Source code for discrimintools.summary._summarycpls
# -*- coding: utf-8 -*-
from pandas import concat
#intern function
from ._eval_predict import eval_predict
[docs]
def summaryCPLS(
obj,ncp=2,digits=4,detailed=False,to_markdown=False,tablefmt="github",**kwargs
):
"""
Printing summaries of Partial Least Squares for Classification model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
An object of class :class:`~discrimintools.CPLS`.
ncp : `int <https://docs.python.org/3/library/functions.html#int>`_,, default = 2
Number of pls components.
digits : `int <https://docs.python.org/3/library/functions.html#int>`_, default = 4
The number of decimal printed.
detailed : `bool <https://docs.python.org/3/library/functions.html#bool>`_, default = `False <https://docs.python.org/3/library/constants.html#False>`_
To print detailed summaries.
to_markdown: `bool <https://docs.python.org/3/library/functions.html#bool>`_, default = `False <https://docs.python.org/3/library/constants.html#False>`_
To print summaries in `markdown <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html>`_-friendly format. Requires the `tabulate <https://pypi.org/project/tabulate/>`_. package.
tablefmt : `str <https://docs.python.org/3/library/functions.html#func-str>`_, default = "github"
The table format.
**kwargs :
additionals parameters. These parameters will be passed to `tabulate <https://pypi.org/project/tabulate/>`_.
Returns
-------
NoneType
See also
--------
:class:`~discrimintools.summaryCANDISC`
Printing summaries of Canonical Discriminant Analysis model.
:class:`~discrimintools.summaryDA`
Printing summaries of Discriminant Analysis model.
:class:`~discrimintools.summaryDiCA`
Printing summaries of Discriminant Correspondence Analysis model.
:class:`~discrimintools.summaryDISCRIM`
Printing summaries of Discriminant Analysis (linear and quadratic) model.
:class:`~discrimintools.summaryGFALDA`
Printing summaries of General Factor Analysis Linear Discriminant Analysis model.
:class:`~discrimintools.summaryMDA`
Printing summaries of Mixed Discriminant Analysis model.
:class:`~discrimintools.summaryPLSDA`
Printing summaries of Partial Least Squares Discriminant Analysis model.
:class:`~discrimintools.summaryPLSLDA`
Printing summaries of Partial Least Squares Linear Discriminant Analysis model.
:class:`~discrimintools.summarySTEPDISC`
Printing summaries of Stepwise Discriminant Analysis model.
Examples
--------
>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import CPLS, summaryCPLS
>>> DTrain = load_dataset("breast") # load training data
>>> y, X = D["Class"], D.drop(columns=["Class"]) # split into X and y
>>> clf = CPLS()
>>> clf.fit(X,y)
CPLS()
>>> summaryCPLS(clf)
Partial Least Squares for Classification - Results
Class Level Information:
Frequency Proportion Prior Probability
negative 458 0.6552 0.6552
positive 241 0.3448 0.3448
Classification functions coefficients:
positive VIP
Constant -0.424881 NaN
ucellsize 0.085323 1.203759
normnucl 0.053251 1.034559
mitoses 0.003001 0.693292
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#check if self is an object of class CPLS
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
if obj.model_ != "cpls":
raise ValueError("'self' must be an object of class CPLS")
print(" Partial Least Squares for Classification - Results ")
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#class level information
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nClass Level Information:")
class_infos = obj.classes_.infos.round(decimals=digits)
if to_markdown:
class_infos = class_infos.to_markdown(tablefmt=tablefmt,**kwargs)
print(class_infos)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#importance of pls components
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nImportance of PLS components:")
eig = obj.explained_variance_.iloc[:min(ncp,obj.call_.n_components),:].round(decimals=digits)
if to_markdown:
eig = eig.to_markdown(tablefmt=tablefmt,**kwargs)
print(eig)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#classification functions coefficients
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nClassification functions coefficients:")
coef = concat((obj.coef_, obj.vip_.vip),axis=1).round(decimals=digits)
if to_markdown:
coef = coef.to_markdown(tablefmt=tablefmt,**kwargs)
print(coef)
if detailed:
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#classification summary for calibration data
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
eval_predict(obj,digits,to_markdown,tablefmt,**kwargs)
