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https://github.com/MI2DataLab/pyBreakDown
Python implementation of R package breakDown
https://github.com/MI2DataLab/pyBreakDown
Last synced: 2 months ago
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Python implementation of R package breakDown
- Host: GitHub
- URL: https://github.com/MI2DataLab/pyBreakDown
- Owner: MI2DataLab
- License: other
- Created: 2018-03-21T19:46:53.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T21:18:41.000Z (11 months ago)
- Last Synced: 2024-01-18T03:48:44.703Z (5 months ago)
- Language: Python
- Homepage:
- Size: 466 KB
- Stars: 41
- Watchers: 6
- Forks: 4
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-production-machine-learning - pyBreakDown - A model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. (Explaining Black Box Models and Datasets)
- awesome-machine-learning-interpretability - pyBreakDown
- awesome-production-machine-learning - pyBreakDown - A model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. (Explaining Black Box Models and Datasets)
- Awesome-AIML-Data-Ops - pyBreakDown - A model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. (Explaining Black Box Models and Datasets)
- awesome-production-machine-learning - pyBreakDown - A model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. (Explaining Black Box Models and Datasets)
README
# pyBreakDown
**Please note that the Break Down method is moved to the [dalex](http://dalex.drwhy.ai/) Python package which is actively maintained. If you will experience any problem with pyBreakDown please consider the [dalex](http://dalex.drwhy.ai/) implementation at https://dalex.drwhy.ai/python/api/.**
Python implementation of breakDown package (https://github.com/pbiecek/breakDown).
Docs: https://pybreakdown.readthedocs.io.
## Requirements
Nothing fancy, just python 3.5.2+ and pip.
## Installation
Install directly from github
```
git clone https://github.com/bondyra/pyBreakDown
cd ./pyBreakDown
python3 setup.py install # (or use pip install . instead)
```
## Basic usage
### Load dataset
```python
from sklearn import datasets
```
```python
x = datasets.load_boston()
```
```python
data = x.data
```
```python
feature_names = x.feature_names
```
```python
y = x.target
```
### Prepare model
```python
import numpy as np
```
```python
from sklearn import tree
```
```python
model = tree.DecisionTreeRegressor()
```
### Train model
```python
train_data = data[1:300,:]
train_labels=y[1:300]
```
```python
model = model.fit(train_data,y=train_labels)
```
### Explain predictions on test data
```python
#necessary imports
from pyBreakDown.explainer import Explainer
from pyBreakDown.explanation import Explanation
```
```python
#make explainer object
exp = Explainer(clf=model, data=train_data, colnames=feature_names)
```
```python
#make explanation object that contains all information
explanation = exp.explain(observation=data[302,:],direction="up")
```
### Text form of explanations
```python
#get information in text form
explanation.text()
```
Feature Contribution Cumulative
Intercept = 1 29.1 29.1
RM = 6.495 -1.98 27.12
TAX = 329.0 -0.2 26.92
B = 383.61 -0.12 26.79
CHAS = 0.0 -0.07 26.72
NOX = 0.433 -0.02 26.7
RAD = 7.0 0.0 26.7
INDUS = 6.09 0.01 26.71
DIS = 5.4917 -0.04 26.66
ZN = 34.0 0.01 26.67
PTRATIO = 16.1 0.04 26.71
AGE = 18.4 0.06 26.77
CRIM = 0.09266 1.33 28.11
LSTAT = 8.67 4.6 32.71
Final prediction 32.71
Baseline = 0
```python
#customized text form
explanation.text(fwidth=40, contwidth=40, cumulwidth = 40, digits=4)
```
Feature Contribution Cumulative
Intercept = 1 29.1 29.1
RM = 6.495 -1.9826 27.1174
TAX = 329.0 -0.2 26.9174
B = 383.61 -0.1241 26.7933
CHAS = 0.0 -0.0686 26.7247
NOX = 0.433 -0.0241 26.7007
RAD = 7.0 0.0 26.7007
INDUS = 6.09 0.0074 26.708
DIS = 5.4917 -0.0438 26.6642
ZN = 34.0 0.0077 26.6719
PTRATIO = 16.1 0.0385 26.7104
AGE = 18.4 0.0619 26.7722
CRIM = 0.09266 1.3344 28.1067
LSTAT = 8.67 4.6037 32.7104
Final prediction 32.7104
Baseline = 0
### Visual form of explanations
```python
explanation.visualize()
```
```python
#customize height, width and dpi of plot
explanation.visualize(figsize=(8,5),dpi=100)
```
```python
#for different baselines than zero
explanation = exp.explain(observation=data[302,:],direction="up",useIntercept=True) # baseline==intercept
explanation.visualize(figsize=(8,5),dpi=100)
```
