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Add SelectKBest docs
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Arkadiusz Kondas
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[](http://php-ml.readthedocs.org/)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://coveralls.io/github/php-ai/php-ml?branch=coveralls)
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[](https://coveralls.io/github/php-ai/php-ml?branch=master)
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[](https://scrutinizer-ci.com/g/php-ai/php-ml/?branch=master)
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<a href="http://www.yegor256.com/2016/10/23/award-2017.html">
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@ -89,6 +89,7 @@ Example scripts are available in a separate repository [php-ai/php-ml-examples](
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* [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)
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* Feature Selection
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* [Variance Threshold](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-selection/variance-threshold/)
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* [SelectKBest](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-selection/selectkbest/)
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* Preprocessing
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* [Normalization](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/normalization/)
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* [Imputation missing values](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/imputation-missing-values/)
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@ -6,7 +6,7 @@
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[](http://php-ml.readthedocs.org/)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://coveralls.io/github/php-ai/php-ml?branch=coveralls)
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[](https://coveralls.io/github/php-ai/php-ml?branch=master)
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[](https://scrutinizer-ci.com/g/php-ai/php-ml/?branch=master)
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<a href="http://www.yegor256.com/2016/10/23/award-2017.html">
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@ -78,6 +78,7 @@ Example scripts are available in a separate repository [php-ai/php-ml-examples](
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* [Stratified Random Split](machine-learning/cross-validation/stratified-random-split.md)
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* Feature Selection
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* [Variance Threshold](machine-learning/feature-selection/variance-threshold.md)
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* [SelectKBest](machine-learning/feature-selection/selectkbest.md)
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* Preprocessing
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* [Normalization](machine-learning/preprocessing/normalization.md)
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* [Imputation missing values](machine-learning/preprocessing/imputation-missing-values.md)
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96
docs/machine-learning/feature-selection/selectkbest.md
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96
docs/machine-learning/feature-selection/selectkbest.md
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# SelectKBest
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`SelectKBest` - select features according to the k highest scores.
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## Constructor Parameters
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* $k (int) - number of top features to select, rest will be removed (default: 10)
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* $scoringFunction (ScoringFunction) - function that take samples and targets and return array with scores (default: ANOVAFValue)
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```php
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use Phpml\FeatureSelection\SelectKBest;
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$transformer = new SelectKBest(2);
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```
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## Example of use
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As an example we can perform feature selection on Iris dataset to retrieve only the two best features as follows:
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```php
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Dataset\Demo\IrisDataset;
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$dataset = new IrisDataset();
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$selector = new SelectKBest(2);
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$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
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$selector->transform($samples);
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/*
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$samples[0] = [1.4, 0.2];
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*/
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```
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## Scores
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You can get a array with the calculated score for each feature.
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A higher value means that a given feature is better suited for learning.
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Of course, the rating depends on the scoring function used.
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```
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Dataset\Demo\IrisDataset;
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$dataset = new IrisDataset();
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$selector = new SelectKBest(2);
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$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
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$selector->scores();
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/*
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..array(4) {
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[0]=>
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float(119.26450218451)
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[1]=>
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float(47.364461402997)
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[2]=>
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float(1179.0343277002)
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[3]=>
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float(959.32440572573)
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}
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*/
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```
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## Scoring function
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Available scoring functions:
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For classification:
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- **ANOVAFValue**
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The one-way ANOVA tests the null hypothesis that 2 or more groups have the same population mean.
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The test is applied to samples from two or more groups, possibly with differing sizes.
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For regression:
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- **UnivariateLinearRegression**
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Quick linear model for testing the effect of a single regressor, sequentially for many regressors.
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This is done in 2 steps:
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- 1. The cross correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *std(y)).
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- 2. It is converted to an F score
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## Pipeline
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`SelectKBest` implements `Transformer` interface so it can be used as part of pipeline:
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```php
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Classification\SVC;
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use Phpml\FeatureExtraction\TfIdfTransformer;
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use Phpml\Pipeline;
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$transformers = [
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new TfIdfTransformer(),
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new SelectKBest(3)
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];
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$estimator = new SVC();
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$pipeline = new Pipeline($transformers, $estimator);
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```
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- Stratified Random Split: machine-learning/cross-validation/stratified-random-split.md
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- Feature Selection:
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- VarianceThreshold: machine-learning/feature-selection/variance-threshold.md
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- SelectKBest: machine-learning/feature-selection/selectkbest.md
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- Preprocessing:
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- Normalization: machine-learning/preprocessing/normalization.md
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- Imputation missing values: machine-learning/preprocessing/imputation-missing-values.md
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@ -16,7 +16,7 @@ use Phpml\Math\Statistic\Mean;
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*
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* 1. The cross correlation between each regressor and the target is computed,
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* that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *std(y)).
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* 2. It is converted to an F score then to a p-value.
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* 2. It is converted to an F score.
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*
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* Ported from scikit-learn f_regression function (http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression)
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*/
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@ -7,7 +7,6 @@ namespace Phpml\Tests;
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use Phpml\Classification\SVC;
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use Phpml\FeatureExtraction\TfIdfTransformer;
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use Phpml\FeatureExtraction\TokenCountVectorizer;
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use Phpml\FeatureSelection\ScoringFunction\ANOVAFValue;
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\ModelManager;
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use Phpml\Pipeline;
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@ -108,7 +107,7 @@ class PipelineTest extends TestCase
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$this->assertEquals($expected, $predicted);
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}
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public function testPipelineTransformersWithTargets() : void
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public function testPipelineTransformersWithTargets(): void
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{
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$samples = [[1, 2, 1], [1, 3, 4], [5, 2, 1], [1, 3, 3], [1, 3, 4], [0, 3, 5]];
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$targets = ['a', 'a', 'a', 'b', 'b', 'b'];
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