myPCA.py

# coding: utf-8

"""Generic usage of Principal Component Analysis"""

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
import numpy as np


def circleOfCorrelations(pc_infos, ebouli):
	plt.Circle((0,0),radius=10, color='g', fill=False)
	circle1=plt.Circle((0,0),radius=1, color='g', fill=False)
	fig = plt.gcf()
	fig.gca().add_artist(circle1)
	for idx in range(len(pc_infos["PC-0"])):
		x = pc_infos["PC-0"][idx]
		y = pc_infos["PC-1"][idx]
		plt.plot([0.0,x],[0.0,y],'k-')
		plt.plot(x, y, 'rx')
		plt.annotate(pc_infos.index[idx], xy=(x,y))
	plt.xlabel("PC-0 (%s%%)" % str(ebouli[0])[:4].lstrip("0."))
	plt.ylabel("PC-1 (%s%%)" % str(ebouli[1])[:4].lstrip("0."))
	plt.xlim((-1,1))
	plt.ylim((-1,1))
	plt.title("Circle of Correlations")

def myScatter(df):
	# http://stackoverflow.com/a/23010837/1565438
	axs = pd.tools.plotting.scatter_matrix(df, diagonal='kde')
	for ax in axs[:,0]: # the left boundary
	    ax.grid('off', axis='both')
	    ax.set_ylabel(ax.get_ylabel(), rotation=0, labelpad=len(ax.get_ylabel())+40)
	    ax.set_yticks([])

	for ax in axs[-1,:]: # the lower boundary
	    ax.grid('off', axis='both')
	    ax.set_xlabel(ax.get_xlabel(), rotation=90)
	    ax.set_xticks([])

def myPCA(df, scatter=False):
	if scatter:
		myScatter(df)
		plt.show()
	# Normalize data
	df_norm = (df - df.mean()) / df.std()
	# PCA
	pca = PCA(n_components='mle')
	pca_res = pca.fit_transform(df_norm.values)
	# Ebouli
	ebouli = pd.Series(pca.explained_variance_ratio_)
	ebouli.plot(kind='bar', title="Ebouli des valeurs propres")
	plt.show()
	# Circle of correlations
	# http://stackoverflow.com/a/22996786/1565438
	coef = np.transpose(pca.components_)
	cols = ['PC-'+str(x) for x in range(len(ebouli))]
	pc_infos = pd.DataFrame(coef, columns=cols, index=df_norm.columns)
	circleOfCorrelations(pc_infos, ebouli)
	plt.show()
	# Plot PCA
	dat = pd.DataFrame(pca_res, columns=cols)
	plt.scatter(dat["PC-0"],dat["PC-1"])
	plt.xlabel("PC-0 (%s%%)" % str(ebouli[0])[:4].lstrip("0."))
	plt.ylabel("PC-1 (%s%%)" % str(ebouli[1])[:4].lstrip("0."))
	plt.title("PCA")
	plt.show()

if __name__ == '__main__':
	pass
	# # An example with IRIS dataset
	# from sklearn import datasets
	# iris = datasets.load_iris()
	# df = pd.DataFrame(iris.data, columns=iris.feature_names)
	# myPCA(df, scatter=True)

Any relation to https://rasbt.github.io/mlxtend/user_guide/plotting/plot_pca_correlation_graph/ ?