import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets from sklearn.decomposition import PCA # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. y = iris.target x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 plt.figure(2, figsize=(8, 6)) plt.clf() # Plot the training points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor="k") plt.xlabel("Sepal length") plt.ylabel("Sepal width") plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.xticks(()) plt.yticks(()) # To getter a better understanding of interaction of the dimensions # plot the first three PCA dimensions fig = plt.figure(1, figsize=(8, 6)) ax = Axes3D(fig, elev=-150, azim=110) X_reduced = PCA(n_components=3).fit_transform(iris.data) ax.scatter( X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y, cmap=plt.cm.Set1, edgecolor="k", s=40, ) ax.set_title("First three PCA directions") ax.set_xlabel("1st eigenvector") ax.w_xaxis.set_ticklabels([]) ax.set_ylabel("2nd eigenvector") ax.w_yaxis.set_ticklabels([]) ax.set_zlabel("3rd eigenvector") ax.w_zaxis.set_ticklabels([]) plt.show()