Axis aligned artifacts

Left: Original data distribution. Right: Learned co-displacement, darker is lower.

Notice the echoes around (10,-10) and (-10, 10)

There are minor artifacts created by choosing axis aligned cuts in RRCF, similar to what was noted with IsoForest. 

	import numpy as np
	import pandas as pd
	import rrcf
	import matplotlib.pyplot as plt
	include_anomaly=False
	# Construct data with two modes and full of anomalies
	X1 = np.random.multivariate_normal([0,0], [[1,0],[0,1]], 1000)
	X2 = np.random.multivariate_normal([10,10], [[1,0],[0,1]], 1000)
	if include_anomaly:
	XA = np.random.uniform(-5, 15, size=200).reshape((100, 2))
	X = np.concatenate([X1, X2, XA])
	else:
	X = np.concatenate([X1, X2])
	# plot the original data
	fig, ax = plt.subplots()
	ax.plot(X[:,0], X[:,1], '.')
	fig.show()
	num_trees = 300
	tree_size = 256
	n = X.shape[0]
	# Construct forest
	forest = []
	while len(forest) < num_trees:
	# Select random subsets of points uniformly from point set
	ixs = np.random.choice(n, size=(n // tree_size, tree_size),
	replace=False)
	# Add sampled trees to forest
	trees = [rrcf.RCTree(X[ix], index_labels=ix) for ix in ixs]
	forest.extend(trees)
	# prepare grid for codisp measurement
	xvals, yvals = np.arange(-10, 20, 0.5), np.arange(-10, 20, 0.5)
	nx, ny = len(xvals), len(yvals)
	xv, yv = np.meshgrid(xvals, yvals)
	codisp = np.zeros((nx, ny))
	# measure codisp across space
	for i in range(nx):
	for j in range(ny):
	temp = []
	for tree in forest:
	point = np.array([xv[i,j], yv[i,j]])
	tree.insert_point(point, index='test')
	temp.append(tree.codisp('test'))
	tree.forget_point('test')
	codisp[i,j] = np.mean(temp)
	# plot codisp
	fig, axs = plt.subplots(ncols=2, sharex=True, sharey=True)
	axs[0].plot(X[:,0], X[:,1], '.', ms=2)
	axs[0].set_aspect(1)
	axs[1].imshow(codisp, origin='lower',
	extent = [np.min(xvals), np.max(xvals), np.min(yvals), np.max(yvals)])
	fig.show()
	fig.savefig("bias.png")

view raw rrcf_bias.py hosted with ❤ by GitHub