In [ ]:
#position des variables (importance des variables)
plotFAMD(afdm,choice='var',repel=True)
#version de python -> il faut au moins une version 3.10
import sys
sys.version
'3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:00:38) [MSC v.1934 64 bit (AMD64)]'
#la libraire -> version 0.0.3 (ce jour)
#!pip install scientisttools
#attention aux dépendances -- plusieurs à installer à la main
#ATTENTION : le code ici est relatif à ma configuration
#!pip install mapply
#!pip install adjustText
#!pip install factor_analyzer
#... et d'autres selon votre configuration... voir fichier environnement (yaml)
#les fonctions
from scientisttools.decomposition import FAMD
from scientisttools.extractfactor import get_eig,get_famd_ind,get_famd_col,get_famd_var,get_famd_mod,summaryFAMD
from scientisttools.pyplot import plotFAMD,plot_eigenvalues,plot_cosines,plot_contrib,plot_correlation_circle
#changement de dossier
import os
os.chdir("C:/Users/ricco/Desktop/demo")
#chargement des données
import pandas
D = pandas.read_excel("Tennis_Players_AFDM.xlsx",index_col=0)
D
| Taille | Lateralite | MainsRevers | Titres | Finales | TitresGC | RolandGarros | BestClassDouble | |
|---|---|---|---|---|---|---|---|---|
| Joueur | ||||||||
| Agassi | 180 | droitier | deux | 60 | 30 | 8 | vainqueur | 123 |
| Becker | 191 | droitier | une | 49 | 28 | 6 | demi | 6 |
| Borg | 180 | droitier | deux | 64 | 25 | 11 | vainqueur | 890 |
| Connors | 178 | gaucher | deux | 109 | 52 | 8 | demi | 370 |
| Courier | 185 | droitier | deux | 23 | 13 | 4 | vainqueur | 20 |
| Edberg | 187 | droitier | une | 41 | 36 | 6 | finale | 1 |
| Kafelnikov | 190 | droitier | deux | 26 | 20 | 2 | vainqueur | 4 |
| Kuerten | 190 | droitier | une | 20 | 9 | 3 | vainqueur | 38 |
| Lendl | 187 | droitier | une | 94 | 50 | 8 | vainqueur | 20 |
| McEnroe | 180 | gaucher | une | 77 | 31 | 7 | finale | 1 |
| Nastase | 180 | droitier | une | 58 | 38 | 2 | vainqueur | 59 |
| Rafter | 185 | droitier | une | 11 | 14 | 2 | demi | 6 |
| Safin | 193 | droitier | deux | 15 | 12 | 2 | demi | 71 |
| Sampras | 185 | droitier | une | 64 | 24 | 14 | demi | 27 |
| Vilas | 180 | gaucher | une | 62 | 40 | 4 | vainqueur | 175 |
| Wilander | 182 | droitier | deux | 33 | 27 | 7 | vainqueur | 3 |
| Djokovic | 188 | droitier | deux | 79 | 34 | 17 | vainqueur | 114 |
| Federer | 185 | droitier | une | 103 | 54 | 20 | vainqueur | 24 |
| Murray | 191 | droitier | deux | 46 | 22 | 3 | finale | 51 |
| Nadal | 185 | gaucher | deux | 85 | 37 | 19 | vainqueur | 26 |
#documentation
help(FAMD)
Help on class FAMD in module scientisttools.decomposition:
class FAMD(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin)
| FAMD(normalize=True, n_components=None, row_labels=None, quanti_labels=None, quali_labels=None, row_sup_labels=None, quanti_sup_labels=None, quali_sup_labels=None, graph=False, figsize=None)
|
| Factor Analysis of Mixed Data
|
| Performs Factor Analysis of Mixed Data (FAMD) with supplementary
| individuals, supplementary quantitative variables and supplementary
| categorical variables.
|
| Parameters:
| -----------
| see scientisttools.decomposition.PCA and scientisttools.decomposition.MCA
|
| Method resolution order:
| FAMD
| sklearn.base.BaseEstimator
| sklearn.base.TransformerMixin
| sklearn.utils._set_output._SetOutputMixin
| builtins.object
|
| Methods defined here:
|
| __init__(self, normalize=True, n_components=None, row_labels=None, quanti_labels=None, quali_labels=None, row_sup_labels=None, quanti_sup_labels=None, quali_sup_labels=None, graph=False, figsize=None)
| Initialize self. See help(type(self)) for accurate signature.
|
| fit(self, X)
|
| fit_transform(self, X, y=None)
| Fit the model with X and apply the dimensionality reduction on X.
|
| Parameters
| ----------
| X : pd.DataFrame, shape (n_samples, n_features)
| New data, where n_samples in the number of samples
| and n_features is the number of features.
|
| y : None
| y is ignored
|
| Returns
| -------
| X_new : array-like, shape (n_samples, n_components)
|
| transform(self, X)
| Apply the dimensionality reduction on X
|
| X is projected on the first axes previous extracted from a training set.
|
| Parameters
| ----------
| X : DataFrame, shape (n_rows_sup, n_columns)
| New data, where n_row_sup is the number of supplementary
| row points and n_columns is the number of columns
| X rows correspond to supplementary row points that are
| projected on the axes
| X is a table containing numeric values
|
| y : None
| y is ignored
|
| Returns
| -------
| X_new : DataFrame of float, shape (n_rows_sup, n_components_)
| X_new : coordinates of the projections of the supplementary
| row points on the axes.
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.BaseEstimator:
|
| __getstate__(self)
|
| __repr__(self, N_CHAR_MAX=700)
| Return repr(self).
|
| __setstate__(self, state)
|
| get_params(self, deep=True)
| Get parameters for this estimator.
|
| Parameters
| ----------
| deep : bool, default=True
| If True, will return the parameters for this estimator and
| contained subobjects that are estimators.
|
| Returns
| -------
| params : dict
| Parameter names mapped to their values.
|
| set_params(self, **params)
| Set the parameters of this estimator.
|
| The method works on simple estimators as well as on nested objects
| (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
| parameters of the form ``<component>__<parameter>`` so that it's
| possible to update each component of a nested object.
|
| Parameters
| ----------
| **params : dict
| Estimator parameters.
|
| Returns
| -------
| self : estimator instance
| Estimator instance.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sklearn.base.BaseEstimator:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.utils._set_output._SetOutputMixin:
|
| set_output(self, *, transform=None)
| Set output container.
|
| See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
| for an example on how to use the API.
|
| Parameters
| ----------
| transform : {"default", "pandas"}, default=None
| Configure output of `transform` and `fit_transform`.
|
| - `"default"`: Default output format of a transformer
| - `"pandas"`: DataFrame output
| - `None`: Transform configuration is unchanged
|
| Returns
| -------
| self : estimator instance
| Estimator instance.
|
| ----------------------------------------------------------------------
| Class methods inherited from sklearn.utils._set_output._SetOutputMixin:
|
| __init_subclass__(auto_wrap_output_keys=('transform',), **kwargs) from builtins.type
| This method is called when a class is subclassed.
|
| The default implementation does nothing. It may be
| overridden to extend subclasses.
#plus d'infos sur les paramètres
from scientisttools.decomposition import PCA
help(PCA)
Help on class PCA in module scientisttools.decomposition:
class PCA(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin)
| PCA(normalize=True, n_components=None, row_labels=None, col_labels=None, row_sup_labels=None, quanti_sup_labels=None, quali_sup_labels=None, graph=False, figsize=None)
|
| Principal Component Analysis
|
| This class inherits from sklearn BaseEstimator and TransformerMixin class
|
| This is a standard Principal Component Analysis implementation
| bases on the Singular Value Decomposition
|
| Performs Principal Component Analysis (PCA) with supplementary
| individuals, supplementary quantitative variables and supplementary
| categorical variables.
|
| Parameters
| ----------
| normalize : bool, default = True
| - If True : the data are scaled to unit variance.
| - If False : the data are not scaled to unit variance.
|
| n_components : int, float or None, default = None
| Number of components to keep.
| - If n_components is None, keep all the components.
| - If 0 <= n_components < 1, select the number of components such
| that the amount of variance that needs to be explained is
| greater than the percentage specified by n_components.
| - If 1 <= n_components :
| - If n_components is int, select a number of components
| equal to n_components
| - If n_components is float, select the higher number of
| components lower than n_components
|
| row_labels : array of strings or None, default = None
| - If row_labels is an array of strings : this array provides the
| row labels.
| If the shape of the array doesn't match with the number of
| rows : labels are automatically computed for each row.
| - If row_labels is None : labels are automatically computed for
| each row.
|
| col_labels : array of strings or None, default = None
| - If col_labels is an array of strings : this array provides the
| column labels.
| If the shape of the array doesn't match with the number of
| columns : labels are automatically computed for each
| column.
| - If col_labels is None : labels are automatically computed for
| each column.
|
| row_sup_labels : array of strings or None, defulat = None
| This array provides the supplementary individuals labels
|
| quanti_sup_labels : arrays of strings or None, default = None
| This array provides the quantitative supplementary variables labels
|
| quali_sup_labels : array of strings or None, default = None
| This array provides the categorical supplementary variables labels
|
| graph : boolean
|
| figsize : tuple or None
|
| Attributes
| ----------
| n_components_ : int
| The estimated number of components.
|
| row_labels_ : array of strings
| Labels for the rows.
|
| col_labels_ : array of strings
| Labels for the columns.
|
| row_sup_labels_ : array of strings or None
| Labels of supplementary individuals labels
|
| quanti_sup_labels_ : arrays f strings or None
| Labels of quantitative supplementary variables
|
| quali_sup_labels_ :
|
| mod_sup_labels_ : list of strings
| labels for the categories supplementary
|
| short_sup_labels_ : list of strings
| Short labels for the categories supplementary
|
| eig_ : array of float
| A 4 x n_components_ matrix containing all the eigenvalues
| (1st row), difference (2nd row) the percentage of variance (3rd row) and the
| cumulative percentage of variance (4th row).
|
| eigen_vectors_ : array of float
| Eigen vectors extracted from the Principal Components Analysis.
|
| row_coord_ : ndarray of shape (n_rows,n_components_)
| A n_rows x n_components_ matrix containing the row coordinates.
|
| col_coord_ : ndarray of shape (n_columns,n_components_)
| A n_columns x n_components_ matrix containing the column
| coordinates.
|
| row_contrib_ : ndarray of shape (n_rows,n_components_)
| A n_rows x n_components_ matrix containing the row
| contributions.
|
| col_contrib_ : ndarray of shape (n_columns,n_components_)
| A n_columns x n_components_ matrix containing the column
| contributions.
|
| row_cos2_ : ndarray of shape (n_rows,n_components_)
| A n_rows x n_components_ matrix containing the row cosines.
|
| col_cos2_ : ndarray of shape (n_columns,n_components_)
| A n_columns x n_components_ matrix containing the column
| cosines.
|
| col_cor_ : ndarray of shape (n_columns,n_components_)
| A n_columns x n_components_ matrix containing the correlations
| between variables (= columns) and axes.
|
| means_ : ndarray of shape (n_columns,)
| The mean for each variable (= for each column).
|
| std_ : ndarray of shape (n_columns,)
| The standard deviation for each variable (= for each column).
|
| ss_col_coord_ : ndarray of shape (n_columns,)
| The sum of squared of columns coordinates.
|
| model_ : string
| The model fitted = 'pca'
|
| Method resolution order:
| PCA
| sklearn.base.BaseEstimator
| sklearn.base.TransformerMixin
| sklearn.utils._set_output._SetOutputMixin
| builtins.object
|
| Methods defined here:
|
| __init__(self, normalize=True, n_components=None, row_labels=None, col_labels=None, row_sup_labels=None, quanti_sup_labels=None, quali_sup_labels=None, graph=False, figsize=None)
| Initialize self. See help(type(self)) for accurate signature.
|
| fit(self, X, y=None)
| Fit the model to X
|
| Parameters
| ----------
| X : DataFrame of float, shape (n_rows, n_columns)
|
| y : None
| y is ignored
|
| Returns:
| --------
| self : object
| Returns the instance itself
|
| fit_transform(self, X, y=None)
| Fit the model with X and apply the dimensionality reduction on X.
|
| Parameters
| ----------
| X : pd.DataFrame, shape (n_samples, n_features)
| New data, where n_samples in the number of samples
| and n_features is the number of features.
|
| Returns
| -------
| X_new : array-like, shape (n_samples, n_components)
|
| transform(self, X, y=None)
| Apply the dimensionality reduction on X
|
| X is projected on the first axes previous extracted from a training set.
|
| Parameters
| ----------
| X : DataFrame of float, shape (n_rows_sup, n_columns)
| New data, where n_row_sup is the number of supplementary
| row points and n_columns is the number of columns
| X rows correspond to supplementary row points that are
| projected on the axes
| X is a table containing numeric values
|
| y : None
| y is ignored
|
| Returns
| -------
| X_new : DataFrame of float, shape (n_rows_sup, n_components_)
| X_new : coordinates of the projections of the supplementary
| row points on the axes.
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.BaseEstimator:
|
| __getstate__(self)
|
| __repr__(self, N_CHAR_MAX=700)
| Return repr(self).
|
| __setstate__(self, state)
|
| get_params(self, deep=True)
| Get parameters for this estimator.
|
| Parameters
| ----------
| deep : bool, default=True
| If True, will return the parameters for this estimator and
| contained subobjects that are estimators.
|
| Returns
| -------
| params : dict
| Parameter names mapped to their values.
|
| set_params(self, **params)
| Set the parameters of this estimator.
|
| The method works on simple estimators as well as on nested objects
| (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
| parameters of the form ``<component>__<parameter>`` so that it's
| possible to update each component of a nested object.
|
| Parameters
| ----------
| **params : dict
| Estimator parameters.
|
| Returns
| -------
| self : estimator instance
| Estimator instance.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sklearn.base.BaseEstimator:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.utils._set_output._SetOutputMixin:
|
| set_output(self, *, transform=None)
| Set output container.
|
| See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
| for an example on how to use the API.
|
| Parameters
| ----------
| transform : {"default", "pandas"}, default=None
| Configure output of `transform` and `fit_transform`.
|
| - `"default"`: Default output format of a transformer
| - `"pandas"`: DataFrame output
| - `None`: Transform configuration is unchanged
|
| Returns
| -------
| self : estimator instance
| Estimator instance.
|
| ----------------------------------------------------------------------
| Class methods inherited from sklearn.utils._set_output._SetOutputMixin:
|
| __init_subclass__(auto_wrap_output_keys=('transform',), **kwargs) from builtins.type
| This method is called when a class is subclassed.
|
| The default implementation does nothing. It may be
| overridden to extend subclasses.
#instaciation
afdm = FAMD(n_components = 2,
row_labels=list(D.index[0:16]), #jusqu'à Wilander
row_sup_labels=list(D.index[16:]), #à partir de Djokovic
quanti_labels=['Taille','Titres','Finales','TitresGC'],
quanti_sup_labels=['BestClassDouble'],
quali_labels=['Lateralite','MainsRevers','RolandGarros'])
#entraînement
afdm.fit(D)
FAMD(n_components=2, quali_labels=['Lateralite', 'MainsRevers', 'RolandGarros'],
quanti_labels=['Taille', 'Titres', 'Finales', 'TitresGC'],
quanti_sup_labels=['BestClassDouble'],
row_labels=['Agassi', 'Becker', 'Borg', 'Connors', 'Courier', 'Edberg',
'Kafelnikov', 'Kuerten', 'Lendl', 'McEnroe', 'Nastase',
'Rafter', 'Safin', 'Sampras', 'Vilas', 'Wilander'],
row_sup_labels=['Djokovic', 'Federer', 'Murray', 'Nadal'])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. FAMD(n_components=2, quali_labels=['Lateralite', 'MainsRevers', 'RolandGarros'],
quanti_labels=['Taille', 'Titres', 'Finales', 'TitresGC'],
quanti_sup_labels=['BestClassDouble'],
row_labels=['Agassi', 'Becker', 'Borg', 'Connors', 'Courier', 'Edberg',
'Kafelnikov', 'Kuerten', 'Lendl', 'McEnroe', 'Nastase',
'Rafter', 'Safin', 'Sampras', 'Vilas', 'Wilander'],
row_sup_labels=['Djokovic', 'Federer', 'Murray', 'Nadal'])#valeurs propres
print(get_eig(afdm))
eigenvalue difference proportion cumulative Dim.1 3.125964 1.701771 39.074556 39.074556 Dim.2 1.424193 0.286425 17.802418 56.876974
#summary
summaryFAMD(afdm,to_markdown=True)
Factor Analysis of Mixed Data - Results Importance of components | | Dim.1 | Dim.2 | |:------------------------|--------:|--------:| | Variance | 3.126 | 1.424 | | Difference | 1.702 | 0.286 | | % of var. | 39.075 | 17.802 | | Cumulative of % of var. | 39.075 | 56.877 | Individuals (the 10 first) | | d(i,G) | p(i) | I(i,G) | Dim.1 | ctr | cos2 | Dim.2 | ctr | cos2 | |:-----------|---------:|-------:|---------:|--------:|-------:|-------:|--------:|-------:|-------:| | Agassi | 1.954 | 0.062 | 0.239 | -0.555 | 0.616 | 0.081 | -1.73 | 13.129 | 0.783 | | Becker | 2.275 | 0.062 | 0.323 | 0.835 | 1.395 | 0.135 | 1.241 | 6.763 | 0.298 | | Borg | 2.423 | 0.062 | 0.367 | -0.716 | 1.025 | 0.087 | -1.875 | 15.431 | 0.599 | | Connors | 4.292 | 0.062 | 1.152 | -3.52 | 24.775 | 0.673 | -0.681 | 2.035 | 0.025 | | Courier | 2.245 | 0.062 | 0.315 | 1.66 | 5.51 | 0.547 | -1.165 | 5.96 | 0.269 | | Edberg | 2.968 | 0.062 | 0.551 | -0.23 | 0.106 | 0.006 | 2.088 | 19.138 | 0.495 | | Kafelnikov | 2.489 | 0.062 | 0.387 | 1.985 | 7.879 | 0.636 | -0.77 | 2.601 | 0.096 | | Kuerten | 2.727 | 0.062 | 0.465 | 2.269 | 10.297 | 0.693 | 0.52 | 1.186 | 0.036 | | Lendl | 2.834 | 0.062 | 0.502 | -1.497 | 4.484 | 0.279 | -0.038 | 0.006 | 0 | | McEnroe | 3.768 | 0.062 | 0.887 | -2.504 | 12.537 | 0.442 | 1.949 | 16.67 | 0.268 | Supplementary individuals | | Dist | Dim.1 | cos2 | Dim.2 | cos2 | |:---------|-------:|--------:|-------:|--------:|-------:| | Djokovic | 3.864 | -1.162 | 0.09 | -1.678 | 0.189 | | Federer | 5.206 | -3.131 | 0.362 | -0.717 | 0.019 | | Murray | 3.385 | 1.042 | 0.095 | 1.127 | 0.111 | | Nadal | 4.847 | -2.89 | 0.355 | -1.543 | 0.101 | Continuous variables | | Dim.1 | ctr | cos2 | Dim.2 | ctr | cos2 | |:---------|--------:|-------:|-------:|--------:|------:|-------:| | Taille | 0.763 | 18.621 | 0.582 | 0.34 | 8.097 | 0.115 | | Titres | -0.936 | 28.023 | 0.876 | -0.074 | 0.385 | 0.005 | | Finales | -0.864 | 23.86 | 0.746 | 0.007 | 0.003 | 0 | | TitresGC | -0.557 | 9.943 | 0.311 | -0.181 | 2.301 | 0.033 | Supplementary continuous variable | | Dim.1 | cos2 | Dim.2 | cos2 | |:----------------|--------:|-------:|--------:|-------:| | BestClassDouble | -0.328 | 0.108 | -0.49 | 0.24 | Categories | | d(k,G) | p(k) | I(k,G) | Dim.1 | ctr | cos2 | vtest | Dim.2 | ctr | cos2 | vtest | |:-----------------------|---------:|-------:|---------:|--------:|-------:|-------:|--------:|--------:|-------:|-------:|--------:| | Lateralite_droitier | 0.48 | 0.116 | 0.027 | 0.602 | 3.016 | 1.572 | 2.747 | -0.113 | 0.509 | 0.055 | -0.761 | | Lateralite_gaucher | 2.082 | 0.027 | 0.116 | -2.61 | 13.071 | 1.572 | -2.747 | 0.488 | 2.205 | 0.055 | 0.761 | | MainsRevers_deux | 1.134 | 0.062 | 0.08 | 0.294 | 0.386 | 0.067 | 0.567 | -1.062 | 24.335 | 0.877 | -3.04 | | MainsRevers_une | 0.882 | 0.08 | 0.062 | -0.228 | 0.3 | 0.067 | -0.567 | 0.826 | 18.927 | 0.877 | 3.04 | | RolandGarros_demi | 1.483 | 0.045 | 0.098 | 0.294 | 0.276 | 0.039 | 0.434 | 0.49 | 3.706 | 0.109 | 1.073 | | RolandGarros_finale | 2.646 | 0.018 | 0.125 | -1.367 | 2.391 | 0.267 | -1.132 | 2.019 | 25.113 | 0.582 | 2.476 | | RolandGarros_vainqueur | 0.882 | 0.08 | 0.062 | 0.14 | 0.114 | 0.025 | 0.349 | -0.721 | 14.419 | 0.668 | -2.653 | Categorical variables | | eta2.1 | cos2.1 | eta2.2 | cos2.2 | |:-------------|---------:|---------:|---------:|---------:| | Lateralite | 0.503 | 0.503 | 0.039 | 0.039 | | MainsRevers | 0.021 | 0.021 | 0.616 | 0.616 | | RolandGarros | 0.087 | 0.043 | 0.616 | 0.308 |
#position des variables (importance des variables)
plotFAMD(afdm,choice='var',repel=True)
#récupérer les informations sur les variabes
info_var = get_famd_col(afdm)
info_var
{'corr': Taille Titres Finales TitresGC
Taille 1.000000 -0.592010 -0.529501 -0.364681
Titres -0.592010 1.000000 0.873995 0.601113
Finales -0.529501 0.873995 1.000000 0.333035
TitresGC -0.364681 0.601113 0.333035 1.000000,
'pcorr': Taille Titres Finales TitresGC
Taille 1.000000 -0.215440 -0.043335 -0.033367
Titres -0.215440 1.000000 0.863026 0.653212
Finales -0.043335 0.863026 1.000000 -0.495977
TitresGC -0.033367 0.653212 -0.495977 1.000000,
'coord': Dim.1 Dim.2
Taille 0.762939 0.339587
Titres -0.935937 -0.074027
Finales -0.863628 0.006709
TitresGC -0.557497 -0.181030,
'cos2': Dim.1 Dim.2
Taille 0.582077 0.115319
Titres 0.875979 0.005480
Finales 0.745854 0.000045
TitresGC 0.310803 0.032772,
'contrib': Dim.1 Dim.2
Taille 18.620702 8.097177
Titres 28.022668 0.384778
Finales 23.859953 0.003161
TitresGC 9.942619 2.301077,
'ftest': Dim.1 Dim.2
Taille 3.617129 1.275016
Titres -6.144733 -0.267397
Finales -4.714070 0.024191
TitresGC -2.268591 -0.659986,
'quanti_sup': {'corr': Taille Titres Finales TitresGC
BestClassDouble -0.442624 0.350269 0.160395 0.40567,
'coord': Dim.1 Dim.2
BestClassDouble -0.328395 -0.490059,
'cos2': Dim.1 Dim.2
BestClassDouble 0.107843 0.240158,
'ftest': Dim.1 Dim.2
BestClassDouble -1.229594 -1.933074}}
#coordonnées --> corrélations des variables avec les facteurs
info_var['coord']
| Dim.1 | Dim.2 | |
|---|---|---|
| Taille | 0.762939 | 0.339587 |
| Titres | -0.935937 | -0.074027 |
| Finales | -0.863628 | 0.006709 |
| TitresGC | -0.557497 | -0.181030 |
#cercle des corrélations
import matplotlib.pyplot as plt
fig,axe = plt.subplots(figsize=(6,6))
plot_correlation_circle(afdm,ax=axe)
plt.show()
#COS2 - qualité de représentation
info_var['cos2']
| Dim.1 | Dim.2 | |
|---|---|---|
| Taille | 0.582077 | 0.115319 |
| Titres | 0.875979 | 0.005480 |
| Finales | 0.745854 | 0.000045 |
| TitresGC | 0.310803 | 0.032772 |
#contributions des variables quantitatives
plot_contrib(afdm,choice='var',axis=0)
#coordonnées des modalités
pandas.DataFrame(afdm.mod_coord_,index=afdm.mod_labels_,columns=afdm.dim_index_)
| Dim.1 | Dim.2 | |
|---|---|---|
| Lateralite_droitier | 0.602308 | -0.112717 |
| Lateralite_gaucher | -2.610001 | 0.488441 |
| MainsRevers_deux | 0.293516 | -1.062163 |
| MainsRevers_une | -0.228291 | 0.826127 |
| RolandGarros_demi | 0.293940 | 0.490470 |
| RolandGarros_finale | -1.367098 | 2.018646 |
| RolandGarros_vainqueur | 0.140499 | -0.721071 |
#positions dans le premier plan factoriel
plotFAMD(afdm,choice='mod',repel=True, xlim=(-2.5,2.5), ylim=(-2.5,2.5))
#contributions des modalités
plot_contrib(afdm,choice='mod',axis=0)
#directement représentation graphique
plotFAMD(afdm,choice='ind',repel=True,ind_sup=True)
#contributions des individus (top 5)
plot_contrib(afdm,choice='ind',axis=0,top_contrib=5)
