Implement OneHotEncoder (#384)

Author: akondas

Diff for: CHANGELOG.md

@@ -4,9 +4,16 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## [0.9.0] - Unreleased
 ### Added
 - [Preprocessing] Implement LabelEncoder
+- [Preprocessing] Implement ColumnFilter
+- [Preprocessing] Implement LambdaTransformer
+- [Preprocessing] Implement NumberConverter
+- [Preprocessing] Implement OneHotEncoder
+- [Workflow] Implement FeatureUnion
+- [Metric] Add Regression metrics: meanSquaredError, meanSquaredLogarithmicError, meanAbsoluteError, medianAbsoluteError, r2Score, maxError
+- [Regression] Implement DecisionTreeRegressor 
 
 ## [0.8.0] - 2019-03-20
 ### Added

Diff for: README.md

@@ -107,6 +107,7 @@ Public datasets are available in a separate repository [php-ai/php-ml-datasets](
     * LambdaTransformer
     * NumberConverter
     * ColumnFilter
+    * OneHotEncoder
 * Feature Extraction
     * [Token Count Vectorizer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/token-count-vectorizer/)
         * NGramTokenizer

Diff for: src/Preprocessing/OneHotEncoder.php

@@ -0,0 +1,66 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Phpml\Preprocessing;
+
+use Phpml\Exception\InvalidArgumentException;
+
+final class OneHotEncoder implements Preprocessor
+{
+    /**
+     * @var bool
+     */
+    private $ignoreUnknown;
+
+    /**
+     * @var array
+     */
+    private $categories = [];
+
+    public function __construct(bool $ignoreUnknown = false)
+    {
+        $this->ignoreUnknown = $ignoreUnknown;
+    }
+
+    public function fit(array $samples, ?array $targets = null): void
+    {
+        foreach (array_keys(array_values(current($samples))) as $column) {
+            $this->fitColumn($column, array_values(array_unique(array_column($samples, $column))));
+        }
+    }
+
+    public function transform(array &$samples, ?array &$targets = null): void
+    {
+        foreach ($samples as &$sample) {
+            $sample = $this->transformSample(array_values($sample));
+        }
+    }
+
+    private function fitColumn(int $column, array $values): void
+    {
+        $count = count($values);
+        foreach ($values as $index => $value) {
+            $map = array_fill(0, $count, 0);
+            $map[$index] = 1;
+            $this->categories[$column][$value] = $map;
+        }
+    }
+
+    private function transformSample(array $sample): array
+    {
+        $encoded = [];
+        foreach ($sample as $column => $feature) {
+            if (!isset($this->categories[$column][$feature]) && !$this->ignoreUnknown) {
+                throw new InvalidArgumentException(sprintf('Missing category "%s" for column %s in trained encoder', $feature, $column));
+            }
+
+            $encoded = array_merge(
+                $encoded,
+                $this->categories[$column][$feature] ?? array_fill(0, count($this->categories[$column]), 0)
+            );
+        }
+
+        return $encoded;
+    }
+}

Diff for: tests/Preprocessing/OneHotEncoderTest.php

@@ -0,0 +1,66 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Phpml\Tests\Preprocessing;
+
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Preprocessing\OneHotEncoder;
+use PHPUnit\Framework\TestCase;
+
+final class OneHotEncoderTest extends TestCase
+{
+    public function testOneHotEncodingWithoutIgnoreUnknown(): void
+    {
+        $samples = [
+            ['fish', 'New York', 'regression'],
+            ['dog', 'New York', 'regression'],
+            ['fish', 'Vancouver', 'classification'],
+            ['dog', 'Vancouver', 'regression'],
+        ];
+
+        $encoder = new OneHotEncoder();
+        $encoder->fit($samples);
+        $encoder->transform($samples);
+
+        self::assertEquals([
+            [1, 0, 1, 0, 1, 0],
+            [0, 1, 1, 0, 1, 0],
+            [1, 0, 0, 1, 0, 1],
+            [0, 1, 0, 1, 1, 0],
+        ], $samples);
+    }
+
+    public function testThrowExceptionWhenUnknownCategory(): void
+    {
+        $encoder = new OneHotEncoder();
+        $encoder->fit([
+            ['fish', 'New York', 'regression'],
+            ['dog', 'New York', 'regression'],
+            ['fish', 'Vancouver', 'classification'],
+            ['dog', 'Vancouver', 'regression'],
+        ]);
+        $samples = [['fish', 'New York', 'ka boom']];
+
+        $this->expectException(InvalidArgumentException::class);
+
+        $encoder->transform($samples);
+    }
+
+    public function testIgnoreMissingCategory(): void
+    {
+        $encoder = new OneHotEncoder(true);
+        $encoder->fit([
+            ['fish', 'New York', 'regression'],
+            ['dog', 'New York', 'regression'],
+            ['fish', 'Vancouver', 'classification'],
+            ['dog', 'Vancouver', 'regression'],
+        ]);
+        $samples = [['ka', 'boom', 'riko']];
+        $encoder->transform($samples);
+
+        self::assertEquals([
+            [0, 0, 0, 0, 0, 0],
+        ], $samples);
+    }
+}