Update to phpunit 8 and bump min php to 7.2 (#367)

* Update to phpunit 8

* Require at least PHP 7.2

.gitignore

@@ -1,4 +1,5 @@
 /vendor/
-.php_cs.cache
 /build
 /tests/Performance/Data/*.csv
+.php_cs.cache
+.phpunit.result.cache

.travis.yml

@@ -4,14 +4,10 @@ matrix:
   fast_finish: true
 
   include:
-    - os: linux
-      php: '7.1'
-      env: DISABLE_XDEBUG="true" STATIC_ANALYSIS="true"
-
     - os: linux
       php: '7.2'
-      env: PHPUNIT_FLAGS="--coverage-clover build/logs/clover.xml"
-      
+      env: PHPUNIT_FLAGS="--coverage-clover build/logs/clover.xml" DISABLE_XDEBUG="true" STATIC_ANALYSIS="true"
+
     - os: linux
       php: '7.3'
 

README.md

@@ -1,6 +1,6 @@
 # PHP-ML - Machine Learning library for PHP
 
-[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.1-8892BF.svg)](https://php.net/)
+[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.2-8892BF.svg)](https://php.net/)
 [![Latest Stable Version](https://img.shields.io/packagist/v/php-ai/php-ml.svg)](https://packagist.org/packages/php-ai/php-ml)
 [![Build Status](https://travis-ci.org/php-ai/php-ml.svg?branch=master)](https://travis-ci.org/php-ai/php-ml)
 [![Documentation Status](https://readthedocs.org/projects/php-ml/badge/?version=master)](http://php-ml.readthedocs.org/)
@@ -15,7 +15,7 @@
 
 Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
 
-PHP-ML requires PHP >= 7.1.
+PHP-ML requires PHP >= 7.2.
 
 Simple example of classification:
 ```php

composer.json

@@ -20,14 +20,14 @@
         }
     ],
     "require": {
-        "php": "^7.1"
+        "php": "^7.2"
     },
     "require-dev": {
         "phpbench/phpbench": "^0.14.0",
         "phpstan/phpstan-phpunit": "^0.11",
         "phpstan/phpstan-shim": "^0.11",
         "phpstan/phpstan-strict-rules": "^0.11",
-        "phpunit/phpunit": "^7.0.0",
+        "phpunit/phpunit": "^8.0",
         "symplify/coding-standard": "^5.1",
         "symplify/easy-coding-standard": "^5.1"
     },

docs/index.md

@@ -1,6 +1,6 @@
 # PHP-ML - Machine Learning library for PHP
 
-[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.1-8892BF.svg)](https://php.net/)
+[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.2-8892BF.svg)](https://php.net/)
 [![Latest Stable Version](https://img.shields.io/packagist/v/php-ai/php-ml.svg)](https://packagist.org/packages/php-ai/php-ml)
 [![Build Status](https://travis-ci.org/php-ai/php-ml.svg?branch=master)](https://travis-ci.org/php-ai/php-ml)
 [![Documentation Status](https://readthedocs.org/projects/php-ml/badge/?version=master)](http://php-ml.readthedocs.org/)
@@ -15,7 +15,7 @@
 
 Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
 
-PHP-ML requires PHP >= 7.1.
+PHP-ML requires PHP >= 7.2.
 
 Simple example of classification:
 ```php

tests/Classification/Linear/LogisticRegressionTest.php

@@ -171,12 +171,12 @@ public function testPredictProbabilitySingleSample(): void
 
         $zero = $method->invoke($predictor, [0.1, 0.1], 0);
         $one = $method->invoke($predictor, [0.1, 0.1], 1);
-        self::assertEquals(1, $zero + $one, '', 1e-6);
+        self::assertEqualsWithDelta(1, $zero + $one, 1e-6);
         self::assertTrue($zero > $one);
 
         $zero = $method->invoke($predictor, [0.9, 0.9], 0);
         $one = $method->invoke($predictor, [0.9, 0.9], 1);
-        self::assertEquals(1, $zero + $one, '', 1e-6);
+        self::assertEqualsWithDelta(1, $zero + $one, 1e-6);
         self::assertTrue($zero < $one);
     }
 
@@ -213,9 +213,9 @@ public function testPredictProbabilityMultiClassSample(): void
         $two = $method->invoke($predictor, [3.0, 9.5], 2);
         $not_two = $method->invoke($predictor, [3.0, 9.5], 'not_2');
 
-        self::assertEquals(1, $zero + $not_zero, '', 1e-6);
-        self::assertEquals(1, $one + $not_one, '', 1e-6);
-        self::assertEquals(1, $two + $not_two, '', 1e-6);
+        self::assertEqualsWithDelta(1, $zero + $not_zero, 1e-6);
+        self::assertEqualsWithDelta(1, $one + $not_one, 1e-6);
+        self::assertEqualsWithDelta(1, $two + $not_two, 1e-6);
         self::assertTrue($zero < $two);
         self::assertTrue($one < $two);
     }

tests/DimensionReduction/KernelPCATest.php

@@ -33,22 +33,22 @@ public function testKernelPCA(): void
             [-0.13128352866095], [-0.20865959471756], [-0.17531601535848], [0.4240660966961],
             [0.36351946685163], [-0.14334173054136], [0.22454914091011], [0.15035027480881], ];
 
-        $kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15);
+        $kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 1, 15.);
         $reducedData = $kpca->fit($data);
 
         // Due to the fact that the sign of values can be flipped
         // during the calculation of eigenValues, we have to compare
         // absolute value of the values
         array_map(function ($val1, $val2) use ($epsilon): void {
-            self::assertEquals(abs($val1), abs($val2), '', $epsilon);
+            self::assertEqualsWithDelta(abs($val1), abs($val2), $epsilon);
         }, $transformed, $reducedData);
 
         // Fitted KernelPCA object can also transform an arbitrary sample of the
         // same dimensionality with the original dataset
         $newData = [1.25, 2.25];
         $newTransformed = [0.18956227539216];
         $newTransformed2 = $kpca->transform($newData);
-        self::assertEquals(abs($newTransformed[0]), abs($newTransformed2[0]), '', $epsilon);
+        self::assertEqualsWithDelta(abs($newTransformed[0]), abs($newTransformed2[0]), $epsilon);
     }
 
     public function testKernelPCAThrowWhenKernelInvalid(): void

tests/DimensionReduction/LDATest.php

@@ -51,7 +51,7 @@ public function testLDA(): void
             // absolute value of the values
             $row1 = array_map('abs', $row1);
             $row2 = array_map('abs', $row2);
-            self::assertEquals($row1, $row2, '', $epsilon);
+            self::assertEqualsWithDelta($row1, $row2, $epsilon);
         };
         array_map($check, $control, $transformed2);
 

tests/DimensionReduction/PCATest.php

@@ -42,7 +42,7 @@ public function testPCA(): void
         // during the calculation of eigenValues, we have to compare
         // absolute value of the values
         array_map(function ($val1, $val2) use ($epsilon): void {
-            self::assertEquals(abs($val1), abs($val2), '', $epsilon);
+            self::assertEqualsWithDelta(abs($val1), abs($val2), $epsilon);
         }, $transformed, $reducedData);
 
         // Test fitted PCA object to transform an arbitrary sample of the
@@ -52,7 +52,7 @@ public function testPCA(): void
             $newRow2 = $pca->transform($row);
 
             array_map(function ($val1, $val2) use ($epsilon): void {
-                self::assertEquals(abs($val1), abs($val2), '', $epsilon);
+                self::assertEqualsWithDelta(abs($val1), abs($val2), $epsilon);
             }, $newRow, $newRow2);
         }
     }

tests/FeatureExtraction/TfIdfTransformerTest.php

@@ -54,6 +54,6 @@ public function testTfIdfTransformation(): void
         $transformer = new TfIdfTransformer($samples);
         $transformer->transform($samples);
 
-        self::assertEquals($tfIdfSamples, $samples, '', 0.001);
+        self::assertEqualsWithDelta($tfIdfSamples, $samples, 0.001);
     }
 }

tests/FeatureSelection/ScoringFunction/ANOVAFValueTest.php

@@ -15,10 +15,9 @@ public function testScoreForANOVAFValue(): void
         $dataset = new IrisDataset();
         $function = new ANOVAFValue();
 
-        self::assertEquals(
+        self::assertEqualsWithDelta(
             [119.2645, 47.3644, 1179.0343, 959.3244],
             $function->score($dataset->getSamples(), $dataset->getTargets()),
-            '',
             0.0001
         );
     }

tests/FeatureSelection/ScoringFunction/UnivariateLinearRegressionTest.php

@@ -15,7 +15,7 @@ public function testRegressionScore(): void
         $targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
 
         $function = new UnivariateLinearRegression();
-        self::assertEquals([6.97286, 6.48558], $function->score($samples, $targets), '', 0.0001);
+        self::assertEqualsWithDelta([6.97286, 6.48558], $function->score($samples, $targets), 0.0001);
     }
 
     public function testRegressionScoreWithoutCenter(): void
@@ -24,6 +24,6 @@ public function testRegressionScoreWithoutCenter(): void
         $targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
 
         $function = new UnivariateLinearRegression(false);
-        self::assertEquals([1.74450, 18.08347], $function->score($samples, $targets), '', 0.0001);
+        self::assertEqualsWithDelta([1.74450, 18.08347], $function->score($samples, $targets), 0.0001);
     }
 }