<?php

namespace Rubix\ML\Tests\Transformers;

use Rubix\ML\Datasets\Unlabeled;
use Rubix\ML\Transformers\Stateful;
use Rubix\ML\Transformers\Transformer;
use Rubix\ML\Transformers\OneHotEncoder;
use PHPUnit\Framework\TestCase;

/**
 * @group Transformers
 * @covers \Rubix\ML\Transformers\OneHotEncoder
 */
class OneHotEncoderTest extends TestCase
{
    /**
     * @var OneHotEncoder
     */
    protected $transformer;

    /**
     * @before
     */
    protected function setUp() : void
    {
        $this->transformer = new OneHotEncoder();
    }

    /**
     * @test
     */
    public function build() : void
    {
        $this->assertInstanceOf(OneHotEncoder::class, $this->transformer);
        $this->assertInstanceOf(Transformer::class, $this->transformer);
        $this->assertInstanceOf(Stateful::class, $this->transformer);
    }

    /**
     * @test
     */
    public function fitTransform() : void
    {
        $dataset = new Unlabeled([
            ['nice', 'furry', 'friendly'],
            ['mean', 'furry', 'loner'],
            ['nice', 'rough', 'friendly'],
            ['mean', 'rough', 'friendly'],
        ]);

        $this->transformer->fit($dataset);

        $this->assertTrue($this->transformer->fitted());

        $categories = $this->transformer->categories();

        $this->assertIsArray($categories);
        $this->assertCount(3, $categories);
        $this->assertContainsOnly('array', $categories);

        $dataset->apply($this->transformer);

        $expected = [
            [1, 0, 1, 0, 1, 0],
            [0, 1, 1, 0, 0, 1],
            [1, 0, 0, 1, 1, 0],
            [0, 1, 0, 1, 1, 0],
        ];

        $this->assertEquals($expected, $dataset->samples());
    }
}