maxDepth = $maxDepth;
}
public function train(array $samples, array $targets): void
{
$this->samples = array_merge($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 === []) {
$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)
);
}
}
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 mixed $baseValue
*/
public function getGiniIndex($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) {
$part += ($countMatrix[$label][$i] / (float) $sum) ** 2;
}
}
$giniParts[$i] = (1 - $part) * $sum;
}
return array_sum($giniParts) / count($colValues);
}
/**
* This method is used to set number of columns to be used
* when deciding a split at an internal node of the tree.
* 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.
*
* @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;
}
/**
* A string array to represent columns. Useful when HTML output or
* column importances are desired to be inspected.
*
* @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;
}
public function getHtml(): string
{
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.
*/
public function getFeatureImportances(): array
{
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 ($total > 0) {
array_walk($this->featureImportances, function (&$importance) use ($total): void {
$importance /= $total;
});
arsort($this->featureImportances);
}
return $this->featureImportances;
}
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 !== null && $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) {
$split->isTerminal = true;
arsort($remainingTargets);
$split->classValue = (string) key($remainingTargets);
} else {
if (isset($leftRecords[0])) {
$split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1);
}
if (isset($rightRecords[0])) {
$split->rightLeaf = $this->getSplitLeaf($rightRecords, $depth + 1);
}
}
return $split;
}
protected function getBestSplit(array $records): DecisionTreeLeaf
{
$targets = array_intersect_key($this->targets, array_flip($records));
$samples = (array) array_combine(
$records,
$this->preprocess(array_intersect_key($this->samples, array_flip($records)))
);
$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);
if ($baseValue === null) {
continue;
}
$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*(.*)/", (string) $split->value, $matches);
$split->operator = $matches[1];
$split->numericValue = (float) $matches[2];
}
$bestSplit = $split;
$bestGiniVal = $gini;
}
}
return $bestSplit;
}
/**
* Returns available features/columns to the tree for the decision making
* process.
*
* If a number is given with setNumFeatures() method, then a random selection
* of features up to this number is returned.
*
* If some features are manually selected by use of setSelectedFeatures(),
* then only these features are returned
*
* If any of above methods were not called beforehand, then all features
* are returned by default.
*/
protected function getSelectedFeatures(): array
{
$allFeatures = range(0, $this->featureCount - 1);
if ($this->numUsableFeatures === 0 && count($this->selectedFeatures) === 0) {
return $allFeatures;
}
if (count($this->selectedFeatures) > 0) {
return $this->selectedFeatures;
}
$numFeatures = $this->numUsableFeatures;
if ($numFeatures > $this->featureCount) {
$numFeatures = $this->featureCount;
}
shuffle($allFeatures);
$selectedFeatures = array_slice($allFeatures, 0, $numFeatures);
sort($selectedFeatures);
return $selectedFeatures;
}
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);
}
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 (count($floatValues) > 0) {
return false;
}
if (count($numericValues) !== $count) {
return true;
}
$distinctValues = array_count_values($columnValues);
return count($distinctValues) <= $count / 5;
}
/**
* Used to set predefined features to consider while deciding which column to use for a split
*/
protected function setSelectedFeatures(array $selectedFeatures): void
{
$this->selectedFeatures = $selectedFeatures;
}
/**
* Collects and returns an array of internal nodes that use the given
* column as a split criterion
*/
protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node): array
{
if ($node->isTerminal) {
return [];
}
$nodes = [];
if ($node->columnIndex === $column) {
$nodes[] = $node;
}
$lNodes = [];
$rNodes = [];
if ($node->leftLeaf !== null) {
$lNodes = $this->getSplitNodesByColumn($column, $node->leftLeaf);
}
if ($node->rightLeaf !== null) {
$rNodes = $this->getSplitNodesByColumn($column, $node->rightLeaf);
}
return array_merge($nodes, $lNodes, $rNodes);
}
/**
* @return mixed
*/
protected function predictSample(array $sample)
{
$node = $this->tree;
do {
if ($node->isTerminal) {
return $node->classValue;
}
if ($node->evaluate($sample)) {
$node = $node->leftLeaf;
} else {
$node = $node->rightLeaf;
}
} while ($node);
return $this->labels[0];
}
}