In this example we will look at how to use the K-Nearest_Neighbor algorithm for classification. We will use a modified version of the Video Store data set for this example. We will use the "Incidentals" attribute as the target attribute for classification (the class attribute). The goal is to be able to classify an unseen instance as "Yes" or "No" given the values of "Incidentals" from training instances.¶

In [1]:
import numpy as np
import pandas as pd

In [2]:
vstable = pd.read_csv("http://facweb.cs.depaul.edu/mobasher/classes/csc478/data/Video_Store_2.csv", index_col=0)

vstable.shape

Out[2]:
(50, 7)
In [3]:
vstable.head()

Out[3]:
Gender Income Age Rentals Avg Per Visit Genre Incidentals
Cust ID
1 M 45000 25 32 2.5 Action Yes
2 F 54000 33 12 3.4 Drama No
3 F 32000 20 42 1.6 Comedy No
4 F 59000 70 16 4.2 Drama Yes
5 M 37000 35 25 3.2 Action Yes

We will be splitting the data into a test and training partions with the test partition to be used for evaluating model error-rate and the training partition to be used to find the K nearest neighbors. Before spliting the data we need to do a random reshuffling to make sure the instances are randomized.¶

In [4]:
vs = vstable.reindex(np.random.permutation(vstable.index))

Out[4]:
Gender Income Age Rentals Avg Per Visit Genre Incidentals
Cust ID
48 F 52000 47 14 1.6 Drama No
12 F 26000 22 32 2.9 Action Yes
47 F 69000 35 22 2.8 Drama Yes
44 M 35000 24 24 1.7 Drama No
42 M 32000 25 26 2.2 Action Yes
7 F 29000 45 19 3.8 Drama No
34 F 29000 32 19 2.9 Action Yes
43 F 49000 28 48 3.3 Drama Yes
35 M 74000 29 43 4.6 Action Yes
25 M 1000 16 25 1.4 Comedy Yes
In [5]:
len(vs)

Out[5]:
50
In [6]:
vs_names = vs.columns.values
vs_names

Out[6]:
array(['Gender', 'Income', 'Age', 'Rentals', 'Avg Per Visit', 'Genre',
'Incidentals'], dtype=object)

The target attribute for classification is Incidentals:¶

In [7]:
vs_target = vs.Incidentals


Before we can compute distances we need to convert the data (excluding the target attribute "incidentals" which contains the class labels) into standard spreadsheet format with binary dummy variables created for each categorical attribute.¶

In [8]:
vs = pd.get_dummies(vs[['Gender','Income','Age','Rentals','Avg Per Visit','Genre']])

Out[8]:
Income Age Rentals Avg Per Visit Gender_F Gender_M Genre_Action Genre_Comedy Genre_Drama
Cust ID
48 52000 47 14 1.6 1 0 0 0 1
12 26000 22 32 2.9 1 0 1 0 0
47 69000 35 22 2.8 1 0 0 0 1
44 35000 24 24 1.7 0 1 0 0 1
42 32000 25 26 2.2 0 1 1 0 0
7 29000 45 19 3.8 1 0 0 0 1
34 29000 32 19 2.9 1 0 1 0 0
43 49000 28 48 3.3 1 0 0 0 1
35 74000 29 43 4.6 0 1 1 0 0
25 1000 16 25 1.4 0 1 0 1 0

To be able to evaluate the accuracy of our predictions, we will split the data into training and test sets. In this case, we will use 80% for training and the remaining 20% for testing. Note that we must also do the same split to the target attribute.¶

In [9]:
tpercent = 0.8
tsize = int(np.floor(tpercent * len(vs)))
vs_train = vs[:tsize]
vs_test = vs[tsize:]

In [10]:
print(vs_train.shape)
print(vs_test.shape)

(40, 9)
(10, 9)

In [11]:
vs_train.head(10)

Out[11]:
Income Age Rentals Avg Per Visit Gender_F Gender_M Genre_Action Genre_Comedy Genre_Drama
Cust ID
48 52000 47 14 1.6 1 0 0 0 1
12 26000 22 32 2.9 1 0 1 0 0
47 69000 35 22 2.8 1 0 0 0 1
44 35000 24 24 1.7 0 1 0 0 1
42 32000 25 26 2.2 0 1 1 0 0
7 29000 45 19 3.8 1 0 0 0 1
34 29000 32 19 2.9 1 0 1 0 0
43 49000 28 48 3.3 1 0 0 0 1
35 74000 29 43 4.6 0 1 1 0 0
25 1000 16 25 1.4 0 1 0 1 0
In [12]:
vs_test

Out[12]:
Income Age Rentals Avg Per Visit Gender_F Gender_M Genre_Action Genre_Comedy Genre_Drama
Cust ID
23 2000 15 30 2.5 1 0 0 1 0
27 62000 47 32 3.6 1 0 0 0 1
49 31000 25 42 3.4 0 1 1 0 0
17 36000 35 28 3.5 0 1 0 0 1
31 49000 56 15 3.2 1 0 0 1 0
36 29000 21 34 2.3 1 0 0 1 0
40 17000 19 32 1.8 0 1 1 0 0
4 59000 70 16 4.2 1 0 0 0 1
11 41000 22 48 2.3 1 0 0 0 1
6 18000 20 29 1.7 0 1 1 0 0

Splitting the target attribute ("Incidentals") accordingly:¶

In [13]:
vs_target_train = vs_target[0:int(tsize)]
vs_target_test = vs_target[int(tsize):len(vs)]

In [14]:
vs_target_train.head()

Out[14]:
Cust ID
48     No
12    Yes
47    Yes
44     No
42    Yes
Name: Incidentals, dtype: object
In [15]:
vs_target_test

Out[15]:
Cust ID
23     No
27     No
49    Yes
17    Yes
31     No
36     No
40     No
4     Yes
11    Yes
6      No
Name: Incidentals, dtype: object

Next, we normalize the attributes so that everything is in [0,1] scale. We can use the normalization functions from the kNN module in Ch. 2 of the text. In this case, however, we will use the more flexible and robust scaler function from the preprocessing module of scikit-learn package.¶

In [16]:
from sklearn import preprocessing

In [17]:
min_max_scaler = preprocessing.MinMaxScaler()
min_max_scaler.fit(vs_train)

Out[17]:
MinMaxScaler(copy=True, feature_range=(0, 1))
In [18]:
vs_train_norm = min_max_scaler.fit_transform(vs_train)
vs_test_norm = min_max_scaler.fit_transform(vs_test)


Note that MinMaxScaler returns a Numpy nd-array).¶

In [19]:
np.set_printoptions(precision=2, linewidth=100)

print(vs_train_norm[:10])

[[0.58 0.86 0.13 0.14 1.   0.   0.   0.   1.  ]
[0.28 0.17 0.59 0.5  1.   0.   1.   0.   0.  ]
[0.77 0.53 0.33 0.47 1.   0.   0.   0.   1.  ]
[0.39 0.22 0.38 0.17 0.   1.   0.   0.   1.  ]
[0.35 0.25 0.44 0.31 0.   1.   1.   0.   0.  ]
[0.32 0.81 0.26 0.75 1.   0.   0.   0.   1.  ]
[0.32 0.44 0.26 0.5  1.   0.   1.   0.   0.  ]
[0.55 0.33 1.   0.61 1.   0.   0.   0.   1.  ]
[0.83 0.36 0.87 0.97 0.   1.   1.   0.   0.  ]
[0.   0.   0.41 0.08 0.   1.   0.   1.   0.  ]]

In [20]:
print(vs_test_norm[:10])

[[0.   0.   0.45 0.32 1.   0.   0.   1.   0.  ]
[1.   0.58 0.52 0.76 1.   0.   0.   0.   1.  ]
[0.48 0.18 0.82 0.68 0.   1.   1.   0.   0.  ]
[0.57 0.36 0.39 0.72 0.   1.   0.   0.   1.  ]
[0.78 0.75 0.   0.6  1.   0.   0.   1.   0.  ]
[0.45 0.11 0.58 0.24 1.   0.   0.   1.   0.  ]
[0.25 0.07 0.52 0.04 0.   1.   1.   0.   0.  ]
[0.95 1.   0.03 1.   1.   0.   0.   0.   1.  ]
[0.65 0.13 1.   0.24 1.   0.   0.   0.   1.  ]
[0.27 0.09 0.42 0.   0.   1.   1.   0.   0.  ]]


For consitency, we'll also convert the training and test target labels into Numpy arrays.¶

In [21]:
vs_target_train = np.array(vs_target_train)
vs_target_test = np.array(vs_target_test)

In [22]:
print(vs_target_train)
print("\n")
print(vs_target_test)

['No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'No' 'No' 'No' 'No' 'Yes' 'Yes' 'No'
'Yes' 'Yes' 'No' 'Yes' 'No' 'No' 'No' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'Yes' 'Yes' 'Yes' 'Yes'
'No' 'Yes' 'No' 'No' 'Yes' 'Yes']

['No' 'No' 'Yes' 'Yes' 'No' 'No' 'No' 'Yes' 'Yes' 'No']


The following function illustrates how we can perform a k-nearest-neighbor search. It takes an instance x to be classifed and a data matrix D (assumed to be a 2d Numpy array) as inputs. It also takes K (the desired number of nearest-neighbors to be identified), and "measure" as arguments. The "measure" argument allows us to use either Euclidean distance (measure=0) or (the inverse of) Cosine similarity (measure = 1) as the distance function:¶

In [23]:
def knn_search(x, D, K, measure):
""" find K nearest neighbors of an instance x among the instances in D """
if measure == 0:
# euclidean distances from the other points
dists = np.sqrt(((D - x)**2).sum(axis=1))
elif measure == 1:
# first find the vector norm for each instance in D as wel as the norm for vector x
D_norm = np.array([np.linalg.norm(D[i]) for i in range(len(D))])
x_norm = np.linalg.norm(x)
# Compute Cosine: divide the dot product o x and each instance in D by the product of the two norms
sims = np.dot(D,x)/(D_norm * x_norm)
# The distance measure will be the inverse of Cosine similarity
dists = 1 - sims
idx = np.argsort(dists) # sorting
# return the indexes of K nearest neighbors
return idx[:K], dists

In [24]:
# Let's use vs_test_norm[0] as a test instance x and find its K nearest neighbors
neigh_idx, distances = knn_search(vs_test_norm[0], vs_train_norm, 5, 0)

In [25]:
vs_test.head(1)

Out[25]:
Income Age Rentals Avg Per Visit Gender_F Gender_M Genre_Action Genre_Comedy Genre_Drama
Cust ID
23 2000 15 30 2.5 1 0 0 1 0
In [26]:
print(neigh_idx)
print("\nNearest Neigbors:")
vs_train.iloc[neigh_idx]

[12 28 11  9 38]

Nearest Neigbors:

Out[26]:
Income Age Rentals Avg Per Visit Gender_F Gender_M Genre_Action Genre_Comedy Genre_Drama
Cust ID
19 24000 25 41 3.1 1 0 0 1 0
3 32000 20 42 1.6 1 0 0 1 0
39 68000 35 19 3.9 1 0 0 1 0
25 1000 16 25 1.4 0 1 0 1 0
29 15000 18 37 2.1 1 0 1 0 0
In [27]:
print(distances[neigh_idx])

[0.57 0.57 1.05 1.43 1.45]

In [28]:
# Let's see how the nearest neighbors of the test instance labeled the target attribute "incidentals"

neigh_labels = vs_target_train[neigh_idx]
print(neigh_labels)

['No' 'No' 'No' 'Yes' 'Yes']


Now that we know the nearest neighbors, we need to find the majority class label among them. The majority class would be the class assgined to the new instance x.¶

In [29]:
from collections import Counter
print(Counter(neigh_labels))

Counter({'No': 3, 'Yes': 2})

In [30]:
Counter(neigh_labels).most_common(1)

Out[30]:
[('No', 3)]

Next, we'll use the Knn module from Chapter 2 of Machine Learning in Action. Before importing the whole module, let's illustrate what the code does by stepping through it with some specific input values.¶

In [31]:
dataSetSize = vs_train_norm.shape[0]
print(dataSetSize)

40

In [32]:
inX = vs_test_norm[0]   # Again we'll use the first instance in the test data for this example
diffMat = np.tile(inX, (dataSetSize,1)) - vs_train_norm  # Create dataSetSize copies of inX, as rows of a 2D matrix
# Compute a matrix of differences
print(diffMat[:5,:])

[[-0.58 -0.86  0.33  0.18  0.    0.    0.    1.   -1.  ]
[-0.28 -0.17 -0.14 -0.18  0.    0.   -1.    1.    0.  ]
[-0.77 -0.53  0.12 -0.15  0.    0.    0.    1.   -1.  ]
[-0.39 -0.22  0.07  0.15  1.   -1.    0.    1.   -1.  ]
[-0.35 -0.25  0.02  0.01  1.   -1.   -1.    1.    0.  ]]

In [33]:
sqDiffMat = diffMat**2  # The matrix of squared differences
sqDistances = sqDiffMat.sum(axis=1)  # 1D array of the sum of squared differences (one element for each training instance)
distances = sqDistances**0.5  # and finally the matrix of Euclidean distances to inX
print(distances)

[1.79 1.47 1.71 2.06 2.05 1.72 1.54 1.67 2.33 1.43 2.14 1.05 0.57 2.14 2.12 2.08 1.5  2.13 1.67
1.95 1.67 1.92 1.81 1.63 1.75 1.68 1.46 2.01 0.57 2.01 2.17 2.21 2.09 2.1  2.04 1.81 1.87 1.98
1.45 2.18]

In [34]:
sortedDistIndicies = distances.argsort() # the indices of the training instances in increasing order of distance to inX
print(sortedDistIndicies)

[12 28 11  9 38 26  1 16  6 23  7 18 20 25  2  5 24  0 35 22 36 21 19 37 29 27 34  4  3 15 32 33 14
17 10 13 30 39 31  8]


To see how the test instance should be classified, we need to find the majority class among the neighbors (here we do not use distance weighting; only a simply voting approach)¶

In [35]:
classCount={}
k = 5  # We'll use the top 5 neighbors
for i in range(k):
voteIlabel = vs_target_train[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1  # add to the count of the label or retun 1 for first occurrence
print(sortedDistIndicies[i], voteIlabel, classCount[voteIlabel])

12 No 1
28 No 2
11 No 3
9 Yes 1
38 Yes 2


Now, let's find the predicted class for the test instance.¶

In [36]:
import operator
# Create a dictionary for the class labels with cumulative occurrences across the neighbors as values
# Dictionary will be ordered in decreasing order of the lable values (so the majority class label will
# be the first dictonary element)
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
print(sortedClassCount)
print(sortedClassCount[0][0])

[('No', 3), ('Yes', 2)]
No


A better way to find the majority class given a list of class labels from neighbors is to use a Python Counter:¶

In [37]:
from collections import Counter

k = 5  # We'll use the top 5 neighbors
vote = vs_target_train[sortedDistIndicies[0:k]]
maj_class = Counter(vote).most_common(1)

print(vote)

print(maj_class)

print("Class label for the classified instance: ", maj_class[0][0])

['No' 'No' 'No' 'Yes' 'Yes']
[('No', 3)]
Class label for the classified instance:  No

In [ ]:



Let's know import a module containing a modified version of the kNN classifier implementation from Chapter 2 of MLA book. We will step through all test instances, use our Knn classifier to predict a class label for each instance, and in each case we compare the predicted label to the actual value from the target test labels.¶

In [48]:
# kNN_new.py must be in the working folder (or you can specify the path in the import statement below)

import kNN_new

In [50]:
numTestVecs = len(vs_target_test)
print(numTestVecs)

10

In [61]:
errorCount = 0.0
for i in range(numTestVecs):
# classify0 function uses Euclidean distance to find k nearest neighbors
classifierResult = kNN_new.classify0(vs_test_norm[i,:], vs_train_norm, vs_target_train, 3)
print("Predicted Label: ", classifierResult, "==> Actual Label: ", vs_target_test[i])
print()
if (classifierResult != vs_target_test[i]):
errorCount += 1.0

print("the total error rate is: ", errorCount/float(numTestVecs))

Predicted Label:  No ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  No ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  No ==> Actual Label:  No

Predicted Label:  No ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  No ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  No

the total error rate is:  0.5


I have added a new classifier function to the kNN module that uses Cosine similarity instead of Euclidean distance:¶

In [63]:
errorCount = 0.0
for i in range(numTestVecs):
# classify1 function uses inverse of Cosine similarity to find k nearest neighbors
classifierResult2 = kNN_new.classify1(vs_test_norm[i,:], vs_train_norm, vs_target_train, 3)
print("Predicted Label: ", classifierResult, "==> Actual Label: ", vs_target_test[i])
print()
if (classifierResult != vs_target_test[i]):
errorCount += 1.0

print("the total error rate is: ", errorCount/float(numTestVecs))

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  No

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  Yes

Predicted Label:  Yes ==> Actual Label:  No

the total error rate is:  0.6

In [ ]: