Labeled#
A Labeled dataset is used to train supervised learners and for testing a model by providing the ground-truth. In addition to the standard dataset API, a labeled dataset can perform operations such as stratification and sorting the dataset using the label column.
Note
Since PHP silently converts integer strings (ex. '1'
) to integers in some circumstances, you should not use integer strings as class labels. Instead, use an appropriate non-integer string class name such as 'class 1'
, '#1'
, or 'first'
.
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | samples | array | A 2-dimensional array consisting of rows of samples and columns with feature values. | |
2 | labels | array | A 1-dimensional array of labels that correspond to each sample in the dataset. | |
2 | verify | true | bool | Should we verify the data? |
Example#
use Rubix\ML\Datasets\Labeled;
$samples = [
[0.1, 20, 'furry'],
[2.0, -5, 'rough'],
[0.01, 5, 'furry'],
];
$labels = ['not monster', 'monster', 'not monster'];
$dataset = new Labeled($samples, $labels);
Additional Methods#
Selectors#
Return the labels of the dataset in an array:
public labels() : array
Return a single label at the given row offset:
public label(int $offset) : mixed
Return all of the possible outcomes i.e. the unique labels in an array:
public possibleOutcomes() : array
print_r($dataset->possibleOutcomes());
Array
(
[0] => female
[1] => male
)
Data Types#
Return the data type of the label:
public labelType() : Rubix\ML\DataType
echo $dataset->labelType();
continuous
Stratification#
Group samples by their class label and return them in their own dataset:
public stratifyByLabel() : array
$strata = $dataset->stratifyByLabel();
Split the dataset into left and right subsets such that the proportions of class labels remain intact:
public stratifiedSplit($ratio = 0.5) : array
[$training, $testing] = $dataset->stratifiedSplit(0.8);
Return k equal size subsets of the dataset such that class proportions remain intact:
public stratifiedFold($k = 10) : array
$folds = $dataset->stratifiedFold(3);
Transform Labels#
Transform the labels in the dataset using a callback function and return self for method chaining:
public transformLabels(callable $fn) : self
Note
The callback function called for each individual label and should return the transformed label as a continuous or categorical value.
$dataset->transformLabels('intval');
//
$dataset->transformLabels(function ($label) {
return $label > 0.5 ? 'yes' : 'no';
});
Describe by Label#
Describe the features of the dataset broken down by categorical label:
public describeByLabel() : Report
echo $dataset->describeByLabel();
{
"not monster": [
{
"type": "categorical",
"num categories": 2,
"probabilities": {
"friendly": 0.75,
"loner": 0.25
}
},
{
"type": "continuous",
"mean": 1.125,
"variance": 12.776875,
"standard deviation": 3.574475485997911,
"skewness": -1.0795676577113944,
"kurtosis": -0.7175867765792474,
"min": -5,
"25%": 0.6999999999999993,
"median": 2.75,
"75%": 3.175,
"max": 4
}
],
"monster": [
{
"type": "categorical",
"num categories": 2,
"probabilities": {
"loner": 0.5,
"friendly": 0.5
}
},
{
"type": "continuous",
"mean": -1.25,
"standard deviation": 0.25,
"skewness": 0,
"kurtosis": -2,
"min": -1.5,
"25%": -1.375,
"median": -1.25,
"75%": -1.125,
"max": -1
}
]
}