cl-random-forest

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• cl-random-forest

[[http://quickdocs.org/cl-random-forest/][http://quickdocs.org/badge/cl-random-forest.svg]] [[https://github.com/masatoi/cl-random-forest/actions?query=workflow%3ACI][https://github.com/masatoi/cl-random-forest/workflows/CI/badge.svg]]

Cl-random-forest is a implementation of Random Forest for multiclass classification and univariate regression written in Common Lisp. It also includes a implementation of Global Refinement of Random Forest (Ren, Cao, Wei and Sun. "Global Refinement of Random Forest" CVPR2015). This refinement makes faster and more accurate than standard Random Forest.

** Features and Limitations

• Faster and more accurate than other major implementations such as scikit-learn (Python/Cython) or ranger (R/C++)

| | scikit-learn | ranger | cl-random-forest | | MNIST | 96.95%, 41.72sec | 97.17%, 69.34sec | 98.29%, 12.68sec | | letter | 96.38%, 2.569sec | 96.42%, 1.828sec | 97.32%, 3.497sec | | covtype | 94.89%, 263.7sec | 83.95%, 139.0sec | 96.01%, 103.9sec | | usps | 93,47%, 3.583sec | 93.57%, 11.70sec | 94.96%, 0.686sec |

• Supporting parallelization of training and prediction (tested on SBCL, CCL)

• It also includes Global Pruning algorithm of Random Forest which can make the model extremely compact

• Currently, multivariate regression is not implemented

** Installation

In quicklisp’s local-projects directory,

#+BEGIN_SRC git clone https://github.com/masatoi/cl-online-learning.git git clone https://github.com/masatoi/cl-random-forest.git #+END_SRC

In Lisp,

#+BEGIN_SRC lisp (ql:quickload :cl-random-forest) #+END_SRC

When using Roswell,

#+BEGIN_SRC ros install masatoi/cl-online-learning masatoi/cl-random-forest #+END_SRC

** Usage *** Classification **** Prepare training dataset A dataset consists of a target vector and a input data matrix. For classification, the target vector should be a fixnum simple-vector and the data matrix should be a 2-dimensional single-float array whose row corresponds one datum. Note that the target is a integer starting from 0. For example, the following dataset is valid for 4-class classification with 2-dimensional input.

#+BEGIN_SRC lisp (defparameter target (make-array 11 :element-type 'fixnum :initial-contents '(0 0 1 1 2 2 2 3 3 3 3)))

(defparameter datamatrix (make-array '(11 2) :element-type 'single-float :initial-contents '((-1.0 -2.0) (-2.0 -1.5) (1.0 -2.0) (3.0 -1.5) (-2.0 2.0) (-3.0 1.0) (-2.0 1.0) (3.0 2.0) (2.0 2.0) (1.0 2.0) (1.0 1.0)))) #+END_SRC

[[./docs/img/clrf-example-simple.png]]

**** Make Decision Tree

To construct a decision tree, MAKE-DTREE function is available. This function receives the number of classes, the data matrix and the target vector and then returns a decision tree object. This function also receives optionally the max depth of the tree and the minimum number of samples in the region the tree divides and the number of trials of splits.

#+BEGIN_SRC lisp (defparameter n-class 4)

(defparameter dtree (make-dtree n-class datamatrix target :max-depth 5 :min-region-samples 1 :n-trial 10)) #+END_SRC

Next, make a prediction from the constructed decision tree with PREDICT-DTREE function. For example, to predict the first datum in the data matrix with this decision tree, do as follows.

#+BEGIN_SRC lisp (predict-dtree dtree datamatrix 0) ;; => 0 (correct class id) #+END_SRC

To make predictions for the entire dataset and calculate the accuracy, use TEST-DTREE function.

#+BEGIN_SRC lisp (test-dtree dtree datamatrix target) ;; Accuracy: 100.0%, Correct: 11, Total: 11 #+END_SRC

**** Make Random Forest

To construct a random forest, MAKE-FOREST function is available. In addition to the MAKE-DTREE function arguments, this function receives optionally the number of decision trees and the bagging ratio that is used for sampling from training data to construct each sub decision trees.

#+BEGIN_SRC lisp (defparameter forest (make-forest n-class datamatrix target :n-tree 10 :bagging-ratio 1.0 :max-depth 5 :min-region-samples 1 :n-trial 10)) #+END_SRC

Prediction and test of random forest are done in the almost same way as decision trees. PREDICT-FOREST function and TEST-FOREST function are available for each purpose.

#+BEGIN_SRC lisp (predict-forest forest datamatrix 0) ;; => 0 (correct class id)

(test-forest forest datamatrix target) ;; Accuracy: 100.0%, Correct: 11, Total: 11 #+END_SRC

**** Global Refinement of Random Forest

Cl-random-forest has a way to improve pre-trained random forest using global information between each decision trees. For this purpose, we make an another dataset from original dataset and pre-trained random forest. When an original datum input into the random forest, the datum enters into a region which corresponds one leaf node for each decision trees. The datum of the new dataset represents which position of leaf node the original datum entered for each decision tree. Then we train a linear classifier (AROW) using this new dataset and the original target.

#+BEGIN_SRC lisp ;; Make refine learner (defparameter forest-learner (make-refine-learner forest))

;; Make refine dataset (defparameter forest-refine-dataset (make-refine-dataset forest datamatrix))

;; Train refine learner (train-refine-learner forest-learner forest-refine-dataset target)

;; Test refine learner (test-refine-learner forest-learner forest-refine-dataset target) #+END_SRC

This TRAIN-REFINE-LEARNER function can be used to learn the dataset collectively, but it may be necessary to call this function several times until learning converges. TRAIN-REFINE-LEARNER-PROCESS function is used for training until converged.

#+BEGIN_SRC lisp (train-refine-learner-process forest-learner forest-refine-dataset target forest-refine-dev-dataset dev-target) #+END_SRC

**** Global Pruning of Random Forest

***** Pruning Global pruning is a method for compactization of the model size of the random forest using information of the global-refinement learner. A leaf node in a decision tree is no longer necessary when its corresponding element of the weight vector of the global-refinement learner has a small value norm.

To prune a forest destructively, after training the global-refinement learner, run PRUNING! function.

#+BEGIN_SRC lisp ;; Prune forest (pruning! forest forest-learner 0.1) #+END_SRC

The third argument is pruning rate. In this case, 10% leaf nodes are deleted.

***** Re-learning

After pruning, it is required to re-learn the global-refinement learner.

#+BEGIN_SRC lisp ;; Re-learning of refine-learner (setf forest-refine-dataset (make-refine-dataset forest datamatrix)) (setf forest-learner (make-refine-learner forest)) (train-refine-learner forest-learner forest-refine-dataset target) (test-refine-learner forest-learner forest-refine-dataset target) #+END_SRC

The following figure shows the accuracy for test dataset and the number of leaf nodes when repeating pruning and re-learning on the MNIST dataset. We can see that the performance hardly changes even if the number of leaf nodes decreases to about 1/10.

[[./docs/img/clrf-mnist-pruning.png]]

**** Parallelization The following several functions can be parallelized with [[https://lparallel.org/][lparallel]].

• MAKE-FOREST
• MAKE-REGRESSION-FOREST
• MAKE-REFINE-DATASET
• TRAIN-REFINE-LEARNER

To enable/disable parallelization, set lparallel's kernel object. For example, to enable parallelization with 4 threads,

#+BEGIN_SRC lisp ;; Enable parallelization (setf lparallel:kernel (lparallel:make-kernel 4))

;; Disable parallelization (setf lparallel:kernel nil) #+END_SRC

*** Regression **** Prepare training dataset In case of classification, the target is a vector of integer values, whereas in regression is a vector of continuous values.

#+BEGIN_SRC lisp (defparameter n 100)

(defparameter datamatrix (let ((arr (make-array (list n 1) :element-type 'single-float))) (loop for i from 0 below n do (setf (aref arr i 0) (random-uniform (- pi) pi))) arr))

(defparameter target (let ((arr (make-array n :element-type 'single-float))) (loop for i from 0 below n do (setf (aref arr i) (+ (sin (aref datamatrix i 0)) (random-normal :sd 0.1)))) arr))

(defparameter test (let ((arr (make-array (list n 1) :element-type 'single-float))) (loop for i from 0 below n for x from (- pi) to pi by (/ (* 2 pi) n) do (setf (aref arr i 0) x)) arr))

(defparameter test-target (let ((arr (make-array n :element-type 'single-float))) (loop for i from 0 below n do (setf (aref arr i) (sin (aref test i 0)))) arr)) #+END_SRC

**** Make Regression Tree

#+BEGIN_SRC lisp ;; Make regression tree (defparameter rtree (make-rtree datamatrix target :max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing (test-rtree rtree test test-target) ; RMSE: 0.09220732459820888

;; Make a prediction for first data point of test dataset (predict-rtree rtree test 0) ; => -0.08374452528780077 #+END_SRC

**** Make Random Forest for Regression

#+BEGIN_SRC lisp ;; Make regression tree forest (defparameter rforest (make-regression-forest datamatrix target :n-tree 100 :bagging-ratio 0.6 :max-depth 5 :min-region-samples 5 :n-trial 10))

;; Testing (test-regression-forest rforest test test-target) ; RMSE: 0.05006872795207973

;; Make a prediction for first data point of test dataset (predict-regression-forest rforest test 0) ; => -0.16540771296145781 #+END_SRC

[[./docs/img/clrf-regression.png]]

** Author Satoshi Imai ([email protected])

** Licence This software is released under the MIT License, see LICENSE.txt.