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https://github.com/guillermo-navas-palencia/optbinning
Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.
https://github.com/guillermo-navas-palencia/optbinning
batch-processing binning counterfactual-explanations credit-scoring mdlp optimization python scorecard stream streaming-data woe woebinning
Last synced: about 1 month ago
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Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.
- Host: GitHub
- URL: https://github.com/guillermo-navas-palencia/optbinning
- Owner: guillermo-navas-palencia
- License: apache-2.0
- Created: 2019-12-31T11:17:44.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-28T23:10:08.000Z (about 2 months ago)
- Last Synced: 2024-10-28T23:18:26.906Z (about 2 months ago)
- Topics: batch-processing, binning, counterfactual-explanations, credit-scoring, mdlp, optimization, python, scorecard, stream, streaming-data, woe, woebinning
- Language: Python
- Homepage: http://gnpalencia.org/optbinning/
- Size: 10.7 MB
- Stars: 454
- Watchers: 18
- Forks: 100
- Open Issues: 33
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-list - OptBinning - Monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations. (Data Processing / Data Pre-processing & Loading)
README
==========
OptBinning
==========
.. image:: https://github.com/guillermo-navas-palencia/optbinning/workflows/CI/badge.svg
:target: https://github.com/guillermo-navas-palencia/optbinning/workflows/CI/badge.svg
.. image:: https://img.shields.io/github/license/guillermo-navas-palencia/optbinning
:target: https://img.shields.io/github/license/guillermo-navas-palencia/optbinning
.. image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue
:target: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue
.. image:: https://img.shields.io/pypi/v/optbinning?color=blueviolet
:target: https://img.shields.io/pypi/v/optbinning?color=blueviolet
.. image:: https://static.pepy.tech/badge/optbinning
:target: https://pepy.tech/project/optbinning
.. image:: https://static.pepy.tech/badge/optbinning/month
:target: https://pepy.tech/project/optbinning/month
**OptBinning** is a library written in Python implementing a rigorous and flexible mathematical programming formulation to solve the optimal binning problem for a binary, continuous and multiclass target type, incorporating constraints not previously addressed.
* **Papers**:
* Optimal binning: mathematical programming formulation. http://arxiv.org/abs/2001.08025
* Optimal counterfactual explanations for scorecard modelling. https://arxiv.org/abs/2104.08619
* **Blog**: Optimal binning for streaming data. http://gnpalencia.org/blog/2020/binning_data_streams/
.. list-table::
* - .. figure:: doc/source/_images/binning_binary.png
- .. figure:: doc/source/_images/binning_data_stream.gif
.. list-table::
* - .. figure:: doc/source/_images/binning_2d_readme.png
- .. figure:: doc/source/_images/binning_2d_readme_woe.png
.. contents:: **Table of Contents**
Installation
============
To install the current release of OptBinning from PyPI:
.. code-block:: text
pip install optbinning
To include batch and stream binning algorithms (this option is not required for most users):
.. code-block:: text
pip install optbinning[distributed]
To include support for the `ecos `_ solver:
.. code-block:: text
pip install optbinning[ecos]
To install from source, download or clone the git repository
.. code-block:: text
git clone https://github.com/guillermo-navas-palencia/optbinning.git
cd optbinning
python setup.py install
Dependencies
------------
OptBinning requires
* matplotlib
* numpy (>=1.16.1)
* ortools (>=9.4)
* pandas
* ropwr (>=1.0.0)
* scikit-learn (>=1.0.2)
* scipy (>=1.6.0)
OptBinning[distributed] requires additional packages
* pympler
* tdigest
Getting started
===============
Please visit the OptBinning documentation (**current** release) http://gnpalencia.org/optbinning/. If your are new to OptBinning, you can get started following the `tutorials `_ and checking the API references.
Tutorials
---------
* `Optimal binning tutorials `_
* `Binning process tutorials `_
* `Scorecard and counterfactual tutorials `_
* `Optimal piecewise binning tutorials `_
* `Batch and stream optimal binning tutorials `_
* `Optimal binning under uncertainty `_
* `Optimal binning 2D `_
Example: Optimal binning with binary target
-------------------------------------------
Let's load a well-known dataset from the UCI repository and choose a variable to discretize and the binary target.
.. code-block:: python
import pandas as pd
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
variable = "mean radius"
x = df[variable].values
y = data.target
Import and instantiate an ``OptimalBinning`` object class. We pass the variable name, its data type, and a solver, in this case, we choose the constraint programming solver. Fit the optimal binning object with arrays ``x`` and ``y``.
.. code-block:: python
from optbinning import OptimalBinning
optb = OptimalBinning(name=variable, dtype="numerical", solver="cp")
optb.fit(x, y)
Check status and retrieve optimal split points
.. code-block:: python
>>> optb.status
'OPTIMAL'
>>> optb.splits
array([11.42500019, 12.32999992, 13.09499979, 13.70499992, 15.04500008,
16.92500019])
The optimal binning algorithms return a binning table; a binning table displays the binned data and several metrics for each bin. Call the method ``build``, which returns a pandas.DataFrame.
.. code-block:: python
>>> optb.binning_table.build()
.. code-block:: text
Bin Count Count (%) Non-event Event Event rate WoE IV JS
0 [-inf, 11.43) 118 0.207381 3 115 0.974576 -3.12517 0.962483 0.087205
1 [11.43, 12.33) 79 0.138840 3 76 0.962025 -2.71097 0.538763 0.052198
2 [12.33, 13.09) 68 0.119508 7 61 0.897059 -1.64381 0.226599 0.025513
3 [13.09, 13.70) 49 0.086116 10 39 0.795918 -0.839827 0.052131 0.006331
4 [13.70, 15.05) 83 0.145870 28 55 0.662651 -0.153979 0.003385 0.000423
5 [15.05, 16.93) 54 0.094903 44 10 0.185185 2.00275 0.359566 0.038678
6 [16.93, inf) 118 0.207381 117 1 0.008475 5.28332 2.900997 0.183436
7 Special 0 0.000000 0 0 0.000000 0 0.000000 0.000000
8 Missing 0 0.000000 0 0 0.000000 0 0.000000 0.000000
Totals 569 1.000000 212 357 0.627417 5.043925 0.393784
You can use the method ``plot`` to visualize the histogram and WoE or event rate curve. Note that the Bin ID corresponds to the binning table index.
.. code-block:: python
>>> optb.binning_table.plot(metric="woe")
.. image:: doc/source/_images/binning_readme_example_woe.png
:target: doc/source/_images/binning_readme_example_woe.png
Optionally, you can show the binning plot with the actual bin widths.
.. code-block:: python
>>> optb.binning_table.plot(metric="woe", style="actual", add_special=False, add_missing=False)
.. image:: doc/source/_images/binning_readme_example_split_woe.png
:target: doc/source/_images/binning_readme_example_split_woe.png
Now that we have checked the binned data, we can transform our original data into WoE or event rate values.
.. code-block:: python
x_transform_woe = optb.transform(x, metric="woe")
x_transform_event_rate = optb.transform(x, metric="event_rate")
The ``analysis`` method performs a statistical analysis of the binning table, computing the statistics Gini index, Information Value (IV), Jensen-Shannon divergence, and the quality score. Additionally, several statistical significance tests between consecutive bins of the contingency table are performed.
.. code-block:: python
>>> optb.binning_table.analysis()
.. code-block:: text
---------------------------------------------
OptimalBinning: Binary Binning Table Analysis
---------------------------------------------
General metrics
Gini index 0.87541620
IV (Jeffrey) 5.04392547
JS (Jensen-Shannon) 0.39378376
Hellinger 0.47248971
Triangular 1.25592041
KS 0.72862164
HHI 0.15727342
HHI (normalized) 0.05193260
Cramer's V 0.80066760
Quality score 0.00000000
Monotonic trend descending
Significance tests
Bin A Bin B t-statistic p-value P[A > B] P[B > A]
0 1 0.252432 6.153679e-01 0.684380 3.156202e-01
1 2 2.432829 1.188183e-01 0.948125 5.187465e-02
2 3 2.345804 1.256207e-01 0.937874 6.212635e-02
3 4 2.669235 1.023052e-01 0.955269 4.473083e-02
4 5 29.910964 4.523477e-08 1.000000 9.814594e-12
5 6 19.324617 1.102754e-05 0.999999 1.216668e-06
Print overview information about the options settings, problem statistics, and the solution of the computation.
.. code-block:: python
>>> optb.information(print_level=2)
.. code-block:: text
optbinning (Version 0.20.0)
Copyright (c) 2019-2024 Guillermo Navas-Palencia, Apache License 2.0
Begin options
name mean radius * U
dtype numerical * d
prebinning_method cart * d
solver cp * d
divergence iv * d
max_n_prebins 20 * d
min_prebin_size 0.05 * d
min_n_bins no * d
max_n_bins no * d
min_bin_size no * d
max_bin_size no * d
min_bin_n_nonevent no * d
max_bin_n_nonevent no * d
min_bin_n_event no * d
max_bin_n_event no * d
monotonic_trend auto * d
min_event_rate_diff 0 * d
max_pvalue no * d
max_pvalue_policy consecutive * d
gamma 0 * d
class_weight no * d
cat_cutoff no * d
user_splits no * d
user_splits_fixed no * d
special_codes no * d
split_digits no * d
mip_solver bop * d
time_limit 100 * d
verbose False * d
End options
Name : mean radius
Status : OPTIMAL
Pre-binning statistics
Number of pre-bins 9
Number of refinements 1
Solver statistics
Type cp
Number of booleans 26
Number of branches 58
Number of conflicts 0
Objective value 5043922
Best objective bound 5043922
Timing
Total time 0.04 sec
Pre-processing 0.00 sec ( 0.33%)
Pre-binning 0.00 sec ( 5.54%)
Solver 0.04 sec ( 93.03%)
model generation 0.03 sec ( 85.61%)
optimizer 0.01 sec ( 14.39%)
Post-processing 0.00 sec ( 0.30%)
Example: Optimal binning 2D with binary target
----------------------------------------------
In this case, we choose two variables to discretized and the binary target.
.. code-block:: python
import pandas as pd
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
variable1 = "mean radius"
variable2 = "worst concavity"
x = df[variable1].values
y = df[variable2].values
z = data.target
Import and instantiate an ``OptimalBinning2D`` object class. We pass the variable names, and monotonic trends. Fit the optimal binning object with arrays ``x``, ``y`` and ``z``.
.. code-block:: python
from optbinning import OptimalBinning2D
optb = OptimalBinning2D(name_x=variable1, name_y=variable2, monotonic_trend_x="descending",
monotonic_trend_y="descending", min_bin_size=0.05)
optb.fit(x, y, z)
Show binning table:
.. code-block:: python
>>> optb.binning_table.build()
.. code-block:: text
Bin x Bin y Count Count (%) Non-event Event Event rate WoE IV JS
0 (-inf, 13.70) (-inf, 0.21) 219 0.384886 1 218 0.995434 -4.863346 2.946834 0.199430
1 [13.70, inf) (-inf, 0.21) 48 0.084359 5 43 0.895833 -1.630613 0.157946 0.017811
2 (-inf, 13.09) [0.21, 0.38) 48 0.084359 1 47 0.979167 -3.328998 0.422569 0.037010
3 [13.09, 15.05) [0.21, 0.38) 46 0.080844 17 29 0.630435 -0.012933 0.000013 0.000002
4 [15.05, inf) [0.21, 0.32) 32 0.056239 29 3 0.093750 2.789833 0.358184 0.034271
5 [15.05, inf) [0.32, inf) 129 0.226714 128 1 0.007752 5.373180 3.229133 0.201294
6 (-inf, 15.05) [0.38, inf) 47 0.082601 31 16 0.340426 1.182548 0.119920 0.014173
7 Special Special 0 0.000000 0 0 0.000000 0.000000 0.000000 0.000000
8 Missing Missing 0 0.000000 0 0 0.000000 0.000000 0.000000 0.000000
Totals 569 1.000000 212 357 0.627417 7.234600 0.503991
Similar to the optimal binning, you can generate a histogram 2D to visualize WoE and event rate.
.. code-block:: python
>>> optb.binning_table.plot(metric="event_rate")
.. image:: doc/source/_images/binning_2d_readme_example.png
:target: doc/source/_images/binning_2d_readme_example.png
Example: Scorecard with continuous target
-----------------------------------------
Let's load the California housing dataset.
.. code-block:: python
import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import HuberRegressor
from optbinning import BinningProcess
from optbinning import Scorecard
data = fetch_california_housing()
target = "target"
variable_names = data.feature_names
X = pd.DataFrame(data.data, columns=variable_names)
y = data.target
Instantiate a binning process, an estimator, and a scorecard with scaling
method and reverse mode.
.. code-block:: python
binning_process = BinningProcess(variable_names)
estimator = HuberRegressor(max_iter=200)
scorecard = Scorecard(binning_process=binning_process, estimator=estimator,
scaling_method="min_max",
scaling_method_params={"min": 0, "max": 100},
reverse_scorecard=True)
scorecard.fit(X, y)
Print overview information about the options settings, problems statistics,
and the number of selected variables after the binning process.
.. code-block:: python
>>> scorecard.information(print_level=2)
.. code-block:: text
optbinning (Version 0.20.0)
Copyright (c) 2019-2024 Guillermo Navas-Palencia, Apache License 2.0
Begin options
binning_process yes * U
estimator yes * U
scaling_method min_max * U
scaling_method_params yes * U
intercept_based False * d
reverse_scorecard True * U
rounding False * d
verbose False * d
End options
Statistics
Number of records 20640
Number of variables 8
Target type continuous
Number of numerical 8
Number of categorical 0
Number of selected 8
Timing
Total time 2.31 sec
Binning process 1.83 sec ( 79.00%)
Estimator 0.41 sec ( 17.52%)
Build scorecard 0.08 sec ( 3.40%)
rounding 0.00 sec ( 0.00%)
.. code-block:: python
>>> scorecard.table(style="summary")
Two scorecard styles are available: ``style="summary"`` shows the variable name, and their corresponding bins and assigned points; ``style="detailed"`` adds information from the corresponding binning table.
.. code-block:: text
Variable Bin Points
0 MedInc [-inf, 1.90) 9.869224
1 MedInc [1.90, 2.16) 10.896940
2 MedInc [2.16, 2.37) 11.482997
3 MedInc [2.37, 2.66) 12.607805
4 MedInc [2.66, 2.88) 13.609078
.. ... ... ...
2 Longitude [-118.33, -118.26) 10.470401
3 Longitude [-118.26, -118.16) 9.092391
4 Longitude [-118.16, inf) 10.223936
5 Longitude Special 1.376862
6 Longitude Missing 1.376862
[94 rows x 3 columns]
.. code-block:: python
>>> scorecard.table(style="detailed")
.. code-block:: text
Variable Bin id Bin Count Count (%) ... Zeros count WoE IV Coefficient Points
0 MedInc 0 [-inf, 1.90) 2039 0.098789 ... 0 -0.969609 0.095786 0.990122 9.869224
1 MedInc 1 [1.90, 2.16) 1109 0.053731 ... 0 -0.836618 0.044952 0.990122 10.896940
2 MedInc 2 [2.16, 2.37) 1049 0.050824 ... 0 -0.760779 0.038666 0.990122 11.482997
3 MedInc 3 [2.37, 2.66) 1551 0.075145 ... 0 -0.615224 0.046231 0.990122 12.607805
4 MedInc 4 [2.66, 2.88) 1075 0.052083 ... 0 -0.485655 0.025295 0.990122 13.609078
.. ... ... ... ... ... ... ... ... ... ... ...
2 Longitude 2 [-118.33, -118.26) 1120 0.054264 ... 0 -0.011006 0.000597 0.566265 10.470401
3 Longitude 3 [-118.26, -118.16) 1127 0.054603 ... 0 -0.322802 0.017626 0.566265 9.092391
4 Longitude 4 [-118.16, inf) 6530 0.316376 ... 0 -0.066773 0.021125 0.566265 10.223936
5 Longitude 5 Special 0 0.000000 ... 0 -2.068558 0.000000 0.566265 1.376862
6 Longitude 6 Missing 0 0.000000 ... 0 -2.068558 0.000000 0.566265 1.376862
[94 rows x 14 columns]
Compute score and predicted target using the fitted estimator.
.. code-block:: python
score = scorecard.score(X)
y_pred = scorecard.predict(X)
Example: Counterfactual explanations for scorecard with continuous target
-------------------------------------------------------------------------
First, we load the dataset and a scorecard previously developed.
.. code-block:: python
import pandas as pd
from optbinning import Scorecard
from optbinning.scorecard import Counterfactual
from sklearn.datasets import load_boston
data = load_boston()
X = pd.DataFrame(data.data, columns=data.feature_names)
scorecard = Scorecard.load("myscorecard.pkl")
We create a new Counterfactual instance that is fitted with the dataset
used during the scorecard development. Then, we select a sample from which to generate
counterfactual explanations.
.. code-block:: python
cf = Counterfactual(scorecard=scorecard)
cf.fit(X)
query = X.iloc[0, :].to_frame().T
The scorecard model predicts 26.8. However, we would like to find out what needs to be
changed to return a prediction greater or equal to 30.
.. code-block:: python
>>> query
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.09 1.0 296.0 15.3 396.9 4.98
>>> scorecard.predict(query)
array([26.83423364])
We can generate a single counterfactual explanation:
.. code-block:: python
>>> cf.generate(query=query, y=30, outcome_type="continuous", n_cf=1, max_changes=3,
hard_constraints=["min_outcome"])
>>> cf.status
'OPTIMAL'
>>> cf.display(show_only_changes=True, show_outcome=True)
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT outcome
0 [0.04, 0.07) - - - [0.45, 0.50) [6.94, 7.44) - - - - - - - 31.28763
Or simultaneously three counterfactuals, enforcing diversity on the feature values and selecting only a few actionable features.
.. code-block:: python
>>> cf.generate(query=query, y=30, outcome_type="continuous", n_cf=3, max_changes=3,
hard_constraints=["diversity_values", "min_outcome"],
actionable_features=["CRIM", "NOX", "RM", "PTRATIO"])
>>> cf.status
'OPTIMAL'
>>> cf.display(show_only_changes=True, show_outcome=True)
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT outcome
0 [0.03, 0.04) - - - [0.42, 0.45) [6.94, 7.44) - - - - - - - 31.737844
0 [0.04, 0.07) - - - - [7.44, inf) - - - - [17.85, 18.55) - - 36.370086
0 - - - - [0.45, 0.50) [6.68, 6.94) - - - - [-inf, 15.15) - - 30.095258
Benchmarks
==========
The following table shows how OptBinning compares to `scorecardpy `_ 0.1.9.1.1 on a selection of variables from the public dataset, Home Credit Default Risk - Kaggle’s competition `Link `_. This dataset contains 307511 samples.The experiments were run on Intel(R) Core(TM) i5-3317 CPU at 1.70GHz, using a single core, running Linux. For scorecardpy, we use default settings only increasing the maximum number of bins ``bin_num_limit=20``. For OptBinning, we use default settings (``max_n_prebins=20``) only changing the maximum allowed p-value between consecutive bins, ``max_pvalue=0.05``.
To compare softwares we use the shifted geometric mean, typically used in mathematical optimization benchmarks: http://plato.asu.edu/bench.html. Using the shifted (by 1 second) geometric mean we found that **OptBinning** is **17x** faster than scorecardpy, with an average IV increment of **12%**. Besides the speed and IV gains, OptBinning includes many more constraints and monotonicity options.
+----------------------------+------------------+----------------+-----------------+---------------+
| Variable | scorecardpy_time | scorecardpy_IV | optbinning_time | optbinning_IV |
+============================+==================+================+=================+===============+
| AMT_INCOME_TOTAL | 6.18 s | 0.010606 | 0.363 s | 0.011705 |
+----------------------------+------------------+----------------+-----------------+---------------+
| NAME_CONTRACT_TYPE (C) | 3.72 s | 0.015039 | 0.148 s | 0.015039 |
+----------------------------+------------------+----------------+-----------------+---------------+
| AMT_CREDIT | 7.10 s | 0.053593 | 0.634 s | 0.059311 |
+----------------------------+------------------+----------------+-----------------+---------------+
| ORGANIZATION_TYPE (C) | 6.31 s | 0.063098 | 0.274 s | 0.071520 |
+----------------------------+------------------+----------------+-----------------+---------------+
| AMT_ANNUITY | 6.51 s | 0.024295 | 0.648 s | 0.031179 |
+----------------------------+------------------+----------------+-----------------+---------------+
| AMT_GOODS_PRICE | 6.95 s | 0.056923 | 0.401 s | 0.092032 |
+----------------------------+------------------+----------------+-----------------+---------------+
| NAME_HOUSING_TYPE (C) | 3.57 s | 0.015055 | 0.140 s | 0.015055 |
+----------------------------+------------------+----------------+-----------------+---------------+
| REGION_POPULATION_RELATIVE | 4.33 s | 0.026578 | 0.392 s | 0.035567 |
+----------------------------+------------------+----------------+-----------------+---------------+
| DAYS_BIRTH | 5.18 s | 0.081270 | 0.564 s | 0.086539 |
+----------------------------+------------------+----------------+-----------------+---------------+
| OWN_CAR_AGE | 4.85 s | 0.021429 | 0.055 s | 0.021890 |
+----------------------------+------------------+----------------+-----------------+---------------+
| OCCUPATION_TYPE (C) | 4.24 s | 0.077606 | 0.201 s | 0.079540 |
+----------------------------+------------------+----------------+-----------------+---------------+
| APARTMENTS_AVG | 5.61 s | 0.032247(*) | 0.184 s | 0.032415 |
+----------------------------+------------------+----------------+-----------------+---------------+
| BASEMENTAREA_AVG | 5.14 s | 0.022320 | 0.119 s | 0.022639 |
+----------------------------+------------------+----------------+-----------------+---------------+
| YEARS_BUILD_AVG | 4.49 s | 0.016033 | 0.055 s | 0.016932 |
+----------------------------+------------------+----------------+-----------------+---------------+
| EXT_SOURCE_2 | 5.21 s | 0.298463 | 0.606 s | 0.321417 |
+----------------------------+------------------+----------------+-----------------+---------------+
| EXT_SOURCE_3 | 5.08 s | 0.316352 | 0.303 s | 0.334975 |
+----------------------------+------------------+----------------+-----------------+---------------+
| **TOTAL** | **84.47 s** |**1.130907** | **5.087 s** | **1.247756** |
+----------------------------+------------------+----------------+-----------------+---------------+
(C): categorical variable.
(*): max p-value between consecutive bins > 0.05.
The binning of variables with monotonicity trend peak or valley can benefit from the option ``monotonic_trend="auto_heuristic"`` at the expense of finding a suboptimal solution for some cases. The following table compares the options ``monotonic_trend="auto"`` and ``monotonic_trend="auto_heuristic"``,
+----------------------------+----------------+----------------+----------------+----------------+
| Variable | auto_time | auto_IV | heuristic_time | heuristic_IV |
+============================+================+================+================+================+
| AMT_INCOME_TOTAL | 0.363 s | 0.011705 | 0.322 s | 0.011705 |
+----------------------------+----------------+----------------+----------------+----------------+
| AMT_CREDIT | 0.634 s | 0.059311 | 0.469 s | 0.058643 |
+----------------------------+----------------+----------------+----------------+----------------+
| AMT_ANNUITY | 0.648 s | 0.031179 | 0.505 s | 0.031179 |
+----------------------------+----------------+----------------+----------------+----------------+
| AMT_GOODS_PRICE | 0.401 s | 0.092032 | 0.299 s | 0.092032 |
+----------------------------+----------------+----------------+----------------+----------------+
| REGION_POPULATION_RELATIVE | 0.392 s | 0.035567 | 0.244 s | 0.035567 |
+----------------------------+----------------+----------------+----------------+----------------+
| **TOTAL** | **2.438 s** | **0.229794** | **1.839 s** | **0.229126** |
+----------------------------+----------------+----------------+----------------+----------------+
Observe that CPU time is reduced by 25% losing less than 1% in IV. The differences in CPU time are more noticeable as the
number of bins increases, see http://gnpalencia.org/optbinning/tutorials/tutorial_binary_large_scale.html.
Contributing
============
Found a bug? Want to contribute with a new feature, improve documentation, or add examples? We encourage you to create pull requests and/or open GitHub issues. Thanks! :octocat: :tada: :+1:
Who uses OptBinning?
====================
We would like to list companies using OptBinning. Please send a PR with your company name and @githubhandle if you may.
Currently **officially** using OptBinning:
1. `Jeitto `_ [`@BrennerPablo `_ & `@ds-mauri `_ & `@GabrielSGoncalves `_]
2. `Bilendo `_ [`@FlorianKappert `_ & `@JakobBeyer `_]
3. `Aplazame `_
4. `Praelexis Credit `_
5. `ING `_
6. `DBRS Morningstar `_
7. `Loginom `_
8. `Risika `_
9. `Tamara `_
10. `BBVA AI Factory `_
11. `N26 `_
12. `Home Credit International `_
13. `Farm Credit Canada `_
Citation
========
If you use OptBinning in your research/work, please cite the paper using the following BibTeX::
@article{Navas-Palencia2020OptBinning,
title = {Optimal binning: mathematical programming formulation},
author = {Guillermo Navas-Palencia},
year = {2020},
eprint = {2001.08025},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
volume = {abs/2001.08025},
url = {http://arxiv.org/abs/2001.08025},
}
@article{Navas-Palencia2021Counterfactual,
title = {Optimal Counterfactual Explanations for Scorecard modelling},
author = {Guillermo Navas-Palencia},
year = {2021},
eprint = {2104.08619},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
volume = {abs/2104.08619},
url = {http://arxiv.org/abs/2104.08619},
}