Responsible Machine Learning in Python

## Project description

# dalex

dalex: Responsible Machine Learning in Python

## Overview

Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection.

The `dalex`

package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working.
The main `Explainer`

object creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of model-level and predict-level explanations. Moreover, there are fairness methods and interactive exploration dashboards available to the user.

The philosophy behind `dalex`

explanations is described in the Explanatory Model Analysis e-book.

## Installation

The `dalex`

package is available on PyPI

```
pip install dalex -U
```

### Resources: https://dalex.drwhy.ai/python

### API reference: https://dalex.drwhy.ai/python/api

## Authors

Main authors of the `dalex`

package are:

Under the supervision of Przemyslaw Biecek.

Other contributors:

- Piotr Piatyszek maintains the
`arena`

module - Jakub Wisnewski maintains the
`fairness`

module

## Citation

If you use `dalex`

, please cite our paper:

```
@article{dalex,
title={{dalex: Responsible Machine Learning with Interactive
Explainability and Fairness in Python}},
author={Hubert Baniecki and Wojciech Kretowicz and Piotr Piatyszek
and Jakub Wisniewski and Przemyslaw Biecek},
year={2020},
journal={arXiv:2012.14406},
url={https://arxiv.org/abs/2012.14406}
}
```

## Changelog

## v1.1.0 (18/04/2021)

#### breaking changes

- fixed concurrent random seeds when
`processes > 1`

(#392), which means that the results of parallel computation will vary between`v1.1.0`

and previous versions

#### fixes

`GroupFairnessX.plot(type='fairness_check')`

generates ticks according to the x-axis range (#409)`GroupFainressRegression.plot(type='density')`

has a more readable hover - only for outliers (#409)`BreakDown.plot()`

wrongly displayed the "+all factors" bar when`max_vars < p`

(#401)`GroupFairnessClassification.plot(type='metric_scores')`

did not handle`NaN`

's (#399)

#### features

- Experimental support for regression models in the
`fairness`

module. Added`GroupFairnessRegression`

object, with the`plot`

method having two types:`fairness_check`

and`density`

.`Explainer.model_fairness`

method now depends on the`model_type`

attribute. (#391) - added
`N`

parameter to the`predict_parts`

method which is`None`

by default (#402) `epsilon`

is now an argument of the`GroupFairnessClassification`

object (#397)

## v1.0.1 (19/02/2021)

#### fixes

- fixed broken range on
`yaxis`

in`fairness_check`

plot (#376) - warnings because
`np.float`

is depracated since`numpy`

v1.20 (#384)

#### other

- added
`ipython`

to test dependencies

## v1.0.0 (29/12/2020)

#### breaking changes

These are summed up in (#368):

- rename modules:
`dataset_level`

into`model_explanations`

,`instance_level`

into`predict_explanations`

,`_arena`

module into`arena`

- use
`__dir__`

method to define autocompletion in IPython environment - show only`['Explainer', 'Arena', 'fairness', 'datasets']`

- add
`plot`

method and`result`

attribute to`LimeExplanation`

(use`lime.explanation.Explanation.as_pyplot_figure()`

and`lime.explanation.Explanation.as_list()`

) `CeterisParibus.plot(variable_type='categorical')`

now has horizontal barplots -`horizontal_spacing=None`

by default (varies on`variable_type`

). Also, once again added the "dot" for observation value.`predict_fn`

in`predict_surrogate`

now uses`predict_function`

(trying to make it work for more frameworks)

#### fixes

- fixed wrong verbose output when any value in
`y_hat/residuals`

was an`int`

not`float`

- added proper
`"-"`

sign to negative dropout losses in`VariableImportance.plot`

#### features

- added
`geom='bars'`

to`AggregateProfiles.plot`

to force the categorical plot - added
`geom='roc'`

and`geom='lift'`

to`ModelPerformance.plot`

- added Fairness plot to Arena

#### other

- remove
`colorize`

from`Explainer`

- updated the documentation, refactored code (import modules not functions, unify variable names in
`object.py`

, move utils funcitons from`checks.py`

to`utils.py`

, etc.) - added license notice next to data

## v0.4.1 (02/12/2020)

- added support for
`h2o.estimators.*`

(#332) - added
`tensorflow.python.keras.engine.functional.Functional`

to the`tensorflow`

list - updated the
`plotly`

dependency to`>=4.12.0`

- code maintenance:
`yhat`

,`check_data`

#### fixes

- fixed
`check_if_empty_fields()`

used in loading the`Explainer`

from a pickle file, since several checks were changed - fixed
`plot()`

method in`GroupFairnessClassification`

as it omitted plotting a metric when`NaN`

was present in metric ratios (result) - fixed
`dragons`

and`HR`

datasets having`,`

delimeter instead of`.`

, which transformed numerical columns into categorical. - fixed representation of the
`ShapWrapper`

class (removed`_repr_html_`

method)

#### features

- allow for
`y`

to be a`pandas.DataFrame`

(converted) - allow for
`data`

,`y`

to be a`H2OFrame`

(converted) - added
`label`

parameter to all the relevant`dx.Explainer`

methods, which overrides the default label in explanation's`result`

- now using
`GradientExplainer`

for`tf.keras.engine.sequential.Sequential`

, added proper warning when`shap_explainer_type`

is`None`

(#366)

#### defaults

- unify verbose output of
`Explainer`

## v0.4.0 (17/11/2020)

- added new
`arena`

module, which adds the backend for Arena dashboard @piotrpiatyszek

#### features

- added new aliases to
`dx.Explainer`

methods (#350) in`model_parts`

it is`{'permutational': 'variable_importance', 'feature_importance': 'variable_importance'}`

, in`model_profile`

it is`{'pdp': 'partial', 'ale': 'accumulated'}`

- added
`Arena`

object for dashboard backend. See https://github.com/ModelOriented/Arena - new
`fairness`

plot types:`stacked`

,`radar`

,`performance_and_fairness`

,`heatmap`

,`ceteris_paribus_cutoff`

- upgraded
`fairness_check()`

## v0.3.0 (26/10/2020)

- added new
`fairness`

module, which will focus on bias detection, visualization and mitigation @jakwisn

#### fixes

- removed unnecessary warning when
`precalculate=False and verbose=False`

(#340)

#### features

- added
`model_fairness`

method to the`Explainer`

, which performs fairness explanation - added
`GroupFairnessClassification`

object, with the`plot`

method having two types:`fairness_check`

and`metric_scores`

#### defaults

- added the
`N=50000`

argument to`ResidualDiagnostics.plot`

, which samples observations from the`result`

parameter to omit performance issues when`smooth=True`

(#341)

## dalex 0.2.2

- added support for
`tensorflow.python.keras.engine.sequential.Sequential`

and`tensorflow.python.keras.engine.training.Model`

(#326) - updated the
`tqdm`

dependency to`>=4.48.2`

,`pandas`

dependency to`>=1.1.2`

and`numpy`

dependency to`>=1.18.4`

#### fixes

- fixed the wrong order of
`Explainer`

verbose messages - fixed a bug that caused
`model_info`

parameter to be overwritten by the default values - fixed a bug occurring when the variable from
`groups`

was not of`str`

type (#327) - fixed
`model_profile`

:`variable_type='categorical'`

not working when user passed`variables`

parameter (#329) + the reverse order of bars in`'categorical'`

plots + (again) added`variable_splits_type`

parameter to`model_profile`

to specify how grid points shall be calculated (#266) + allow for both`'quantile'`

and`'quantiles'`

types (alias)

#### features

- added informative error messages when importing optional dependencies (#316)
- allow for
`data`

and`y`

to be`None`

- added checks in`Explainer`

methods

#### defaults

- wrong parameter name
`title_x`

changed to`y_title`

in`CeterisParibus.plot`

and`AggregatedProfiles.plot`

(#317) - now warning the user in
`Explainer`

when`predict_function`

returns an error or doesn't return`numpy.ndarray (1d)`

(#325)

## dalex 0.2.1

- updated the
`pandas`

dependency to`>=1.1.0`

#### fixes

`ModelPerformance.plot`

now uses a drwhy color palette- use
`unique`

method instead of`np.unique`

in`variable_splits`

(#293) `v0.2.0`

didn't export new datasets- fixed a bug where
`predict_parts(type='shap')`

calculated wrong`contributions`

(#300) `model_profile`

uses observation mean instead of profile mean in`_yhat_`

centering- fixed barplot baseline in categorical
`model_profile`

and`predict_profile`

plots (#297) - fixed
`model_profile(type='accumulated')`

giving wrong results (#302) - vertical/horizontal lines in plots now end on the plot edges

#### features

- added new
`type='shap_wrapper'`

to`predict_parts`

and`model_parts`

methods, which returns a new`ShapWrapper`

object. It contains the main result attribute (`shapley_values`

) and the plot method (`force_plot`

and`summary_plot`

respectively). These come from the shap package `Explainer.predict`

method now accepts`numpy.ndarray`

- added the
`ResidualDiagnostics`

object with a`plot`

method - added
`model_diagnostics`

method to the`Explainer`

, which performs residual diagnostics - added
`predict_surrogate`

method to the`Explainer`

, which is a wrapper for the`lime`

tabular explanation from the lime package - added
`model_surrogate`

method to the`Explainer`

, which creates a basic surrogate decision tree or linear model from the black-box model using the scikit-learn package - added a
`_repr_html_`

method to all of the explanation objects (it prints the`result`

attribute) - added
`dalex.__version__`

- added informative error messages in
`Explainer`

methods when`y`

is of wrong type (#294) `CeterisParibus.plot(variable_type='categorical')`

now allows for multiple observations- new verbose checks for
`model_type`

- add
`type`

to`model_info`

in`dump`

and`dumps`

for R compatibility (#303) `ModelPerformance.result`

now has`label`

as index

#### defaults

- removed
`_grid_`

column in`AggregatedProfiles.result`

and`center`

only works with`type=accumulated`

- use
`Pipeline._final_estimator`

to extract`model_class`

of the actual model - use
`model._estimator_type`

to extract`model_type`

if possible

## dalex 0.2.0

- major documentation update (#270)
- unified the order of function parameters

#### fixes

`v0.1.9`

had wrong`_original_`

column in`predict_profile`

`vertical_spacing`

acts as intended in`VariableImportance.plot`

when`split='variable'`

`loss_function='auc'`

now uses`loss_one_minus_auc`

as this should be a descending measure- plots are now saved with the original height and width
`model_profile`

now properly passes the`variables`

parameter to`CeterisParibus`

`variables`

parameter in`predict_profile`

now can also be a string

#### features

- use
`px.express`

instead of core`plotly`

to make`model_profile`

and`predict_profile`

plots; thus, enhance performance and scalability - added
`verbose`

parameter where`tqdm`

is used to verbose progress bar - added
`loss_one_minus_auc`

function that can be used with`loss_function='1-auc'`

in`model_parts`

- added new example data sets:
`apartments`

,`dragons`

and`hr`

- added
`color`

,`opacity`

,`title_x`

parameters to`model_profile`

and`predict_profile`

plots (#236), changed tooltips and legends (#262) - added
`geom='profiles'`

parameter to`model_profile`

plot and`raw_profiles`

attribute to`AggregatedProfiles`

- added
`variable_splits_type`

parameter to`predict_profile`

to specify how grid points shall be calculated (#266) - added
`variable_splits_with_obs`

parameter to`predict_profile`

function to extend split points with observation variable values (#269) - added
`variable_splits`

parameter to`model_profile`

#### defaults

- use different
`loss_function`

for classification and regression (#248) - models that use
`proba`

yhats now get`model_type='classification'`

if it's not specified - use uniform way of grid points calculation in
`predict_profile`

and`model_profile`

(see`variable_splits_type`

parameter) - add the variable values of
`new_observation`

to`variable_splits`

in`predict_profile`

(see`variable_splits_with_obs`

parameter) - use
`N=1000`

in`model_parts`

and`N=300`

in`model_profile`

to comply with the R version `keep_raw_permutation`

is now set to`False`

instead of`None`

in`model_parts`

`intercept`

parameter in`model_profile`

is now named`center`

## dalex 0.1.9

*feature:*added`random_state`

parameter for`predict_parts(type='shap')`

and`model_profile`

for reproducible calculations*fix:*fixed`random_state`

parameter in`model_parts`

*feature:*multiprocessing added for:`model_profile`

,`model_parts`

,`predict_profile`

and`predict_parts(type='shap')`

, through the`processes`

parameter*fix:*significantly improved the speed of`accumulated`

and`conditional`

types in`model_profile`

*bugfix:*use pd.api.types.is_numeric_dtype() instead of`np.issubdtype()`

to cover more types; e.g. it caused errors with`string`

type*defaults:*use pd.convert_dtypes() on the result of`CeterisParibus`

to fix variable dtypes and later allow for a concatenation without the dtype conversion*fix:*`variables`

parameter now can be a single`str`

value*fix:*number rounding in`predict_parts`

,`model_parts`

(#245)*fix:*CP calculations for models that take only variables as an input

## dalex 0.1.8

*bugfix:*`variable_splits`

parameter now works correctly in`predict_profile`

*bugfix:*fix baseline for 3+ models in`AggregatedProfiles.plot`

(#234)*printing:*now rounding numbers in`Explainer`

messages*fix:*minor checks fixes in`instance_level`

*bugfix:*`AggregatedProfiles.plot`

now works with`groups`

## dalex 0.1.7

*feature:*parameter`N`

in`model_profile`

can be set to`None`

, to select all observations*input:*`groups`

and`variable`

parameters in`model_profile`

can be:`str`

,`list`

,`numpy.ndarray`

,`pandas.Series`

*fix:*`check_label`

returned only a first letter*bugfix:*removed the conversion of`all_variables`

to`str`

in`prepare_all_variables`

, which caused an error in`model_profile`

(#214)*defaults:*change numpy data variable names from numbers to strings

## dalex 0.1.6

*fix:*change`short_name`

encoding in`fifa`

dataset (utf8->ascii)*fix:*remove`scipy`

dependency*defaults:*default`loss_root_mean_square`

in model parts changed to`rmse`

*bugfix:*checks related to`new_observation`

in`BreakDown, Shap, CeterisParibus`

now work for multiple inputs (#207)*bugfix:*`CeterisParibus.fit`

and`CeterisParibus.plot`

now work for more types of`new_observation.index`

, but won't work for a`bolean`

type (#211)

## dalex 0.1.5

*feature:*add`xgboost`

package compatibility (#188)*feature:*added`model_class`

parameter to`Explainer`

to handle wrapped models*feature:*`Exaplainer`

attribute`model_info`

remembers if parameters are default*bugfix:*`variable_groups`

parameter now works correctly in`model_parts`

*fix:*changed parameter order in`Explainer`

:`model_type`

,`model_info`

,`colorize`

*documentation:*`model_parts`

documentation is updated*feature:*new`show`

parameter in`plot`

methods that (`if False`

) returns`plotly Figure`

(#190)*feature:*`load_fifa()`

function which loads the preprocessed players_20 dataset*fix:*`CeterisParibus.plot`

tooltip

## dalex 0.1.4

*feature:*new`Explainer.residual`

method which uses`residual_function`

to calculate`residuals`

*feature:*new`dump`

and`dumps`

methods for saving`Explainer`

in a binary form;`load`

and`loads`

methods for loading`Explainer`

from binary form*fix:*`Explainer`

constructor verbose text*bugfix:*`B:=B+1`

-`Shap`

now stores average results as`B=0`

and path results as`B=1,2,...`

*bugfix:*`Explainer.model_performance`

method uses`self.model_type`

when`model_type`

is`None`

*bugfix:*values in`BreakDown`

and`Shap`

are now rounded to 4 significant places (#180)*bugfix:*`Shap`

by default uses`path='average'`

,`sign`

column is properly updated and bars in`plot`

are sorted by`abs(contribution)`

## dalex 0.1.3

- release of the
`dalex`

package `Explainer`

object with`predict`

,`predict_parts`

,`predict_profile`

,`model_performance`

,`model_parts`

and`model_profile`

methods`BreakDown`

,`Shap`

,`CeterisParibus`

,`ModelPerformance`

,`VariableImportance`

and`AggregatedProfiles`

objects with a`plot`

method`load_titanic()`

function which loads the`titanic_imputed`

dataset

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