knnplots.py

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors
import numpy as np

def plotvector(XTrain, yTrain, XTest, yTest, weights, upperLim = 310):
	results = []
	for n in range(1, upperLim, 4):
    	    clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)
    	    clf = clf.fit(XTrain, yTrain)
    	    preds = clf.predict(XTest)
    	    accuracy = clf.score(XTest, yTest)
    	    results.append([n, accuracy])
 
	results = np.array(results)
	return(results)

def plotaccuracy(XTrain, yTrain, XTest, yTest, upperLim):
	pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform")
	pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance")
	line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")
	line2 = plt.plot(pltvector2[:,0], pltvector2[:,1],  label = "distance")
	plt.legend(loc=3)
	plt.ylim(0.5, 1)
	plt.title("Accuracy with Increasing K")
	plt.show()



def decisionplot(XTrain, yTrain, n_neighbors, weights):
	h = .02  # step size in the mesh
    
	Xtrain = XTrain[:, :2] # we only take the first two features.
    
	# Create color maps
	cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
	cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])

	clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
	clf.fit(Xtrain, yTrain)

	# Plot the decision boundary. For that, we will assign a color to each
	# point in the mesh [x_min, m_max]x[y_min, y_max].
	x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1
	y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     	np.arange(y_min, y_max, h))
	Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

	# Put the result into a color plot
	Z = Z.reshape(xx.shape)
	plt.figure()
	plt.pcolormesh(xx, yy, Z, cmap = cmap_light)

	# Plot also the training points
	plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)
	plt.xlim(xx.min(), xx.max())
	plt.ylim(yy.min(), yy.max())
	plt.title("2-Class classification (k = %i, weights = '%s')"
          	% (n_neighbors, weights))
	plt.show()