exmatrix 0.1.0

A Python package to the ExMatrix method, supporting Random Forest models interpretability.

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:

pip install exmatrix

Creating the Random Forest model with sklearn

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

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

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()

ExMatrix Local Expanations for Instance 13

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()

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()

Developing

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

python setup.py sdist bdist_wheel
pip install -e .