Source code for discrimintools.summary._summaryplslda
# -*- coding: utf-8 -*-
from pandas import concat
#intern function
from ._eval_predict import eval_predict
[docs]
def summaryPLSLDA(
obj,digits=4,detailed=False,to_markdown=False,tablefmt="github",**kwargs
):
"""
Printing summaries of Partial Least Squares Linear Discriminant Analysis model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
An object of class :class:`~discrimintools.PLSLDA`.
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.summaryCPLS`
Printing summaries of Partial Least Squares for Classification 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.summarySTEPDISC`
Printing summaries of Stepwise Discriminant Analysis model.
Examples
--------
>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import PLSLDA, summaryPLSLDA
>>> D = load_dataset("breast")
>>> y, X = DTrain["Class"], D.drop(columns=["Class"])
>>> clf = PLSLDA()
>>> clf.fit(X,y)
PLSLDA()
>>> summaryPLSLDA(clf)
Partial Least Squares Linear Discriminant Analysis - Results
Class Level Information:
Frequency Proportion Prior Probability
negative 458 0.6552 0.6552
positive 241 0.3448 0.3448
Importance of PLS components:
Proportion (%) Cumulative (%)
Can1 69.1520 69.1520
Can2 20.1981 89.3501
Raw Canonical and Classification Functions Coefficients:
Can1 Can2 negative positive
Constant -1.6329 -0.1467 1.1027 -7.2713
ucellsize 0.2302 -0.1985 -0.4268 0.8112
normnucl 0.2003 0.0083 -0.2664 0.5063
mitoses 0.2119 0.4687 -0.0150 0.0285
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#check if self is an object of class PLSLDA
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
if obj.model_ != "plslda":
raise ValueError("'self' must be an object of class PLSLDA")
print(" Partial Least Squares Linear Discriminant Analysis - 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_.round(decimals=digits)
if to_markdown:
eig = eig.to_markdown(tablefmt=tablefmt,**kwargs)
print(eig)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#raw canonical coefficients and classification functions coefficients
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nRaw Canonical and Classification Functions Coefficients:")
coef = concat((obj.cancoef_.raw,obj.coef_.raw),axis=1).round(decimals=digits)
if to_markdown:
coef = coef.to_markdown(tablefmt=tablefmt,**kwargs)
print(coef)
if detailed:
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#manova summary
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nMultivariate Analysis of Variance (MANOVA) Summary:")
manova = obj.lda_.statistics_.performance.round(decimals=digits)
if to_markdown:
manova = manova.to_markdown(tablefmt=tablefmt,**kwargs)
print(manova)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#classification functions coefficients
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
class_eval = concat((obj.lda_.coef_,obj.lda_.vip_.vip),axis=1).round(decimals=digits)
print("\nLDA Classification functions & Statistical Evaluation:")
if to_markdown:
class_eval = class_eval.to_markdown(tablefmt=tablefmt,**kwargs)
print(class_eval)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#classification summary for calibration data
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
eval_predict(obj,digits,to_markdown,tablefmt,**kwargs)
