Black Tree AutoML

Installation

Download the executable file for Black Tree for MacOS 10.10 or later, which is free for non-commercial purposes:

Simply download the application file, open it, and this causes source code files to be downloaded to the following directory:

“/Users/[yourname]/Documents/BlackTree/TemplateCode/”.

This will create some modest delay, only the first time the application is run. Note that because of Mac security settings, you must right-click the application file the first time you open it, and select “Open”, which will cause a security prompt to appear notifying you that you downloaded the application from the Internet, and that you’d like to open it anyway.

Then, open Octave, which you can download for free, and type the following into the command line, and press Enter:

addpath(“/Users/[yourname]/Documents/BlackTree/TemplateCode/”)

savepath

This completes the installation of Black Tree.

Running Black Tree

Data Classification / Clustering

To run Data Classification / Clustering, simply (1) select a Training File and Output Path, (2) select an Algorithm, (3) press “EXE”, then (4) type “BlackTree”, no spaces, into Octave’s command line, and press Enter, that’s it. Note that the Free Version does not allow you to select both a training and testing file, and instead runs the selected algorithm on a single dataset. Both the Pro and Massive Versions of course allow for Training / Testing. Note that the dataset must be an M x (N+1), plain text, CSV file, with integer classifier labels in column N+1. You can download the datasets mentioned on the homepage in a ready-to-use format from DropBox (UCI Datasets, MNIST Fashion, MRI Dataset). The accuracies and runtimes on the homepage were generated using Supervised Delta Classification.

Image Classification

For Image Classification, you must first install Octave’s image package (also free), which can be done by typing the following into Octave’s command line:

pkg install -forge image

If for whatever reason, the package fails to download (e.g., no internet connection), then you can manually install the image package by downloading it from SourceForge, and then typing the following code into Octave’s command line:

pkg install [local_filename],

where “local_filename” is the full path and filename to the image package on your machine.

Then, select the first image in the dataset (this is the “Training File”). Black Tree will automatically load and process the entire folder of images, though they must be sequentially named files, with identical names and extensions (e.g., “MNIST_fashion1.jpg”, “MNIST_fashion2.jpg”, … ). The classifiers for the images must be in the same folder, in a plain text, CSV file named “class_vector.txt”, in order, with no other information or spacing. Then select an Output Path, select “Image Classification” as the algorithm, and enter the number of training images (M) in the dataset in CMND, as M,0, and press “EXE”. This tells the algorithms that there are M training images, and 0 testing images (recall, the Free Version does not allow for Testing datasets). Then press “EXE”, and simply type the word “BlackTree” (no spaces) into Octave’s command line, and press Enter.

As noted, you cannot select the prediction algorithm, as it is automatically fixed to Supervised Delta Classification for Image Classification. Moreover, you cannot select the image pre-processing algorithm, which is described in Section 1.3 of Vectorized Deep Learning, that generates super-pixel representations of each image in the dataset, thereby compressing them, allowing for extremely fast clustering and prediction. Finally, the images must be greyscale, and the dataset cannot contain more than 2,500 images.