From 0b9cf6545fe945ebd81968bd15f46c4959965f8d Mon Sep 17 00:00:00 2001 From: Hakim Date: Fri, 23 Dec 2022 11:17:48 +0100 Subject: [PATCH 1/9] fix: include last pair in RIPA score --- wefe/metrics/RIPA.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wefe/metrics/RIPA.py b/wefe/metrics/RIPA.py index 74b191b..796735f 100644 --- a/wefe/metrics/RIPA.py +++ b/wefe/metrics/RIPA.py @@ -112,7 +112,7 @@ def _calc_metric( # calculating the ripa score for each attribute word with each target pair for word in range(len(attribute_embeddings_0)): ripa_scores[attributes[word]] = [] - for index in range(target_length - 1): + for index in range(target_length): bvec = self._b_vec( target_embeddings_0[index], target_embeddings_1[index] ) From f3a933fa4e975f28be510aeef78fdc8b9bac8bd5 Mon Sep 17 00:00:00 2001 From: Pablo Badilla Date: Mon, 26 Dec 2022 21:37:43 -0300 Subject: [PATCH 2/9] precommit installation and execution --- .coveragerc | 2 +- .pre-commit-config.yaml | 21 + MANIFEST.in | 1 - README.rst | 60 +- docs/Makefile | 2 +- docs/_static/css/theme_overrides.css | 2 +- docs/_static/js/copybutton.js | 2 +- docs/_templates/numpydoc_docstring.py | 16 - docs/api/api.rst | 4 +- .../measurement_framework.rst | 78 +- .../mitigation_framework.rst | 26 +- docs/conceptual_guides/references.rst | 13 +- docs/conf.py | 10 +- docs/examples/multilingual.rst | 13 +- docs/examples/replications.rst | 54 +- docs/examples/wefe_case_study.rst | 26 +- docs/getting_started/about.rst | 64 +- docs/getting_started/quick_start.rst | 48 +- docs/index.rst | 16 +- docs/logos/WEFE.svg | 2 +- docs/logos/WEFE_2.svg | 2 +- docs/logos/WEFE_2_BLANCO.svg | 2 +- docs/logos/WEFE_BLANCO.svg | 2 +- docs/user_guide/contribute.rst | 206 +++--- docs/user_guide/loading_embeddings.rst | 48 +- docs/user_guide/measurement_user_guide.rst | 143 ++-- docs/user_guide/mitigation_user_guide.rst | 139 ++-- examples/Contributing.ipynb | 2 +- examples/User_Guide.ipynb | 2 +- examples/measurement_user_guide.ipynb | 2 +- mypy.ini | 2 +- pyproject.toml | 77 ++ requirements-dev.txt | 7 +- requirements.txt | 2 +- tests/debias/conftest.py | 1 + tests/debias/test_base_debias.py | 1 + tests/debias/test_double_hard_debias.py | 1 + tests/debias/test_half_sibling_regression.py | 1 + tests/debias/test_hard_debias.py | 1 + tests/debias/test_multiclass_hard_debias.py | 1 + ...est_repulsion_attraction_neutralization.py | 1 + tests/metrics/conftest.py | 1 + tests/metrics/test_ECT.py | 1 + tests/metrics/test_MAC.py | 1 + tests/metrics/test_RIPA.py | 1 + tests/metrics/test_RND.py | 1 + tests/metrics/test_RNSB.py | 1 + tests/metrics/test_WEAT.py | 1 + tests/metrics/test_base_metric.py | 1 + tests/test_preprocessing.py | 1 + tests/test_query.py | 1 + tests/test_word_embedding_model.py | 1 + wefe/datasets/data/negative-words.txt | 28 +- wefe/datasets/data/positive-words.txt | 28 +- wefe/datasets/data/weat_w2v____old.txt | 694 +++++++++--------- wefe/debias/double_hard_debias.py | 1 + wefe/debias/half_sibling_regression.py | 1 + wefe/debias/hard_debias.py | 1 + wefe/debias/multiclass_hard_debias.py | 1 + .../repulsion_attraction_neutralization.py | 1 + wefe/metrics/MAC.py | 25 +- wefe/metrics/RND.py | 1 + wefe/metrics/RNSB.py | 36 +- wefe/metrics/WEAT.py | 1 + wefe/metrics/example_metric.py | 1 + wefe/word_embedding_model.py | 3 +- 66 files changed, 1034 insertions(+), 902 deletions(-) create mode 100644 .pre-commit-config.yaml delete mode 100644 docs/_templates/numpydoc_docstring.py create mode 100644 pyproject.toml diff --git a/.coveragerc b/.coveragerc index c604b3a..35657a5 100644 --- a/.coveragerc +++ b/.coveragerc @@ -18,4 +18,4 @@ exclude_lines = if 0: if __name__ == .__main__.: if self.verbose: -show_missing = True \ No newline at end of file +show_missing = True diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..fa81bac --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,21 @@ +repos: + - repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + name: isort (python) + - repo: https://github.com/psf/black + rev: 22.1.0 + hooks: + - id: black + additional_dependencies: ['click==8.0.4'] + - repo: https://github.com/pycqa/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v2.3.0 + hooks: + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace diff --git a/MANIFEST.in b/MANIFEST.in index 876e9b0..d6ab8d6 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,2 @@ include requirements.txt include wefe/datasets/data/* - diff --git a/README.rst b/README.rst index a82039f..322062b 100644 --- a/README.rst +++ b/README.rst @@ -29,19 +29,19 @@ WEFE: The Word Embedding Fairness Evaluation Framework :alt: WEFE Logo :align: center -*Word Embedding Fairness Evaluation* (WEFE) is an open source library for -measuring an mitigating bias in word embedding models. -It generalizes many existing fairness metrics into a unified framework and +*Word Embedding Fairness Evaluation* (WEFE) is an open source library for +measuring an mitigating bias in word embedding models. +It generalizes many existing fairness metrics into a unified framework and provides a standard interface for: - Encapsulating existing fairness metrics from previous work and designing new ones. - Encapsulating the test words used by fairness metrics into standard objects called queries. -- Computing a fairness metric on a given pre-trained word embedding model +- Computing a fairness metric on a given pre-trained word embedding model using user-given queries. -WEFE also standardizes the process of mitigating bias through an interface similar +WEFE also standardizes the process of mitigating bias through an interface similar to the ``scikit-learn`` ``fit-transform``. This standardization separates the mitigation process into two stages: @@ -55,18 +55,18 @@ The official documentation can be found at this `link `_ Bibtex: -.. code-block:: latex +.. code-block:: latex @InProceedings{wefe2020, title = {WEFE: The Word Embeddings Fairness Evaluation Framework}, author = {Badilla, Pablo and Bravo-Marquez, Felipe and Pérez, Jorge}, booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}}, - publisher = {International Joint Conferences on Artificial Intelligence Organization}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, pages = {430--436}, year = {2020}, month = {7}, @@ -274,12 +274,12 @@ Contributors ------------ -We thank all our contributors who have allowed WEFE to grow, especially -`stolenpyjak `_ and +We thank all our contributors who have allowed WEFE to grow, especially +`stolenpyjak `_ and `mspl13 `_ for implementing new metrics. -We also thank `alan-cueva `_ for initiating the development -of metrics for contextualized embedding models and +We also thank `alan-cueva `_ for initiating the development +of metrics for contextualized embedding models and `harshvr15 `_ for the examples of multi-language bias measurement. Thank you very much 😊! diff --git a/docs/Makefile b/docs/Makefile index ef2103c..4b4c556 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -181,4 +181,4 @@ xml: pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." \ No newline at end of file + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/docs/_static/css/theme_overrides.css b/docs/_static/css/theme_overrides.css index a858562..188cc57 100644 --- a/docs/_static/css/theme_overrides.css +++ b/docs/_static/css/theme_overrides.css @@ -51,4 +51,4 @@ .wy-side-nav-search>a img.logo { width: 500px; max-width: 100%; -} \ No newline at end of file +} diff --git a/docs/_static/js/copybutton.js b/docs/_static/js/copybutton.js index e05a086..cf82822 100644 --- a/docs/_static/js/copybutton.js +++ b/docs/_static/js/copybutton.js @@ -60,4 +60,4 @@ $(document).ready(function () { button.data('hidden', 'false'); } }); -}); \ No newline at end of file +}); diff --git a/docs/_templates/numpydoc_docstring.py b/docs/_templates/numpydoc_docstring.py deleted file mode 100644 index 81fdb40..0000000 --- a/docs/_templates/numpydoc_docstring.py +++ /dev/null @@ -1,16 +0,0 @@ -{{index}} -{{summary}} -{{extended_summary}} -{{parameters}} -{{returns}} -{{yields}} -{{other_parameters}} -{{attributes}} -{{raises}} -{{warns}} -{{warnings}} -{{see_also}} -{{notes}} -{{references}} -{{examples}} -{{methods}} diff --git a/docs/api/api.rst b/docs/api/api.rst index da2fcd7..d152e3e 100644 --- a/docs/api/api.rst +++ b/docs/api/api.rst @@ -85,7 +85,7 @@ Preprocessing ============= -The following functions allow transforming sets of words and queries to embeddings. +The following functions allow transforming sets of words and queries to embeddings. The documentation of the functions in this section are intended as a guide for WEFE developers. .. autosummary:: @@ -101,7 +101,7 @@ The documentation of the functions in this section are intended as a guide for W Utils ===== -Collection of assorted utils. +Collection of assorted utils. .. autosummary:: :toctree: generated/ diff --git a/docs/conceptual_guides/measurement_framework.rst b/docs/conceptual_guides/measurement_framework.rst index 1734179..1f26c7d 100644 --- a/docs/conceptual_guides/measurement_framework.rst +++ b/docs/conceptual_guides/measurement_framework.rst @@ -3,45 +3,45 @@ Measurement Framework ===================== -Below we present the main aspects of the measurement framework developed at WEFE. +Below we present the main aspects of the measurement framework developed at WEFE. .. note:: - - If you want to see tutorials on how to apply queries, visit :ref:`bias measurement` + + If you want to see tutorials on how to apply queries, visit :ref:`bias measurement` in the User Guide. -Target set +Target set ---------- -A target word set (denoted by :math:`T`) corresponds to a -set of words intended to denote a particular social group,which is defined by a -certain criterion. This criterion can be any character, trait or origin that -distinguishes groups of people from each other e.g., gender, social class, age, -and ethnicity. For example, if the criterion is gender we can use it to -distinguish two groups, `women and men`. Then, a set of target words -representing the social group "*women*" could contain words like *'she'*, -*'woman'*, *'girl'*, etc. Analogously a set of target words the representing the +A target word set (denoted by :math:`T`) corresponds to a +set of words intended to denote a particular social group,which is defined by a +certain criterion. This criterion can be any character, trait or origin that +distinguishes groups of people from each other e.g., gender, social class, age, +and ethnicity. For example, if the criterion is gender we can use it to +distinguish two groups, `women and men`. Then, a set of target words +representing the social group "*women*" could contain words like *'she'*, +*'woman'*, *'girl'*, etc. Analogously a set of target words the representing the social group '*men'* could include *'he'*, *'man'*, *'boy'*, etc. Attribute set ------------- -An attribute word set (denoted by :math:`A`) is a set of words -representing some attitude, characteristic, trait, occupational field, etc. +An attribute word set (denoted by :math:`A`) is a set of words +representing some attitude, characteristic, trait, occupational field, etc. that can be associated with individuals from any social group. For example, -the set of *science* attribute words could contain words such as +the set of *science* attribute words could contain words such as *'technology'*, *'physics'*, *'chemistry'*, while the *art* attribute words could have words like *'poetry'*, *'dance'*, *'literature'*. Query ----- -Queries are the main building blocks used by fairness metrics to measure bias -of word embedding models. -Formally, a query is a pair :math:`Q=(\mathcal{T},\mathcal{A})` in which -:math:`T` is a set of target word sets, and :math:`A` is a set of attribute +Queries are the main building blocks used by fairness metrics to measure bias +of word embedding models. +Formally, a query is a pair :math:`Q=(\mathcal{T},\mathcal{A})` in which +:math:`T` is a set of target word sets, and :math:`A` is a set of attribute word sets. For example, consider the target word sets: @@ -70,17 +70,17 @@ Then the following is a query in our framework \end{equation} When a set of queries :math:`\mathcal{Q} = {Q_1, Q_2, \dots, Q_n}` is intended -to measure a single type of bias, we say that the set has a -**Bias Criterion**. -Examples of bias criteria are gender, ethnicity, religion, politics, +to measure a single type of bias, we say that the set has a +**Bias Criterion**. +Examples of bias criteria are gender, ethnicity, religion, politics, social class, among others. .. warning:: - To accurately study the biases contained in word embeddings, queries may - contain words that could be offensive to certain groups or individuals. - The relationships studied between these words DO NOT represent the ideas, - thoughts or beliefs of the authors of this library. + To accurately study the biases contained in word embeddings, queries may + contain words that could be offensive to certain groups or individuals. + The relationships studied between these words DO NOT represent the ideas, + thoughts or beliefs of the authors of this library. This warning applies to all documentation. @@ -89,27 +89,27 @@ Query Template -------------- A query template is simply a pair :math:`(t,a)\in\mathbb{N}\times\mathbb{N}`. -We say that query :math:`Q=(\mathcal{T},\mathcal{A})` satisfies a +We say that query :math:`Q=(\mathcal{T},\mathcal{A})` satisfies a template :math:`(t,a)` if :math:`|\mathcal{T}|=t` and :math:`|\mathcal{A}|=a`. Fairness Measure ---------------- -A fairness metric is a function that quantifies the degree of association -between target and attribute words in a word embedding model. -In our framework, every fairness metric is defined as a function that has a +A fairness metric is a function that quantifies the degree of association +between target and attribute words in a word embedding model. +In our framework, every fairness metric is defined as a function that has a query and a model as input, and produces a real number as output. Several fairness metrics have been proposed in the literature. But not all of them share a common input template for queries. -Thus, we assume that every fairness metric comes with a template that -essentially defines the shape of the input queries supported by the metric. +Thus, we assume that every fairness metric comes with a template that +essentially defines the shape of the input queries supported by the metric. -Formally, let :math:`F` be a fairness metric with template :math:`s_F=(t_F,a_F)`. -Given an embedding model :math:`\mathbf{M}` and a query :math:`Q` that -satisfies :math:`s_F`, the metric produces the value -:math:`F(\mathbf{M},Q)\in \mathbb{R}` that quantifies the degree of bias of +Formally, let :math:`F` be a fairness metric with template :math:`s_F=(t_F,a_F)`. +Given an embedding model :math:`\mathbf{M}` and a query :math:`Q` that +satisfies :math:`s_F`, the metric produces the value +:math:`F(\mathbf{M},Q)\in \mathbb{R}` that quantifies the degree of bias of :math:`\mathbf{M}` with respect to query :math:`Q`. @@ -120,17 +120,17 @@ Standard usage pattern of WEFE The following flow chart shows how to perform a bias measurement using a gender query, word2vec embeddings and the WEAT metric. - + .. image:: ../images/diagram_1.png :alt: Bias measurement diagram. -To see the implementation of this query using WEFE, refer to +To see the implementation of this query using WEFE, refer to the :ref:`Quick start ` section. Metrics Implemented So Far -------------------------- -WEFE implements the following bias measurement metrics: +WEFE implements the following bias measurement metrics: - Word Embedding Association Test (:class:`~wefe.metrics.WEAT.WEAT`) diff --git a/docs/conceptual_guides/mitigation_framework.rst b/docs/conceptual_guides/mitigation_framework.rst index 988bb77..1aabbbe 100644 --- a/docs/conceptual_guides/mitigation_framework.rst +++ b/docs/conceptual_guides/mitigation_framework.rst @@ -4,27 +4,27 @@ Mitigation Framework ==================== .. note:: - If you want to see tutorials on how to mitigate (debias) bias in word embedding + If you want to see tutorials on how to mitigate (debias) bias in word embedding models, visit :ref:`Bias Mitigation ` in the User Guide. -WEFE standardizes all mitigation methods through an interface inherited from -``scikit-learn`` basic data transformations: the ``fit-transform`` interface. +WEFE standardizes all mitigation methods through an interface inherited from +``scikit-learn`` basic data transformations: the ``fit-transform`` interface. Fit method ---------- -The first step ``fit``, consists in learning the corresponding mitigation -transformation, which usually corresponds to a matrix projection of the -embedding space. -This method is quite flexible: it can accept multiple sets of words and other parameters. +The first step ``fit``, consists in learning the corresponding mitigation +transformation, which usually corresponds to a matrix projection of the +embedding space. +This method is quite flexible: it can accept multiple sets of words and other parameters. Transform method ---------------- The ``transform`` method applies the transformation learned in the previous step to -words residing in the original embedding space. The method is rigid and only accepts -lists of words that should be mitigated (``target``) or words that should be omitted +words residing in the original embedding space. The method is rigid and only accepts +lists of words that should be mitigated (``target``) or words that should be omitted (``ignore``). The process by which debiasing methods are used is shown in the following Figure. @@ -37,14 +37,14 @@ The process by which debiasing methods are used is shown in the following Figure Mitigation Methods Implemented So Far ------------------------------------- -WEFE implements the following bias mitigation (debias) metrics: +WEFE implements the following bias mitigation (debias) metrics: -- :class:`~wefe.debias.hard_debias.HardDebias`, +- :class:`~wefe.debias.hard_debias.HardDebias`, - :class:`~wefe.debias.multiclass_hard_debias.MulticlassHardDebias`, - :class:`~wefe.debias.double_hard_debias.DoubleHardDebias`, - :class:`~wefe.debias.repulsion_attraction_neutralization.RepulsionAttractionNeutralization`, - :class:`~wefe.debias.half_sibling_regression.HalfSiblingRegression`, -Except for ``MulticlassHardDebias``, all methods are limited to binary criteria, -such as gender. \ No newline at end of file +Except for ``MulticlassHardDebias``, all methods are limited to binary criteria, +such as gender. diff --git a/docs/conceptual_guides/references.rst b/docs/conceptual_guides/references.rst index 8bad7ad..76cdfb9 100644 --- a/docs/conceptual_guides/references.rst +++ b/docs/conceptual_guides/references.rst @@ -1,11 +1,11 @@ References ========== -The intention of this section is to provide a list of the works on which WEFE -relies as well as a rough reference of works on measuring and mitigating bias -in word embeddings. +The intention of this section is to provide a list of the works on which WEFE +relies as well as a rough reference of works on measuring and mitigating bias +in word embeddings. -Measurements and Case Studies +Measurements and Case Studies ----------------------------- @@ -35,11 +35,10 @@ A Survey on Bias and Fairness in Machine Learning - `Bakarov, A. (2018). A survey of word embeddings evaluation methods. arXiv preprint arXiv:1801.09536. `_ - `Camacho-Collados, J., & Pilehvar, M. T. (2018). From word to sense embeddings: A survey on vector representations of meaning. Journal of Artificial Intelligence Research, 63, 743-788. `_ -Bias in Contextualized Word Embeddings +Bias in Contextualized Word Embeddings - `Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., & Chang, K. W. (2019). Gender bias in contextualized word embeddings. arXiv preprint arXiv:1904.03310. `_ - `Basta, C., Costa-jussà, M. R., & Casas, N. (2019). Evaluating the underlying gender bias in contextualized word embeddings. arXiv preprint arXiv:1904.08783. `_ - `Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019). Measuring bias in contextualized word representations. arXiv preprint arXiv:1906.07337. `_ - `Tan, Y. C., & Celis, L. E. (2019). Assessing social and intersectional biases in contextualized word representations. In Advances in Neural Information Processing Systems (pp. 13209-13220). `_ -- `Stereoset: A Measure of Bias in Language Models `_ - +- `Stereoset: A Measure of Bias in Language Models `_ diff --git a/docs/conf.py b/docs/conf.py index b2ee3fc..1c50115 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -15,7 +15,8 @@ import os import sys -import sphinx_rtd_theme +# import sphinx +# import sphinx_rtd_theme # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the @@ -51,7 +52,6 @@ numpydoc_show_class_members = False # pngmath / imgmath compatibility layer for different sphinx versions -import sphinx if os.environ.get("NO_MATHJAX"): extensions.append("sphinx.ext.imgmath") @@ -93,7 +93,7 @@ # built documents. # # The short X.Y version. -from wefe import __version__ +from wefe import __version__ # noqa: E402 # __version__ = '0.0.1' version = __version__ @@ -237,9 +237,9 @@ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', + # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', + # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. "preamble": r""" \usepackage{amsmath}\usepackage{amsfonts}\usepackage{bm} diff --git a/docs/examples/multilingual.rst b/docs/examples/multilingual.rst index 3adb9b2..e4f24fb 100644 --- a/docs/examples/multilingual.rst +++ b/docs/examples/multilingual.rst @@ -2,12 +2,12 @@ Multilingual Gender Bias Measurement Examples ============================================= -The notebooks located in -`multilingual examples folder `_ +The notebooks located in +`multilingual examples folder `_ show how to measure gender bias in static embeddings from gender queries in different languages. -The word embedding models in different languages are obtained from the flair library. +The word embedding models in different languages are obtained from the flair library. The notebooks are self-contained: they contain everything necessary to load the embeddings and execute the queries and allow flair to be installed in case it is not already installed. @@ -17,10 +17,9 @@ Swedish, Dutch. .. warning:: - The words sets used in the notebooks were translated using google translator. + The words sets used in the notebooks were translated using google translator. Therefore, it is possible that some concepts may have been mistranslated and may - require some correction. The original English concepts can be loaded using + require some correction. The original English concepts can be loaded using `load_weat `_ - util. + util. Read the instructions carefully and use the notebooks and its results with caution! - diff --git a/docs/examples/replications.rst b/docs/examples/replications.rst index 503acc7..4cea241 100644 --- a/docs/examples/replications.rst +++ b/docs/examples/replications.rst @@ -2,26 +2,26 @@ Previous Studies Replication =============================== -All replications of other studies that WEFE has currently implemented are in the Examples folder. +All replications of other studies that WEFE has currently implemented are in the Examples folder. Below we list some examples: Semantics derived automatically from language corpora contain human-like biases (WEAT) ====================================================================================== -The following -`notebook `__ +The following +`notebook `__ reproduces the experiments performed in the following paper: Semantics derived automatically from language corpora contain human-like biases. Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan -.. note:: +.. note:: - Due to the formulation of the metric and the methods to transform the word to embeddings, - our results are not exactly the same as those reported in - the original paper. However, our results are still very similar to those + Due to the formulation of the metric and the methods to transform the word to embeddings, + our results are not exactly the same as those reported in + the original paper. However, our results are still very similar to those in the original paper. @@ -29,30 +29,30 @@ reproduces the experiments performed in the following paper: A transparent framework for evaluating unintended demographic bias in word embeddings (RNSB) ============================================================================================ -The following -`notebook `__ +The following +`notebook `__ replicates the experiments carried out in the following paper: Chris Sweeney and Maryam Najafian. A transparent framework for evaluating unintended demographic bias in word embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1662–1667, 2019. -.. note:: +.. note:: - Due to the formulation of the metric (it trains a logistic regression in - each execution) our results are not exactly the same as those reported in - the original paper. However, our results are still very similar to those + Due to the formulation of the metric (it trains a logistic regression in + each execution) our results are not exactly the same as those reported in + the original paper. However, our results are still very similar to those in the original paper. >>> from wefe.datasets import load_bingliu >>> from wefe.metrics import RNSB >>> from wefe.query import Query ->>> from wefe.word_embedding import ->>> +>>> from wefe.word_embedding import +>>> >>> import pandas as pd >>> import plotly.express as px >>> import gensim.downloader as api ->>> +>>> >>> # load the target word sets. >>> # In this case each word is an objective set because each of them represents a different social group. >>> RNSB_words = [ @@ -60,13 +60,13 @@ replicates the experiments carried out in the following paper: >>> ['french'], ['norwegian'], ['american'], ['indian'], ['dutch'], ['russian'], >>> ['scottish'], ['italian'] >>> ] ->>> +>>> >>> bing_liu = load_bingliu() ->>> +>>> >>> # Create the query >>> query = Query(RNSB_words, >>> [bing_liu['positive_words'], bing_liu['negative_words']]) ->>> +>>> >>> # Fetch the models >>> glove = (api.load('glove-wiki-gigaword-300'), >>> 'glove-wiki-gigaword-300') @@ -74,12 +74,12 @@ replicates the experiments carried out in the following paper: >>> conceptnet = (api.load('conceptnet-numberbatch-17-06-300'), >>> 'conceptnet-numberbatch-17', >>> vocab_prefix='/c/en/') ->>> +>>> >>> # Run the queries >>> glove_results = RNSB().run_query(query, glove) >>> conceptnet_results = RNSB().run_query(query, conceptnet) ->>> ->>> +>>> +>>> >>> # Show the results obtained with glove >>> glove_fig = px.bar( >>> pd.DataFrame(glove_results['negative_sentiment_distribution'], @@ -113,7 +113,7 @@ replicates the experiments carried out in the following paper: >>> columns=['Word', 'Sentiment distribution']) >>> fair_distribution['Sentiment distribution'] = np.ones( >>> fair_distribution.shape[0]) / fair_distribution.shape[0] ->>> +>>> >>> fair_distribution_fig = px.bar(fair_distribution, x='Word', >>> y='Sentiment distribution', >>> title='Fair negative sentiment distribution') @@ -124,9 +124,9 @@ replicates the experiments carried out in the following paper: .. image:: ../images/fair_rnsb.png :alt: Fair RNSB sentiment distribution -.. note:: +.. note:: - This code is not executed when compiling the documentation due to the long - processing time. + This code is not executed when compiling the documentation due to the long + processing time. Instead, the tables and plots of these results were embedded. - The code is available for execution in the following `notebook `__. \ No newline at end of file + The code is available for execution in the following `notebook `__. diff --git a/docs/examples/wefe_case_study.rst b/docs/examples/wefe_case_study.rst index bea6bf0..adb551e 100644 --- a/docs/examples/wefe_case_study.rst +++ b/docs/examples/wefe_case_study.rst @@ -2,31 +2,31 @@ WEFE Case Study Replication =========================== -The following code replicates the case study presented in our paper: +The following code replicates the case study presented in our paper: -P. Badilla, F. Bravo-Marquez, and J. Pérez +P. Badilla, F. Bravo-Marquez, and J. Pérez WEFE: The Word Embeddings Fairness Evaluation Framework In Proceedings of the -29th International Joint Conference on Artificial Intelligence and the 17th -Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan. +29th International Joint Conference on Artificial Intelligence and the 17th +Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan. In this study we evaluate: - Multiple queries grouped according to different criteria (gender, ethnicity, religion) -- Multiple embeddings (:code:`word2vec-google-news`, :code:`glove-wikipedia`, - :code:`glove-twitter`, :code:`conceptnet`, :code:`lexvec`, +- Multiple embeddings (:code:`word2vec-google-news`, :code:`glove-wikipedia`, + :code:`glove-twitter`, :code:`conceptnet`, :code:`lexvec`, :code:`fasttext-wiki-news`) -- Multiple metrics (:code:`WEAT` and its variant, :code:`WEAT effect size`, - :code:`RND`, :code:`RNSB`). +- Multiple metrics (:code:`WEAT` and its variant, :code:`WEAT effect size`, + :code:`RND`, :code:`RNSB`). -After grouping the results by each criterion and metric, the rankings of the -bias scores of each embedding model are calculated and plotted. -An overall ranking is also computed, which is simply the sum of all rankings +After grouping the results by each criterion and metric, the rankings of the +bias scores of each embedding model are calculated and plotted. +An overall ranking is also computed, which is simply the sum of all rankings by model and metric. -Finally, the matrix of correlations between these rankings is calculated and +Finally, the matrix of correlations between these rankings is calculated and plotted. The code for this experiment is relatively long to run. -A Jupyter Notebook with the code is provided in the +A Jupyter Notebook with the code is provided in the following `link `_. diff --git a/docs/getting_started/about.rst b/docs/getting_started/about.rst index 07081e9..12d7b50 100644 --- a/docs/getting_started/about.rst +++ b/docs/getting_started/about.rst @@ -2,12 +2,12 @@ About ===== -*Word Embedding Fairness Evaluation* (WEFE) is an open source library that implements -many fairness metrics and mitigation methods (debias) in a unified framework. -It also provides a standard interface for designing new ones. +*Word Embedding Fairness Evaluation* (WEFE) is an open source library that implements +many fairness metrics and mitigation methods (debias) in a unified framework. +It also provides a standard interface for designing new ones. -The main goal of the library is to provide a ready-to-use tool that allows the -user to run bias measures and mitigation methods in a straightforward manner +The main goal of the library is to provide a ready-to-use tool that allows the +user to run bias measures and mitigation methods in a straightforward manner through well-designed and documented interfaces. In bias measurement, WEFE provides a standard interface for: @@ -15,18 +15,18 @@ In bias measurement, WEFE provides a standard interface for: - Encapsulating existing fairness metrics. - Encapsulating the test words used by fairness metrics into standard objects called queries. -- Computing a fairness metric on a given pre-trained word embedding model +- Computing a fairness metric on a given pre-trained word embedding model using user-given queries. -On the other hand, WEFE standardizes all mitigation methods through an interface -inherited from `scikit-learn `_ basic data transformations: -the ``fit-transform`` interface. This standardization separates the mitigation +On the other hand, WEFE standardizes all mitigation methods through an interface +inherited from `scikit-learn `_ basic data transformations: +the ``fit-transform`` interface. This standardization separates the mitigation process into two stages: - The first step, ``fit``, learn the corresponding mitigation transformation. - The ``transform`` method applies the transformation learned in the previous step - to words residing in the original embedding space. + to words residing in the original embedding space. .. note:: @@ -34,8 +34,8 @@ process into two stages: :ref:`measurement framework` or :ref:`mitigation framework` respectively, in the Conceptual Guides Section. - For practical tutorials on how to measure or mitigate bias, visit - :ref:`bias measurement` or :ref:`bias mitigation` respectively + For practical tutorials on how to measure or mitigate bias, visit + :ref:`bias measurement` or :ref:`bias mitigation` respectively in the WEFE User Guide. Motivation and objectives @@ -50,7 +50,7 @@ groups and attributes. Although previous studies have begun to measure bias in embeddings, they are limited both in the types of bias measured (gender, ethnic) and in the models -tested. +tested. Moreover, each study proposes its own metric, which makes the relationship between the results obtained unclear. @@ -60,10 +60,10 @@ to their bias. We originally proposed WEFE as a theoretical framework that formalizes the main building blocks for measuring bias in word embedding models. -The purpose of developing this framework was to run a case study that consistently +The purpose of developing this framework was to run a case study that consistently compares and ranks different embedding models. -Seeing the possibility that other research teams are facing the same problem, -we decided to improve this code and publish it as a library, hoping that it +Seeing the possibility that other research teams are facing the same problem, +we decided to improve this code and publish it as a library, hoping that it can be useful for their studies. We later realized that the library had the potential to cover more areas than just @@ -72,9 +72,9 @@ resulted in a new bias mitigation module and multiple enhancements and fixes. The main objectives we want to achieve with this library are: -- To provide a ready-to-use tool that allows the user to run bias tests in a - straightforward manner. -- To provide a ready-to-use tool that allows the user to mitigate bias by means of a +- To provide a ready-to-use tool that allows the user to run bias tests in a + straightforward manner. +- To provide a ready-to-use tool that allows the user to mitigate bias by means of a simple `fit-transform` interface. - To provide simple interface and utils to develop new metrics and mitigation methods. @@ -95,19 +95,19 @@ Please cite the following paper if using this package in an academic publication `P. Badilla, F. Bravo-Marquez, and J. Pérez WEFE: The Word Embeddings Fairness Evaluation Framework In Proceedings of the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan. `__ -The author's version can be found at the following +The author's version can be found at the following `link `__. Bibtex: -.. code-block:: latex +.. code-block:: latex @InProceedings{wefe2020, title = {WEFE: The Word Embeddings Fairness Evaluation Framework}, author = {Badilla, Pablo and Bravo-Marquez, Felipe and Pérez, Jorge}, booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}}, - publisher = {International Joint Conferences on Artificial Intelligence Organization}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, pages = {430--436}, year = {2020}, month = {7}, @@ -123,7 +123,7 @@ We expect in the future to: - Implement measurement framework for contextualized embedding models. - Implement new queries on different criteria. -- Create a single script that evaluates different embedding models under different bias criteria. +- Create a single script that evaluates different embedding models under different bias criteria. - From the previous script, rank as many embeddings available on the web as possible. - Implement a simple visualization module. - Implement p-values mixin that applies for all metrics that accept two targets. @@ -133,7 +133,7 @@ License WEFE is licensed under the BSD 3-Clause License. -Details of the license on this +Details of the license on this `link `__. Team @@ -147,12 +147,12 @@ Team Contributors ------------ -We thank all our contributors who have allowed WEFE to grow, especially -`stolenpyjak `_ and +We thank all our contributors who have allowed WEFE to grow, especially +`stolenpyjak `_ and `mspl13 `_ for implementing new metrics. -We also thank `alan-cueva `_ for initiating the development -of metrics for contextualized embedding models and +We also thank `alan-cueva `_ for initiating the development +of metrics for contextualized embedding models and `harshvr15 `_ for the examples of multi-language bias measurement. Thank you very much 😊! @@ -160,13 +160,13 @@ Thank you very much 😊! Contact ------- -Please write to pablo.badilla at ug.chile.cl for inquiries about the software. -You are also welcome to do a pull request or publish an issue in the +Please write to pablo.badilla at ug.chile.cl for inquiries about the software. +You are also welcome to do a pull request or publish an issue in the `WEFE repository on Github `_. Acknowledgments =============== -This work was funded by the +This work was funded by the `Millennium Institute for Foundational Research on Data (IMFD) `_. -It is also sponsored by `National Center of Artificial Intelligence of Chile (CENIA) `_. \ No newline at end of file +It is also sponsored by `National Center of Artificial Intelligence of Chile (CENIA) `_. diff --git a/docs/getting_started/quick_start.rst b/docs/getting_started/quick_start.rst index 3ea3da9..ccdbd56 100644 --- a/docs/getting_started/quick_start.rst +++ b/docs/getting_started/quick_start.rst @@ -4,14 +4,14 @@ Quick Start =========== -In this tutorial we show you how to install WEFE and then how to run a +In this tutorial we show you how to install WEFE and then how to run a basic query. Download and setup ================== -There are two different ways to install WEFE: +There are two different ways to install WEFE: - To install the package with pip, run in a console:: @@ -19,7 +19,7 @@ There are two different ways to install WEFE: - To install the package with conda, run in a console:: - conda install -c pbadilla wefe + conda install -c pbadilla wefe Run your first Query @@ -27,15 +27,15 @@ Run your first Query .. warning:: - If you are not familiar with the concepts of query, target and attribute - set, please visit the `the framework section `_ - on the library's about page. + If you are not familiar with the concepts of query, target and attribute + set, please visit the `the framework section `_ + on the library's about page. These concepts be widely used in the following sections. -In the following code we show how to implement the example query presented -in WEFE's home page: A gender Query using WEAT metrics on the glove-twitter Word -Embedding model. +In the following code we show how to implement the example query presented +in WEFE's home page: A gender Query using WEAT metrics on the glove-twitter Word +Embedding model. The following graphic shows the flow of the query execution: @@ -44,15 +44,15 @@ The following graphic shows the flow of the query execution: The programming of the previous flow can be separated into three steps: -- Load the Word Embedding model. -- Create the Query. +- Load the Word Embedding model. +- Create the Query. - Run the Query using the WEAT metric over the Word Embedding Model. These stages be implemented next: -1. Load the Word Embedding pretrained model from :code:`gensim` and then, +1. Load the Word Embedding pretrained model from :code:`gensim` and then, create a :code:`` instance with it. -This object took a gensim's :code:`KeyedVectors` object and a model name as +This object took a gensim's :code:`KeyedVectors` object and a model name as parameters. As we said previously, for this example, we use :code:`glove-twitter-25'` embedding model. @@ -62,11 +62,11 @@ As we said previously, for this example, we use :code:`glove-twitter-25'` embedd >>> from wefe.metrics.WEAT import WEAT >>> import gensim.downloader as api >>> ->>> # load glove +>>> # load glove >>> twitter_25 = api.load('glove-twitter-25') >>> model = WordEmbeddingModel(twitter_25, 'glove twitter dim=25') -2. Create the Query with a loaded, fetched or custom target and attribute +2. Create the Query with a loaded, fetched or custom target and attribute word sets. In this case, we manually set both target words and attribute words. @@ -81,23 +81,23 @@ words. >>> query = Query(target_sets, attribute_sets, target_sets_names, >>> attribute_sets_names) -3. Instantiate the metric to be used and then, execute :code:`run_query` +3. Instantiate the metric to be used and then, execute :code:`run_query` with the parameters created in the past steps. In this case we use the -`WEAT `_ metric. +`WEAT `_ metric. >>> # instance a WEAT metric ->>> weat = WEAT() +>>> weat = WEAT() >>> result = weat.run_query(query, model) >>> print(result) { - 'query_name': 'Female Terms and Male Terms wrt Arts and Science', - 'result': 0.2595698336760204, - 'weat': 0.2595698336760204, - 'effect_size': 1.452482230821006, + 'query_name': 'Female Terms and Male Terms wrt Arts and Science', + 'result': 0.2595698336760204, + 'weat': 0.2595698336760204, + 'effect_size': 1.452482230821006, 'p_value': nan } -A score greater than 0 indicates that there is indeed a biased relationship between -women and the arts with respect to men and science. +A score greater than 0 indicates that there is indeed a biased relationship between +women and the arts with respect to men and science. For more advanced usage, visit user the :ref:`bias measurement` in the User Guide. diff --git a/docs/index.rst b/docs/index.rst index 35c2cec..4e5b31a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,18 +2,18 @@ Welcome to the WEFE documentation! ================================== -**WEFE: The Word Embeddings Fairness Evaluation Framework** is an open source -library for measuring and mitigating bias in word embedding models. +**WEFE: The Word Embeddings Fairness Evaluation Framework** is an open source +library for measuring and mitigating bias in word embedding models. The following pages contain the documentation about WEFE: -how to install the package, how to use it and how to contribute, as well as -the detailed API documentation and extensive examples. +how to install the package, how to use it and how to contribute, as well as +the detailed API documentation and extensive examples. .. toctree:: :maxdepth: 2 :caption: Getting Started - + getting_started/about.rst getting_started/quick_start.rst @@ -24,11 +24,11 @@ the detailed API documentation and extensive examples. user_guide/measurement_user_guide.rst user_guide/mitigation_user_guide.rst user_guide/loading_embeddings.rst - + .. toctree:: :maxdepth: 2 :caption: API - + api/api.rst .. toctree:: @@ -58,4 +58,4 @@ Repository ========== You can find the project repository at the following link: -`WEFE repository on Github `_. \ No newline at end of file +`WEFE repository on Github `_. diff --git a/docs/logos/WEFE.svg b/docs/logos/WEFE.svg index f7034b6..15d29cd 100644 --- a/docs/logos/WEFE.svg +++ b/docs/logos/WEFE.svg @@ -11,4 +11,4 @@ - \ No newline at end of file + diff --git a/docs/logos/WEFE_2.svg b/docs/logos/WEFE_2.svg index 4311b8c..7b1b14c 100644 --- a/docs/logos/WEFE_2.svg +++ b/docs/logos/WEFE_2.svg @@ -51,4 +51,4 @@ - \ No newline at end of file + diff --git a/docs/logos/WEFE_2_BLANCO.svg b/docs/logos/WEFE_2_BLANCO.svg index 26f518c..6872863 100644 --- a/docs/logos/WEFE_2_BLANCO.svg +++ b/docs/logos/WEFE_2_BLANCO.svg @@ -47,4 +47,4 @@ - \ No newline at end of file + diff --git a/docs/logos/WEFE_BLANCO.svg b/docs/logos/WEFE_BLANCO.svg index eb91cdc..17e6858 100644 --- a/docs/logos/WEFE_BLANCO.svg +++ b/docs/logos/WEFE_BLANCO.svg @@ -11,4 +11,4 @@ - \ No newline at end of file + diff --git a/docs/user_guide/contribute.rst b/docs/user_guide/contribute.rst index f2a6be2..030f1b5 100644 --- a/docs/user_guide/contribute.rst +++ b/docs/user_guide/contribute.rst @@ -2,9 +2,9 @@ Contributing ============ -There are many ways to contribute to the library: +There are many ways to contribute to the library: -- Implement new metrics. +- Implement new metrics. - Implement new mitigation methods. - Create more examples and use cases. - Help improve the documentation. @@ -22,7 +22,7 @@ You can download the library by running the following command :: To contribute, simply create a pull request. -Verify that your code is well documented, to implement unit tests and +Verify that your code is well documented, to implement unit tests and follow the black coding style. Development Requirements @@ -37,8 +37,8 @@ of WEFE documentation, run :: Testing ======= -All unit tests are located in the wefe/tests folder and are based on the -``pytest`` framework. +All unit tests are located in the wefe/tests folder and are based on the +``pytest`` framework. To run the tests, execute:: @@ -56,13 +56,13 @@ And then:: Build the documentation ======================= -The documentation is created using sphinx. It can be found in the docs folder +The documentation is created using sphinx. It can be found in the docs folder at the project's root folder. The documentation includes the API description and some tutorials. To compile the documentation, run the following commands:: cd doc - make html + make html Then, you can visit the documentation at ``docs/_build/html/index.html`` @@ -71,7 +71,7 @@ How to implement your own metric ================================ The following guide is intended to show how to implement a metric using WEFE. -You can find a notebook version of this tutorial at the following +You can find a notebook version of this tutorial at the following `link `__. Create the class @@ -98,10 +98,10 @@ Below are some examples of templates: # two target sets and one attribute set required to execute this metric. template_1 = (2, 1) - + # two target sets and two attribute sets required to execute this metric. template_2 = (2, 2) - + # one or more (unlimited) target sets and one attribute set required to execute this metric. template_3 = ('n', 1) @@ -111,7 +111,7 @@ following code scheme: .. code:: python3 from wefe.metrics.base_metric import BaseMetric - + class ExampleMetric(BaseMetric): metric_template = (2, 1) metric_name = 'Example Metric' @@ -128,7 +128,7 @@ operations before executing the mathematical calculations: Validate the parameters ~~~~~~~~~~~~~~~~~~~~~~~ -This call checks the main parameters provided to the ``run_query`` and will raise an +This call checks the main parameters provided to the ``run_query`` and will raise an exception if it finds a problem with them. .. code:: python @@ -156,7 +156,7 @@ This call transforms all the word sets of a query into embeddings. This step could return either: -- ``None`` if any of the sets lost percentage more words than the number of words +- ``None`` if any of the sets lost percentage more words than the number of words allowed by ``lost_vocabulary_threshold`` parameter (specified as percentage float). In this case the metric would be expected to return nan in its results. @@ -187,24 +187,24 @@ like using the following query: from wefe.utils import load_test_model # a few embeddings of WEAT experiments from wefe.datasets.datasets import load_weat # the word sets of WEAT experiments from wefe.preprocessing import get_embeddings_from_query - - + + weat = load_weat() model = load_test_model() - + flowers = weat['flowers'] weapons = weat['weapons'] pleasant = weat['pleasant_5'] query = Query([flowers, weapons], [pleasant], ['Flowers', 'Weapons'], ['Pleasant']) - + embeddings = get_embeddings_from_query( model=model, query=query, # other params... ) target_sets, attribute_sets = embeddings - + If you inspect ``target_sets``, it would look like the following dictionary: @@ -254,19 +254,19 @@ Using the steps previously seen, a sample metric is implemented: from typing import Any, Dict, Union, List, Callable import numpy as np - + from wefe.metrics.base_metric import BaseMetric from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel - - + + class ExampleMetric(BaseMetric): - + # replace with the parameters of your metric metric_template = (2, 1) # cardinalities of the targets and attributes sets that your metric will accept. - metric_name = 'Example Metric' + metric_name = 'Example Metric' metric_short_name = 'EM' - + def run_query( self, query: Query, @@ -280,17 +280,17 @@ Using the steps previously seen, a sample metric is implemented: **kwargs: Any, ) -> Dict[str, Any]: """Calculate the Example Metric metric over the provided parameters. - + Parameters ---------- query : Query - A Query object that contains the target and attribute word sets to + A Query object that contains the target and attribute word sets to be tested. - + word_embedding : WordEmbeddingModel - A WordEmbeddingModel object that contains certain word embedding + A WordEmbeddingModel object that contains certain word embedding pretrained model. - + lost_vocabulary_threshold : float, optional Specifies the proportional limit of words that any set of the query is allowed to lose when transforming its words into embeddings. @@ -338,21 +338,21 @@ Using the steps previously seen, a sample metric is implemented: normalize : bool, optional True indicates that embeddings will be normalized, by default False - + warn_not_found_words : bool, optional Specifies if the function will warn (in the logger) the words that were not found in the model's vocabulary , by default False. - + Returns ------- Dict[str, Any] - A dictionary with the query name, the resulting score of the metric, + A dictionary with the query name, the resulting score of the metric, and other scores. """ # check the types of the provided arguments (only the defaults). self._check_input(query, model) - + # transform query word sets into embeddings embeddings = get_embeddings_from_query( model=model, @@ -363,7 +363,7 @@ Using the steps previously seen, a sample metric is implemented: normalize=normalize, warn_not_found_words=warn_not_found_words, ) - + # if there is any/some set has less words than the allowed limit, # return the default value (nan) if embeddings is None: @@ -375,22 +375,22 @@ Using the steps previously seen, a sample metric is implemented: 'results_by_word' : np.nan, # if available, values by word (optional) # ... } - + # get the targets and attribute sets transformed into embeddings. target_sets, attribute_sets = embeddings - + # commonly, you only will need the embeddings of the sets. # this can be obtained by using: target_embeddings = list(target_sets.values()) attribute_embeddings = list(attribute_sets.values()) - - + + """ # From here, the code can vary quite a bit depending on what you need. # It is recommended to calculate the metric operations in another method(s). - results = calc_metric() - - # The final step is to return query and result. + results = calc_metric() + + # The final step is to return query and result. # You can return other scores, metrics by word or metrics by set, etc. return { 'query_name': query.query_name, # the name of the evaluated query @@ -401,7 +401,7 @@ Using the steps previously seen, a sample metric is implemented: ... } """ - + Implement the logic of the metric @@ -425,61 +425,61 @@ the above. from scipy.spatial import distance import numpy as np - + from wefe.metrics import BaseMetric from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel from wefe.preprocessing import get_embeddings_from_query - + class ExampleMetric(BaseMetric): - + # replace with the parameters of your metric metric_template = ( 2, 1 ) # cardinalities of the targets and attributes sets that your metric will accept. metric_name = 'Example Metric' metric_short_name = 'EM' - + def _calc_metric(self, target_embeddings, attribute_embeddings): """Calculates the metric. - + Parameters ---------- target_embeddings : np.array - An array with dicts. Each dict represents an target set. + An array with dicts. Each dict represents an target set. A dict is composed with a word and its embedding as key, value respectively. attribute_embeddings : np.array - An array with dicts. Each dict represents an attribute set. + An array with dicts. Each dict represents an attribute set. A dict is composed with a word and its embedding as key, value respectively. - + Returns ------- np.float The value of the calculated metric. """ - + # get the embeddings from the dicts target_embeddings_0 = np.array(list(target_embeddings[0].values())) target_embeddings_1 = np.array(list(target_embeddings[1].values())) - + attribute_embeddings_0 = np.array( list(attribute_embeddings[0].values())) - + # calculate the average embedding by target and attribute set. target_embeddings_0_avg = np.mean(target_embeddings_0, axis=0) target_embeddings_1_avg = np.mean(target_embeddings_1, axis=0) attribute_embeddings_0_avg = np.mean(attribute_embeddings_0, axis=0) - + # calculate the distances between the target sets and the attribute set dist_target_0_attr = distance.cosine(target_embeddings_0_avg, attribute_embeddings_0_avg) dist_target_1_attr = distance.cosine(target_embeddings_1_avg, attribute_embeddings_0_avg) - + # subtract the distances metric_result = dist_target_0_attr - dist_target_1_attr return metric_result - + def run_query( self, query: Query, @@ -493,26 +493,26 @@ the above. **kwargs: Any, ) -> Dict[str, Any]: """Calculate the Example Metric metric over the provided parameters. - + Parameters ---------- query : Query - A Query object that contains the target and attribute word sets to + A Query object that contains the target and attribute word sets to be tested. - + word_embedding : WordEmbeddingModel - A WordEmbeddingModel object that contains certain word embedding + A WordEmbeddingModel object that contains certain word embedding pretrained model. - + lost_vocabulary_threshold : float, optional Specifies the proportional limit of words that any set of the query is allowed to lose when transforming its words into embeddings. In the case that any set of the query loses proportionally more words than this limit, the result values will be np.nan, by default 0.2 - + preprocessors : List[Dict[str, Union[str, bool, Callable]]] A list with preprocessor options. - + A ``preprocessor`` is a dictionary that specifies what processing(s) are performed on each word before its looked up in the model vocabulary. For example, the ``preprocessor`` @@ -520,9 +520,9 @@ the above. and remove the accent from each word before searching for them in the model vocabulary. Note that an empty dictionary ``{}`` indicates that no preprocessing is done. - + The possible options for a preprocessor are: - + * ``lowercase``: ``bool``. Indicates that the words are transformed to lowercase. * ``uppercase``: ``bool``. Indicates that the words are transformed to @@ -535,36 +535,36 @@ the above. * ``preprocessor``: ``Callable``. It receives a function that operates on each word. In the case of specifying a function, it overrides the default preprocessor (i.e., the previous options stop working). - + A list of preprocessor options allows searching for several variants of the words into the model. For example, the preprocessors ``[{}, {"lowercase": True, "strip_accents": True}]`` - ``{}`` allows searching first for the original words in the vocabulary of the model. - In case some of them are not found, ``{"lowercase": True, "strip_accents": True}`` + ``{}`` allows searching first for the original words in the vocabulary of the model. + In case some of them are not found, ``{"lowercase": True, "strip_accents": True}`` is executed on these words and then they are searched in the model vocabulary. - + strategy : str, optional The strategy indicates how it will use the preprocessed words: 'first' will include only the first transformed word found. 'all' will include all transformed words found, by default "first". - + normalize : bool, optional True indicates that embeddings will be normalized, by default False - + warn_not_found_words : bool, optional Specifies if the function will warn (in the logger) the words that were not found in the model's vocabulary , by default False. - + Returns ------- Dict[str, Any] - A dictionary with the query name, the resulting score of the metric, + A dictionary with the query name, the resulting score of the metric, and other scores. """ # check the types of the provided arguments (only the defaults). self._check_input(query, model) - + # transform query word sets into embeddings embeddings = get_embeddings_from_query( model=model, @@ -575,7 +575,7 @@ the above. normalize=normalize, warn_not_found_words=warn_not_found_words, ) - + # if there is any/some set has less words than the allowed limit, # return the default value (nan) if embeddings is None: @@ -587,17 +587,17 @@ the above. 'results_by_word' : np.nan, # if available, values by word (optional) # ... } - + # get the targets and attribute sets transformed into embeddings. target_sets, attribute_sets = embeddings - + # commonly, you only will need the embeddings of the sets. # this can be obtained by using: target_embeddings = list(target_sets.values()) attribute_embeddings = list(attribute_sets.values()) - + result = self._calc_metric(target_embeddings, attribute_embeddings) - + # return the results. return {"query_name": query.query_name, "result": result, 'em': result} @@ -608,24 +608,24 @@ Now, let us try it out: from wefe.query import Query from wefe.utils import load_weat_w2v # a few embeddings of WEAT experiments from wefe.datasets.datasets import load_weat # the word sets of WEAT experiments - + weat = load_weat() model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '') - + flowers = weat['flowers'] weapons = weat['weapons'] pleasant = weat['pleasant_5'] query = Query([flowers, weapons], [pleasant], ['Flowers', 'Weapons'], ['Pleasant']) - - + + results = ExampleMetric().run_query(query, model) print(results) .. parsed-literal:: {'query_name': 'Flowers and Weapons wrt Pleasant', 'result': -0.10210171341896057, 'em': -0.10210171341896057} - + We have completely defined a new metric. Congratulations! @@ -648,18 +648,18 @@ Some comments regarding the implementation of new metrics: distances folder of the `repository `__. - + Mitigation Method Implementation Guide ====================================== The main idea when implementing a mitigation method is that it has to follow the logic -of the transformations in scikit-learn. -That is, you must separate the logic of the calculation of the mitigation -transformation (`fit`) with the application of the transformation on the model +of the transformations in scikit-learn. +That is, you must separate the logic of the calculation of the mitigation +transformation (`fit`) with the application of the transformation on the model (`transform`). -In practical terms, every WEFE transformation must extend the `BaseDebias` class. +In practical terms, every WEFE transformation must extend the `BaseDebias` class. `BaseDebias` has two abstract methods that must be implemented: `fit` and `transform`. @@ -667,9 +667,9 @@ Fit --- -`fit` is the method in charge of calculating the bias mitigation transformation +`fit` is the method in charge of calculating the bias mitigation transformation that will be subsequently applied to the model. -`BaseDebias` implements it as an abstract method that requires only one argument: +`BaseDebias` implements it as an abstract method that requires only one argument: `model`, which expects a `WordEmbeddingModel` instance. .. code:: python3 @@ -690,12 +690,12 @@ that will be subsequently applied to the model. raise NotImplementedError() -The idea of requesting model at this point is that the calculation of the +The idea of requesting model at this point is that the calculation of the transformation commonly requires some words from the model vocabulary. -As each bias mitigation method is different, it is expected that these can receive more +As each bias mitigation method is different, it is expected that these can receive more parameters than those listed above. In, `HardDebias`, `fit` is defined using the default -parameter `model` plus `definitional_pairs` and `equalize_pairs`, which are +parameter `model` plus `definitional_pairs` and `equalize_pairs`, which are specific to `HardDebias`: .. code:: python3 @@ -783,8 +783,8 @@ in `fit` on the embedding model. It must always receive the same 4 arguments: only on the word embeddings of this set. If `None` is provided, the debias will be performed on all words (except those specified in ignore). by default `None`. -- `ignore`: A set of words or None. If target is `None` and a set of words is specified -- in ignore, the debias method will perform the debias in all words except those +- `ignore`: A set of words or None. If target is `None` and a set of words is specified +- in ignore, the debias method will perform the debias in all words except those - specified in this set, by default `None`. - `copy`: If `True`, the debias will be performed on a copy of the model. If `False`, the debias will be applied on the same model delivered, causing @@ -839,13 +839,13 @@ method. Some useful initial checks and operations for this method: - The arguments can be checked through the `_check_transform_args` `BaseDebias` method. -- You can also check whether the method is trained or not using the `check_is_fitted` - method. This is a wrapper of the original scikit-learn that can be imported from the +- You can also check whether the method is trained or not using the `check_is_fitted` + method. This is a wrapper of the original scikit-learn that can be imported from the utils module. -- In case `copy` argument is `True`, you must duplicate the model and work on the +- In case `copy` argument is `True`, you must duplicate the model and work on the replica. It is recommended to use `deepcopy` of the `copy` module for such purposes. -The following code segment (obtained from `HardDebias`) shows an example of how to +The following code segment (obtained from `HardDebias`) shows an example of how to execute the points mentioned above: .. code:: python @@ -919,8 +919,8 @@ execute the points mentioned above: ) Unfortunately it is impossible to cover much more without losing generality. -However, we recommend checking the code structure shown in `HardDebias` or -`MulticlassHardDebias` classes to guide you through the process of implementing a new +However, we recommend checking the code structure shown in `HardDebias` or +`MulticlassHardDebias` classes to guide you through the process of implementing a new mitigation method. You can also open an issue in the repository to comment on any questions you may have -in the implementation. \ No newline at end of file +in the implementation. diff --git a/docs/user_guide/loading_embeddings.rst b/docs/user_guide/loading_embeddings.rst index 716fd5b..b042082 100644 --- a/docs/user_guide/loading_embeddings.rst +++ b/docs/user_guide/loading_embeddings.rst @@ -2,9 +2,9 @@ Loading Embeddings From Different Sources ========================================= -WEFE depends on gensim's :code:`KeyedVectors` to operate the word +WEFE depends on gensim's :code:`KeyedVectors` to operate the word embeddings models. -Therefore, any embedding you want to experiment with must be a model loaded +Therefore, any embedding you want to experiment with must be a model loaded through gensim's APIs or any library that extends it. In technical terms, the minimum requirement for WEFE to operate with a model @@ -24,14 +24,14 @@ to be used in the following sections. >>> from wefe.word_embedding_model import WordEmbeddingModel >>> from wefe.metrics.WEAT import WEAT >>> from wefe.datasets.datasets import load_weat ->>> +>>> >>> # load the weat word sets >>> word_sets = load_weat() ->>> +>>> >>> # create the query >>> query = Query([word_sets['male_terms'], word_sets['female_terms']], >>> [word_sets['career'], word_sets['family']], ->>> ['Male terms', 'Female terms'], +>>> ['Male terms', 'Female terms'], >>> ['Career', 'Family']) >>> >>> # instantiate the metric @@ -40,19 +40,19 @@ to be used in the following sections. Load from Gensim API ==================== -Gensim provides an -`extensive list of pre-trained models `_ +Gensim provides an +`extensive list of pre-trained models `_ that can be used directly. Below we show an example of use. >>> import gensim.downloader as api ->>> +>>> >>> # Load from gensim.downloader some model, for example: glove-twitter-25 >>> glove_25_keyed_vectors = api.load('glove-twitter-25') ->>> +>>> >>> # The resulting object is already a BaseKeyedVectors subclass object. >>> # so we can wrap directly using . >>> glove_25_model = WordEmbeddingModel(glove_25_keyed_vectors, 'glove-25') ->>> +>>> >>> # Execute the query >>> result = weat.run_query(query, glove_25_model) >>> print(result) @@ -67,8 +67,8 @@ As we said before, any model that is loaded with gensim and extends In this section we will see how to load a word2vec model and Fasttext. .. note:: - Gensim is not directly compatible with glove model file format. - However, they provide a + Gensim is not directly compatible with glove model file format. + However, they provide a `script `_ that allows you to transform any glove model into a word2vec format. @@ -81,10 +81,10 @@ The procedure is quite simple: first we download word2vec binary file from its s and then we load it using the :code:`KeyedVectors.load_word2vec_format` function. >>> from gensim.models import KeyedVectors ->>> +>>> >>> w2v_embeddings = KeyedVectors.load_word2vec_format("/path/to/your/embeddings/model", binary=True) >>> word2vec = WordEmbeddingModel(w2v_embeddings, 'word2vec') ->>> +>>> >>> result = weat.run_query(query, word2vec) >>> result {'query_name': 'Male terms and Female terms wrt Career and Family', @@ -98,15 +98,15 @@ The same method works for :code:`Fasttext`. >>> from gensim.models import KeyedVectors >>> fast_embeddings = KeyedVectors.load_word2vec_format('path/to/fast/embeddings.vec') ->>> +>>> >>> fast = WordEmbeddingModel(fast_embeddings, 'fast') >>> result = weat.run_query(query, fast) ->>> +>>> >>> result {'query_name': 'Male terms and Female terms wrt Career and Family', 'result': 0.34870023} -While we load FastText here as :code:`KeyedVectors` (i.e., in word2vec format), +While we load FastText here as :code:`KeyedVectors` (i.e., in word2vec format), it can also be used via :code:`FastTextKeyedVectors`. @@ -114,21 +114,21 @@ Flair ===== WEFE does not support flair interfaces. -However, you can use static embeddings of flair +However, you can use static embeddings of flair ( -`Classic Word Embeddings `_ +`Classic Word Embeddings `_ ) which are based on gensim's :code:`KeyedVectors`, to load embedding models. The following code is an example of this: >>> from flair.embeddings import WordEmbeddings >>> from wefe.utils import flair_to_gensim ->>> ->>> # could be any of the Classic Word Embeddings model list. +>>> +>>> # could be any of the Classic Word Embeddings model list. >>> flair_model_name = "glove" ->>> +>>> >>> flair_model = flair_to_gensim(WordEmbeddings(flair_model_name)) >>> wefe_model = WordEmbeddingModel(flair_model, flair_model_name) ->>> +>>> >>> result = weat.run_query(query, wefe_model) >>> print(result) -{'query_name': 'Male terms and Female terms wrt Career and Family', 'result': 1.0486683} \ No newline at end of file +{'query_name': 'Male terms and Female terms wrt Career and Family', 'result': 1.0486683} diff --git a/docs/user_guide/measurement_user_guide.rst b/docs/user_guide/measurement_user_guide.rst index e7a97c9..674fdd8 100644 --- a/docs/user_guide/measurement_user_guide.rst +++ b/docs/user_guide/measurement_user_guide.rst @@ -19,7 +19,7 @@ using the package to measure bias. The following sections show: To accurately study and reduce biases contained in word embeddings, queries may contain words that could be offensive to certain groups or individuals. The relationships studied between these words DO NOT represent the - ideas, thoughts or beliefs of the authors of this library. + ideas, thoughts or beliefs of the authors of this library. This warning applies to all documentation. .. note:: @@ -32,7 +32,7 @@ using the package to measure bias. The following sections show: .. note:: For a list of metrics implemented in WEFE, refer to the - :ref:`metrics section` of the API reference. + :ref:`metrics section` of the API reference. Run a Query @@ -40,9 +40,9 @@ Run a Query The following subsections explains how to run a simple query that measures gender bias on -`Glove `_. +`Glove `_. The example uses the Word Embedding Association Test (:class:`~wefe.metrics.WEAT.WEAT`) -metric quantifying the bias in the embeddings model. Below we show the three usual +metric quantifying the bias in the embeddings model. Below we show the three usual steps for performing a query in WEFE: .. note:: @@ -51,9 +51,9 @@ steps for performing a query in WEFE: between two sets of target words (sets of words intended to denote a social groups as men and women) and two sets of attribute words (sets of words representing some attitude, characteristic, trait, occupational field, - etc. that can be associated with individuals from any social group). + etc. that can be associated with individuals from any social group). - The closer its value is to 0, the less biased the model is. + The closer its value is to 0, the less biased the model is. Visit the metrics documentation (:class:`~wefe.metrics.WEAT.WEAT`) for more information. @@ -71,18 +71,18 @@ twitter dataset loaded using `gensim-data `_. + Visit `gensim-data repository `_. to find the complete list of published pre-trained models ready to use. .. code:: ipython3 import gensim.downloader as api - + from wefe.datasets import load_weat from wefe.metrics import WEAT from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel - + twitter_25 = api.load("glove-twitter-25") # WordEmbeddingModel receives as first argument a KeyedVectors model # and the second argument the model name. @@ -132,7 +132,7 @@ included in the ``datasets`` module. target_sets_names=["Female terms", "Male Terms"], attribute_sets_names=["Family", "Careers"], ) - + gender_query @@ -205,7 +205,7 @@ warn errors or modify which calculation method the metric use. .. note:: - Each metric implements the ``run_query`` method with different arguments. + Each metric implements the ``run_query`` method with different arguments. Visit their API documentation for more information. @@ -286,7 +286,7 @@ to find the names in the model vocabulary. # load the weat word sets. word_sets = load_weat() - + # print a set of european american common names. print(word_sets["european_american_names_5"]) @@ -348,11 +348,11 @@ names with respect to pleasant and unpleasant attributes. Word Preprocessors ~~~~~~~~~~~~~~~~~~ -Any ``run_query`` method allows preprocessing each word before they are searched in the model's +Any ``run_query`` method allows preprocessing each word before they are searched in the model's vocabulary through the parameter ``preprocessors`` (list of one or more preprocessor). This parameter accepts a list of individual preprocessors, which are defined below: -A ``preprocessor`` is a dictionary that specifies what processing(s) are +A ``preprocessor`` is a dictionary that specifies what processing(s) are performed on each word before its looked up in the model vocabulary. For example, the ``preprocessor`` ``{'lowecase': True, 'strip_accents': True}`` allows you to lowercase @@ -367,20 +367,20 @@ The possible options for a preprocessor are: - ``titlecase``: ``bool``. Indicates that the words are transformed to titlecase. - ``strip_accents``: ``bool``, ``{'ascii', 'unicode'}``: Specifies that the accents of the words are eliminated. The stripping type can be specified. True uses 'unicode' by default. -- ``preprocessor``: ``Callable``. It receives a function that operates on each word. - In the case of specifying a function, it overrides the default preprocessor +- ``preprocessor``: ``Callable``. It receives a function that operates on each word. + In the case of specifying a function, it overrides the default preprocessor (i.e., the previous options stop working). A list of preprocessor options allows searching for several variants of the words into the model. For example, the preprocessors ``[{}, {"lowercase": True, "strip_accents": True}]`` -``{}`` allows searching first for the original words in the vocabulary of the model. -In case some of them are not found, ``{"lowercase": True, "strip_accents": True}`` +``{}`` allows searching first for the original words in the vocabulary of the model. +In case some of them are not found, ``{"lowercase": True, "strip_accents": True}`` is executed on these words and then they are searched in the model vocabulary. -By default (in case there is more than one preprocessor in the list) the first -preprocessed word found in the embeddings model is used. +By default (in case there is more than one preprocessor in the list) the first +preprocessed word found in the embeddings model is used. This behavior can be controlled by the ``strategy`` parameter of ``run_query``. In the following example, we provide a list with only one @@ -440,7 +440,7 @@ that converts to lowercase and removes accents. ], warn_not_found_words=True, ) - + result @@ -484,7 +484,7 @@ lowercase words without accents. ], warn_not_found_words=True, ) - + result @@ -525,7 +525,7 @@ preprocessors. This can be controlled by specifying the parameter strategy="all", warn_not_found_words=True, ) - + result @@ -570,7 +570,7 @@ dataset. The queries are executed using :class:`~wefe.metrics.WEAT.WEAT` metric. .. code:: ipython3 import gensim.downloader as api - + from wefe.datasets import load_weat from wefe.metrics import RNSB, WEAT from wefe.query import Query @@ -589,9 +589,9 @@ dimensions. model_1 = WordEmbeddingModel(api.load("glove-twitter-25"), "glove twitter dim=25") model_2 = WordEmbeddingModel(api.load("glove-twitter-50"), "glove twitter dim=50") model_3 = WordEmbeddingModel(api.load("glove-twitter-100"), "glove twitter dim=100") - + models = [model_1, model_2, model_3] - + Load the word sets and create the queries @@ -605,7 +605,7 @@ three queries are intended to measure gender bias. # Load the WEAT word sets word_sets = load_weat() - + # Create gender queries gender_query_1 = Query( [word_sets["male_terms"], word_sets["female_terms"]], @@ -613,21 +613,21 @@ three queries are intended to measure gender bias. ["Male terms", "Female terms"], ["Career", "Family"], ) - + gender_query_2 = Query( [word_sets["male_terms"], word_sets["female_terms"]], [word_sets["science"], word_sets["arts"]], ["Male terms", "Female terms"], ["Science", "Arts"], ) - + gender_query_3 = Query( [word_sets["male_terms"], word_sets["female_terms"]], [word_sets["math"], word_sets["arts_2"]], ["Male terms", "Female terms"], ["Math", "Arts"], ) - + gender_queries = [gender_query_1, gender_query_2, gender_query_3] @@ -674,11 +674,11 @@ set of queries through the parameter ``queries_set_name``. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -747,7 +747,7 @@ words before searching for them in the models’ vocabularies. metric_params={"preprocessors": [{"lowercase": True}]}, queries_set_name="Gender Queries", ) - + WEAT_gender_results @@ -760,11 +760,11 @@ words before searching for them in the models’ vocabularies. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -821,7 +821,7 @@ a ``run_queries`` execution into a `plotly `_ barplo .. code:: ipython3 from wefe.utils import plot_queries_results, run_queries - + # Plot the results plot_queries_results(WEAT_gender_results).show() @@ -860,8 +860,8 @@ the results dataframe. The aggregation functions available are: Notice that some functions are more appropriate for certain metrics. For metrics returning only positive numbers, all the previous aggregation functions would be OK. In contrast, metrics that return real values - (e.g., :class:`~wefe.metrics.WEAT.WEAT` , :class:`~wefe.metrics.RND.RND` , etc…), - aggregation functions such as sum would make positive and negative outputs to cancel + (e.g., :class:`~wefe.metrics.WEAT.WEAT` , :class:`~wefe.metrics.RND.RND` , etc…), + aggregation functions such as sum would make positive and negative outputs to cancel each other. .. code:: ipython3 @@ -888,11 +888,11 @@ the results dataframe. The aggregation functions available are: .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -981,11 +981,11 @@ results using the parameter ``return_only_aggregation`` .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -1051,8 +1051,8 @@ To show these problems, suppose we have: - Three ``Glove`` models of 25, 50 and 100 dimensions trained on the same twitter dataset. -Then we run :func:`~wefe.utils.run_queries` on this set of model-queries using -:class:`~wefe.metrics.WEAT.WEAT`, and to corroborate the results obtained, we also use +Then we run :func:`~wefe.utils.run_queries` on this set of model-queries using +:class:`~wefe.metrics.WEAT.WEAT`, and to corroborate the results obtained, we also use Relative Negative Sentiment Bias (:class:`~wefe.metrics.RNSB.RNSB`). 1. The first problem occurs when the bias scores obtained from one set @@ -1145,9 +1145,9 @@ Gender Bias Model Ranking ["Male terms", "Female terms"], ["Math", "Arts"], ) - + gender_queries = [gender_query_1, gender_query_2, gender_query_3] - + # run the queries using WEAT WEAT_gender_results = run_queries( WEAT, @@ -1158,7 +1158,7 @@ Gender Bias Model Ranking return_only_aggregation=True, queries_set_name="Gender Queries", ) - + # run the queries using WEAT effect size WEAT_EZ_gender_results = run_queries( WEAT, @@ -1169,7 +1169,7 @@ Gender Bias Model Ranking return_only_aggregation=True, queries_set_name="Gender Queries", ) - + # run the queries using RNSB RNSB_gender_results = run_queries( RNSB, @@ -1189,12 +1189,12 @@ values. .. code:: ipython3 from wefe.utils import create_ranking - + # create the ranking gender_ranking = create_ranking( [WEAT_gender_results, WEAT_EZ_gender_results, RNSB_gender_results] ) - + gender_ranking @@ -1207,11 +1207,11 @@ values. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -1261,7 +1261,7 @@ Ethnicity Bias Model Ranking .. code:: ipython3 - + # define the queries ethnicity_query_1 = Query( [word_sets["european_american_names_5"], word_sets["african_american_names_5"]], @@ -1269,16 +1269,16 @@ Ethnicity Bias Model Ranking ["European Names", "African Names"], ["Pleasant", "Unpleasant"], ) - + ethnicity_query_2 = Query( [word_sets["european_american_names_7"], word_sets["african_american_names_7"]], [word_sets["pleasant_9"], word_sets["unpleasant_9"]], ["European Names", "African Names"], ["Pleasant 2", "Unpleasant 2"], ) - + ethnicity_queries = [ethnicity_query_1, ethnicity_query_2] - + # run the queries using WEAT WEAT_ethnicity_results = run_queries( WEAT, @@ -1289,7 +1289,7 @@ Ethnicity Bias Model Ranking return_only_aggregation=True, queries_set_name="Ethnicity Queries", ) - + # run the queries using WEAT effect size WEAT_EZ_ethnicity_results = run_queries( WEAT, @@ -1300,7 +1300,7 @@ Ethnicity Bias Model Ranking return_only_aggregation=True, queries_set_name="Ethnicity Queries", ) - + # run the queries using RNSB RNSB_ethnicity_results = run_queries( RNSB, @@ -1319,7 +1319,7 @@ Ethnicity Bias Model Ranking ethnicity_ranking = create_ranking( [WEAT_ethnicity_results, WEAT_EZ_gender_results, RNSB_ethnicity_results] ) - + ethnicity_ranking @@ -1333,11 +1333,11 @@ Ethnicity Bias Model Ranking .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -1394,7 +1394,7 @@ a different criterion-metric ranking. .. code:: ipython3 from wefe.utils import plot_ranking - + fig = plot_ranking(gender_ranking) fig.show() @@ -1419,10 +1419,10 @@ Correlating Rankings Having obtained rankings by metric for each embeddings, it would be ideal to see and analyze the degree of agreement between them. -A high concordance between the rankings allows us to state with some certainty that -all metrics evaluated the embedding models in a similar way and therefore, +A high concordance between the rankings allows us to state with some certainty that +all metrics evaluated the embedding models in a similar way and therefore, that the ordering of embeddings by bias calculated makes sense. -On the other hand, a low degree of agreement shows the opposite: the rankings do not +On the other hand, a low degree of agreement shows the opposite: the rankings do not allow to clearly establish which embedding is less biased than another. The level of concordance of the rankings can be evaluated by calculating @@ -1432,7 +1432,7 @@ calculate the correlations between rankings. .. code:: ipython3 from wefe.utils import calculate_ranking_correlations, plot_ranking_correlations - + correlations = calculate_ranking_correlations(gender_ranking) correlations @@ -1447,11 +1447,11 @@ calculate the correlations between rankings. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -1493,7 +1493,7 @@ calculate the correlations between rankings. .. note:: ``calculate_ranking_correlations`` uses the ``corr()`` ``pandas`` - dataframe method. The type of correlation that is calculated can be changed + dataframe method. The type of correlation that is calculated can be changed through the method parameter. The available options are: ``'pearson'``, ``'spearman'``, ``'kendall'``. By default, the spearman correlation is calculated. @@ -1515,11 +1515,11 @@ In this example, Kendall’s correlation is used. .dataframe tbody tr th:only-of-type { vertical-align: middle; } - + .dataframe tbody tr th { vertical-align: top; } - + .dataframe thead th { text-align: right; } @@ -1573,4 +1573,3 @@ WEFE also provides a function for graphing the correlations: In this case, only two of the three rankings show similar results. - diff --git a/docs/user_guide/mitigation_user_guide.rst b/docs/user_guide/mitigation_user_guide.rst index 7d3e752..885e620 100644 --- a/docs/user_guide/mitigation_user_guide.rst +++ b/docs/user_guide/mitigation_user_guide.rst @@ -3,8 +3,8 @@ Bias Mitigation (Debias) ======================== -The following guide is designed to present the more general details on -using the package to mitigate (debias) bias in word embedding models. +The following guide is designed to present the more general details on +using the package to mitigate (debias) bias in word embedding models. The following sections show: - run :class:`~wefe.debias.hard_debias.HardDebias` mitigation method on an @@ -12,17 +12,17 @@ The following sections show: - apply the ``target`` parameter when executing the transformation. - apply the ``ignore`` parameter when executing the transformation. - apply the ``copy`` parameter when executing the transformation. -- run :class:`~wefe.debias.multiclass_hard_debias.MulticlassHardDebias` mitigation +- run :class:`~wefe.debias.multiclass_hard_debias.MulticlassHardDebias` mitigation method on an word embedding model to mitigate ethnic bias. .. note:: For a list of metrics implemented in WEFE, refer to the - :ref:`debias-API` of the API reference. + :ref:`debias-API` of the API reference. .. note:: - If you want to know more about WEFE's standardization of debias methods, + If you want to know more about WEFE's standardization of debias methods, visit :ref:`mitigation framework` in the conceptual guides. @@ -30,8 +30,8 @@ Hard Debias ----------- -Hard debias is a method that allows mitigating biases through geometric operations -on embeddings. +Hard debias is a method that allows mitigating biases through geometric operations +on embeddings. This method is binary because it only allows 2 classes of the same bias criterion, such as male or female. @@ -40,7 +40,7 @@ such as male or female. For a multiclass debias (such as for Latinos, Asians and Whites), it is recommended to visit :class:`~wefe.debias.multiclass_hard_debias.MulticlassHardDebias` class. - + The main idea of this method is: @@ -93,13 +93,13 @@ Hard Debias, you have to provide the the ``definitional_pairs``, the optionally, a debias ``criterion_name`` (to name the debiased model). -The code shown below shows how to run Hard Debias from gender to the test model +The code shown below shows how to run Hard Debias from gender to the test model provided by wefe (reduced word2vec). .. code:: ipython3 from wefe.utils import load_test_model - + model = load_test_model() # load a reduced version of word2vec model @@ -118,14 +118,14 @@ Load the required word sets. from wefe.datasets import fetch_debiaswe from wefe.debias.hard_debias import HardDebias - + debiaswe_wordsets = fetch_debiaswe() - + definitional_pairs = debiaswe_wordsets["definitional_pairs"] equalize_pairs = debiaswe_wordsets["equalize_pairs"] gender_specific = debiaswe_wordsets["gender_specific"] - - + + print(f"definitional_pairs: \n{definitional_pairs}") print(f"equalize_pairs: \n{equalize_pairs}") print(f"gender_specific: \n{gender_specific}") @@ -134,24 +134,24 @@ Load the required word sets. .. parsed-literal:: - definitional_pairs: + definitional_pairs: [['woman', 'man'], ['girl', 'boy'], ['she', 'he'], ['mother', 'father'], ['daughter', 'son'], ['gal', 'guy'], ['female', 'male'], ['her', 'his'], ['herself', 'himself'], ['Mary', 'John']] - equalize_pairs: + equalize_pairs: [['monastery', 'convent'], ['spokesman', 'spokeswoman'], ['Catholic_priest', 'nun'], ['Dad', 'Mom'], ['Men', 'Women'], ['councilman', 'councilwoman'], ['grandpa', 'grandma'], ['grandsons', 'granddaughters'], ['prostate_cancer', 'ovarian_cancer'], ['testosterone', 'estrogen'], ['uncle', 'aunt'], ['wives', 'husbands'], ['Father', 'Mother'], ['Grandpa', 'Grandma'], ['He', 'She'], ['boy', 'girl'], ['boys', 'girls'], ['brother', 'sister'], ['brothers', 'sisters'], ['businessman', 'businesswoman'], ['chairman', 'chairwoman'], ['colt', 'filly'], ['congressman', 'congresswoman'], ['dad', 'mom'], ['dads', 'moms'], ['dudes', 'gals'], ['ex_girlfriend', 'ex_boyfriend'], ['father', 'mother'], ['fatherhood', 'motherhood'], ['fathers', 'mothers'], ['fella', 'granny'], ['fraternity', 'sorority'], ['gelding', 'mare'], ['gentleman', 'lady'], ['gentlemen', 'ladies'], ['grandfather', 'grandmother'], ['grandson', 'granddaughter'], ['he', 'she'], ['himself', 'herself'], ['his', 'her'], ['king', 'queen'], ['kings', 'queens'], ['male', 'female'], ['males', 'females'], ['man', 'woman'], ['men', 'women'], ['nephew', 'niece'], ['prince', 'princess'], ['schoolboy', 'schoolgirl'], ['son', 'daughter'], ['sons', 'daughters'], ['twin_brother', 'twin_sister']] - gender_specific: + gender_specific: ['he', 'his', 'He', 'her', 'she', 'him', 'She', 'man', 'women', 'men', 'His', 'woman', 'spokesman', 'wife', 'himself', 'son', 'mother', 'father', 'chairman', 'daughter', 'husband', 'guy', 'girls', 'girl', 'Her', 'boy', 'King', 'boys', 'brother', 'Chairman', 'spokeswoman', 'female', 'sister', 'Women', 'Man', 'male', 'herself', 'Lions', 'Lady', 'brothers', 'dad', 'actress', 'mom', 'sons', 'girlfriend', 'Kings', 'Men', 'daughters', 'Prince', 'Queen', 'teenager', 'lady', 'Bulls', 'boyfriend', 'sisters', 'Colts', 'mothers', 'Sir', 'king', 'businessman', 'Boys', 'grandmother', 'grandfather', 'deer', 'cousin', 'Woman', 'ladies', 'Girls', 'Father', 'uncle', 'PA', 'Boy', 'Councilman', 'mum', 'Brothers', 'MA', 'males', 'Girl', 'Mom', 'Guy', 'Queens', 'congressman', 'Dad', 'Mother', 'grandson', 'twins', 'bull', 'queen', 'businessmen', 'wives', 'widow', 'nephew', 'bride', 'females', 'aunt', 'Congressman', 'prostate_cancer', 'lesbian', 'chairwoman', 'fathers', 'Son', 'moms', 'Ladies', 'maiden', 'granddaughter', 'younger_brother', 'Princess', 'Guys', 'lads', 'Ma', 'Sons', 'lion', 'Bachelor', 'gentleman', 'fraternity', 'bachelor', 'niece', 'Lion', 'Sister', 'bulls', 'husbands', 'prince', 'colt', 'salesman', 'Bull', 'Sisters', 'hers', 'dude', 'Spokesman', 'beard', 'filly', 'Actress', 'Him', 'princess', 'Brother', 'lesbians', 'councilman', 'actresses', 'Viagra', 'gentlemen', 'stepfather', 'Deer', 'monks', 'Beard', 'Uncle', 'ex_girlfriend', 'lad', 'sperm', 'Daddy', 'testosterone', 'MAN', 'Female', 'nephews', 'maid', 'daddy', 'mare', 'fiance', 'Wife', 'fiancee', 'kings', 'dads', 'waitress', 'Male', 'maternal', 'heroine', 'feminist', 'Mama', 'nieces', 'girlfriends', 'Councilwoman', 'sir', 'stud', 'Mothers', 'mistress', 'lions', 'estranged_wife', 'womb', 'Brotherhood', 'Statesman', 'grandma', 'maternity', 'estrogen', 'ex_boyfriend', 'widows', 'gelding', 'diva', 'teenage_girls', 'nuns', 'Daughter', 'czar', 'ovarian_cancer', 'HE', 'Monk', 'countrymen', 'Grandma', 'teenage_girl', 'penis', 'bloke', 'nun', 'Husband', 'brides', 'housewife', 'spokesmen', 'suitors', 'menopause', 'monastery', 'patriarch', 'Beau', 'motherhood', 'brethren', 'stepmother', 'Dude', 'prostate', 'Moms', 'hostess', 'twin_brother', 'Colt', 'schoolboy', 'eldest', 'brotherhood', 'Godfather', 'fillies', 'stepson', 'congresswoman', 'Chairwoman', 'Daughters', 'uncles', 'witch', 'Mommy', 'monk', 'viagra', 'paternity', 'suitor', 'chick', 'Pa', 'fiancé', 'sorority', 'macho', 'Spokeswoman', 'businesswoman', 'eldest_son', 'gal', 'statesman', 'schoolgirl', 'fathered', 'goddess', 'hubby', 'mares', 'stepdaughter', 'blokes', 'dudes', 'socialite', 'strongman', 'Witch', 'fiancée', 'uterus', 'grandsons', 'Bride', 'studs', 'mama', 'Aunt', 'godfather', 'hens', 'hen', 'mommy', 'Babe', 'estranged_husband', 'Fathers', 'elder_brother', 'boyhood', 'baritone', 'Diva', 'Lesbian', 'grandmothers', 'grandpa', 'boyfriends', 'feminism', 'countryman', 'stallion', 'heiress', 'queens', 'Grandpa', 'witches', 'aunts', 'semen', 'fella', 'granddaughters', 'chap', 'knight', 'widower', 'Maiden', 'salesmen', 'convent', 'KING', 'vagina', 'beau', 'babe', 'HIS', 'beards', 'handyman', 'twin_sister', 'maids', 'gals', 'housewives', 'Gentlemen', 'horsemen', 'Businessman', 'obstetrics', 'fatherhood', 'beauty_queen', 'councilwoman', 'princes', 'matriarch', 'colts', 'manly', 'ma', 'fraternities', 'Spokesmen', 'pa', 'fellas', 'Gentleman', 'councilmen', 'dowry', 'barbershop', 'Monks', 'WOMAN', 'fraternal', 'ballerina', 'manhood', 'Dads', 'heroines', 'granny', 'gynecologist', 'princesses', 'Goddess', 'yo', 'Granny', 'knights', 'eldest_daughter', 'HER', 'underage_girls', 'masculinity', 'Girlfriend', 'bro', 'Grandmother', 'grandfathers', 'crown_prince', 'Restless', 'paternal', 'Queen_Mother', 'Boyfriend', 'womens', 'Males', 'SHE', 'Countess', 'stepchildren', 'Belles', 'bachelors', 'matron', 'momma', 'Legs', 'maidens', 'goddesses', 'landlady', 'sisterhood', 'Grandfather', 'Fraternity', 'Majesty', 'Babes', 'lass', 'maternal_grandmother', 'blondes', "ma'am", 'Womens', 'divorcee', 'Momma', 'fathering', 'Effie', 'Lad', 'womanhood', 'missus', 'Sisterhood', 'granddad', 'Mens', 'papa', 'gf', 'sis', 'Husbands', 'Hen', 'womanizer', 'gynecological', 'stepsister', 'Handsome', 'Prince_Charming', 'BOY', 'stepdad', 'teen_ager', 'GIRL', 'dame', 'Sorority', 'beauty_pageants', 'raspy', 'harem', 'maternal_grandfather', 'Hes', 'deliveryman', 'septuagenarian', 'damsel', 'paternal_grandmother', 'paramour', 'paternal_grandparents', 'Nun', 'DAD', 'mothering', 'shes', "HE_'S", 'Nuns', 'teenage_daughters', 'auntie', 'widowed_mother', 'Girlfriends', 'FATHER', 'virile', 'COUPLE', 'grandmas', 'Hubby', 'nan', 'vixen', 'Joan_Crawford', 'stepdaughters', 'endometrial_cancer', 'stepsons', 'loins', 'Grandson', 'Mitchells', 'erections', 'Matron', 'Fella', 'daddies', 'ter', 'Sweetie', 'Dudes', 'Princesses', 'Lads', 'lioness', 'Mamma', 'virility', 'bros', 'womenfolk', 'Heir', 'BROTHERS', 'manliness', 'patriarchs', 'earl', 'sisterly', 'Whore', 'Gynaecology', 'countess', 'convents', 'Oratory', 'witch_doctor', 'mamas', 'yah', 'aunty', 'aunties', 'Heiress', 'lasses', 'Breasts', 'fairer_sex', 'sorority_sisters', 'WIFE', 'Laurels', 'penile', 'nuh', 'mah', 'toms', 'mam', 'Granddad', 'premenopausal_women', 'Granddaddy', 'nana', 'coeds', 'dames', 'herdsman', 'Mammy', 'Fellas', 'Niece', 'menfolk', 'Grandad', 'bloods', 'Gramps', 'damsels', 'Granddaughter', 'mamma', 'concubine', 'Oros', 'Blarney', 'filial', 'broads', 'Ethel_Kennedy', 'ACTRESS', 'Tit', 'fianc', 'Hunk', 'Night_Shift', 'wifey', 'Lothario', 'Holy_Roman_Emperor', 'horse_breeder', 'grandnephew', 'Lewises', 'Muscular', 'feminist_movement', 'Sanan', 'womenâ_€_™', 'Fiancee', 'dowries', 'Carmelite', 'rah', 'n_roller', 'bay_filly', 'belles', 'Uncles', 'PRINCESS', 'womans', 'Homeboy', 'Blokes', 'Charmer', 'codger', 'Delta_Zeta', 'courtesans', 'grandaughter', 'SISTER', 'Highness', 'grandbabies', 'crone', 'Skip_Away', 'noblewoman', 'bf', 'jane', 'philandering_husband', 'Sisqo', 'mammy', 'daugher', 'director_Skip_Bertman', 'DAUGHTER', 'Royal_Highness', 'mannish', 'spinsters', 'Missus', 'madame', 'Godfathers', 'saleswomen', 'beaus', 'Risha', 'luh', 'sah', 'negligee', 'Womenâ_€_™', 'Hos', 'salesgirl', 'grandmom', 'Grandmas', 'Lawsons', 'countrywomen', 'Booby', 'darlin', 'Sheiks', 'boyz', 'wifes', 'Bayi', 'Il_Duce', 'â_€_œMy', 'fem', 'daugther', 'Potti', 'hussy', 'tch', 'Gelding', 'stemmed_roses', 'Damson', 'puh', 'Tylers', 'neice', 'Mutha', 'GRANDMOTHER', 'youse', 'spurned_lover', 'mae', 'Britt_Ekland', 'clotheshorse', 'Carlita_Kilpatrick', 'Cambest', 'Pretty_Polly', 'banshees', 'male_chauvinist', 'Arliss', 'mommas', 'maidservant', 'Gale_Harold', 'Little_Bo_Peep', 'Cleavers', 'hags', 'blowsy', 'Queen_Elizabeth_I.', 'lassies', 'papas', 'BABE', 'ugly_ducklings', 'Jims', 'hellion', 'Beautician', 'coalminer', 'relaxin', 'El_Mahroug', 'Victoria_Secret_Angel', 'shepherdess', 'Mosco', 'Slacks', 'nanna', 'wifely', 'tomboys', 'LAH', 'hast', 'apo', 'Kaplans', 'milkmaid', 'Robin_Munis', 'John_Barleycorn', 'royal_highness', 'Meanie', 'NAH', 'trollop', 'roh', 'Jewess', 'Sheik_Hamad', 'mumsy', 'Big_Pussy', 'chil_dren', 'Aunt_Bea', 'basso', 'sista', 'girlies', 'nun_Sister', 'chica', 'Bubbas', 'massa', 'Southern_belles', 'Nephews', 'castrations', 'Mister_Ed', 'Grandsons', 'Calaf', 'Malachy_McCourt', 'Shamash', 'hey_hey', 'Harmen', 'sonofabitch', 'Donovans', 'Grannie', 'Kalinka', 'hisself', 'Devean', 'goatherd', 'hinds', 'El_Corredor', 'Kens', 'notorious_womanizer', 'goh', 'Mommas', 'washerwoman', 'Samaira', 'Coo_Coo', 'Governess', 'grandsire', 'PRINCE_WILLIAM', 'gramma', 'him.He', 'Coptic_priest', 'Corbie', 'Kennys', 'thathe', 'Pa_Pa', 'Bristols', 'Hotep', 'snowy_haired', 'El_Prado_Ire', 'Girl_hitmaker', 'Hurleys', 'St._Meinrad', 'sexually_perverted', 'authoress', 'Prudie', 'raven_haired_beauty', 'Bonos', 'domestic_shorthair', 'brothas', 'nymphet', 'Neelma', 'Seita', 'stud_muffin', 'St._Judes', 'yenta', 'bare_shouldered', 'Pinkney_Sr.', 'PRINCE_CHARLES', 'Bisutti', 'sistas', 'Blanche_Devereaux', 'Momoa', 'Quiff', 'Scotswoman', 'balaclava_clad_men', 'Louis_Leakey', 'dearie', 'vacuum_cleaner_salesman', 'grandads', 'postulant', 'SARAH_JESSICA_PARKER', 'AUNT', 'Prince_Dauntless', 'Dalys', 'Darkie', 'Czar_Nicholas', 'Lion_Hearted', 'Boy_recliner', 'baby_mamas', 'giantess', 'Lawd', 'GRANNY', 'fianc_e', 'Bilqis', 'WCTU', 'famly', 'Ellas', 'feminazis', 'Pentheus', 'MAMAS', 'Town_Criers', 'Saggy', 'youngman', 'grandam', 'divorcé', 'bosomed', 'roon', 'Simmentals', 'eponymous_heroine', 'LEYLAND', "REE'", "cain't", 'Evelynn', "WAH'", 'sistah', 'Horners', 'Elsie_Poncher', 'Coochie', 'rat_terriers', 'Limousins', 'Buchinski', 'Schicchi', 'Carpitcher', 'Khwezi', "HAH'", 'Shazza', 'Mackeson', "ROH'", 'kuya', 'novice_nun', 'Shei', 'Elmasri', 'ladykiller', '6yo', 'Yenta', 'SHEL', 'pater', 'Souse', 'Tahirah', 'comedian_Rodney_Dangerfield', 'Shottle', 'carryin', 'Sath', "fa'afafine", 'royal_consort', 'hus_band', 'maternal_uncles', 'dressing_provocatively', 'dreamgirl', 'millionaire_industrialist', 'Georgie_Girl', 'Must_Be_Obeyed', 'joh', 'Arabian_stallion', 'ahr', 'mso_para_margin_0in', "SOO'", 'Biddles', 'Chincoteague_Volunteer_Fire', 'Lisa_Miceli', 'gorgeous_brunette', 'fiancŽ', 'Moved_fluently', 'Afternoon_Deelites', 'biker_dude', 'Vito_Spatafore', 'MICK_JAGGER', 'Adesida', 'Reineman', 'witz', 'Djamila', 'Glenroe', 'daddys', 'Romanzi', 'gentlewomen', 'Dandie_Dinmont_terrier', 'Excess_Ire', 'By_SYVJ_Staff', 'zan', 'CONFESSIONS', 'Magees', 'wimmin', 'tash', 'Theatrical_Ire', 'Prince_Charmings', 'chocolate_eclair', 'bron', 'daughers', 'Felly', 'fiftyish', 'Spritely', 'GRANDPA', 'distaffer', 'Norbertines', "DAH'", 'leader_Muammar_Gadaffi', 'swains', 'Prince_Tomohito', 'Honneur', 'Soeur', 'jouster', 'Pharaoh_Amenhotep_III', 'QUEEN_ELIZABETH_II', "Ne'er", 'Galileo_Ire', 'Fools_Crow', 'Lannisters', 'Devines', 'gonzales', 'columnist_Ann_Landers', 'Moseleys', 'hiz', 'busch', 'roastee', 'toyboys', 'Sheffields', 'grandaunt', 'Galvins', 'Giongo', 'geh', 'flame_haired_actress', 'Grammarian', 'Greg_Evigan', 'frontierswoman', 'Debele', 'rabs', 'nymphets', 'aai', 'BREE', 'Shaqs', 'ZAY', 'pappa', 'Housa', 'refrigerator_repairman', 'artificial_inseminations', 'chickie', 'Rippa', 'teenager_Tracy_Turnblad', 'homebred_colt', 'Abigaille', 'hen_pecked_husband', 'businesman', 'her.She', 'Kaikeyi', 'Stittsworth', 'self_proclaimed_redneck', 'Khella', 'NeW', 'Evers_Swindell', 'Asmerom_Gebreselassie', 'Boy_recliners', 'Cliff_Claven', 'Legge_Bourke', 'Costos', "d'_honneur", 'sistahs', 'Cabble', 'sahn', 'CROW_AGENCY_Mont', 'jezebel', 'Harrolds', 'ROSARIO_DAWSON', 'INXS_frontman_Michael_Hutchence', 'Gursikh', 'Dadas', 'VIAGA', 'keen_horsewoman', 'Theodoric', 'Eldery', 'lihn', 'Alice_Kramden', 'Santarina', 'radical_cleric_al_Sadr', 'Curleys', "SY'", 'Fidaa', 'Saptapadi', 'Actor_Sean_Astin', 'Kellita_Smith', 'Doly', 'Libertina', 'Money_McBags', 'Chief_Bearhart', 'choirgirl', 'chestnut_stallion', 'VIGRA', 'BY_JIM_McCONNELL', 'Sal_Vitale', 'Trivia_buffs', 'kumaris', 'fraternal_lodge', 'galpals', 'Borino_Quinn', 'lina', 'LATEST_Rapper', 'Bezar', 'Manro', 'bakla', 'Grisetti', 'blond_bimbo', 'spinster_aunt', 'gurls', 'hiswife', 'paleface', 'Charlye', 'hippie_chicks', 'Khalifas', 'Picture_JUSTIN_SANSON', 'Hepburns', 'yez', 'ALDER', 'Sanussi', 'Lil_Sis', 'McLoughlins', 'Barbra_Jean', 'Lulua', 'thatshe', 'actress_Shohreh_Aghdashloo', 'SIR_ANTHONY_HOPKINS', 'Gloddy', "ZAH'", "ORANGE_'S", 'Danielle_Bimber', 'grandmum', 'Kulkis', 'Brazington', 'Marisa_Lenhard_CFA', 'SIR_JOHN', 'Clareman', 'Aqila', 'Heavily_tattooed', 'Libbys', 'thim', 'elocutionist', 'submissives', 'Inja', 'rahm', 'Agnes_Gooch', 'fake_tits', 'nancy_boys', 'Swaidan', "SHAH'", "ain'ta_bed", 'Shumail_Raj', 'Duchesse', 'diethylstilbestrol_DES', 'colt_foal', 'unfaithful_lover', 'Maseri', 'nevah', 'SAHN', 'Barths', 'Toughkenamon', 'GUEST_STARS', 'him.But', 'Donna_Claspell', 'gingham_dresses', 'Massage_Parlour', 'wae', 'Wasacz', 'Magistra', 'vihl', 'Smriti_Iraani', 'boyish_haircut', 'workingwoman', 'borthers', 'Capuchin_friars', 'Nejma', 'yes_sirs', 'bivocational_pastor', 'Grafters', 'HOPWOOD', 'Nicknamed_Godzilla', 'yos', 'Berkenfield', 'Missis', 'sitcom_Designing_Women', 'Kafoa', 'trainer_Emma_Lavelle', 'sadomasochistic_dungeon', 'iht', 'desperates', 'predessor', 'wolf_cub', 'indigenous_Peruvians', 'Livia_Soprano', 'troh', 'colt_sired', 'BOND_HILL', 'ihl', 'Drydens', 'rahs', 'Piserchia', 'Sonny_Corinthos', 'bankrobber', 'Fwank', 'feisty_redhead', 'booze_guzzling', 'COOPERS', "actress_Q'orianka_Kilcher", 'Cortezar', 'twe', 'Jacoub', 'Cindy_Iannarelli', 'Hell_Raiser', 'Fondly_referred', 'Bridal_Shoppe', 'Noleta', 'Christinas', 'IAGRA', 'LaTanya_Richardson', 'Sang_Bender', 'Assasins', 'sorrel_gelding', 'septugenarian', 'Hissy', 'Muqtada_al_Sadr_mook', 'Pfeni', 'MADRID_AFX_Banco_Santander', 'tuchis', 'LeVaughn', 'Gadzicki', 'transvestite_hooker', 'Fame_jockey_Laffit', 'nun_Sister_Mary', 'SAMSONOV', 'Mayflower_Madam', 'Shaque', 'well.He', 'Trainer_Julio_Canani', 'sorrel_mare', 'minivehicle_joint_venture', 'wife_Dwina', "Aasiya_AH'_see", 'Baratheon', "Rick_O'Shay", 'Mammies', 'goatie', 'Nell_Gwynne', 'charmingly_awkward', 'Slamma', 'DEHL', 'Lorenzo_Borghese', 'ALMA_Wis.', 'Anne_Scurria', 'father_Peruvians_alternately', 'JULIE_ANDREWS', 'Slim_Pickins', 'Victoria_Secret_stunner', "BY'", 'Sanam_Devdas', 'pronounced_luh', 'Pasha_Selim', '中华', 'rson', 'maternal_grandmothers', 'IOWA_CITY_Ia', 'Madame_de_Tourvel', "JAY'", 'Sheika_Mozah_bint_Nasser', 'Hotsy_Totsy', "D'_Ginto", 'singer_Johnny_Paycheck', 'uterine_prolapse_surgery', 'SCOTTDALE_Pa.', 'AdelaideNow_reports', 'Marcus_Schenkenberg', 'Clyse', 'Obiter_Dicta', 'comic_Sam_Kinison', 'bitties', 'ROCKVILLE_Ind.', 'swimsuit_calendars', 'Decicio_Smith', 'Ma_ma', 'Rie_Miyazawa', 'celibate_chastity', 'gwah', "ZAY'", 'HER_Majesty', 'Defrere', 'Las_Madrinas', '簿_聂_翻', 'Bea_Hamill', 'ARCADIA_Calif._Trainer', 'Bold_Badgett', 'stakes_victress', 'Hoppin_Frog', 'Narumiya', 'Flayfil', 'hardman_Vinnie_Jones', 'Marilyn_Monroe_lookalike', 'Kivanc_Tatlitug', 'Persis_Khambatta', 'SINKING_SPRING_Pa.', 'len_3rd', 'DEAR_TRYING', 'Farndon_Cheshire', 'Krishna_Madiga', 'daughter_Princess_Chulabhorn', 'Marshall_Rooster_Cogburn', 'Kitty_Kiernan', 'Yokich', 'Jarou', 'Serdaris', 'ee_ay', 'Montifiore', 'Chuderewicz', 'Samuel_Le_Bihan', 'filly_Proud_Spell', 'Umm_Hiba', 'pronounced_koo', 'Sandy_Fonzo', "KOR'", 'Fielder_Civil_kisses', 'Federalsburg_Maryland', 'Nikah_ceremony', 'Brinke_Stevens', 'Yakama_Tribal_Council', 'Capuchin_Father', 'wife_Callista_Bisek', 'Beau_Dare', 'Bedoni', 'Arjun_Punj', 'JOHNNY_KNOXVILLE', 'cap_tain', 'Alderwood_Boys', 'Chi_Eta_Phi', 'ringleader_Charles_Graner', 'Savoies', 'Lalla_Salma', 'Mrs._Potiphar', 'fahn', 'name_Taylor_Sumers', 'Vernita_Green', 'Bollywood_baddie', 'BENBROOK_Texas', 'Assemblyman_Lou_Papan', 'virgin_brides', 'Cho_Eun', 'CATHY_Freeman', 'Uncle_Saul', 'Lao_Brewery', 'Ibo_tribe', 'ruf', 'rival_Edurne_Pasaban', 'Hei_Shangri_La', 'Mommy_dearest', 'interest_Angola_Sonogal', 'Ger_Monsun', 'PUSSYCAT_DOLL', 'Crown_Jewels_Condoms', 'Lord_Marke', 'Patootie', 'Nora_Bey', 'huntin_shootin', 'Minister_Raymond_Tshibanda', 'La_Nina_la_NEEN', 'signature_Whoppers', 'estranged_hubby_Kevin_Federline', "UR'", 'pill_poppin', "GEHR'", 'purebred_Arabians', 'husbandly_duties', 'VIAGRA_TIMING', 'Hereford_heifer', 'hushed_monotone_voice', 'Pola_Uddin', 'Wee_Jimmy_Krankie', 'Kwakwanso', 'Our_Galvinator', 'shoh', 'Codependency_Anonymous_Group', "LA'", "Taufa'ahau", 'Invincible_Spirit_colt', "SAH'_dur", 'MOUNT_CARMEL_Pa.', 'watches_attentively', 'SNL_spinoffs', 'Seth_Nitschke', 'Duns_Berwickshire', 'defendant_Colleen_LaRose', "Silky_O'Sullivan", 'Highcliff_Farm', "REN'", 'Comestar', 'Satisfied_Frog', 'Jai_Maharashtra', 'ATTICA_Ind.', 'lover_Larry_Birkhead', 'Tami_Megal', 'chauvinist_pigs', 'Phi_sorority', 'Micronesian_immigrant', 'Lia_Boldt', 'Sugar_Tits', 'actress_Kathy_Najimy', 'zhoo', 'Colombo_underboss', 'Katsav_accusers', 'Bess_Houdini', 'rap_mogul_Diddy', 'companions_Khin_Khin', 'Van_Het', 'Mastoi_tribe', 'VITALY', 'ROLLING_STONES_rocker', 'womanizing_cad', 'LILY_COLE', 'paternal_grandfathers', 'Lt._Col._Kurt_Kosmatka', 'Kasseem_Jr.', 'Ji_Ji', 'Wilburforce', 'VIAGRA_DOSE', 'English_Sheepdogs', 'pronounced_Kah', 'Htet_Htet_Oo', 'Brisk_Breeze', 'Eau_du', 'BY_MELANIE_EVANS', 'Neovasc_Medical', 'British_funnyman_RICKY', '4YO_mare', 'Hemaida', 'MONKTON', 'Mrs_Mujuru', 'BaGhana_BaGhana', 'Shaaban_Abdel_Rahim', 'Edward_Jazlowiecki_lawyer', 'Ajman_Stud', 'manly_pharaoh_even', 'Serra_Madeira_Islands', "FRAY'", 'panto_dames', 'Khin_Myo', 'dancer_Karima_El_Mahroug', 'CROWN_Princess', 'Baseball_HOFer', 'Hasta_la_Pasta', 'GIRLS_NEXT_DOOR', 'Benedict_Groeschel', 'Bousamra', 'Ruby_Rubacuori_Ruby', 'Monde_Bleu', 'Un_homme_qui', 'Taylor_Sumers', 'Rapper_EMINEM', 'Joe_Menchetti', "VAY'", 'supermodel_NAOMI_CAMPBELL', 'Supermodel_GISELE_BUNDCHEN', 'Au_Lait', 'Radar_Installed', 'THOMAS_TOWNSHIP_Mich.', 'Rafinesque', 'Herman_Weinrich', 'Abraxas_Antelope', 'raspy_voiced_rocker', 'Manurewa_Cosmopolitan_Club', 'Paraone', 'THE_LEOPARD', 'Boy_Incorporated_LZB', 'Dansili_filly', 'Lumpy_Rutherford', 'unwedded_bliss', 'Bhavna_Sharma', 'Scarvagh', 'en_flagrante', 'Mottu_Maid', 'Dowager_Queen', 'NEEN', 'model_Monika_Zsibrita', 'ROSIE_PEREZ', 'Mattock_Ranger', 'Valorous', 'Surpreme', 'Marwari_businessmen', 'Grandparents_aunts', 'Kimberley_Vlaeminck', 'Lyn_Treece_Boys', 'PDX_Update', 'Virsa_Punjab', 'eyelash_fluttering', 'Pi_fraternity', 'HUNTLEIGH_Mo.', 'novelist_Jilly_Cooper', 'Naha_Shuri_temple', 'Yasmine_Al_Massri', 'Mu_Gamma_Xi', 'Mica_Ertegun', 'Ocleppo', 'VIAGRA_CONTRAINDICATIONS', 'daughter_PEACHES', 'trainer_Geoff_Wragg', 'OVERNIGHT_DELIVERY', 'Fitts_retiree', 'de_Tourvel', 'Lil_Lad', 'north_easterner', 'Aol_Weird_News', 'Somewhat_improbably', 'Sikh_panth', 'Worcester_2m_7f', 'Zainab_Jah', 'OLYMPIC_medalist', 'Enoch_Petrucelly', 'collie_Lassie', "LOW'", 'clumsiness_Holloway', 'ayr', "OHR'", 'ROLLING_STONES_guitarist', "LAH'_nee", 'Ian_Beefy_Botham', 'Awapuni_trainer', 'Glamorous_Granny', 'Chiang_Ching', 'MidAtlantic_Cardiovascular_Associates', 'Yeke', 'Seaforth_Huron_Expositor', 'Westley_Cary_Elwes', 'Cate_Blanchett_Veronica_Guerin', 'Bellas_Gate', 'witch_Glinda', 'wives_mistresses', 'Woodsville_Walmart', '2YO_colt', 'Manav_Sushant_Singh', 'Pupi_Avati_Il', 'Sigma_Beta_Rho', 'Bishop_Christopher_Senyonjo', 'Vodou_priest', 'Rubel_Chowdhury', 'Claddagh_Ring', "TAH'_duh_al", "al_Sadr_mook_TAH'", 'ROBIN_GIBB', "GAHN'", 'BY_THOMAS_RANSON', 'sister_Carine_Jena', 'Lyphard_mare', 'summa_cum', 'Semenya_grandmother_Maputhi', 'Clare_Nuns', 'Talac', 'sex_hormones_androgens', 'majeste', 'Saint_Ballado_mare', 'Carrie_Huchel', 'Mae_Dok', 'wife_Dieula', 'Earnest_Sirls', 'spoof_bar_mitzvah', 'von_Boetticher', 'Audwin_Mosby', 'Case_presentationWe', 'Vincent_Papandrea', "KRAY'", 'Sergi_Benavent', 'Le_Poisson', 'Von_Cramm', 'Patti_Mell', 'Raymi_Coya', 'Benjamin_BeBe_Winans', 'Nana_Akosua', 'Auld_Acquaintance', 'Desire_Burunga', 'Company_Wrangler_Nestea', 'ask_Krisy_Plourde', 'JUANITA_BYNUM', 'livia', 'GAMB', 'Gail_Rosario_Dawson', 'Ramgarhia_Sikh', 'Catholic_nun_Sister', 'FOUR_WEDDINGS_AND', 'Robyn_Scherer', 'brother_King_Athelstan', 'Santo_Loquasto_Fences', 'Wee_Frees', 'MARISOL', 'Soliloquy_Stakes', 'Whatever_Spoetzl', "Marc'Aurelio", 'mon_petit', 'Sabbar_al_Mashhadani', "KAY'_lee", "m_zah_MAH'", 'BY_TAMI_ALTHOFF', 'hobbit_Samwise_Gamgee', 'Bahiya_Hariri_sister', 'daddy_Larry_Birkhead', 'Sow_Tracey_Ullman', 'coach_Viljo_Nousiainen', 'Carmen_Lebbos', 'conjoined_twins_Zainab', 'Rob_Komosa', 'ample_bosomed', 'Ageing_rocker', 'psychic_Oda'] - ---------------------------------------------------------------------- - + ---------------------------------------------------------------------- + Instantiate and fit the parameters of the debias transformation. -In the fit stage, parameters such as bias direction are calculated and embeddings are +In the fit stage, parameters such as bias direction are calculated and embeddings are prepared for the equalization stage. .. code:: ipython3 hd = HardDebias(verbose=False, criterion_name="gender") - + hd.fit( model, definitional_pairs=definitional_pairs, equalize_pairs=equalize_pairs, ) @@ -230,7 +230,7 @@ between the original model and the debiased model. from wefe.datasets import load_weat from wefe.query import Query from wefe.metrics import WEAT - + weat_wordset = load_weat() weat = WEAT() @@ -246,19 +246,19 @@ Next, we measure the gender bias exposed by query 1 (Male terms and Female terms ["Career", "Family"], ) print(gender_query_1, "\n", "-" * 70, "\n") - + biased_results_1 = weat.run_query(gender_query_1, model, normalize=True) debiased_results_1 = weat.run_query( gender_query_1, gender_debiased_model, normalize=True ) - + print("Debiased vs Biased (absolute values)") print( round(abs(debiased_results_1["weat"]), 3), "<", round(abs(biased_results_1["weat"]), 3), ) - + @@ -266,9 +266,9 @@ Next, we measure the gender bias exposed by query 1 (Male terms and Female terms - ---------------------------------------------------------------------- - + - Attribute sets:[['executive', 'management', 'professional', 'corporation', 'salary', 'office', 'business', 'career'], ['home', 'parents', 'children', 'family', 'cousins', 'marriage', 'wedding', 'relatives']]> + ---------------------------------------------------------------------- + Debiased vs Biased (absolute values) 0.047 < 0.463 @@ -285,9 +285,9 @@ Next, we measure the gender bias exposed by query 2 (Male Names and Female Names ["Male Names", "Female Names"], ["Pleasant", "Unpleasant"], ) - + print(gender_query_2, "\n", "-" * 70, "\n") - + biased_results_2 = weat.run_query( gender_query_2, model, normalize=True, preprocessors=[{}, {"lowercase": True}] ) @@ -297,14 +297,14 @@ Next, we measure the gender bias exposed by query 2 (Male Names and Female Names normalize=True, preprocessors=[{}, {"lowercase": True}], ) - + print("Debiased vs Biased (absolute values)") print( round(abs(debiased_results_2["weat"]), 3), "<", round(abs(biased_results_2["weat"]), 3), ) - + @@ -312,9 +312,9 @@ Next, we measure the gender bias exposed by query 2 (Male Names and Female Names - ---------------------------------------------------------------------- - + - Attribute sets:[['caress', 'freedom', 'health', 'love', 'peace', 'cheer', 'friend', 'heaven', 'loyal', 'pleasure', 'diamond', 'gentle', 'honest', 'lucky', 'rainbow', 'diploma', 'gift', 'honor', 'miracle', 'sunrise', 'family', 'happy', 'laughter', 'paradise', 'vacation'], ['abuse', 'crash', 'filth', 'murder', 'sickness', 'accident', 'death', 'grief', 'poison', 'stink', 'assault', 'disaster', 'hatred', 'pollute', 'tragedy', 'divorce', 'jail', 'poverty', 'ugly', 'cancer', 'kill', 'rotten', 'vomit', 'agony', 'prison']]> + ---------------------------------------------------------------------- + Debiased vs Biased (absolute values) 0.055 < 0.074 @@ -325,11 +325,11 @@ Target Parameter ~~~~~~~~~~~~~~~~ If a set of words is specified in ``target`` parameter, the debias method is performed -only on the embeddings associated with this set. +only on the embeddings associated with this set. In the case of providing ``None``, the transformation is performed on all vocabulary words except those specified in ignore. By default ``None``. -In the following example, the target parameter is used to execute the transformation +In the following example, the target parameter is used to execute the transformation only on the career and family word set: .. code:: ipython3 @@ -352,11 +352,11 @@ only on the career and family word set: "wedding", "relatives", ] - + hd = HardDebias(verbose=False, criterion_name="gender").fit( model, definitional_pairs=definitional_pairs, equalize_pairs=equalize_pairs, ) - + gender_debiased_model = hd.transform(model, target=targets, copy=True) @@ -373,7 +373,7 @@ only on the career and family word set: Next, a bias test is run on the mitigated embeddings associated with the -target words. +target words. In this case, the value of the metric is lower on the query executed on the mitigated model than on the original one. @@ -389,19 +389,19 @@ These results indicate that there was a mitigation of bias on embeddings of thes ["Career", "Family"], ) print(gender_query_1, "\n", "-" * 70, "\n") - + biased_results_1 = weat.run_query(gender_query_1, model, normalize=True) debiased_results_1 = weat.run_query( gender_query_1, gender_debiased_model, normalize=True ) - + print("Debiased vs Biased (absolute values)") print( round(abs(debiased_results_1["weat"]), 3), "<", round(abs(biased_results_1["weat"]), 3), ) - + @@ -409,9 +409,9 @@ These results indicate that there was a mitigation of bias on embeddings of thes - ---------------------------------------------------------------------- - + - Attribute sets:[['executive', 'management', 'professional', 'corporation', 'salary', 'office', 'business', 'career'], ['home', 'parents', 'children', 'family', 'cousins', 'marriage', 'wedding', 'relatives']]> + ---------------------------------------------------------------------- + Debiased vs Biased (absolute values) 0.047 < 0.463 @@ -424,7 +424,7 @@ equalized. .. warning:: The equalization process can modify embeddings that have not been marked in the target. - In Hard Debias, equalization can be deactivated by delivering an empty + In Hard Debias, equalization can be deactivated by delivering an empty equalize set (``[]``). @@ -437,9 +437,9 @@ equalized. ["Male Names", "Female Names"], ["Pleasant", "Unpleasant"], ) - + print(gender_query_2, "\n", "-" * 70, "\n") - + biased_results_2 = weat.run_query( gender_query_2, model, normalize=True, preprocessors=[{}, {"lowercase": True}] ) @@ -449,7 +449,7 @@ equalized. normalize=True, preprocessors=[{}, {"lowercase": True}], ) - + print("Debiased vs Biased (absolute values)") print( round(abs(debiased_results_2["weat"]), 3), @@ -463,9 +463,9 @@ equalized. - ---------------------------------------------------------------------- - + - Attribute sets:[['caress', 'freedom', 'health', 'love', 'peace', 'cheer', 'friend', 'heaven', 'loyal', 'pleasure', 'diamond', 'gentle', 'honest', 'lucky', 'rainbow', 'diploma', 'gift', 'honor', 'miracle', 'sunrise', 'family', 'happy', 'laughter', 'paradise', 'vacation'], ['abuse', 'crash', 'filth', 'murder', 'sickness', 'accident', 'death', 'grief', 'poison', 'stink', 'assault', 'disaster', 'hatred', 'pollute', 'tragedy', 'divorce', 'jail', 'poverty', 'ugly', 'cancer', 'kill', 'rotten', 'vomit', 'agony', 'prison']]> + ---------------------------------------------------------------------- + Debiased vs Biased (absolute values) 0.08 > 0.074 @@ -485,7 +485,7 @@ the ``save`` method to store the method in a file. .. code:: ipython3 gender_debiased_model.wv.save("gender_debiased_glove.kv") - + Multiclass Hard Debias @@ -507,39 +507,39 @@ The usage is very similar to Hard Debias with the difference that the from wefe.datasets import fetch_debias_multiclass from wefe.debias.multiclass_hard_debias import MulticlassHardDebias - + multiclass_debias_wordsets = fetch_debias_multiclass() weat_wordsets = load_weat() weat = WEAT() - + ethnicity_definitional_sets = multiclass_debias_wordsets["ethnicity_definitional_sets"] ethnicity_equalize_sets = list( multiclass_debias_wordsets["ethnicity_analogy_templates"].values() ) - + print(f"ethnicity_definitional_sets: \n{ethnicity_definitional_sets}") print(f"ethnicity_equalize_sets: \n{ethnicity_equalize_sets}") print("-" * 70, "\n") - + mhd = MulticlassHardDebias(verbose=False, criterion_name="ethnicity") mhd.fit( model=model, definitional_sets=ethnicity_definitional_sets, equalize_sets=ethnicity_equalize_sets, ) - + ethnicity_debiased_model = mhd.transform(model, copy=True) .. parsed-literal:: - ethnicity_definitional_sets: + ethnicity_definitional_sets: [['black', 'caucasian', 'asian'], ['african', 'caucasian', 'asian'], ['black', 'white', 'asian'], ['africa', 'america', 'asia'], ['africa', 'america', 'china'], ['africa', 'europe', 'asia']] - ethnicity_equalize_sets: + ethnicity_equalize_sets: [['manager', 'executive', 'redneck', 'hillbilly', 'leader', 'farmer'], ['doctor', 'engineer', 'laborer', 'teacher'], ['slave', 'musician', 'runner', 'criminal', 'homeless']] - ---------------------------------------------------------------------- - + ---------------------------------------------------------------------- + copy argument is True. Transform will attempt to create a copy of the original model. This may fail due to lack of memory. Model copy created successfully. @@ -568,9 +568,9 @@ The following code compares the execution of a query measuring ethnic bias in th ["european_american_names", "african_american_names"], ["white_biased_words", "black_biased_words"], ) - + print(ethnicity_query, "\n", "-" * 70, "\n") - + biased_results = weat.run_query( ethnicity_query, model, normalize=True, preprocessors=[{}, {"lowercase": True}], ) @@ -580,7 +580,7 @@ The following code compares the execution of a query measuring ethnic bias in th normalize=True, preprocessors=[{}, {"lowercase": True}], ) - + print("Debiased vs Biased (absolute values)") print( round(abs(debiased_results_2["weat"]), 3), @@ -594,9 +594,8 @@ The following code compares the execution of a query measuring ethnic bias in th - ---------------------------------------------------------------------- - + - Attribute sets:[['manager', 'executive', 'redneck', 'hillbilly', 'leader', 'farmer'], ['slave', 'musician', 'runner', 'criminal', 'homeless']]> + ---------------------------------------------------------------------- + Debiased vs Biased (absolute values) 0.08 < 0.074 - diff --git a/examples/Contributing.ipynb b/examples/Contributing.ipynb index 1ef6f6f..8a80c34 100644 --- a/examples/Contributing.ipynb +++ b/examples/Contributing.ipynb @@ -792,4 +792,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examples/User_Guide.ipynb b/examples/User_Guide.ipynb index c35b34f..add7555 100644 --- a/examples/User_Guide.ipynb +++ b/examples/User_Guide.ipynb @@ -8752,4 +8752,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examples/measurement_user_guide.ipynb b/examples/measurement_user_guide.ipynb index e22d29d..ef49e5a 100644 --- a/examples/measurement_user_guide.ipynb +++ b/examples/measurement_user_guide.ipynb @@ -2125,4 +2125,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/mypy.ini b/mypy.ini index 26ed65d..7d39e3d 100644 --- a/mypy.ini +++ b/mypy.ini @@ -1,4 +1,4 @@ [mypy] ignore_missing_imports = True show_column_numbers = True -follow_imports = silent \ No newline at end of file +follow_imports = silent diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..dc9c99e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,77 @@ +[tool.isort] +profile = "black" + +[tool.black] +line-length = 88 + +[tool.ruff] +line-length = 88 +extend-select = [ + "W", + "I", + "D100", + "D101", + "D102", + "D103", + "D104", + "D105", + "D106", + "D107", + "D200", + "D201", + "D202", + "D204", + "D205", + "D206", + "D207", + "D208", + "D209", + "D210", + "D211", + "D212", + "D214", + "D215", + "D300", + "D301", + "D400", + "D402", + "D403", + "D404", + "D405", + "D406", + "D407", + "D408", + "D409", + "D410", + "D411", + "D412", + "D413", + "D414", + "D415", + "D416", + "D417", + "D418", + "D419", + "N", +] +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", +] diff --git a/requirements-dev.txt b/requirements-dev.txt index 31a0894..cdadf5b 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -6,10 +6,11 @@ black==22.6.0 isort==5.10.1 mypy==0.812 Sphinx==5.0.2 -sphinx-gallery==0.11.1 +sphinx-gallery==0.11.1 sphinx-rtd-theme==1.0.0 sphinx-copybutton==0.5.0 -numpydoc==1.4.0 +numpydoc==1.5.0 docutils==0.16 torch==1.12.1 -ipython==7.34.0 \ No newline at end of file +ipython==7.34.0 +ruff==0.0.194 diff --git a/requirements.txt b/requirements.txt index 20a4782..2d521fc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,4 @@ plotly>=4.2.0 requests>=2.22.0 tqdm>=4.30.0 semantic_version>=2.8.0 -six \ No newline at end of file +six diff --git a/tests/debias/conftest.py b/tests/debias/conftest.py index 35f4344..39febcb 100644 --- a/tests/debias/conftest.py +++ b/tests/debias/conftest.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.datasets.datasets import fetch_debias_multiclass, fetch_debiaswe, load_weat from wefe.query import Query from wefe.utils import load_test_model diff --git a/tests/debias/test_base_debias.py b/tests/debias/test_base_debias.py index 653685c..7ea8ad5 100644 --- a/tests/debias/test_base_debias.py +++ b/tests/debias/test_base_debias.py @@ -2,6 +2,7 @@ from typing import List import pytest + from wefe.debias.base_debias import BaseDebias from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/debias/test_double_hard_debias.py b/tests/debias/test_double_hard_debias.py index ce76ff6..f7deb07 100644 --- a/tests/debias/test_double_hard_debias.py +++ b/tests/debias/test_double_hard_debias.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.debias.double_hard_debias import DoubleHardDebias from wefe.metrics import WEAT from wefe.query import Query diff --git a/tests/debias/test_half_sibling_regression.py b/tests/debias/test_half_sibling_regression.py index 5498cbd..eddecf9 100644 --- a/tests/debias/test_half_sibling_regression.py +++ b/tests/debias/test_half_sibling_regression.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.debias.half_sibling_regression import HalfSiblingRegression from wefe.metrics import WEAT from wefe.query import Query diff --git a/tests/debias/test_hard_debias.py b/tests/debias/test_hard_debias.py index 86884c9..513ecae 100644 --- a/tests/debias/test_hard_debias.py +++ b/tests/debias/test_hard_debias.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.debias.hard_debias import HardDebias from wefe.metrics import WEAT from wefe.query import Query diff --git a/tests/debias/test_multiclass_hard_debias.py b/tests/debias/test_multiclass_hard_debias.py index ebf2c4b..aff0887 100644 --- a/tests/debias/test_multiclass_hard_debias.py +++ b/tests/debias/test_multiclass_hard_debias.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.debias.multiclass_hard_debias import MulticlassHardDebias from wefe.metrics import MAC, WEAT from wefe.query import Query diff --git a/tests/debias/test_repulsion_attraction_neutralization.py b/tests/debias/test_repulsion_attraction_neutralization.py index dc5f084..45ce76c 100644 --- a/tests/debias/test_repulsion_attraction_neutralization.py +++ b/tests/debias/test_repulsion_attraction_neutralization.py @@ -2,6 +2,7 @@ from typing import Dict, List import numpy as np + from wefe.debias.repulsion_attraction_neutralization import ( RepulsionAttractionNeutralization, ) diff --git a/tests/metrics/conftest.py b/tests/metrics/conftest.py index 68add16..c502cbe 100644 --- a/tests/metrics/conftest.py +++ b/tests/metrics/conftest.py @@ -2,6 +2,7 @@ from typing import Dict, List import pytest + from wefe.datasets.datasets import load_weat from wefe.query import Query from wefe.utils import load_test_model diff --git a/tests/metrics/test_ECT.py b/tests/metrics/test_ECT.py index 201d457..6daf4db 100644 --- a/tests/metrics/test_ECT.py +++ b/tests/metrics/test_ECT.py @@ -2,6 +2,7 @@ from typing import Any, Dict import numpy as np + from wefe.metrics import ECT from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_MAC.py b/tests/metrics/test_MAC.py index 5850fc3..52e232a 100644 --- a/tests/metrics/test_MAC.py +++ b/tests/metrics/test_MAC.py @@ -2,6 +2,7 @@ from typing import Any, Dict import numpy as np + from wefe.metrics import MAC from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_RIPA.py b/tests/metrics/test_RIPA.py index 749d476..1224121 100644 --- a/tests/metrics/test_RIPA.py +++ b/tests/metrics/test_RIPA.py @@ -2,6 +2,7 @@ from typing import Any, Dict import numpy as np + from wefe.metrics import RIPA from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_RND.py b/tests/metrics/test_RND.py index 87481cb..dd11196 100644 --- a/tests/metrics/test_RND.py +++ b/tests/metrics/test_RND.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.metrics import RND from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_RNSB.py b/tests/metrics/test_RNSB.py index cceb16e..84605ab 100644 --- a/tests/metrics/test_RNSB.py +++ b/tests/metrics/test_RNSB.py @@ -2,6 +2,7 @@ from typing import Any, Dict import numpy as np + from wefe.metrics import RNSB from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_WEAT.py b/tests/metrics/test_WEAT.py index ab7a2bf..25ff7f8 100644 --- a/tests/metrics/test_WEAT.py +++ b/tests/metrics/test_WEAT.py @@ -2,6 +2,7 @@ from typing import Any, Dict import numpy as np + from wefe.metrics import WEAT from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/metrics/test_base_metric.py b/tests/metrics/test_base_metric.py index 986fa49..d299c28 100644 --- a/tests/metrics/test_base_metric.py +++ b/tests/metrics/test_base_metric.py @@ -1,4 +1,5 @@ import pytest + from wefe.metrics.base_metric import BaseMetric from wefe.query import Query from wefe.word_embedding_model import WordEmbeddingModel diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py index 7af0201..e018477 100644 --- a/tests/test_preprocessing.py +++ b/tests/test_preprocessing.py @@ -3,6 +3,7 @@ import numpy as np import pytest + from wefe.datasets.datasets import load_weat from wefe.preprocessing import ( _warn_not_found_words, diff --git a/tests/test_query.py b/tests/test_query.py index 53ba8ea..3cdcbe4 100644 --- a/tests/test_query.py +++ b/tests/test_query.py @@ -2,6 +2,7 @@ from typing import Dict, List import pytest + from wefe.datasets.datasets import load_weat from wefe.query import Query diff --git a/tests/test_word_embedding_model.py b/tests/test_word_embedding_model.py index a1a0af1..38d700d 100644 --- a/tests/test_word_embedding_model.py +++ b/tests/test_word_embedding_model.py @@ -5,6 +5,7 @@ import semantic_version from gensim.models import FastText, KeyedVectors, Word2Vec from gensim.test.utils import common_texts + from wefe.word_embedding_model import WordEmbeddingModel gensim_version = semantic_version.Version.coerce(gensim.__version__) diff --git a/wefe/datasets/data/negative-words.txt b/wefe/datasets/data/negative-words.txt index 3a126c2..254f11e 100644 --- a/wefe/datasets/data/negative-words.txt +++ b/wefe/datasets/data/negative-words.txt @@ -1,31 +1,31 @@ ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; -; +; ; Opinion Lexicon: Negative ; ; This file contains a list of NEGATIVE opinion words (or sentiment words). ; -; This file and the papers can all be downloaded from +; This file and the papers can all be downloaded from ; http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html ; ; If you use this list, please cite the following paper: ; -; Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." -; Proceedings of the ACM SIGKDD International Conference on Knowledge -; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, -; Washington, USA, +; Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." +; Proceedings of the ACM SIGKDD International Conference on Knowledge +; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, +; Washington, USA, ; -; Notes: -; 1. The appearance of an opinion word in a sentence does not necessarily -; mean that the sentence expresses a positive or negative opinion. +; Notes: +; 1. The appearance of an opinion word in a sentence does not necessarily +; mean that the sentence expresses a positive or negative opinion. ; See the paper below: ; -; Bing Liu. "Sentiment Analysis and Subjectivity." An chapter in -; Handbook of Natural Language Processing, Second Edition, +; Bing Liu. "Sentiment Analysis and Subjectivity." An chapter in +; Handbook of Natural Language Processing, Second Edition, ; (editors: N. Indurkhya and F. J. Damerau), 2010. ; -; 2. You will notice many misspelled words in the list. They are not -; mistakes. They are included as these misspelled words appear -; frequently in social media content. +; 2. You will notice many misspelled words in the list. They are not +; mistakes. They are included as these misspelled words appear +; frequently in social media content. ; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; diff --git a/wefe/datasets/data/positive-words.txt b/wefe/datasets/data/positive-words.txt index 3d7c8fc..f923143 100644 --- a/wefe/datasets/data/positive-words.txt +++ b/wefe/datasets/data/positive-words.txt @@ -1,30 +1,30 @@ ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; -; +; ; Opinion Lexicon: Positive ; ; This file contains a list of POSITIVE opinion words (or sentiment words). ; -; This file and the papers can all be downloaded from +; This file and the papers can all be downloaded from ; http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html ; ; If you use this list, please cite the following paper: ; -; Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." -; Proceedings of the ACM SIGKDD International Conference on Knowledge -; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, -; Washington, USA, -; Notes: -; 1. The appearance of an opinion word in a sentence does not necessarily -; mean that the sentence expresses a positive or negative opinion. +; Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." +; Proceedings of the ACM SIGKDD International Conference on Knowledge +; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, +; Washington, USA, +; Notes: +; 1. The appearance of an opinion word in a sentence does not necessarily +; mean that the sentence expresses a positive or negative opinion. ; See the paper below: ; -; Bing Liu. "Sentiment Analysis and Subjectivity." An chapter in -; Handbook of Natural Language Processing, Second Edition, +; Bing Liu. "Sentiment Analysis and Subjectivity." An chapter in +; Handbook of Natural Language Processing, Second Edition, ; (editors: N. Indurkhya and F. J. Damerau), 2010. ; -; 2. You will notice many misspelled words in the list. They are not -; mistakes. They are included as these misspelled words appear -; frequently in social media content. +; 2. You will notice many misspelled words in the list. They are not +; mistakes. They are included as these misspelled words appear +; frequently in social media content. ; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; diff --git a/wefe/datasets/data/weat_w2v____old.txt b/wefe/datasets/data/weat_w2v____old.txt index 3b43b6c..720ae33 100644 --- a/wefe/datasets/data/weat_w2v____old.txt +++ b/wefe/datasets/data/weat_w2v____old.txt @@ -1,348 +1,348 @@ 347 300 -management -0.15332031 0.103515625 -0.08984375 -0.09472656 -0.15136719 0.10205078 0.103027344 -0.29101562 0.11230469 -0.017089844 -0.10546875 -0.0032348633 -0.11230469 0.021484375 -0.15917969 -0.09814453 -0.025390625 -0.049560547 -0.14453125 0.0061950684 -0.07910156 -0.021728516 -0.05908203 0.09667969 0.09667969 0.17480469 -0.13378906 0.17871094 -0.09375 -0.09423828 0.14746094 0.08935547 -0.115722656 0.0076293945 0.018798828 -0.059814453 -0.030517578 0.0074768066 0.15429688 -0.04272461 0.01550293 -0.12695312 -0.03100586 0.0138549805 0.009277344 -0.37695312 -0.11669922 0.036621094 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wefe.debias.base_debias import BaseDebias from wefe.preprocessing import get_embeddings_from_tuples from wefe.utils import check_is_fitted diff --git a/wefe/debias/half_sibling_regression.py b/wefe/debias/half_sibling_regression.py index 2476db5..57551c6 100644 --- a/wefe/debias/half_sibling_regression.py +++ b/wefe/debias/half_sibling_regression.py @@ -5,6 +5,7 @@ import numpy as np from tqdm import tqdm + from wefe.debias.base_debias import BaseDebias from wefe.preprocessing import get_embeddings_from_tuples from wefe.utils import check_is_fitted diff --git a/wefe/debias/hard_debias.py b/wefe/debias/hard_debias.py index 7da2dd8..9d0eb4f 100644 --- a/wefe/debias/hard_debias.py +++ b/wefe/debias/hard_debias.py @@ -6,6 +6,7 @@ import numpy as np from sklearn.decomposition import PCA from tqdm import tqdm + from wefe.debias.base_debias import BaseDebias from wefe.preprocessing import get_embeddings_from_tuples from wefe.utils import check_is_fitted diff --git a/wefe/debias/multiclass_hard_debias.py b/wefe/debias/multiclass_hard_debias.py index 51f979e..f7544cd 100644 --- a/wefe/debias/multiclass_hard_debias.py +++ b/wefe/debias/multiclass_hard_debias.py @@ -6,6 +6,7 @@ import numpy as np from sklearn.decomposition import PCA from tqdm import tqdm + from wefe.debias.base_debias import BaseDebias from wefe.preprocessing import get_embeddings_from_tuples from wefe.utils import check_is_fitted diff --git a/wefe/debias/repulsion_attraction_neutralization.py b/wefe/debias/repulsion_attraction_neutralization.py index 3cb4d7a..ac5c51d 100644 --- a/wefe/debias/repulsion_attraction_neutralization.py +++ b/wefe/debias/repulsion_attraction_neutralization.py @@ -5,6 +5,7 @@ import numpy as np from sklearn.decomposition import PCA from tqdm import tqdm + from wefe.debias.base_debias import BaseDebias from wefe.preprocessing import EmbeddingDict, get_embeddings_from_tuples from wefe.utils import check_is_fitted diff --git a/wefe/metrics/MAC.py b/wefe/metrics/MAC.py index 363c40e..403fcd1 100644 --- a/wefe/metrics/MAC.py +++ b/wefe/metrics/MAC.py @@ -3,6 +3,7 @@ import numpy as np from scipy.spatial import distance + from wefe.metrics.base_metric import BaseMetric from wefe.preprocessing import get_embeddings_from_query from wefe.query import Query @@ -22,9 +23,10 @@ class MAC(BaseMetric): For each target set: For each word embedding in the target set: For each attribute set: - Calculate the cosine similarity of the target embedding and each attribute - embedding of the set. - Calculate the mean of the cosines similarities and store it in a array. + Calculate the cosine similarity of the target embedding and + each attribute embedding of the set. + Calculate the mean of the cosines similarities and store it in a + array. Average all the mean cosine similarities and return the calculated score. The closer the value is to 1, the less biased the query will be. @@ -41,7 +43,7 @@ class MAC(BaseMetric): | Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. | [2]: https://github.com/TManzini/DebiasMulticlassWordEmbedding/blob/master/Debiasing/evalBias.py - """ + """ # noqa: E501 metric_template = ("n", "n") metric_name = "Mean Average Cosine Similarity" @@ -267,7 +269,11 @@ def run_query( >>> # instance the metric and run the query >>> MAC().run_query(ethnicity_query, model) { - "query_name": "Black words, White words and Asian words wrt Black biased words, White biased words and Asian biased words", + "query_name": ( + "Black words, White words and Asian words wrt Black " + "biased words, White biased words and Asian biased " + "words" + ), "result": 0.9462675075454171, "mac": 0.9462675075454171, "targets_eval": { @@ -310,6 +316,7 @@ def run_query( }, } + We can also test words that represent religious groups and beliefs: >>> from wefe.word_embedding_model import WordEmbeddingModel @@ -344,7 +351,10 @@ def run_query( >>> # instance the metric and run the query >>> MAC().run_query(religion_query, model, warn_not_found_words=True) { - "query_name": "judaism, christianity and islam wrt jew biased words, christian biased words and musilm biased words", + "query_name": ( + "judaism, christianity and islam wrt jew biased words," + " christian biased words and musilm biased words" + ), "result": 0.8589896201628209, "mac": 0.8589896201628209, "targets_eval": { @@ -431,8 +441,7 @@ def run_query( }, }, } - - """ + """ # noqa: E501 # check the types of the provided arguments (only the defaults). self._check_input(query, model, locals()) diff --git a/wefe/metrics/RND.py b/wefe/metrics/RND.py index 5b8fe4e..0879f33 100644 --- a/wefe/metrics/RND.py +++ b/wefe/metrics/RND.py @@ -3,6 +3,7 @@ import numpy as np from sklearn.metrics.pairwise import cosine_similarity + from wefe.metrics.base_metric import BaseMetric from wefe.preprocessing import get_embeddings_from_query from wefe.query import Query diff --git a/wefe/metrics/RNSB.py b/wefe/metrics/RNSB.py index 023e80b..9b770e3 100644 --- a/wefe/metrics/RNSB.py +++ b/wefe/metrics/RNSB.py @@ -9,6 +9,7 @@ from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report from sklearn.model_selection import train_test_split + from wefe.metrics.base_metric import BaseMetric from wefe.preprocessing import get_embeddings_from_query from wefe.query import Query @@ -81,7 +82,7 @@ class RNSB(BaseMetric): Computational Linguistics, pages 1662–1667, 2019. | [2]: https://github.com/ChristopherSweeney/AIFairness/blob/master/python_notebooks/Measuring_and_Mitigating_Word_Embedding_Bias.ipynb - """ + """ # noqa: E501 metric_template = ("n", 2) metric_name = "Relative Negative Sentiment Bias" @@ -90,11 +91,11 @@ class RNSB(BaseMetric): def _train_classifier( self, attribute_embeddings_dict: List[Dict[str, np.ndarray]], - estimator: BaseEstimator = LogisticRegression, - estimator_params: Dict[str, Any] = {"max_iter": 10000}, - random_state: Union[int, None] = None, - holdout: bool = True, - print_model_evaluation: bool = False, + estimator: BaseEstimator, + estimator_params: Dict[str, Any], + random_state: Union[int, None], + holdout: bool, + print_model_evaluation: bool, ) -> Tuple[BaseEstimator, float]: """Train the sentiment classifier from the provided attribute embeddings. @@ -114,6 +115,16 @@ def _train_classifier( A seed that allows making the execution of the query reproducible, by default None + holdout: bool, optional + True indicates that a holdout (split attributes in train/test sets) will + be executed before running the model training. + This option allows for evaluating the performance of the classifier + (can be printed using print_model_evaluation=True) at the cost of training + the classifier with fewer examples. False disables this functionality. + Note that holdout divides into 80% train and 20% test, performs a shuffle + and tries to maintain the original ratio of the classes via stratify param, + by default True. + print_model_evaluation : bool, optional Indicates whether the classifier evaluation is printed after the training process is completed, by default False @@ -301,11 +312,11 @@ def run_query( model : WordEmbeddingModel A word embedding model. - estimator : BaseEstimator, optional + estimator : BaseEstimator A scikit-learn classifier class that implements predict_proba function, by default None, - estimator_params : dict, optional + estimator_params : dict Parameters that will use the classifier, by default { 'solver': 'liblinear', 'max_iter': 10000, } @@ -731,7 +742,13 @@ def run_query( ... ) >>> results { - "query_name": "Target set 0, Target set 1, Target set 2, Target set 3, Target set 4, Target set 5, Target set 6, Target set 7, Target set 8, Target set 9, Target set 10, Target set 11, Target set 12, Target set 13 and Target set 14 wrt Positive words and Negative words", + "query_name": ( + "Target set 0, Target set 1, Target set 2, Target set 3, " + "Target set 4, Target set 5, Target set 6, Target set 7, " + "Target set 8, Target set 9, Target set 10, Target set 11, " + "Target set 12, Target set 13 and Target set 14 " + "wrt Positive words and Negative words" + ), "result": 0.6313118439654091, "rnsb": 0.6313118439654091, "negative_sentiment_probabilities": { @@ -771,6 +788,7 @@ def run_query( } + """ # check the types of the provided arguments (only the defaults). self._check_input(query, model, locals()) diff --git a/wefe/metrics/WEAT.py b/wefe/metrics/WEAT.py index e1f5153..addd8d0 100644 --- a/wefe/metrics/WEAT.py +++ b/wefe/metrics/WEAT.py @@ -5,6 +5,7 @@ import numpy as np from sklearn.metrics.pairwise import cosine_similarity + from wefe.metrics.base_metric import BaseMetric from wefe.preprocessing import get_embeddings_from_query from wefe.query import Query diff --git a/wefe/metrics/example_metric.py b/wefe/metrics/example_metric.py index a74f1f7..707c5ef 100644 --- a/wefe/metrics/example_metric.py +++ b/wefe/metrics/example_metric.py @@ -4,6 +4,7 @@ import numpy as np from scipy.spatial import distance + from wefe.metrics.base_metric import BaseMetric from wefe.preprocessing import get_embeddings_from_query from wefe.query import Query diff --git a/wefe/word_embedding_model.py b/wefe/word_embedding_model.py index 2fd1a4e..3cad6e9 100644 --- a/wefe/word_embedding_model.py +++ b/wefe/word_embedding_model.py @@ -194,7 +194,8 @@ def __repr__(self) -> str: ) return ( - f"" ) except AttributeError: From 8ac2d44cf7b3c3700308d59d7aa0a60fff7c8f41 Mon Sep 17 00:00:00 2001 From: Pablo Badilla Date: Mon, 26 Dec 2022 21:43:47 -0300 Subject: [PATCH 3/9] add pre-commit to github jobs --- .github/workflows/ci.yaml | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 62c0d7e..d26004d 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -8,7 +8,26 @@ on: branches: - "master" - "develop" + jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - name: "Check out repository code" + uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: "3.10" + cache: "pip" + - name: Install Pre-commit + run: | + python -m pip install --upgrade pip + pip install pre-commit + - name: "Run pre-commit" + run: | + pre-commit run --all-files + pytest: runs-on: ubuntu-latest From 2736daf2b63fd27fb58d8d3a8527cd6f2812b526 Mon Sep 17 00:00:00 2001 From: Pablo Badilla Date: Wed, 28 Dec 2022 20:34:11 -0300 Subject: [PATCH 4/9] metrics type annotations --- .github/workflows/ci.yaml | 2 +- pyproject.toml | 70 ++++++++++++-------------- setup.py | 8 +-- wefe/datasets/datasets.py | 96 ++++++++++++++++-------------------- wefe/debias/base_debias.py | 12 +++-- wefe/metrics/ECT.py | 1 - wefe/metrics/MAC.py | 11 +++-- wefe/metrics/RIPA.py | 26 +++++----- wefe/metrics/RND.py | 12 ++--- wefe/metrics/RNSB.py | 4 +- wefe/metrics/WEAT.py | 37 ++++++++++---- wefe/word_embedding_model.py | 29 +++++++---- 12 files changed, 161 insertions(+), 147 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index d26004d..40396e9 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -1,4 +1,4 @@ -name: Tests +name: Tests and code review on: push: branches: diff --git a/pyproject.toml b/pyproject.toml index dc9c99e..04fa215 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,55 +5,47 @@ profile = "black" line-length = 88 [tool.ruff] -line-length = 88 -extend-select = [ +select = [ + "E", + "F", "W", "I", - "D100", - "D101", - "D102", - "D103", - "D104", - "D105", - "D106", + "D", + "YTT", + "ANN", + "S", + "B", + "A", + "C", + "Q", + "RET", + "TID252", + "SIM118", + "ARG", + +] + +ignore = [ + "ANN101", + "S101", + "BLE001", + "ANN401", "D107", - "D200", - "D201", - "D202", - "D204", - "D205", - "D206", - "D207", - "D208", - "D209", - "D210", - "D211", + "D203", "D212", - "D214", - "D215", - "D300", - "D301", - "D400", + "D213", "D402", - "D403", - "D404", - "D405", - "D406", - "D407", - "D408", - "D409", - "D410", - "D411", - "D412", "D413", - "D414", "D415", "D416", "D417", - "D418", - "D419", - "N", + "ANN002", + "ANN003", + "B006", + ] + +line-length = 88 exclude = [ ".bzr", ".direnv", diff --git a/setup.py b/setup.py index c066e36..b4c2868 100644 --- a/setup.py +++ b/setup.py @@ -2,14 +2,10 @@ """The Word Embeddings Fairness Evaluation Framework""" import codecs -import os from setuptools import find_packages, setup -# get __version__ from _version.py -ver_file = os.path.join("wefe", "_version.py") -with open(ver_file) as f: - exec(f.read()) +import wefe DISTNAME = "wefe" DESCRIPTION = "The Word Embedding Fairness Evaluation Framework" @@ -21,7 +17,7 @@ URL = "https://github.com/dccuchile/wefe" LICENSE = "new BSD" DOWNLOAD_URL = "https://github.com/dccuchile/wefe" -VERSION = __version__ +VERSION = wefe.__version__ INSTALL_REQUIRES = [ "numpy", "scipy", diff --git a/wefe/datasets/datasets.py b/wefe/datasets/datasets.py index c52edd3..a2628c7 100644 --- a/wefe/datasets/datasets.py +++ b/wefe/datasets/datasets.py @@ -11,19 +11,20 @@ def fetch_eds( occupations_year: int = 2015, top_n_race_occupations: int = 10 ) -> Dict[str, List[str]]: - """Fetch the word sets used in the experiments of the work *Word Embeddings - *Quantify 100 Years Of Gender And Ethnic Stereotypes*. + """Fetch the sets of words used in the experiments of the _Word Embeddings + Quantify 100 Years Of Gender And Ethnic Stereotypes_ work. - This dataset includes gender (male, female), ethnicity (asian, black, white) and - religion (christianity and islam) and adjetives (appearence, intelligence, - otherization, sensitive) word sets. + This dataset includes the following word sets: + - gender: male, female. + - ethnicity: asian, black, white. + - religion: christianity, judaism and islam. + - adjetives: appearence, intelligence, otherization, sensitive. References ---------- - Word Embeddings quantify 100 years of gender and ethnic stereotypes. - Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Proceedings of - the National Academy of Sciences, 115(16), E3635-E3644. - + | [1]: Word Embeddings quantify 100 years of gender and ethnic stereotypes. + | Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). + | Proceedings of the National Academy of Sciences, 115(16), E3635-E3644. Parameters ---------- @@ -42,8 +43,7 @@ def fetch_eds( ------- dict A dictionary with the word sets. - """ - + """ # noqa: D205 EDS_BASE_URL = ( "https://raw.githubusercontent.com/nikhgarg/" "EmbeddingDynamicStereotypes/master/data/" @@ -157,19 +157,17 @@ def fetch_debiaswe() -> Dict[str, Union[List[str], list]]: References ---------- - - Man is to Computer Programmer as Woman is to Homemaker? - Debiasing Word Embeddings by Tolga Bolukbasi, Kai-Wei Chang, James Zou, - Venkatesh Saligrama, and Adam Kalai. - Proceedings of NIPS 2016. + | [1]: Man is to Computer Programmer as Woman is to Homemaker? + | Debiasing Word Embeddings by Tolga Bolukbasi, Kai-Wei Chang, James Zou, + Venkatesh Saligrama, and Adam Kalai. + | Proceedings of NIPS 2016. Returns ------- - dict + Dict[str, Union[List[str], list]] A dictionary in which each key correspond to the name of the set and its values correspond to the word set. - """ - + """ # noqa: D205 DEBIAS_WE_BASE_URL = ( "https://raw.githubusercontent.com/tolga-b/debiaswe/master/data/" ) @@ -201,7 +199,7 @@ def fetch_debiaswe() -> Dict[str, Union[List[str], list]]: ) as json_file: professions = json.loads(json_file.read().decode()) - word_sets_dict = { + return { "male_terms": male_words, "female_terms": female_words, "definitional_pairs": definitional_pairs, @@ -210,8 +208,6 @@ def fetch_debiaswe() -> Dict[str, Union[List[str], list]]: "professions": professions, } - return word_sets_dict - def load_bingliu() -> Dict[str, List[str]]: """Load the Bing-Liu sentiment lexicon. @@ -247,17 +243,15 @@ def load_bingliu() -> Dict[str, List[str]]: word.decode("latin-1").strip() for word in bingliu_pos_bytes.readlines() ][30:] - bingliu_lexicon = { + return { "positive_words": positive_words, "negative_words": negative_words, } - return bingliu_lexicon - def fetch_debias_multiclass() -> Dict[str, Union[List[str], list]]: """Fetch the word sets used in the paper Black Is To Criminals as Caucasian - Is To Police: Detecting And Removing Multiclass Bias In Word Embeddings. + Is To Police: Detecting And Removing Multiclass Bias In Word Embeddings. This dataset contains gender (male, female), ethnicity (asian, black, white) and religion (christianity, judaism and islam) word sets. @@ -284,9 +278,7 @@ def fetch_debias_multiclass() -> Dict[str, Union[List[str], list]]: dict A dictionary in which each key correspond to the name of the set and its values correspond to the word set. - - """ - + """ # noqa: D205, E501 BASE_URL = ( "https://raw.githubusercontent.com/TManzini/" "DebiasMulticlassWordEmbedding/master/Debiasing/data/vocab/" @@ -345,7 +337,7 @@ def fetch_debias_multiclass() -> Dict[str, Union[List[str], list]]: religion_eval_target = religion["eval_targets"] - word_sets_dict = { + return { # preprocessed word sets "male_terms": male_terms, "female_terms": female_terms, @@ -374,7 +366,6 @@ def fetch_debias_multiclass() -> Dict[str, Union[List[str], list]]: "ethnicity_eval_target": ethnicity_eval_target, "religion_eval_target": religion_eval_target, } - return word_sets_dict def fetch_gn_glove() -> Dict[str, List[str]]: @@ -382,17 +373,17 @@ def fetch_gn_glove() -> Dict[str, List[str]]: This dataset contain two sets of 221 female and male related words. - Reference - --------- - Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K. W. (2018, January). Learning - Gender-Neutral Word Embeddings. In EMNLP. + References + ---------- + | [1]: Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K. W. (2018, January). + | Learning Gender-Neutral Word Embeddings. + | In EMNLP. Returns ------- Dict[str, List[str]] A dictionary with male and female word sets. """ - BASE_URL = "https://raw.githubusercontent.com/uclanlp/gn_glove/master/wordlist/" FILES = [ "female_word_file.txt", @@ -408,38 +399,37 @@ def fetch_gn_glove() -> Dict[str, List[str]]: male_terms = file.read().decode().split("\n") male_terms = list(filter(lambda x: x != "", male_terms)) - word_sets_dict = { + return { "male_terms": male_terms, "female_terms": female_terms, } - return word_sets_dict def load_weat() -> Dict[str, List[str]]: - """Load the word sets used in the paper *Semantics Derived Automatically* - *From Language Corpora Contain Human-Like Biases*. - It includes gender (male, female), ethnicity (black, white) - and pleasant, unpleasant word sets, among others. + """Load the word sets used in the experiments of the + _Semantics Derived Automatically From Language Corpora Contain Human-Like Biases_ + work. + + It includes the following word sets: + - gender (male, female) + - ethnicity (black, white) + - pleasant, unpleasant + - among others. References ---------- - - Semantics derived automatically from language corpora contain human-like - biases. - Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). - Science, 356(6334), 183-186. + | [1]: Semantics derived automatically from language corpora contain human-like + biases. + | Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). + | Science, 356(6334), 183-186. Returns ------- word_sets_dict : dict A dictionary with the word sets. - - - """ + """ # noqa: D205 resource_package = __name__ resource_path = "/".join(("data", "WEAT.json")) weat_data = pkg_resources.resource_string(resource_package, resource_path) - data = json.loads(weat_data.decode()) - - return data + return json.loads(weat_data.decode()) diff --git a/wefe/debias/base_debias.py b/wefe/debias/base_debias.py index 9156061..dc4bc76 100644 --- a/wefe/debias/base_debias.py +++ b/wefe/debias/base_debias.py @@ -116,19 +116,20 @@ def _check_transform_args( target: Optional[List[str]] = None, ignore: Optional[List[str]] = None, copy: bool = True, - ): - # check model + ) -> None: + # check if model is a WordEmbeddingModel if not isinstance(model, WordEmbeddingModel): raise TypeError( f"model should be a WordEmbeddingModel instance, got {model}." ) - # check target + # check if target is a list if target is not None and not isinstance(target, list): raise TypeError( f"target should be None or a list of strings, got {target}." ) + # check if all elements of the list are strings if isinstance(target, list): for idx, word in enumerate(target): if not isinstance(word, str): @@ -137,12 +138,13 @@ def _check_transform_args( f", got: {word} at index {idx} " ) - # check ignore + # check ignore parameter type (none or list) if ignore is not None and not isinstance(ignore, list): raise TypeError( f"ignore should be None or a list of strings, got {ignore}." ) + # if ignore is a list, check that all its elements are string if isinstance(ignore, list): for idx, word in enumerate(ignore): if not isinstance(word, str): @@ -160,7 +162,7 @@ def _check_sets_sizes( sets: List[List[str]], set_name: str, set_size: Union[int, str], - ): + ) -> None: if len(sets) == 0: raise ValueError("") diff --git a/wefe/metrics/ECT.py b/wefe/metrics/ECT.py index c3b337c..40d7dfa 100644 --- a/wefe/metrics/ECT.py +++ b/wefe/metrics/ECT.py @@ -36,7 +36,6 @@ class ECT(BaseMetric): References ---------- - | [1]: Dev, S., & Phillips, J. (2019, April). Attenuating Bias in Word vectors. | [2]: https://github.com/sunipa/Attenuating-Bias-in-Word-Vec """ diff --git a/wefe/metrics/MAC.py b/wefe/metrics/MAC.py index 403fcd1..1e9d3e3 100644 --- a/wefe/metrics/MAC.py +++ b/wefe/metrics/MAC.py @@ -1,5 +1,5 @@ """Mean Average Cosine Similarity (MAC) implementation.""" -from typing import Any, Callable, Dict, List, Union +from typing import Any, Callable, Dict, List, Tuple, Union import numpy as np from scipy.spatial import distance @@ -33,7 +33,6 @@ class MAC(BaseMetric): References ---------- - | [1]: Thomas Manzini, Lim Yao Chong,Alan W Black, and Yulia Tsvetkov. | Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings. @@ -67,10 +66,14 @@ def _calc_s(self, t: np.ndarray, A_j: np.ndarray) -> np.number: """ return np.mean([distance.cosine(t, a_i) for a_i in A_j]) - def _calc_mac(self, T, A): + def _calc_mac( + self, + T: Dict[str, Dict[str, np.ndarray]], + A: Dict[str, Dict[str, np.ndarray]], + ) -> Tuple[np.number, Dict[str, Dict[str, Dict[str, np.number]]]]: # dict that will store the s scores by target word and attribute set. - targets_eval = {} + targets_eval: Dict[str, Dict[str, Dict[str, np.number]]] = {} # list that will store the s scores targets_eval_scores = [] diff --git a/wefe/metrics/RIPA.py b/wefe/metrics/RIPA.py index 74b191b..042ad75 100644 --- a/wefe/metrics/RIPA.py +++ b/wefe/metrics/RIPA.py @@ -57,38 +57,40 @@ class RIPA(BaseMetric): metric_name = "Relational Inner Product Association" metric_short_name = "RIPA" - def _b_vec(self, word1, word2): + def _b_vec(self, word_vec_1: np.ndarray, word_vec_2: np.ndarray) -> np.ndarray: # calculating the relation vector - vec = np.array(word1) - np.array(word2) + vec = np.array(word_vec_1) - np.array(word_vec_2) norm = np.linalg.norm(vec) return vec / norm - def _ripa_calc(self, word_vec, bvec): + def _ripa_calc(self, word_vec: np.ndarray, b_vec: np.ndarray) -> np.number: # calculating the dot product of the relation vector with the attribute # word vector - return np.dot(word_vec, bvec) + return np.dot(word_vec, b_vec) def _calc_metric( - self, target_embeddings, attribute_embeddings - ) -> Tuple[np.floating, Dict[str, Dict[str, np.floating]]]: + self, + target_embeddings: List[Dict[str, np.ndarray]], + attribute_embeddings: List[Dict[str, np.ndarray]], + ) -> Tuple[np.number, Dict[str, Dict[str, np.number]]]: """Calculate the metric. Parameters ---------- - target_embeddings : np.array + target_embeddings : List[Dict[str, np.ndarray]] An array with dicts. Each dict represents an target set. A dict is composed with a word and its embedding as key, value respectively. - attribute_embeddings : np.array + attribute_embeddings : List[Dict[str, np.ndarray]] An array with dicts. Each dict represents an attribute set. A dict is composed with a word and its embedding as key, value respectively. Returns ------- - np.float - The value of the calculated metric, averaged across all the RIPA scores of + Tuple[np.float, Dict[str, Dict[str, np.float]]] + A tuple with: + - the value of the calculated metric, averaged across all the RIPA scores of the attributes. - dict - The mean value ± the standard deviation (across all target pairs) of the + - the mean value ± the standard deviation (across all target pairs) of the attribute word's RIPA score. """ # word vectors from the embedding model for all the words in each of the diff --git a/wefe/metrics/RND.py b/wefe/metrics/RND.py index 0879f33..538d153 100644 --- a/wefe/metrics/RND.py +++ b/wefe/metrics/RND.py @@ -50,15 +50,15 @@ def __calc_distance( ) -> float: if distance_type == "norm": return np.linalg.norm(np.subtract(vec1, vec2)) - elif distance_type == "cos": + if distance_type == "cos": # c = np.dot(vec1, vec2) / np.linalg.norm(vec1) / np.linalg.norm(vec2) c = cosine_similarity([vec1], [vec2]).flatten() return c[0] - else: - raise ValueError( - 'distance_type can be either "norm" or "cos", ' - "got: {} ".format(distance_type) - ) + + raise ValueError( + 'distance_type can be either "norm" or "cos", ' + "got: {} ".format(distance_type) + ) def __calc_rnd( self, diff --git a/wefe/metrics/RNSB.py b/wefe/metrics/RNSB.py index 9b770e3..8006b23 100644 --- a/wefe/metrics/RNSB.py +++ b/wefe/metrics/RNSB.py @@ -269,7 +269,9 @@ def _calc_rnsb( # set the probabilities for each word in a dict. negative_sentiment_probabilities = { word: prob - for word, prob in zip(flatten_target_words, negative_probabilities) + for word, prob in zip( + flatten_target_words, negative_probabilities, strict=True + ) } return kl_divergence, negative_sentiment_probabilities diff --git a/wefe/metrics/WEAT.py b/wefe/metrics/WEAT.py index addd8d0..19c1dcf 100644 --- a/wefe/metrics/WEAT.py +++ b/wefe/metrics/WEAT.py @@ -81,18 +81,35 @@ class WEAT(BaseMetric): metric_name = "Word Embedding Association Test" metric_short_name = "WEAT" - def _calc_s(self, w, A, B) -> np.number: + def _calc_s( + self, + w: np.ndarray, + A: np.ndarray, + B: np.ndarray, + ) -> np.number: A_mean_sim = np.mean(cosine_similarity([w], A), dtype=np.float64) B_mean_sim = np.mean(cosine_similarity([w], B), dtype=np.float64) return A_mean_sim - B_mean_sim - def _calc_weat(self, X, Y, A, B) -> np.number: + def _calc_weat( + self, + X: np.ndarray, + Y: np.ndarray, + A: np.ndarray, + B: np.ndarray, + ) -> np.number: first_term = np.sum([self._calc_s(x, A, B) for x in X], dtype=np.float64) second_term = np.sum([self._calc_s(y, A, B) for y in Y], dtype=np.float64) return first_term - second_term - def _calc_effect_size(self, X, Y, A, B) -> np.number: + def _calc_effect_size( + self, + X: np.ndarray, + Y: np.ndarray, + A: np.ndarray, + B: np.ndarray, + ) -> np.number: first_term = np.mean([self._calc_s(x, A, B) for x in X], dtype=np.float64) second_term = np.mean([self._calc_s(y, A, B) for y in Y], dtype=np.float64) @@ -111,7 +128,7 @@ def _calc_p_value( method: str, test_type: str, verbose: bool, - ): + ) -> np.number: # TODO: Add joblib or other library to parallelize this function. # TODO: Test this function # TODO: Implement exact and bootstrap methods @@ -159,8 +176,8 @@ def test_function(calculated, original): raise TypeError(f"verbose should be bool instance, got {verbose}.") # get attribute embeddings - attribute_0 = list(attribute_embeddings[0].values()) - attribute_1 = list(attribute_embeddings[1].values()) + attribute_0 = np.array(list(attribute_embeddings[0].values())) + attribute_1 = np.array(list(attribute_embeddings[1].values())) # generate the pool of target and attribute embeddings. target_0_dict, target_1_dict = target_embeddings @@ -447,10 +464,10 @@ def run_query( target_embeddings = list(target_sets.values()) attribute_embeddings = list(attribute_sets.values()) - target_0 = list(target_embeddings[0].values()) - target_1 = list(target_embeddings[1].values()) - attribute_0 = list(attribute_embeddings[0].values()) - attribute_1 = list(attribute_embeddings[1].values()) + target_0 = np.array(list(target_embeddings[0].values())) + target_1 = np.array(list(target_embeddings[1].values())) + attribute_0 = np.array(list(attribute_embeddings[0].values())) + attribute_1 = np.array(list(attribute_embeddings[1].values())) # if the requested value is the effect size: weat_effect_size = self._calc_effect_size( diff --git a/wefe/word_embedding_model.py b/wefe/word_embedding_model.py index 3cad6e9..4ede484 100644 --- a/wefe/word_embedding_model.py +++ b/wefe/word_embedding_model.py @@ -1,5 +1,5 @@ """A Word Embedding contanier based on gensim BaseKeyedVectors.""" -from typing import Dict, Sequence, Union +from typing import Any, Dict, Sequence, Union import gensim import numpy as np @@ -25,7 +25,7 @@ class WordEmbeddingModel: def __init__( self, wv: BaseKeyedVectors, name: str = None, vocab_prefix: str = None - ): + ) -> None: """Initialize the word embedding model. Parameters @@ -110,7 +110,7 @@ def __init__( else: self.name = name - def __eq__(self, other) -> bool: + def __eq__(self, other: Any) -> bool: """Check if other is the same WordEmbeddingModel that self. Parameters @@ -150,10 +150,10 @@ def __getitem__(self, key: str) -> Union[np.ndarray, None]: """ if key in self.vocab: return self.wv[key] - else: - return None - def __contains__(self, key): + return None + + def __contains__(self, key: str) -> bool: """Check if a word exists in the model's vocabulary. Parameters @@ -169,6 +169,17 @@ def __contains__(self, key): return key in self.vocab def __repr__(self) -> str: + """Custom representation for WordEmbeddingModels. + + Format: + + + + Returns + ------- + str + The generated representation. + """ try: if self.name == "Unnamed model" and self.vocab_prefix is not None: return ( @@ -203,7 +214,7 @@ def __repr__(self) -> str: # defined. return "" - def normalize(self): + def normalize(self) -> None: """Normalize word embeddings in the model by using the L2 norm. Use the `init_sims` function of the gensim's `KeyedVectors` class. @@ -216,7 +227,7 @@ def normalize(self): else: raise TypeError("The model does not have the init_sims method implemented.") - def update(self, word: str, embedding: np.ndarray): + def update(self, word: str, embedding: np.ndarray) -> None: """Update the value of an embedding of the model. If the method is executed with a word that is not in the vocabulary, an @@ -283,7 +294,7 @@ def batch_update( self, words: Sequence[str], embeddings: Union[Sequence[np.ndarray], np.ndarray], - ): + ) -> None: """Update a batch of embeddings. This method calls `update_embedding` method with each of the word-embedding From 77488710dc0306a6f179210b4a75f58011338c8b Mon Sep 17 00:00:00 2001 From: Pablo Badilla Date: Mon, 2 Jan 2023 13:25:30 -0300 Subject: [PATCH 5/9] correct several ruff minor issues --- .github/workflows/ci.yaml | 2 +- pyproject.toml | 12 +++- wefe/debias/double_hard_debias.py | 11 ++-- wefe/debias/half_sibling_regression.py | 9 ++- wefe/debias/hard_debias.py | 5 +- wefe/debias/multiclass_hard_debias.py | 10 +++- .../repulsion_attraction_neutralization.py | 22 ++++--- wefe/metrics/RNSB.py | 4 +- wefe/metrics/WEAT.py | 40 +++++++------ wefe/metrics/base_metric.py | 17 +++--- wefe/metrics/example_metric.py | 6 +- wefe/preprocessing.py | 4 +- wefe/query.py | 59 ++++++++++++++----- wefe/utils.py | 58 ++++++++++-------- 14 files changed, 162 insertions(+), 97 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 40396e9..973be62 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -18,7 +18,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v3 with: - python-version: "3.10" + python-version: "3.7" cache: "pip" - name: Install Pre-commit run: | diff --git a/pyproject.toml b/pyproject.toml index 04fa215..e907011 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,10 +18,19 @@ select = [ "A", "C", "Q", - "RET", + "S", "TID252", "SIM118", "ARG", + "PLC", + "PLE", + "PLR", + "PLW", + "PGH", + "TID", + "SIM", + "ICN", + "C4", ] @@ -42,6 +51,7 @@ ignore = [ "ANN002", "ANN003", "B006", + "B905", ] diff --git a/wefe/debias/double_hard_debias.py b/wefe/debias/double_hard_debias.py index 9e87a63..2afd125 100644 --- a/wefe/debias/double_hard_debias.py +++ b/wefe/debias/double_hard_debias.py @@ -169,7 +169,7 @@ def _check_sets_size( self, sets: List[List[str]], set_name: str, - ): + ) -> None: for idx, set_ in enumerate(sets): if len(set_) != 2: @@ -296,7 +296,7 @@ def _drop_frecuency_features( def _identify_bias_subspace( self, - defining_pairs_embeddings, + defining_pairs_embeddings: List[Dict[str, np.ndarray]], verbose: bool = False, ) -> PCA: @@ -310,7 +310,7 @@ def _identify_bias_subspace( for embedding in embedding_dict_pair.values(): # Substract the center of the pair to the embedding matrix.append(embedding - center) - matrix = np.array(matrix) # type: ignore + matrix = np.array(matrix) pca = PCA(**self.pca_args) pca.fit(matrix) @@ -371,8 +371,7 @@ def fit( bias_representation: List[str], **fit_params, ) -> BaseDebias: - """Compute the bias direction and obtain the principal components of the entire - set of vectors. + """Compute the bias direction and get the principal components of the vectors. Parameters ---------- @@ -386,12 +385,12 @@ def fit( bias_representation: List[str] Two words that represents each bias group. In case of gender "he" and "she". + Returns ------- BaseDebias The debias method fitted. """ - self.definitional_pairs = definitional_pairs self._check_sets_sizes(self.definitional_pairs, "definitional", set_size=2) diff --git a/wefe/debias/half_sibling_regression.py b/wefe/debias/half_sibling_regression.py index 57551c6..09d9b29 100644 --- a/wefe/debias/half_sibling_regression.py +++ b/wefe/debias/half_sibling_regression.py @@ -191,7 +191,12 @@ def _subtract_bias_information( debiased_vectors = non_bias_vectors - bias_information return debiased_vectors - def _get_indexes(self, model, target: List[str], non_bias: List[str]) -> List[int]: + def _get_indexes( + self, + model: WordEmbeddingModel, + target: List[str], + non_bias: List[str], + ) -> List[int]: return [non_bias.index(word) for word in target if word in model] def fit( @@ -325,7 +330,7 @@ def transform( target = target elif ignore: - target = list(set(list(self.non_bias_dict.keys())) - set(ignore)) + target = list(set(self.non_bias_dict.keys()) - set(ignore)) indexes = self._get_indexes(model, target, list(self.non_bias_dict.keys())) diff --git a/wefe/debias/hard_debias.py b/wefe/debias/hard_debias.py index 9d0eb4f..274a0d3 100644 --- a/wefe/debias/hard_debias.py +++ b/wefe/debias/hard_debias.py @@ -75,7 +75,6 @@ class HardDebias(BaseDebias): Examples -------- - .. note:: For more information on the use of mitigation methods, visit @@ -242,7 +241,7 @@ def _neutralize( bias_direction: np.ndarray, target: Optional[List[str]], ignore: Optional[List[str]], - ): + ) -> None: if target is not None: target_ = set(target) @@ -268,7 +267,7 @@ def _equalize( embedding_model: WordEmbeddingModel, equalize_pairs_embeddings: List[EmbeddingDict], bias_direction: np.ndarray, - ): + ) -> None: for equalize_pair_embeddings in equalize_pairs_embeddings: if ( isinstance(equalize_pair_embeddings, dict) diff --git a/wefe/debias/multiclass_hard_debias.py b/wefe/debias/multiclass_hard_debias.py index f7544cd..d4e55a3 100644 --- a/wefe/debias/multiclass_hard_debias.py +++ b/wefe/debias/multiclass_hard_debias.py @@ -145,7 +145,11 @@ def _identify_bias_subspace( return pca - def _project_onto_subspace(self, vector, subspace): + def _project_onto_subspace( + self, + vector: np.ndarray, + subspace: np.ndarray, + ) -> np.ndarray: v_b = np.zeros_like(vector) for component in subspace: v_b += np.dot(vector.transpose(), component) * component @@ -157,7 +161,7 @@ def _neutralize( bias_subspace: np.ndarray, target: Optional[List[str]], ignore: Optional[List[str]], - ): + ) -> None: if target is not None: target_ = set(target) else: @@ -189,7 +193,7 @@ def _equalize( model: WordEmbeddingModel, equalize_sets_embeddings: List[EmbeddingDict], bias_subspace: np.ndarray, - ): + ) -> None: for equalize_tuple_embeddings in equalize_sets_embeddings: words = equalize_tuple_embeddings.keys() diff --git a/wefe/debias/repulsion_attraction_neutralization.py b/wefe/debias/repulsion_attraction_neutralization.py index ac5c51d..5dca0d5 100644 --- a/wefe/debias/repulsion_attraction_neutralization.py +++ b/wefe/debias/repulsion_attraction_neutralization.py @@ -13,7 +13,7 @@ try: import torch - import torch.nn as nn + from torch import nn except ModuleNotFoundError: raise ModuleNotFoundError( "PyTorch is required to run RepulsionAttractionNeutralization method. " @@ -50,7 +50,7 @@ def __init__( self.repulsion_set = repulsion_set self.bias_direction = bias_direction - def _repulsion(self): + def _repulsion(self) -> torch.Tensor: if not isinstance(self.repulsion_set, bool): cos_similarity = self._cosine_similarity(self.w_b, self.repulsion_set) repulsion = torch.abs(cos_similarity).mean(dim=0) @@ -71,7 +71,12 @@ def _neutralization(self) -> torch.Tensor: neutralization = torch.abs(self.w_b.dot(self.bias_direction)).mean(dim=0) return neutralization - def _objective_function(self, w1, w2, w3) -> torch.Tensor: + def _objective_function( + self, + w1: np.number, + w2: np.number, + w3: np.number, + ) -> torch.Tensor: return ( self._repulsion() * w1 + self._attraction() * w2 @@ -95,7 +100,7 @@ def __init__( w: np.ndarray, repulsion_set: List[np.ndarray], bias_direction: np.ndarray, - weights=[0.33, 0.33, 0.33], + weights: Sequence = [0.33, 0.33, 0.33], ) -> None: """Initialize a RAN instance. @@ -112,7 +117,7 @@ def __init__( repulsion_set: List[np.ndarray] Set of embeddings to be repeled from word bias_direction: np.array - weights: list, optional + weights: Sequence, optional weights λi that determine the relative importance of one objective function (repulsion, attraction, neutralization) over another. by Defaults [0.33, 0.33, 0.33]. @@ -122,6 +127,7 @@ def __init__( self.model = model self.word = word self.w = torch.FloatTensor(np.array(w)).requires_grad_(True) + if len(repulsion_set) == 0: self.repulsion_set = False else: @@ -137,7 +143,7 @@ def __init__( self.loss = RAN_Loss(self.w, self.w_b, self.repulsion_set, self.bias_direction) - def _forward(self): + def _forward(self) -> torch.Tensor: w1, w2, w3 = self.weights return self.loss._objective_function(w1, w2, w3) @@ -364,7 +370,7 @@ def _check_sets_size( self, sets: Sequence[Sequence[str]], set_name: str, - ): + ) -> None: for idx, set_ in enumerate(sets): if len(set_) != 2: @@ -459,7 +465,7 @@ def _debias( weights, ) optimizer = torch.optim.Adam(ran.parameters(), lr=learning_rate) - for epoch in range(epochs): + for _ in range(epochs): optimizer.zero_grad() out = ran._forward() out.backward() diff --git a/wefe/metrics/RNSB.py b/wefe/metrics/RNSB.py index 8006b23..9b770e3 100644 --- a/wefe/metrics/RNSB.py +++ b/wefe/metrics/RNSB.py @@ -269,9 +269,7 @@ def _calc_rnsb( # set the probabilities for each word in a dict. negative_sentiment_probabilities = { word: prob - for word, prob in zip( - flatten_target_words, negative_probabilities, strict=True - ) + for word, prob in zip(flatten_target_words, negative_probabilities) } return kl_divergence, negative_sentiment_probabilities diff --git a/wefe/metrics/WEAT.py b/wefe/metrics/WEAT.py index 19c1dcf..0cd1485 100644 --- a/wefe/metrics/WEAT.py +++ b/wefe/metrics/WEAT.py @@ -12,6 +12,25 @@ from wefe.word_embedding_model import EmbeddingDict, WordEmbeddingModel +def _left_sided_test_function(calculated: np.number, original: np.number) -> bool: + return calculated < original + + +def _right_sided_test_function(calculated: np.number, original: np.number) -> bool: + return calculated > original + + +def _two_sided_test_function(calculated: np.number, original: np.number) -> bool: + return np.abs(calculated) > original + + +TEST_FUNCTION_DISPATCHER = { + "left-sided": _left_sided_test_function, + "right-sided": _right_sided_test_function, + "two-sided": _two_sided_test_function, +} + + class WEAT(BaseMetric): r"""Word Embedding Association Test (WEAT). @@ -145,27 +164,12 @@ def _calc_p_value( f'p value method should be "exact", "approximate"' f", got {method}." ) - # Choose the type of test to be calculated. - if test_type == "left-sided": - - def test_function(calculated, original): - return calculated < original - - elif test_type == "right-sided": - - def test_function(calculated, original): - return calculated > original - - elif test_type == "two-sided": - - def test_function(calculated, original): - return np.abs(calculated) > original - - else: + if test_type not in ["left-sided", "right-sided", "two-sided"]: raise Exception( - f'p value test type should be "left-sided", "right-sided" ' + f'p value test_type should be "left-sided", "right-sided" ' f'or "two-sided", got {test_type}' ) + test_function = TEST_FUNCTION_DISPATCHER[test_type] if not isinstance(iterations, (int, float)): raise TypeError( diff --git a/wefe/metrics/base_metric.py b/wefe/metrics/base_metric.py index 5c82c0b..459e239 100644 --- a/wefe/metrics/base_metric.py +++ b/wefe/metrics/base_metric.py @@ -1,3 +1,4 @@ +"""Base metric class that all metrics must extend..""" from abc import ABC, abstractmethod from typing import Any, Callable, Dict, List, Tuple, Union @@ -23,7 +24,7 @@ class BaseMetric(ABC): metric_short_name: str def _check_input( - self, query: Query, model: WordEmbeddingModel, locals: Dict[str, Any] + self, query: Query, model: WordEmbeddingModel, _locals: Dict[str, Any] ) -> None: """Check if Query and WordEmbeddingModel parameters are valid. @@ -33,6 +34,8 @@ def _check_input( The query that the method will execute. model : WordEmbeddingModel A word embedding model. + _locals: Dict[str, Any] + The extra arguments of run_query. Raises ------ @@ -96,29 +99,29 @@ def _check_input( ) ) - preprocessor_in_args = "preprocessor_args" in locals - secondary_preprocessor_in_args = "secondary_preprocessor_args" in locals + preprocessor_in_args = "preprocessor_args" in _locals + secondary_preprocessor_in_args = "secondary_preprocessor_args" in _locals if preprocessor_in_args and secondary_preprocessor_in_args: raise DeprecationWarning( "preprocessor_args and secondary_preprocessor_args arguments are " "deprecated. Use " - f'preprocessors=[{locals["preprocessor_args"]}, ' - f'{locals["secondary_preprocessor_args"]}] ' + f'preprocessors=[{_locals["preprocessor_args"]}, ' + f'{_locals["secondary_preprocessor_args"]}] ' "instead.\n\nSee https://wefe.readthedocs.io/en/latest/user_guide_" "measurement.html#word-preprocessors for more information." ) if preprocessor_in_args: raise DeprecationWarning( "preprocessor_args argument is deprecated. Use " - f'preprocessors=[{locals["preprocessor_args"]}] ' + f'preprocessors=[{_locals["preprocessor_args"]}] ' "instead.\n\nSee https://wefe.readthedocs.io/en/latest/user_guide_" "measurement.html#word-preprocessors for more information." ) if secondary_preprocessor_in_args: raise DeprecationWarning( "secondary_preprocessor_args is deprecated. Use " - f'preprocessors=[{{}}, {locals["secondary_preprocessor_args"]}] ' + f'preprocessors=[{{}}, {_locals["secondary_preprocessor_args"]}] ' "instead.\n\nSee https://wefe.readthedocs.io/en/latest/user_guide_" "measurement.html#word-preprocessors for more information." ) diff --git a/wefe/metrics/example_metric.py b/wefe/metrics/example_metric.py index 707c5ef..382a006 100644 --- a/wefe/metrics/example_metric.py +++ b/wefe/metrics/example_metric.py @@ -45,7 +45,6 @@ def _calc_metric( np.float The value of the calculated metric. """ - # get the embeddings from the dicts target_embeddings_0 = np.array(list(target_embeddings[0].values())) target_embeddings_1 = np.array(list(target_embeddings[1].values())) @@ -65,9 +64,8 @@ def _calc_metric( target_embeddings_1_avg, attribute_embeddings_0_avg ) - # subtract the distances - metric_result = dist_target_0_attr - dist_target_1_attr - return metric_result + # subtract the distances and return + return dist_target_0_attr - dist_target_1_attr def run_query( self, diff --git a/wefe/preprocessing.py b/wefe/preprocessing.py index 82a2a10..51d4789 100644 --- a/wefe/preprocessing.py +++ b/wefe/preprocessing.py @@ -256,7 +256,7 @@ def _check_lost_vocabulary_threshold( word_set: List[str], word_set_name: str, lost_vocabulary_threshold: float, -): +) -> None: if not isinstance(lost_vocabulary_threshold, (float, np.floating)): raise TypeError( @@ -441,7 +441,7 @@ def get_embeddings_from_tuples( ) raise Exception(msg) - elif verbose: + if verbose: print( f"{len(embedding_sets)}/{len(sets)} sets of " "words were correctly converted to sets of embeddings" diff --git a/wefe/query.py b/wefe/query.py index ecfdb2c..c6d4b3c 100644 --- a/wefe/query.py +++ b/wefe/query.py @@ -1,3 +1,4 @@ +"""Module that implements the Query object.""" from itertools import combinations from typing import Any, Dict, List, Union @@ -13,7 +14,7 @@ def __init__( attribute_sets: List[Any], target_sets_names: Union[List[str], None] = None, attribute_sets_names: Union[List[str], None] = None, - ): + ) -> None: """Initializes the container. It could include a name for each word set. Parameters @@ -78,7 +79,6 @@ def __init__( >>> query.query_name 'Male terms and Female terms wrt Science terms' """ - # check input type if not isinstance(target_sets, (list, np.ndarray)): raise TypeError( @@ -148,8 +148,7 @@ def __init__( "number of elements as target_sets_names " f"(len={len(target_sets_names)})" ) - else: - self.target_sets_names = target_sets_names + self.target_sets_names = target_sets_names # set attribute and attribute sets names. if attribute_sets_names is None: @@ -163,13 +162,33 @@ def __init__( "number of elements as attribute_sets_names " f"(len={len(attribute_sets_names)})" ) - else: - self.attribute_sets_names = attribute_sets_names + self.attribute_sets_names = attribute_sets_names self.query_name = self._get_query_name() - def __eq__(self, other): + def __eq__(self, other: Any) -> bool: + """Check if some object is equal to this query. + + Steps: + - Check other type. + - Compare template. + - Check if the number of target sets in both queries is equal. + - Check if the number of attribute sets in both queries is equal. + - Check if every target set is the same in both queries. + - Check if every attribute set is the same in both queries. + - Check if the names of the target sets are equal in both queries. + - Check if the names of the attribute sets are equal in both queries. + + Parameters + ---------- + other : Any + The object to compare. + Returns + ------- + bool + True if other is the same query, False in any other case. + """ if not isinstance(other, Query): return False @@ -178,22 +197,35 @@ def __eq__(self, other): if self.template[1] != other.template[1]: return False - for target_set, other_target_set in zip(self.target_sets, other.target_sets): + if len(self.target_sets) != len(other.target_sets): + return False + if len(self.attribute_sets) != len(other.attribute_sets): + return False + + for target_set, other_target_set in zip( + self.target_sets, + other.target_sets, + ): if target_set != other_target_set: return False for attribute_set, other_attribute_set in zip( - self.attribute_sets, other.attribute_sets + self.attribute_sets, + other.attribute_sets, ): if attribute_set != other_attribute_set: return False - for names, other_names in zip(self.target_sets_names, other.target_sets_names): + for names, other_names in zip( + self.target_sets_names, + other.target_sets_names, + ): if names != other_names: return False for names, other_names in zip( - self.attribute_sets_names, other.attribute_sets_names + self.attribute_sets_names, + other.attribute_sets_names, ): if names != other_names: return False @@ -224,7 +256,7 @@ def __repr__(self) -> str: return "" def dict(self) -> Dict[str, Any]: - """Generates a dictionary from the Query data + """Generate a dictionary from the Query data. This includes the target and attribute sets, as well as their names, the query name generated from them and the query template. @@ -244,7 +276,7 @@ def dict(self) -> Dict[str, Any]: } def get_subqueries(self, new_template: tuple) -> list: - """Generate the subqueries from this query using the given template""" + """Generate the subqueries from this query using the given template.""" if not isinstance(new_template[0], int): raise TypeError( "The new target cardinality (new_template[0]) must be int. " @@ -321,7 +353,6 @@ def _get_query_name(self) -> str: str The name of the query. """ - target_sets_names = self.target_sets_names attribute_sets_names = self.attribute_sets_names diff --git a/wefe/utils.py b/wefe/utils.py index 8f1531e..8a28fa7 100644 --- a/wefe/utils.py +++ b/wefe/utils.py @@ -22,7 +22,7 @@ from wefe.word_embedding_model import WordEmbeddingModel -def check_is_fitted(estimator, attributes): +def check_is_fitted(estimator, attributes) -> None: msg = ( "This %(name)s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this debias method." @@ -329,7 +329,7 @@ def run_queries( # ----------------------------------------------------------------------------- -def plot_queries_results(results: pd.DataFrame, by: str = "query"): +def plot_queries_results(results: pd.DataFrame, by: str = "query") -> go.Figure: """Plot the results obtained by a run_queries execution. Parameters @@ -338,7 +338,7 @@ def plot_queries_results(results: pd.DataFrame, by: str = "query"): A dataframe that contains the result of having executed run_queries with a set of queries and word embeddings. by : {'query', 'model'}, optional - The aggregation function , by default 'query' + The aggregation function , by default 'query Returns ------- @@ -407,7 +407,7 @@ def create_ranking( results_dataframes: List[pd.DataFrame], method: str = "first", ascending: bool = True, -): +) -> pd.DataFrame: """Create a ranking form the aggregated scores of the provided dataframes. The function will assume that the aggregated scores are in the last column @@ -478,22 +478,25 @@ def create_ranking( return rankings +def _melt_df(results: pd.DataFrame) -> pd.DataFrame: + results = results.copy() + results["exp_name"] = results.index + id_vars = ["exp_name"] + cols = results.columns + values_vars = [col_name for col_name in cols if col_name not in id_vars] + melted_results = pd.melt( + results, id_vars=id_vars, value_vars=values_vars, var_name="Metric" + ) + melted_results.columns = ["Embedding model", "Metric", "Ranking"] + return melted_results + + def plot_ranking( - ranking: pd.DataFrame, title: str = "", use_metric_as_facet: bool = False -): - def melt_df(results): - results = results.copy() - results["exp_name"] = results.index - id_vars = ["exp_name"] - cols = results.columns - values_vars = [col_name for col_name in cols if col_name not in id_vars] - melted_results = pd.melt( - results, id_vars=id_vars, value_vars=values_vars, var_name="Metric" - ) - melted_results.columns = ["Embedding model", "Metric", "Ranking"] - return melted_results + ranking: pd.DataFrame, + use_metric_as_facet: bool = False, +) -> go.Figure: - melted_ranking = melt_df(ranking.copy(deep=True)) + melted_ranking = _melt_df(ranking.copy(deep=True)) if use_metric_as_facet: fig = px.bar( @@ -518,7 +521,7 @@ def melt_df(results): fig.update_layout(showlegend=False) fig.update_yaxes(title_text="") - fig.update_yaxes(tickfont=dict(size=10)) + fig.update_yaxes(tickfont={"size": 10}) # fig.for_each_trace(lambda t: t.update(name=t.name.split('=')[1])) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[1])) return fig @@ -530,7 +533,8 @@ def melt_df(results): def calculate_ranking_correlations( - rankings: pd.DataFrame, method="spearman" + rankings: pd.DataFrame, + method: str = "spearman", ) -> pd.DataFrame: """Calculate the correlation between the calculated rankings. @@ -568,7 +572,11 @@ def calculate_ranking_correlations( return correlation_matrix -def plot_ranking_correlations(correlation_matrix, title=""): +def plot_ranking_correlations( + correlation_matrix: pd.DataFrame, + title: str = "", +) -> go.Figure: + fig = go.Figure( data=go.Heatmap( z=correlation_matrix, @@ -580,7 +588,7 @@ def plot_ranking_correlations(correlation_matrix, title=""): colorscale="Darkmint", ) ) - fig.update_layout(title=title, font=dict(color="#000000")) + fig.update_layout(title=title, font={"color": "#000000"}) return fig @@ -604,18 +612,18 @@ def load_test_model() -> WordEmbeddingModel: return WordEmbeddingModel(test_model, "test_w2v") -def print_doc_table(df): +def print_doc_table(df: pd.DataFrame) -> None: from tabulate import tabulate df_2 = df.reset_index() print(tabulate(df_2, headers=df_2.columns, tablefmt="rst", showindex=False)) -def save_doc_image(fig, name): +def save_doc_image(fig: go.Figure, name: str) -> None: fig.write_image(f"./doc/images/{name}.png", width=1200, height=600, scale=3) -def flair_to_gensim(flair_embedding): +def flair_to_gensim(flair_embedding) -> KeyedVectors: # load model from flair # hack to transform pytorch embedding to gensim's KeyedVectors From cf8e2ce57c0f1ef5956142b17d9fc34a77cc8ec4 Mon Sep 17 00:00:00 2001 From: katotaisei Date: Wed, 15 Nov 2023 11:22:12 +0900 Subject: [PATCH 6/9] add references for WEAT words --- wefe/datasets/data/references.txt | 10 ++++++++++ wefe/datasets/datasets.py | 19 +++++++++++++++++++ 2 files changed, 29 insertions(+) create mode 100644 wefe/datasets/data/references.txt diff --git a/wefe/datasets/data/references.txt b/wefe/datasets/data/references.txt new file mode 100644 index 0000000..02b8dc7 --- /dev/null +++ b/wefe/datasets/data/references.txt @@ -0,0 +1,10 @@ +WEAT.json + flowers, insects, pleasant_5, unpleasant_5, instruments, weapons, european_american_names_5, and african_american_names_5: Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464 + + european_american_names_7 and african_american_names_7: Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review, 94(4), 991–1013. https://doi.org/10.1257/0002828042002561 + + pleasant_9, unpleasant_9, male_names, female_names, career, family, math, arts, male_terms, female_terms, young_people_names, and old_people_names: Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dynamics: Theory, Research, and Practice, 6(1), 101–115. https://doi.org/10.1037/1089-2699.6.1.101 + + science, arts_2, male_terms_2, and female_terms_2: Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math ≠ me. Journal of Personality and Social Psychology, 83(1), 44–59. https://doi.org/10.1037/0022-3514.83.1.44 + + mental_disease, physical_disease, temporary, and permanent: Monteith, L. L., & Pettit, J. W. (2011). Implicit and Explicit Stigmatizing Attitudes and Stereotypes About Depression. Journal of Social and Clinical Psychology, 30(5), 484–505. https://doi.org/10.1521/jscp.2011.30.5.484 diff --git a/wefe/datasets/datasets.py b/wefe/datasets/datasets.py index a2628c7..0771e02 100644 --- a/wefe/datasets/datasets.py +++ b/wefe/datasets/datasets.py @@ -422,6 +422,25 @@ def load_weat() -> Dict[str, List[str]]: biases. | Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). | Science, 356(6334), 183-186. + | [2]: Measuring individual differences in implicit cognition: The implicit + association test. + | Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). + | Journal of Personality and Social Psychology, 74(6), 1464-1480. + | [3]: Are Emily and Greg More Employable Than Lakisha and Jamal? A Field + Experiment on Labor Market Discrimination. + | Bertrand, M., & Mullainathan, S. (2004). + | American Economic Review, 94(4), 991-1013. + | [4]: Harvesting implicit group attitudes and beliefs from a demonstration web + site. + | Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). + | Group Dynamics: Theory, Research, and Practice, 6(1), 101-115. + | [5]: Math = male, me = female, therefore math ≠ me. + | Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). + | Journal of Personality and Social Psychology, 83(1), 44-59. + | [6]: Implicit and Explicit Stigmatizing Attitudes and Stereotypes About + Depression. + | Monteith, L. L., & Pettit, J. W. (2011). + | Journal of Social and Clinical Psychology, 30(5), 484-505. Returns ------- From baeecc79cbb58d1d491ca1934da7e4a6a96030f4 Mon Sep 17 00:00:00 2001 From: katotaisei Date: Wed, 15 Nov 2023 11:58:05 +0900 Subject: [PATCH 7/9] add unpleasant_5b --- wefe/datasets/data/WEAT.json | 29 ++++++++++++++++++++++++++++- wefe/datasets/data/references.txt | 2 +- 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/wefe/datasets/data/WEAT.json b/wefe/datasets/data/WEAT.json index ced9f02..9a0a32c 100644 --- a/wefe/datasets/data/WEAT.json +++ b/wefe/datasets/data/WEAT.json @@ -80,7 +80,7 @@ "paradise", "vacation" ], - "unpleasant_5": [ + "unpleasant_5a": [ "abuse", "crash", "filth", @@ -231,6 +231,33 @@ "Malika", "Yvette" ], + "unpleasant_5b": [ + "abuse", + "crash", + "filth", + "murder", + "sickness", + "accident", + "death", + "grief", + "poison", + "stink", + "assault", + "disaster", + "hatred", + "pollute", + "tragedy", + "bomb", + "divorce", + "jail", + "poverty", + "ugly", + "cancer", + "evil", + "kill", + "rotten", + "vomit" + ], "european_american_names_7": [ "Brad", "Brendan", diff --git a/wefe/datasets/data/references.txt b/wefe/datasets/data/references.txt index 02b8dc7..18bc4c5 100644 --- a/wefe/datasets/data/references.txt +++ b/wefe/datasets/data/references.txt @@ -1,5 +1,5 @@ WEAT.json - flowers, insects, pleasant_5, unpleasant_5, instruments, weapons, european_american_names_5, and african_american_names_5: Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464 + flowers, insects, pleasant_5, unpleasant_5a, instruments, weapons, european_american_names_5, african_american_names_5, and unpleasant_5b: Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464 european_american_names_7 and african_american_names_7: Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review, 94(4), 991–1013. https://doi.org/10.1257/0002828042002561 From 889295b2a9a6b87e4f27b39c2bdc8ea1173f23b8 Mon Sep 17 00:00:00 2001 From: katotaisei Date: Wed, 15 Nov 2023 16:35:15 +0900 Subject: [PATCH 8/9] ignore .python-version --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 40a15ab..7ac9531 100644 --- a/.gitignore +++ b/.gitignore @@ -94,3 +94,6 @@ conda_deploy/ # coverage files cov.xml test-results/junit.xml + +# pyenv +.python-version From dc6600d1b97018cfa8a3c91b9a1d4f9f1714a121 Mon Sep 17 00:00:00 2001 From: katotaisei Date: Wed, 15 Nov 2023 18:14:45 +0900 Subject: [PATCH 9/9] fix european_american_names_5 --- wefe/datasets/data/WEAT.json | 2 -- 1 file changed, 2 deletions(-) diff --git a/wefe/datasets/data/WEAT.json b/wefe/datasets/data/WEAT.json index 9a0a32c..67cf31d 100644 --- a/wefe/datasets/data/WEAT.json +++ b/wefe/datasets/data/WEAT.json @@ -183,8 +183,6 @@ "Courtney", "Heather", "Melanie", - "Sara", - "Amber", "Katie", "Betsy", "Kristin",