knn_iris.py

"""
File: knn_iris.py
Created on Thu May 31 23:19:55 2018
@author: pking70@uw.edu

This module compares the results of knn_our to sklearn's KNeighbors classifier.
This loads sklearn's provided Iris dataset.

Variables:
    test_size    The split ratio of test data to train data.
    X_train      The training samples from Iris or random data.
    y_train      A training set of labels from Iris or random data.
    X_test       The test samples from Iris or random data.
    y_test       A test set of labels from Iris or random data.
    k            The number of neighbors to consider.
    accuracy     The accuracy of our KNN computation
    sklknn       The classifier returned from scikit-learns KNN classification,
                 from which we can get accuracy via its score method.
"""
from knn_data import loadIrisData
from knn_our import ourKnn
from knn_skl import sklKnn

test_size = 0.25    # split ratio: 0.25=one quarter of dataset is set for test

# Load data 
X_train, X_test, y_train, y_test = loadIrisData(test_size)

# How many neighbors?
k=10

# Get prediction accuracy from both ways
accuracy = ourKnn(X_train, y_train, X_test, y_test, k)
sklknn = sklKnn(X_train, y_train, k)

# Compare results
print('Our accuracy: ' + repr(round(accuracy, 2)) + ' %')
print('Scikit-learn accuracy: {:.2f}'.format(100*sklknn.score(X_test, 
      y_test)), '%')