dlpy.metrics.plot_precision_recall

dlpy.metrics.plot_precision_recall(y_true, y_score, pos_label, castable=None, cutstep=0.001, figsize=8, 8, fontsize_spec=None, linewidth=1, id_vars=None)

Plot the precision recall(PR) curve for binary classification tasks.

Parameters

y_truestring or CASColumn

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

y_scorestring or CASColumn

The column of estimated probability for the positive class. 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_score and y_true are CASColumn, they can be in different CASTable.

pos_labelstring, int or float

The positive class label.

castableCASTable, optional

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

cutstepfloat > 0 and < 1, optional

The stepsize of threshold cutoffs. Default=0.001.

figsizetuple, optional

The size of the generated figure. Default=(8, 8).

fontsize_specdict, optional

It specifies the fontsize for ‘xlabel’, ‘ylabel’, ‘xtick’, ‘ytick’ and ‘title’. (e.g. {‘xlabel’:14, ‘ylabel’:14}). If None, it will take the default fontsize, which are {‘xlabel’:16, ‘ylabel’:16, ‘xtick’:14, ‘ytick’:14, ‘title’:20} Default=None.

linewidthfloat, optional

It specify the line width for the ROC curve. Default=1.

id_varsstring or list of strings, optional

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

Returns

matplotlib.axes.Axes

The x-axis is the recall(sensitivity) and the y-axis is the precision.