discrimintools.add_scatter

discrimintools.add_scatter(p, data, axis=[0, 1], geom=('point', 'text'), repel=False, color='steelblue', points_args={'shape': 'o', 'size': 1.5}, text_args={'size': 8})

Add elements points and/or texts to plotnine graph

Parameters:

• p (class) – An object of class ggplot.

• data (DataFrame of shape (n_samples, n_components)) – Input data, where n_samples is the number of samples and n_components is the number of components.

• axis (list, defaul = [0,1]) – Dimensions to be plotted

• geom (str, list or tuple, default=(‘point’,’text’)) – Geometry to be used for the graph. Possible values are the combinaison of [“point”,”text”].

  • ‘point’ to show only points,

  • ‘text’ to show only labels,

  • (‘point’,’text’) to show both types.

• repel (bool, default=False) – To avoid overplotting text labels.

• color (str, default = “steelblue”) – Color for the points and texts.

• point_args (dict, default=dict(shape = “o”, size = 1.5)) – Keywords arguments for geom_point.

• text_args (dict, default=dict(size = 8)) – KeywordS arguments for geom_text.

Returns:

p – A object of class ggplot.

Return type:

class

Examples

>>> from discrimintools.datasets import load_wine
>>> from discrimintools import CANDISC, add_scatter
>>> from plotnine import ggplot, theme_minimal
>>> D = load_wine("train") # load training dataset
>>> y, X = D["Quality"], D.drop(columns=["Quality"]) # split into X and y
>>> clf = CANDISC()
>>> clf.fit(X,y)
CANDISC()
>>> p = add_scatter(ggplot(),clf.ind_.coord,repel=True)+theme_minimal()
>>> print(p)

Fig. 22 Add individuals points - CANDISC