Source code for discrimintools.summary._summarympca
# -*- coding: utf-8 -*-
[docs]
def summaryMPCA(
obj,digits=4,nb_element=10,ncp=3,to_markdown=False,tablefmt = "github",**kwargs
):
"""
Printing summaries of Mixed Principal Component Analysis model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
An object of class :class:`~discrimintools.MPCA`.
digits : `int <https://docs.python.org/3/library/functions.html#int>`_, default = 4
The number of decimal printed.
nb_element: `int <https://docs.python.org/3/library/functions.html#int>`_,, default = 10
Number of element
ncp : `int <https://docs.python.org/3/library/functions.html#int>`_,, default = 3
Number of components
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.summaryGFA`
Printing summaries of General Factor Analysis model.
:class:`~discrimintools.summaryGFALDA`
Printing summaries of General Factor Analysis Linear Discriminant Analysis model.
:class:`~discrimintools.summaryMDA`
Printing summaries of Mixed Discriminant Analysis model.
Examples
--------
>>> from discrimintools.datasets import load_heart
>>> from discrimintools import MPCA, summaryMPCA
>>> D = load_heart("subset")
>>> X = D.drop(columns=["disease"])
>>> clf = MPCA()
>>> clf.fit(X)
MPCA()
>>> summaryMPCA(clf)
Mixed Principal Component Analysis - Results
Eigenvalues informations:
Can1 Can2 Can3 Can4 ... Can7 Can8 Can9 Can10
Variance 3.5040 2.0055 1.8996 1.3246 ... 0.9318 0.8633 0.7418 0.4526
Difference 1.4985 0.1059 0.5750 0.1522 ... 0.0684 0.1215 0.2892 NaN
% of var. 25.0288 14.3249 13.5684 9.4616 ... 6.6555 6.1667 5.2988 3.2328
Cumulative % of var. 25.0288 39.3536 52.9220 62.3837 ... 85.3017 91.4684 96.7672 100.0000
[4 rows x 10 columns]
Individuals (the 10 first):
Can1 Can2
0 0.4813 -0.3691
1 -1.5953 -0.7913
2 0.4499 5.0389
3 1.4228 0.1799
4 0.0194 -0.3433
5 0.0610 -0.3194
6 -3.8725 4.3323
7 -2.5126 -0.3472
8 -0.2793 -0.2635
9 -3.4851 -1.5245
Variables (the 10 first):
Can1 Can2
age -0.5010 0.1554
restbpress -0.3558 0.1148
max_hrate 0.6681 0.0688
asympt -0.7169 -0.2320
atyp_angina 0.5061 0.1254
non_anginal 0.2706 0.0883
typ_angina 0.1312 0.1470
f 0.3192 -0.9171
t -0.3192 0.9171
left_vent_hyper 0.0567 0.1085
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#check if self is an object of class MPCA
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
if obj.model_ != "mpca":
raise ValueError("'self' must be an object of class MPCA")
print(" Mixed Principal Component Analysis - Results ")
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#eigenvalues informations
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nEigenvalues informations:")
eig = obj.eig_.T.round(decimals=digits)
eig.index = ["Variance","Difference","% of var.","Cumulative % of var."]
if to_markdown:
eig = eig.to_markdown(tablefmt=tablefmt,**kwargs)
print(eig)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#individuals informations
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
ind_coord = obj.ind_.coord
if ind_coord.shape[0] > nb_element:
print(f"\nIndividuals (the {nb_element} first):")
else:
print("\nIndividuals:")
ind_coord = ind_coord.iloc[:nb_element,:min(ncp,obj.call_.n_components)].round(decimals=digits)
if to_markdown:
ind_coord = ind_coord.to_markdown(tablefmt=tablefmt,**kwargs)
print(ind_coord)
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#variables informations
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
var_coord = obj.var_.coord
if var_coord.shape[0]>nb_element:
print(f"\nVariables (the {nb_element} first):")
else:
print("\nVariables:")
var_coord = var_coord.iloc[:nb_element,:min(ncp,obj.call_.n_components)].round(decimals=digits)
if to_markdown:
var_coord = var_coord.to_markdown(tablefmt=tablefmt,**kwargs)
print(var_coord)
