Metadata-Version: 2.1
Name: exmatrix
Version: 0.1.2
Summary: A Python package to the ExMatrix method, supporting Random Forest models interpretability.
Home-page: https://gitlab.com/popolinneto/exmatrix
Author: Mario Popolin Neto
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Platform: UNKNOWN
Classifier: License :: Free for non-commercial use
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: drawSvg (>=1.6.0)
Requires-Dist: matplotlib (>=2.1.1)
Requires-Dist: numpy (>=1.16.0)
Requires-Dist: scikit-learn (>=0.20.0)

# ExMatrix Method

The Explainable Matrix (ExMatrix) is a novel method for Random Forest (RF) interpretability based on the visual
representations of logic rules. ExMatrix supports global and local explanations of RF models enabling tasks that involve the overview of models and the auditing of classification processes. The key idea is to explore logic rules by demand using matrix visualizations, where rows are rules, columns are features, and cells are rules predicates.

For presenting the method, we utilize the Iris Dataset.

**Cite us**:  M. Popolin Neto and F. V. Paulovich. Explainable Matrix – Visualization for Global and Local Interpretability of Random Forest Classification Ensembles. In 2020 IEEE Conference on Visual Analytics Science and Technology (VAST), pages aa – bb, Oct 2020.


## Instalation

Run the following to install:

```pyhton
pip install exmatrix
```

## Creating the Random Forest model with sklearn


```python
import numpy as np
import sklearn.datasets as datasets
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score


dataset = datasets.load_iris()

X = dataset.data
y = dataset.target

feature_names = dataset.feature_names
target_names = dataset.target_names


sss = list( StratifiedShuffleSplit( n_splits = 1, test_size = 0.30, random_state = 68269 ).split( X, y ) )
train_indexes = sss[ 0 ][ 0 ]
test_indexes = sss[ 0 ][ 1 ]

X_train, X_test = X[ train_indexes ], X[ test_indexes ]
y_train, y_test = y[ train_indexes ], y[ test_indexes ]


kargs = eval( "{'criterion': 'gini', 'n_estimators': 3, 'max_depth': 3, 'max_leaf_nodes': 4, 'random_state': 68269, 'bootstrap': False}" )
clf = RandomForestClassifier( **kargs )
clf.fit( X_train, y_train )


y_true, y_pred = y_test, clf.predict( X_test )
accuracy = accuracy_score( y_true, y_pred )
print( 'accuracy RF-3', accuracy )
```

    accuracy RF-3 0.9555555555555556


## Interpreting the Random Forest model with ExMatrix


```python
from exmatrix import ExplainableMatrix

exm = ExplainableMatrix( n_features = len( feature_names ), n_classes = len( target_names ), feature_names = np.array( feature_names ), class_names = np.array( target_names ) )
exm.rule_extration( clf, X, y, clf.feature_importances_ )
print( 'n_rules RF-3', exm.n_rules_ )
```

    n_rules RF-3 12


### ExMatrix Global Expanation


```python
exp = exm.explanation( info_text = '\ntrees 3\nmax-depth 3\n\naccuracy 0.96\nerror 0.04\n' )
exp.create_svg( draw_row_labels = True, draw_col_labels = True, draw_rows_line = True, draw_cols_line = True, col_label_degrees = 10, width = 1990, height = 940, margin_bottom = 150 )
exp.save( 'IrisFlowerGE.png', pixel_scale = 5 )
exp.save( 'IrisFlowerGE.svg' )
exp.display_jn()
```




![svg](https://popolinneto.gitlab.io/exmatrix/readme/IrisFlowerGE.svg)



### ExMatrix Local Expanations for Instance 13


```python
exp = exm.explanation( exp_type = 'local-used', x_k = X_test[ 13 ], r_order = 'coverage', f_order = 'importance', info_text = '\ninstance 13\n' )
exp.create_svg( draw_x_k = True, draw_row_labels = True, draw_col_labels = True, draw_rows_line = True, draw_cols_line = True, col_label_degrees = 10, width = 1890, height = 720, margin_bottom = 150 )
exp.save( 'IrisFlowerLEUR-13.png', pixel_scale = 5 )
exp.save( 'IrisFlowerLEUR-13.svg' )
exp.display_jn()
```




![svg](https://popolinneto.gitlab.io/exmatrix/readme/IrisFlowerLEUR-13.svg)




```python
exp = exm.explanation( exp_type = 'local-closest', x_k = X_test[ 13 ], r_order = 'delta change', f_order = 'importance', info_text = '\ninstance 13\n' )
exp.create_svg( draw_x_k = True, draw_deltas = True, cell_background = True, draw_row_labels = True, draw_col_labels = True, draw_rows_line = True, draw_cols_line = True, col_label_degrees = 10, width = 1890, height = 720, margin_bottom = 150 )
exp.save( 'IrisFlowerLESC-13.png', pixel_scale = 5 )
exp.save( 'IrisFlowerLESC-13.svg' )
exp.display_jn()
```




![svg](https://popolinneto.gitlab.io/exmatrix/readme/IrisFlowerLESC-13.svg)



## Developing

Download the exmatrix project on gitlab (https://gitlab.com/popolinneto/exmatrix) and run the following to build and install locally:

```pyhton
python setup.py sdist bdist_wheel
pip install -e .
```


