Update to ML 1.0

README.md

@@ -11,7 +11,7 @@ $ composer create-project rubix/iris
 ```
 
 ## Requirements
-- [PHP](https://php.net) 7.2 or above
+- [PHP](https://php.net) 7.4 or above
 
 ## Tutorial
 
@@ -74,21 +74,13 @@ use Rubix\ML\CrossValidation\Metrics\Accuracy;
 $metric = new Accuracy();
 
 $score = $metric->score($predictions, $testing->labels());
-
-echo 'Accuracy is ' . (string) ($score * 100.0) . '%' . PHP_EOL;
 ```
 
 Now you're ready to run the training script from the command line.
 ```sh
 php train.php
 ```
 
-**Output**
-
-```sh
-Accuracy is 90%
-```
-
 ### Next Steps
 Congratulations on completing the introduction to machine learning in PHP with Rubix ML using the Iris dataset. Now you're ready to experiment on your own. For example, you may want to try different values of `k` or swap out the default [Euclidean](https://docs.rubixml.com/latest/kernels/distance/euclidean.html) distance kernel for another one such as [Manhattan](https://docs.rubixml.com/latest/kernels/distance/manhattan.html) or [Minkowski](https://docs.rubixml.com/latest/kernels/distance/minkowski.html).
 

composer.json

@@ -17,8 +17,8 @@
         }
     ],
     "require": {
-        "php": ">=7.2",
-        "rubix/ml": "^0.3.0"
+        "php": ">=7.4",
+        "rubix/ml": "^1.0"
     },
     "scripts": {
         "train": "@php train.php"

train.php

@@ -2,33 +2,32 @@
 
 include __DIR__ . '/vendor/autoload.php';
 
+use Rubix\ML\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');
 
 $training = Labeled::fromIterator(new NDJSON('dataset.ndjson'));
 
 $testing = $training->randomize()->take(10);
 
 $estimator = new KNearestNeighbors(5);
 
-echo 'Training ...' . PHP_EOL;
+$logger->info('Training');
 
 $estimator->train($training);
 
-echo 'Making predictions ...' . PHP_EOL;
+$logger->info('Making predictions');
 
 $predictions = $estimator->predict($testing);
 
-echo 'Example predictions:' . PHP_EOL;
-
-print_r(array_slice($predictions, 0, 3));
-
 $metric = new Accuracy();
 
 $score = $metric->score($predictions, $testing->labels());
 
-echo 'Accuracy is ' . (string) ($score * 100.0) . '%' . PHP_EOL;
+$logger->info("Accuracy is $score");