mlinsights.helpers

Formatting

mlinsights.helpers.parameters.format_parameters(pdict)

Formats a list of parameters.

Parameters:

pdict – dictionary

Returns:

string

<<<

from mlinsights.helpers.parameters import format_parameters

d = dict(i=2, x=6.7, s="r")
print(format_parameters(d))

>>>

    i=2, s='r', x=6.7

mlinsights.helpers.parameters.format_value(v)

Formats a value to be included in a string.

Parameters:

v – a string

Returns:

a string

mlinsights.helpers.parameters.format_function_call(name, pdict)

Formats a function call with named parameters.

param pdict: dictionary :return: string

<<<

from mlinsights.helpers.parameters import format_function_call

d = dict(i=2, x=6.7, s="r")
print(format_function_call("fct", d))

>>>

    fct(i=2, s='r', x=6.7)

Pipeline

mlinsights.helpers.pipeline.alter_pipeline_for_debugging(pipe)

Overwrite methods transform, predict, predict_proba or decision_function to collect the last inputs and outputs seen in these methods.

Parameters:

pipe – scikit-learn pipeline

The object pipe is modified, it should be copied before calling this function if you need the object untouched after that. The prediction is slower. See notebook Visualize a scikit-learn pipeline.

mlinsights.helpers.pipeline.enumerate_pipeline_models(pipe, coor=None, vs=None)

Enumerates all the models within a pipeline.

Parameters:

• pipe – scikit-learn pipeline

• coor – current coordinate

• vs – subset of variables for the model, None for all

Returns:

iterator on models tuple(coordinate, model)

See example Visualize a scikit-learn pipeline.