DecisionTree - Code Metrics - Inspection of "Update dependecies and coding style fixes" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 01bb82...39747e )

by Arkadiusz

created 2017-03-05 15:45 UTC

DecisionTree C

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	497
Duplicated Lines	0 %

Coupling/Cohesion

Components	1
Dependencies	5

Importance

Changes

Metric	Value
wmc	70
lcom	1
cbo	5
dl	0
loc	497
rs	5.6163
c	0
b	0
f	0

16 Methods

Rating	Name	Size	Complexity
A	__construct()	4	1
B	train()	25	4
A	getColumnTypes()	12	3
C	getSplitLeaf()	57	12
B	getBestSplit()	42	6
B	getSelectedFeatures()	21	5
C	getGiniIndex()	25	7
B	preprocess()	23	5
A	isCategoricalColumn()	22	3
A	setNumFeatures()	10	2
A	setSelectedFeatures()	4	1
A	setColumnNames()	10	3
A	getHtml()	4	1
B	getFeatureImportances()	30	6
B	getSplitNodesByColumn()	23	6
B	predictSample()	16	5

How to fix Complexity

<?php

declare(strict_types=1);

namespace Phpml\Classification;

use Phpml\Exception\InvalidArgumentException;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Classification\DecisionTree\DecisionTreeLeaf;

class DecisionTree implements Classifier
{
    use Trainable, Predictable;

    const CONTINUOUS = 1;
    const NOMINAL = 2;

    /**
     * @var array
     */
    protected $columnTypes;

    /**
     * @var array
     */
    private $labels = [];

    /**
     * @var int
     */
    private $featureCount = 0;

    /**
     * @var DecisionTreeLeaf
     */
    protected $tree = null;

    /**
     * @var int
     */
    protected $maxDepth;

    /**
     * @var int
     */
    public $actualDepth = 0;

    /**
     * @var int
     */
    private $numUsableFeatures = 0;

    /**
     * @var array
     */
    private $selectedFeatures;

    /**
     * @var array
     */
    private $featureImportances = null;

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

    /**
     * @param int $maxDepth
     */
    public function __construct(int $maxDepth = 10)
    {
        $this->maxDepth = $maxDepth;
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $this->samples = array_merge($this->samples, $samples);
        $this->targets = array_merge($this->targets, $targets);

        $this->featureCount = count($this->samples[0]);
        $this->columnTypes = self::getColumnTypes($this->samples);
        $this->labels = array_keys(array_count_values($this->targets));
        $this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1));

        // Each time the tree is trained, feature importances are reset so that
        // we will have to compute it again depending on the new data
        $this->featureImportances = null;


        // If column names are given or computed before, then there is no
        // need to init it and accidentally remove the previous given names
        if ($this->columnNames === null) {
            $this->columnNames = range(0, $this->featureCount - 1);
        } elseif (count($this->columnNames) > $this->featureCount) {
            $this->columnNames = array_slice($this->columnNames, 0, $this->featureCount);
        } elseif (count($this->columnNames) < $this->featureCount) {
            $this->columnNames = array_merge($this->columnNames,
                range(count($this->columnNames), $this->featureCount - 1));
        }
    }

    /**
     * @param array $samples
     * @return array
     */
    public static function getColumnTypes(array $samples) : array
    {
        $types = [];
        $featureCount = count($samples[0]);
        for ($i=0; $i < $featureCount; $i++) {
            $values = array_column($samples, $i);
            $isCategorical = self::isCategoricalColumn($values);
            $types[] = $isCategorical ? self::NOMINAL : self::CONTINUOUS;
        }

        return $types;
    }

    /**
     * @param array $records
     * @param int $depth
     * @return DecisionTreeLeaf
     */
    protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
    {
        $split = $this->getBestSplit($records);
        $split->level = $depth;
        if ($this->actualDepth < $depth) {
            $this->actualDepth = $depth;
        }

        // Traverse all records to see if all records belong to the same class,
        // otherwise group the records so that we can classify the leaf
        // in case maximum depth is reached
        $leftRecords = [];
        $rightRecords= [];
        $remainingTargets = [];
        $prevRecord = null;
        $allSame = true;

        foreach ($records as $recordNo) {
            // Check if the previous record is the same with the current one
            $record = $this->samples[$recordNo];
            if ($prevRecord && $prevRecord != $record) {
                $allSame = false;
            }
            $prevRecord = $record;

            // According to the split criteron, this record will
            // belong to either left or the right side in the next split
            if ($split->evaluate($record)) {
                $leftRecords[] = $recordNo;
            } else {
                $rightRecords[]= $recordNo;
            }

            // Group remaining targets
            $target = $this->targets[$recordNo];
            if (! array_key_exists($target, $remainingTargets)) {
                $remainingTargets[$target] = 1;
            } else {
                $remainingTargets[$target]++;
            }
        }

        if ($allSame || $depth >= $this->maxDepth || count($remainingTargets) === 1) {
            $split->isTerminal = 1;
$answer = 42;

$correct = false;

$correct = (bool) $answer;
            arsort($remainingTargets);
            $split->classValue = key($remainingTargets);
        } else {
            if ($leftRecords) {

                $split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
            }
            if ($rightRecords) {

                $split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
            }
        }

        return $split;
    }

    /**
     * @param array $records
     * @return DecisionTreeLeaf
     */
    protected function getBestSplit(array $records) : DecisionTreeLeaf
    {
        $targets = array_intersect_key($this->targets, array_flip($records));
        $samples = array_intersect_key($this->samples, array_flip($records));
        $samples = array_combine($records, $this->preprocess($samples));
        $bestGiniVal = 1;
        $bestSplit = null;
        $features = $this->getSelectedFeatures();
        foreach ($features as $i) {
            $colValues = [];
            foreach ($samples as $index => $row) {
                $colValues[$index] = $row[$i];
            }
            $counts = array_count_values($colValues);
            arsort($counts);
            $baseValue = key($counts);
            $gini = $this->getGiniIndex($baseValue, $colValues, $targets);
            if ($bestSplit === null || $bestGiniVal > $gini) {
                $split = new DecisionTreeLeaf();
                $split->value = $baseValue;
                $split->giniIndex = $gini;
                $split->columnIndex = $i;
                $split->isContinuous = $this->columnTypes[$i] == self::CONTINUOUS;
                $split->records = $records;

                // If a numeric column is to be selected, then
                // the original numeric value and the selected operator
                // will also be saved into the leaf for future access
                if ($this->columnTypes[$i] == self::CONTINUOUS) {
                    $matches = [];
                    preg_match("/^([<>=]{1,2})\s*(.*)/", strval($split->value), $matches);
                    $split->operator = $matches[1];
                    $split->numericValue = floatval($matches[2]);
                }

                $bestSplit = $split;
                $bestGiniVal = $gini;
            }
        }

        return $bestSplit;
    }

    /**
     * Returns available features/columns to the tree for the decision making
     * process. <br>
     *
     * If a number is given with setNumFeatures() method, then a random selection
     * of features up to this number is returned. <br>
     *
     * If some features are manually selected by use of setSelectedFeatures(),
     * then only these features are returned <br>
     *
     * If any of above methods were not called beforehand, then all features
     * are returned by default.
     *
     * @return array
     */
    protected function getSelectedFeatures() : array
    {
        $allFeatures = range(0, $this->featureCount - 1);
        if ($this->numUsableFeatures === 0 && ! $this->selectedFeatures) {

            return $allFeatures;
        }

        if ($this->selectedFeatures) {

            return $this->selectedFeatures;
        }

        $numFeatures = $this->numUsableFeatures;
        if ($numFeatures > $this->featureCount) {
            $numFeatures = $this->featureCount;
        }
        shuffle($allFeatures);
        $selectedFeatures = array_slice($allFeatures, 0, $numFeatures, false);
        sort($selectedFeatures);

        return $selectedFeatures;
    }

    /**
     * @param $baseValue
     * @param array $colValues
     * @param array $targets
     * @return float
     */
    public function getGiniIndex($baseValue, array $colValues, array $targets) : float
    {
        $countMatrix = [];
        foreach ($this->labels as $label) {
            $countMatrix[$label] = [0, 0];
        }
        foreach ($colValues as $index => $value) {
            $label = $targets[$index];
            $rowIndex = $value === $baseValue ? 0 : 1;
            $countMatrix[$label][$rowIndex]++;
        }
        $giniParts = [0, 0];
        for ($i=0; $i<=1; $i++) {
            $part = 0;
            $sum = array_sum(array_column($countMatrix, $i));
            if ($sum > 0) {
                foreach ($this->labels as $label) {
                    $part += pow($countMatrix[$label][$i] / floatval($sum), 2);
                }
            }
            $giniParts[$i] = (1 - $part) * $sum;
        }

        return array_sum($giniParts) / count($colValues);
    }

    /**
     * @param array $samples
     * @return array
     */
    protected function preprocess(array $samples) : array
    {
        // Detect and convert continuous data column values into
        // discrete values by using the median as a threshold value
        $columns = [];
        for ($i=0; $i<$this->featureCount; $i++) {
            $values = array_column($samples, $i);
            if ($this->columnTypes[$i] == self::CONTINUOUS) {
                $median = Mean::median($values);
                foreach ($values as &$value) {
                    if ($value <= $median) {
                        $value = "<= $median";
                    } else {
                        $value = "> $median";
                    }
                }
            }
            $columns[] = $values;
        }
        // Below method is a strange yet very simple & efficient method
        // to get the transpose of a 2D array
        return array_map(null, ...$columns);
    }

    /**
     * @param array $columnValues
     * @return bool
     */
    protected static function isCategoricalColumn(array $columnValues) : bool
    {
        $count = count($columnValues);

        // There are two main indicators that *may* show whether a
        // column is composed of discrete set of values:
        // 1- Column may contain string values and non-float values
        // 2- Number of unique values in the column is only a small fraction of
        //	  all values in that column (Lower than or equal to %20 of all values)
        $numericValues = array_filter($columnValues, 'is_numeric');
        $floatValues = array_filter($columnValues, 'is_float');
        if ($floatValues) {

            return false;
        }
        if (count($numericValues) !== $count) {
            return true;
        }

        $distinctValues = array_count_values($columnValues);

        return count($distinctValues) <= $count / 5;
    }

    /**
     * This method is used to set number of columns to be used
     * when deciding a split at an internal node of the tree.  <br>
     * If the value is given 0, then all features are used (default behaviour),
     * otherwise the given value will be used as a maximum for number of columns
     * randomly selected for each split operation.
     *
     * @param int $numFeatures
     * @return $this
     * @throws InvalidArgumentException
     */
    public function setNumFeatures(int $numFeatures)
    {
        if ($numFeatures < 0) {
            throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
        }

        $this->numUsableFeatures = $numFeatures;

        return $this;
    }

    /**
     * Used to set predefined features to consider while deciding which column to use for a split
     *
     * @param array $selectedFeatures
     */
    protected function setSelectedFeatures(array $selectedFeatures)
    {
        $this->selectedFeatures = $selectedFeatures;
    }

    /**
     * A string array to represent columns. Useful when HTML output or
     * column importances are desired to be inspected.
     *
     * @param array $names
     * @return $this
     * @throws InvalidArgumentException
     */
    public function setColumnNames(array $names)
    {
        if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
            throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
        }

        $this->columnNames = $names;

        return $this;
    }

    /**
     * @return string
     */
    public function getHtml()
    {
        return $this->tree->getHTML($this->columnNames);
    }

    /**
     * This will return an array including an importance value for
     * each column in the given dataset. The importance values are
     * normalized and their total makes 1.<br/>
     *
     * @return array
     */
    public function getFeatureImportances()
    {
        if ($this->featureImportances !== null) {
            return $this->featureImportances;
        }

        $sampleCount = count($this->samples);
        $this->featureImportances = [];
        foreach ($this->columnNames as $column => $columnName) {
            $nodes = $this->getSplitNodesByColumn($column, $this->tree);

            $importance = 0;
            foreach ($nodes as $node) {
                $importance += $node->getNodeImpurityDecrease($sampleCount);
            }

            $this->featureImportances[$columnName] = $importance;
        }

        // Normalize & sort the importances
        $total = array_sum($this->featureImportances);
        if ($total > 0) {
            foreach ($this->featureImportances as &$importance) {
                $importance /= $total;
            }
            arsort($this->featureImportances);
        }

        return $this->featureImportances;
    }

    /**
     * Collects and returns an array of internal nodes that use the given
     * column as a split criterion
     *
     * @param int $column
     * @param DecisionTreeLeaf $node
     * @return array
     */
    protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
    {
        if (!$node || $node->isTerminal) {
            return [];
        }

        $nodes = [];
        if ($node->columnIndex === $column) {
            $nodes[] = $node;
        }

        $lNodes = [];
        $rNodes = [];
        if ($node->leftLeaf) {
            $lNodes = $this->getSplitNodesByColumn($column, $node->leftLeaf);
        }
        if ($node->rightLeaf) {
            $rNodes = $this->getSplitNodesByColumn($column, $node->rightLeaf);
        }
        $nodes = array_merge($nodes, $lNodes, $rNodes);

        return $nodes;
    }

    /**
     * @param array $sample
     * @return mixed
     */
    protected function predictSample(array $sample)
    {
        $node = $this->tree;
        do {
            if ($node->isTerminal) {
                break;
            }
            if ($node->evaluate($sample)) {
                $node = $node->leftLeaf;
            } else {
                $node = $node->rightLeaf;
            }
        } while ($node);

        return $node ? $node->classValue : $this->labels[0];
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification;
6
7			use Phpml\Exception\InvalidArgumentException;
8			use Phpml\Helper\Predictable;
9			use Phpml\Helper\Trainable;
10			use Phpml\Math\Statistic\Mean;
11			use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
12
13			class DecisionTree implements Classifier
14			{
15			use Trainable, Predictable;
16
17			const CONTINUOUS = 1;
18			const NOMINAL = 2;
19
20			/**
21			* @var array
22			*/
23			protected $columnTypes;
24
25			/**
26			* @var array
27			*/
28			private $labels = [];
29
30			/**
31			* @var int
32			*/
33			private $featureCount = 0;
34
35			/**
36			* @var DecisionTreeLeaf
37			*/
38			protected $tree = null;
39
40			/**
41			* @var int
42			*/
43			protected $maxDepth;
44
45			/**
46			* @var int
47			*/
48			public $actualDepth = 0;
49
50			/**
51			* @var int
52			*/
53			private $numUsableFeatures = 0;
54
55			/**
56			* @var array
57			*/
58			private $selectedFeatures;
59
60			/**
61			* @var array
62			*/
63			private $featureImportances = null;
64
65			/**
66			*
67			* @var array
68			*/
69			private $columnNames = null;
70
71			/**
72			* @param int $maxDepth
73			*/
74			public function __construct(int $maxDepth = 10)
75			{
76			$this->maxDepth = $maxDepth;
77			}
78
79			/**
80			* @param array $samples
81			* @param array $targets
82			*/
83			public function train(array $samples, array $targets)
84			{
85			$this->samples = array_merge($this->samples, $samples);
86			$this->targets = array_merge($this->targets, $targets);
87
88			$this->featureCount = count($this->samples[0]);
89			$this->columnTypes = self::getColumnTypes($this->samples);
90			$this->labels = array_keys(array_count_values($this->targets));
91			$this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1));
92
93			// Each time the tree is trained, feature importances are reset so that
94			// we will have to compute it again depending on the new data
95			$this->featureImportances = null;
			0 ignored issues – show Documentation Bug introduced 2017-02-11 15:02 UTC by Report Bug Copy Issue Report It seems like `null` of type `null` is incompatible with the declared type `array` of property `$featureImportances`. Our type inference engine has found an assignment to a property that is incompatible with the declared type of that property. Either this assignment is in error or the assigned type should be added to the documentation/type hint for that property.. Loading history...
96
97			// If column names are given or computed before, then there is no
98			// need to init it and accidentally remove the previous given names
99			if ($this->columnNames === null) {
100			$this->columnNames = range(0, $this->featureCount - 1);
101			} elseif (count($this->columnNames) > $this->featureCount) {
102			$this->columnNames = array_slice($this->columnNames, 0, $this->featureCount);
103			} elseif (count($this->columnNames) < $this->featureCount) {
104			$this->columnNames = array_merge($this->columnNames,
105			range(count($this->columnNames), $this->featureCount - 1));
106			}
107			}
108
109			/**
110			* @param array $samples
111			* @return array
112			*/
113			public static function getColumnTypes(array $samples) : array
114			{
115			$types = [];
116			$featureCount = count($samples[0]);
117			for ($i=0; $i < $featureCount; $i++) {
118			$values = array_column($samples, $i);
119			$isCategorical = self::isCategoricalColumn($values);
120			$types[] = $isCategorical ? self::NOMINAL : self::CONTINUOUS;
121			}
122
123			return $types;
124			}
125
126			/**
127			* @param array $records
128			* @param int $depth
129			* @return DecisionTreeLeaf
130			*/
131			protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
132			{
133			$split = $this->getBestSplit($records);
134			$split->level = $depth;
135			if ($this->actualDepth < $depth) {
136			$this->actualDepth = $depth;
137			}
138
139			// Traverse all records to see if all records belong to the same class,
140			// otherwise group the records so that we can classify the leaf
141			// in case maximum depth is reached
142			$leftRecords = [];
143			$rightRecords= [];
144			$remainingTargets = [];
145			$prevRecord = null;
146			$allSame = true;
147
148			foreach ($records as $recordNo) {
149			// Check if the previous record is the same with the current one
150			$record = $this->samples[$recordNo];
151			if ($prevRecord && $prevRecord != $record) {
152			$allSame = false;
153			}
154			$prevRecord = $record;
155
156			// According to the split criteron, this record will
157			// belong to either left or the right side in the next split
158			if ($split->evaluate($record)) {
159			$leftRecords[] = $recordNo;
160			} else {
161			$rightRecords[]= $recordNo;
162			}
163
164			// Group remaining targets
165			$target = $this->targets[$recordNo];
166			if (! array_key_exists($target, $remainingTargets)) {
167			$remainingTargets[$target] = 1;
168			} else {
169			$remainingTargets[$target]++;
170			}
171			}
172
173			if ($allSame \|\| $depth >= $this->maxDepth \|\| count($remainingTargets) === 1) {
174			$split->isTerminal = 1;
			0 ignored issues – show Documentation Bug introduced 2017-01-29 10:19 UTC by Report Bug Copy Issue Report The property `$isTerminal` was declared of type `boolean`, but `1` is of type `integer`. Maybe add a type cast? This check looks for assignments to scalar types that may be of the wrong type. To ensure the code behaves as expected, it may be a good idea to add an explicit type cast. $answer = 42; $correct = false; $correct = (bool) $answer; Loading history...
175			arsort($remainingTargets);
176			$split->classValue = key($remainingTargets);
177			} else {
178			if ($leftRecords) {
			0 ignored issues – show Bug Best Practice introduced 2017-01-29 10:19 UTC by Report Bug Copy Issue Report The expression `$leftRecords` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `! empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
179			$split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
180			}
181			if ($rightRecords) {
			0 ignored issues – show Bug Best Practice introduced 2017-01-29 10:19 UTC by Report Bug Copy Issue Report The expression `$rightRecords` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `! empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
182			$split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
183			}
184			}
185
186			return $split;
187			}
188
189			/**
190			* @param array $records
191			* @return DecisionTreeLeaf
192			*/
193			protected function getBestSplit(array $records) : DecisionTreeLeaf
194			{
195			$targets = array_intersect_key($this->targets, array_flip($records));
196			$samples = array_intersect_key($this->samples, array_flip($records));
197			$samples = array_combine($records, $this->preprocess($samples));
198			$bestGiniVal = 1;
199			$bestSplit = null;
200			$features = $this->getSelectedFeatures();
201			foreach ($features as $i) {
202			$colValues = [];
203			foreach ($samples as $index => $row) {
204			$colValues[$index] = $row[$i];
205			}
206			$counts = array_count_values($colValues);
207			arsort($counts);
208			$baseValue = key($counts);
209			$gini = $this->getGiniIndex($baseValue, $colValues, $targets);
210			if ($bestSplit === null \|\| $bestGiniVal > $gini) {
211			$split = new DecisionTreeLeaf();
212			$split->value = $baseValue;
213			$split->giniIndex = $gini;
214			$split->columnIndex = $i;
215			$split->isContinuous = $this->columnTypes[$i] == self::CONTINUOUS;
216			$split->records = $records;
217
218			// If a numeric column is to be selected, then
219			// the original numeric value and the selected operator
220			// will also be saved into the leaf for future access
221			if ($this->columnTypes[$i] == self::CONTINUOUS) {
222			$matches = [];
223			preg_match("/^([<>=]{1,2})\s(.)/", strval($split->value), $matches);
224			$split->operator = $matches[1];
225			$split->numericValue = floatval($matches[2]);
226			}
227
228			$bestSplit = $split;
229			$bestGiniVal = $gini;
230			}
231			}
232
233			return $bestSplit;
234			}
235
236			/**
237			* Returns available features/columns to the tree for the decision making
238			* process. <br>
239			*
240			* If a number is given with setNumFeatures() method, then a random selection
241			* of features up to this number is returned. <br>
242			*
243			* If some features are manually selected by use of setSelectedFeatures(),
244			* then only these features are returned <br>
245			*
246			* If any of above methods were not called beforehand, then all features
247			* are returned by default.
248			*
249			* @return array
250			*/
251			protected function getSelectedFeatures() : array
252			{
253			$allFeatures = range(0, $this->featureCount - 1);
254			if ($this->numUsableFeatures === 0 && ! $this->selectedFeatures) {
			0 ignored issues – show Bug Best Practice introduced 2017-03-05 15:48 UTC by Report Bug Copy Issue Report The expression `$this->selectedFeatures` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
255			return $allFeatures;
256			}
257
258			if ($this->selectedFeatures) {
			0 ignored issues – show Bug Best Practice introduced 2017-02-15 21:05 UTC by Report Bug Copy Issue Report The expression `$this->selectedFeatures` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `! empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
259			return $this->selectedFeatures;
260			}
261
262			$numFeatures = $this->numUsableFeatures;
263			if ($numFeatures > $this->featureCount) {
264			$numFeatures = $this->featureCount;
265			}
266			shuffle($allFeatures);
267			$selectedFeatures = array_slice($allFeatures, 0, $numFeatures, false);
268			sort($selectedFeatures);
269
270			return $selectedFeatures;
271			}
272
273			/**
274			* @param $baseValue
275			* @param array $colValues
276			* @param array $targets
277			* @return float
278			*/
279			public function getGiniIndex($baseValue, array $colValues, array $targets) : float
280			{
281			$countMatrix = [];
282			foreach ($this->labels as $label) {
283			$countMatrix[$label] = [0, 0];
284			}
285			foreach ($colValues as $index => $value) {
286			$label = $targets[$index];
287			$rowIndex = $value === $baseValue ? 0 : 1;
288			$countMatrix[$label][$rowIndex]++;
289			}
290			$giniParts = [0, 0];
291			for ($i=0; $i<=1; $i++) {
292			$part = 0;
293			$sum = array_sum(array_column($countMatrix, $i));
294			if ($sum > 0) {
295			foreach ($this->labels as $label) {
296			$part += pow($countMatrix[$label][$i] / floatval($sum), 2);
297			}
298			}
299			$giniParts[$i] = (1 - $part) * $sum;
300			}
301
302			return array_sum($giniParts) / count($colValues);
303			}
304
305			/**
306			* @param array $samples
307			* @return array
308			*/
309			protected function preprocess(array $samples) : array
310			{
311			// Detect and convert continuous data column values into
312			// discrete values by using the median as a threshold value
313			$columns = [];
314			for ($i=0; $i<$this->featureCount; $i++) {
315			$values = array_column($samples, $i);
316			if ($this->columnTypes[$i] == self::CONTINUOUS) {
317			$median = Mean::median($values);
318			foreach ($values as &$value) {
319			if ($value <= $median) {
320			$value = "<= $median";
321			} else {
322			$value = "> $median";
323			}
324			}
325			}
326			$columns[] = $values;
327			}
328			// Below method is a strange yet very simple & efficient method
329			// to get the transpose of a 2D array
330			return array_map(null, ...$columns);
331			}
332
333			/**
334			* @param array $columnValues
335			* @return bool
336			*/
337			protected static function isCategoricalColumn(array $columnValues) : bool
338			{
339			$count = count($columnValues);
340
341			// There are two main indicators that may show whether a
342			// column is composed of discrete set of values:
343			// 1- Column may contain string values and non-float values
344			// 2- Number of unique values in the column is only a small fraction of
345			// all values in that column (Lower than or equal to %20 of all values)
346			$numericValues = array_filter($columnValues, 'is_numeric');
347			$floatValues = array_filter($columnValues, 'is_float');
348			if ($floatValues) {
			0 ignored issues – show Bug Best Practice introduced 2017-02-19 10:47 UTC by Report Bug Copy Issue Report The expression `$floatValues` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `! empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
349			return false;
350			}
351			if (count($numericValues) !== $count) {
352			return true;
353			}
354
355			$distinctValues = array_count_values($columnValues);
356
357			return count($distinctValues) <= $count / 5;
358			}
359
360			/**
361			* This method is used to set number of columns to be used
362			* when deciding a split at an internal node of the tree. <br>
363			* If the value is given 0, then all features are used (default behaviour),
364			* otherwise the given value will be used as a maximum for number of columns
365			* randomly selected for each split operation.
366			*
367			* @param int $numFeatures
368			* @return $this
369			* @throws InvalidArgumentException
370			*/
371			public function setNumFeatures(int $numFeatures)
372			{
373			if ($numFeatures < 0) {
374			throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
375			}
376
377			$this->numUsableFeatures = $numFeatures;
378
379			return $this;
380			}
381
382			/**
383			* Used to set predefined features to consider while deciding which column to use for a split
384			*
385			* @param array $selectedFeatures
386			*/
387			protected function setSelectedFeatures(array $selectedFeatures)
388			{
389			$this->selectedFeatures = $selectedFeatures;
390			}
391
392			/**
393			* A string array to represent columns. Useful when HTML output or
394			* column importances are desired to be inspected.
395			*
396			* @param array $names
397			* @return $this
398			* @throws InvalidArgumentException
399			*/
400			public function setColumnNames(array $names)
401			{
402			if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
403			throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
404			}
405
406			$this->columnNames = $names;
407
408			return $this;
409			}
410
411			/**
412			* @return string
413			*/
414			public function getHtml()
415			{
416			return $this->tree->getHTML($this->columnNames);
417			}
418
419			/**
420			* This will return an array including an importance value for
421			* each column in the given dataset. The importance values are
422			* normalized and their total makes 1.<br/>
423			*
424			* @return array
425			*/
426			public function getFeatureImportances()
427			{
428			if ($this->featureImportances !== null) {
429			return $this->featureImportances;
430			}
431
432			$sampleCount = count($this->samples);
433			$this->featureImportances = [];
434			foreach ($this->columnNames as $column => $columnName) {
435			$nodes = $this->getSplitNodesByColumn($column, $this->tree);
436
437			$importance = 0;
438			foreach ($nodes as $node) {
439			$importance += $node->getNodeImpurityDecrease($sampleCount);
440			}
441
442			$this->featureImportances[$columnName] = $importance;
443			}
444
445			// Normalize & sort the importances
446			$total = array_sum($this->featureImportances);
447			if ($total > 0) {
448			foreach ($this->featureImportances as &$importance) {
449			$importance /= $total;
450			}
451			arsort($this->featureImportances);
452			}
453
454			return $this->featureImportances;
455			}
456
457			/**
458			* Collects and returns an array of internal nodes that use the given
459			* column as a split criterion
460			*
461			* @param int $column
462			* @param DecisionTreeLeaf $node
463			* @return array
464			*/
465			protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
466			{
467			if (!$node \|\| $node->isTerminal) {
468			return [];
469			}
470
471			$nodes = [];
472			if ($node->columnIndex === $column) {
473			$nodes[] = $node;
474			}
475
476			$lNodes = [];
477			$rNodes = [];
478			if ($node->leftLeaf) {
479			$lNodes = $this->getSplitNodesByColumn($column, $node->leftLeaf);
480			}
481			if ($node->rightLeaf) {
482			$rNodes = $this->getSplitNodesByColumn($column, $node->rightLeaf);
483			}
484			$nodes = array_merge($nodes, $lNodes, $rNodes);
485
486			return $nodes;
487			}
488
489			/**
490			* @param array $sample
491			* @return mixed
492			*/
493			protected function predictSample(array $sample)
494			{
495			$node = $this->tree;
496			do {
497			if ($node->isTerminal) {
498			break;
499			}
500			if ($node->evaluate($sample)) {
501			$node = $node->leftLeaf;
502			} else {
503			$node = $node->rightLeaf;
504			}
505			} while ($node);
506
507			return $node ? $node->classValue : $this->labels[0];
508			}
509			}
510

php-ai / php-ml

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DecisionTree C

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