xrf 0.1.1

ExPLAINABLE rANDOM fORESTS (xrf)

xrf is a Python package that implements random forest classifiers and regressors with example attribution, i.e., the predictions are associated with weight distributions over the training examples, where each prediction is the scalar product of the weights and targets of the training examples. The examples that are used when forming predictions can be constrained by their number or by their cumulative weight. When not constrained, the predictions are identical to what is output by random forest classifiers and regressors as implemented in scikit-learn

Installation

From PyPI

pip install xrf

Documentation

For the complete documentation, see xrf.readthedocs.io.

Quickstart

Classification forests

Let us start by importing the tic-tac-toe dataset from openml.org.

from sklearn.datasets import fetch_openml
from sklearn.preprocessing import OneHotEncoder

dataset = fetch_openml(name="tic-tac-toe", parser="auto")

y = dataset.target.values

X = OneHotEncoder().fit_transform(dataset.data.values).toarray()

Let us split the dataset into a training and a test set.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.75)

Let us now fit an explainable random forest classifier; we can use the same parameters as for standard random forest classifiers as implemented in scikit-learn.

from xrf import XRandomForestClassifier

rfx = XRandomForestClassifier(n_jobs=-1)
rfx.fit(X_train, y_train)

We get the predictions in the usual way, using either predict or predict_proba, here resulting in exactly the same output as the standard random forest classifiers in scikit-learn.

rfx.predict_proba(X_test)

array([[0.05, 0.95],
       [0.56, 0.44],
       [0.4 , 0.6 ],
       ...,
       [0.21, 0.79],
       [0.17, 0.83],
       [0.59, 0.41]])

We may now limit the number of examples involved in a prediction, e.g., to at most 5.

rfx.predict_proba(X_test, k=5)

array([[0.        , 1.        ],
       [0.85416634, 0.14583366],
       [0.34500622, 0.65499378],
       ...,
       [0.27464175, 0.72535825],
       [0.12693503, 0.87306497],
       [1.        , 0.        ]])

Let us also obtain the example attributions, by setting return_examples and return_weights to True.

predictions, examples, weights = rfx.predict_proba(X_test, k=5, 
                                                   return_examples=True, 
                                                   return_weights=True)

Let us also take a look at the example attributions; examples will contain the indexes of the training objects involved in each prediction, while weights will contain the corresponding weights.

examples

array([[ 26, 131,  40, 193, 169],
       [ 48, 121,  52, 164,   6],
       [203, 176, 213, 110,  99],
       ...,
       [ 52, 167, 194, 175,  53],
       [104,  71,  20,  35, 122],
       [ 33,  47, 188, 228, 120]])

weights

array([[0.23050922, 0.21026052, 0.19812573, 0.18882078, 0.17228375],
       [0.24554293, 0.20930998, 0.20651394, 0.19279949, 0.14583366],
       [0.2935989 , 0.25979051, 0.21957101, 0.12543522, 0.10160437],
       ...,
       [0.27464175, 0.23320384, 0.19853987, 0.15467345, 0.13894108],
       [0.32220957, 0.21056097, 0.20287181, 0.13742261, 0.12693503],
       [0.26857466, 0.20863132, 0.20008477, 0.18560888, 0.13710037]])

Regression forests

Let us import the Miami housing dataset from openml.org.

from sklearn.datasets import fetch_openml
from sklearn.preprocessing import OneHotEncoder

dataset = fetch_openml(name="miami_housing", parser="auto")

y = dataset.target.values
X = dataset.data.values

Let us split the dataset into a training and a test set.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.75)

Let us generate and apply an explainable random forest regressor without constraining the number of training examples involved in the predictions.

from xrf import XRandomForestRegressor

rfx = XRandomForestRegressor(n_jobs=-1)
rfx.fit(X_train, y_train)
rfx.predict(X_test)

array([492859., 193170., 260507., ..., 330824., 416856., 241969.])

We may now limit the number of examples involved in a prediction, e.g., to at most 5.

rfx.predict(X_test, k=5)

array([541411.11111111, 196994.81865285, 210900.81300813, ...,
       340516.66666667, 389410.25641026, 241550.27422303])

The example attributions are obtained by setting return_examples and return_weights to True.

predictions, examples, weights = rfx.predict(X_test, k=5,
                                             return_examples=True,
                                             return_weights=True)

We may check that the predictions are the same as the weighted targets of the training examples.

import numpy as np

weighted_predictions = np.sum([weights[i]*y_train[examples[i]] 
                               for i in range(len(weights))], axis=1)

np.allclose(predictions, weighted_predictions)

True

Citing xrf

If you use xrf for a scientific publication, you are kindly requested to cite the following paper:

Boström, H., 2024. Example-Based Explanations of Random Forest Predictions. International Symposium on Intelligent Data Analysis, Springer, pp. 185-196 Link

Bibtex entry:

@inproceedings{xrf,
  title={Example-Based Explanations of Random Forest Predictions},
  author={Bostr{\"o}m, Henrik},
  booktitle={International Symposium on Intelligent Data Analysis},
  pages={185--196},
  year={2024},
  organization={Springer}
}

---

Author: Henrik Boström ([e-mail]) Copyright 2024 Henrik Boström License: BSD 3 clause