From 6f731b2582a4f1ad38adf8f970f7bda3c80e1e0e Mon Sep 17 00:00:00 2001
From: Andrew DalPino <me@andrewdalpino.com>
Date: Thu, 18 Jun 2020 15:12:36 -0500
Subject: [PATCH] Added composer installation instructions

---
 README.md     | 36 +++++++++++++++++++++++-------------
 composer.json |  8 ++++++++
 2 files changed, 31 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index c17b8dd1..bc110aa9 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,14 @@ This is a multilayer feed forward neural network for text sentiment classificati
 - **Difficulty:** Hard
 - **Training time:** Hours
 
+## Installation
+Clone the project locally using [Composer](https://getcomposer.org/):
+```sh
+$ composer create-project rubix/sentiment
+```
+
+> **Note:** Installation may take longer than usual because of the large dataset.
+
 ## Requirements
 - [PHP](https://php.net) 7.2 or above
 
@@ -11,19 +19,6 @@ This is a multilayer feed forward neural network for text sentiment classificati
 - [Tensor extension](https://github.com/RubixML/Tensor) for faster training and inference
 - 12G of system memory or more
 
-## Installation
-Clone the project locally with [Git](https://git-scm.com/):
-```sh
-$ git clone https://github.com/RubixML/Sentiment
-```
-
-> **Note:** Cloning may take longer than usual because of the large dataset.
-
-Install project dependencies with [Composer](http://getcomposer.org/):
-```sh
-$ composer install
-```
-
 ## Tutorial
 
 ### Introduction
@@ -142,6 +137,11 @@ Finally, we save the model so we can load it later in our other scripts.
 $estimator->save();
 ```
 
+Now you're ready to run the training script from the command line.
+```php
+$ php train.php
+```
+
 ### Cross Validation
 To test the generalization performance of the trained network we'll use the testing samples provided to us to generate predictions and then analyze them compared to their ground-truth labels using a cross validation report. Note that we do not use any training data for cross validation because we want to test the model on samples it has never seen before.
 
@@ -202,6 +202,11 @@ To generate the report, pass in the predictions along with the labels from the t
 $results = $report->generate($predictions, $dataset->labels());
 ```
 
+Execute the validation script from the command line like we see below.
+```sh
+$ php validate.php
+```
+
 Take a look at the report and see how well the model performs. According to the example report below, our model is 87% accurate.
 
 ```json
@@ -315,6 +320,11 @@ $prediction = $estimator->predictSample([$text]);
 echo "The sentiment is: $prediction" . PHP_EOL;
 ```
 
+To run the prediction script enter the following on the command line.
+```sh
+php predict.php
+```
+
 **Output**
 
 ```sh
diff --git a/composer.json b/composer.json
index ba6bebf5..b5f2013a 100644
--- a/composer.json
+++ b/composer.json
@@ -25,6 +25,14 @@
         "league/csv": "^9.5",
         "rubix/ml": "^0.1.0-rc3"
     },
+    "suggest": {
+        "ext-tensor": "For faster training and inference"
+    },
+    "scripts": {
+        "predict": "@php predict.php",
+        "train": "@php train.php",
+        "test": "@php validate.php"
+    },
     "config": {
         "preferred-install": "dist",
         "sort-packages": true