RandomForest::getFeatureImportances() - Code Metrics - Inspection of "Fix the implementation of conjugate gradient metho..." - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

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by Arkadiusz

created 2018-01-12 09:53 UTC

RandomForest::getFeatureImportances() B

↳ Parent: RandomForest

Complexity

Conditions	5
Paths	8

Size

Total Lines	27
Code Lines	14

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	27
rs	8.439
c	0
b	0
f	0
cc	5
eloc	14
nc	8
nop	0

<?php

declare(strict_types=1);

namespace Phpml\Classification\Ensemble;

use Exception;
use Phpml\Classification\Classifier;
use Phpml\Classification\DecisionTree;

class RandomForest extends Bagging
{
    /**
     * @var float|string
     */
    protected $featureSubsetRatio = 'log';

    /**
     * @var array
     */
    protected $columnNames = null;

    /**
     * Initializes RandomForest with the given number of trees. More trees
     * may increase the prediction performance while it will also substantially
     * increase the processing time and the required memory
     */
    public function __construct(int $numClassifier = 50)
    {
        parent::__construct($numClassifier);

        $this->setSubsetRatio(1.0);
    }

    /**
     * This method is used to determine how many of the original columns (features)
     * will be used to construct subsets to train base classifiers.<br>
     *
     * Allowed values: 'sqrt', 'log' or any float number between 0.1 and 1.0 <br>
     *
     * Default value for the ratio is 'log' which results in log(numFeatures, 2) + 1
     * features to be taken into consideration while selecting subspace of features
     *
     * @param mixed $ratio string or float should be given
     *
     * @return $this
     *
     * @throws \Exception
     */
    public function setFeatureSubsetRatio($ratio)
    {
        if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
            throw new Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
        }

        if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
            throw new Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' ");
        }

        $this->featureSubsetRatio = $ratio;

        return $this;
    }

    /**
     * RandomForest algorithm is usable *only* with DecisionTree
     *
     * @return $this
     *
     * @throws \Exception
     */
    public function setClassifer(string $classifier, array $classifierOptions = [])
    {
        if ($classifier != DecisionTree::class) {
            throw new Exception('RandomForest can only use DecisionTree as base classifier');
        }

        return parent::setClassifer($classifier, $classifierOptions);
    }

    /**
     * This will return an array including an importance value for
     * each column in the given dataset. Importance values for a column
     * is the average importance of that column in all trees in the forest
     */
    public function getFeatureImportances(): array
    {
        // Traverse each tree and sum importance of the columns
        $sum = [];
        foreach ($this->classifiers as $tree) {
            /* @var $tree DecisionTree */
            $importances = $tree->getFeatureImportances();

            foreach ($importances as $column => $importance) {
                if (array_key_exists($column, $sum)) {
                    $sum[$column] += $importance;
                } else {
                    $sum[$column] = $importance;
                }
            }
        }

        // Normalize & sort the importance values
        $total = array_sum($sum);
        foreach ($sum as &$importance) {
            $importance /= $total;
        }

        arsort($sum);

        return $sum;
    }

    /**
     * A string array to represent the columns is given. They are useful
     * when trying to print some information about the trees such as feature importances
     *
     * @return $this
     */
    public function setColumnNames(array $names)
    {
        $this->columnNames = $names;

        return $this;
    }

    /**
     * @param DecisionTree $classifier
     *
     * @return DecisionTree
     */
    protected function initSingleClassifier(Classifier $classifier): Classifier
    {
        if (is_float($this->featureSubsetRatio)) {
            $featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
        } elseif ($this->featureCount == 'sqrt') {
            $featureCount = (int) sqrt($this->featureCount) + 1;
        } else {
            $featureCount = (int) log($this->featureCount, 2) + 1;
        }

        if ($featureCount >= $this->featureCount) {
            $featureCount = $this->featureCount;
        }

        if ($this->columnNames === null) {
            $this->columnNames = range(0, $this->featureCount - 1);
        }

        return $classifier
interface User
{
    /** @return string */
    public function getPassword();
}

class MyUser implements User
{
    public function getPassword()
    {
        // return something
    }

    public function getDisplayName()
    {
        // return some name.
    }
}

class AuthSystem
{
    public function authenticate(User $user)
    {
        $this->logger->info(sprintf('Authenticating %s.', $user->getDisplayName()));
        // do something.
    }
}
            ->setColumnNames($this->columnNames)
            ->setNumFeatures($featureCount);
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification\Ensemble;
6
7			use Exception;
8			use Phpml\Classification\Classifier;
9			use Phpml\Classification\DecisionTree;
10
11			class RandomForest extends Bagging
12			{
13			/**
14			* @var float\|string
15			*/
16			protected $featureSubsetRatio = 'log';
17
18			/**
19			* @var array
20			*/
21			protected $columnNames = null;
22
23			/**
24			* Initializes RandomForest with the given number of trees. More trees
25			* may increase the prediction performance while it will also substantially
26			* increase the processing time and the required memory
27			*/
28			public function __construct(int $numClassifier = 50)
29			{
30			parent::__construct($numClassifier);
31
32			$this->setSubsetRatio(1.0);
33			}
34
35			/**
36			* This method is used to determine how many of the original columns (features)
37			* will be used to construct subsets to train base classifiers.<br>
38			*
39			* Allowed values: 'sqrt', 'log' or any float number between 0.1 and 1.0 <br>
40			*
41			* Default value for the ratio is 'log' which results in log(numFeatures, 2) + 1
42			* features to be taken into consideration while selecting subspace of features
43			*
44			* @param mixed $ratio string or float should be given
45			*
46			* @return $this
47			*
48			* @throws \Exception
49			*/
50			public function setFeatureSubsetRatio($ratio)
51			{
52			if (is_float($ratio) && ($ratio < 0.1 \|\| $ratio > 1.0)) {
53			throw new Exception('When a float given, feature subset ratio should be between 0.1 and 1.0');
54			}
55
56			if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
57			throw new Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' ");
58			}
59
60			$this->featureSubsetRatio = $ratio;
61
62			return $this;
63			}
64
65			/**
66			* RandomForest algorithm is usable only with DecisionTree
67			*
68			* @return $this
69			*
70			* @throws \Exception
71			*/
72			public function setClassifer(string $classifier, array $classifierOptions = [])
73			{
74			if ($classifier != DecisionTree::class) {
75			throw new Exception('RandomForest can only use DecisionTree as base classifier');
76			}
77
78			return parent::setClassifer($classifier, $classifierOptions);
79			}
80
81			/**
82			* This will return an array including an importance value for
83			* each column in the given dataset. Importance values for a column
84			* is the average importance of that column in all trees in the forest
85			*/
86			public function getFeatureImportances(): array
87			{
88			// Traverse each tree and sum importance of the columns
89			$sum = [];
90			foreach ($this->classifiers as $tree) {
91			/* @var $tree DecisionTree */
92			$importances = $tree->getFeatureImportances();
93
94			foreach ($importances as $column => $importance) {
95			if (array_key_exists($column, $sum)) {
96			$sum[$column] += $importance;
97			} else {
98			$sum[$column] = $importance;
99			}
100			}
101			}
102
103			// Normalize & sort the importance values
104			$total = array_sum($sum);
105			foreach ($sum as &$importance) {
106			$importance /= $total;
107			}
108
109			arsort($sum);
110
111			return $sum;
112			}
113
114			/**
115			* A string array to represent the columns is given. They are useful
116			* when trying to print some information about the trees such as feature importances
117			*
118			* @return $this
119			*/
120			public function setColumnNames(array $names)
121			{
122			$this->columnNames = $names;
123
124			return $this;
125			}
126
127			/**
128			* @param DecisionTree $classifier
129			*
130			* @return DecisionTree
131			*/
132			protected function initSingleClassifier(Classifier $classifier): Classifier
133			{
134			if (is_float($this->featureSubsetRatio)) {
135			$featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
136			} elseif ($this->featureCount == 'sqrt') {
137			$featureCount = (int) sqrt($this->featureCount) + 1;
138			} else {
139			$featureCount = (int) log($this->featureCount, 2) + 1;
140			}
141
142			if ($featureCount >= $this->featureCount) {
143			$featureCount = $this->featureCount;
144			}
145
146			if ($this->columnNames === null) {
147			$this->columnNames = range(0, $this->featureCount - 1);
148			}
149
150			return $classifier
			0 ignored issues – show Bug introduced 2017-11-16 16:57 UTC by Report Bug Copy Issue Report It seems like you code against a concrete implementation and not the interface `Phpml\Classification\Classifier` as the method `setColumnNames()` does only exist in the following implementations of said interface: `Phpml\Classification\DecisionTree`, `Phpml\Classification\Ensemble\RandomForest`. Let’s take a look at an example: interface User { /** @return string / public function getPassword(); } class MyUser implements User { public function getPassword() { // return something } public function getDisplayName() { // return some name. } } class AuthSystem { public function authenticate(User $user) { $this->logger->info(sprintf('Authenticating %s.', $user->getDisplayName())); // do something. } } In the above example, the authenticate() method works fine as long as you just pass instances of MyUser. However, if you now also want to pass a different implementation of User which does not have a getDisplayName() method, the code will break. Available Fixes Change the type-hint for the parameter: class AuthSystem { public function authenticate(MyUser $user) { / ... / } } Add an additional type-check: class AuthSystem { public function authenticate(User $user) { if ($user instanceof MyUser) { $this->logger->info(/* ... /); } // or alternatively if ( ! $user instanceof MyUser) { throw new \LogicException( '$user must be an instance of MyUser, ' .'other instances are not supported.' ); } } } Note:* PHP Analyzer uses reverse abstract interpretation to narrow down the types inside the if block in such a case. Add the method to the interface: interface User { /** @return string / public function getPassword(); /* @return string */ public function getDisplayName(); } Loading history...
151			->setColumnNames($this->columnNames)
152			->setNumFeatures($featureCount);
153			}
154			}
155

php-ai / php-ml

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RandomForest::getFeatureImportances() B

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