Module trustML.metrics.precision

Expand source code 
from trustML.metrics.metric import Metric
from sklearn.metrics import precision_score

class PrecisionSKL(Metric):
    """Precision for sklearn-based classifiers using sklearn. The precision is the ratio tp / (tp + fp)
    where tp is the number of true positives and fp the number of false positives. The precision is 
    intuitively the ability of the classifier not to label as positive a sample that is negative.

    The best value is 1 and the worst value is 0.

    (Extracted from sklearn documentation)

    ADDITIONAL PROPERTIES:
    multiclass_average (str): 'binary' for binary classification problems, for 
    multiclass/multilabel targets, 'micro', 'macro' or 'weighted'.

    Args:
        Metric (Class): Metric abstract class
    """
    
    def __init__(self, additional_properties):
        super().__init__()
        self.multiclass_average = additional_properties["multiclass_average"]

    def assess(self, trained_model, data_x, data_y):
        pred = trained_model.predict(data_x)
        self.score = precision_score(data_y, pred, average=self.multiclass_average)

Classes

class PrecisionSKL (additional_properties)

Precision for sklearn-based classifiers using sklearn. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

The best value is 1 and the worst value is 0.

(Extracted from sklearn documentation)

ADDITIONAL PROPERTIES: multiclass_average (str): 'binary' for binary classification problems, for multiclass/multilabel targets, 'micro', 'macro' or 'weighted'.

Args

Metric : Class
Metric abstract class
Expand source code 
class PrecisionSKL(Metric):
    """Precision for sklearn-based classifiers using sklearn. The precision is the ratio tp / (tp + fp)
    where tp is the number of true positives and fp the number of false positives. The precision is 
    intuitively the ability of the classifier not to label as positive a sample that is negative.

    The best value is 1 and the worst value is 0.

    (Extracted from sklearn documentation)

    ADDITIONAL PROPERTIES:
    multiclass_average (str): 'binary' for binary classification problems, for 
    multiclass/multilabel targets, 'micro', 'macro' or 'weighted'.

    Args:
        Metric (Class): Metric abstract class
    """
    
    def __init__(self, additional_properties):
        super().__init__()
        self.multiclass_average = additional_properties["multiclass_average"]

    def assess(self, trained_model, data_x, data_y):
        pred = trained_model.predict(data_x)
        self.score = precision_score(data_y, pred, average=self.multiclass_average)

Ancestors

Inherited members