Update to Rubix ML 0.3.0

LICENSE.md → LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 The Rubix ML Community
+Copyright (c) 2020 Rubix ML
 Copyright (c) 2020 Andrew DalPino
 
 Permission is hereby granted, free of charge, to any person obtaining a copy

README.md

@@ -28,4 +28,4 @@ On the map ...
 >- Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
 
 ## License
-The code is licensed [MIT](LICENSE.md) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+The code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

composer.json

@@ -3,7 +3,7 @@
     "type": "project",
     "description": "Use the K Nearest Neighbors algorithm to predict the probability of a divorce.",
     "homepage": "https://github.com/RubixML/Divorce",
-    "license": "Apache-2.0",
+    "license": "MIT",
     "keywords": [
         "classification", "cross validation", "dataset", "data science", "divorce prediction",
         "example project", "k nearest neighbors", "knn", "machine learning", "ml", "php",
@@ -12,14 +12,13 @@
     "authors": [
         {
             "name": "Andrew DalPino",
-            "email": "me@andrewdalpino.com",
-            "homepage": "https://andrewdalpino.com",
+            "homepage": "https://github.com/andrewdalpino",
             "role": "Lead Engineer"
         }
     ],
     "require": {
         "php": ">=7.2",
-        "rubix/ml": "^0.1.0-rc2"
+        "rubix/ml": "^0.3.0"
     },
     "scripts": {
         "train": "@php train.php"

train.php

@@ -2,33 +2,32 @@
 
 include __DIR__ . '/vendor/autoload.php';
 
+use Rubix\ML\Other\Loggers\Screen;
 use Rubix\ML\Datasets\Labeled;
 use Rubix\ML\Extractors\NDJSON;
 use Rubix\ML\Classifiers\KNearestNeighbors;
 use Rubix\ML\CrossValidation\Metrics\Accuracy;
 
-echo 'Loading data into memory ...' . PHP_EOL;
+$logger = new Screen();
+
+$logger->info('Loading data into memory');
 
 $dataset = Labeled::fromIterator(new NDJSON('dataset.ndjson'));
 
 [$training, $testing] = $dataset->stratifiedSplit(0.8);
 
 $estimator = new KNearestNeighbors(3);
 
-echo 'Training ...' . PHP_EOL;
+$logger->info('Training');
 
 $estimator->train($training);
 
-echo 'Making predictions ...' . PHP_EOL;
-
-$predictions = $estimator->predict($testing->randomize());
-
-echo 'Example predictions:' . PHP_EOL;
+$logger->info('Making predictions');
 
-print_r(array_slice($predictions, 0, 3));
+$predictions = $estimator->predict($testing);
 
 $metric = new Accuracy();
 
 $score = $metric->score($predictions, $testing->labels());
 
-echo 'Accuracy is ' . (string) ($score * 100.0) . '%' . PHP_EOL;
+$logger->info("Accuracy is $score");