dlpy.metrics.f1_score

dlpy.metrics.f1_score(y_true, y_pred, pos_label, castable=None, id_vars=None)

Compute the f1 score of the binary classification task. f1 score is defined as :math:`

rac{2PR}{P+R}`, where is the precision and is

the recall.

Parameters:

y_true : string or CASColumn

The column of the ground truth labels. If it is a string, then y_pred has to be a string and they both belongs to the same CASTable specified by the castable argument. If it is a CASColumn, then y_pred has to be a CASColumn, and the castable argument is ignored. When both y_pred and y_true are CASColumn, they can be in different CASTables.

y_pred : string or CASColumn

The column of the predicted class labels. If it is a string, then y_true has to be a string and they both belongs to the same CASTable specified by the castable argument. If it is a CASColumn, then y_true has to be a CASColumn, and the castable argument is ignored. When both y_pred and y_true are CASColumn, they can be in different CASTables.

pos_label : string, int or float

The positive class label.

castable : CASTable, optional

The CASTable object to use as the source if the y_pred and y_true are strings. Default = None

id_vars : string or list of strings, optional

Column names that serve as unique id for y_true and y_pred if they are from different CASTables. The column names need to appear in both CASTables, and they serve to match y_true and y_pred appropriately, since observation orders can be shuffled in distributed computing environment. Default = None.

Returns:

score : float