Source code for discrimintools.summary._summarymda
# -*- coding: utf-8 -*-
from pandas import concat
#intern function
from ._eval_predict import eval_predict
[docs]
def summaryMDA(
obj,digits=4,detailed=False,to_markdown=False,tablefmt="github",**kwargs
):
"""Printing summaries of Mixed Discriminant Analysis model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
An object of class :class:`~discrimintools.MDA`.
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.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_heart
>>> from discrimintools import MDA, summaryMDA
>>> D = load_heart("subset") # load training data
>>> y, X = D["disease"], D.drop(columns=["disease"]) # split into X and y
>>> clf = MDA(n_components=5)
>>> clf.fit(X,y)
MDA(n_components=5)
>>> summaryMDA(clf)
Mixed Discriminant Analysis - Results
Importance of components:
Eigenvalue Difference Proportion (%) Cumulative (%)
Can1 3.5040 1.4985 25.0288 25.0288
Can2 2.0055 0.1059 14.3249 39.3536
Can3 1.8996 0.5750 13.5684 52.9220
Can4 1.3246 0.1522 9.4616 62.3837
Can5 1.1724 0.0681 8.3743 70.7580
Raw Canonical Coefficients:
Can1 Can2 Can3 Can4 Can5
Constant -0.0059 0.2104 0.2151 -0.8402 -5.4678
age -0.0333 0.0137 0.0030 0.0189 0.0549
restbpress -0.0109 0.0047 0.0056 -0.0012 0.0297
max_hrate 0.0150 0.0020 0.0027 0.0007 -0.0121
asympt -0.7662 -0.3278 -0.4341 -0.0563 -0.5999
...
left_vent_hyper 0.1982 0.5016 1.7704 1.4714 -1.7099
normal 0.4200 0.4352 -1.7807 -0.1179 0.2904
st_t_wave_abnormality -0.5140 -0.5888 1.6832 -0.1457 -0.0045
no 0.9448 0.1881 0.2183 0.1125 0.1830
yes -0.9448 -0.1881 -0.2183 -0.1125 -0.1830
Projection functions coefficients:
Can1 Can2 Can3 Can4 Can5
age -0.0892 0.0366 0.0081 0.0507 0.1472
restbpress -0.0634 0.0270 0.0322 -0.0067 0.1723
max_hrate 0.1190 0.0162 0.0218 0.0054 -0.0965
asympt -0.0957 -0.0410 -0.0542 -0.0070 -0.0750
atyp_angina 0.0676 0.0221 0.0450 -0.1487 0.0679
...
left_vent_hyper 0.0076 0.0192 0.0676 0.0562 -0.0653
normal 0.0392 0.0406 -0.1662 -0.0110 0.0271
st_t_wave_abnormality -0.0451 -0.0516 0.1475 -0.0128 -0.0004
no 0.1122 0.0224 0.0259 0.0134 0.0217
yes -0.1122 -0.0224 -0.0259 -0.0134 -0.0217
"""
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#check if self is an object of class MDA
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
if obj.model_ != "mda":
raise ValueError("'self' must be an object of class MDA")
print(" Mixed Discriminant Analysis - Results ")
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#add eigenvalues informations
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nImportance of components:")
eig = obj.pipe_["mpca"].eig_.iloc[:obj.call_.n_components,:].round(decimals=digits)
if to_markdown:
eig = eig.to_markdown(tablefmt=tablefmt,**kwargs)
print(eig)
print("\nRaw Canonical Coefficients:")
can_coef_raw = obj.cancoef_.raw.round(decimals=digits)
if to_markdown:
can_coef_raw = can_coef_raw.to_markdown(tablefmt=tablefmt,**kwargs)
print(can_coef_raw)
print("\nProjection functions coefficients:")
proj_coef = obj.cancoef_.projection.round(decimals=digits)
if to_markdown:
proj_coef = proj_coef.to_markdown(tablefmt=tablefmt,**kwargs)
print(proj_coef)
if detailed:
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
#manova summary
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------
print("\nMultivariate Analysis of Variance (MANOVA) Summary:")
manova = obj.pipe_["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.pipe_["lda"].coef_,obj.pipe_["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)
