BuildingMachineLearningSystemsWithPython/ch02/seeds_knn_increasing_k.py at master · charlessoft/BuildingMachineLearningSystemsWithPython

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# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

# It is made available under the MIT License

# Basic imports

from __future__ import print_function

import numpy as np

from matplotlib import pyplot as plt

from load import load_dataset

from sklearn.neighbors import KNeighborsClassifier

from sklearn.cross_validation import cross_val_score

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import StandardScaler

features, labels = load_dataset('seeds')

# Values of k to consider: all in 1 .. 160

ks = np.arange(1,161)

# We build a classifier object here with the default number of neighbors

# (It happens to be 5, but it does not matter as we will be changing it below

classifier = KNeighborsClassifier()

classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)])

# accuracies will hold our results

accuracies = []

for k in ks:

# set the classifier parameter

classifier.set_params(knn__n_neighbors=k)

crossed = cross_val_score(classifier, features, labels)

# Save only the average

accuracies.append(crossed.mean())

accuracies = np.array(accuracies)

# Scale the accuracies by 100 to plot as a percentage instead of as a fraction

plt.plot(ks, accuracies*100)

plt.xlabel('Value for k (nr. of neighbors)')

plt.ylabel('Accuracy (%)')

plt.savefig('figure6.png')