference_tag_list, estimated_tag_list=None, estimated_tag_probabilities=None):\n        """Evaluate estimated against reference\n\n        Parameters\n        ----------\n\n        reference_tag_list : list of dict or dcase_util.containers.MetaDataContainer\n            Reference tag list\n\n        estimated_tag_list : list of dict or dcase_util.containers.MetaDataContainer\n            Estimated tag list\n\n        estimated_tag_probabilities : list of dict or dcase_util.containers.ProbabilityContainer\n            Estimated tag probabilities\n\n        Returns\n        -------\n        self\n\n        """\n\n        if estimated_tag_list is None and estimated_tag_probabilities is None:\n            raise ValueError("Nothing to evaluate, give at least estimated_tag_list or estimated_tag_probabilities")\n\n        # Make sure reference_tag_list is dcase_util.containers.MetaDataContainer\n        if not isinstance(reference_tag_list, dcase_util.containers.MetaDataContainer):\n            reference_tag_list = dcase_util.containers.MetaDataContainer(reference_tag_list)\n\n        # Make sure estimated_tag_list is dcase_util.containers.MetaDataContainer\n        if estimated_tag_list is not None:\n            if not isinstance(estimated_tag_list, dcase_util.containers.MetaDataContainer):\n                estimated_tag_list = dcase_util.containers.MetaDataContainer(estimated_tag_list)\n\n        # Make sure estimated_tag_probabilities is dcase_util.containers.ProbabilityContainer\n        if estimated_tag_probabilities is not None:\n            if not isinstance(estimated_tag_probabilities, dcase_util.containers.ProbabilityContainer):\n                estimated_tag_probabilities = dcase_util.containers.ProbabilityContainer(estimated_tag_probabilities)\n\n        y_true = []\n        y_pred = []\n\n        # Go though reference and estimated list label by label, and file by file\n        for label in self.tag_label_list:\n            for filename in reference_tag_list.unique_files:\n                reference_item = reference_tag_list.filter(filename=filename)[0]\n\n                # Populate y_true based on reference_item\n                if label in reference_item.tags:\n                    self.y_true[label].append(1)\n                    y_true.append(1)\n\n                else:\n                    self.y_true[label].append(0)\n                    y_true.append(0)\n\n                if estimated_tag_list is not None:\n                    # Evaluate based on estimated tags\n\n                    estimated_item = estimated_tag_list.filter(filename=filename)[0]\n\n                    if not estimated_item:\n                        raise ValueError(\n                            "Not all reference files estimated, please check [{file}]".format(\n                                file=filename\n                            )\n                        )\n                    # Store nref\n                    if label in reference_item.tags:\n                        self.tag_wise[label]['Nref'] += 1\n\n                    # Populate y_pred based estimated_item\n                    if label in estimated_item.tags:\n                        self.y_pred[label].append(1)\n                        y_pred.append(1)\n                        self.tag_wise[label]['Nsys'] += 1\n\n                    else:\n                        self.y_pred[label].append(0)\n                        y_pred.append(0)\n\n                    # Accumulate intermediate values\n                    # True positives (TP)\n                    if label in reference_item.tags and label in estimated_item.tags:\n                        self.tag_wise[label]['Ntp'] += 1\n\n                    # True negatives (TN)\n                    if label not in reference_item.tags and label not in estimated_item.tags:\n                        self.tag_wise[label]['Ntn'] += 1\n\n                    # False positives (FP)\n                    if label not in reference_item.tags and label in estimated_item.tags:\n                        self.tag_wise[label]['Nfp'] += 1\n\n                    # False negatives (FN)\n                    if label in reference_item.tags and label not in estimated_item.tags:\n                        self.tag_wise[label]['Nfn'] += 1\n\n                if estimated_tag_probabilities is not None:\n                    # Evaluate based on per tag probabilities\n\n                    estimated_item = estimated_tag_probabilities.filter(filename=filename, label=label)[0]\n                    self.y_pred_score[label].append(float(estimated_item['probability']))\n\n        if estimated_tag_list is not None:\n            # Evaluate based on estimated tags\n\n            self.overall['Nref'] += sum(y_true)\n            self.overall['Nsys'] += sum(y_pred)\n\n            y_true = numpy.array(y_true)\n            y_pred = numpy.array(y_pred)\n\n            self.overall['Ntp'] += sum(y_pred + y_true > 1)\n            self.overall['Ntn'] += sum(y_pred + y_true == 0)\n            self.overall['Nfp'] += sum(y_pred - y_true > 0)\n            self.overall['Nfn'] += sum(y_true - y_pred > 0)\n\n        return self
\n\n
[docs]    def reset(self):\n        """Reset internal state\n        """\n        self.overall = {\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'Ntp': 0.0,\n            'Ntn': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n        }\n\n        self.tag_wise = {}\n        for label in self.tag_label_list:\n            self.tag_wise[label] = {\n                'Nref': 0.0,\n                'Nsys': 0.0,\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n            }\n\n        return self
\n\n    # Results\n
[docs]    def results(self):\n        """All metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        results = {\n            'overall': self.results_overall_metrics(),\n            'class_wise': self.results_class_wise_metrics(),\n            'class_wise_average': self.results_class_wise_average_metrics(),\n        }\n\n        return results
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[docs]    def results_overall_metrics(self):\n        """Overall metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        # F-measure\n        if self.overall['Nref'] > 0:\n            precision = metric.precision(\n                Ntp=self.overall['Ntp'],\n                Nsys=self.overall['Nsys']\n            )\n\n            recall = metric.recall(\n                Ntp=self.overall['Ntp'],\n                Nref=self.overall['Nref']\n            )\n\n            f_measure = metric.f_measure(\n                precision=precision,\n                recall=recall\n            )\n\n        else:\n            precision = None\n            recall = None\n            f_measure = None\n\n        y_true = []\n        y_score = []\n        for tag_id, tag_label in enumerate(self.tag_label_list):\n            y_true += self.y_true[tag_label]\n            y_score += self.y_pred_score[tag_label]\n\n        if y_score:\n            eer = metric.equal_error_rate(\n                y_true=y_true,\n                y_score=y_score\n            )\n\n        else:\n            eer = None\n\n        return {\n                'count': {\n                    'Nref': self.overall['Nref'],\n                    'Nsys': self.overall['Nsys'],\n                },\n                'f_measure': {\n                    'f_measure': f_measure,\n                    'precision': precision,\n                    'recall': recall,\n                },\n                'eer': {\n                    'eer': eer\n                }\n\n        }
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[docs]    def results_class_wise_metrics(self):\n        """Class-wise metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        results = {}\n        for tag_id, tag_label in enumerate(self.tag_label_list):\n            if tag_label not in results:\n                results[tag_label] = {}\n\n            # Counts\n            results[tag_label]['count'] = {\n                'Nref': self.tag_wise[tag_label]['Nref'],\n                'Nsys': self.tag_wise[tag_label]['Nsys'],\n                'Ntp': self.tag_wise[tag_label]['Ntp'],\n                'Ntn': self.tag_wise[tag_label]['Ntn'],\n                'Nfp': self.tag_wise[tag_label]['Nfp'],\n                'Nfn': self.tag_wise[tag_label]['Nfn'],\n            }\n\n            # Equal error rate\n            if self.y_pred_score[tag_label]:\n                results[tag_label]['eer'] = {\n                    'eer': metric.equal_error_rate(\n                        y_true=self.y_true[tag_label],\n                        y_score=self.y_pred_score[tag_label]\n                    )\n                }\n\n            else:\n                results[tag_label]['eer'] = {\n                    'eer': None\n                }\n\n            # F-measure\n            if self.tag_wise[tag_label]['Nref'] > 0:\n\n                precision = metric.precision(\n                    Ntp=self.tag_wise[tag_label]['Ntp'],\n                    Nsys=self.tag_wise[tag_label]['Nsys']\n                )\n\n                recall = metric.recall(\n                    Ntp=self.tag_wise[tag_label]['Ntp'],\n                    Nref=self.tag_wise[tag_label]['Nref']\n                )\n\n                f_measure = metric.f_measure(precision=precision, recall=recall)\n\n            else:\n                precision = None\n                recall = None\n                f_measure = None\n\n            results[tag_label]['f_measure'] = {\n                'f_measure': f_measure,\n                'precision': precision,\n                'recall': recall,\n            }\n\n        return results
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[docs]    def results_class_wise_average_metrics(self):\n        """Class-wise averaged metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        class_wise_results = self.results_class_wise_metrics()\n\n        class_wise_eer = []\n        class_wise_fmeasure = []\n        class_wise_precision = []\n        class_wise_recall = []\n\n        for class_label in class_wise_results:\n            if class_wise_results[class_label]['eer']['eer'] is not None:\n                class_wise_eer.append(class_wise_results[class_label]['eer']['eer'])\n\n            if class_wise_results[class_label]['f_measure']['f_measure'] is not None:\n                class_wise_fmeasure.append(class_wise_results[class_label]['f_measure']['f_measure'])\n                class_wise_precision.append(class_wise_results[class_label]['f_measure']['precision'])\n                class_wise_recall.append(class_wise_results[class_label]['f_measure']['recall'])\n\n        if class_wise_eer:\n            eer = float(numpy.nanmean(class_wise_eer))\n\n        else:\n            eer = None\n\n        if class_wise_fmeasure:\n            f_measure = float(numpy.nanmean(class_wise_fmeasure))\n\n        else:\n            f_measure = None\n\n        if class_wise_precision:\n            precision = float(numpy.nanmean(class_wise_precision))\n\n        else:\n            precision = None\n\n        if class_wise_recall:\n            recall = float(numpy.nanmean(class_wise_recall))\n\n        else:\n            recall = None\n\n        return {\n            'eer': {\n                'eer': eer\n            },\n            'f_measure': {\n                'f_measure': f_measure,\n                'precision': precision,\n                'recall': recall,\n            }\n        }
\n\n    # Reports\n
[docs]    def result_report_parameters(self):\n        """Report metric parameters\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        output = self.ui.data(field='Tags', value=len(self.tag_label_list)) + '\\n'\n        output += self.ui.data(field='Evaluated units', value=int(self.overall['Nref'])) + '\\n'\n\n        return output
\n\n    def result_report_overall(self):\n        """Report overall results\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_overall_metrics()\n\n        output = self.ui.section_header('Overall metrics (micro-average)', indent=2) + '\\n'\n\n        if 'f_measure' in results and results['f_measure']:\n\n            if results['f_measure']['f_measure'] is not None:\n                f_measure = results['f_measure']['f_measure'] * 100\n\n            else:\n                f_measure = None\n\n            if results['f_measure']['precision'] is not None:\n                precision = results['f_measure']['precision'] * 100\n\n            else:\n                precision = None\n\n            if results['f_measure']['recall'] is not None:\n                recall = results['f_measure']['recall'] * 100\n\n            else:\n                recall = None\n\n            output += self.ui.line('F-measure', indent=2) + '\\n'\n            output += self.ui.data(field='F-measure (F1)', value=f_measure, unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Precision', value=precision, unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Recall', value=recall, unit='%', indent=4) + '\\n'\n\n        if 'eer' in results and results['eer']:\n            if results['eer']['eer'] is not None:\n                eer = results['eer']['eer'] * 100\n\n            else:\n                eer = None\n\n            output += self.ui.line('Equal error rate', indent=2) + '\\n'\n            output += self.ui.data(field='Equal error rate (EER)', value=eer, unit='%', indent=4) + '\\n'\n\n        return output\n\n
[docs]    def result_report_class_wise_average(self):\n        """Report class-wise averages\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_average_metrics()\n\n        output = self.ui.section_header('Class-wise average metrics (macro-average)', indent=2) + '\\n'\n\n        if 'f_measure' in results and results['f_measure']:\n            if results['f_measure']['f_measure'] is not None:\n                f_measure = results['f_measure']['f_measure'] * 100\n\n            else:\n                f_measure = None\n\n            if results['f_measure']['precision'] is not None:\n                precision = results['f_measure']['precision'] * 100\n\n            else:\n                precision = None\n\n            if results['f_measure']['recall'] is not None:\n                recall = results['f_measure']['recall'] * 100\n\n            else:\n                recall = None\n\n            output += self.ui.line('F-measure', indent=2) + '\\n'\n            output += self.ui.data(field='F-measure (F1)', value=f_measure, unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Precision', value=precision, unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Recall', value=recall, unit='%', indent=4) + '\\n'\n\n        if 'eer' in results and results['eer']:\n            if results['eer']['eer'] is not None:\n                eer = results['eer']['eer'] * 100\n\n            else:\n                eer = None\n\n            output += self.ui.line('Equal error rate', indent=2) + '\\n'\n            output += self.ui.data(field='Equal error rate (EER)', value=eer, unit='%', indent=4) + '\\n'\n\n        return output
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[docs]    def result_report_class_wise(self):\n        """Report class-wise results\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_metrics()\n\n        output = self.ui.section_header('Class-wise metrics', indent=2) + '\\n'\n        output += self.ui.row(\n            'Tag', 'Nref', 'Nsys', 'F-score', 'Pre', 'Rec', 'EER',\n            widths=[20, 12, 12, 12, 12, 12, 12],\n            separators=[True, False, True, False, False, True, False],\n            indent=4\n        ) + '\\n'\n        output += self.ui.row('-', '-', '-', '-', '-', '-', '-') + '\\n'\n\n        for tag_label in self.tag_label_list:\n            if results[tag_label]['f_measure']['f_measure'] is not None:\n                f_measure = results[tag_label]['f_measure']['f_measure'] * 100\n\n            else:\n                f_measure = None\n\n            if results[tag_label]['f_measure']['precision'] is not None:\n                precision = results[tag_label]['f_measure']['precision'] * 100\n\n            else:\n                precision = None\n\n            if results[tag_label]['f_measure']['recall'] is not None:\n                recall = results[tag_label]['f_measure']['recall'] * 100\n\n            else:\n                recall = None\n\n            if results[tag_label]['eer']['eer'] is not None:\n                eer = results[tag_label]['eer']['eer'] * 100\n\n            else:\n                eer = None\n\n            output += self.ui.row(\n                tag_label,\n                results[tag_label]['count']['Nref'],\n                results[tag_label]['count']['Nsys'],\n                f_measure,\n                precision,\n                recall,\n                eer,\n                types=['str', 'int', 'int', 'float1_percentage', 'float1', 'float1', 'float1_percentage']\n            ) + '\\n'\n\n        return output
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Source code for sed_eval.io

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nI/O\n==================\nFunctions for loading annotations from files in various formats.\n\n.. autosummary::\n    :toctree: generated/\n\n    load_event_list\n    load_scene_list\n    load_file_pair_list\n\n"""\n\nfrom __future__ import absolute_import\nimport csv\nimport dcase_util\n\n\n
[docs]def load_event_list(filename, **kwargs):\n    """Load event list from csv formatted text-file\n\n    Supported formats (see more `dcase_util.containers.MetaDataContainer.load()` method):\n\n    - [event onset (float >= 0)][delimiter][event offset (float >= 0)]\n    - [event onset (float >= 0)][delimiter][event offset (float >= 0)][delimiter][label]\n    - [filename][delimiter][event onset (float >= 0)][delimiter][event offset (float >= 0)][delimiter][event label]\n    - [filename][delimiter][scene_label][delimiter][event onset (float >= 0)][delimiter][event offset (float >= 0)][delimiter][event label]\n    - [filename]\n\n    Supported delimiters: ``,``, ``;``, ``tab``\n\n    Example of event list file::\n\n        21.64715\t23.00552\talert\n        36.91184\t38.27021\talert\n        69.72575\t71.09029\talert\n        63.53990\t64.89827\talert\n        84.25553\t84.83920\talert\n        20.92974\t21.82661\tclearthroat\n        28.39992\t29.29679\tclearthroat\n        80.47837\t81.95937\tclearthroat\n        44.48363\t45.96463\tclearthroat\n        78.13073\t79.05953\tclearthroat\n        15.17031\t16.27235\tcough\n        20.54931\t21.65135\tcough\n        27.79964\t28.90168\tcough\n        75.45959\t76.32490\tcough\n        70.81708\t71.91912\tcough\n        21.23203\t22.55902\tdoorslam\n        7.546220\t9.014880\tdoorslam\n        34.11303\t35.04183\tdoorslam\n        45.86001\t47.32867\tdoorslam\n\n\n    Parameters\n    ----------\n    filename : str\n        Path to the csv-file\n\n    Returns\n    -------\n    list of dict\n        Event list\n\n    """\n\n    return dcase_util.containers.MetaDataContainer().load(filename=filename, **kwargs)
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[docs]def load_scene_list(filename, **kwargs):\n    """Load scene list from csv formatted text-file\n\n    Supported formats (see more `dcase_util.containers.MetaDataContainer.load()` method):\n\n    - [filename][delimiter][scene label]\n    - [filename][delimiter][segment start (float >= 0)][delimiter][segment stop (float >= 0)][delimiter][scene label]\n\n    Supported delimiters: ``,``, ``;``, ``tab``\n\n    Example of scene list file::\n\n        scenes_stereo/supermarket09.wav\tsupermarket\n        scenes_stereo/tubestation10.wav\ttubestation\n        scenes_stereo/quietstreet08.wav\tquietstreet\n        scenes_stereo/restaurant05.wav\trestaurant\n        scenes_stereo/busystreet05.wav\tbusystreet\n        scenes_stereo/openairmarket04.wav\topenairmarket\n        scenes_stereo/quietstreet01.wav\tquietstreet\n        scenes_stereo/supermarket05.wav\tsupermarket\n        scenes_stereo/openairmarket01.wav\topenairmarket\n\n    Parameters\n    ----------\n    filename : str\n        Path to the csv-file\n\n    Returns\n    -------\n    list of dict\n        Scene list\n\n    """\n\n    return dcase_util.containers.MetaDataContainer().load(filename=filename, **kwargs)
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[docs]def load_file_pair_list(filename):\n    """Load file pair list csv formatted text-file\n\n    Format is [reference_file][delimiter][estimated_file]\n\n    Supported delimiters: ``,``, ``;``, ``tab``\n\n    Example of file-list::\n\n        office_snr0_high_v2.txt\toffice_snr0_high_v2_detected.txt\n        office_snr0_med_v2.txt\toffice_snr0_med_v2_detected.txt\n\n    Parameters\n    ----------\n    filename : str\n        Path to the csv-file\n\n    Returns\n    -------\n    file_list: list\n        File pair dicts in a list\n\n    """\n\n    data = []\n\n    input_file = open(filename, 'rt')\n\n    try:\n        dialect = csv.Sniffer().sniff(input_file.readline(), [',', ';', '\\t'])\n\n    except csv.Error:\n        raise ValueError('Unknown delimiter in file [{file}].'.format(file=filename))\n\n    input_file.seek(0)  \n\n    for row in csv.reader(input_file, dialect):\n        if len(row) == 2:\n            data.append(\n                {\n                    'reference_file': row[0],\n                    'estimated_file': row[1]\n                }\n            )\n\n        else:\n            raise ValueError('Unknown file pair list format in file [{file}].'.format(file=filename))\n\n    input_file.close()\n\n    return data
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Source code for sed_eval.metric

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\n\n.. autosummary::\n    :toctree: generated/\n\n    f_measure\n    precision\n    recall\n    \n    accuracy\n    accuracy2\n    balanced_accuracy\n    sensitivity\n    specificity\n\n    error_rate\n    substitution_rate\n    deletion_rate\n    insertion_rate\n\n    equal_error_rate\n\n"""\n\nimport numpy\n\n\n# -- F-measure -- #\n
[docs]def precision(Ntp, Nsys, eps=numpy.spacing(1)):\n    """Precision.\n\n    Wikipedia entry https://en.wikipedia.org/wiki/Precision_and_recall\n\n    Parameters\n    ----------\n    Ntp : int >=0\n        Number of true positives.\n\n    Nsys : int >=0\n        Amount of system output.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    precision: float\n        Precision\n\n    """\n\n    if Nsys == 0:\n        return numpy.nan\n    else:\n        return float(Ntp / float(Nsys))
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[docs]def recall(Ntp, Nref, eps=numpy.spacing(1)):\n    """Recall.\n\n    Wikipedia entry https://en.wikipedia.org/wiki/Precision_and_recall\n\n    Parameters\n    ----------\n    Ntp : int >=0\n        Number of true positives.\n\n    Nref : int >=0\n        Amount of reference.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    recall: float\n        Recall\n\n    """\n\n    if Nref == 0:\n        return numpy.nan\n    else:\n        return float(Ntp / float(Nref))
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[docs]def f_measure(precision, recall, beta=1.0):\n    """F-measure from precision and recall scores.\n\n    Wikipedia entry https://en.wikipedia.org/wiki/F1_score\n\n    Parameters\n    ----------\n    precision : float in (0, 1]\n        Precision.\n\n    recall : float in (0, 1]\n        Recall.\n\n    beta : float > 0\n        Weighting factor for f-measure.\n        Default value 1.0\n\n    Returns\n    -------\n    f_measure: float\n        The weighted f-measure\n\n    """\n\n    if precision == 0 and recall == 0:\n        return 0.0\n\n    return float((1 + beta**2)*precision*recall/((beta**2)*precision + recall))
\n\n\n# -- Accuracy -- #\n
[docs]def sensitivity(Ntp, Nfn, eps=numpy.spacing(1)):\n    """Sensitivity\n\n    Wikipedia entry https://en.wikipedia.org/wiki/Sensitivity_and_specificity\n\n    Parameters\n    ----------\n    Ntp : int >=0\n        Number of true positives.\n\n    Nfn : int >=0\n        Number of false negatives.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    sensitivity: float\n        Sensitivity\n\n    """\n\n    return float(Ntp / (Ntp + Nfn + eps))
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[docs]def specificity(Ntn, Nfp, eps=numpy.spacing(1)):\n    """Specificity\n\n    Wikipedia entry https://en.wikipedia.org/wiki/Sensitivity_and_specificity\n\n    Parameters\n    ----------\n    Ntn : int >= 0\n        Number of true negatives.\n\n    Nfp : int >= 0\n        Number of false positives.\n    \n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    specificity: float\n        Specificity\n\n    """\n\n    return float(Ntn / (Ntn + Nfp + eps))
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[docs]def balanced_accuracy(sensitivity, specificity, factor=0.5):\n    """Balanced accuracy\n\n    Wikipedia entry https://en.wikipedia.org/wiki/Accuracy_and_precision\n\n    Parameters\n    ----------\n    sensitivity : float in [0, 1]\n        sensitivity.\n\n    specificity : float in [0, 1]\n        specificity.\n\n    factor : float in [0, 1]\n        Balancing factor multiplying true positive rate (sensitivity).\n        Default value 0.5\n\n    Returns\n    -------\n    bacc: float\n        Balanced accuracy\n\n    """\n\n    return float(((1-factor) * sensitivity) + (factor * specificity))
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[docs]def accuracy(Ntp, Ntn, Nfp, Nfn, eps=numpy.spacing(1)):\n    """Accuracy\n\n    Parameters\n    ----------\n    Ntp : int >= 0\n        Number of true positives.\n\n    Ntn : int >= 0\n        Number of true negatives.\n\n    Nfp : int >= 0\n        Number of false positives.\n\n    Nfn : int >= 0\n        Number of false negatives.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    acc: float\n        Accuracy\n\n    """\n\n    return float((Ntp + Ntn) / (Ntp + Ntn + Nfn + Nfp + eps))
\n\n\ndef accuracy_corr(Ncorr, N, eps=numpy.spacing(1)):\n    """Accuracy\n\n    Parameters\n    ----------\n    Ncorr : int >= 0\n        Number of correct items.\n\n    N : int >= 0\n        Number of all items.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    acc: float\n        Accuracy.\n\n    """\n\n    return float((Ncorr) / (N + eps))\n\n\n
[docs]def accuracy2(Ntp, Nfp, Nfn, eps=numpy.spacing(1)):\n    """Accuracy\n\n    Implements metric described by [1]_.\n\n    .. [1] Dixon, S.\n           "On the computer recognition of solo piano music".\n           Proceedings of Australasian Computer Music Conference, July 2000, Brisbane, Australia, pp. 31–37.\n           http://www.eecs.qmul.ac.uk/~simond/pub/2000/acmc.pdf\n\n    Parameters\n    ----------\n    Ntp : int >= 0\n        Number of true positives.\n\n    Nfp : int >= 0\n        Number of false positives.\n\n    Nfn : int >= 0\n        Number of false negatives.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    acc: float\n        Accuracy\n\n    """\n\n    return float((Ntp) / (Ntp + Nfn + Nfp + eps))
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[docs]def substitution_rate(Nref, Nsubstitutions, eps=numpy.spacing(1)):\n    """Substitution rate\n\n    Parameters\n    ----------\n    Nref : int >=0\n        Number of entries in the reference.\n\n    Nsubstitutions : int >=0\n        Number of substitutions.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    substitution_rate: float\n        Substitution rate\n\n    """\n\n    return float(Nsubstitutions / (Nref + eps))
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[docs]def deletion_rate(Nref, Ndeletions, eps=numpy.spacing(1)):\n    """Deletion rate\n\n    Parameters\n    ----------\n    Nref : int >=0\n        Number of entries in the reference.\n\n    Ndeletions : int >=0\n        Number of deletions.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    deletion_rate: float\n        Deletion rate\n        \n    """\n\n    return float(Ndeletions / (Nref + eps))
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[docs]def insertion_rate(Nref, Ninsertions, eps=numpy.spacing(1)):\n    """Insertion rate\n\n    Parameters\n    ----------\n    Nref : int >=0\n        Number of entries in the reference.\n\n    Ninsertions : int >=0\n        Number of insertions.\n\n    eps : float\n        eps.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    insertion_rate: float\n        Insertion rate\n\n    """\n\n    return float(Ninsertions / (Nref + eps))
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[docs]def error_rate(substitution_rate_value=0.0, deletion_rate_value=0.0, insertion_rate_value=0.0):\n    """Error rate\n\n    Parameters\n    ----------\n    substitution_rate_value : float >=0\n        Substitution rate.\n        Default value 0\n\n    deletion_rate_value : float >=0\n        Deletion rate.\n        Default value 0\n\n    insertion_rate_value : float >=0\n        Insertion rate.\n        Default value 0\n\n    Returns\n    -------\n    error_rate: float\n        Error rate\n\n    """\n\n    return float(substitution_rate_value + deletion_rate_value + insertion_rate_value)
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[docs]def equal_error_rate(y_true, y_score, eps=numpy.spacing(1)):\n    """Equal error rate (EER)\n\n    EER is calculated from the curve of the false negative rate versus the false positive rate.\n    Implementation is based on https://github.com/pafoster/dcase2016_task4/blob/master/evaluation_scripts/eer.py\n\n    Parameters\n    ----------\n    y_true : numpy.array or list\n        True binary labels in range {0, 1} or {-1, 1}.\n\n    y_score : numpy.array or list\n        Target scores, can either be probability estimates of the positive\n        class or confidence values.\n\n    eps : float\n        Minimum difference considered equal.\n        Default value numpy.spacing(1)\n\n    Returns\n    -------\n    float\n\n    """\n\n    from sklearn import metrics\n\n    if numpy.any(y_true):\n        false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n            y_true=y_true,\n            y_score=y_score,\n            drop_intermediate=True\n        )\n\n        points = [(0, 0)] + list(zip(false_positive_rate, true_positive_rate))\n        for i, point in enumerate(points):\n            if point[0] + eps >= 1 - point[1]:\n                break\n\n        point1 = points[i - 1]\n        point2 = points[i]\n\n        # Interpolate between point1 and point2\n        if abs(point2[0] - point1[0]) < eps:\n            eer = point1[0]\n\n        else:\n            m = (point2[1] - point1[1]) / (point2[0] - point1[0])\n            o = point1[1] - m * point1[0]\n            eer = (1 - o) / (1 + m)\n\n    else:\n        eer = numpy.nan\n\n    return eer
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Source code for sed_eval.scene

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nMetrics\n-------\n\nMain functions:\n\n* :func:`sed_eval.scene.SceneClassificationMetrics.evaluate`: Calculate intermediate values for evaluation and accumulate them.\n* :func:`sed_eval.scene.SceneClassificationMetrics.results`: Calculate and return all metrics.\n* :func:`sed_eval.scene.SceneClassificationMetrics.results_overall_metrics`: Calculate and return overall metrics (micro-averaged).\n* :func:`sed_eval.scene.SceneClassificationMetrics.results_class_wise_metrics`: Calculate and return class-wise metrics.\n* :func:`sed_eval.scene.SceneClassificationMetrics.results_class_wise_average_metrics`: Calculate and return class-wise average metrics (macro-averaged).\n\nFunction :func:`sed_eval.scene.SceneClassificationMetrics.evaluate` takes as a parameter scene lists,\nuse :func:`sed_eval.io.load_scene_list` to read them from a file.\n\nUsage example to evaluate files:\n\n.. code-block:: python\n    :linenos:\n    import sed_eval\n    import dcase_util\n\n    file_list = [\n        {'reference_file': 'fold1_reference.txt', 'estimated_file': 'fold1_estimated.txt'}\n    ]\n\n    data = []\n\n    # Get used scene labels and load data in\n    all_data = []\n    for file_pair in file_list:\n        reference_scene_list = sed_eval.io.load_scene_list(\n            filename=file_pair['reference_file'],\n            csv_header=False,\n            file_format=dcase_util.utils.FileFormat.CSV,\n            fields=['filename', 'scene_label']\n        )\n        estimated_scene_list = sed_eval.io.load_scene_list(\n            filename=file_pair['estimated_file'],\n            csv_header=False,\n            file_format=dcase_util.utils.FileFormat.CSV,\n            fields=['filename', 'onset', 'offset', 'scene_label']\n        )\n\n        data.append(\n            {\n                'reference_scene_list': reference_scene_list,\n                'estimated_scene_list': estimated_scene_list\n            }\n        )\n\n        all_data += reference_scene_list\n\n    scene_labels = sed_eval.sound_event.util.unique_scene_labels(all_data)\n\n    # Create metrics class\n    scene_metrics = sed_eval.scene.SceneClassificationMetrics(\n        scene_labels=scene_labels\n    )\n    for file_pair in data:\n        scene_metrics.evaluate(\n            reference_scene_list=file_pair['reference_scene_list'],\n            estimated_scene_list=file_pair['estimated_scene_list']\n        )\n\n    # Get only certain metrics\n    overall_metrics_results = scene_metrics.results_overall_metrics()\n    print("Accuracy:", overall_metrics_results['accuracy'])\n\n    # Or print all metrics as reports\n    print(scene_metrics)\n\nUsage example to evaluate results stored in variables:\n\n.. code-block:: python\n    :linenos:\n\n    import sed_eval\n    import dcase_util\n\n    reference = dcase_util.containers.MetaDataContainer([\n        {\n            'scene_label': 'supermarket',\n            'file': 'supermarket09.wav'\n        },\n        {\n            'scene_label': 'tubestation',\n            'file': 'tubestation10.wav'\n        },\n        {\n            'scene_label': 'quietstreet',\n            'file': 'quietstreet08.wav'\n        },\n        {\n            'scene_label': 'office',\n            'file': 'office10.wav'\n        },\n        {\n            'scene_label': 'bus',\n            'file': 'bus01.wav'\n        },\n    ])\n\n    estimated = dcase_util.containers.MetaDataContainer([\n        {\n            'scene_label': 'supermarket',\n            'file': 'supermarket09.wav'\n        },\n        {\n            'scene_label': 'bus',\n            'file': 'tubestation10.wav'\n        },\n        {\n            'scene_label': 'quietstreet',\n            'file': 'quietstreet08.wav'\n        },\n        {\n            'scene_label': 'park',\n            'file': 'office10.wav'\n        },\n        {\n            'scene_label': 'car',\n            'file': 'bus01.wav'\n        },\n    ])\n\n    scene_labels = sed_eval.sound_event.util.unique_scene_labels(reference)\n\n    scene_metrics = sed_eval.scene.SceneClassificationMetrics(scene_labels)\n    scene_metrics.evaluate(\n        reference_scene_list=reference,\n        estimated_scene_list=estimated\n    )\n\n    print(scene_metrics)\n\n.. autosummary::\n    :toctree: generated/\n\n    SceneClassificationMetrics\n    SceneClassificationMetrics.evaluate\n    SceneClassificationMetrics.results\n    SceneClassificationMetrics.results_overall_metrics\n    SceneClassificationMetrics.results_class_wise_metrics\n    SceneClassificationMetrics.results_class_wise_average_metrics\n    SceneClassificationMetrics.result_report_parameters\n    SceneClassificationMetrics.result_report_class_wise\n    SceneClassificationMetrics.result_report_class_wise_average\n    SceneClassificationMetrics.reset\n\n"""\n\nfrom __future__ import absolute_import\nimport numpy\nimport dcase_util\nfrom . import metric\n\n\n
[docs]class SceneClassificationMetrics:\n
[docs]    def __init__(self, scene_labels=None):\n        self.accuracies_per_class = None\n        self.scene_label_list = scene_labels\n\n        self.overall = {\n            'Ncorr': 0.0,\n            'Nref': 0.0,\n            'Nsys': 0.0\n        }\n\n        self.scene_wise = {}\n        for label in self.scene_label_list:\n            self.scene_wise[label] = {\n                'Ncorr': 0.0,\n                'Nref': 0.0,\n                'Nsys': 0.0\n            }\n\n        self.ui = dcase_util.ui.FancyStringifier()
\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type, value, traceback):\n        return self.results()\n\n    def __str__(self):\n        """Print result reports"""\n\n        output = self.ui.section_header('Scene classification metrics') + '\\n'\n        output += self.result_report_parameters() + '\\n'\n        output += self.result_report_class_wise_average() + '\\n'\n        output += self.result_report_class_wise() + '\\n'\n\n        return output\n\n
[docs]    def evaluate(self, reference_scene_list, estimated_scene_list=None, estimated_scene_probabilities=None):\n        """Evaluate file pair (reference and estimated)\n\n        Parameters\n        ----------\n\n        reference_scene_list : list of dict or dcase_util.containers.MetaDataContainer\n            Reference scene list.\n            Default value None\n\n        estimated_scene_list : list of dict or dcase_util.containers.MetaDataContainer\n            Estimated scene list.\n            Default value None\n\n        estimated_scene_probabilities : dcase_util.containers.ProbabilityContainer\n            Estimated scene probabilities. Currently not used.\n            Default value None\n\n        Returns\n        -------\n        self\n\n        """\n\n        if estimated_scene_list is None and estimated_scene_probabilities is None:\n            raise ValueError("Nothing to evaluate, give at least estimated_scene_list or estimated_scene_probabilities")\n\n        # Make sure reference_scene_list is dcase_util.containers.MetaDataContainer\n        if not isinstance(estimated_scene_list, dcase_util.containers.MetaDataContainer):\n            reference_scene_list = dcase_util.containers.MetaDataContainer(reference_scene_list)\n\n        # Make sure estimated_scene_list is dcase_util.containers.MetaDataContainer\n        if not isinstance(estimated_scene_list, dcase_util.containers.MetaDataContainer):\n            estimated_scene_list = dcase_util.containers.MetaDataContainer(estimated_scene_list)\n\n        # Make sure estimated_tag_probabilities is dcase_util.containers.ProbabilityContainer\n        if estimated_scene_probabilities is not None:\n            if not isinstance(estimated_scene_probabilities, dcase_util.containers.ProbabilityContainer):\n                estimated_scene_probabilities = dcase_util.containers.ProbabilityContainer(estimated_scene_probabilities)\n\n        # Translate "file" field to "filename"\n        for item in reference_scene_list:\n            if 'filename' not in item and 'file' in item:\n                item['filename'] = item['file']\n\n        for item in estimated_scene_list:\n            if 'filename' not in item and 'file' in item:\n                item['filename'] = item['file']\n\n        y_true = []\n        y_pred = []\n\n        for estimated_item in estimated_scene_list:\n            reference_item_matched = {}\n            for reference_item in reference_scene_list:\n                if estimated_item['filename'] == reference_item['filename']:\n                    reference_item_matched = reference_item\n                    break\n\n            if not reference_item_matched:\n                raise ValueError(\n                    "Cannot find reference_item for estimated item [{item}]".format(item=estimated_item['file'])\n                )\n\n            y_true.append(reference_item_matched['scene_label'])\n            y_pred.append(estimated_item['scene_label'])\n\n        y_true = numpy.array(y_true)\n        y_pred = numpy.array(y_pred)\n\n        Ncorr_overall = 0\n        for scene_id, scene_label in enumerate(self.scene_label_list):\n            true_id = numpy.where(y_true == scene_label)[0]\n            pred_id = numpy.where(y_pred == scene_label)[0]\n\n            Ncorr = 0\n            for id in true_id:\n                if id in pred_id:\n                    Ncorr += 1\n\n            Ncorr_overall += Ncorr\n            self.scene_wise[scene_label]['Ncorr'] += Ncorr\n            self.scene_wise[scene_label]['Nref'] += true_id.shape[0]\n            self.scene_wise[scene_label]['Nsys'] += pred_id.shape[0]\n\n        self.overall['Ncorr'] += Ncorr_overall\n        self.overall['Nref'] += y_true.shape[0]\n        self.overall['Nsys'] += y_pred.shape[0]\n\n        return self
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[docs]    def reset(self):\n        """Reset internal state\n        """\n        self.overall = {\n            'Ncorr': 0.0,\n            'Nref': 0.0,\n            'Nsys': 0.0\n        }\n\n        self.scene_wise = {}\n        for label in self.scene_label_list:\n            self.scene_wise[label] = {\n                'Ncorr': 0.0,\n                'Nref': 0.0,\n                'Nsys': 0.0\n            }
\n\n    # Reports\n
[docs]    def result_report_parameters(self):\n        """Report metric parameters\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        output = self.ui.data(field='Scene labels', value=len(self.scene_label_list)) + '\\n'\n        output += self.ui.data(field='Evaluated units', value=int(self.overall['Nref'])) + '\\n'\n\n        return output
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[docs]    def result_report_class_wise(self):\n        """Report class-wise results\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_metrics()\n\n        output = self.ui.section_header('Class-wise metrics', indent=2) + '\\n'\n\n        output += self.ui.row(\n            'Scene label', 'Ncorr', 'Nref', 'Accuracy',\n            widths=[20, 12, 12, 12],\n            separators=[True, False, True, False],\n            indent=4\n        ) + '\\n'\n\n        output += self.ui.row('-', '-', '-', '-') + '\\n'\n\n        for scene_label in self.scene_label_list:\n            output += self.ui.row(\n                scene_label,\n                results[scene_label]['count']['Ncorr'],\n                results[scene_label]['count']['Nref'],\n                results[scene_label]['accuracy']['accuracy'] * 100,\n                types=['str', 'int', 'int', 'float1_percentage']\n            ) + '\\n'\n\n        return output
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[docs]    def result_report_class_wise_average(self):\n        """Report class-wise averages\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_average_metrics()\n\n        output = self.ui.section_header('Class-wise average metrics (macro-average)', indent=2) + '\\n'\n\n        output += self.ui.line('Accuracy', indent=2) + '\\n'\n        output += self.ui.data(field='Accuracy',\n                               value=float(results['accuracy']['accuracy']) * 100, unit='%', indent=4) + '\\n'\n\n        return output
\n\n    # Results\n
[docs]    def results(self):\n        """All metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        results = {\n            'overall': self.results_overall_metrics(),\n            'class_wise': self.results_class_wise_metrics(),\n            'class_wise_average': self.results_class_wise_average_metrics()\n        }\n\n        return results
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[docs]    def results_overall_metrics(self):\n        """Overall metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        if self.overall['Nsys'] != 0:\n            accuracy = self.overall['Ncorr'] / float(self.overall['Nsys'])\n        else:\n            accuracy = None\n\n        return {\n            'count': self.overall,\n            'accuracy': accuracy\n        }
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[docs]    def results_class_wise_metrics(self):\n        """Class-wise metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        results = {}\n        for scene_id, scene_label in enumerate(self.scene_label_list):\n            if scene_label not in results:\n                results[scene_label] = {}\n\n            results[scene_label]['count'] = {}\n            results[scene_label]['count']['Ncorr'] = self.scene_wise[scene_label]['Ncorr']\n            results[scene_label]['count']['Nref'] = self.scene_wise[scene_label]['Nref']\n            results[scene_label]['count']['Nsys'] = self.scene_wise[scene_label]['Nsys']\n            results[scene_label]['accuracy'] = {\n                'accuracy': metric.accuracy_corr(\n                    Ncorr=self.scene_wise[scene_label]['Ncorr'],\n                    N=self.scene_wise[scene_label]['Nref']\n                )\n            }\n\n        return results
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[docs]    def results_class_wise_average_metrics(self):\n        """Class-wise averaged metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        scene_wise_results = self.results_class_wise_metrics()\n\n        scene_wise_accuracy = []\n        for scene_label in scene_wise_results:\n            scene_wise_accuracy.append(scene_wise_results[scene_label]['accuracy']['accuracy'])\n\n        return {\n            'accuracy': {\n                'accuracy': float(numpy.mean(scene_wise_accuracy))\n            }\n        }
\n\n    # Metrics\n    def class_wise_accuracy(self, scene_label):\n        """Class-wise accuracy\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        if len(self.accuracies_per_class.shape) == 2:\n            return {\n                'accuracy': float(numpy.mean(self.accuracies_per_class[:, self.scene_label_list.index(scene_label)]))\n            }\n\n        else:\n            return {\n                'accuracy': float(numpy.mean(self.accuracies_per_class[self.scene_label_list.index(scene_label)]))\n            }
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Source code for sed_eval.sound_event

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nSegment-based metrics, main functions:\n\n* :func:`sed_eval.sound_event.SegmentBasedMetrics.evaluate`: Calculate intermediate values for evaluation and accumulate them.\n* :func:`sed_eval.sound_event.SegmentBasedMetrics.results`: Calculate and return all metrics.\n* :func:`sed_eval.sound_event.SegmentBasedMetrics.results_overall_metrics`: Calculate and return overall metrics (micro-averaged).\n* :func:`sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_metrics`: Calculate and return class-wise metrics.\n* :func:`sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_average_metrics`: Calculate and return class-wise average metrics (macro-averaged).\n\nEvent-based metrics, main functions:\n\n* :func:`sed_eval.sound_event.EventBasedMetrics.evaluate`: Calculate intermediate values for evaluation and accumulate them.\n* :func:`sed_eval.sound_event.EventBasedMetrics.results`: Calculate and return all metrics.\n* :func:`sed_eval.sound_event.EventBasedMetrics.results_overall_metrics`: Calculate and return overall metrics (micro-averaged).\n* :func:`sed_eval.sound_event.EventBasedMetrics.results_class_wise_metrics`: Calculate and return class-wise metrics.\n* :func:`sed_eval.sound_event.EventBasedMetrics.results_class_wise_average_metrics`: Calculate and return class-wise average metrics (macro-averaged).\n\nFunctions :func:`sed_eval.sound_event.SegmentBasedMetrics.evaluate` and :func:`sed_eval.sound_event.EventBasedMetrics.evaluate`\ntake as a parameter event lists, use :func:`sed_eval.io.load_event_list` to read them from a file.\n\n\nUsage example when reading event lists from disk (you can run example in path ``tests/data/sound_event``):\n\n.. code-block:: python\n    :linenos:\n\n    import sed_eval\n    import dcase_util\n\n    file_list = [\n        {\n         'reference_file': 'office_snr0_high_v2.txt',\n         'estimated_file': 'office_snr0_high_v2_detected.txt'\n        },\n        {\n         'reference_file': 'office_snr0_med_v2.txt',\n         'estimated_file': 'office_snr0_med_v2_detected.txt'\n        }\n    ]\n\n    data = []\n\n    # Get used event labels\n    all_data = dcase_util.containers.MetaDataContainer()\n    for file_pair in file_list:\n        reference_event_list = sed_eval.io.load_event_list(\n            filename=file_pair['reference_file']\n        )\n        estimated_event_list = sed_eval.io.load_event_list(\n            filename=file_pair['estimated_file']\n        )\n\n        data.append({'reference_event_list': reference_event_list,\n                     'estimated_event_list': estimated_event_list})\n\n        all_data += reference_event_list\n\n    event_labels = all_data.unique_event_labels\n\n    # Start evaluating\n\n    # Create metrics classes, define parameters\n    segment_based_metrics = sed_eval.sound_event.SegmentBasedMetrics(\n        event_label_list=event_labels,\n        time_resolution=1.0\n    )\n\n    event_based_metrics = sed_eval.sound_event.EventBasedMetrics(\n        event_label_list=event_labels,\n        t_collar=0.250\n    )\n\n    # Go through files\n    for file_pair in data:\n        segment_based_metrics.evaluate(\n            reference_event_list=file_pair['reference_event_list'],\n            estimated_event_list=file_pair['estimated_event_list']\n        )\n\n        event_based_metrics.evaluate(\n            reference_event_list=file_pair['reference_event_list'],\n            estimated_event_list=file_pair['estimated_event_list']\n        )\n\n    # Get only certain metrics\n    overall_segment_based_metrics = segment_based_metrics.results_overall_metrics()\n    print("Accuracy:", overall_segment_based_metrics['accuracy']['accuracy'])\n\n    # Or print all metrics as reports\n    print(segment_based_metrics)\n    print(event_based_metrics)\n\nUsage example to evaluate results stored in variables:\n\n.. code-block:: python\n    :linenos:\n\n    import sed_eval\n    import dcase_util\n\n    reference_event_list = dcase_util.containers.MetaDataContainer(\n        [\n            {\n                'event_label': 'car',\n                'event_onset': 0.0,\n                'event_offset': 2.5,\n                'file': 'audio/street/b099.wav',\n                'scene_label': 'street'\n            },\n            {\n                'event_label': 'car',\n                'event_onset': 2.8,\n                'event_offset': 4.5,\n                'file': 'audio/street/b099.wav',\n                'scene_label': 'street'\n            },\n            {\n                'event_label': 'car',\n                'event_onset': 6.0,\n                'event_offset': 10.0,\n                'file': 'audio/street/b099.wav',\n                'scene_label': 'street'\n            }\n        ]\n    )\n\n    estimated_event_list = dcase_util.containers.MetaDataContainer(\n        [\n            {\n                'event_label': 'car',\n                'event_onset': 1.0,\n                'event_offset': 3.5,\n                'file': 'audio/street/b099.wav',\n                'scene_label': 'street'\n            },\n            {\n                'event_label': 'car',\n                'event_onset': 7.0,\n                'event_offset': 8.0,\n                'file': 'audio/street/b099.wav',\n                'scene_label': 'street'\n            }\n        ]\n    )\n\n    segment_based_metrics = sed_eval.sound_event.SegmentBasedMetrics(\n        event_label_list=reference_event_list.unique_event_labels,\n        time_resolution=1.0\n    )\n    event_based_metrics = sed_eval.sound_event.EventBasedMetrics(\n        event_label_list=reference_event_list.unique_event_labels,\n        t_collar=0.250\n    )\n\n    for filename in reference_event_list.unique_files:\n        reference_event_list_for_current_file = reference_event_list.filter(\n            filename=filename\n        )\n\n        estimated_event_list_for_current_file = estimated_event_list.filter(\n            filename=filename\n        )\n\n        segment_based_metrics.evaluate(\n            reference_event_list=reference_event_list_for_current_file,\n            estimated_event_list=estimated_event_list_for_current_file\n        )\n\n        event_based_metrics.evaluate(\n            reference_event_list=reference_event_list_for_current_file,\n            estimated_event_list=estimated_event_list_for_current_file\n        )\n\n    # Get only certain metrics\n    overall_segment_based_metrics = segment_based_metrics.results_overall_metrics()\n    print("Accuracy:", overall_segment_based_metrics['accuracy']['accuracy'])\n\n    # Or print all metrics as reports\n    print(segment_based_metrics)\n    print(event_based_metrics)\n\nSegment based metrics\n^^^^^^^^^^^^^^^^^^^^^\n\n.. autosummary::\n    :toctree: generated/\n\n    SegmentBasedMetrics\n    SegmentBasedMetrics.evaluate\n    SegmentBasedMetrics.results\n    SegmentBasedMetrics.results_overall_metrics\n    SegmentBasedMetrics.results_class_wise_metrics\n    SegmentBasedMetrics.results_class_wise_average_metrics\n    SegmentBasedMetrics.result_report_parameters\n    SegmentBasedMetrics.result_report_overall\n    SegmentBasedMetrics.result_report_class_wise\n    SegmentBasedMetrics.result_report_class_wise_average\n    SegmentBasedMetrics.reset\n\n.. autoclass:: SegmentBasedMetrics\n   :members:\n\nEvent based metrics\n^^^^^^^^^^^^^^^^^^^\n\n.. autosummary::\n    :toctree: generated/\n\n    EventBasedMetrics\n    EventBasedMetrics.evaluate\n    EventBasedMetrics.results\n    EventBasedMetrics.results_overall_metrics\n    EventBasedMetrics.results_class_wise_metrics\n    EventBasedMetrics.results_class_wise_average_metrics\n    EventBasedMetrics.result_report_parameters\n    EventBasedMetrics.result_report_overall\n    EventBasedMetrics.result_report_class_wise\n    EventBasedMetrics.result_report_class_wise_average\n    EventBasedMetrics.reset\n\n.. autoclass:: EventBasedMetrics\n   :members:\n   :undoc-members:\n   :inherited-members:\n\n"""\n\nfrom __future__ import absolute_import\nimport numpy\nimport math\nimport dcase_util\nfrom . import metric\nfrom . import util\n\n\nclass SoundEventMetrics(object):\n    """Base class for sound event detection metrics.\n\n    """\n    def __init__(self,\n                 empty_system_output_handling=None):\n        """Constructor\n\n        Parameters\n        ----------\n\n        empty_system_output_handling : str\n            Controls how empty system output is handled, i.e. when Nsys = 0. Default behaviour is to show NaN when e.g.\n            computing precision (Ntp / Nsys).\n            Use 'zero_score' to force these score to zero.\n            Default value None\n\n        """\n\n        self.event_label_list = []\n        self.ui = dcase_util.ui.FancyStringifier()\n        self.empty_system_output_handling = empty_system_output_handling\n\n    # Reports\n    def result_report_overall(self):\n        """Report overall results\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_overall_metrics()\n\n        output = self.ui.section_header('Overall metrics (micro-average)', indent=2) + '\\n'\n\n        if results['f_measure']:\n            output += self.ui.line('F-measure', indent=2) + '\\n'\n            output += self.ui.data(field='F-measure (F1)', value=float(results['f_measure']['f_measure']) * 100,\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Precision', value=float(results['f_measure']['precision']) * 100,\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Recall', value=float(results['f_measure']['recall']) * 100,\n                                   unit='%', indent=4) + '\\n'\n\n        if results['error_rate']:\n            output += self.ui.line('Error rate', indent=2) + '\\n'\n            output += self.ui.data(field='Error rate (ER)', value=float(results['error_rate']['error_rate']),\n                                   indent=4) + '\\n'\n            output += self.ui.data(field='Substitution rate', value=float(results['error_rate']['substitution_rate']),\n                                   indent=4) + '\\n'\n            output += self.ui.data(field='Deletion rate', value=float(results['error_rate']['deletion_rate']),\n                                   indent=4) + '\\n'\n            output += self.ui.data(field='Insertion rate', value=float(results['error_rate']['insertion_rate']),\n                                   indent=4) + '\\n'\n\n        if results['accuracy']:\n            output += self.ui.line('Accuracy', indent=2) + '\\n'\n            output += self.ui.data(field='Sensitivity', value=float(results['accuracy']['sensitivity']*100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Specificity', value=float(results['accuracy']['specificity'] * 100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Balanced accuracy', value=float(results['accuracy']['balanced_accuracy'] * 100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Accuracy', value=float(results['accuracy']['accuracy'] * 100),\n                                   unit='%', indent=4) + '\\n'\n\n        return output\n\n    def result_report_class_wise_average(self):\n        """Report class-wise averages\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_average_metrics()\n\n        output = self.ui.section_header('Class-wise average metrics (macro-average)', indent=2) + '\\n'\n\n        if results['f_measure']:\n            output += self.ui.line('F-measure', indent=2) + '\\n'\n            output += self.ui.data(field='F-measure (F1)', value=float(results['f_measure']['f_measure']) * 100,\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Precision', value=float(results['f_measure']['precision']) * 100,\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Recall', value=float(results['f_measure']['recall']) * 100,\n                                   unit='%', indent=4) + '\\n'\n\n        if results['error_rate']:\n            output += self.ui.line('Error rate', indent=2) + '\\n'\n            output += self.ui.data(field='Error rate (ER)', value=float(results['error_rate']['error_rate']),\n                                   indent=4) + '\\n'\n            output += self.ui.data(field='Deletion rate', value=float(results['error_rate']['deletion_rate']),\n                                   indent=4) + '\\n'\n            output += self.ui.data(field='Insertion rate', value=float(results['error_rate']['insertion_rate']),\n                                   indent=4) + '\\n'\n\n        if results['accuracy']:\n            output += self.ui.line('Accuracy', indent=2) + '\\n'\n            output += self.ui.data(field='Sensitivity', value=float(results['accuracy']['sensitivity']*100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Specificity', value=float(results['accuracy']['specificity'] * 100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Balanced accuracy', value=float(results['accuracy']['balanced_accuracy'] * 100),\n                                   unit='%', indent=4) + '\\n'\n            output += self.ui.data(field='Accuracy', value=float(results['accuracy']['accuracy'] * 100),\n                                   unit='%', indent=4) + '\\n'\n\n        output += "  \\n"\n\n        return output\n\n    def result_report_class_wise(self):\n        """Report class-wise results\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        results = self.results_class_wise_metrics()\n\n        accuracy_present = True\n        for event_label in self.event_label_list:\n            if 'accuracy' not in results[event_label]['accuracy']:\n                accuracy_present = False\n\n        output = self.ui.section_header('Class-wise metrics', indent=2) + '\\n'\n\n        headers = ['Event label', 'Nref', 'Nsys', 'F', 'Pre', 'Rec', 'ER', 'Del', 'Ins']\n        sep = ['-', '-', '-', '-', '-', '-', '-', '-', '-']\n        widths = [15, 8, 8, 9, 9, 9, 9, 9, 9]\n        separators = [True, False, True, False, False, True, False, False, True]\n        if accuracy_present:\n            headers += ['Sens', 'Spec', 'Bacc', 'Acc']\n            sep += ['-', '-', '-', '-']\n            widths += [9, 9, 9, 9]\n            separators += [False, False, False, False]\n\n        output += self.ui.row(*headers, widths=widths, indent=4, separators=separators) + '\\n'\n        output += self.ui.row(*sep) + '\\n'\n        for event_label in self.event_label_list:\n            data = [\n                event_label,\n                results[event_label]['count']['Nref'],\n                results[event_label]['count']['Nsys'],\n                results[event_label]['f_measure']['f_measure'] * 100,\n                results[event_label]['f_measure']['precision'] * 100,\n                results[event_label]['f_measure']['recall'] * 100,\n                results[event_label]['error_rate']['error_rate'],\n                results[event_label]['error_rate']['deletion_rate'],\n                results[event_label]['error_rate']['insertion_rate']\n            ]\n\n            types = [\n                'str15',\n                'int',\n                'int',\n                'float1_percentage',\n                'float1_percentage',\n                'float1_percentage',\n                'float2',\n                'float2',\n                'float2',\n            ]\n\n            if accuracy_present:\n                data += [\n                    results[event_label]['accuracy']['sensitivity'] * 100,\n                    results[event_label]['accuracy']['specificity'] * 100,\n                    results[event_label]['accuracy']['balanced_accuracy'] * 100,\n                    results[event_label]['accuracy']['accuracy'] * 100\n                ]\n\n                types += [\n                    'float1_percentage',\n                    'float1_percentage',\n                    'float1_percentage',\n                    'float1_percentage',\n                ]\n\n            output += self.ui.row(*data, types=types) + '\\n'\n\n        return output\n\n    # Metrics / overall\n    def overall_f_measure(self):\n        return {}\n\n    def overall_error_rate(self):\n        return {}\n\n    def overall_accuracy(self, factor=0.5):\n        return {}\n    \n    # Metrics / class-wise\n    def class_wise_count(self, event_label):\n        return {}\n    \n    def class_wise_f_measure(self, event_label):\n        return {}\n\n    def class_wise_error_rate(self, event_label):\n        return {}\n\n    def class_wise_accuracy(self, event_label):\n        return {}\n\n    # Results\n    def results_overall_metrics(self):\n        """Overall metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        return {\n            'f_measure': self.overall_f_measure(),\n            'error_rate': self.overall_error_rate(),\n            'accuracy': self.overall_accuracy()\n        }\n\n    def results_class_wise_metrics(self):\n        """Class-wise metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        results = {}\n        for event_label in self.event_label_list:\n            if event_label not in results:\n                results[event_label] = {}\n\n            results[event_label]['f_measure'] = self.class_wise_f_measure(event_label)\n            results[event_label]['accuracy'] = self.class_wise_accuracy(event_label)\n            results[event_label]['error_rate'] = self.class_wise_error_rate(event_label)\n            results[event_label]['count'] = self.class_wise_count(event_label)\n\n        return results\n\n    def results_class_wise_average_metrics(self):\n        """Class-wise averaged metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        event_wise_results = self.results_class_wise_metrics()\n\n        event_wise_f_measure = []\n        event_wise_precision = []\n        event_wise_recall = []\n        \n        event_wise_error_rate = []\n        event_wise_deletion_rate = []\n        event_wise_insertion_rate = []\n        \n        event_wise_sensitivity = []\n        event_wise_specificity = []\n        event_wise_balanced_accuracy = []\n        event_wise_accuracy = []\n\n        for event_label in event_wise_results:\n            # F-measure\n            event_wise_f_measure.append(event_wise_results[event_label]['f_measure']['f_measure'])\n            event_wise_precision.append(event_wise_results[event_label]['f_measure']['precision'])\n            event_wise_recall.append(event_wise_results[event_label]['f_measure']['recall'])\n            \n            # Error rate\n            event_wise_error_rate.append(event_wise_results[event_label]['error_rate']['error_rate'])\n            event_wise_deletion_rate.append(event_wise_results[event_label]['error_rate']['deletion_rate'])\n            event_wise_insertion_rate.append(event_wise_results[event_label]['error_rate']['insertion_rate'])\n\n            # Accuracy\n            if 'sensitivity' in event_wise_results[event_label]['accuracy']:\n                event_wise_sensitivity.append(event_wise_results[event_label]['accuracy']['sensitivity'])\n\n            if 'specificity' in event_wise_results[event_label]['accuracy']:\n                event_wise_specificity.append(event_wise_results[event_label]['accuracy']['specificity'])\n\n            if 'balanced_accuracy' in event_wise_results[event_label]['accuracy']:\n                event_wise_balanced_accuracy.append(event_wise_results[event_label]['accuracy']['balanced_accuracy'])\n\n            if 'accuracy' in event_wise_results[event_label]['accuracy']:\n                event_wise_accuracy.append(event_wise_results[event_label]['accuracy']['accuracy'])\n\n        if event_wise_f_measure:\n            event_wise_f_measure_dict = {\n                'f_measure': float(numpy.nanmean(event_wise_f_measure)),\n                'precision': float(numpy.nanmean(event_wise_precision)),\n                'recall': float(numpy.nanmean(event_wise_recall))\n            }\n\n        else:\n            event_wise_f_measure_dict = {}\n\n        if event_wise_error_rate:\n            event_wise_error_rate_dict = {\n                'error_rate': float(numpy.nanmean(event_wise_error_rate)),\n                'deletion_rate': float(numpy.nanmean(event_wise_deletion_rate)),\n                'insertion_rate': float(numpy.nanmean(event_wise_insertion_rate))\n            }\n\n        else:\n            event_wise_error_rate_dict = {}\n\n        if event_wise_accuracy:\n            event_wise_accuracy_dict = {\n                'sensitivity': float(numpy.nanmean(event_wise_sensitivity)),\n                'specificity': float(numpy.nanmean(event_wise_specificity)),\n                'balanced_accuracy': float(numpy.nanmean(event_wise_balanced_accuracy)),\n                'accuracy': float(numpy.nanmean(event_wise_accuracy))\n            }\n\n        else:\n            event_wise_accuracy_dict = {}\n\n        return {\n            'f_measure': event_wise_f_measure_dict,\n            'error_rate': event_wise_error_rate_dict,\n            'accuracy': event_wise_accuracy_dict\n        }\n\n    def results(self):\n        """All metrics\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        return {\n            'overall': self.results_overall_metrics(),\n            'class_wise': self.results_class_wise_metrics(),\n            'class_wise_average': self.results_class_wise_average_metrics()\n        }\n\n\n
[docs]class SegmentBasedMetrics(SoundEventMetrics):\n
[docs]    def __init__(self,\n                 event_label_list,\n                 time_resolution=1.0):\n        """Constructor\n\n        Parameters\n        ----------\n        event_label_list : list, numpy.array\n            List of unique event labels\n\n        time_resolution : float (0,]\n            Segment size used in the evaluation, in seconds.\n            Default value 1.0\n\n        """\n\n        SoundEventMetrics.__init__(self)\n\n        if isinstance(event_label_list, numpy.ndarray) and len(event_label_list.shape) == 1:\n            # We have numpy array, convert it to list\n            event_label_list = event_label_list.tolist()\n\n        if not isinstance(event_label_list, list):\n            raise ValueError(\n                "event_label_list needs to be list or numpy.array"\n            )\n\n        if not isinstance(time_resolution, float) or time_resolution <= 0.0:\n            raise ValueError(\n                "time_resolution needs to be float > 0"\n            )\n\n        self.event_label_list = event_label_list\n        self.evaluated_length_seconds = 0.0\n        self.evaluated_files = 0\n\n        self.time_resolution = time_resolution\n\n        self.overall = {\n            'Ntp': 0.0,\n            'Ntn': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'ER': 0.0,\n            'S': 0.0,\n            'D': 0.0,\n            'I': 0.0,\n        }\n\n        self.class_wise = {}\n        for class_label in self.event_label_list:\n            self.class_wise[class_label] = {\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n                'Nref': 0.0,\n                'Nsys': 0.0,\n            }
\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type, value, traceback):\n        return self.results()\n\n    def __str__(self):\n        """Print result reports"""\n\n        output = self.ui.section_header('Segment based metrics') + '\\n'\n        output += self.result_report_parameters() + '\\n'\n        output += self.result_report_overall() + '\\n'\n        output += self.result_report_class_wise_average() + '\\n'\n        output += self.result_report_class_wise() + '\\n'\n\n        return output\n\n
[docs]    def evaluate(self, reference_event_list, estimated_event_list, evaluated_length_seconds=None):\n        """Evaluate file pair (reference and estimated)\n\n        Parameters\n        ----------\n\n        reference_event_list : list of dict or dcase_util.containers.MetaDataContainer\n            Reference event list.\n\n        estimated_event_list : list of dict or dcase_util.containers.MetaDataContainer\n            Estimated event list.\n\n        evaluated_length_seconds : float, optional\n            Evaluated length. If none given, maximum offset is used.\n            Default value None\n\n        Returns\n        -------\n        self\n\n        """\n\n        # Make sure input is dcase_util.containers.MetaDataContainer\n        if not isinstance(reference_event_list, dcase_util.containers.MetaDataContainer):\n            reference_event_list = dcase_util.containers.MetaDataContainer(reference_event_list)\n\n        if not isinstance(estimated_event_list, dcase_util.containers.MetaDataContainer):\n            estimated_event_list = dcase_util.containers.MetaDataContainer(estimated_event_list)\n\n        # Check that input event list have event only from one file\n        reference_files = reference_event_list.unique_files\n        if len(reference_files) > 1:\n            raise ValueError(\n                "reference_event_list contains events from multiple files. Evaluate only file by file."\n            )\n\n        estimated_files = estimated_event_list.unique_files\n        if len(estimated_files) > 1:\n            raise ValueError(\n                "estimated_event_list contains events from multiple files. Evaluate only file by file."\n            )\n\n        # Evaluate only valid events\n        valid_reference_event_list = dcase_util.containers.MetaDataContainer()\n        for item in reference_event_list:\n            if 'event_onset' in item and 'event_offset' in item and 'event_label' in item:\n                valid_reference_event_list.append(item)\n\n            elif 'onset' in item and 'offset' in item and 'event_label' in item:\n                valid_reference_event_list.append(item)\n\n        reference_event_list = valid_reference_event_list\n\n        valid_estimated_event_list = dcase_util.containers.MetaDataContainer()\n        for item in estimated_event_list:\n            if 'event_onset' in item and 'event_offset' in item and 'event_label' in item:\n                valid_estimated_event_list.append(item)\n\n            elif 'onset' in item and 'offset' in item and 'event_label' in item:\n                valid_estimated_event_list.append(item)\n\n        estimated_event_list = valid_estimated_event_list\n\n        # Convert event list into frame-based representation\n        reference_event_roll = util.event_list_to_event_roll(\n            source_event_list=reference_event_list,\n            event_label_list=self.event_label_list,\n            time_resolution=self.time_resolution\n        )\n\n        estimated_event_roll = util.event_list_to_event_roll(\n            source_event_list=estimated_event_list,\n            event_label_list=self.event_label_list,\n            time_resolution=self.time_resolution\n        )\n\n        if evaluated_length_seconds is None:\n            evaluated_length_seconds = max(reference_event_list.max_offset, estimated_event_list.max_offset)\n            evaluated_length_segments = int(math.ceil(evaluated_length_seconds * 1 / float(self.time_resolution)))\n\n        else:\n            evaluated_length_segments = int(math.ceil(evaluated_length_seconds * 1 / float(self.time_resolution)))\n\n        self.evaluated_length_seconds += evaluated_length_seconds\n        self.evaluated_files += 1\n\n        reference_event_roll, estimated_event_roll = util.match_event_roll_lengths(\n            reference_event_roll,\n            estimated_event_roll,\n            evaluated_length_segments\n        )\n\n        # Compute segment-based overall metrics\n        for segment_id in range(0, reference_event_roll.shape[0]):\n            annotated_segment = reference_event_roll[segment_id, :]\n            system_segment = estimated_event_roll[segment_id, :]\n\n            Ntp = sum(system_segment + annotated_segment > 1)\n            Ntn = sum(system_segment + annotated_segment == 0)\n            Nfp = sum(system_segment - annotated_segment > 0)\n            Nfn = sum(annotated_segment - system_segment > 0)\n\n            Nref = sum(annotated_segment)\n            Nsys = sum(system_segment)\n\n            S = min(Nref, Nsys) - Ntp\n            D = max(0, Nref - Nsys)\n            I = max(0, Nsys - Nref)\n\n            self.overall['Ntp'] += Ntp\n            self.overall['Ntn'] += Ntn\n            self.overall['Nfp'] += Nfp\n            self.overall['Nfn'] += Nfn\n            self.overall['Nref'] += Nref\n            self.overall['Nsys'] += Nsys\n            self.overall['S'] += S\n            self.overall['D'] += D\n            self.overall['I'] += I\n\n        # Compute segment-based class-wise metrics\n        for class_id, class_label in enumerate(self.event_label_list):\n            annotated_segment = reference_event_roll[:, class_id]\n            system_segment = estimated_event_roll[:, class_id]\n\n            Ntp = sum(system_segment + annotated_segment > 1)\n            Ntn = sum(system_segment + annotated_segment == 0)\n            Nfp = sum(system_segment - annotated_segment > 0)\n            Nfn = sum(annotated_segment - system_segment > 0)\n\n            Nref = sum(annotated_segment)\n            Nsys = sum(system_segment)\n\n            self.class_wise[class_label]['Ntp'] += Ntp\n            self.class_wise[class_label]['Ntn'] += Ntn\n            self.class_wise[class_label]['Nfp'] += Nfp\n            self.class_wise[class_label]['Nfn'] += Nfn\n            self.class_wise[class_label]['Nref'] += Nref\n            self.class_wise[class_label]['Nsys'] += Nsys\n\n        return self
\n\n
[docs]    def reset(self):\n        """Reset internal state"""\n\n        self.overall = {\n            'Ntp': 0.0,\n            'Ntn': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'ER': 0.0,\n            'S': 0.0,\n            'D': 0.0,\n            'I': 0.0,\n        }\n\n        self.class_wise = {}\n        for class_label in self.event_label_list:\n            self.class_wise[class_label] = {\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n                'Nref': 0.0,\n                'Nsys': 0.0,\n            }\n\n        return self
\n\n    # Metrics\n
[docs]    def overall_f_measure(self):\n        """Overall f-measure metrics (f_measure, precision, and recall)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n        if self.overall['Nsys'] == 0 and self.empty_system_output_handling == 'zero_score':\n            precision = 0\n\n        else:\n            precision = metric.precision(\n                Ntp=self.overall['Ntp'],\n                Nsys=self.overall['Nsys']\n            )\n\n        recall = metric.recall(\n            Ntp=self.overall['Ntp'],\n            Nref=self.overall['Nref']\n        )\n\n        f_measure = metric.f_measure(\n            precision=precision,\n            recall=recall\n        )\n\n        return {\n            'f_measure': f_measure,\n            'precision': precision,\n            'recall': recall\n        }
\n\n
[docs]    def overall_error_rate(self):\n        """Overall error rate metrics (error_rate, substitution_rate, deletion_rate, and insertion_rate)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        substitution_rate = metric.substitution_rate(\n            Nref=self.overall['Nref'],\n            Nsubstitutions=self.overall['S']\n        )\n\n        deletion_rate = metric.deletion_rate(\n            Nref=self.overall['Nref'],\n            Ndeletions=self.overall['D']\n        )\n\n        insertion_rate = metric.insertion_rate(\n            Nref=self.overall['Nref'],\n            Ninsertions=self.overall['I']\n        )\n\n        error_rate = metric.error_rate(\n            substitution_rate_value=substitution_rate,\n            deletion_rate_value=deletion_rate,\n            insertion_rate_value=insertion_rate\n        )\n\n        return {\n            'error_rate': error_rate,\n            'substitution_rate': substitution_rate,\n            'deletion_rate': deletion_rate,\n            'insertion_rate': insertion_rate\n        }
\n\n
[docs]    def overall_accuracy(self, factor=0.5):\n        """Overall accuracy metrics (sensitivity, specificity, accuracy, and balanced_accuracy)\n\n        Parameters\n        ----------\n        factor : float [0-1]\n            Balance factor.\n            Default value 0.5\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        sensitivity = metric.sensitivity(\n            Ntp=self.overall['Ntp'],\n            Nfn=self.overall['Nfn']\n        )\n\n        specificity = metric.specificity(\n            Ntn=self.overall['Ntn'],\n            Nfp=self.overall['Nfp']\n        )\n\n        balanced_accuracy = metric.balanced_accuracy(\n            sensitivity=sensitivity,\n            specificity=specificity,\n            factor=factor\n        )\n\n        accuracy = metric.accuracy(\n            Ntp=self.overall['Ntp'],\n            Ntn=self.overall['Ntn'],\n            Nfp=self.overall['Nfp'],\n            Nfn=self.overall['Nfn']\n        )\n\n        return {\n            'accuracy': accuracy,\n            'balanced_accuracy': balanced_accuracy,\n            'sensitivity': sensitivity,\n            'specificity': specificity\n        }
\n\n
[docs]    def class_wise_count(self, event_label):\n        """Class-wise counts (Nref and Nsys)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        return {\n            'Nref': float(self.class_wise[event_label]['Nref']),\n            'Nsys': float(self.class_wise[event_label]['Nsys'])\n        }
\n\n
[docs]    def class_wise_f_measure(self, event_label):\n        """Class-wise f-measure metrics (f_measure, precision, and recall)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n        if self.class_wise[event_label]['Nsys'] == 0 and self.empty_system_output_handling == 'zero_score':\n            precision = 0\n\n        else:\n            precision = metric.precision(\n                Ntp=self.class_wise[event_label]['Ntp'],\n                Nsys=self.class_wise[event_label]['Nsys']\n            )\n\n        recall = metric.recall(\n            Ntp=self.class_wise[event_label]['Ntp'],\n            Nref=self.class_wise[event_label]['Nref']\n        )\n\n        f_measure = metric.f_measure(\n            precision=precision,\n            recall=recall\n        )\n\n        return {\n            'f_measure': f_measure,\n            'precision': precision,\n            'recall': recall\n        }
\n\n
[docs]    def class_wise_error_rate(self, event_label):\n        """Class-wise error rate metrics (error_rate, deletion_rate, and insertion_rate)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        deletion_rate = metric.deletion_rate(\n            Nref=self.class_wise[event_label]['Nref'],\n            Ndeletions=self.class_wise[event_label]['Nfn']\n        )\n\n        insertion_rate = metric.insertion_rate(\n            Nref=self.class_wise[event_label]['Nref'],\n            Ninsertions=self.class_wise[event_label]['Nfp']\n        )\n\n        error_rate = metric.error_rate(\n            deletion_rate_value=deletion_rate,\n            insertion_rate_value=insertion_rate\n        )\n\n        return {\n            'error_rate': error_rate,\n            'deletion_rate': deletion_rate,\n            'insertion_rate': insertion_rate\n        }
\n\n
[docs]    def class_wise_accuracy(self, event_label, factor=0.5):\n        """Class-wise accuracy metrics (sensitivity, specificity, accuracy, and balanced_accuracy)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        sensitivity = metric.sensitivity(\n            Ntp=self.class_wise[event_label]['Ntp'],\n            Nfn=self.class_wise[event_label]['Nfn']\n        )\n\n        specificity = metric.specificity(\n            Ntn=self.class_wise[event_label]['Ntn'],\n            Nfp=self.class_wise[event_label]['Nfp']\n        )\n\n        balanced_accuracy = metric.balanced_accuracy(\n            sensitivity=sensitivity,\n            specificity=specificity,\n            factor=factor\n        )\n\n        accuracy = metric.accuracy(\n            Ntp=self.class_wise[event_label]['Ntp'],\n            Ntn=self.class_wise[event_label]['Ntn'],\n            Nfp=self.class_wise[event_label]['Nfp'],\n            Nfn=self.class_wise[event_label]['Nfn']\n        )\n\n        return {\n            'accuracy': accuracy,\n            'balanced_accuracy': balanced_accuracy,\n            'sensitivity': sensitivity,\n            'specificity': specificity\n        }
\n\n    # Reports\n
[docs]    def result_report_parameters(self):\n        """Report metric parameters\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        output = self.ui.data(field='Evaluated length', value=self.evaluated_length_seconds, unit='sec') + '\\n'\n        output += self.ui.data(field='Evaluated files', value=self.evaluated_files) + '\\n'\n\n        if self.time_resolution < 1:\n            output += self.ui.data(field='Segment length', value=self.time_resolution * 1000, unit='ms') + '\\n'\n\n        else:\n            output += self.ui.data(field='Segment length', value=self.time_resolution, unit='sec') + '\\n'\n\n        return output
\n\n\n
[docs]class EventBasedMetrics(SoundEventMetrics):\n
[docs]    def __init__(self,\n                 event_label_list,\n                 evaluate_onset=True,\n                 evaluate_offset=True,\n                 t_collar=0.200,\n                 percentage_of_length=0.5,\n                 event_matching_type='optimal',\n                 **kwargs):\n        """Constructor\n\n        Parameters\n        ----------\n        event_label_list : list\n            List of unique event labels\n\n        evaluate_onset : bool\n            Evaluate onset.\n            Default value True\n\n        evaluate_offset : bool\n            Evaluate offset.\n            Default value True\n\n        t_collar : float (0,]\n            Time collar used when evaluating validity of the onset and offset, in seconds.\n            Default value 0.2\n\n        percentage_of_length : float in [0, 1]\n            Second condition, percentage of the length within which the estimated offset has to be in order to be\n            consider valid estimation.\n            Default value 0.5\n\n        event_matching_type : str\n            Event matching type. Set 'optimal' for graph-based matching, or 'greedy' for always select first found match.\n            Greedy type of event matching is kept for backward compatibility. Both event matching types produce\n            very similar results, however, greedy matching can be sensitive to the order of reference events.\n            Use default 'optimal' event matching, if you do not intend to compare your results to old results.\n            Default value 'optimal'\n\n        """\n\n        SoundEventMetrics.__init__(self, **kwargs)\n\n        if isinstance(event_label_list, numpy.ndarray) and len(event_label_list.shape) == 1:\n            # We have numpy array, convert it to list\n            event_label_list = event_label_list.tolist()\n\n        if not isinstance(event_label_list, list):\n            raise ValueError(\n                "event_label_list needs to be list or numpy.array"\n            )\n\n        if not isinstance(t_collar, float) or t_collar <= 0.0:\n            raise ValueError(\n                "t_collar needs to be float > 0"\n            )\n\n        if not isinstance(percentage_of_length, float) or percentage_of_length < 0.0 or percentage_of_length > 1.0:\n            raise ValueError(\n                "t_collar percentage_of_length to be float in [0, 1]"\n            )\n\n        self.event_label_list = event_label_list\n        self.evaluated_length = 0.0\n        self.evaluated_files = 0\n        if not evaluate_onset and not evaluate_offset:\n            raise ValueError("Both evaluate_onset and evaluate_offset cannot be set to False")\n\n        self.evaluate_onset = evaluate_onset\n        self.evaluate_offset = evaluate_offset\n\n        self.t_collar = t_collar\n        self.percentage_of_length = percentage_of_length\n        self.event_matching_type = event_matching_type\n        self.overall = {\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'Nsubs': 0.0,\n            'Ntp': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n        }\n        self.class_wise = {}\n\n        for class_label in self.event_label_list:\n            self.class_wise[class_label] = {\n                'Nref': 0.0,\n                'Nsys': 0.0,\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n            }
\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type, value, traceback):\n        return self.results()\n\n    def __str__(self):\n        """Print result reports"""\n\n        if self.evaluate_onset and self.evaluate_offset:\n            title = "Event based metrics (onset-offset)"\n\n        elif self.evaluate_onset and not self.evaluate_offset:\n            title = "Event based metrics (onset only)"\n\n        elif not self.evaluate_onset and self.evaluate_offset:\n            title = "Event based metrics (offset only)"\n\n        else:\n            title = "Event based metrics"\n\n        output = self.ui.section_header(title) + '\\n'\n\n        output += self.result_report_parameters() + '\\n'\n        output += self.result_report_overall() + '\\n'\n        output += self.result_report_class_wise_average() + '\\n'\n        output += self.result_report_class_wise() + '\\n'\n\n        return output\n\n
[docs]    def evaluate(self, reference_event_list, estimated_event_list):\n        """Evaluate file pair (reference and estimated)\n\n        Parameters\n        ----------\n\n        reference_event_list : event list\n            Reference event list\n\n        estimated_event_list : event list\n            Estimated event list\n\n        Returns\n        -------\n        self\n\n        """\n\n        # Make sure input is dcase_util.containers.MetaDataContainer\n        if not isinstance(reference_event_list, dcase_util.containers.MetaDataContainer):\n            reference_event_list = dcase_util.containers.MetaDataContainer(reference_event_list)\n\n        if not isinstance(estimated_event_list, dcase_util.containers.MetaDataContainer):\n            estimated_event_list = dcase_util.containers.MetaDataContainer(estimated_event_list)\n\n        # Check that input event list have event only from one file\n        reference_files = reference_event_list.unique_files\n        if len(reference_files) > 1:\n            raise ValueError(\n                "reference_event_list contains events from multiple files. Evaluate only file by file."\n            )\n\n        estimated_files = estimated_event_list.unique_files\n        if len(estimated_files) > 1:\n            raise ValueError(\n                "estimated_event_list contains events from multiple files. Evaluate only file by file."\n            )\n\n        # Evaluate only valid events\n        valid_reference_event_list = dcase_util.containers.MetaDataContainer()\n        for item in reference_event_list:\n            if 'event_onset' in item and 'event_offset' in item and 'event_label' in item:\n                valid_reference_event_list.append(item)\n\n            elif 'onset' in item and 'offset' in item and 'event_label' in item:\n                valid_reference_event_list.append(item)\n\n        reference_event_list = valid_reference_event_list\n\n        valid_estimated_event_list = dcase_util.containers.MetaDataContainer()\n        for item in estimated_event_list:\n            if 'event_onset' in item and 'event_offset' in item and 'event_label' in item:\n                valid_estimated_event_list.append(item)\n\n            elif 'onset' in item and 'offset' in item and 'event_label' in item:\n                valid_estimated_event_list.append(item)\n\n        estimated_event_list = valid_estimated_event_list\n\n        self.evaluated_length += reference_event_list.max_offset\n        self.evaluated_files += 1\n\n        # Overall metrics\n\n        # Total number of detected and reference events\n        Nsys = len(estimated_event_list)\n        Nref = len(reference_event_list)\n\n        if self.event_matching_type == 'optimal':\n            label_hit_matrix = numpy.zeros((len(reference_event_list), len(estimated_event_list)), dtype=bool)\n            for j in range(0, len(reference_event_list)):\n                for i in range(0, len(estimated_event_list)):\n                    label_hit_matrix[j, i] = reference_event_list[j]['event_label'] == estimated_event_list[i]['event_label']\n\n            hit_matrix = label_hit_matrix\n            if self.evaluate_onset:\n                onset_hit_matrix = numpy.zeros((len(reference_event_list), len(estimated_event_list)), dtype=bool)\n                for j in range(0, len(reference_event_list)):\n                    for i in range(0, len(estimated_event_list)):\n                        onset_hit_matrix[j, i] = self.validate_onset(\n                            reference_event=reference_event_list[j],\n                            estimated_event=estimated_event_list[i],\n                            t_collar=self.t_collar\n                        )\n\n                hit_matrix *= onset_hit_matrix\n\n            if self.evaluate_offset:\n                offset_hit_matrix = numpy.zeros((len(reference_event_list), len(estimated_event_list)), dtype=bool)\n                for j in range(0, len(reference_event_list)):\n                    for i in range(0, len(estimated_event_list)):\n                        offset_hit_matrix[j, i] = self.validate_offset(\n                            reference_event=reference_event_list[j],\n                            estimated_event=estimated_event_list[i],\n                            t_collar=self.t_collar,\n                            percentage_of_length=self.percentage_of_length\n                        )\n\n                hit_matrix *= offset_hit_matrix\n\n            hits = numpy.where(hit_matrix)\n            G = {}\n            for ref_i, est_i in zip(*hits):\n                if est_i not in G:\n                    G[est_i] = []\n\n                G[est_i].append(ref_i)\n\n            matching = sorted(util.bipartite_match(G).items())\n\n            ref_correct = numpy.zeros(Nref, dtype=bool)\n            sys_correct = numpy.zeros(Nsys, dtype=bool)\n            for item in matching:\n                ref_correct[item[0]] = True\n                sys_correct[item[1]] = True\n\n            Ntp = len(matching)\n            # Substitutions\n            Nsubs = 0\n\n            ref_leftover = numpy.nonzero(numpy.logical_not(ref_correct))[0]\n            sys_leftover = numpy.nonzero(numpy.logical_not(sys_correct))[0]\n            sys_counted = numpy.zeros(Nsys, dtype=bool)\n            for j in ref_leftover:\n                for i in sys_leftover:\n                    if not sys_counted[i]:\n                        if self.evaluate_onset:\n                            onset_condition = self.validate_onset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar\n                            )\n\n                        else:\n                            onset_condition = True\n\n                        if self.evaluate_offset:\n                            offset_condition = self.validate_offset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar,\n                                percentage_of_length=self.percentage_of_length\n                            )\n\n                        else:\n                            offset_condition = True\n\n                        if onset_condition and offset_condition:\n                            sys_counted[i] = True\n                            Nsubs += 1\n                            break\n\n        elif self.event_matching_type == 'greedy':\n            sys_correct = numpy.zeros(Nsys, dtype=bool)\n            ref_correct = numpy.zeros(Nref, dtype=bool)\n\n            # Number of correctly detected events\n            for j in range(0, len(reference_event_list)):\n                for i in range(0, len(estimated_event_list)):\n                    if not sys_correct[i]:  # skip already matched events\n                        label_condition = reference_event_list[j]['event_label'] == estimated_event_list[i]['event_label']\n\n                        if self.evaluate_onset:\n                            onset_condition = self.validate_onset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar\n                            )\n\n                        else:\n                            onset_condition = True\n\n                        if self.evaluate_offset:\n                            offset_condition = self.validate_offset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar,\n                                percentage_of_length=self.percentage_of_length\n                            )\n\n                        else:\n                            offset_condition = True\n\n                        if label_condition and onset_condition and offset_condition:\n                            ref_correct[j] = True\n                            sys_correct[i] = True\n                            break\n\n            Ntp = numpy.sum(sys_correct)\n\n            ref_leftover = numpy.nonzero(numpy.logical_not(ref_correct))[0]\n            sys_leftover = numpy.nonzero(numpy.logical_not(sys_correct))[0]\n\n            # Substitutions\n            Nsubs = 0\n            sys_counted = numpy.zeros(Nsys, dtype=bool)\n            for j in ref_leftover:\n                for i in sys_leftover:\n                    if not sys_counted[i]:\n                        if self.evaluate_onset:\n                            onset_condition = self.validate_onset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar\n                            )\n\n                        else:\n                            onset_condition = True\n\n                        if self.evaluate_offset:\n                            offset_condition = self.validate_offset(\n                                reference_event=reference_event_list[j],\n                                estimated_event=estimated_event_list[i],\n                                t_collar=self.t_collar,\n                                percentage_of_length=self.percentage_of_length\n                            )\n\n                        else:\n                            offset_condition = True\n\n                        if onset_condition and offset_condition:\n                            sys_counted[i] = True\n                            Nsubs += 1\n                            break\n\n        Nfp = Nsys - Ntp - Nsubs\n        Nfn = Nref - Ntp - Nsubs\n\n        self.overall['Nref'] += Nref\n        self.overall['Nsys'] += Nsys\n        self.overall['Ntp'] += Ntp\n        self.overall['Nsubs'] += Nsubs\n        self.overall['Nfp'] += Nfp\n        self.overall['Nfn'] += Nfn\n\n        # Class-wise metrics\n        for class_id, class_label in enumerate(self.event_label_list):\n            Nref = 0.0\n            Nsys = 0.0\n            Ntp = 0.0\n\n            # Count event frequencies in the ground truth\n            for i in range(0, len(reference_event_list)):\n                if reference_event_list[i]['event_label'] == class_label:\n                    Nref += 1\n\n            # Count event frequencies in the system output\n            for i in range(0, len(estimated_event_list)):\n                if estimated_event_list[i]['event_label'] == class_label:\n                    Nsys += 1\n\n            if self.event_matching_type == 'optimal':\n                class_reference_event_list = reference_event_list.filter(event_label=class_label)\n                class_estimated_event_list = estimated_event_list.filter(event_label=class_label)\n\n                hit_matrix = numpy.ones((len(class_reference_event_list), len(class_estimated_event_list)), dtype=bool)\n                if self.evaluate_onset:\n                    onset_hit_matrix = numpy.zeros((len(class_reference_event_list), len(class_estimated_event_list)), dtype=bool)\n                    for j in range(0, len(class_reference_event_list)):\n                        for i in range(0, len(class_estimated_event_list)):\n                            onset_hit_matrix[j, i] = self.validate_onset(\n                                reference_event=class_reference_event_list[j],\n                                estimated_event=class_estimated_event_list[i],\n                                t_collar=self.t_collar\n                            )\n\n                    hit_matrix *= onset_hit_matrix\n\n                if self.evaluate_offset:\n                    offset_hit_matrix = numpy.zeros((len(class_reference_event_list), len(class_estimated_event_list)), dtype=bool)\n                    for j in range(0, len(class_reference_event_list)):\n                        for i in range(0, len(class_estimated_event_list)):\n                            offset_hit_matrix[j, i] = self.validate_offset(\n                                reference_event=class_reference_event_list[j],\n                                estimated_event=class_estimated_event_list[i],\n                                t_collar=self.t_collar,\n                                percentage_of_length=self.percentage_of_length\n                            )\n\n                    hit_matrix *= offset_hit_matrix\n\n                hits = numpy.where(hit_matrix)\n                G = {}\n                for ref_i, est_i in zip(*hits):\n                    if est_i not in G:\n                        G[est_i] = []\n\n                    G[est_i].append(ref_i)\n\n                matching = sorted(util.bipartite_match(G).items())\n\n                ref_correct = numpy.zeros(int(Nref), dtype=bool)\n                sys_correct = numpy.zeros(int(Nsys), dtype=bool)\n\n                for item in matching:\n                    ref_correct[item[0]] = True\n                    sys_correct[item[1]] = True\n\n                Ntp = len(matching)\n\n            elif self.event_matching_type == 'greedy':\n                sys_counted = numpy.zeros(len(estimated_event_list), dtype=bool)\n                for j in range(0, len(reference_event_list)):\n                    if reference_event_list[j]['event_label'] == class_label:\n                        for i in range(0, len(estimated_event_list)):\n                            if estimated_event_list[i]['event_label'] == class_label and not sys_counted[i]:\n                                if self.evaluate_onset:\n                                    onset_condition = self.validate_onset(\n                                        reference_event=reference_event_list[j],\n                                        estimated_event=estimated_event_list[i],\n                                        t_collar=self.t_collar\n                                    )\n\n                                else:\n                                    onset_condition = True\n\n                                if self.evaluate_offset:\n                                    offset_condition = self.validate_offset(\n                                        reference_event=reference_event_list[j],\n                                        estimated_event=estimated_event_list[i],\n                                        t_collar=self.t_collar,\n                                        percentage_of_length=self.percentage_of_length\n                                    )\n\n                                else:\n                                    offset_condition = True\n\n                                if onset_condition and offset_condition:\n                                    sys_counted[i] = True\n                                    Ntp += 1\n                                    break\n\n            Nfp = Nsys - Ntp\n            Nfn = Nref - Ntp\n\n            self.class_wise[class_label]['Nref'] += Nref\n            self.class_wise[class_label]['Nsys'] += Nsys\n\n            self.class_wise[class_label]['Ntp'] += Ntp\n            self.class_wise[class_label]['Nfp'] += Nfp\n            self.class_wise[class_label]['Nfn'] += Nfn\n\n        return self
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[docs]    def reset(self):\n        """Reset internal state\n        """\n\n        self.overall = {\n            'Nref': 0.0,\n            'Nsys': 0.0,\n            'Nsubs': 0.0,\n            'Ntp': 0.0,\n            'Nfp': 0.0,\n            'Nfn': 0.0,\n        }\n        self.class_wise = {}\n\n        for class_label in self.event_label_list:\n            self.class_wise[class_label] = {\n                'Nref': 0.0,\n                'Nsys': 0.0,\n                'Ntp': 0.0,\n                'Ntn': 0.0,\n                'Nfp': 0.0,\n                'Nfn': 0.0,\n            }\n\n        return self
\n\n    @staticmethod\n
[docs]    def validate_onset(reference_event, estimated_event, t_collar=0.200):\n        """Validate estimated event based on event onset\n\n        Parameters\n        ----------\n        reference_event : dict\n            Reference event.\n\n        estimated_event: dict\n            Estimated event.\n\n        t_collar : float > 0, seconds\n            Time collar with which the estimated onset has to be in order to be consider valid estimation.\n            Default value 0.2\n\n        Returns\n        -------\n        bool\n\n        """\n\n        # Detect field naming style used and validate onset\n        if 'event_onset' in reference_event and 'event_onset' in estimated_event:\n            return math.fabs(reference_event['event_onset'] - estimated_event['event_onset']) <= t_collar\n\n        elif 'onset' in reference_event and 'onset' in estimated_event:\n            return math.fabs(reference_event['onset'] - estimated_event['onset']) <= t_collar
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[docs]    def validate_offset(reference_event, estimated_event, t_collar=0.200, percentage_of_length=0.5):\n        """Validate estimated event based on event offset\n\n        Parameters\n        ----------\n        reference_event : dict\n            Reference event.\n\n        estimated_event : dict\n            Estimated event.\n\n        t_collar : float > 0, seconds\n            First condition, Time collar with which the estimated offset has to be in order to be consider valid estimation.\n            Default value 0.2\n\n        percentage_of_length : float in [0, 1]\n            Second condition, percentage of the length within which the estimated offset has to be in order to be\n            consider valid estimation.\n            Default value 0.5\n\n        Returns\n        -------\n        bool\n\n        """\n\n        # Detect field naming style used and validate onset\n        if 'event_offset' in reference_event and 'event_offset' in estimated_event:\n            annotated_length = reference_event['event_offset'] - reference_event['event_onset']\n\n            return math.fabs(reference_event['event_offset'] - estimated_event['event_offset']) <= max(t_collar, percentage_of_length * annotated_length)\n\n        elif 'offset' in reference_event and 'offset' in estimated_event:\n            annotated_length = reference_event['offset'] - reference_event['onset']\n\n            return math.fabs(reference_event['offset'] - estimated_event['offset']) <= max(t_collar, percentage_of_length * annotated_length)
\n\n    # Metrics\n
[docs]    def overall_f_measure(self):\n        """Overall f-measure metrics (f_measure, precision, and recall)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n        """\n\n        if self.overall['Nsys'] == 0 and self.empty_system_output_handling == 'zero_score':\n            precision = 0\n\n        else:\n            precision = metric.precision(\n                Ntp=self.overall['Ntp'],\n                Nsys=self.overall['Nsys']\n            )\n\n        recall = metric.recall(\n            Ntp=self.overall['Ntp'],\n            Nref=self.overall['Nref']\n        )\n\n        f_measure = metric.f_measure(\n            precision=precision,\n            recall=recall\n        )\n\n        return {\n            'f_measure': f_measure,\n            'precision': precision,\n            'recall': recall\n        }
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[docs]    def overall_error_rate(self):\n        """Overall error rate metrics (error_rate, substitution_rate, deletion_rate, and insertion_rate)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        substitution_rate = metric.substitution_rate(\n            Nref=self.overall['Nref'],\n            Nsubstitutions=self.overall['Nsubs']\n        )\n\n        deletion_rate = metric.deletion_rate(\n            Nref=self.overall['Nref'],\n            Ndeletions=self.overall['Nfn']\n        )\n\n        insertion_rate = metric.insertion_rate(\n            Nref=self.overall['Nref'],\n            Ninsertions=self.overall['Nfp']\n        )\n\n        error_rate = metric.error_rate(\n            substitution_rate_value=substitution_rate,\n            deletion_rate_value=deletion_rate,\n            insertion_rate_value=insertion_rate\n        )\n\n        return {\n            'error_rate': error_rate,\n            'substitution_rate': substitution_rate,\n            'deletion_rate': deletion_rate,\n            'insertion_rate': insertion_rate\n        }
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[docs]    def class_wise_count(self, event_label):\n        """Class-wise counts (Nref and Nsys)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        return {\n            'Nref': self.class_wise[event_label]['Nref'],\n            'Nsys': self.class_wise[event_label]['Nsys']\n        }
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[docs]    def class_wise_f_measure(self, event_label):\n        """Class-wise f-measure metrics (f_measure, precision, and recall)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n        if self.class_wise[event_label]['Nsys'] == 0 and self.empty_system_output_handling == 'zero_score':\n            precision = 0\n\n        else:\n            precision = metric.precision(\n                Ntp=self.class_wise[event_label]['Ntp'],\n                Nsys=self.class_wise[event_label]['Nsys']\n            )\n\n        recall = metric.recall(\n            Ntp=self.class_wise[event_label]['Ntp'],\n            Nref=self.class_wise[event_label]['Nref']\n        )\n\n        f_measure = metric.f_measure(\n            precision=precision,\n            recall=recall\n        )\n\n        return {\n            'f_measure': f_measure,\n            'precision': precision,\n            'recall': recall\n        }
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[docs]    def class_wise_error_rate(self, event_label):\n        """Class-wise error rate metrics (error_rate, deletion_rate, and insertion_rate)\n\n        Returns\n        -------\n        dict\n            results in a dictionary format\n\n        """\n\n        deletion_rate = metric.deletion_rate(\n            Nref=self.class_wise[event_label]['Nref'],\n            Ndeletions=self.class_wise[event_label]['Nfn']\n        )\n\n        insertion_rate = metric.insertion_rate(\n            Nref=self.class_wise[event_label]['Nref'],\n            Ninsertions=self.class_wise[event_label]['Nfp']\n        )\n\n        error_rate = metric.error_rate(\n            deletion_rate_value=deletion_rate,\n            insertion_rate_value=insertion_rate\n        )\n\n        return {\n            'error_rate': error_rate,\n            'deletion_rate': deletion_rate,\n            'insertion_rate': insertion_rate\n        }
\n\n    # Reports\n
[docs]    def result_report_parameters(self):\n        """Report metric parameters\n\n        Returns\n        -------\n        str\n            result report in string format\n\n        """\n\n        output = self.ui.data(field='Evaluated length', value=self.evaluated_length, unit='sec') + '\\n'\n        output += self.ui.data(field='Evaluated files', value=self.evaluated_files) + '\\n'\n\n        output += self.ui.data(field='Evaluate onset', value=self.evaluate_onset) + '\\n'\n        output += self.ui.data(field='Evaluate offset', value=self.evaluate_offset) + '\\n'\n\n        if self.t_collar < 1:\n            output += self.ui.data(field='T collar', value=self.t_collar*1000, unit='ms') + '\\n'\n\n        else:\n            output += self.ui.data(field='T collar', value=self.t_collar, unit='sec') + '\\n'\n\n        output += self.ui.data(field='Offset (length)', value=self.percentage_of_length*100, unit='%') + '\\n'\n\n        return output
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Source code for sed_eval.test

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\n\n.. autosummary::\n    :toctree: generated/\n\n    mcnemar\n\n"""\n\nimport numpy\n\n\n
[docs]def mcnemar(reference, estimated_a, estimated_b):\n    """McNemar's test\n\n    Wikipedia entry https://en.wikipedia.org/wiki/McNemar%27s_test\n\n    Parameters\n    ----------\n    reference : list\n        Reference value\n\n    estimated_a : list\n        System output A\n\n    estimated_b : list\n        System output B\n\n    Returns\n    -------\n    float\n        exact P-value\n\n    """\n\n    if len(reference) != len(estimated_a) or len(reference) != len(estimated_b):\n        raise ValueError('Input arrays needs to be same length.')\n\n    # Convert lists into numpy.array\n    reference = numpy.array(reference)\n    estimated_a = numpy.array(estimated_a)\n    estimated_b = numpy.array(estimated_b)\n\n    # Intermediate values\n    correct_a = estimated_a == reference\n    correct_b = estimated_b == reference\n\n    incorrect_a = estimated_a != reference\n    incorrect_b = estimated_b != reference\n\n    # Contingency table values\n    b = float( numpy.sum( numpy.logical_and(incorrect_a, correct_b) ) )\n    c = float( numpy.sum( numpy.logical_and(correct_a, incorrect_b) ) )\n\n    # Continuity corrected version of the McNemar test to approximate the binomial exact-P-value\n    # Edwards, A (1948). "Note on the "correction for continuity" in testing the significance of the difference\n    # between correlated proportions". Psychometrika. 13: 185–187\n    if b + c > 0:\n        return (numpy.abs(b - c) - 1)**2 / (b + c)\n    else:\n        return 0
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Source code for sed_eval.util.event_list

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nEvent list handling\n"""\n\nimport dcase_util\n\n__all__ = ['filter_event_list',\n           'unique_files',\n           'unique_event_labels',\n           'max_event_offset']\n\n\n
[docs]def filter_event_list(event_list, scene_label=None, event_label=None, filename=None):\n    """Filter event list based on given fields\n\n    Parameters\n    ----------\n    event_list : list, shape=(n,)\n        A list containing event dicts\n\n    scene_label : str\n        Scene label\n\n    event_label : str\n        Event label\n\n    filename : str\n        Filename\n\n    Returns\n    -------\n    event_list: list, shape=(n,)\n        A list containing event dicts\n\n    """\n\n    return dcase_util.containers.MetaDataContainer(event_list).filter(\n        filename=filename,\n        scene_label=scene_label,\n        event_label=event_label\n    )
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[docs]def unique_files(event_list):\n    """Find the unique files\n\n    Parameters\n    ----------\n    event_list : list or dcase_util.containers.MetaDataContainer\n        A list containing event dicts\n\n    Returns\n    -------\n    list\n        Unique filenames in alphabetical order\n\n    """\n\n    if isinstance(event_list, dcase_util.containers.MetaDataContainer):\n        return event_list.unique_files\n\n    else:\n        files = {}\n        for event in event_list:\n            if 'file' in event:\n                files[event['file']] = event['file']\n\n            elif 'filename' in event:\n                files[event['filename']] = event['filename']\n\n        files = list(files.keys())\n        files.sort()\n        return files
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[docs]def unique_event_labels(event_list):\n    """Find the unique event labels\n\n    Parameters\n    ----------\n    event_list : list or dcase_util.containers.MetaDataContainer\n        A list containing event dicts\n\n    Returns\n    -------\n    list\n        Unique labels in alphabetical order\n\n    """\n\n    if isinstance(event_list, dcase_util.containers.MetaDataContainer):\n        return event_list.unique_event_labels\n\n    else:\n        labels = []\n        for event in event_list:\n            if 'event_label' in event and event['event_label'] not in labels:\n                labels.append(event['event_label'])\n\n        labels.sort()\n        return labels
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[docs]def max_event_offset(event_list):\n    """Find the offset (end-time) of last event\n\n    Parameters\n    ----------\n    event_list : list or dcase_util.containers.MetaDataContainer\n        A list containing event dicts\n\n    Returns\n    -------\n    float > 0\n        maximum offset\n\n    """\n\n    if isinstance(event_list, dcase_util.containers.MetaDataContainer):\n        return event_list.max_offset\n\n    else:\n        max_offset = 0\n        for event in event_list:\n            if 'event_offset' in event:\n                if event['event_offset'] > max_offset:\n                    max_offset = event['event_offset']\n\n            elif 'offset' in event:\n                if event['offset'] > max_offset:\n                    max_offset = event['offset']\n\n        return max_offset
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Source code for sed_eval.util.event_matching

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""\nEvent matching\n"""\n\n
[docs]def bipartite_match(graph):\n    """\n    Find maximum cardinality matching of a bipartite graph (U,V,E).\n    Function is borrowed from mir_eval toolbox (https://github.com/craffel/mir_eval).\n\n    The input format is a dictionary mapping members of U to a list of their neighbors in V.\n    The output is a dict M mapping members of V to their matches in U.\n\n    Parameters\n    ----------\n    graph : dictionary : left-vertex -> list of right vertices\n        The input bipartite graph.  Each edge need only be specified once.\n\n    Returns\n    -------\n    matching : dictionary : right-vertex -> left vertex\n        A maximal bipartite matching.\n\n    """\n    # Implementation is after _bipartite_match function in mir_eval toolbox:\n    # Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang,\n    # and Daniel P. W. Ellis, "mir_eval: A Transparent Implementation of Common MIR Metrics",\n    # Proceedings of the 15th International Conference on Music Information Retrieval, 2014.\n    #\n    # _bipartite_match function:\n    # https://github.com/craffel/mir_eval/blob/master/mir_eval/util.py#L547\n    #\n    # Function is originally adapted from:\n    #\n    # Hopcroft-Karp bipartite max-cardinality matching and max independent set\n    # David Eppstein, UC Irvine, 27 Apr 2002\n\n    # initialize greedy matching (redundant, but faster than full search)\n    matching = {}\n    for u in graph:\n        for v in graph[u]:\n            if v not in matching:\n                matching[v] = u\n                break\n\n    while True:\n        # structure residual graph into layers\n        # pred[u] gives the neighbor in the previous layer for u in U\n        # preds[v] gives a list of neighbors in the previous layer for v in V\n        # unmatched gives a list of unmatched vertices in final layer of V,\n        # and is also used as a flag value for pred[u] when u is in the first\n        # layer\n\n        preds = {}\n        unmatched = []\n        pred = dict([(u, unmatched) for u in graph])\n\n        for v in matching:\n            del pred[matching[v]]\n\n        layer = list(pred)\n\n        # repeatedly extend layering structure by another pair of layers\n        while layer and not unmatched:\n            new_layer = {}\n            for u in layer:\n                for v in graph[u]:\n                    if v not in preds:\n                        new_layer.setdefault(v, []).append(u)\n\n            layer = []\n            for v in new_layer:\n                preds[v] = new_layer[v]\n\n                if v in matching:\n                    layer.append(matching[v])\n                    pred[matching[v]] = v\n\n                else:\n                    unmatched.append(v)\n\n        # did we finish layering without finding any alternating paths?\n        if not unmatched:\n            unlayered = {}\n            for u in graph:\n                for v in graph[u]:\n                    if v not in preds:\n                        unlayered[v] = None\n\n            return matching\n\n        def recurse(v):\n            """Recursively search backward through layers to find alternating\n            paths.  recursion returns true if found path, false otherwise\n            """\n\n            if v in preds:\n                L = preds[v]\n                del preds[v]\n                for u in L:\n                    if u in pred:\n                        pu = pred[u]\n                        del pred[u]\n                        if pu is unmatched or recurse(pu):\n                            matching[v] = u\n                            return True\n\n            return False\n\n        for v in unmatched:\n            recurse(v)
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Source code for sed_eval.util.event_roll

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""Event roll handling\n\n"""\nfrom __future__ import absolute_import\nimport math\nimport numpy\nfrom . import event_list\nimport dcase_util\n\n\n
[docs]def event_list_to_event_roll(source_event_list, event_label_list=None, time_resolution=0.01):\n    """Convert event list into event roll, binary activity matrix\n\n    Parameters\n    ----------\n    source_event_list : list, shape=(n,)\n        A list containing event dicts\n\n    event_label_list : list, shape=(k,) or None\n        A list of containing unique labels in alphabetical order\n        (Default value = None)\n    time_resolution : float > 0\n        Time resolution in seconds of the event roll\n        (Default value = 0.01)\n\n    Returns\n    -------\n\n    event_roll: np.ndarray, shape=(m,k)\n        Event roll\n\n    """\n\n    if isinstance(source_event_list, dcase_util.containers.MetaDataContainer):\n        max_offset_value = source_event_list.max_offset\n\n        if event_label_list is None:\n            event_label_list = source_event_list.unique_event_labels\n\n    elif isinstance(source_event_list, list):\n        max_offset_value = event_list.max_event_offset(source_event_list)\n\n        if event_label_list is None:\n            event_label_list = event_list.unique_event_labels(source_event_list)\n\n    else:\n        raise ValueError('Unknown source_event_list type.')\n\n    # Initialize event roll\n    event_roll = numpy.zeros((int(math.ceil(max_offset_value * 1 / time_resolution)), len(event_label_list)))\n\n    # Fill-in event_roll\n    for event in source_event_list:\n        pos = event_label_list.index(event['event_label'])\n\n        if 'event_onset' in event and 'event_offset' in event:\n            event_onset = event['event_onset']\n            event_offset = event['event_offset']\n\n        elif 'onset' in event and 'offset' in event:\n            event_onset = event['onset']\n            event_offset = event['offset']\n\n        onset = int(math.floor(event_onset * 1 / float(time_resolution)))\n        offset = int(math.ceil(event_offset * 1 / float(time_resolution)))\n\n        event_roll[onset:offset, pos] = 1\n\n    return event_roll
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[docs]def pad_event_roll(event_roll, length):\n    """Pad event roll's length to given length\n\n    Parameters\n    ----------\n    event_roll: np.ndarray, shape=(m,k)\n        Event roll\n\n    length : int\n        Length to be padded\n\n    Returns\n    -------\n    event_roll: np.ndarray, shape=(m,k)\n        Padded event roll\n\n    """\n\n    if length > event_roll.shape[0]:\n        padding = numpy.zeros((length-event_roll.shape[0], event_roll.shape[1]))\n        event_roll = numpy.vstack((event_roll, padding))\n\n    return event_roll
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[docs]def match_event_roll_lengths(event_roll_a, event_roll_b, length=None):\n    """Fix the length of two event rolls\n\n    Parameters\n    ----------\n    event_roll_a: np.ndarray, shape=(m1,k)\n        Event roll A\n\n    event_roll_b: np.ndarray, shape=(m2,k)\n        Event roll B\n\n    length: int, optional\n        Length of the event roll, if none given, shorter event roll is padded to match longer one.\n\n    Returns\n    -------\n    event_roll_a: np.ndarray, shape=(max(m1,m2),k)\n        Padded event roll A\n\n    event_roll_b: np.ndarray, shape=(max(m1,m2),k)\n        Padded event roll B\n\n    """\n\n    # Fix durations of both event_rolls to be equal\n    if length is None:\n        length = max(event_roll_b.shape[0], event_roll_a.shape[0])\n\n    else:\n        length = int(length)\n\n    if length < event_roll_a.shape[0]:\n        event_roll_a = event_roll_a[0:length, :]\n\n    else:\n        event_roll_a = pad_event_roll(\n            event_roll=event_roll_a,\n            length=length\n        )\n\n    if length < event_roll_b.shape[0]:\n        event_roll_b = event_roll_b[0:length, :]\n\n    else:\n        event_roll_b = pad_event_roll(\n            event_roll=event_roll_b,\n            length=length\n        )\n\n    return event_roll_a, event_roll_b
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Source code for sed_eval.util.scene_list

\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n"""Scene list handling\n"""\n\nimport dcase_util\n\n__all__ = ['unique_scene_labels']\n\n\n
[docs]def unique_scene_labels(scene_list):\n    """Find the unique scene labels\n\n    Parameters\n    ----------\n    scene_list : list, shape=(n,)\n        A list containing scene dicts\n\n    Returns\n    -------\n    labels: list, shape=(n,)\n        Unique labels in alphabetical order\n\n    """\n    if isinstance(scene_list, dcase_util.containers.MetaDataContainer):\n        return scene_list.unique_scene_labels\n\n    else:\n        labels = []\n        for item in scene_list:\n            if 'scene_label' in item and item['scene_label'] not in labels:\n                labels.append(item['scene_label'])\n\n        labels.sort()\n        return labels
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\n \n\n\n \n\n \n \n \n \n \n\n \n\n \n \n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_sources/audio_tag.rst.txt", "content": ".. _audio_tag:\n\nAudio Tagging\n=============\n\nThe goal of audio tagging is to assign tags (one or many) to audio signal. Tags can be considered as sound events\nwithout timing information (onset or offset).\n\n.. automodule:: sed_eval.audio_tag" }, { "path": "docs/_sources/changelog.rst.txt", "content": "Changes\n=======\n\nv0.2.0\n------\n\n- Audio tagging evaluator class added\n- Equal error rate metric added\n- Meta data reading is using `dcase_util` library now\n\nv0.1.4\n------\n\n- Checks for sound event evaluator to prevent evaluation with event lists containing data from multiple source files\n- Update documentation\n\nv0.1.3\n------\n\n- McNemar's test added\n- Added utility methods for EventList class\n\nv0.1.2\n------\n\n- Minor fixes\n\nv0.1.1\n------\n\n- Fixing indexing error in event roll creation (event offsets affected)\n\nv0.1.0\n------\n\n- First public release." }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.evaluate.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.evaluate\n===============================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.evaluate" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.reset.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.reset\n============================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.reset" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.result_report_class_wise.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.result_report_class_wise\n===============================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.result_report_class_wise" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.result_report_class_wise_average.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.result_report_class_wise_average\n=======================================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.result_report_class_wise_average" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.result_report_parameters.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.result_report_parameters\n===============================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.result_report_parameters" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.results.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.results\n==============================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.results" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_average_metrics.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_average_metrics\n=========================================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.results_class_wise_average_metrics" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_metrics.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.results_class_wise_metrics\n=================================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.results_class_wise_metrics" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.results_overall_metrics.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics.results_overall_metrics\n==============================================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. automethod:: AudioTaggingMetrics.results_overall_metrics" }, { "path": "docs/_sources/generated/sed_eval.audio_tag.AudioTaggingMetrics.rst.txt", "content": "sed_eval.audio_tag.AudioTaggingMetrics\n======================================\n\n.. currentmodule:: sed_eval.audio_tag\n\n.. autoclass:: AudioTaggingMetrics\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~AudioTaggingMetrics.__init__\n ~AudioTaggingMetrics.evaluate\n ~AudioTaggingMetrics.reset\n ~AudioTaggingMetrics.result_report_class_wise\n ~AudioTaggingMetrics.result_report_class_wise_average\n ~AudioTaggingMetrics.result_report_overall\n ~AudioTaggingMetrics.result_report_parameters\n ~AudioTaggingMetrics.results\n ~AudioTaggingMetrics.results_class_wise_average_metrics\n ~AudioTaggingMetrics.results_class_wise_metrics\n ~AudioTaggingMetrics.results_overall_metrics\n \n \n\n \n \n " }, { "path": "docs/_sources/generated/sed_eval.io.load_event_list.rst.txt", "content": "sed_eval.io.load_event_list\n===========================\n\n.. currentmodule:: sed_eval.io\n\n.. autofunction:: load_event_list" }, { "path": "docs/_sources/generated/sed_eval.io.load_file_pair_list.rst.txt", "content": "sed_eval.io.load_file_pair_list\n===============================\n\n.. currentmodule:: sed_eval.io\n\n.. autofunction:: load_file_pair_list" }, { "path": "docs/_sources/generated/sed_eval.io.load_scene_list.rst.txt", "content": "sed_eval.io.load_scene_list\n===========================\n\n.. currentmodule:: sed_eval.io\n\n.. autofunction:: load_scene_list" }, { "path": "docs/_sources/generated/sed_eval.metric.accuracy.rst.txt", "content": "sed_eval.metric.accuracy\n========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: accuracy" }, { "path": "docs/_sources/generated/sed_eval.metric.accuracy2.rst.txt", "content": "sed_eval.metric.accuracy2\n=========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: accuracy2" }, { "path": "docs/_sources/generated/sed_eval.metric.balanced_accuracy.rst.txt", "content": "sed_eval.metric.balanced_accuracy\n=================================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: balanced_accuracy" }, { "path": "docs/_sources/generated/sed_eval.metric.deletion_rate.rst.txt", "content": "sed_eval.metric.deletion_rate\n=============================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: deletion_rate" }, { "path": "docs/_sources/generated/sed_eval.metric.equal_error_rate.rst.txt", "content": "sed_eval.metric.equal_error_rate\n================================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: equal_error_rate" }, { "path": "docs/_sources/generated/sed_eval.metric.error_rate.rst.txt", "content": "sed_eval.metric.error_rate\n==========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: error_rate" }, { "path": "docs/_sources/generated/sed_eval.metric.f_measure.rst.txt", "content": "sed_eval.metric.f_measure\n=========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: f_measure" }, { "path": "docs/_sources/generated/sed_eval.metric.insertion_rate.rst.txt", "content": "sed_eval.metric.insertion_rate\n==============================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: insertion_rate" }, { "path": "docs/_sources/generated/sed_eval.metric.precision.rst.txt", "content": "sed_eval.metric.precision\n=========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: precision" }, { "path": "docs/_sources/generated/sed_eval.metric.recall.rst.txt", "content": "sed_eval.metric.recall\n======================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: recall" }, { "path": "docs/_sources/generated/sed_eval.metric.sensitivity.rst.txt", "content": "sed_eval.metric.sensitivity\n===========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: sensitivity" }, { "path": "docs/_sources/generated/sed_eval.metric.specificity.rst.txt", "content": "sed_eval.metric.specificity\n===========================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: specificity" }, { "path": "docs/_sources/generated/sed_eval.metric.substitution_rate.rst.txt", "content": "sed_eval.metric.substitution_rate\n=================================\n\n.. currentmodule:: sed_eval.metric\n\n.. autofunction:: substitution_rate" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.evaluate.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.evaluate\n==================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.evaluate" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.reset.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.reset\n===============================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.reset" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.result_report_class_wise.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.result_report_class_wise\n==================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.result_report_class_wise" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.result_report_class_wise_average.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.result_report_class_wise_average\n==========================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.result_report_class_wise_average" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.result_report_parameters.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.result_report_parameters\n==================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.result_report_parameters" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.results.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.results\n=================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.results" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.results_class_wise_average_metrics.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.results_class_wise_average_metrics\n============================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.results_class_wise_average_metrics" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.results_class_wise_metrics.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.results_class_wise_metrics\n====================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.results_class_wise_metrics" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.results_overall_metrics.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics.results_overall_metrics\n=================================================================\n\n.. currentmodule:: sed_eval.scene\n\n.. automethod:: SceneClassificationMetrics.results_overall_metrics" }, { "path": "docs/_sources/generated/sed_eval.scene.SceneClassificationMetrics.rst.txt", "content": "sed_eval.scene.SceneClassificationMetrics\n=========================================\n\n.. currentmodule:: sed_eval.scene\n\n.. autoclass:: SceneClassificationMetrics\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SceneClassificationMetrics.__init__\n ~SceneClassificationMetrics.class_wise_accuracy\n ~SceneClassificationMetrics.evaluate\n ~SceneClassificationMetrics.reset\n ~SceneClassificationMetrics.result_report_class_wise\n ~SceneClassificationMetrics.result_report_class_wise_average\n ~SceneClassificationMetrics.result_report_parameters\n ~SceneClassificationMetrics.results\n ~SceneClassificationMetrics.results_class_wise_average_metrics\n ~SceneClassificationMetrics.results_class_wise_metrics\n ~SceneClassificationMetrics.results_overall_metrics\n \n \n\n \n \n " }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.evaluate.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.evaluate\n===============================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.evaluate" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.reset.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.reset\n============================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.reset" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.result_report_class_wise.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.result_report_class_wise\n===============================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.result_report_class_wise" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.result_report_class_wise_average.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.result_report_class_wise_average\n=======================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.result_report_class_wise_average" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.result_report_overall.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.result_report_overall\n============================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.result_report_overall" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.result_report_parameters.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.result_report_parameters\n===============================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.result_report_parameters" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.results.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.results\n==============================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.results" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.results_class_wise_average_metrics.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.results_class_wise_average_metrics\n=========================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.results_class_wise_average_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.results_class_wise_metrics.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.results_class_wise_metrics\n=================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.results_class_wise_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.results_overall_metrics.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics.results_overall_metrics\n==============================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: EventBasedMetrics.results_overall_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.EventBasedMetrics.rst.txt", "content": "sed_eval.sound_event.EventBasedMetrics\n======================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. autoclass:: EventBasedMetrics\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~EventBasedMetrics.__init__\n ~EventBasedMetrics.class_wise_accuracy\n ~EventBasedMetrics.class_wise_count\n ~EventBasedMetrics.class_wise_error_rate\n ~EventBasedMetrics.class_wise_f_measure\n ~EventBasedMetrics.evaluate\n ~EventBasedMetrics.overall_accuracy\n ~EventBasedMetrics.overall_error_rate\n ~EventBasedMetrics.overall_f_measure\n ~EventBasedMetrics.reset\n ~EventBasedMetrics.result_report_class_wise\n ~EventBasedMetrics.result_report_class_wise_average\n ~EventBasedMetrics.result_report_overall\n ~EventBasedMetrics.result_report_parameters\n ~EventBasedMetrics.results\n ~EventBasedMetrics.results_class_wise_average_metrics\n ~EventBasedMetrics.results_class_wise_metrics\n ~EventBasedMetrics.results_overall_metrics\n ~EventBasedMetrics.validate_offset\n ~EventBasedMetrics.validate_onset\n \n \n\n \n \n " }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.evaluate.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.evaluate\n=================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.evaluate" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.reset.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.reset\n==============================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.reset" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.result_report_class_wise.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.result_report_class_wise\n=================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.result_report_class_wise" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.result_report_class_wise_average.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.result_report_class_wise_average\n=========================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.result_report_class_wise_average" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.result_report_overall.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.result_report_overall\n==============================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.result_report_overall" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.result_report_parameters.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.result_report_parameters\n=================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.result_report_parameters" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.results.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.results\n================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.results" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_average_metrics.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_average_metrics\n===========================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.results_class_wise_average_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_metrics.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.results_class_wise_metrics\n===================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.results_class_wise_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.results_overall_metrics.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics.results_overall_metrics\n================================================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. automethod:: SegmentBasedMetrics.results_overall_metrics" }, { "path": "docs/_sources/generated/sed_eval.sound_event.SegmentBasedMetrics.rst.txt", "content": "sed_eval.sound_event.SegmentBasedMetrics\n========================================\n\n.. currentmodule:: sed_eval.sound_event\n\n.. autoclass:: SegmentBasedMetrics\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SegmentBasedMetrics.__init__\n ~SegmentBasedMetrics.class_wise_accuracy\n ~SegmentBasedMetrics.class_wise_count\n ~SegmentBasedMetrics.class_wise_error_rate\n ~SegmentBasedMetrics.class_wise_f_measure\n ~SegmentBasedMetrics.evaluate\n ~SegmentBasedMetrics.overall_accuracy\n ~SegmentBasedMetrics.overall_error_rate\n ~SegmentBasedMetrics.overall_f_measure\n ~SegmentBasedMetrics.reset\n ~SegmentBasedMetrics.result_report_class_wise\n ~SegmentBasedMetrics.result_report_class_wise_average\n ~SegmentBasedMetrics.result_report_overall\n ~SegmentBasedMetrics.result_report_parameters\n ~SegmentBasedMetrics.results\n ~SegmentBasedMetrics.results_class_wise_average_metrics\n ~SegmentBasedMetrics.results_class_wise_metrics\n ~SegmentBasedMetrics.results_overall_metrics\n \n \n\n \n \n " }, { "path": "docs/_sources/generated/sed_eval.test.mcnemar.rst.txt", "content": "sed_eval.test.mcnemar\n=====================\n\n.. currentmodule:: sed_eval.test\n\n.. autofunction:: mcnemar" }, { "path": "docs/_sources/generated/sed_eval.util.event_list.filter_event_list.rst.txt", "content": "sed_eval.util.event_list.filter_event_list\n==========================================\n\n.. currentmodule:: sed_eval.util.event_list\n\n.. autofunction:: filter_event_list" }, { "path": "docs/_sources/generated/sed_eval.util.event_list.max_event_offset.rst.txt", "content": "sed_eval.util.event_list.max_event_offset\n=========================================\n\n.. currentmodule:: sed_eval.util.event_list\n\n.. autofunction:: max_event_offset" }, { "path": "docs/_sources/generated/sed_eval.util.event_list.unique_event_labels.rst.txt", "content": "sed_eval.util.event_list.unique_event_labels\n============================================\n\n.. currentmodule:: sed_eval.util.event_list\n\n.. autofunction:: unique_event_labels" }, { "path": "docs/_sources/generated/sed_eval.util.event_list.unique_files.rst.txt", "content": "sed_eval.util.event_list.unique_files\n=====================================\n\n.. currentmodule:: sed_eval.util.event_list\n\n.. autofunction:: unique_files" }, { "path": "docs/_sources/generated/sed_eval.util.event_matching.bipartite_match.rst.txt", "content": "sed_eval.util.event_matching.bipartite_match\n============================================\n\n.. currentmodule:: sed_eval.util.event_matching\n\n.. autofunction:: bipartite_match" }, { "path": "docs/_sources/generated/sed_eval.util.event_roll.event_list_to_event_roll.rst.txt", "content": "sed_eval.util.event_roll.event_list_to_event_roll\n=================================================\n\n.. currentmodule:: sed_eval.util.event_roll\n\n.. autofunction:: event_list_to_event_roll" }, { "path": "docs/_sources/generated/sed_eval.util.event_roll.match_event_roll_lengths.rst.txt", "content": "sed_eval.util.event_roll.match_event_roll_lengths\n=================================================\n\n.. currentmodule:: sed_eval.util.event_roll\n\n.. autofunction:: match_event_roll_lengths" }, { "path": "docs/_sources/generated/sed_eval.util.event_roll.pad_event_roll.rst.txt", "content": "sed_eval.util.event_roll.pad_event_roll\n=======================================\n\n.. currentmodule:: sed_eval.util.event_roll\n\n.. autofunction:: pad_event_roll" }, { "path": "docs/_sources/generated/sed_eval.util.scene_list.unique_scene_labels.rst.txt", "content": "sed_eval.util.scene_list.unique_scene_labels\n============================================\n\n.. currentmodule:: sed_eval.util.scene_list\n\n.. autofunction:: unique_scene_labels" }, { "path": "docs/_sources/glossary.rst.txt", "content": "Glossary\n========\n\n.. glossary::\n acoustic scene\n Descriptor for surrounding audio environment, for example \"outdoor market\", \"busy street\", \"office\".\n\n event label\n Textual description of sound event, usually one or two words.\n\n event offset\n End of the event instance as a time-stamp (in seconds).\n\n event onset\n Start of the event instance as a time-stamp (in seconds).\n\n macro-average\n Intermediate statistics are aggregated class-wise, class-based metrics are calculated,\n then average of class based metrics; each class has equal influence on the final metric value. (see :ref:`averaging`)\n\n micro-average\n Intermediate statistics are aggregated over all test data, then metrics are calculated;\n each instance has equal influence on the final metric value. (see :ref:`averaging`)\n\n scene label\n Textual label used to identify acoustic scene.\n\n sound event\n Audio segment that is attributed to a specific sound source and is perceived as an entity.\n Marked as having onset and offset and labeled with textual descriptors related to the sound source,\n for example \"dog barking\", \"car passing by\".\n\n" }, { "path": "docs/_sources/index.rst.txt", "content": "``sed_eval`` - Evaluation toolbox for Sound Event Detection\n===========================================================\n\n- Toni Heittola (toni.heittola@tut.fi, `GitHub `_, `Home `_)\n- Annamaria Mesaros (annamaria.mesaros@tut.fi, `Home `_)\n\n.. figure:: _static/evaluation_overview.png\n :target: _static/evaluation_overview.png\n :align: center\n :width: 100%\n\n\n``sed_eval`` is an open source Python toolbox which provides a standardized, and transparent way to evaluate\nsound event detection systems (see :ref:`sound_event`). In addition to this, it provides tools for evaluating acoustic\nscene classification systems, as the fields are closely related (see :ref:`scene`).\n\nThe toolbox can be used in any of the following ways:\n\n* By using the included evaluator scripts directly (see :ref:`install` and :ref:`evaluators`). This is suitable if the system to be evaluated is implemented using some other platform than Python.\n* By importing it and calling it from your own Python code (see :ref:`install` and :ref:`sed_eval_quickstart`)\n\nCiting\n------\n\nIf you use ``sed_eval`` in a research project, please cite the following paper:\n\nAnnamaria Mesaros, Toni Heittola, and Tuomas Virtanen, \"Metrics for polyphonic sound event detection\", Applied Sciences, 6(6):162, 2016 [`HTML `_][`PDF `_]\n\n.. _installation:\n\nGetting started\n---------------\n.. toctree::\n :maxdepth: 1\n\n install\n tutorial\n\nAPI documentation\n-----------------\n\n.. toctree::\n :maxdepth: 1\n\n sound_event\n scene\n audio_tag\n metric\n test\n util\n io\n\nReference\n---------\n\n.. toctree::\n :maxdepth: 1\n\n glossary\n changelog\n\n* :ref:`genindex`\n\n\n" }, { "path": "docs/_sources/install.rst.txt", "content": ".. _install:\n\nInstallation instructions\n=========================\n\nUsing pip\n---------\n\nThe latest stable release is available on PyPI, and you can install with pip::\n\n pip install sed_eval\n\n\nAlternatively you can download or clone toolbox and use ``pip`` to handle dependencies::\n\n unzip sed_eval.zip\n pip install -e sed_eval\n\nor::\n\n git clone https://github.com/TUT-ARG/sed_eval.git\n pip install -e sed_eval\n\n\nUsing ``setyp.py``\n------------------\n\nYou can install ``sed_eval`` from source by first installing the dependencies::\n\n pip install -r requirements.txt\n\nand then running::\n\n python setup.py install\n\n\nTo uninstall the toolbox if it was installed with ``setyp.py``:\n\n``python setup.py install --record files.txt`` to get files associated with toolbox\n``cat files.txt | xargs rm -rf`` to remove the files recorded by the previous step.\n\nYou can also install the toolbox in *develop* mode::\n\n python setup.py develop\n\nToolbox can be uninstalled::\n\n python setup.py develop --uninstall\n\nRequirements\n------------\n\nFollowing libraries are required:\n\n - numpy >= 1.7.0\n - dcase_util >= 0.2.4" }, { "path": "docs/_sources/io.rst.txt", "content": ".. _io:\n\n.. automodule:: sed_eval.io\n" }, { "path": "docs/_sources/metric.rst.txt", "content": ".. _metric:\n\nMetrics\n==================\n\n.. automodule:: sed_eval.metric" }, { "path": "docs/_sources/scene.rst.txt", "content": ".. _scene:\n\nAcoustic Scene Classification\n=============================\n\nThe goal of acoustic scene classification is to classify a test recording into one of predefined classes that\ncharacterizes the environment in which it was recorded — for example \"outdoor market\", \"busy street\", \"office\".\n\nClassification performance is measured using accuracy: the number of correctly classified segments\namong the total number of test segments.\n\n.. automodule:: sed_eval.scene\n" }, { "path": "docs/_sources/sound_event.rst.txt", "content": ".. _sound_event:\n\nSound Event Detection\n=====================\n\nThe task of sound event detection involves locating and classifying\nsounds in audio recordings - estimating onset and offset for distinct\nsound event instances and providing a textual descriptor for each. The\nusual approach for this problem is supervised learning with sound event\nclasses defined in advance.\n\nMetrics are defined for polyphonic sound event detection, in which the\nground truth and system output contain overlapping sound event instances.\n\nTwo types of metrics are implemented:\n\n- **segment-based metrics** - the ground truth and system output are compared in a fixed time grid; sound events are marked as active or inactive in each segment;\n- **event-based metrics** - the ground truth and system output are compared at event instance level;\n\nIntermediate statistics\n-----------------------\n\nSegment-based\n^^^^^^^^^^^^^\n\n- *true positive*: the ground truth and system output both indicate an event to be active in that segment\n- *false positive*: the ground truth indicates an event to be inactive in that segment, but the system output indicates it as active\n- *false negative*: the ground truth indicates an event to be active in that segment, but the system output indicates it as inactive.\n- *true negative*: the ground truth and system output both indicate an event to be inactive.\n\nSegment-based metrics implementation allow selecting the desired segment length for evaluation (see ``time_resolution`` parameter).\n\nEvent-based\n^^^^^^^^^^^\n\n- *true positive*: an event in the system output that has a temporal position overlapping with the temporal position of an event with the same label in the ground truth. A *collar* is usually allowed for the onset and offset, or a tolerance with respect to the ground truth event duration.\n- *false positive*: an event in the system output that has no correspondence to an event with same label in the ground truth within the allowed tolerance;\n- *false negative*: an event in the ground truth that has no correspondence to an event with same label in the system output within the allowed tolerance.\n- *true negative*: event-based metrics have no meaningful true negatives.\n\nEvent-based metrics implementation allow selecting the desired collar size (see ``t_collar`` parameter) and use of onset only or onset and offset conditions for evaluation (see ``evaluate_onset`` and ``evaluate_offset`` parameters).\n\n.. _averaging:\n\nAveraging\n^^^^^^^^^\n\n\n\n**Micro-averaging** - intermediate statistics are aggregated over all test\ndata, then metrics are calculated; each instance has equal influence on\nthe final metric value;\n\n**Macro-averaging** - intermediate statistics are aggregated class-wise,\nclass-based metrics are calculated, then average of class based metrics;\neach class has equal influence on the final metric value.\n\nMicro and macro averages can result in very different values when\nclasses are highly unbalanced or performance on individual classes is\nvery different.\n\nCross-validation\n^^^^^^^^^^^^^^^^\n\nRecommended calculation for a cross-validation setup is to run all\ntrain/test folds and perform evaluation at the end (no fold-wise\nevaluation!).\nThe reason is that folds are most often unbalanced due to the multilabel\nnature of the problem, and this results in biases when averaging. For\nmore details, consult [1]_.\n\n\nImplemented metrics\n-------------------\n\nPrecision, Recall and F-score\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. math::\n\n P=\\frac{TP}{TP+FP},\\quad R=\\frac{TP}{TP+FN},\\quad F=\\frac{2 \\cdot P \\cdot R}{P+R}\n\n\nThese can be calculated segment based or event based, micro or macro averaged.\n\nSensitivity and specificity\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. math::\n Sensitivity = \\frac{TP}{TP+FN},\\quad\n Specificity = \\frac{TN}{TN+FP}\n\nAccuracy\n^^^^^^^^\n\n.. math::\n accuracy = \\frac{TP+TN}{TP+TN+FP+FN}\n\n.. math::\n accuracy2 = \\frac{TP}{TP+FP+FN}\n\nBalanced accuracy\n^^^^^^^^^^^^^^^^^\n\n.. math::\n BACC = factor \\cdot \\frac{TP}{TP+FN} +(1-factor) \\cdot \\frac{TN}{TN+FP}\n\nSpecificity and accuracy variants are only calculated as segment-based\nmetrics.\n\n\nError Rate\n^^^^^^^^^^\n\n**Segment-based**\n\n**Substitutions** in segment *k* - *S(k)* - the number of ground truth events\nfor which a correct event was not output, yet something else was. One\nsubstitution is equivalent to having one false positives and one false\nnegatives in the same segment. There is no need to designate which\nerroneous event substitutes which.\n\n**Insertions** in segment *k* - *I(k)* - events in system output that are not\ncorrect (false positives after substitutions are accounted for).\n\n**Deletions** in segment *k* - *D(k)* - events in ground truth that are not\ncorrect (false positives after substitutions are accounted for).\n\n.. math::\n &S(k) = min(FN(k),FP(k)) \\nonumber \\\\\n &D(k) = max(0,FN(k)-FP(k)) \\\\\n &I(k)= max(0,FP(k)-FN(k)) \\nonumber\n\n.. math::\n ER=\\frac{\\sum_{k=1}^K{S(k)}+\\sum_{k=1}^K{D(k)}+\\sum_{k=1}^K{I(k)}}{\\sum_{k=1}^K{N(k)}}\n\n*N(k)* is the number of events in segment *k* in ground truth.\n\n**Event-based**\n\n**Substitutions** - events in system output with correct temporal position\nbut incorrect class label\n\n**Insertions** - events in system output unaccounted for as correct or\nsubstituted\n\n**Deletions** - events in ground truth unaccounted for as correct or substituted\n\n.. math::\n ER=\\frac{S + D + I}{N}\n\n*N* is the total number of events in ground truth.\n\nCode\n----\n\n.. automodule:: sed_eval.sound_event\n\nRerefences\n----------\n\n.. [1] Forman, G. and Scholz, M.\n \"Apples-to-Apples in Cross-Validation Studies: Pitfalls in Classifier Performance Measurement\".\n SIGKDD Explor. Newsl. 12, 1, November 2010, pp. 49-57.\n http://kdd.org/exploration_files/v12-1-p49-forman-sigkdd.pdf" }, { "path": "docs/_sources/test.rst.txt", "content": ".. _test:\n\nTests\n=====\n\n.. automodule:: sed_eval.test" }, { "path": "docs/_sources/tutorial.rst.txt", "content": "Tutorial\n========\n\n.. _sed_eval:\n\n``sed_eval`` -- Evaluation toolbox for Sound Event Detection\n------------------------------------------------------------\n\nThe structure of the ``sed_eval`` toolbox is as follows:\n\n* For evaluating the **sound event detection system** (SED system later), there are two types of metrics available: segment-based and event-based. For both types, there is a metric class ``SegmentBasedMetrics`` and ``EventBasedMetrics``. A member function ``evaluate()`` is used to go through system output (estimated event list) and ground truth (reference event list) pairs. The ``results()`` function is used to get the metric values in dictionary. There are also functions to return results as a formatted string for convenience (e.g. ``result_report_overall()``), or one can just print class instance.\n* For evaluating the **acoustic scene classification system**, there is similar evaluation class, ``SceneClassificationMetrics``, as for SED system evaluation.\n\n``sed_eval`` also includes the following additional submodules:\n\n* ``io`` which contains convenience functions for loading annotations\n* ``util`` which includes miscellaneous functions to handle event lists (list of event items), event roll (event activity indicator matrix used in evaluation), and scene list.\n\n\n.. _evaluators:\n\nQuickstart: Using the evaluators\n--------------------------------\n\nThe easiest way to evaluate systems with ``sed_eval`` is to use provided evaluators.\nEvaluators are Python scripts which can be run from the command prompt and utilize ``sed_eval`` to compute metrics\naccording to reference and estimated annotations you provide.\nTo use the evaluators, you must first install ``sed_eval`` and its dependencies (see :ref:`installation`).\nThe evaluator scripts can be found in the ``sed_eval`` repository in the ``evaluators`` folder:\n\nhttps://github.com/TUT-ARG/sed_eval/tree/master/evaluators\n\nCurrently there are two evaluators available, one for evaluating the sound event detection systems and one for\nevaluating acoustic scene classification systems.\n\nSound event detection\n^^^^^^^^^^^^^^^^^^^^^\n\nTo get usage help:\n\n``./sound_event_eval.py --help``\n\nEvaluator takes as argument a csv-formatted file-list. The list contains pairs of filenames, one pair per row: first the filename of the reference event list\nfile and the second the estimated event list file. Format is [reference_file][delimiter][estimated_file], and supported delimiters are ``,``, ``;``, ``tab``.\n\nExample of file-list::\n\n office_snr0_high_v2.txt\toffice_snr0_high_v2_detected.txt\n office_snr0_med_v2.txt\toffice_snr0_med_v2_detected.txt\n\nEvent list is csv-formatted text-file. Supported formats for the file are:\n\n- [event onset (float >= 0)][delimiter][event offset (float >= 0)]\n- [event onset (float >= 0)][delimiter][event offset (float >= 0)][delimiter][label]\n- [filename][delimiter][scene_label][delimiter][event onset (float >= 0)][delimiter][event offset (float >= 0)][delimiter][event label]\n\nSupported delimiters: ``,``, ``;``, ``tab``\n\nExample of event list file::\n\n 21.64715\t23.00552\talert\n 36.91184\t38.27021\talert\n 69.72575\t71.09029\talert\n 63.53990\t64.89827\talert\n 84.25553\t84.83920\talert\n 20.92974\t21.82661\tclearthroat\n 28.39992\t29.29679\tclearthroat\n 80.47837\t81.95937\tclearthroat\n 44.48363\t45.96463\tclearthroat\n 78.13073\t79.05953\tclearthroat\n 15.17031\t16.27235\tcough\n 20.54931\t21.65135\tcough\n 27.79964\t28.90168\tcough\n 75.45959\t76.32490\tcough\n 70.81708\t71.91912\tcough\n 21.23203\t22.55902\tdoorslam\n 7.546220\t9.014880\tdoorslam\n 34.11303\t35.04183\tdoorslam\n 45.86001\t47.32867\tdoorslam\n\nTo get segment-based and event-based metrics report printed, run:\n\n``./sound_event_eval.py file_list.txt``\n\nTo get segment-based and event-based metrics saved in YAML-format, run:\n\n``./sound_event_eval.py file_list.txt -o results.yaml``\n\nAcoustic scene classification\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``./scene_eval.py --help``\n\nEvaluator takes as argument a csv-formatted file-list. The list contains pairs of filenames, one pair per row: first the filename of the reference scene list\nfile and the second the estimated scene list file. Format is [reference_file][delimiter][estimated_file], and supported delimiters are ``,``, ``;``, ``tab``.\n\nExample of file-list::\n\n fold1_reference.txt\tfold1_estimated.txt\n fold2_reference.txt\tfold2_estimated.txt\n fold3_reference.txt\tfold3_estimated.txt\n fold4_reference.txt\tfold4_estimated.txt\n fold5_reference.txt\tfold5_estimated.txt\n\n\nScene list is csv-formatted text-file. Supported formats for the file are:\n\n- [filename][delimiter][scene label]\n- [filename][delimiter][segment start (float >= 0)][delimiter][segment stop (float >= 0)][delimiter][scene label]\n\nSupported delimiters: ``,``, ``;``, ``tab``\n\nExample of scene list file::\n\n scenes_stereo/supermarket09.wav\tsupermarket\n scenes_stereo/tubestation10.wav\ttubestation\n scenes_stereo/quietstreet08.wav\tquietstreet\n scenes_stereo/restaurant05.wav\trestaurant\n scenes_stereo/busystreet05.wav\tbusystreet\n scenes_stereo/openairmarket04.wav\topenairmarket\n scenes_stereo/quietstreet01.wav\tquietstreet\n scenes_stereo/supermarket05.wav\tsupermarket\n scenes_stereo/openairmarket01.wav\topenairmarket\n\n\nTo get metrics printed, run:\n\n``./scene_eval.py file_list.txt``\n\nTo get metrics saved in YAML-format, run:\n\n``./scene_eval.py file_list.txt -o results.yaml``\n\n\n.. _sed_eval_quickstart:\n\nQuickstart: Using ``sed_eval`` in Python code\n---------------------------------------------\n\nAfter ``sed_eval`` is installed (see :ref:`installation`), it can be imported to your Python code as follows:\n\n``import sed_eval``\n\nSound event detection\n^^^^^^^^^^^^^^^^^^^^^\n\nUsage example when reading event lists from disk (you can run example in path ``tests/data/sound_event``):\n\n.. code-block:: python\n :linenos:\n\n import sed_eval\n import dcase_util\n\n file_list = [\n {\n 'reference_file': 'office_snr0_high_v2.txt',\n 'estimated_file': 'office_snr0_high_v2_detected.txt'\n },\n {\n 'reference_file': 'office_snr0_med_v2.txt',\n 'estimated_file': 'office_snr0_med_v2_detected.txt'\n }\n ]\n\n data = []\n\n # Get used event labels\n all_data = dcase_util.containers.MetaDataContainer()\n for file_pair in file_list:\n reference_event_list = sed_eval.io.load_event_list(\n filename=file_pair['reference_file']\n )\n estimated_event_list = sed_eval.io.load_event_list(\n filename=file_pair['estimated_file']\n )\n\n data.append({'reference_event_list': reference_event_list,\n 'estimated_event_list': estimated_event_list})\n\n all_data += reference_event_list\n\n event_labels = all_data.unique_event_labels\n\n # Start evaluating\n\n # Create metrics classes, define parameters\n segment_based_metrics = sed_eval.sound_event.SegmentBasedMetrics(\n event_label_list=event_labels,\n time_resolution=1.0\n )\n\n event_based_metrics = sed_eval.sound_event.EventBasedMetrics(\n event_label_list=event_labels,\n t_collar=0.250\n )\n\n # Go through files\n for file_pair in data:\n segment_based_metrics.evaluate(\n reference_event_list=file_pair['reference_event_list'],\n estimated_event_list=file_pair['estimated_event_list']\n )\n\n event_based_metrics.evaluate(\n reference_event_list=file_pair['reference_event_list'],\n estimated_event_list=file_pair['estimated_event_list']\n )\n\n # Get only certain metrics\n overall_segment_based_metrics = segment_based_metrics.results_overall_metrics()\n print(\"Accuracy:\", overall_segment_based_metrics['accuracy']['accuracy'])\n\n # Or print all metrics as reports\n print(segment_based_metrics)\n print(event_based_metrics)\n\nUsage example to evaluate results stored in variables:\n\n.. code-block:: python\n :linenos:\n\n import sed_eval\n import dcase_util\n\n reference_event_list = dcase_util.containers.MetaDataContainer(\n [\n {\n 'event_label': 'car',\n 'event_onset': 0.0,\n 'event_offset': 2.5,\n 'file': 'audio/street/b099.wav',\n 'scene_label': 'street'\n },\n {\n 'event_label': 'car',\n 'event_onset': 2.8,\n 'event_offset': 4.5,\n 'file': 'audio/street/b099.wav',\n 'scene_label': 'street'\n },\n {\n 'event_label': 'car',\n 'event_onset': 6.0,\n 'event_offset': 10.0,\n 'file': 'audio/street/b099.wav',\n 'scene_label': 'street'\n }\n ]\n )\n\n estimated_event_list = dcase_util.containers.MetaDataContainer(\n [\n {\n 'event_label': 'car',\n 'event_onset': 1.0,\n 'event_offset': 3.5,\n 'file': 'audio/street/b099.wav',\n 'scene_label': 'street'\n },\n {\n 'event_label': 'car',\n 'event_onset': 7.0,\n 'event_offset': 8.0,\n 'file': 'audio/street/b099.wav',\n 'scene_label': 'street'\n }\n ]\n )\n\n segment_based_metrics = sed_eval.sound_event.SegmentBasedMetrics(\n event_label_list=reference_event_list.unique_event_labels,\n time_resolution=1.0\n )\n event_based_metrics = sed_eval.sound_event.EventBasedMetrics(\n event_label_list=reference_event_list.unique_event_labels,\n t_collar=0.250\n )\n\n for filename in reference_event_list.unique_files:\n reference_event_list_for_current_file = reference_event_list.filter(\n filename=filename\n )\n\n estimated_event_list_for_current_file = estimated_event_list.filter(\n filename=filename\n )\n\n segment_based_metrics.evaluate(\n reference_event_list=reference_event_list_for_current_file,\n estimated_event_list=estimated_event_list_for_current_file\n )\n\n event_based_metrics.evaluate(\n reference_event_list=reference_event_list_for_current_file,\n estimated_event_list=estimated_event_list_for_current_file\n )\n\n # Get only certain metrics\n overall_segment_based_metrics = segment_based_metrics.results_overall_metrics()\n print(\"Accuracy:\", overall_segment_based_metrics['accuracy']['accuracy'])\n\n # Or print all metrics as reports\n print(segment_based_metrics)\n print(event_based_metrics)\n\nAcoustic scene classification\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nUsage example to evaluate files:\n\n.. code-block:: python\n :linenos:\n\n import sed_eval\n import dcase_util\n\n file_list = [\n {'reference_file': 'fold1_reference.txt', 'estimated_file': 'fold1_estimated.txt'}\n ]\n\n data = []\n\n # Get used scene labels and load data in\n all_data = []\n for file_pair in file_list:\n reference_scene_list = sed_eval.io.load_scene_list(\n filename=file_pair['reference_file'],\n csv_header=False,\n file_format=dcase_util.utils.FileFormat.CSV,\n fields=['filename', 'scene_label']\n )\n estimated_scene_list = sed_eval.io.load_scene_list(\n filename=file_pair['estimated_file'],\n csv_header=False,\n file_format=dcase_util.utils.FileFormat.CSV,\n fields=['filename', 'onset', 'offset', 'scene_label']\n )\n\n data.append(\n {\n 'reference_scene_list': reference_scene_list,\n 'estimated_scene_list': estimated_scene_list\n }\n )\n\n all_data += reference_scene_list\n\n scene_labels = sed_eval.sound_event.util.unique_scene_labels(all_data)\n\n # Create metrics class\n scene_metrics = sed_eval.scene.SceneClassificationMetrics(\n scene_labels=scene_labels\n )\n for file_pair in data:\n scene_metrics.evaluate(\n reference_scene_list=file_pair['reference_scene_list'],\n estimated_scene_list=file_pair['estimated_scene_list']\n )\n\n # Get only certain metrics\n overall_metrics_results = scene_metrics.results_overall_metrics()\n print(\"Accuracy:\", overall_metrics_results['accuracy'])\n\n # Or print all metrics as reports\n print(scene_metrics)\n\nUsage example to evaluate results stored in variables:\n\n.. code-block:: python\n :linenos:\n\n import sed_eval\n import dcase_util\n\n reference = dcase_util.containers.MetaDataContainer([\n {\n 'scene_label': 'supermarket',\n 'file': 'supermarket09.wav'\n },\n {\n 'scene_label': 'tubestation',\n 'file': 'tubestation10.wav'\n },\n {\n 'scene_label': 'quietstreet',\n 'file': 'quietstreet08.wav'\n },\n {\n 'scene_label': 'office',\n 'file': 'office10.wav'\n },\n {\n 'scene_label': 'bus',\n 'file': 'bus01.wav'\n },\n ])\n\n estimated = dcase_util.containers.MetaDataContainer([\n {\n 'scene_label': 'supermarket',\n 'file': 'supermarket09.wav'\n },\n {\n 'scene_label': 'bus',\n 'file': 'tubestation10.wav'\n },\n {\n 'scene_label': 'quietstreet',\n 'file': 'quietstreet08.wav'\n },\n {\n 'scene_label': 'park',\n 'file': 'office10.wav'\n },\n {\n 'scene_label': 'car',\n 'file': 'bus01.wav'\n },\n ])\n\n scene_labels = sed_eval.sound_event.util.unique_scene_labels(reference)\n\n scene_metrics = sed_eval.scene.SceneClassificationMetrics(scene_labels)\n scene_metrics.evaluate(\n reference_scene_list=reference,\n estimated_scene_list=estimated\n )\n\n print(scene_metrics)" }, { "path": "docs/_sources/util.rst.txt", "content": ".. _util:\n\n.. automodule:: sed_eval.util\n" }, { "path": "docs/_static/basic.css", "content": "/*\n * basic.css\n * ~~~~~~~~~\n *\n * Sphinx stylesheet -- basic theme.\n *\n * :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.\n * :license: BSD, see LICENSE for details.\n *\n */\n\n/* -- main layout ----------------------------------------------------------- */\n\ndiv.clearer {\n clear: both;\n}\n\n/* -- relbar ---------------------------------------------------------------- */\n\ndiv.related {\n width: 100%;\n font-size: 90%;\n}\n\ndiv.related h3 {\n display: none;\n}\n\ndiv.related ul {\n margin: 0;\n padding: 0 0 0 10px;\n list-style: none;\n}\n\ndiv.related li {\n display: inline;\n}\n\ndiv.related li.right {\n float: right;\n margin-right: 5px;\n}\n\n/* -- sidebar 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"docs/_static/doctools.js", "content": "/*\n * doctools.js\n * ~~~~~~~~~~~\n *\n * Sphinx JavaScript utilities for all documentation.\n *\n * :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.\n * :license: BSD, see LICENSE for details.\n *\n */\n\n/**\n * select a different prefix for underscore\n */\n$u = _.noConflict();\n\n/**\n * make the code below compatible with browsers without\n * an installed firebug like debugger\nif (!window.console || !console.firebug) {\n var names = [\"log\", \"debug\", \"info\", \"warn\", \"error\", \"assert\", \"dir\",\n \"dirxml\", \"group\", \"groupEnd\", \"time\", \"timeEnd\", \"count\", \"trace\",\n \"profile\", \"profileEnd\"];\n window.console = {};\n for (var i = 0; i < names.length; ++i)\n window.console[names[i]] = function() {};\n}\n */\n\n/**\n * small helper function to urldecode strings\n */\njQuery.urldecode = function(x) {\n return decodeURIComponent(x).replace(/\\+/g, ' ');\n};\n\n/**\n * small helper function to urlencode strings\n */\njQuery.urlencode = encodeURIComponent;\n\n/**\n * This function returns the parsed url parameters of the\n * current request. Multiple values per key are supported,\n * it will always return arrays of strings for the value parts.\n */\njQuery.getQueryParameters = function(s) {\n if (typeof s == 'undefined')\n s = document.location.search;\n var parts = s.substr(s.indexOf('?') + 1).split('&');\n var result = {};\n for (var i = 0; i < parts.length; i++) {\n var tmp = parts[i].split('=', 2);\n var key = jQuery.urldecode(tmp[0]);\n var value = jQuery.urldecode(tmp[1]);\n if (key in result)\n result[key].push(value);\n else\n result[key] = [value];\n }\n return result;\n};\n\n/**\n * highlight a given string on a jquery object by wrapping it in\n * span elements with the given class name.\n */\njQuery.fn.highlightText = function(text, className) {\n function highlight(node) {\n if (node.nodeType == 3) {\n var val = node.nodeValue;\n var pos = val.toLowerCase().indexOf(text);\n if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {\n var span = document.createElement(\"span\");\n span.className = className;\n span.appendChild(document.createTextNode(val.substr(pos, text.length)));\n node.parentNode.insertBefore(span, node.parentNode.insertBefore(\n document.createTextNode(val.substr(pos + text.length)),\n node.nextSibling));\n node.nodeValue = val.substr(0, pos);\n }\n }\n else if (!jQuery(node).is(\"button, select, textarea\")) {\n jQuery.each(node.childNodes, function() {\n highlight(this);\n });\n }\n }\n return this.each(function() {\n highlight(this);\n });\n};\n\n/*\n * backward compatibility for jQuery.browser\n * This will be supported until firefox bug is fixed.\n */\nif (!jQuery.browser) {\n jQuery.uaMatch = function(ua) {\n ua = ua.toLowerCase();\n\n var match = /(chrome)[ \\/]([\\w.]+)/.exec(ua) ||\n /(webkit)[ \\/]([\\w.]+)/.exec(ua) ||\n /(opera)(?:.*version|)[ \\/]([\\w.]+)/.exec(ua) ||\n /(msie) ([\\w.]+)/.exec(ua) ||\n ua.indexOf(\"compatible\") < 0 && /(mozilla)(?:.*? rv:([\\w.]+)|)/.exec(ua) ||\n [];\n\n return {\n browser: match[ 1 ] || \"\",\n version: match[ 2 ] || \"0\"\n };\n };\n jQuery.browser = {};\n jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;\n}\n\n/**\n * Small JavaScript module for the documentation.\n */\nvar Documentation = {\n\n init : function() {\n this.fixFirefoxAnchorBug();\n this.highlightSearchWords();\n this.initIndexTable();\n \n },\n\n /**\n * i18n support\n */\n TRANSLATIONS : {},\n PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },\n LOCALE : 'unknown',\n\n // gettext and ngettext don't access this so that the functions\n // can safely bound to a different name (_ = Documentation.gettext)\n gettext : function(string) {\n var translated = Documentation.TRANSLATIONS[string];\n if (typeof translated == 'undefined')\n return string;\n return (typeof translated == 'string') ? translated : translated[0];\n },\n\n ngettext : function(singular, plural, n) {\n var translated = Documentation.TRANSLATIONS[singular];\n if (typeof translated == 'undefined')\n return (n == 1) ? singular : plural;\n return translated[Documentation.PLURALEXPR(n)];\n },\n\n addTranslations : function(catalog) {\n for (var key in catalog.messages)\n this.TRANSLATIONS[key] = catalog.messages[key];\n this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');\n this.LOCALE = catalog.locale;\n },\n\n /**\n * add context elements like header anchor links\n */\n addContextElements : function() {\n $('div[id] > :header:first').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this headline')).\n appendTo(this);\n });\n $('dt[id]').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this definition')).\n appendTo(this);\n });\n },\n\n /**\n * workaround a firefox stupidity\n * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075\n */\n fixFirefoxAnchorBug : function() {\n if (document.location.hash)\n window.setTimeout(function() {\n document.location.href += '';\n }, 10);\n },\n\n /**\n * highlight the search words provided in the url in the text\n */\n highlightSearchWords : function() {\n var params = $.getQueryParameters();\n var terms = (params.highlight) ? params.highlight[0].split(/\\s+/) : [];\n if (terms.length) {\n var body = $('div.body');\n if (!body.length) {\n body = $('body');\n }\n window.setTimeout(function() {\n $.each(terms, function() {\n body.highlightText(this.toLowerCase(), 'highlighted');\n });\n }, 10);\n $('

' + _('Hide Search Matches') + '

')\n .appendTo($('#searchbox'));\n }\n },\n\n /**\n * init the domain index toggle buttons\n */\n initIndexTable : function() {\n var togglers = $('img.toggler').click(function() {\n var src = $(this).attr('src');\n var idnum = $(this).attr('id').substr(7);\n $('tr.cg-' + idnum).toggle();\n if (src.substr(-9) == 'minus.png')\n $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');\n else\n $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');\n }).css('display', '');\n if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {\n togglers.click();\n }\n },\n\n /**\n * helper function to hide the search marks again\n */\n hideSearchWords : function() {\n $('#searchbox .highlight-link').fadeOut(300);\n $('span.highlighted').removeClass('highlighted');\n },\n\n /**\n * make the url absolute\n */\n makeURL : function(relativeURL) {\n return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;\n },\n\n /**\n * get the current relative url\n */\n getCurrentURL : function() {\n var path = document.location.pathname;\n var parts = path.split(/\\//);\n $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\\//), function() {\n if (this == '..')\n parts.pop();\n });\n var url = parts.join('/');\n return path.substring(url.lastIndexOf('/') + 1, path.length - 1);\n },\n\n initOnKeyListeners: function() {\n $(document).keyup(function(event) {\n var activeElementType = document.activeElement.tagName;\n // don't navigate when in search box or textarea\n if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {\n switch (event.keyCode) {\n case 37: // left\n var prevHref = $('link[rel=\"prev\"]').prop('href');\n if (prevHref) {\n window.location.href = prevHref;\n return false;\n }\n case 39: // right\n var nextHref = $('link[rel=\"next\"]').prop('href');\n if (nextHref) {\n window.location.href = nextHref;\n return false;\n }\n }\n }\n });\n }\n};\n\n// quick alias for translations\n_ = Documentation.gettext;\n\n$(document).ready(function() {\n Documentation.init();\n});" }, { "path": "docs/_static/jquery-3.1.0.js", "content": "/*eslint-disable no-unused-vars*/\n/*!\n * jQuery JavaScript Library v3.1.0\n * https://jquery.com/\n *\n * Includes Sizzle.js\n * https://sizzlejs.com/\n *\n * Copyright jQuery Foundation and other contributors\n * Released under the MIT license\n * https://jquery.org/license\n *\n * Date: 2016-07-07T21:44Z\n */\n( function( global, factory ) {\n\n\t\"use strict\";\n\n\tif ( typeof module === \"object\" && typeof module.exports === \"object\" ) {\n\n\t\t// For CommonJS and CommonJS-like environments where a proper `window`\n\t\t// is present, execute the factory and get jQuery.\n\t\t// For environments that do not have a `window` with a `document`\n\t\t// (such as Node.js), expose a factory as module.exports.\n\t\t// This accentuates the need for the creation of a real `window`.\n\t\t// e.g. var jQuery = require(\"jquery\")(window);\n\t\t// See ticket #14549 for more info.\n\t\tmodule.exports = global.document ?\n\t\t\tfactory( global, true ) :\n\t\t\tfunction( w ) {\n\t\t\t\tif ( !w.document ) {\n\t\t\t\t\tthrow new Error( \"jQuery requires a window with a document\" );\n\t\t\t\t}\n\t\t\t\treturn factory( w );\n\t\t\t};\n\t} else {\n\t\tfactory( global );\n\t}\n\n// Pass this if window is not defined yet\n} )( typeof window !== \"undefined\" ? window : this, function( window, noGlobal ) {\n\n// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1\n// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode\n// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common\n// enough that all such attempts are guarded in a try block.\n\"use strict\";\n\nvar arr = [];\n\nvar document = window.document;\n\nvar getProto = Object.getPrototypeOf;\n\nvar slice = arr.slice;\n\nvar concat = arr.concat;\n\nvar push = arr.push;\n\nvar indexOf = arr.indexOf;\n\nvar class2type = {};\n\nvar toString = class2type.toString;\n\nvar hasOwn = class2type.hasOwnProperty;\n\nvar fnToString = hasOwn.toString;\n\nvar ObjectFunctionString = fnToString.call( Object );\n\nvar support = {};\n\n\n\n\tfunction DOMEval( code, doc ) {\n\t\tdoc = doc || document;\n\n\t\tvar script = doc.createElement( \"script\" );\n\n\t\tscript.text = code;\n\t\tdoc.head.appendChild( script ).parentNode.removeChild( script );\n\t}\n/* global Symbol */\n// Defining this global in .eslintrc would create a danger of using the global\n// unguarded in another place, it seems safer to define global only for this module\n\n\n\nvar\n\tversion = \"3.1.0\",\n\n\t// Define a local copy of jQuery\n\tjQuery = function( selector, context ) {\n\n\t\t// The jQuery object is actually just the init constructor 'enhanced'\n\t\t// Need init if jQuery is called (just allow error to be thrown if not included)\n\t\treturn new jQuery.fn.init( selector, context );\n\t},\n\n\t// Support: Android <=4.0 only\n\t// Make sure we trim BOM and NBSP\n\trtrim = /^[\\s\\uFEFF\\xA0]+|[\\s\\uFEFF\\xA0]+$/g,\n\n\t// Matches dashed string for camelizing\n\trmsPrefix = /^-ms-/,\n\trdashAlpha = /-([a-z])/g,\n\n\t// Used by jQuery.camelCase as callback to replace()\n\tfcamelCase = function( all, letter ) {\n\t\treturn letter.toUpperCase();\n\t};\n\njQuery.fn = jQuery.prototype = {\n\n\t// The current version of jQuery being used\n\tjquery: version,\n\n\tconstructor: jQuery,\n\n\t// The default length of a jQuery object is 0\n\tlength: 0,\n\n\ttoArray: function() {\n\t\treturn slice.call( this );\n\t},\n\n\t// Get the Nth element in the matched element set OR\n\t// Get the whole matched element set as a clean array\n\tget: function( num ) {\n\t\treturn num != null ?\n\n\t\t\t// Return just the one element from the set\n\t\t\t( num < 0 ? this[ num + this.length ] : this[ num ] ) :\n\n\t\t\t// Return all the elements in a clean array\n\t\t\tslice.call( this );\n\t},\n\n\t// Take an array of elements and push it onto the stack\n\t// (returning the new matched element set)\n\tpushStack: function( elems ) {\n\n\t\t// Build a new jQuery matched element set\n\t\tvar ret = jQuery.merge( this.constructor(), elems );\n\n\t\t// Add the old object onto the stack (as a reference)\n\t\tret.prevObject = this;\n\n\t\t// Return the newly-formed element set\n\t\treturn ret;\n\t},\n\n\t// Execute a callback for every element in the matched set.\n\teach: function( callback ) {\n\t\treturn jQuery.each( this, callback );\n\t},\n\n\tmap: function( callback ) {\n\t\treturn this.pushStack( jQuery.map( this, function( elem, i ) {\n\t\t\treturn callback.call( elem, i, elem );\n\t\t} ) );\n\t},\n\n\tslice: function() {\n\t\treturn this.pushStack( slice.apply( this, arguments ) );\n\t},\n\n\tfirst: function() {\n\t\treturn this.eq( 0 );\n\t},\n\n\tlast: function() {\n\t\treturn this.eq( -1 );\n\t},\n\n\teq: function( i ) {\n\t\tvar len = this.length,\n\t\t\tj = +i + ( i < 0 ? len : 0 );\n\t\treturn this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );\n\t},\n\n\tend: function() {\n\t\treturn this.prevObject || this.constructor();\n\t},\n\n\t// For internal use only.\n\t// Behaves like an Array's method, not like a jQuery method.\n\tpush: push,\n\tsort: arr.sort,\n\tsplice: arr.splice\n};\n\njQuery.extend = jQuery.fn.extend = function() {\n\tvar options, name, src, copy, copyIsArray, clone,\n\t\ttarget = arguments[ 0 ] || {},\n\t\ti = 1,\n\t\tlength = arguments.length,\n\t\tdeep = false;\n\n\t// Handle a deep copy situation\n\tif ( typeof target === \"boolean\" ) {\n\t\tdeep = target;\n\n\t\t// Skip the boolean and the target\n\t\ttarget = arguments[ i ] || {};\n\t\ti++;\n\t}\n\n\t// Handle case when target is a string or something (possible in deep copy)\n\tif ( typeof target !== \"object\" && !jQuery.isFunction( target ) ) {\n\t\ttarget = {};\n\t}\n\n\t// Extend jQuery itself if only one argument is passed\n\tif ( i === length ) {\n\t\ttarget = this;\n\t\ti--;\n\t}\n\n\tfor ( ; i < length; i++ ) {\n\n\t\t// Only deal with non-null/undefined values\n\t\tif ( ( options = arguments[ i ] ) != null ) {\n\n\t\t\t// Extend the base object\n\t\t\tfor ( name in options ) {\n\t\t\t\tsrc = target[ name ];\n\t\t\t\tcopy = options[ name ];\n\n\t\t\t\t// Prevent never-ending loop\n\t\t\t\tif ( target === copy ) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\t// Recurse if we're merging plain objects or arrays\n\t\t\t\tif ( deep && copy && ( jQuery.isPlainObject( copy ) ||\n\t\t\t\t\t( copyIsArray = jQuery.isArray( copy ) ) ) ) {\n\n\t\t\t\t\tif ( copyIsArray ) {\n\t\t\t\t\t\tcopyIsArray = false;\n\t\t\t\t\t\tclone = src && jQuery.isArray( src ) ? src : [];\n\n\t\t\t\t\t} else {\n\t\t\t\t\t\tclone = src && jQuery.isPlainObject( src ) ? src : {};\n\t\t\t\t\t}\n\n\t\t\t\t\t// Never move original objects, clone them\n\t\t\t\t\ttarget[ name ] = jQuery.extend( deep, clone, copy );\n\n\t\t\t\t// Don't bring in undefined values\n\t\t\t\t} else if ( copy !== undefined ) {\n\t\t\t\t\ttarget[ name ] = copy;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Return the modified object\n\treturn target;\n};\n\njQuery.extend( {\n\n\t// Unique for each copy of jQuery on the page\n\texpando: \"jQuery\" + ( version + Math.random() ).replace( /\\D/g, \"\" ),\n\n\t// Assume jQuery is ready without the ready module\n\tisReady: true,\n\n\terror: function( msg ) {\n\t\tthrow new Error( msg );\n\t},\n\n\tnoop: function() {},\n\n\tisFunction: function( obj ) {\n\t\treturn jQuery.type( obj ) === \"function\";\n\t},\n\n\tisArray: Array.isArray,\n\n\tisWindow: function( obj ) {\n\t\treturn obj != null && obj === obj.window;\n\t},\n\n\tisNumeric: function( obj ) {\n\n\t\t// As of jQuery 3.0, isNumeric is limited to\n\t\t// strings and numbers (primitives or objects)\n\t\t// that can be coerced to finite numbers (gh-2662)\n\t\tvar type = jQuery.type( obj );\n\t\treturn ( type === \"number\" || type === \"string\" ) &&\n\n\t\t\t// parseFloat NaNs numeric-cast false positives (\"\")\n\t\t\t// ...but misinterprets leading-number strings, particularly hex literals (\"0x...\")\n\t\t\t// subtraction forces infinities to NaN\n\t\t\t!isNaN( obj - parseFloat( obj ) );\n\t},\n\n\tisPlainObject: function( obj ) {\n\t\tvar proto, Ctor;\n\n\t\t// Detect obvious negatives\n\t\t// Use toString instead of jQuery.type to catch host objects\n\t\tif ( !obj || toString.call( obj ) !== \"[object Object]\" ) {\n\t\t\treturn false;\n\t\t}\n\n\t\tproto = getProto( obj );\n\n\t\t// Objects with no prototype (e.g., `Object.create( null )`) are plain\n\t\tif ( !proto ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Objects with prototype are plain iff they were constructed by a global Object function\n\t\tCtor = hasOwn.call( proto, \"constructor\" ) && proto.constructor;\n\t\treturn typeof Ctor === \"function\" && fnToString.call( Ctor ) === ObjectFunctionString;\n\t},\n\n\tisEmptyObject: function( obj ) {\n\n\t\t/* eslint-disable no-unused-vars */\n\t\t// See https://github.com/eslint/eslint/issues/6125\n\t\tvar name;\n\n\t\tfor ( name in obj ) {\n\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\ttype: function( obj ) {\n\t\tif ( obj == null ) {\n\t\t\treturn obj + \"\";\n\t\t}\n\n\t\t// Support: Android <=2.3 only (functionish RegExp)\n\t\treturn typeof obj === \"object\" || typeof obj === \"function\" ?\n\t\t\tclass2type[ toString.call( obj ) ] || \"object\" :\n\t\t\ttypeof obj;\n\t},\n\n\t// Evaluates a script in a global context\n\tglobalEval: function( code ) {\n\t\tDOMEval( code );\n\t},\n\n\t// Convert dashed to camelCase; used by the css and data modules\n\t// Support: IE <=9 - 11, Edge 12 - 13\n\t// Microsoft forgot to hump their vendor prefix (#9572)\n\tcamelCase: function( string ) {\n\t\treturn string.replace( rmsPrefix, \"ms-\" ).replace( rdashAlpha, fcamelCase );\n\t},\n\n\tnodeName: function( elem, name ) {\n\t\treturn elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();\n\t},\n\n\teach: function( obj, callback ) {\n\t\tvar length, i = 0;\n\n\t\tif ( isArrayLike( obj ) ) {\n\t\t\tlength = obj.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tfor ( i in obj ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn obj;\n\t},\n\n\t// Support: Android <=4.0 only\n\ttrim: function( text ) {\n\t\treturn text == null ?\n\t\t\t\"\" :\n\t\t\t( text + \"\" ).replace( rtrim, \"\" );\n\t},\n\n\t// results is for internal usage only\n\tmakeArray: function( arr, results ) {\n\t\tvar ret = results || [];\n\n\t\tif ( arr != null ) {\n\t\t\tif ( isArrayLike( Object( arr ) ) ) {\n\t\t\t\tjQuery.merge( ret,\n\t\t\t\t\ttypeof arr === \"string\" ?\n\t\t\t\t\t[ arr ] : arr\n\t\t\t\t);\n\t\t\t} else {\n\t\t\t\tpush.call( ret, arr );\n\t\t\t}\n\t\t}\n\n\t\treturn ret;\n\t},\n\n\tinArray: function( elem, arr, i ) {\n\t\treturn arr == null ? -1 : indexOf.call( arr, elem, i );\n\t},\n\n\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t// push.apply(_, arraylike) throws on ancient WebKit\n\tmerge: function( first, second ) {\n\t\tvar len = +second.length,\n\t\t\tj = 0,\n\t\t\ti = first.length;\n\n\t\tfor ( ; j < len; j++ ) {\n\t\t\tfirst[ i++ ] = second[ j ];\n\t\t}\n\n\t\tfirst.length = i;\n\n\t\treturn first;\n\t},\n\n\tgrep: function( elems, callback, invert ) {\n\t\tvar callbackInverse,\n\t\t\tmatches = [],\n\t\t\ti = 0,\n\t\t\tlength = elems.length,\n\t\t\tcallbackExpect = !invert;\n\n\t\t// Go through the array, only saving the items\n\t\t// that pass the validator function\n\t\tfor ( ; i < length; i++ ) {\n\t\t\tcallbackInverse = !callback( elems[ i ], i );\n\t\t\tif ( callbackInverse !== callbackExpect ) {\n\t\t\t\tmatches.push( elems[ i ] );\n\t\t\t}\n\t\t}\n\n\t\treturn matches;\n\t},\n\n\t// arg is for internal usage only\n\tmap: function( elems, callback, arg ) {\n\t\tvar length, value,\n\t\t\ti = 0,\n\t\t\tret = [];\n\n\t\t// Go through the array, translating each of the items to their new values\n\t\tif ( isArrayLike( elems ) ) {\n\t\t\tlength = elems.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Go through every key on the object,\n\t\t} else {\n\t\t\tfor ( i in elems ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Flatten any nested arrays\n\t\treturn concat.apply( [], ret );\n\t},\n\n\t// A global GUID counter for objects\n\tguid: 1,\n\n\t// Bind a function to a context, optionally partially applying any\n\t// arguments.\n\tproxy: function( fn, context ) {\n\t\tvar tmp, args, proxy;\n\n\t\tif ( typeof context === \"string\" ) {\n\t\t\ttmp = fn[ context ];\n\t\t\tcontext = fn;\n\t\t\tfn = tmp;\n\t\t}\n\n\t\t// Quick check to determine if target is callable, in the spec\n\t\t// this throws a TypeError, but we will just return undefined.\n\t\tif ( !jQuery.isFunction( fn ) ) {\n\t\t\treturn undefined;\n\t\t}\n\n\t\t// Simulated bind\n\t\targs = slice.call( arguments, 2 );\n\t\tproxy = function() {\n\t\t\treturn fn.apply( context || this, args.concat( slice.call( arguments ) ) );\n\t\t};\n\n\t\t// Set the guid of unique handler to the same of original handler, so it can be removed\n\t\tproxy.guid = fn.guid = fn.guid || jQuery.guid++;\n\n\t\treturn proxy;\n\t},\n\n\tnow: Date.now,\n\n\t// jQuery.support is not used in Core but other projects attach their\n\t// properties to it so it needs to exist.\n\tsupport: support\n} );\n\nif ( typeof Symbol === \"function\" ) {\n\tjQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];\n}\n\n// Populate the class2type map\njQuery.each( \"Boolean Number String Function Array Date RegExp Object Error Symbol\".split( \" \" ),\nfunction( i, name ) {\n\tclass2type[ \"[object \" + name + \"]\" ] = name.toLowerCase();\n} );\n\nfunction isArrayLike( obj ) {\n\n\t// Support: real iOS 8.2 only (not reproducible in simulator)\n\t// `in` check used to prevent JIT error (gh-2145)\n\t// hasOwn isn't used here due to false negatives\n\t// regarding Nodelist length in IE\n\tvar length = !!obj && \"length\" in obj && obj.length,\n\t\ttype = jQuery.type( obj );\n\n\tif ( type === \"function\" || jQuery.isWindow( obj ) ) {\n\t\treturn false;\n\t}\n\n\treturn type === \"array\" || length === 0 ||\n\t\ttypeof length === \"number\" && length > 0 && ( length - 1 ) in obj;\n}\nvar Sizzle =\n/*!\n * Sizzle CSS Selector Engine v2.3.0\n * https://sizzlejs.com/\n *\n * Copyright jQuery Foundation and other contributors\n * Released under the MIT license\n * http://jquery.org/license\n *\n * Date: 2016-01-04\n */\n(function( window ) {\n\nvar i,\n\tsupport,\n\tExpr,\n\tgetText,\n\tisXML,\n\ttokenize,\n\tcompile,\n\tselect,\n\toutermostContext,\n\tsortInput,\n\thasDuplicate,\n\n\t// Local document vars\n\tsetDocument,\n\tdocument,\n\tdocElem,\n\tdocumentIsHTML,\n\trbuggyQSA,\n\trbuggyMatches,\n\tmatches,\n\tcontains,\n\n\t// Instance-specific data\n\texpando = \"sizzle\" + 1 * new Date(),\n\tpreferredDoc = window.document,\n\tdirruns = 0,\n\tdone = 0,\n\tclassCache = createCache(),\n\ttokenCache = createCache(),\n\tcompilerCache = createCache(),\n\tsortOrder = function( a, b ) {\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t}\n\t\treturn 0;\n\t},\n\n\t// Instance methods\n\thasOwn = ({}).hasOwnProperty,\n\tarr = [],\n\tpop = arr.pop,\n\tpush_native = arr.push,\n\tpush = arr.push,\n\tslice = arr.slice,\n\t// Use a stripped-down indexOf as it's faster than native\n\t// https://jsperf.com/thor-indexof-vs-for/5\n\tindexOf = function( list, elem ) {\n\t\tvar i = 0,\n\t\t\tlen = list.length;\n\t\tfor ( ; i < len; i++ ) {\n\t\t\tif ( list[i] === elem ) {\n\t\t\t\treturn i;\n\t\t\t}\n\t\t}\n\t\treturn -1;\n\t},\n\n\tbooleans = \"checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped\",\n\n\t// Regular expressions\n\n\t// http://www.w3.org/TR/css3-selectors/#whitespace\n\twhitespace = \"[\\\\x20\\\\t\\\\r\\\\n\\\\f]\",\n\n\t// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier\n\tidentifier = \"(?:\\\\\\\\.|[\\\\w-]|[^\\0-\\\\xa0])+\",\n\n\t// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors\n\tattributes = \"\\\\[\" + whitespace + \"*(\" + identifier + \")(?:\" + whitespace +\n\t\t// Operator (capture 2)\n\t\t\"*([*^$|!~]?=)\" + whitespace +\n\t\t// \"Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]\"\n\t\t\"*(?:'((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\"|(\" + identifier + \"))|)\" + whitespace +\n\t\t\"*\\\\]\",\n\n\tpseudos = \":(\" + identifier + \")(?:\\\\((\" +\n\t\t// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:\n\t\t// 1. quoted (capture 3; capture 4 or capture 5)\n\t\t\"('((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\")|\" +\n\t\t// 2. simple (capture 6)\n\t\t\"((?:\\\\\\\\.|[^\\\\\\\\()[\\\\]]|\" + attributes + \")*)|\" +\n\t\t// 3. anything else (capture 2)\n\t\t\".*\" +\n\t\t\")\\\\)|)\",\n\n\t// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter\n\trwhitespace = new RegExp( whitespace + \"+\", \"g\" ),\n\trtrim = new RegExp( \"^\" + whitespace + \"+|((?:^|[^\\\\\\\\])(?:\\\\\\\\.)*)\" + whitespace + \"+$\", \"g\" ),\n\n\trcomma = new RegExp( \"^\" + whitespace + \"*,\" + whitespace + \"*\" ),\n\trcombinators = new RegExp( \"^\" + whitespace + \"*([>+~]|\" + whitespace + \")\" + whitespace + \"*\" ),\n\n\trattributeQuotes = new RegExp( \"=\" + whitespace + \"*([^\\\\]'\\\"]*?)\" + whitespace + \"*\\\\]\", \"g\" ),\n\n\trpseudo = new RegExp( pseudos ),\n\tridentifier = new RegExp( \"^\" + identifier + \"$\" ),\n\n\tmatchExpr = {\n\t\t\"ID\": new RegExp( \"^#(\" + identifier + \")\" ),\n\t\t\"CLASS\": new RegExp( \"^\\\\.(\" + identifier + \")\" ),\n\t\t\"TAG\": new RegExp( \"^(\" + identifier + \"|[*])\" ),\n\t\t\"ATTR\": new RegExp( \"^\" + attributes ),\n\t\t\"PSEUDO\": new RegExp( \"^\" + pseudos ),\n\t\t\"CHILD\": new RegExp( \"^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\\\(\" + whitespace +\n\t\t\t\"*(even|odd|(([+-]|)(\\\\d*)n|)\" + whitespace + \"*(?:([+-]|)\" + whitespace +\n\t\t\t\"*(\\\\d+)|))\" + whitespace + \"*\\\\)|)\", \"i\" ),\n\t\t\"bool\": new RegExp( \"^(?:\" + booleans + \")$\", \"i\" ),\n\t\t// For use in libraries implementing .is()\n\t\t// We use this for POS matching in `select`\n\t\t\"needsContext\": new RegExp( \"^\" + whitespace + \"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\\\(\" +\n\t\t\twhitespace + \"*((?:-\\\\d)?\\\\d*)\" + whitespace + \"*\\\\)|)(?=[^-]|$)\", \"i\" )\n\t},\n\n\trinputs = /^(?:input|select|textarea|button)$/i,\n\trheader = /^h\\d$/i,\n\n\trnative = /^[^{]+\\{\\s*\\[native \\w/,\n\n\t// Easily-parseable/retrievable ID or TAG or CLASS selectors\n\trquickExpr = /^(?:#([\\w-]+)|(\\w+)|\\.([\\w-]+))$/,\n\n\trsibling = /[+~]/,\n\n\t// CSS escapes\n\t// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters\n\trunescape = new RegExp( \"\\\\\\\\([\\\\da-f]{1,6}\" + whitespace + \"?|(\" + whitespace + \")|.)\", \"ig\" ),\n\tfunescape = function( _, escaped, escapedWhitespace ) {\n\t\tvar high = \"0x\" + escaped - 0x10000;\n\t\t// NaN means non-codepoint\n\t\t// Support: Firefox<24\n\t\t// Workaround erroneous numeric interpretation of +\"0x\"\n\t\treturn high !== high || escapedWhitespace ?\n\t\t\tescaped :\n\t\t\thigh < 0 ?\n\t\t\t\t// BMP codepoint\n\t\t\t\tString.fromCharCode( high + 0x10000 ) :\n\t\t\t\t// Supplemental Plane codepoint (surrogate pair)\n\t\t\t\tString.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );\n\t},\n\n\t// CSS string/identifier serialization\n\t// https://drafts.csswg.org/cssom/#common-serializing-idioms\n\trcssescape = /([\\0-\\x1f\\x7f]|^-?\\d)|^-$|[^\\x80-\\uFFFF\\w-]/g,\n\tfcssescape = function( ch, asCodePoint ) {\n\t\tif ( asCodePoint ) {\n\n\t\t\t// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER\n\t\t\tif ( ch === \"\\0\" ) {\n\t\t\t\treturn \"\\uFFFD\";\n\t\t\t}\n\n\t\t\t// Control characters and (dependent upon position) numbers get escaped as code points\n\t\t\treturn ch.slice( 0, -1 ) + \"\\\\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + \" \";\n\t\t}\n\n\t\t// Other potentially-special ASCII characters get backslash-escaped\n\t\treturn \"\\\\\" + ch;\n\t},\n\n\t// Used for iframes\n\t// See setDocument()\n\t// Removing the function wrapper causes a \"Permission Denied\"\n\t// error in IE\n\tunloadHandler = function() {\n\t\tsetDocument();\n\t},\n\n\tdisabledAncestor = addCombinator(\n\t\tfunction( elem ) {\n\t\t\treturn elem.disabled === true;\n\t\t},\n\t\t{ dir: \"parentNode\", next: \"legend\" }\n\t);\n\n// Optimize for push.apply( _, NodeList )\ntry {\n\tpush.apply(\n\t\t(arr = slice.call( preferredDoc.childNodes )),\n\t\tpreferredDoc.childNodes\n\t);\n\t// Support: Android<4.0\n\t// Detect silently failing push.apply\n\tarr[ preferredDoc.childNodes.length ].nodeType;\n} catch ( e ) {\n\tpush = { apply: arr.length ?\n\n\t\t// Leverage slice if possible\n\t\tfunction( target, els ) {\n\t\t\tpush_native.apply( target, slice.call(els) );\n\t\t} :\n\n\t\t// Support: IE<9\n\t\t// Otherwise append directly\n\t\tfunction( target, els ) {\n\t\t\tvar j = target.length,\n\t\t\t\ti = 0;\n\t\t\t// Can't trust NodeList.length\n\t\t\twhile ( (target[j++] = els[i++]) ) {}\n\t\t\ttarget.length = j - 1;\n\t\t}\n\t};\n}\n\nfunction Sizzle( selector, context, results, seed ) {\n\tvar m, i, elem, nid, match, groups, newSelector,\n\t\tnewContext = context && context.ownerDocument,\n\n\t\t// nodeType defaults to 9, since context defaults to document\n\t\tnodeType = context ? context.nodeType : 9;\n\n\tresults = results || [];\n\n\t// Return early from calls with invalid selector or context\n\tif ( typeof selector !== \"string\" || !selector ||\n\t\tnodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {\n\n\t\treturn results;\n\t}\n\n\t// Try to shortcut find operations (as opposed to filters) in HTML documents\n\tif ( !seed ) {\n\n\t\tif ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {\n\t\t\tsetDocument( context );\n\t\t}\n\t\tcontext = context || document;\n\n\t\tif ( documentIsHTML ) {\n\n\t\t\t// If the selector is sufficiently simple, try using a \"get*By*\" DOM method\n\t\t\t// (excepting DocumentFragment context, where the methods don't exist)\n\t\t\tif ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {\n\n\t\t\t\t// ID selector\n\t\t\t\tif ( (m = match[1]) ) {\n\n\t\t\t\t\t// Document context\n\t\t\t\t\tif ( nodeType === 9 ) {\n\t\t\t\t\t\tif ( (elem = context.getElementById( m )) ) {\n\n\t\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\t\tif ( elem.id === m ) {\n\t\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t// Element context\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\tif ( newContext && (elem = newContext.getElementById( m )) &&\n\t\t\t\t\t\t\tcontains( context, elem ) &&\n\t\t\t\t\t\t\telem.id === m ) {\n\n\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t// Type selector\n\t\t\t\t} else if ( match[2] ) {\n\t\t\t\t\tpush.apply( results, context.getElementsByTagName( selector ) );\n\t\t\t\t\treturn results;\n\n\t\t\t\t// Class selector\n\t\t\t\t} else if ( (m = match[3]) && support.getElementsByClassName &&\n\t\t\t\t\tcontext.getElementsByClassName ) {\n\n\t\t\t\t\tpush.apply( results, context.getElementsByClassName( m ) );\n\t\t\t\t\treturn results;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Take advantage of querySelectorAll\n\t\t\tif ( support.qsa &&\n\t\t\t\t!compilerCache[ selector + \" \" ] &&\n\t\t\t\t(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {\n\n\t\t\t\tif ( nodeType !== 1 ) {\n\t\t\t\t\tnewContext = context;\n\t\t\t\t\tnewSelector = selector;\n\n\t\t\t\t// qSA looks outside Element context, which is not what we want\n\t\t\t\t// Thanks to Andrew Dupont for this workaround technique\n\t\t\t\t// Support: IE <=8\n\t\t\t\t// Exclude object elements\n\t\t\t\t} else if ( context.nodeName.toLowerCase() !== \"object\" ) {\n\n\t\t\t\t\t// Capture the context ID, setting it first if necessary\n\t\t\t\t\tif ( (nid = context.getAttribute( \"id\" )) ) {\n\t\t\t\t\t\tnid = nid.replace( rcssescape, fcssescape );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tcontext.setAttribute( \"id\", (nid = expando) );\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prefix every selector in the list\n\t\t\t\t\tgroups = tokenize( selector );\n\t\t\t\t\ti = groups.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tgroups[i] = \"#\" + nid + \" \" + toSelector( groups[i] );\n\t\t\t\t\t}\n\t\t\t\t\tnewSelector = groups.join( \",\" );\n\n\t\t\t\t\t// Expand context for sibling selectors\n\t\t\t\t\tnewContext = rsibling.test( selector ) && testContext( context.parentNode ) ||\n\t\t\t\t\t\tcontext;\n\t\t\t\t}\n\n\t\t\t\tif ( newSelector ) {\n\t\t\t\t\ttry {\n\t\t\t\t\t\tpush.apply( results,\n\t\t\t\t\t\t\tnewContext.querySelectorAll( newSelector )\n\t\t\t\t\t\t);\n\t\t\t\t\t\treturn results;\n\t\t\t\t\t} catch ( qsaError ) {\n\t\t\t\t\t} finally {\n\t\t\t\t\t\tif ( nid === expando ) {\n\t\t\t\t\t\t\tcontext.removeAttribute( \"id\" );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// All others\n\treturn select( selector.replace( rtrim, \"$1\" ), context, results, seed );\n}\n\n/**\n * Create key-value caches of limited size\n * @returns {function(string, object)} Returns the Object data after storing it on itself with\n *\tproperty name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)\n *\tdeleting the oldest entry\n */\nfunction createCache() {\n\tvar keys = [];\n\n\tfunction cache( key, value ) {\n\t\t// Use (key + \" \") to avoid collision with native prototype properties (see Issue #157)\n\t\tif ( keys.push( key + \" \" ) > Expr.cacheLength ) {\n\t\t\t// Only keep the most recent entries\n\t\t\tdelete cache[ keys.shift() ];\n\t\t}\n\t\treturn (cache[ key + \" \" ] = value);\n\t}\n\treturn cache;\n}\n\n/**\n * Mark a function for special use by Sizzle\n * @param {Function} fn The function to mark\n */\nfunction markFunction( fn ) {\n\tfn[ expando ] = true;\n\treturn fn;\n}\n\n/**\n * Support testing using an element\n * @param {Function} fn Passed the created element and returns a boolean result\n */\nfunction assert( fn ) {\n\tvar el = document.createElement(\"fieldset\");\n\n\ttry {\n\t\treturn !!fn( el );\n\t} catch (e) {\n\t\treturn false;\n\t} finally {\n\t\t// Remove from its parent by default\n\t\tif ( el.parentNode ) {\n\t\t\tel.parentNode.removeChild( el );\n\t\t}\n\t\t// release memory in IE\n\t\tel = null;\n\t}\n}\n\n/**\n * Adds the same handler for all of the specified attrs\n * @param {String} attrs Pipe-separated list of attributes\n * @param {Function} handler The method that will be applied\n */\nfunction addHandle( attrs, handler ) {\n\tvar arr = attrs.split(\"|\"),\n\t\ti = arr.length;\n\n\twhile ( i-- ) {\n\t\tExpr.attrHandle[ arr[i] ] = handler;\n\t}\n}\n\n/**\n * Checks document order of two siblings\n * @param {Element} a\n * @param {Element} b\n * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b\n */\nfunction siblingCheck( a, b ) {\n\tvar cur = b && a,\n\t\tdiff = cur && a.nodeType === 1 && b.nodeType === 1 &&\n\t\t\ta.sourceIndex - b.sourceIndex;\n\n\t// Use IE sourceIndex if available on both nodes\n\tif ( diff ) {\n\t\treturn diff;\n\t}\n\n\t// Check if b follows a\n\tif ( cur ) {\n\t\twhile ( (cur = cur.nextSibling) ) {\n\t\t\tif ( cur === b ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn a ? 1 : -1;\n}\n\n/**\n * Returns a function to use in pseudos for input types\n * @param {String} type\n */\nfunction createInputPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn name === \"input\" && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for buttons\n * @param {String} type\n */\nfunction createButtonPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn (name === \"input\" || name === \"button\") && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for :enabled/:disabled\n * @param {Boolean} disabled true for :disabled; false for :enabled\n */\nfunction createDisabledPseudo( disabled ) {\n\t// Known :disabled false positives:\n\t// IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset)\n\t// not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable\n\treturn function( elem ) {\n\n\t\t// Check form elements and option elements for explicit disabling\n\t\treturn \"label\" in elem && elem.disabled === disabled ||\n\t\t\t\"form\" in elem && elem.disabled === disabled ||\n\n\t\t\t// Check non-disabled form elements for fieldset[disabled] ancestors\n\t\t\t\"form\" in elem && elem.disabled === false && (\n\t\t\t\t// Support: IE6-11+\n\t\t\t\t// Ancestry is covered for us\n\t\t\t\telem.isDisabled === disabled ||\n\n\t\t\t\t// Otherwise, assume any non-