From de781dd9b0f22a8d65cb41247ffbb5498154ec42 Mon Sep 17 00:00:00 2001
From: Andrew DalPino <me@andrewdalpino.com>
Date: Thu, 6 Aug 2020 18:15:29 -0500
Subject: [PATCH] Update to Rubix ML 0.1.0

---
 README.md     | 20 +++++++++++++++++---
 composer.json |  3 +--
 train.php     | 11 +++++------
 validate.php  |  4 +++-
 4 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 20b055c..9c6eb67 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@ Since Softmax Classifier implements the [Verbose](https://docs.rubixml.com/en/la
 ```php
 use Rubix\ML\Other\Loggers\Screen;
 
-$estimator->setLogger(new Screen('HAR'));
+$estimator->setLogger(new Screen());
 ```
 
 ### Training
@@ -91,6 +91,17 @@ During training, the learner will record the training loss at each epoch which w
 $losses = $estimator->steps();
 ```
 
+Then we'll save the losses to a CSV file using the `toCSV` method on the [Unlabeled]() dataset object.
+
+```php
+use Rubix\ML\Datasets\Unlabeled;
+use function Rubix\ML\array_transpose;
+
+Unlabeled::build(array_transpose([$losses]))
+    ->toCSV(['losses'])
+    ->write('progress.csv');
+```
+
 This is an example of a line plot of the Cross Entropy cost function from a training session. As you can see, the model learns quickly during the early epochs with slower training nearing the final stage as the learner fine-tunes the model parameters.
 
 ![Cross Entropy Loss](https://raw.githubusercontent.com/RubixML/HAR/master/docs/images/training-loss.svg?sanitize=true)
@@ -150,10 +161,14 @@ $report = new AggregateReport([
 ]);
 ```
 
-Now, generate the report using the predictions and labels from the testing set.
+Now, generate the report using the predictions and labels from the testing set. In addition, we'll echo the report to the console and save the results to a JSON file for reference later.
 
 ```php
 $results = $report->generate($predictions, $dataset->labels());
+
+echo $results;
+
+$results->toJSON()->write('report.json');
 ```
 
 To execute the validation script, enter the following command at the command prompt.
@@ -187,7 +202,6 @@ The output of the report should look something like the output below. Nice work!
             "cardinality": 2947
         },
     }
-
 ]
 ```
 
diff --git a/composer.json b/composer.json
index 9d4ef1c..33a3142 100644
--- a/composer.json
+++ b/composer.json
@@ -21,8 +21,7 @@
     ],
     "require": {
         "php": ">=7.2",
-        "league/csv": "^9.5",
-        "rubix/ml": "^0.1.0-rc2"
+        "rubix/ml": "^0.1.0"
     },
     "suggest": {
         "ext-tensor": "For faster training and inference"
diff --git a/train.php b/train.php
index 5400e02..c0c529d 100644
--- a/train.php
+++ b/train.php
@@ -12,7 +12,7 @@
 use Rubix\ML\NeuralNet\Optimizers\Momentum;
 use Rubix\ML\Persisters\Filesystem;
 use Rubix\ML\Other\Loggers\Screen;
-use League\Csv\Writer;
+use Rubix\ML\Datasets\Unlabeled;
 
 use function Rubix\ML\array_transpose;
 
@@ -30,7 +30,7 @@
     new Filesystem('har.model')
 );
 
-$estimator->setLogger(new Screen('HAR'));
+$estimator->setLogger(new Screen());
 
 echo 'Training ...' . PHP_EOL;
 
@@ -38,10 +38,9 @@
 
 $losses = $estimator->steps();
 
-$writer = Writer::createFromPath('progress.csv', 'w+');
-
-$writer->insertOne(['loss']);
-$writer->insertAll(array_transpose([$losses]));
+Unlabeled::build(array_transpose([$losses]))
+    ->toCSV(['losses'])
+    ->write('progress.csv');
 
 echo 'Progress saved to progress.csv' . PHP_EOL;
 
diff --git a/validate.php b/validate.php
index a4a640a..b231594 100644
--- a/validate.php
+++ b/validate.php
@@ -29,6 +29,8 @@
 
 $results = $report->generate($predictions, $dataset->labels());
 
-file_put_contents('report.json', json_encode($results, JSON_PRETTY_PRINT));
+echo $results;
+
+$results->toJSON()->write('report.json');
 
 echo 'Report saved to report.json' . PHP_EOL;
\ No newline at end of file