knn.py

# All the libraries we need for KNN
import numpy as np
import matplotlib.pyplot as plt

from sklearn.neighbors import KNeighborsClassifier
# This is used for our dataset
from sklearn.datasets import load_breast_cancer


# =============================================================================
# We are using sklearn datasets to create the set of data points about breast cancer
# Data is the set data points
# target is the classification of those data points. 
# More information can be found at:
#https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer
# =============================================================================
dataCancer = load_breast_cancer()

# The data[:, x:n] gets two features for the data given. 
# The : part gets all the rows in the matrix. And 0:2 gets the first 2 columns 
# If you want to get a different two features you can replace 0:2 with 1:3, 2:4,... 28:30, 
# there are 30 features in the set so it can only go up to 30.
# If we wanted to plot a 3 dimensional plot then the difference between x and n needs to be 3 instead of two
data = dataCancer.data[:, 0:2]
target = dataCancer.target

# =============================================================================
# This creates the KNN classifier and specifies the algorithm being used and the k
# nearest neighbors used for the algorithm. more information can about KNeighborsClassifier
# can be found at: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
#
# Then it trains the model using the breast cancer dataset. 
# =============================================================================
model = KNeighborsClassifier(n_neighbors = 9, algorithm = 'auto')
model.fit(data, target)


# plots the points 
plt.scatter(data[:, 0], data[:, 1], c=target, s=30, cmap=plt.cm.prism)

# Creates the axis bounds for the grid
axis = plt.gca()
x_limit = axis.get_xlim()
y_limit = axis.get_ylim()

# Creates a grid to evaluate model
x = np.linspace(x_limit[0], x_limit[1])
y = np.linspace(y_limit[0], y_limit[1])
X, Y = np.meshgrid(x, y)
xy = np.c_[X.ravel(), Y.ravel()]

# Creates the line that will separate the data
boundary = model.predict(xy)
boundary = boundary.reshape(X.shape)


# Plot the decision boundary
axis.contour(X, Y,  boundary, colors = 'k')
         
# Shows the graph
plt.show()