Developer API#

AbstractPlot#

class sklearn_evaluation.plot.plot.AbstractPlot(*, label=None)#

An abstract class for all class-based plots

dump(path)#

Serialize the plot as .json to the given path.

classmethod from_dump(path)#

Instantiates a plot object from a path to a JSON file. A default implementation is provided, but you might override it.

abstract classmethod from_raw_data()#

Takes raw unaggregated (for an example of aggregated vs unaggregated data see the constructor docstring) data, compute statistics and initializes the object. This is the method that users typically use. (e.g., they pass y_true, and y_pred here, we aggregate and call the constructor).

Apart from input data, this method must have the same argument as the constructor.

All arguments beyond the input data must be keyword-only (add a * argument between the input and the rest of the arguments).

abstract plot(ax=None)#

All plotting related code must be here with one optional argument ax=None. Must assign, self.ax_, and self.figure_ attributes and return self.

class sklearn_evaluation.plot.plot.AbstractComposedPlot#
abstract plot(ax=None)#

All plotting related code must be here with one optional argument ax=None. Must return self.

MyBar#

class sklearn_evaluation.plot._example.MyBar(count, *, color=None, name=None)#

Bar plot. This is an internal plot targeted for developers, not intended for end-users.

Parameters:

  • count (dict) – A dictionary whose keys are labels and values are counts

  • color (string, default=None) – Color for the bars, must be a valid matplotlib color

  • name (string, default=None) – A value to identify this plot

Notes

New in version 0.9.

Examples

Create plot:

from sklearn_evaluation.plot._example import MyBar
MyBar.from_raw_data(["banana", "banana", "apple", "pineapple", "apple"],
                        color="lightblue")
../_images/developer-api-1.png

Compare plots:

from sklearn_evaluation.plot._example import MyBar
one = MyBar.from_raw_data(["banana", "banana", "apple", "pineapple", "apple"])
another = MyBar.from_raw_data(["banana", "apple",  "pineapple"])
one + another
../_images/developer-api-2_00.png
../_images/developer-api-2_01.png
../_images/developer-api-2_02.png
classmethod from_raw_data(things_to_count, *, color=None, name=None)#

check typical naming: such as y_pred, y_score, y_true

Parameters:

  • things_to_count (list) – The list of elements to count

  • color (string, default=None) – Color for the bars, must be a valid matplotlib color

  • name (string, default=None) – A value to identify this plot

plot(ax=None)#

Create the plot

Parameters:

ax (matplotlib.Axes) – An Axes object to add the plot to

bar#

sklearn_evaluation.plot._example.my_bar(things_to_count, ax=None, color=None)#
Parameters:

  • things_to_count (list) – The list of elements to count

  • color (string, default=None) – Color for the bars, must be a valid matplotlib color

Examples

from sklearn_evaluation.plot._example import my_bar
my_bar(["banana", "banana", "apple", "pineapple", "apple"],
       color="lightblue")
../_images/developer-api-3.png