This page was generated from examples/anchor_tabular_iris.ipynb.

Anchor explanations on the Iris dataset

import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from alibi.explainers import AnchorTabular

Load iris dataset

dataset = load_iris()
feature_names = dataset.feature_names
class_names = list(dataset.target_names)

Define training and test set

idx = 145
X_train,Y_train = dataset.data[:idx,:], dataset.target[:idx]
X_test, Y_test = dataset.data[idx+1:,:], dataset.target[idx+1:]

Train Random Forest model

np.random.seed(0)
clf = RandomForestClassifier(n_estimators=50)
clf.fit(X_train, Y_train)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=50,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

Define predict function

predict_fn = lambda x: clf.predict_proba(x)

Initialize and fit anchor explainer for tabular data

explainer = AnchorTabular(predict_fn, feature_names)

Discretize the ordinal features into quartiles

explainer.fit(X_train, disc_perc=(25, 50, 75))

AnchorTabular(meta={
    'name': 'AnchorTabular',
    'type': ['blackbox'],
    'explanations': ['local'],
    'params': {'seed': None, 'disc_perc': (25, 50, 75)}
})

Getting an anchor

Below, we get an anchor for the prediction of the first observation in the test set. An anchor is a sufficient condition - that is, when the anchor holds, the prediction should be the same as the prediction for this instance.

idx = 0
print('Prediction: ', class_names[explainer.predictor(X_test[idx].reshape(1, -1))[0]])

Prediction:  virginica

We set the precision threshold to 0.95. This means that predictions on observations where the anchor holds will be the same as the prediction on the explained instance at least 95% of the time.

explanation = explainer.explain(X_test[idx], threshold=0.95)
print('Anchor: %s' % (' AND '.join(explanation.anchor)))
print('Precision: %.2f' % explanation.precision)
print('Coverage: %.2f' % explanation.coverage)

Anchor: petal width (cm) > 1.80 AND sepal width (cm) <= 2.80
Precision: 0.98
Coverage: 0.32