Example projection visualizer with Yellowbrick

pca_projection_yb.py

from yellowbrick.features.projection import ProjectionVisualizer

from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import  StandardScaler
from sklearn.datasets import make_regression


class Visualizer(ProjectionVisualizer):

    def __init__(self, **kwargs):
        super(Visualizer, self).__init__(**kwargs)
        self.transformer = Pipeline([
            ('scale', StandardScaler()),
            ('pca', PCA(self.projection))
        ])

    def fit(self, X, y=None):
        super(ProjectionVisualizer, self).fit(X, y)
        self.transformer.fit(X)
        return self

    def transform(self, X, y=None):
        Xp = self.transformer.transform(X)
        self.draw(Xp, y)
        return Xp


if __name__ == "__main__":
    X, y = make_regression(n_samples=500, n_features=22, n_informative=8)
    oz = Visualizer()
    oz.fit_transform(X, y)
    oz.poof()