Source code for discrimintools.summary._summarygfa
# -*- coding: utf-8 -*-
[docs]
def summaryGFA(
obj,digits=4,nb_element=10,ncp=3,to_markdown=False,tablefmt="github",**kwargs
):
"""
Printing summaries of General Factor Analysis model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
an object of class :class:`~discrimintools.GFA`.
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.summaryGFALDA`
Printing summaries of General Factor Analysis Linear Discriminant Analysis model.
:class:`~discrimintools.summaryMPCA`
Printing summaries of Mixed Principal Component Analysis model.
:class:`~discrimintools.summaryMDA`
Printing summaries of Mixed Discriminant Analysis model.
Examples
--------
>>> from discrimintools.datasets import load_alcools, load_canines, load_heart
>>> from discrimintools import GFA, summaryGFA
The :class:`~discrimintools.GFA` performs principal component analysis (PCA) ...
>>> #PCA
>>> D = load_alcools("train")
>>> X = D.drop(columns=["TYPE"])
>>> clf = GFA()
>>> clf.fit(X)
GFA()
>>> summaryGFA(clf)
General Factor Analysis - Results
Eigenvalues informations:
Can1 Can2 Can3 Can4 Can5 Can6 Can7 Can8
Variance 2.7988 1.7188 1.4034 1.0444 0.5368 0.2024 0.1851 0.1103
Difference 1.0799 0.3154 0.3590 0.5076 0.3344 0.0173 0.0748 NaN
% of var. 34.9848 21.4856 17.5428 13.0554 6.7101 2.5295 2.3136 1.3782
Cumulative % of var. 34.9848 56.4703 74.0132 87.0686 93.7786 96.3081 98.6218 100.0000
Individuals (the 10 first):
Can1 Can2
0 -1.4901 -1.1150
1 -0.8484 1.0139
2 -1.7262 0.6568
3 -1.7259 -1.1717
4 -3.6258 -1.4189
5 -0.9469 2.3607
6 -0.7407 1.8183
7 -3.4476 -1.4497
8 -1.6847 -0.9451
9 -3.7593 -1.0701
Variables:
Can1 Can2
MEOH 0.8711 0.0601
ACET 0.1017 0.4556
BU1 0.7630 -0.0353
BU2 -0.0493 0.7563
ISOP 0.7692 0.0130
MEPR 0.8078 0.1149
PRO1 -0.3263 0.8465
ACAL 0.3073 0.4522
multiple correspondence analysis (MCA)...
>>> #MCA
>>> D = load_canines("train")
>>> X = D.drop(columns=["Fonction"])
>>> clf = GFA()
>>> clf.fit(X)
GFA()
>>> summaryGFA(clf)
General Factor Analysis - Results
Eigenvalues informations:
Can1 Can2 Can3 Can4 ... Can7 Can8 Can9 Can10
Variance 0.4816 0.3847 0.2110 0.1576 ... 0.0815 0.0457 0.0235 0.0077
Difference 0.0969 0.1738 0.0534 0.0074 ... 0.0358 0.0221 0.0158 NaN
% of var. 28.8964 23.0842 12.6572 9.4532 ... 4.8877 2.7402 1.4125 0.4628
Cumulative % of var. 28.8964 51.9806 64.6379 74.0911 ... 95.3845 98.1247 99.5372 100.0000
[4 rows x 10 columns]
Individuals (the 10 first):
Can1 Can2
Beauceron -0.3172 -0.4177
Basset 0.2541 1.1012
Berger All -0.4864 -0.4644
Boxer 0.4474 -0.8818
Bull-Dog 1.0134 0.5499
Bull-Mastif -0.7526 0.5469
Caniche 0.9123 -0.0162
Chihuahua 0.8408 0.8439
Cocker 0.7333 0.0791
Colley -0.1173 -0.5261
Variables (the 10 first):
Can1 Can2
Taille+ 0.8511 -1.2317
Taille++ -0.8367 -0.0206
Taille- 1.1850 0.9239
Poids+ -0.3054 -0.8189
Poids++ -1.0151 0.9739
Poids- 1.1689 0.8243
Veloc+ 0.6037 -0.8878
Veloc++ -0.8921 -0.3718
Veloc- 0.3199 1.0449
Intell+ 0.3694 -0.2855
and factor analysis of mixed data (FAMD).
>>> #FAMD
>>> D = load_heart("subset")
>>> X = D.drop(columns=["disease"])
>>> clf = GFA()
>>> clf.fit(X,y)
GFA()
>>> summaryGFA(clf)
General Factor Analysis - Results
Eigenvalues informations:
Can1 Can2 Can3 Can4 ... Can7 Can8 Can9 Can10
Variance 2.3749 1.1618 1.0641 1.0243 ... 0.7950 0.7223 0.4739 0.4363
Difference 1.2131 0.0978 0.0398 0.0441 ... 0.0727 0.2483 0.0376 NaN
% of var. 23.7495 11.6184 10.6408 10.2431 ... 7.9497 7.2227 4.7392 4.3629
Cumulative % of var. 23.7495 35.3678 46.0086 56.2517 ... 83.6751 90.8979 95.6371 100.0000
[4 rows x 10 columns]
Individuals (the 10 first):
Can1 Can2
0 -1.0697 -0.9664
1 0.8760 -0.8987
2 -0.6487 3.0729
3 -0.8548 1.0046
4 0.2919 -0.3426
5 0.2375 -0.2891
6 2.2459 5.0207
7 2.3149 0.3848
8 0.7471 -0.1014
9 2.8908 -0.2045
Variables (the 10 first):
Can1 Can2
age 0.6420 0.3410
restbpress 0.4544 0.2595
max_hrate -0.7802 0.0723
asympt 1.0310 -0.4093
atyp_angina -1.0302 -0.0652
non_anginal -0.8281 1.1019
typ_angina -1.3976 1.0534
f -0.1198 -0.1910
t 1.4456 2.3035
left_vent_hyper -1.2533 3.6476
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#check if self is an object of class GFA
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
if obj.model_ != "gfa":
raise ValueError("'self' must be an object of class GFA")
print(" General Factor Analysis - Results ")
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#eigenvalues informations
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nEigenvalues informations:")
eig = obj.eig_.T.round(decimals=digits)
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,:ncp].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,:ncp].round(decimals=digits)
if to_markdown:
var_coord = var_coord.to_markdown(tablefmt=tablefmt,**kwargs)
print(var_coord)
