75 |
76 | [](https://cran.r-project.org/package=DALEXtra) [](https://travis-ci.org/ModelOriented/DALEXtra) [](https://codecov.io/github/ModelOriented/DALEXtra?branch=master) [](http://drwhy.ai/#BackBone)
78 |
79 | The `DALEXtra` package is an extension pack for [DALEX](https://modeloriented.github.io/DALEX) package.
80 | This package provides easy to use connectors for models created with scikitlearn, keras, H2O, mljar and mlr.
81 |
82 | Vignettes:
83 |
84 | * [General introduction: DALEX with scikitlearn models](https://raw.githack.com/pbiecek/DALEX_docs/master/vignettes/How_to_use_DALEXtra_to_explain_and_visualize_scikitlearn_models.html)
85 |
86 | --------------------
87 |
88 | ### [survex](http://github.com/ModelOriented/survex) 
89 |
90 | [](https://cran.r-project.org/package=survex) [](https://github.com/ModelOriented/survex/actions/workflows/R-CMD-check.yaml) [](https://codecov.io/github/ModelOriented/survex?branch=master) [](http://drwhy.ai/#BackBone)
92 |
93 | The survex package provides model-agnostic explanations for machine learning survival models. It is based on the DALEX package.
94 |
95 | Due to a functional type of prediction, either in the form of survival function or cumulative hazard function, standard model-agnostic explanations cannot be applied directly to survival analysis machine learning models. The survex package contains implementations of explanation methods specific to survival analysis, as well as extensions of existing ones for classification or regression.
96 |
97 | Vignettes:
98 |
99 | * [Package usage](https://modeloriented.github.io/survex/articles/survex-usage.html)
100 | * [Creating custom explainers](https://modeloriented.github.io/survex/articles/custom-explainers.html)
101 | * [Integration with `mlr3proba`](https://modeloriented.github.io/survex/articles/mlr3proba-usage.html)
102 |
103 | --------------------
104 |
105 | ### [ingredients](http://github.com/ModelOriented/ingredients) 
106 |
107 | [](https://cran.r-project.org/package=ingredients) [](https://travis-ci.org/ModelOriented/ingredients) [](https://codecov.io/github/ModelOriented/ingredients?branch=master) [](http://drwhy.ai/#eXtraAI)
109 |
110 | The `ingredients` package is a collection of tools for assessment of feature importance and feature effects.
111 |
112 | Key functions: `feature_importance()` for assessment of global level feature importance, `ceteris_paribus()` for calculation of the Ceteris Paribus / What-If Profiles, `partial_dependency()` for Partial Dependency Plots, `conditional_dependency()` for Conditional Dependency Plots also called M Plots, `accumulated_dependency()` for Accumulated Local Effects Plots, `cluster_profiles()` for aggregation of Ceteris Paribus Profiles, generic `print()` and `plot()` for better usability of selected explainers, generic `plotD3()` for interactive D3 based explanations, and generic `describe()` for explanations in natural language.
113 |
114 |
115 | Vignettes:
116 |
117 | * [General introduction: Survival on the RMS Titanic](https://modeloriented.github.io/ingredients/articles/vignette_titanic.html),
118 | * [Aspects importance](https://modeloriented.github.io/ingredients/articles/vignette_aspect_importance.html),
119 | * [Explanations in natural language](https://modeloriented.github.io/ingredients/articles/Describing-Explanations.html)
120 |
121 | --------------------
122 |
123 | ### [iBreakDown](http://github.com/ModelOriented/iBreakDown) 
124 |
125 | [](https://cran.r-project.org/package=iBreakDown) [](https://travis-ci.org/ModelOriented/iBreakDown) [](https://codecov.io/github/ModelOriented/iBreakDown?branch=master) [](http://drwhy.ai/#eXtraAI)
127 |
128 | The `iBreakDown` package is a model agnostic tool for explanation of predictions from black boxes ML models.
129 | Break Down Table shows contributions of every variable to a final prediction.
130 | Break Down Plot presents variable contributions in a concise graphical way.
131 | SHAP (Shapley Additive Attributions) values are calculated as average from random Break Down profiles.
132 | This package works for binary classifiers as well as regression models.
133 |
134 | `iBreakDown` is a successor of the [breakDown](https://github.com/pbiecek/breakDown) package. It is faster (complexity O(p) instead of O(p^2)). It supports interactions and interactive explainers with D3.js plots.
135 |
136 | Vignettes:
137 |
138 | * [General introduction: Survival on the RMS Titanic](https://modeloriented.github.io/iBreakDown/articles/vignette_iBreakDown_titanic.html),
139 | * [Explanations in natural language](https://modeloriented.github.io/iBreakDown/articles/vignette_iBreakDown_description.html)
140 |
141 | --------------------
142 |
143 | ### [auditor](http://github.com/ModelOriented/auditor) 
144 |
145 | [](https://cran.r-project.org/package=auditor) [](https://travis-ci.org/ModelOriented/auditor) [](https://codecov.io/github/ModelOriented/auditor?branch=master) [](http://drwhy.ai/#eXtraAI)
147 |
148 | The `auditor` package is a tool for model-agnostic validation. Implemented techniques facilitate assessing and comparing the goodness of fit and performance of models. In addition, they may be used for the analysis of the similarity of residuals and for the identification of outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Due to the flexible and consistent grammar, it is simple to validate models of any classes.
149 |
150 | Learn more:
151 |
152 | * Preprint: [auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic](https://arxiv.org/abs/1809.07763),
153 | * [List of implemented audits](https://github.com/ModelOriented/auditor#a-short-overview-of-plots)
154 |
155 |
156 | --------------------
157 |
158 | ### [fairmodels](http://github.com/ModelOriented/fairmodels) 
159 |
160 | [](https://cran.r-project.org/package=fairmodels) [](https://github.com/ModelOriented/fairmodels/actions) [](https://codecov.io/gh/ModelOriented/fairmodels?branch=master) [](http://drwhy.ai/#eXtraAI)
161 |
162 | Flexible tool for bias detection, visualization, and mitigation. Use models explained with [DALEX](https://modeloriented.github.io/DALEX/) and calculate fairness classification metrics based on confusion matrices using `fairness_check()` or try newly developed module for regression models using `fairness_check_regression()`. R package fairmodels allows to compare and gain information about various machine learning models. Mitigate bias with various pre-processing and post-processing techniques. *Make sure your models are classifying protected groups similarly*.
163 |
164 | Learn more:
165 |
166 | * [fairmodels website](https://fairmodels.drwhy.ai/)
167 | * Compas recidivism data use case: [Basic tutorial](https://modeloriented.github.io/fairmodels/articles/Basic_tutorial.html)
168 | * Bias mitigation techniques on Adult data: [Advanced tutorial](https://modeloriented.github.io/fairmodels/articles/Advanced_tutorial.html)
169 |
170 |
171 | --------------------
172 |
173 | ### [vivo](http://github.com/ModelOriented/vivo) 
174 |
175 | [](https://cran.r-project.org/package=vivo) [](https://travis-ci.org/ModelOriented/vivo) [](https://codecov.io/github/ModelOriented/vivo?branch=master) [](http://drwhy.ai/#eXtraAI)
177 |
178 | The `vivo` package helps to calculate instance level variable importance (measure of local sensitivity). The importance measure is based on Ceteris Paribus profiles and can be calculated in eight variants. Select the variant that suits your needs by setting parameters: `absolute_deviation`, `point` and `density`.
179 |
180 | Learn more:
181 |
182 | * [Intuition for Ceteris Paribus Oscillations](https://github.com/ModelOriented/vivo#intuition)
183 |
184 | --------------------
185 |
186 | ### [randomForestExplainer](http://github.com/ModelOriented/randomForestExplainer) 
187 |
188 | [](https://cran.r-project.org/package=randomForestExplainer) [](https://travis-ci.org/ModelOriented/randomForestExplainer) [](https://codecov.io/github/ModelOriented/randomForestExplainer?branch=master) [](http://drwhy.ai/#eXtraAI)
190 |
191 | The `randomForestExplainer` package helps to understand what is happening inside a Random Forest model. This package helps to explore main effects and pairwise interactions, depth distribution, conditional responses and feature importance.
192 |
193 | Learn more:
194 |
195 | * Vignettes: [Boston data: Understanding random forests with randomForestExplainer](https://modeloriented.github.io/randomForestExplainer/articles/randomForestExplainer.html), [Glioblastoma data: Understanding random forests with randomForestExplainer](https://cdn.staticaly.com/gh/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer/inst/doc/randomForestExplainer.html)
196 | * [Cheatsheet](https://github.com/ModelOriented/randomForestExplainer/blob/master/materials/cheatsheet.pdf)
197 |
198 | --------------------
199 |
200 | ### [xspliner](http://github.com/ModelOriented/xspliner)
201 |
202 | [](https://cran.r-project.org/package=xspliner) [](https://travis-ci.org/ModelOriented/xspliner) [](https://codecov.io/github/ModelOriented/xspliner?branch=master) [](http://drwhy.ai/#AutoMat)
204 |
205 | The `xspliner` package is a collection of tools for training interpretable surrogate ML models. The package helps to build simple, interpretable models that inherits informations provided by more complicated ones - resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm. Provided functionality offers graphical and statistical evaluation both for overall model and its components.
206 |
207 | --------------------
208 |
209 | ### [shapper](http://github.com/ModelOriented/shapper) 
210 |
211 | [](https://cran.r-project.org/package=shapper) [](https://travis-ci.org/ModelOriented/shapper) [](https://codecov.io/github/ModelOriented/shapper?branch=master) [](http://drwhy.ai/#eXtraAI)
213 |
214 | The `shapper` is an R wrapper of SHAP python library.
215 | It accesses python implementation through `reticulate` connector.
216 |
217 |
218 | --------------------
219 |
220 | ### [drifter](http://github.com/ModelOriented/drifter)
221 |
222 | [](https://cran.r-project.org/package=drifter) [](https://travis-ci.org/ModelOriented/drifter) [](https://codecov.io/github/ModelOriented/drifter?branch=master)[](http://drwhy.ai/#eXtraAI)
224 |
225 | The `drifter` is an R package that identifies concept drift in model structure or in data structure.
226 |
227 | Machine learning models are often fitted and validated on historical data under an assumption that data are stationary. The most popular techniques for validation (k-fold cross-validation, repeated cross-validation, and so on) test models on data with the same distribution as training data.
228 |
229 | Yet, in many practical applications, deployed models are working in a changing environment. After some time, due to changes in the environment, model performance may degenerate, as model may be less reliable.
230 |
231 | Concept drift refers to the change in the data distribution or in the relationships between variables over time. Think about model for energy consumption for a school, over time the school may be equipped with larger number of devices of with more power-efficient devices that may affect the model performance.
232 |
233 |
234 | --------------------
235 |
236 | ### [EIX](http://github.com/ModelOriented/EIX)
237 |
238 | [](https://cran.r-project.org/package=EIX) [](https://travis-ci.org/ModelOriented/EIX) [](https://codecov.io/github/ModelOriented/EIX?branch=master) [](http://drwhy.ai/#eXtraAI)
240 |
241 | The `EIX` package implements set of techniques to explore and explain `XGBoost` and `LightGBM` models. Main functions of this package cover various variable importance measures and well as functions for identification of interactions between variables.
242 |
243 | Learn more:
244 |
245 | * [Cheatsheet](https://github.com/ModelOriented/EIX#cheatsheets)
246 |
247 | --------------------
248 |
249 | ### [modelStudio](http://github.com/ModelOriented/modelStudio) 
250 |
251 | [](https://cran.r-project.org/package=modelStudio) [](https://travis-ci.org/ModelOriented/modelStudio) [](https://codecov.io/github/ModelOriented/modelStudio?branch=master) [](http://drwhy.ai/#AutoMat)
253 |
254 | The `modelStudio` package **automates the explanatory analysis of machine learning predictive models**. It generates advanced interactive model explanations in the form of a **serverless HTML site** with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks (e.g. `mlr/mlr3`, `xgboost`, `caret`, `h2o`, `parsnip`, `tidymodels`, `scikit-learn`, `lightgbm`, `keras/tensorflow`).
255 |
256 | The main `modelStudio()` function computes various (instance and model-level) explanations and produces a **customisable dashboard**, which consists of multiple panels for plots with their short descriptions. Easily **save** the dashboard and **share** it with others. Tools for [Explanatory Model Analysis](https://ema.drwhy.ai/) unite with tools for Exploratory Data Analysis to give a broad overview of the model behavior.
257 |
258 | Learn more:
259 |
260 | * [Getting started](https://modeloriented.github.io/modelStudio/#demo)
261 | * [Vignette: perks and features](https://modelstudio.drwhy.ai/articles/ms-perks-features.html)
262 | * [JOSS paper: Interactive Studio with Explanations for ML Predictive Models](https://joss.theoj.org/papers/10.21105/joss.01798)
263 |
264 | --------------------
265 |
266 | ### [arenar](https://github.com/ModelOriented/arenar)
267 |
268 | [](https://cran.r-project.org/package=arenar) [](https://github.com/ModelOriented/ArenaR/actions) [](https://codecov.io/gh/ModelOriented/ArenaR?branch=master) [](http://drwhy.ai/#AutoMat)
269 |
270 | Arena is an interactive tool that allows you to explore and compare any model regardless of its internal structure.
271 |
272 | The `arenar` package can be run in two modes - live (R runs in the background and calculates all necessary explanations) and serverless (all necessary explanations are calculated earlier).
273 |
274 | Using the Arena is trivially simple. Examples with different levels of advancement are available:
275 |
276 | * [Arena website](http://arenar.drwhy.ai/)
277 | * [arenar documentation](https://arenar.drwhy.ai/)
278 |
279 | --------------------
280 |
281 | ### [modelDown](http://github.com/ModelOriented/modelDown)
282 |
283 | [](https://cran.r-project.org/package=modelDown) [](https://travis-ci.org/ModelOriented/modelDown) [](https://codecov.io/github/ModelOriented/modelDown?branch=master) [](http://drwhy.ai/#AutoMat)
285 |
286 | The`modelDown` package generates a website with HTML summaries for predictive models. Is uses `DALEX` explainers to compute and plot summaries of how given models behave. We can see how well models behave (*Model Performance*, *Auditor*), how much each variable contributes to predictions (*Variable Response*) and which variables are the most important for a given model (*Variable Importance*). We can also compare Concept Drift for pairs of models (*Drifter*). Additionally, data available on the website can be easily recreated in current R session (using `archivist` package).
287 |
288 | Learn more:
289 |
290 | * [Getting started](https://modeloriented.github.io/modelDown/getting-started)
291 | * [JOSS paper: modelDown: automated website generator with interpretable documentation for predictive machine learning models](https://joss.theoj.org/papers/10.21105/joss.01444)
292 |
293 | --------------------
294 |
295 | ### [rSAFE](http://github.com/ModelOriented/rSAFE) 
296 |
297 | [](https://cran.r-project.org/package=rSAFE) [](https://travis-ci.org/ModelOriented/rSAFE) [](https://codecov.io/github/ModelOriented/rSAFE?branch=master)[](http://drwhy.ai/#AutoMat)
299 |
300 | The `rSAFE` package is a model agnostic tool for making an interpretable white-box model more accurate using alternative black-box model called surrogate model. Based on the complicated model, such as neural network or random forest, new features are being extracted and then used in the process of fitting a simpler interpretable model, improving its overall performance.
301 |
302 | Learn more:
303 |
304 | * article [Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering](https://www.sciencedirect.com/science/article/pii/S016792362100066X)
305 | * package [website](https://modeloriented.github.io/rSAFE/)
306 |
307 | --------------------
308 |
309 | ### [EloML](http://github.com/ModelOriented/EloML) 
310 |
311 | [](https://cran.r-project.org/package=EloML) [](https://travis-ci.org/ModelOriented/EloML) [](https://codecov.io/github/ModelOriented/EloML?branch=master) [](http://drwhy.ai/#AutoMat)
313 |
314 | The `EloML` package provides Elo rating system for machine learning models. Elo Predictive Power (EPP) score helps to assess model performance based Elo ranking system.
315 |
316 | Learn more:
317 |
318 | * [arxiv](https://arxiv.org/abs/1908.09213)
319 |
320 |
321 | --------------------
322 |
323 | ### [archivist](http://github.com/pbiecek/archivist)
324 |
325 | [](https://cran.r-project.org/package=archivist) [](https://travis-ci.org/pbiecek/archivist) [](https://codecov.io/github/pbiecek/archivist?branch=master) [](http://drwhy.ai/#BackBone)
327 |
328 | The `archivist` package automate serialization and deserialization of R objects. Objects are stored with additional metadata to facilitate reproducibility and governance of data science projects.
329 |
330 | Everything that exists in R is an object. `archivist` is an R package that stores copies of all objects along with their metadata. It helps to manage and recreate objects with final or partial results from data analysis. Use `archivist` to record every result, to share these results with future you or with others, to search through repository of objects created in the past but needed now.
331 |
332 | Learn more:
333 |
334 | * [Cheatsheet](https://github.com/pbiecek/archivist/raw/master/cheatsheets/archivistCheatsheet.png)
335 | * [JSS article](http://doi.org/10.18637/jss.v082.i11)
336 |
337 |
338 | --------------------
339 |
340 |
341 | Tools that are useful during the model lifetime.  stands for our internal tools.
342 |
343 | ### 1. Data preparation
344 |
345 | * [dataMaid](https://cran.r-project.org/web/packages/dataMaid/index.html); A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process
346 | * [inspectdf](https://cran.r-project.org/web/packages/inspectdf/index.html); A collection of utilities for columnwise summary, comparison and visualisation of data frames.
347 | * [validate](https://github.com/data-cleaning/validate); Professional data validation for the R environment
348 | * [errorlocate](https://github.com/data-cleaning/errorlocate); Find and replace erroneous fields in data using validation rules
349 | * [ggplot2](https://ggplot2.tidyverse.org/); System for declaratively creating graphics, based on The Grammar of Graphics.
350 |
351 | ### 2. Data understanding
352 |
353 | * Model Agnostic Variable Importance Scores. Surrogate learning = Train an elastic model and measure feature importance in such model. See [DALEX](https://github.com/pbiecek/DALEX/), [Model Class Reliance MCR](https://arxiv.org/abs/1801.01489)
354 | * [vip](https://github.com/koalaverse/vip) Variable importance plots
355 | * [SAFE](https://github.com/MI2DataLab/SAFE)  Surrogate learning = Train an elastic model and extract feature transformations.
356 | * [xspliner](https://github.com/ModelOriented/xspliner)  Using surrogate black-boxes to train interpretable spline based additive models
357 | * [factorMerger](https://github.com/MI2DataLab/factorMerger)  Set of tools for factors merging [paper](https://arxiv.org/abs/1709.04412)
358 | * [ingredients](https://github.com/ModelOriented/ingredients)  Set of tools for model level feature effects and feature importance.
359 |
360 | ### 4. Model assembly
361 |
362 | * [mlr](https://github.com/mlr-org/mlr) Machine Learning in R [paper](http://jmlr.org/papers/v17/15-066.html)
363 | * [caret](https://github.com/topepo/caret) Classification And Regression Training [paper](https://www.jstatsoft.org/article/view/v028i05)
364 |
365 | ### 5. Model audit
366 |
367 | * [auditor](https://github.com/MI2DataLab/auditor)  model verification, validation, and error analysis [vigniette](https://mi2datalab.github.io/auditor/articles/model_performance_audit.html)
368 | * [DALEX](https://github.com/pbiecek/DALEX/)  Descriptive mAchine Learning EXplanations
369 | * [iml](https://github.com/christophM/iml); interpretable machine learning R package
370 | * [randomForestExplainer](https://github.com/MI2DataLab/randomForestExplainer)  A set of tools to understand what is happening inside a Random Forest
371 | * [survxai](https://github.com/MI2DataLab/survxai)  Explanations for survival models [paper](http://joss.theoj.org/papers/dcc9d53e8a1b1f613d59b9658b113fff)
372 |
373 | ### 6. Model delivery
374 |
375 | * [iBreakDown](https://github.com/ModelOriented/iBreakDown), [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown)  Model Agnostic Explainers for Individual Predictions (with interactions)
376 | * [ceterisParibus](https://github.com/pbiecek/ceterisParibus), [pyCeterisParibus](https://github.com/ModelOriented/pyCeterisParibus), [ceterisParibusD3](https://github.com/MI2DataLab/ceterisParibusExt/tree/master/ceterisParibusD3) and [ingredients](https://github.com/ModelOriented/ingredients)  Ceteris Paribus Plots (What-If plots) for explanations of a single observation
377 | * [localModel](https://github.com/ModelOriented/localModel) and [live](https://github.com/MI2DataLab/live/) 
378 | LIME-like explanations with interpretable features based on Ceteris Paribus curves.
379 | * [lime](https://github.com/thomasp85/lime); Local Interpretable Model-Agnostic Explanations (R port of original Python package)
380 | * [shapper](https://github.com/ModelOriented/shapper)  An R wrapper of SHAP python library
381 | * [modelDown](https://github.com/ModelOriented/modelDown)  modelDown generates a website with HTML summaries for predictive models
382 | * [modelStudio](https://github.com/ModelOriented/modelStudio)  modelStudio generates an interactive dashboard with D3 based summaries for predictive models
383 | * [drifter](https://github.com/ModelOriented/drifter)  Concept Drift and Concept Shift Detection for Predictive Models
384 | * [archivist](https://github.com/pbiecek/archivist)  A set of tools for datasets and plots archiving [paper](http://doi.org/10.18637/jss.v082.i11)
385 |
386 |
387 | ## Family of Model Explainers
388 |
389 | 
390 |
391 |
392 | ## Architecture of DrWhy
393 |
394 | `DrWhy` works on fully trained predictive models. Models can be created with any tool.
395 |
396 | `DrWhy` uses `DALEX` package to wrap model with additional metadata required for explanations, like validation data, predict function etc.
397 |
398 | Explainers for predictive models can be created with model agnostic or model specific functions implemented in various packages.
399 |
400 | 
401 |
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100 | The DrWhy manifesto
89 |Przemyslaw Biecek
90 | 91 |2020-03-04
92 | 93 | 94 |manifesto.RmdI simply love the tidyverse manifesto written by Hadley Wickham.
101 |Especially one point, that I believe is a key differentiator between DrWhy tools and many other tools for XAI.
102 |
103 |
113 | 104 | Design for humans
105 |Copied from tidyverse manifesto
106 |Design your API primarily so that it is easy to use by humans. Computer efficiency is a secondary concern because the bottleneck in most data analysis is thinking time, not computing time.
107 |-
108 |
Invest time in naming your functions. Evocative function names make your API easier to use and remember.
109 | Favour explicit, lengthy names, over short, implicit, names. Save the shortest names for the most important operations.
110 | Think about how autocomplete can also make an API that’s easy to write. Make sure that function families are identified by a common prefix, not a common suffix. This makes autocomplete more helpful, as you can jog your memory with the prompts. For smaller packages, this may mean that every function has a common prefix (e.g. stringr, xml2, rvest).
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Przemyslaw Biecek. Author, maintainer.
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2 | Sticky-kit v1.1.2 | WTFPL | Leaf Corcoran 2015 | http://leafo.net
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6 | u=a.outerHeight(!0),r=a.css("float"),h&&h.css({width:a.outerWidth(!0),height:u,display:a.css("display"),"vertical-align":a.css("vertical-align"),"float":r}),e))return l()};x();if(u!==C)return D=void 0,c=q,z=E,l=function(){var b,l,e,k;if(!G&&(e=!1,null!=z&&(--z,0>=z&&(z=E,x(),e=!0)),e||A.height()===I||x(),e=f.scrollTop(),null!=D&&(l=e-D),D=e,m?(w&&(k=e+u+c>C+n,v&&!k&&(v=!1,a.css({position:"fixed",bottom:"",top:c}).trigger("sticky_kit:unbottom"))),e
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92 |
94 |
419 | It takes a village to raise a child model.
The way how we do predictive modeling is very ineffective. We spend way too much time on manual time consuming and easy to automate activities like data cleaning and exploration, crisp modeling, model validation. Instead of focusing on model understanding, productisation and communication.
96 |Here we gather tools that can be use to make out work more efficient through the whole model lifecycle. The unified grammar beyond DrWhy.AI universe is described in the Predictive Models: Visual Exploration, Explanation and Debugging book.
97 |
98 |
103 | 99 | Lifecycle for Predictive Models
100 |The DrWhy is based on an unified Model Development Process inspired by RUP. Find an overview in the diagram below.
101 |
104 |
116 |
405 |
410 | 105 | The DrWhy.AI family
106 |Packages in the DrWhy.AI family of models may be divided into four classes.
-
108 |
Model adapters. Predictive models created with different tools have different structures, and different interfaces. Model adapters create uniform wrappers. This way other packages may operate on models in an unified way.
DALEXis a lightweight package with generic interface.DALEXtrais a package with extensions for heavyweight interfaces likescikitlearn,h2o,mlr.
109 | Model agnostic explainers. These packages implement specific methods for model exploration. They can be applied to a single model or they can compare different models.
ingredientsimplements variable specific techniques like Ceteris Paribus, Partial Dependency, Permutation based Feature Importance.iBreakDownimplements techniques for variable attribution, like Break Down or SHAPley values.auditorimplements techniques for model validation, residual diagnostic and performance diagnostic.
110 | Model specific explainers. These packages implement model specific techniques.
randomForestExplainerimplements techniques for exploration ofrandomForestmodels.EIXimplements techniques for exploration of gbm and xgboost models.cr19implements techniques for exploration of survival models.
111 | Automated exploration. These packages combine series of model exploration techniques and produce an automated report of website for model exploration.
modelStudioimplements a dashboard generator for local and global interactive model exploration.modelDownimplements a HTML website generator for global model cross comparison.
112 |
Here is a more detailed overview.
115 |116 |
117 |
118 | DALEX
120 |
128 |
129 |
118 | DALEX 
119 |
120 |
The DALEX package (Descriptive mAchine Learning EXplanations) helps to understand how complex models are working. The main function explain creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers. Recent developments from the area of Interpretable Machine Learning/eXplainable Artificial Intelligence.
122 |DALEX wraps methods from other packages, i.e. ‘pdp’ (Greenwell 2017) doi:10.32614/RJ-2017-016, ‘ALEPlot’ (Apley 2018) <arXiv:1612.08468>, ‘factorMerger’ (Sitko and Biecek 2017) <arXiv:1709.04412>, ‘breakDown’ package (Staniak and Biecek 2018) doi:10.32614/RJ-2018-072, (Fisher at al. 2018) <arXiv:1801.01489>.
123 |Vignettes:
124 | 127 |128 |
130 |
131 | DALEXtra
133 |
140 |
141 |
131 | DALEXtra
132 |
133 |
The DALEXtra package is an extension pack for DALEX package. This package provides easy to use connectors for models created with scikitlearn, keras, H2O, mljar and mlr.
Vignettes:
136 | 139 |140 |
142 |
143 | ingredients
145 |
157 |
158 |
143 | ingredients
144 |
145 |
The ingredients package is a collection of tools for assessment of feature importance and feature effects.
Key functions: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the Ceteris Paribus / What-If Profiles, partial_dependency() for Partial Dependency Plots, conditional_dependency() for Conditional Dependency Plots also called M Plots, accumulated_dependency() for Accumulated Local Effects Plots, cluster_profiles() for aggregation of Ceteris Paribus Profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive D3 based explanations, and generic describe() for explanations in natural language.
Vignettes:
149 |-
150 |
- 151 | General introduction: Survival on the RMS Titanic, 152 |
- 153 | Aspects importance, 154 |
- Explanations in natural language 155 |
157 |
159 |
160 | iBreakDown
162 |
172 |
173 |
160 | iBreakDown
161 |
162 |
The iBreakDown package is a model agnostic tool for explanation of predictions from black boxes ML models. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. SHAP (Shapley Additive Attributions) values are calculated as average from random Break Down profiles. This package works for binary classifiers as well as regression models.
iBreakDown is a successor of the breakDown package. It is faster (complexity O(p) instead of O(p^2)). It supports interactions and interactive explainers with D3.js plots.
Vignettes:
166 |-
167 |
- 168 | General introduction: Survival on the RMS Titanic, 169 |
- Explanations in natural language 170 |
172 |
174 |
175 | auditor
177 |
185 |
186 |
175 | auditor
176 |
177 |
The auditor package is a tool for model-agnostic validation. Implemented techniques facilitate assessing and comparing the goodness of fit and performance of models. In addition, they may be used for the analysis of the similarity of residuals and for the identification of outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Due to the flexible and consistent grammar, it is simple to validate models of any classes.
Learn more:
180 |-
181 |
- Preprint: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic, 182 |
- List of implemented audits 183 |
185 |
187 |
188 | vivo
190 |
197 |
198 |
188 | vivo
189 |
190 |
The vivo package helps to calculate instance level variable importance (measure of local sensitivity). The importance measure is based on Ceteris Paribus profiles and can be calculated in eight variants. Select the variant that suits your needs by setting parameters: absolute_deviation, point and density.
Learn more:
193 |-
194 |
- Intuition for Ceteris Paribus Oscillations 195 |
197 |
199 |
200 | randomForestExplainer
202 |
211 |
212 |
200 | randomForestExplainer
201 |
202 |
The randomForestExplainer package helps to understand what is happening inside a Random Forest model. This package helps to explore main effects and pairwise interactions, depth distribution, conditional responses and feature importance.
Learn more:
205 |-
206 |
- Vignettes: Boston data: Understanding random forests with randomForestExplainer, Glioblastoma data: Understanding random forests with randomForestExplainer 207 | 208 |
- Cheatsheet 209 |
211 |
213 |
214 | rSAFE
216 |
219 |
220 |
214 | rSAFE
215 |
216 |
The rSAFE package is a model agnostic tool for making an interpretable white-box model more accurate using alternative black-box model called surrogate model. Based on the complicated model, such as neural network or random forest, new features are being extracted and then used in the process of fitting a simpler interpretable model, improving its overall performance.
219 |
221 |
222 | xspliner
224 |
227 |
228 |
222 | xspliner 
223 |
224 |
The xspliner package is a collection of tools for training interpretable surrogate ML models. The package helps to build simple, interpretable models that inherits informations provided by more complicated ones - resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm. Provided functionality offers graphical and statistical evaluation both for overall model and its components.
227 |
229 |
230 | shapper
232 |
235 |
236 |
230 | shapper
231 |
232 |
The shapper is an R wrapper of SHAP python library. It accesses python implementation through reticulate connector.
235 |
237 |
246 |
247 | 238 | drifter 239 |
240 |
The drifter is an R package that identifies concept drift in model structure or in data structure.
Machine learning models are often fitted and validated on historical data under an assumption that data are stationary. The most popular techniques for validation (k-fold cross-validation, repeated cross-validation, and so on) test models on data with the same distribution as training data.
243 |Yet, in many practical applications, deployed models are working in a changing environment. After some time, due to changes in the environment, model performance may degenerate, as model may be less reliable.
244 |Concept drift refers to the change in the data distribution or in the relationships between variables over time. Think about model for energy consumption for a school, over time the school may be equipped with larger number of devices of with more power-efficient devices that may affect the model performance.
245 |246 |
248 |
258 |
259 | 249 | EIX 250 |
251 |
The EIX package implements set of techniques to explore and explain XGBoost and LightGBM models. Main functions of this package cover various variable importance measures and well as functions for identification of interactions between variables.
Learn more:
254 |-
255 |
- Cheatsheet 256 |
258 |
260 |
261 | modelStudio
263 |
273 |
274 |
261 | modelStudio
262 |
263 |
The modelStudio package automates the Explanatory Analysis of Machine Learning predictive models. Generate advanced interactive and animated model explanations in the form of a serverless HTML site with only one line of code.
The main modelStudio() function computes various (instance and dataset level) model explanations and produces an interactive, customisable dashboard made with D3.js. It consists of multiple panels for plots with their short descriptions. Easily save and share the dashboard with others. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior.
Learn more:
267 |-
268 |
- Getting started 269 |
- Vignette: perks and features 270 |
- JOSS paper: Interactive Studio with Explanations for ML Predictive Models 271 |
273 |
275 |
286 |
287 | 276 | modelDown 277 |
278 |
ThemodelDown package generates a website with HTML summaries for predictive models. Is uses DALEX explainers to compute and plot summaries of how given models behave. We can see how well models behave (Model Performance, Auditor), how much each variable contributes to predictions (Variable Response) and which variables are the most important for a given model (Variable Importance). We can also compare Concept Drift for pairs of models (Drifter). Additionally, data available on the website can be easily recreated in current R session (using archivist package).
Learn more:
281 |-
282 |
- Getting started 283 |
- JOSS paper: modelDown: automated website generator with interpretable documentation for predictive machine learning models 284 |
286 |
288 |
289 | EloML
291 |
298 |
299 |
289 | EloML
290 |
291 |
The EloML package provides Elo rating system for machine learning models. Elo Predictive Power (EPP) score helps to assess model performance based Elo ranking system.
Learn more:
294 |-
295 |
- arxiv 296 |
298 |
300 |
312 |
314 | 301 | archivist 302 |
303 |
The archivist package automate serialization and deserialization of R objects. Objects are stored with additional metadata to facilitate reproducibility and governance of data science projects.
Everything that exists in R is an object. archivist is an R package that stores copies of all objects along with their metadata. It helps to manage and recreate objects with final or partial results from data analysis. Use archivist to record every result, to share these results with future you or with others, to search through repository of objects created in the past but needed now.
Learn more:
307 |-
308 |
- Cheatsheet 309 |
- JSS article 310 |
312 |
Tools that are useful during the model lifetime. 
stands for our internal tools.
315 |
330 | 316 | 1. Data preparation
317 |-
318 |
- 319 | dataMaid; A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process 320 |
- 321 | inspectdf; A collection of utilities for columnwise summary, comparison and visualisation of data frames. 322 |
- 323 | validate; Professional data validation for the R environment 324 |
- 325 | errorlocate; Find and replace erroneous fields in data using validation rules 326 |
- 327 | ggplot2; System for declaratively creating graphics, based on The Grammar of Graphics. 328 |
331 |
349 |
361 | 332 | 2. Data understanding
333 |-
334 |
- Model Agnostic Variable Importance Scores. Surrogate learning = Train an elastic model and measure feature importance in such model. See DALEX, Model Class Reliance MCR 335 | 336 |
- 337 | vip Variable importance plots 338 |
-
339 | SAFE Surrogate learning = Train an elastic model and extract feature transformations.
340 |
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341 | xspliner Using surrogate black-boxes to train interpretable spline based additive models
342 |
-
343 | factorMerger Set of tools for factors merging paper
344 |
345 |
-
346 | ingredients Set of tools for model level feature effects and feature importance.
347 |
362 |
379 | 363 | 5. Model audit
364 |-
365 |
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366 | auditor model verification, validation, and error analysis vigniette
367 |
368 |
-
369 | DALEX Descriptive mAchine Learning EXplanations
370 |
- 371 | iml; interpretable machine learning R package 372 |
-
373 | randomForestExplainer A set of tools to understand what is happening inside a Random Forest
374 |
-
375 | survxai Explanations for survival models paper
376 |
377 |
380 |
404 | 381 | 6. Model delivery
382 |-
383 |
-
384 | iBreakDown, pyBreakDown Model Agnostic Explainers for Individual Predictions (with interactions)
385 |
-
386 | ceterisParibus, pyCeterisParibus, ceterisParibusD3 and ingredients Ceteris Paribus Plots (What-If plots) for explanations of a single observation
387 |
-
388 | localModel and live LIME-like explanations with interpretable features based on Ceteris Paribus curves.
389 |
- 390 | lime; Local Interpretable Model-Agnostic Explanations (R port of original Python package) 391 |
-
392 | shapper An R wrapper of SHAP python library
393 |
-
394 | modelDown modelDown generates a website with HTML summaries for predictive models
395 |
-
396 | modelStudio modelStudio generates an interactive dashboard with D3 based summaries for predictive models
397 |
-
398 | drifter Concept Drift and Concept Shift Detection for Predictive Models
399 |
-
400 | archivist A set of tools for datasets and plots archiving paper
401 |
402 |
411 |
418 | 412 | Architecture of DrWhy
413 |DrWhy works on fully trained predictive models. Models can be created with any tool.
DrWhy uses DALEX package to wrap model with additional metadata required for explanations, like validation data, predict function etc.
Explainers for predictive models can be created with model agnostic or model specific functions implemented in various packages.
416 |
64 |
65 |
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117 |
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119 |
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121 |
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147 |
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165 |
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1 | /* Sticky footer */
2 |
3 | /**
4 | * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
5 | * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
6 | *
7 | * .Site -> body > .container
8 | * .Site-content -> body > .container .row
9 | * .footer -> footer
10 | *
11 | * Key idea seems to be to ensure that .container and __all its parents__
12 | * have height set to 100%
13 | *
14 | */
15 |
16 | html, body {
17 | height: 100%;
18 | }
19 |
20 | body > .container .row {
21 | flex: 1 0 auto;
22 | }
23 |
24 | footer {
25 | margin-top: 45px;
26 | padding: 35px 0 36px;
27 | border-top: 1px solid #e5e5e5;
28 | color: #666;
29 | display: flex;
30 | flex-shrink: 0;
31 | }
32 | footer p {
33 | margin-bottom: 0;
34 | }
35 | footer div {
36 | flex: 1;
37 | }
38 | footer .pkgdown {
39 | text-align: right;
40 | }
41 | footer p {
42 | margin-bottom: 0;
43 | }
44 |
45 | img.icon {
46 | float: right;
47 | }
48 |
49 | img {
50 | max-width: 100%;
51 | }
52 |
53 | /* Fix bug in bootstrap (only seen in firefox) */
54 | summary {
55 | display: list-item;
56 | }
57 |
58 | /* Typographic tweaking ---------------------------------*/
59 |
60 | .contents .page-header {
61 | margin-top: calc(-60px + 1em);
62 | }
63 |
64 | /* Section anchors ---------------------------------*/
65 |
66 | a.anchor {
67 | margin-left: -30px;
68 | display:inline-block;
69 | width: 30px;
70 | height: 30px;
71 | visibility: hidden;
72 |
73 | background-image: url(./link.svg);
74 | background-repeat: no-repeat;
75 | background-size: 20px 20px;
76 | background-position: center center;
77 | }
78 |
79 | .hasAnchor:hover a.anchor {
80 | visibility: visible;
81 | }
82 |
83 | @media (max-width: 767px) {
84 | .hasAnchor:hover a.anchor {
85 | visibility: hidden;
86 | }
87 | }
88 |
89 |
90 | /* Fixes for fixed navbar --------------------------*/
91 |
92 | .contents h1, .contents h2, .contents h3, .contents h4 {
93 | padding-top: 60px;
94 | margin-top: -60px;
95 | }
96 |
97 | /* Sidebar --------------------------*/
98 |
99 | #sidebar {
100 | margin-top: 30px;
101 | position: -webkit-sticky;
102 | position: sticky;
103 | top: 70px;
104 | }
105 | #sidebar h2 {
106 | font-size: 1.5em;
107 | margin-top: 1em;
108 | }
109 |
110 | #sidebar h2:first-child {
111 | margin-top: 0;
112 | }
113 |
114 | #sidebar .list-unstyled li {
115 | margin-bottom: 0.5em;
116 | }
117 |
118 | #sidebar .nav nav-pills .nav-stacked > li > a:focus, .nav-pills > li > a:focus {
119 | background-color: #4a3c89 !important;
120 | color: #fff;
121 | }
122 | #sidebar .nav nav-pills .nav-stacked > li.active > a, .nav-pills > li.active > a{
123 | background-color: #370f54 !important;
124 | }
125 | #sidebar .nav nav-pills .nav-stacked > li > a, .nav-pills > li > a{
126 | background-color: #4a3c89 !important;
127 | color: #fff;
128 | }
129 |
130 | .orcid {
131 | height: 16px;
132 | /* margins are required by official ORCID trademark and display guidelines */
133 | margin-left:4px;
134 | margin-right:4px;
135 | vertical-align: middle;
136 | }
137 |
138 | /* Reference index & topics ----------------------------------------------- */
139 |
140 | .ref-index th {font-weight: normal;}
141 |
142 | .ref-index td {vertical-align: top;}
143 | .ref-index .icon {width: 40px;}
144 | .ref-index .alias {width: 40%;}
145 | .ref-index-icons .alias {width: calc(40% - 40px);}
146 | .ref-index .title {width: 60%;}
147 |
148 | .ref-arguments th {text-align: right; padding-right: 10px;}
149 | .ref-arguments th, .ref-arguments td {vertical-align: top;}
150 | .ref-arguments .name {width: 20%;}
151 | .ref-arguments .desc {width: 80%;}
152 |
153 | /* Nice scrolling for wide elements --------------------------------------- */
154 |
155 | table {
156 | display: block;
157 | overflow: auto;
158 | }
159 |
160 | /* Syntax highlighting ---------------------------------------------------- */
161 |
162 | pre {
163 | word-wrap: normal;
164 | word-break: normal;
165 | border: 1px solid #eee;
166 | }
167 |
168 | pre, code {
169 | background-color: #f8f8f8;
170 | color: #333;
171 | }
172 |
173 | pre code {
174 | overflow: auto;
175 | word-wrap: normal;
176 | white-space: pre;
177 | }
178 |
179 | pre .img {
180 | margin: 5px 0;
181 | }
182 |
183 | pre .img img {
184 | background-color: #fff;
185 | display: block;
186 | height: auto;
187 | }
188 |
189 | code a, pre a {
190 | color: #375f84;
191 | }
192 |
193 | a.sourceLine:hover {
194 | text-decoration: none;
195 | }
196 |
197 | .fl {color: #1514b5;}
198 | .fu {color: #000000;} /* function */
199 | .ch,.st {color: #036a07;} /* string */
200 | .kw {color: #264D66;} /* keyword */
201 | .co {color: #888888;} /* comment */
202 |
203 | .message { color: black; font-weight: bolder;}
204 | .error { color: orange; font-weight: bolder;}
205 | .warning { color: #6A0366; font-weight: bolder;}
206 |
207 | /* Clipboard --------------------------*/
208 |
209 | .hasCopyButton {
210 | position: relative;
211 | }
212 |
213 | .btn-copy-ex {
214 | position: absolute;
215 | right: 0;
216 | top: 0;
217 | visibility: hidden;
218 | }
219 |
220 | .hasCopyButton:hover button.btn-copy-ex {
221 | visibility: visible;
222 | }
223 |
224 | /* headroom.js ------------------------ */
225 |
226 | .headroom {
227 | will-change: transform;
228 | transition: transform 200ms linear;
229 | }
230 | .headroom--pinned {
231 | transform: translateY(0%);
232 | }
233 | .headroom--unpinned {
234 | transform: translateY(-100%);
235 | }
236 |
237 | /* mark.js ----------------------------*/
238 |
239 | mark {
240 | background-color: rgba(255, 255, 51, 0.5);
241 | border-bottom: 2px solid rgba(255, 153, 51, 0.3);
242 | padding: 1px;
243 | }
244 |
245 | /* vertical spacing after htmlwidgets */
246 | .html-widget {
247 | margin-bottom: 10px;
248 | }
249 |
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/docs/pkgdown.js:
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1 | /* http://gregfranko.com/blog/jquery-best-practices/ */
2 | (function($) {
3 | $(function() {
4 |
5 | $('.navbar-fixed-top').headroom();
6 |
7 | $('body').css('padding-top', $('.navbar').height() + 10);
8 | $(window).resize(function(){
9 | $('body').css('padding-top', $('.navbar').height() + 10);
10 | });
11 |
12 | $('body').scrollspy({
13 | target: '#sidebar',
14 | offset: 60
15 | });
16 |
17 | $('[data-toggle="tooltip"]').tooltip();
18 |
19 | var cur_path = paths(location.pathname);
20 | var links = $("#navbar ul li a");
21 | var max_length = -1;
22 | var pos = -1;
23 | for (var i = 0; i < links.length; i++) {
24 | if (links[i].getAttribute("href") === "#")
25 | continue;
26 | // Ignore external links
27 | if (links[i].host !== location.host)
28 | continue;
29 |
30 | var nav_path = paths(links[i].pathname);
31 |
32 | var length = prefix_length(nav_path, cur_path);
33 | if (length > max_length) {
34 | max_length = length;
35 | pos = i;
36 | }
37 | }
38 |
39 | // Add class to parent
67 |
68 |
119 |
120 |
121 |
122 |
123 |
124 |
170 |
171 |
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173 |
174 |
175 |
176 |
177 |
178 |
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130 |
131 |
132 | The DrWhy logo, using ASCII art
128 | 129 |DrWhy_logo.Rd
133 |
134 |
137 |
138 | The DrWhy logo, using ASCII art
135 | 136 |DrWhy_logo()
139 |
140 |
141 | Examples
142 |148 |DrWhy_logo()#> _____ 143 | #> ____ _ _ _ _ |___ | 144 | #> | \ ___ | | | | |_ _ _| _| 145 | #> | | | _| | | | | | | |_| 146 | #> |____/|_| |_____|_|_|_ |_| 147 | #> |___|
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/logo.R
3 | \name{DrWhy_logo}
4 | \alias{DrWhy_logo}
5 | \title{The DrWhy logo, using ASCII art}
6 | \usage{
7 | DrWhy_logo()
8 | }
9 | \description{
10 | The DrWhy logo, using ASCII art
11 | }
12 | \examples{
13 | DrWhy_logo()
14 | }
15 |
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1 | library(testthat)
2 | library(DrWhy)
3 |
4 | test_check("DrWhy")
5 |
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1 | context("Check code vector")
2 |
3 | test_that("Is there DALEX2",{
4 | expect_true("DALEX2" %in% core)
5 | })
6 |
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1 | ---
2 | title: "The DrWhy manifesto"
3 | author: "Przemyslaw Biecek"
4 | date: "`r Sys.Date()`"
5 | output: rmarkdown::html_vignette
6 | vignette: >
7 | %\VignetteIndexEntry{The DrWhy manifesto}
8 | %\VignetteEngine{knitr::rmarkdown}
9 | %\VignetteEncoding{UTF-8}
10 | ---
11 |
12 | I simply love the [tidyverse manifesto](https://github.com/tidyverse/tidyverse/blob/master/vignettes/manifesto.Rmd) written by Hadley Wickham.
13 |
14 | Especially one point, that I believe is a key differentiator between DrWhy tools and many other tools for XAI.
15 |
16 | ## Design for humans
17 |
18 | *Copied from tidyverse manifesto*
19 |
20 | Design your API primarily so that it is easy to use by humans. Computer efficiency is a secondary concern because the bottleneck in most data analysis is thinking time, not computing time.
21 |
22 | * Invest time in naming your functions. Evocative function names
23 | make your API easier to use and remember.
24 |
25 | * Favour explicit, lengthy names, over short, implicit, names.
26 | Save the shortest names for the most important operations.
27 |
28 | * Think about how autocomplete can also make an API that's easy to
29 | write. Make sure that function families are identified by a
30 | common prefix, not a common suffix. This makes autocomplete more
31 | helpful, as you can jog your memory with the prompts. For smaller
32 | packages, this may mean that every function has a common prefix
33 | (e.g. stringr, xml2, rvest).
34 |
35 |
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