├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── ROADMAP.md ├── docs ├── .nojekyll ├── _images │ ├── bias.png │ ├── readme-1.png │ ├── readme-10.png │ ├── readme-11.png │ ├── readme-12.png │ ├── readme-13.png │ ├── readme-14.png │ ├── readme-15.png │ ├── readme-16.png │ ├── readme-2.png │ ├── readme-3.png │ ├── readme-4.png │ ├── readme-5.png │ ├── readme-6.png │ ├── readme-7.png │ ├── readme-8.png │ └── readme-9.png ├── _sources │ ├── gettingstarted.rst.txt │ ├── index.rst.txt │ └── xai.rst.txt ├── _static │ ├── .nojekyll │ ├── basic.css │ ├── bias.png │ ├── css │ │ ├── .nojekyll │ │ ├── badge_only.css │ │ └── theme.css │ ├── doctools.js │ ├── documentation_options.js │ ├── file.png │ ├── fonts │ │ ├── .nojekyll │ │ ├── Inconsolata-Bold.ttf │ │ ├── Inconsolata-Regular.ttf │ │ ├── Inconsolata.ttf │ │ ├── Lato-Bold.ttf │ │ ├── Lato-Regular.ttf │ │ ├── Lato │ │ │ ├── lato-bold.eot │ │ │ ├── lato-bold.ttf │ │ │ ├── lato-bold.woff │ │ │ ├── lato-bold.woff2 │ │ │ ├── lato-bolditalic.eot │ │ │ ├── lato-bolditalic.ttf │ │ │ ├── lato-bolditalic.woff │ │ │ ├── lato-bolditalic.woff2 │ │ │ ├── lato-italic.eot │ │ │ ├── lato-italic.ttf │ │ │ ├── lato-italic.woff │ │ │ ├── lato-italic.woff2 │ │ │ ├── lato-regular.eot │ │ │ ├── lato-regular.ttf │ │ │ ├── lato-regular.woff │ │ │ └── lato-regular.woff2 │ │ ├── RobotoSlab-Bold.ttf │ │ ├── RobotoSlab-Regular.ttf │ │ ├── RobotoSlab │ │ │ ├── roboto-slab-v7-bold.eot │ │ │ ├── roboto-slab-v7-bold.ttf │ │ │ ├── roboto-slab-v7-bold.woff │ │ │ ├── roboto-slab-v7-bold.woff2 │ │ │ ├── roboto-slab-v7-regular.eot │ │ │ ├── roboto-slab-v7-regular.ttf │ │ │ ├── roboto-slab-v7-regular.woff │ │ │ └── roboto-slab-v7-regular.woff2 │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.svg │ │ ├── fontawesome-webfont.ttf │ │ ├── fontawesome-webfont.woff │ │ └── fontawesome-webfont.woff2 │ ├── jquery-3.2.1.js │ ├── jquery.js │ ├── js │ │ ├── .nojekyll │ │ ├── modernizr.min.js │ │ └── theme.js │ ├── language_data.js │ ├── minus.png │ ├── plus.png │ ├── pygments.css │ ├── readme-1.png │ ├── readme-10.png │ ├── readme-11.png │ ├── readme-12.png │ ├── readme-13.png │ ├── readme-14.png │ ├── readme-15.png │ ├── readme-16.png │ ├── readme-2.png │ ├── readme-3.png │ ├── readme-4.png │ ├── readme-5.png │ ├── readme-6.png │ ├── readme-7.png │ ├── readme-8.png │ ├── readme-9.png │ ├── searchtools.js │ ├── tech.jpg │ ├── underscore-1.3.1.js │ └── underscore.js ├── genindex.html ├── gettingstarted.html ├── index.html ├── objects.inv ├── py-modindex.html ├── search.html ├── searchindex.js └── xai.html ├── docs_src ├── Makefile ├── make.bat └── source │ ├── _static │ ├── bias.png │ ├── readme-1.png │ ├── readme-10.png │ ├── readme-11.png │ ├── readme-12.png │ ├── readme-13.png │ ├── readme-14.png │ ├── readme-15.png │ ├── readme-16.png │ ├── readme-2.png │ ├── readme-3.png │ ├── readme-4.png │ ├── readme-5.png │ ├── readme-6.png │ ├── readme-7.png │ ├── readme-8.png │ ├── readme-9.png │ └── tech.jpg │ ├── conf.py │ ├── gettingstarted.rst │ ├── index.rst │ └── xai.rst ├── env.yml ├── examples ├── .ipynb_checkpoints │ └── XData Example Usage-checkpoint.ipynb ├── README.md ├── XAI Tabular Data Example Usage.ipynb ├── XAI Tabular Data Example Usage_files │ ├── XAI Tabular Data Example Usage_10_1.png │ ├── XAI Tabular Data Example Usage_15_0.png │ ├── XAI Tabular Data Example Usage_17_0.png │ ├── XAI Tabular Data Example Usage_18_0.png │ ├── XAI Tabular Data Example Usage_18_1.png │ ├── XAI Tabular Data Example Usage_18_2.png │ ├── XAI Tabular Data Example Usage_19_0.png │ ├── XAI Tabular Data Example Usage_20_0.png │ ├── XAI Tabular Data Example Usage_21_0.png │ ├── XAI Tabular Data Example Usage_22_0.png │ ├── XAI Tabular Data Example Usage_22_1.png │ ├── XAI Tabular Data Example Usage_22_2.png │ ├── XAI Tabular Data Example Usage_23_0.png │ ├── XAI Tabular Data Example Usage_24_0.png │ ├── XAI Tabular Data Example Usage_24_1.png │ ├── XAI Tabular Data Example Usage_24_2.png │ ├── XAI Tabular Data Example Usage_25_1.png │ ├── XAI Tabular Data Example Usage_26_1.png │ ├── XAI Tabular Data Example Usage_27_1.png │ ├── XAI Tabular Data Example Usage_28_1.png │ ├── XAI Tabular Data Example Usage_29_1.png │ ├── XAI Tabular Data Example Usage_30_1.png │ ├── XAI Tabular Data Example Usage_31_1.png │ ├── XAI Tabular Data Example Usage_4_0.png │ ├── XAI Tabular Data Example Usage_5_0.png │ ├── XAI Tabular Data Example Usage_6_0.png │ ├── XAI Tabular Data Example Usage_8_0.png │ └── XAI Tabular Data Example Usage_9_0.png └── data │ └── adult.data ├── images ├── bias.png ├── mlops-link.png ├── readme-10.png ├── readme-11.png ├── readme-12.png ├── readme-13.png ├── readme-14.png ├── readme-15.png ├── readme-16.png ├── readme-3.png ├── readme-4.png ├── readme-5.png ├── readme-6.png ├── readme-7.png ├── readme-8.png ├── readme-9.png ├── readme-balance-split.jpg ├── readme-balance-upsample.jpg ├── readme-confusion-matrix.jpg ├── readme-correlation-dendogram.jpg ├── readme-correlation-matrix.jpg ├── readme-csv-head.jpg ├── readme-groups.jpg ├── readme-imbalance-gender.jpg ├── readme-imbalance-multiple.jpg ├── readme-metrics-column.jpg ├── readme-metrics-multiple.jpg ├── readme-metrics-plot.jpg ├── tech.jpg └── video.jpg ├── meta.yaml ├── mkdocs.yml ├── requirements.txt ├── setup.py └── xai ├── __init__.py └── data ├── __init__.py └── census.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled source # 2 | ################### 3 | *.com 4 | *.class 5 | *.dll 6 | *.exe 7 | *.o 8 | *.so 9 | 10 | # Packages # 11 | ############ 12 | # it's better to unpack these files and commit the raw source 13 | # git has its own built in compression methods 14 | *.7z 15 | *.dmg 16 | *.gz 17 | *.iso 18 | *.jar 19 | *.rar 20 | *.tar 21 | *.zip 22 | 23 | # Logs and databases # 24 | ###################### 25 | *.log 26 | *.sql 27 | *.sqlite 28 | 29 | # OS generated files # 30 | ###################### 31 | .DS_Store 32 | .DS_Store? 33 | ._* 34 | .Spotlight-V100 35 | .Trashes 36 | ehthumbs.db 37 | Thumbs.db 38 | 39 | # Python 40 | __pycache__ 41 | *.pyc 42 | 43 | # Logs 44 | logs 45 | *.log 46 | npm-debug.log* 47 | yarn-debug.log* 48 | yarn-error.log* 49 | 50 | # Runtime data 51 | pids 52 | *.pid 53 | *.seed 54 | *.pid.lock 55 | 56 | # Directory for instrumented libs generated by jscoverage/JSCover 57 | lib-cov 58 | 59 | # Coverage directory used by tools like istanbul 60 | coverage 61 | 62 | # nyc test coverage 63 | .nyc_output 64 | 65 | # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files) 66 | .grunt 67 | 68 | # Bower dependency directory (https://bower.io/) 69 | bower_components 70 | 71 | # node-waf configuration 72 | .lock-wscript 73 | 74 | # Compiled binary addons (http://nodejs.org/api/addons.html) 75 | build/Release 76 | 77 | # Dependency directories 78 | node_modules/ 79 | 80 | # Tags 81 | tags 82 | tags.* 83 | .tags 84 | .tags.* 85 | 86 | docs_src/build/ 87 | .mypy_cache 88 | xai.egg-info 89 | .eggs 90 | .ipynb_checkpoints 91 | 92 | xai.egg-info/ 93 | build/ 94 | dist/ 95 | 96 | dist/ 97 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2018 The Institute for Ethical AI & Machine Learning 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | this software and associated documentation files (the "Software"), to deal in 5 | the Software without restriction, including without limitation the rights to 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | the Software, and to permit persons to whom the Software is furnished to do so, 8 | subject to the following conditions: 9 | 10 | The above copyright notice and this permission notice shall be included in all 11 | copies or substantial portions of the Software. 12 | 13 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | SOFTWARE. 20 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.md 2 | include requirements.txt 3 | recursive-include xai *.pickle 4 | recursive-exclude * __pycache__ 5 | recursive-include xai *.csv 6 | recursive-exclude * *.py[co] 7 | recursive-include docs *.md *.jpg *.png *.gif 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![GitHub](https://img.shields.io/badge/Release-ALPHA-yellow.svg) 2 | ![GitHub](https://img.shields.io/badge/Version-0.0.5_ALPHA-lightgrey.svg) 3 | ![GitHub](https://img.shields.io/badge/Python-3.5_|_3.6_|_3.7-blue.svg) 4 | ![GitHub](https://img.shields.io/badge/License-MIT-lightgrey.svg) 5 | 6 | # XAI - An eXplainability toolbox for machine learning 7 | 8 | XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by [The Institute for Ethical AI & ML](http://ethical.institute/), and it was developed based on the [8 principles for Responsible Machine Learning](http://ethical.institute/principles.html). 9 | 10 | You can find the documentation at [https://ethicalml.github.io/xai/index.html](https://ethicalml.github.io/xai/index.html). You can also check out our [talk at Tensorflow London](https://www.youtube.com/watch?v=GZpfBhQJ0H4) where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library. 11 | 12 | ## YouTube video showing how to use XAI to mitigate undesired biases 13 | 14 | 15 | 16 | 19 | 22 | 23 | 24 | 27 | 30 | 31 | 32 |
17 | This video of the talk presented at the PyData London 2019 Conference which provides an overview on the motivations for machine learning explainability as well as techniques to introduce explainability and mitigate undesired biases using the XAI Library. 18 | 20 | 21 |
25 | Do you want to learn about more awesome machine learning explainability tools? Check out our community-built "Awesome Machine Learning Production & Operations" list which contains an extensive list of tools for explainability, privacy, orchestration and beyond. 26 | 28 | 29 |
33 | 34 | # 0.1.0 35 | 36 | If you want to see a fully functional demo in action clone this repo and run the Example Jupyter Notebook in the Examples folder. 37 | 38 | ## What do we mean by eXplainable AI? 39 | 40 | We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring. 41 | 42 | We provide a visual overview of these three steps mentioned above in this diagram: 43 | 44 | 45 | 46 | # XAI Quickstart 47 | 48 | ## Installation 49 | 50 | The XAI package is on PyPI. To install you can run: 51 | 52 | ``` 53 | pip install xai 54 | ``` 55 | 56 | Alternatively you can install from source by cloning the repo and running: 57 | 58 | ``` 59 | python setup.py install 60 | ``` 61 | 62 | ## Usage 63 | 64 | You can find example usage in the examples folder. 65 | 66 | ### 1) Data Analysis 67 | 68 | With XAI you can identify imbalances in the data. For this, we will load the census dataset from the XAI library. 69 | 70 | ``` python 71 | import xai.data 72 | df = xai.data.load_census() 73 | df.head() 74 | ``` 75 | 76 | 77 | #### View class imbalances for all categories of one column 78 | ``` python 79 | ims = xai.imbalance_plot(df, "gender") 80 | ``` 81 | 82 | 83 | #### View imbalances for all categories across multiple columns 84 | ``` python 85 | im = xai.imbalance_plot(df, "gender", "loan") 86 | ``` 87 | 88 | 89 | #### Balance classes using upsampling and/or downsampling 90 | ``` python 91 | bal_df = xai.balance(df, "gender", "loan", upsample=0.8) 92 | ``` 93 | 94 | 95 | #### Perform custom operations on groups 96 | ``` python 97 | groups = xai.group_by_columns(df, ["gender", "loan"]) 98 | for group, group_df in groups: 99 | print(group) 100 | print(group_df["loan"].head(), "\n") 101 | ``` 102 | 103 | 104 | #### Visualise correlations as a matrix 105 | ``` python 106 | _ = xai.correlations(df, include_categorical=True, plot_type="matrix") 107 | ``` 108 | 109 | 110 | #### Visualise correlations as a hierarchical dendogram 111 | ``` python 112 | _ = xai.correlations(df, include_categorical=True) 113 | ``` 114 | 115 | 116 | #### Create a balanced validation and training split dataset 117 | ``` python 118 | # Balanced train-test split with minimum 300 examples of 119 | # the cross of the target y and the column gender 120 | x_train, y_train, x_test, y_test, train_idx, test_idx = \ 121 | xai.balanced_train_test_split( 122 | x, y, "gender", 123 | min_per_group=300, 124 | max_per_group=300, 125 | categorical_cols=categorical_cols) 126 | 127 | x_train_display = bal_df[train_idx] 128 | x_test_display = bal_df[test_idx] 129 | 130 | print("Total number of examples: ", x_test.shape[0]) 131 | 132 | df_test = x_test_display.copy() 133 | df_test["loan"] = y_test 134 | 135 | _= xai.imbalance_plot(df_test, "gender", "loan", categorical_cols=categorical_cols) 136 | ``` 137 | 138 | 139 | ### 2) Model Evaluation 140 | 141 | We are able to also analyse the interaction between inference results and input features. For this, we will train a single layer deep learning model. 142 | 143 | ``` 144 | model = build_model(proc_df.drop("loan", axis=1)) 145 | 146 | model.fit(f_in(x_train), y_train, epochs=50, batch_size=512) 147 | 148 | probabilities = model.predict(f_in(x_test)) 149 | predictions = list((probabilities >= 0.5).astype(int).T[0]) 150 | ``` 151 | 152 | 153 | #### Visualise permutation feature importance 154 | ``` python 155 | def get_avg(x, y): 156 | return model.evaluate(f_in(x), y, verbose=0)[1] 157 | 158 | imp = xai.feature_importance(x_test, y_test, get_avg) 159 | 160 | imp.head() 161 | ``` 162 | 163 | 164 | #### Identify metric imbalances against all test data 165 | ``` python 166 | _= xai.metrics_plot( 167 | y_test, 168 | probabilities) 169 | ``` 170 | 171 | 172 | #### Identify metric imbalances across a specific column 173 | ``` python 174 | _ = xai.metrics_plot( 175 | y_test, 176 | probabilities, 177 | df=x_test_display, 178 | cross_cols=["gender"], 179 | categorical_cols=categorical_cols) 180 | ``` 181 | 182 | 183 | #### Identify metric imbalances across multiple columns 184 | ``` python 185 | _ = xai.metrics_plot( 186 | y_test, 187 | probabilities, 188 | df=x_test_display, 189 | cross_cols=["gender", "ethnicity"], 190 | categorical_cols=categorical_cols) 191 | ``` 192 | 193 | 194 | #### Draw confusion matrix 195 | ``` python 196 | xai.confusion_matrix_plot(y_test, pred) 197 | ``` 198 | 199 | 200 | #### Visualise the ROC curve against all test data 201 | ``` python 202 | _ = xai.roc_plot(y_test, probabilities) 203 | ``` 204 | 205 | 206 | #### Visualise the ROC curves grouped by a protected column 207 | ``` python 208 | protected = ["gender", "ethnicity", "age"] 209 | _ = [xai.roc_plot( 210 | y_test, 211 | probabilities, 212 | df=x_test_display, 213 | cross_cols=[p], 214 | categorical_cols=categorical_cols) for p in protected] 215 | ``` 216 | 217 | 218 | #### Visualise accuracy grouped by probability buckets 219 | ``` python 220 | d = xai.smile_imbalance( 221 | y_test, 222 | probabilities) 223 | ``` 224 | 225 | 226 | #### Visualise statistical metrics grouped by probability buckets 227 | ``` python 228 | d = xai.smile_imbalance( 229 | y_test, 230 | probabilities, 231 | display_breakdown=True) 232 | ``` 233 | 234 | 235 | #### Visualise benefits of adding manual review on probability thresholds 236 | ``` python 237 | d = xai.smile_imbalance( 238 | y_test, 239 | probabilities, 240 | bins=9, 241 | threshold=0.75, 242 | manual_review=0.375, 243 | display_breakdown=False) 244 | ``` 245 | 246 | 247 | 248 | 249 | 250 | -------------------------------------------------------------------------------- /ROADMAP.md: -------------------------------------------------------------------------------- 1 | 2 | # XAI - eXplainableAI Roadmap 3 | 4 | This page contains the roadmap for the XAI library. 5 | 6 | * Add AIC and BIC model evaluation metrics 7 | * Add numbers on the charts for accuracy metrics 8 | * Enable metrics for multiclass 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Here you will find everything 10 | 11 | Indices and tables 12 | ================== 13 | 14 | * :ref:`genindex` 15 | * :doc:`xai` 16 | * :ref:`search` 17 | 18 | -------------------------------------------------------------------------------- /docs/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. xai - WordCount Python documentation master file, created by 2 | sphinx-quickstart on Fri Jun 23 15:52:18 2017. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to the XAI docs - eXplainable machine learning 7 | =========================================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 4 11 | 12 | 13 | Welcome to the ``xai`` documentation. Here you will the installation guide, the quick start guide, and the detailed docstrings code documentation of the xai library. 14 | 15 | The documentation is currently under construction - for the meantime you can check out the main Github repository for the code (https://github.com/EthicalML/xai). 16 | 17 | About 18 | ================ 19 | 20 | XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by `The Institute for Ethical AI & ML `_, and it was developed based on the `8 principles for Responsible Machine Learning `_. 21 | 22 | You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our `talk at Tensorflow London `_ where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library. 23 | 24 | 0.0.4 - ALPHA Version 25 | ------------------------ 26 | 27 | This library is currently in early stage developments and hence it will be quite unstable due to the fast updates. It is important to bare this in mind if using it in production. 28 | 29 | What do we mean by eXplainable AI? 30 | --------------------------------------- 31 | 32 | We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring. 33 | 34 | We provide a visual overview of these three steps mentioned above in this diagram: 35 | 36 | .. image:: _static/bias.png 37 | 38 | XAI Quickstart 39 | ==================================== 40 | 41 | Installation 42 | ------------------ 43 | 44 | The XAI package is on PyPI. To install you can run: 45 | 46 | .. parsed-literal:: 47 | 48 | pip install xai 49 | 50 | Alternatively you can install from source by cloning the repo and running: 51 | 52 | .. parsed-literal:: 53 | 54 | python setup.py install 55 | 56 | Usage 57 | --------- 58 | 59 | You can find example usage in the examples folder. 60 | 61 | 1) Data Analysis 62 | ^^^^^^^^^^^^^^^^^^^^^^^^^^ 63 | 64 | 65 | With XAI you can identify imbalances in the data. For this, we will load the census dataset from the XAI library. 66 | 67 | .. parsed-literal:: 68 | 69 | import xai.data 70 | df = xai.data.load_census() 71 | df.head() 72 | 73 | .. image:: _static/readme-1.png 74 | 75 | View class imbalances for protected columns 76 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 77 | 78 | .. parsed-literal:: 79 | 80 | protected_cols = ["gender", "ethnicity", "age"] 81 | ims = xai.show_imbalances(df, protected_cols) 82 | 83 | .. image:: _static/readme-2.png 84 | 85 | View imbalance of one column 86 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 87 | 88 | 89 | .. parsed-literal:: 90 | 91 | im = xai.show_imbalance(df, "gender") 92 | 93 | .. image:: _static/readme-3.png 94 | 95 | View imbalance of one column intersected with another 96 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 97 | 98 | 99 | .. parsed-literal:: 100 | 101 | im = xai.show_imbalance(df, "gender", cross=["loan"]) 102 | 103 | .. image:: _static/readme-4.png 104 | 105 | Balance the class using upsampling and/or downsampling 106 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 107 | 108 | 109 | .. parsed-literal:: 110 | bal_df = xai.balance(df, "gender", cross=["loan"], upsample=1.0) 111 | 112 | .. image:: _static/readme-5.png 113 | 114 | Create a balanced test-train split (should be done pre-balancing) 115 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 116 | 117 | 118 | .. parsed-literal:: 119 | 120 | # Balanced train-test split with minimum 300 examples of 121 | # the cross of the target y and the column gender 122 | x_train, y_train, x_test, y_test = xai.balanced_train_test_split( 123 | x, y, cross=["gender"], 124 | categorical_cols=categorical_cols, min_per_class=300) 125 | 126 | # Visualise the imbalances of gender and the target 127 | df_test = x_test.copy() 128 | df_test["loan"] = y_test 129 | _= xai.show_imbalance(df_test, "gender", cross=["loan"], categorical_cols=categorical_cols) 130 | 131 | .. image:: _static/readme-16.png 132 | 133 | 2) Model Evaluation 134 | ^^^^^^^^^^^^^^^^^^^^^^^^^ 135 | 136 | We are able to also analyse the interaction between inference results and input features. For this, we will train a single layer deep learning model. 137 | 138 | .. parsed-literal:: 139 | 140 | model = build_model(proc_df.drop("loan", axis=1)) 141 | model.fit(f_in(x_train), y_train, epochs=50, batch_size=512) 142 | 143 | probabilities = model.predict(f_in(x_test)) 144 | predictions = list((probabilities >= 0.5).astype(int).T[0]) 145 | 146 | .. image:: _static/readme-15.png 147 | 148 | Visualise permutation feature importance 149 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 150 | 151 | 152 | .. parsed-literal:: 153 | 154 | def get_avg(x, y): 155 | return model.evaluate(f_in(x), y, verbose=0)[1] 156 | 157 | imp = xai.feature_importance(x_test, y_test, get_avg) 158 | 159 | imp.head() 160 | 161 | .. image:: _static/readme-6.png 162 | 163 | Identify metric imbalances against all test data 164 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 165 | 166 | 167 | .. parsed-literal:: 168 | 169 | _= xai.metrics_imbalance( 170 | x_test, 171 | y_test, 172 | probabilities) 173 | 174 | .. image:: _static/readme-7.png 175 | 176 | Identify metric imbalances grouped by protected columns 177 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 178 | 179 | 180 | .. parsed-literal:: 181 | _= xai.metrics_imbalances( 182 | x_test, 183 | y_test, 184 | probabilities, 185 | columns=protected, 186 | categorical_cols=categorical_cols) 187 | 188 | .. image:: _static/readme-8.png 189 | 190 | Visualise the ROC curve against all test data 191 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 192 | 193 | 194 | .. parsed-literal:: 195 | 196 | _= xai.roc_imbalance( 197 | x_test, 198 | y_test, 199 | probabilities) 200 | 201 | .. image:: _static/readme-9.png 202 | 203 | Visualise the ROC curves grouped by protected columns 204 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 205 | 206 | 207 | .. parsed-literal:: 208 | _= xai.roc_imbalances( 209 | x_test, 210 | y_test, 211 | probabilities, 212 | columns=protected, 213 | categorical_cols=categorical_cols) 214 | 215 | .. image:: _static/readme-10.png 216 | 217 | Visualise the precision-recall curve by protected columns 218 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 219 | 220 | 221 | .. parsed-literal:: 222 | 223 | _= xai.pr_imbalances( 224 | x_test, 225 | y_test, 226 | probabilities, 227 | columns=protected, 228 | categorical_cols=categorical_cols) 229 | 230 | .. image:: _static/readme-11.png 231 | 232 | Visualise accuracy grouped by probability buckets 233 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 234 | 235 | 236 | .. parsed-literal:: 237 | 238 | d = xai.smile_imbalance( 239 | y_test, 240 | probabilities) 241 | 242 | .. image:: _static/readme-12.png 243 | 244 | Visualise statistical metrics grouped by probability buckets 245 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 246 | 247 | 248 | .. parsed-literal:: 249 | 250 | d = xai.smile_imbalance( 251 | y_test, 252 | probabilities, 253 | display_breakdown=True) 254 | 255 | .. image:: _static/readme-13.png 256 | 257 | Visualise benefits of adding manual review on probability thresholds 258 | """""""""""""""""""""""""""""""""""""""""""""""""""""" 259 | 260 | 261 | .. parsed-literal:: 262 | 263 | d = xai.smile_imbalance( 264 | y_test, 265 | probabilities, 266 | bins=9, 267 | threshold=0.75, 268 | manual_review=0.375, 269 | display_breakdown=False) 270 | 271 | .. image:: _static/readme-14.png 272 | 273 | 274 | 275 | xai Python Docstrings 276 | =========================== 277 | 278 | 279 | Submodules 280 | ------------ 281 | 282 | xai\.data module 283 | ----------------- 284 | 285 | .. automodule:: xai.data 286 | :members: 287 | :undoc-members: 288 | :show-inheritance: 289 | 290 | 291 | Module contents 292 | ----------------- 293 | 294 | .. automodule:: xai 295 | :members: 296 | :undoc-members: 297 | :show-inheritance: 298 | 299 | -------------------------------------------------------------------------------- /docs/_sources/xai.rst.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EthicalML/xai/f31a13bc94a91a9163161fed5793556b1eb3ff3c/docs/_sources/xai.rst.txt -------------------------------------------------------------------------------- /docs/_static/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EthicalML/xai/f31a13bc94a91a9163161fed5793556b1eb3ff3c/docs/_static/.nojekyll -------------------------------------------------------------------------------- /docs/_static/basic.css: -------------------------------------------------------------------------------- 1 | /* 2 | * basic.css 3 | * ~~~~~~~~~ 4 | * 5 | * Sphinx stylesheet -- basic theme. 6 | * 7 | * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS. 8 | * :license: BSD, see LICENSE for details. 9 | * 10 | */ 11 | 12 | /* -- main layout ----------------------------------------------------------- */ 13 | 14 | div.clearer { 15 | clear: both; 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16 | 17 | /** 18 | * make the code below compatible with browsers without 19 | * an installed firebug like debugger 20 | if (!window.console || !console.firebug) { 21 | var names = ["log", "debug", "info", "warn", "error", "assert", "dir", 22 | "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", 23 | "profile", "profileEnd"]; 24 | window.console = {}; 25 | for (var i = 0; i < names.length; ++i) 26 | window.console[names[i]] = function() {}; 27 | } 28 | */ 29 | 30 | /** 31 | * small helper function to urldecode strings 32 | */ 33 | jQuery.urldecode = function(x) { 34 | return decodeURIComponent(x).replace(/\+/g, ' '); 35 | }; 36 | 37 | /** 38 | * small helper function to urlencode strings 39 | */ 40 | jQuery.urlencode = encodeURIComponent; 41 | 42 | /** 43 | * This function returns the parsed url parameters of the 44 | * current request. Multiple values per key are supported, 45 | * it will always return arrays of strings for the value parts. 46 | */ 47 | jQuery.getQueryParameters = function(s) { 48 | if (typeof s === 'undefined') 49 | s = document.location.search; 50 | var parts = s.substr(s.indexOf('?') + 1).split('&'); 51 | var result = {}; 52 | for (var i = 0; i < parts.length; i++) { 53 | var tmp = parts[i].split('=', 2); 54 | var key = jQuery.urldecode(tmp[0]); 55 | var value = jQuery.urldecode(tmp[1]); 56 | if (key in result) 57 | result[key].push(value); 58 | else 59 | result[key] = [value]; 60 | } 61 | return result; 62 | }; 63 | 64 | /** 65 | * highlight a given string on a jquery object by wrapping it in 66 | * span elements with the given class name. 67 | */ 68 | jQuery.fn.highlightText = function(text, className) { 69 | function highlight(node, addItems) { 70 | if (node.nodeType === 3) { 71 | var val = node.nodeValue; 72 | var pos = val.toLowerCase().indexOf(text); 73 | if (pos >= 0 && 74 | !jQuery(node.parentNode).hasClass(className) && 75 | !jQuery(node.parentNode).hasClass("nohighlight")) { 76 | var span; 77 | var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); 78 | if (isInSVG) { 79 | span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); 80 | } else { 81 | span = document.createElement("span"); 82 | span.className = className; 83 | } 84 | span.appendChild(document.createTextNode(val.substr(pos, text.length))); 85 | node.parentNode.insertBefore(span, node.parentNode.insertBefore( 86 | document.createTextNode(val.substr(pos + text.length)), 87 | node.nextSibling)); 88 | node.nodeValue = val.substr(0, pos); 89 | if (isInSVG) { 90 | var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); 91 | var bbox = node.parentElement.getBBox(); 92 | rect.x.baseVal.value = bbox.x; 93 | rect.y.baseVal.value = bbox.y; 94 | rect.width.baseVal.value = bbox.width; 95 | rect.height.baseVal.value = bbox.height; 96 | rect.setAttribute('class', className); 97 | addItems.push({ 98 | "parent": node.parentNode, 99 | "target": rect}); 100 | } 101 | } 102 | } 103 | else if (!jQuery(node).is("button, select, textarea")) { 104 | jQuery.each(node.childNodes, function() { 105 | highlight(this, addItems); 106 | }); 107 | } 108 | } 109 | var addItems = []; 110 | var result = this.each(function() { 111 | highlight(this, addItems); 112 | }); 113 | for (var i = 0; i < addItems.length; ++i) { 114 | jQuery(addItems[i].parent).before(addItems[i].target); 115 | } 116 | return result; 117 | }; 118 | 119 | /* 120 | * backward compatibility for jQuery.browser 121 | * This will be supported until firefox bug is fixed. 122 | */ 123 | if (!jQuery.browser) { 124 | jQuery.uaMatch = function(ua) { 125 | ua = ua.toLowerCase(); 126 | 127 | var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || 128 | /(webkit)[ \/]([\w.]+)/.exec(ua) || 129 | /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || 130 | /(msie) ([\w.]+)/.exec(ua) || 131 | ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || 132 | []; 133 | 134 | return { 135 | browser: match[ 1 ] || "", 136 | version: match[ 2 ] || "0" 137 | }; 138 | }; 139 | jQuery.browser = {}; 140 | jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; 141 | } 142 | 143 | /** 144 | * Small JavaScript module for the documentation. 145 | */ 146 | var Documentation = { 147 | 148 | init : function() { 149 | this.fixFirefoxAnchorBug(); 150 | this.highlightSearchWords(); 151 | this.initIndexTable(); 152 | if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { 153 | this.initOnKeyListeners(); 154 | } 155 | }, 156 | 157 | /** 158 | * i18n support 159 | */ 160 | TRANSLATIONS : {}, 161 | PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, 162 | LOCALE : 'unknown', 163 | 164 | // gettext and ngettext don't access this so that the functions 165 | // can safely bound to a different name (_ = Documentation.gettext) 166 | gettext : function(string) { 167 | var translated = Documentation.TRANSLATIONS[string]; 168 | if (typeof translated === 'undefined') 169 | return string; 170 | return (typeof translated === 'string') ? translated : translated[0]; 171 | }, 172 | 173 | ngettext : function(singular, plural, n) { 174 | var translated = Documentation.TRANSLATIONS[singular]; 175 | if (typeof translated === 'undefined') 176 | return (n == 1) ? singular : plural; 177 | return translated[Documentation.PLURALEXPR(n)]; 178 | }, 179 | 180 | addTranslations : function(catalog) { 181 | for (var key in catalog.messages) 182 | this.TRANSLATIONS[key] = catalog.messages[key]; 183 | this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); 184 | this.LOCALE = catalog.locale; 185 | }, 186 | 187 | /** 188 | * add context elements like header anchor links 189 | */ 190 | addContextElements : function() { 191 | $('div[id] > :header:first').each(function() { 192 | $('\u00B6'). 193 | attr('href', '#' + this.id). 194 | attr('title', _('Permalink to this headline')). 195 | appendTo(this); 196 | }); 197 | $('dt[id]').each(function() { 198 | $('\u00B6'). 199 | attr('href', '#' + this.id). 200 | attr('title', _('Permalink to this definition')). 201 | appendTo(this); 202 | }); 203 | }, 204 | 205 | /** 206 | * workaround a firefox stupidity 207 | * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 208 | */ 209 | fixFirefoxAnchorBug : function() { 210 | if (document.location.hash && $.browser.mozilla) 211 | window.setTimeout(function() { 212 | document.location.href += ''; 213 | }, 10); 214 | }, 215 | 216 | /** 217 | * highlight the search words provided in the url in the text 218 | */ 219 | highlightSearchWords : function() { 220 | var params = $.getQueryParameters(); 221 | var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; 222 | if (terms.length) { 223 | var body = $('div.body'); 224 | if (!body.length) { 225 | body = $('body'); 226 | } 227 | window.setTimeout(function() { 228 | $.each(terms, function() { 229 | body.highlightText(this.toLowerCase(), 'highlighted'); 230 | }); 231 | }, 10); 232 | $('

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

') 234 | .appendTo($('#searchbox')); 235 | } 236 | }, 237 | 238 | /** 239 | * init the domain index toggle buttons 240 | */ 241 | initIndexTable : function() { 242 | var togglers = $('img.toggler').click(function() { 243 | var src = $(this).attr('src'); 244 | var idnum = $(this).attr('id').substr(7); 245 | $('tr.cg-' + idnum).toggle(); 246 | if (src.substr(-9) === 'minus.png') 247 | $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); 248 | else 249 | $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); 250 | }).css('display', ''); 251 | if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { 252 | togglers.click(); 253 | } 254 | }, 255 | 256 | /** 257 | * helper function to hide the search marks again 258 | */ 259 | hideSearchWords : function() { 260 | $('#searchbox .highlight-link').fadeOut(300); 261 | $('span.highlighted').removeClass('highlighted'); 262 | }, 263 | 264 | /** 265 | * make the url absolute 266 | */ 267 | makeURL : function(relativeURL) { 268 | return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; 269 | }, 270 | 271 | /** 272 | * get the current relative url 273 | */ 274 | getCurrentURL : function() { 275 | var path = document.location.pathname; 276 | var parts = path.split(/\//); 277 | $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { 278 | if (this === '..') 279 | parts.pop(); 280 | }); 281 | var url = parts.join('/'); 282 | return path.substring(url.lastIndexOf('/') + 1, path.length - 1); 283 | }, 284 | 285 | initOnKeyListeners: function() { 286 | $(document).keyup(function(event) { 287 | var activeElementType = document.activeElement.tagName; 288 | // don't navigate when in search box or textarea 289 | if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { 290 | switch (event.keyCode) { 291 | case 37: // left 292 | var prevHref = $('link[rel="prev"]').prop('href'); 293 | if (prevHref) { 294 | window.location.href = prevHref; 295 | return false; 296 | } 297 | case 39: // right 298 | var nextHref = $('link[rel="next"]').prop('href'); 299 | if (nextHref) { 300 | window.location.href = nextHref; 301 | return false; 302 | } 303 | } 304 | } 305 | }); 306 | } 307 | }; 308 | 309 | // quick alias for translations 310 | _ = Documentation.gettext; 311 | 312 | $(document).ready(function() { 313 | Documentation.init(); 314 | }); 315 | -------------------------------------------------------------------------------- /docs/_static/documentation_options.js: -------------------------------------------------------------------------------- 1 | var DOCUMENTATION_OPTIONS = { 2 | URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), 3 | VERSION: '0.1', 4 | LANGUAGE: 'None', 5 | COLLAPSE_INDEX: false, 6 | FILE_SUFFIX: '.html', 7 | HAS_SOURCE: true, 8 | SOURCELINK_SUFFIX: '.txt', 9 | NAVIGATION_WITH_KEYS: false 10 | }; -------------------------------------------------------------------------------- /docs/_static/file.png: 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re2 = new RegExp(mgr1); 170 | if (re2.test(stem)) 171 | w = stem; 172 | } 173 | 174 | // Step 5 175 | re = /^(.+?)e$/; 176 | if (re.test(w)) { 177 | var fp = re.exec(w); 178 | stem = fp[1]; 179 | re = new RegExp(mgr1); 180 | re2 = new RegExp(meq1); 181 | re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); 182 | if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) 183 | w = stem; 184 | } 185 | re = /ll$/; 186 | re2 = new RegExp(mgr1); 187 | if (re.test(w) && re2.test(w)) { 188 | re = /.$/; 189 | w = w.replace(re,""); 190 | } 191 | 192 | // and turn initial Y back to y 193 | if (firstch == "y") 194 | w = firstch.toLowerCase() + w.substr(1); 195 | return w; 196 | } 197 | } 198 | 199 | 200 | 201 | 202 | 203 | var splitChars = (function() { 204 | var result = {}; 205 | var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, 206 | 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, 207 | 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, 208 | 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, 209 | 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, 210 | 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, 211 | 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, 212 | 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, 213 | 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, 214 | 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; 215 | var i, j, start, end; 216 | for (i = 0; i < singles.length; i++) { 217 | result[singles[i]] = true; 218 | } 219 | var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], 220 | [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], 221 | [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], 222 | [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], 223 | [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], 224 | [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], 225 | [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], 226 | [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], 227 | [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], 228 | [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], 229 | [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], 230 | [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], 231 | [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], 232 | [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], 233 | [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], 234 | [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], 235 | [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], 236 | [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], 237 | [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], 238 | [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], 239 | [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], 240 | [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], 241 | [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], 242 | [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], 243 | [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], 244 | [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], 245 | [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], 246 | [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], 247 | [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], 248 | [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], 249 | [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], 250 | [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], 251 | [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], 252 | [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], 253 | [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], 254 | [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], 255 | [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], 256 | [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], 257 | [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], 258 | [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], 259 | [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], 260 | [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], 261 | [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], 262 | [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], 263 | [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], 264 | [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], 265 | [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], 266 | [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], 267 | [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; 268 | for (i = 0; i < ranges.length; i++) { 269 | start = ranges[i][0]; 270 | end = ranges[i][1]; 271 | for (j = start; j <= end; j++) { 272 | result[j] = true; 273 | } 274 | } 275 | return result; 276 | })(); 277 | 278 | function splitQuery(query) { 279 | var result = []; 280 | var start = -1; 281 | for (var i = 0; i < query.length; i++) { 282 | if (splitChars[query.charCodeAt(i)]) { 283 | if (start !== -1) { 284 | result.push(query.slice(start, i)); 285 | start = -1; 286 | } 287 | } else if (start === -1) { 288 | start = i; 289 | } 290 | } 291 | if (start !== -1) { 292 | result.push(query.slice(start)); 293 | } 294 | return result; 295 | } 296 | 297 | 298 | -------------------------------------------------------------------------------- /docs/_static/minus.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EthicalML/xai/f31a13bc94a91a9163161fed5793556b1eb3ff3c/docs/_static/minus.png 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function to further tweak the score for each result 18 | // The function takes a result array [filename, title, anchor, descr, score] 19 | // and returns the new score. 20 | /* 21 | score: function(result) { 22 | return result[4]; 23 | }, 24 | */ 25 | 26 | // query matches the full name of an object 27 | objNameMatch: 11, 28 | // or matches in the last dotted part of the object name 29 | objPartialMatch: 6, 30 | // Additive scores depending on the priority of the object 31 | objPrio: {0: 15, // used to be importantResults 32 | 1: 5, // used to be objectResults 33 | 2: -5}, // used to be unimportantResults 34 | // Used when the priority is not in the mapping. 35 | objPrioDefault: 0, 36 | 37 | // query found in title 38 | title: 15, 39 | partialTitle: 7, 40 | // query found in terms 41 | term: 5, 42 | partialTerm: 2 43 | }; 44 | } 45 | 46 | if (!splitQuery) { 47 | function splitQuery(query) { 48 | return query.split(/\s+/); 49 | } 50 | } 51 | 52 | /** 53 | * Search Module 54 | */ 55 | var Search = { 56 | 57 | _index : null, 58 | _queued_query : null, 59 | _pulse_status : -1, 60 | 61 | htmlToText : function(htmlString) { 62 | var htmlElement = document.createElement('span'); 63 | htmlElement.innerHTML = htmlString; 64 | $(htmlElement).find('.headerlink').remove(); 65 | docContent = $(htmlElement).find('[role=main]')[0]; 66 | return docContent.textContent || docContent.innerText; 67 | }, 68 | 69 | init : function() { 70 | var params = $.getQueryParameters(); 71 | if (params.q) { 72 | var query = params.q[0]; 73 | $('input[name="q"]')[0].value = query; 74 | this.performSearch(query); 75 | } 76 | }, 77 | 78 | loadIndex : function(url) { 79 | $.ajax({type: "GET", url: url, data: null, 80 | dataType: "script", cache: true, 81 | complete: function(jqxhr, textstatus) { 82 | if (textstatus != "success") { 83 | document.getElementById("searchindexloader").src = url; 84 | } 85 | }}); 86 | }, 87 | 88 | setIndex : function(index) { 89 | var q; 90 | this._index = index; 91 | if ((q = this._queued_query) !== null) { 92 | this._queued_query = null; 93 | Search.query(q); 94 | } 95 | }, 96 | 97 | hasIndex : function() { 98 | return this._index !== null; 99 | }, 100 | 101 | deferQuery : function(query) { 102 | this._queued_query = query; 103 | }, 104 | 105 | stopPulse : function() { 106 | this._pulse_status = 0; 107 | }, 108 | 109 | startPulse : function() { 110 | if (this._pulse_status >= 0) 111 | return; 112 | function pulse() { 113 | var i; 114 | Search._pulse_status = (Search._pulse_status + 1) % 4; 115 | var dotString = ''; 116 | for (i = 0; i < Search._pulse_status; i++) 117 | dotString += '.'; 118 | Search.dots.text(dotString); 119 | if (Search._pulse_status > -1) 120 | window.setTimeout(pulse, 500); 121 | } 122 | pulse(); 123 | }, 124 | 125 | /** 126 | * perform a search for something (or wait until index is loaded) 127 | */ 128 | performSearch : function(query) { 129 | // create the required interface elements 130 | this.out = $('#search-results'); 131 | this.title = $('

' + _('Searching') + '

').appendTo(this.out); 132 | this.dots = $('').appendTo(this.title); 133 | this.status = $('

 

').appendTo(this.out); 134 | this.output = $('