watex.view.EvalPlot.plotPCA#

EvalPlot.plotPCA(n_components=None, *, n_axes=None, biplot=False, pc1_label='Axis 1', pc2_label='Axis 2', plot_dict=None, **pca_kws)[source]#

Plot PCA component analysis using decomposition.

PCA identifies the axis that accounts for the largest amount of variance in the train set X. It also finds a second axis orthogonal to the first one, that accounts for the largest amount of remaining variance.

Parameters

  • n_components (Number of dimension to preserve. If`n_components`) – is ranged between float 0. to 1., it indicates the number of variance ratio to preserve. If None as default value the number of variance to preserve is 95%.

  • n_axes (Number of importance components to retrieve the) – variance ratio. Default is 2. The first two importance components with most variance ratio.

  • biplot (bool,) – biplot plots PCA features importance (pc1 and pc2) and visualize the level of variance and direction of components for different variables. Refer to `Serafeim Loukas`_

  • pc1_label (str, default ='Axis 1') – the first component with most variance held in ‘Axis 1’. Can be modified to any other axis for instance ‘Axis 3’ to replace the component in ‘Axis 1’ to the one in Axis 3 and so one. This will allow to visualize the position of each level of variance for each variable.

  • pc2_label (str, default ='Axis 2',) – the second component with most variance held in ‘Axis 2’. Can be modified to any other axis for instance ‘Axis 6’ to replace the component in ‘Axis 2’ to the one in Axis 6 and so one.

  • plot_dict (dict,) – dictionnary of font and properties for markers for each sample corresponding to the label_values.

  • pca_kws (dict,) – additional keyword arguments passed to watex.analysis.dimensionality.nPCA

Returns

``self`` – self for easy method chaining.

Return type

EvalPlot instance

Notes

By default, nPCA methods plots the first two principal components named pc1_label for axis 1 and pc2_label for axis 2. If you want to plot the first component pc1 vs the third components`pc2` set the pc2_label to Axis 3 and set the n_components to 3 that is the max reduced columns to retrieve, otherwise an users warning will be displayed. Commonly Algorithm should automatically detect the digit 3 in the litteral pc1_labels including Axis (e.g. ‘Axis 3`) and will consider as the third component `pc3 `. The same process is available for other axis.

Examples

>>> from watex.datasets import load_bagoue
>>> from watex.view.mlplot import EvalPlot
>>> X , y = load_bagoue(as_frame =True )
>>> b=EvalPlot(tname ='flow', encode_labels=True ,
                  scale = True )
>>> b.fit_transform (X, y)
>>> b.plotPCA (n_components= 2 )
...
>>> # pc1 and pc2 labels > n_components -> raises user warnings
>>> b.plotPCA (n_components= 2 , biplot=False, pc1_label='Axis 3',
               pc2_label='axis 4')
... UserWarning: Number of components and axes might be consistent;
    '2'and '4 are given; default two components are used.
>>> b.plotPCA (n_components= 8 , biplot=False, pc1_label='Axis3',
               pc2_label='axis4')
    # works fine since n_components are greater to the number of axes
... EvalPlot(tname= None, objective= None, scale= True, ... ,
             sns_height= 4.0, sns_aspect= 0.7, verbose= 0)