Knn R, Knearest neighbor classifier implementation in R programming from scratch
KNearest neighbor algorithm implement in R Programming from scratch
In the introduction to knearestneighbor algorithm article, we have learned the core concepts of the knn algorithm. Also learned about the applications using knn algorithm to solve the real world problems.
In this post, we will be implementing KNearest Neighbor Algorithm on a dummy data set using R programming language from scratch. Along the way, we will implement a prediction model to predict classes for data.
Knn Implementation in R
Why we need to implement knn algorithm from scratch in R Programming Language
Implementation of KNearest Neighbor algorithm in R language from scratch will help us to apply the concepts of Knn algorithm. As we are going implement each every component of the knn algorithm and the other components like how to use the datasets and find the accuracy of our implemented model etc.
Problem Set
We will use a sample dataset extracted from ionosphere database by John Hopkins University. We have converted the database into a small dataset so as to simplify the learning curve for our readers.
Our objective is to program a Knn classifier in R programming language without using any machine learning package. We have two classes “g”(good) or “b”(bad), it is the response of radar from the ionosphere. The classifier could be capable of predicting “g” or “b” class for new records from training data.
Ionosphere Dataset Description
This dummy dataset consists of 6 attributes and 30 records. Out Of these 5 attributes are continuous variables with values ranging from 1 to +1 i.e, [1,+1]. Last(6th) attribute is a categorical variable with values as “g”(good) or “b”(bad) according to the definition summarized above. This is a binary classification task.
KNearest Neighbor Algorithm Pseudocode
Let (X_{i}, C_{i}) where i = 1, 2……., n be data points. X_{i} denotes feature values & C_{i} denotes labels for X_{i }for each i.
Assuming the number of classes as ‘c’
C_{i} ∈ {1, 2, 3, ……, c} for all values of i
Let x be a point for which label is not known, and we would like to find the label class using knearest neighbor algorithms.
Procedure:
 Calculate “d(x, x_{i})” i =1, 2, ….., n; where d denotes the Euclidean distance between the points.
 Arrange the calculated n Euclidean distances in nondecreasing order.
 Let k be a +ve integer, take the first k distances from this sorted list.
 Find those kpoints corresponding to these kdistances.
 Let k_{i} denotes the number of points belonging to the i^{th} class among k points i.e. k ≥ 0
 If k_{i} >k_{j} ∀ i ≠ j then put x in class i.
Let’s use the above pseudocode for implementing the knn algorithm in R Language.
Prerequisites:
 Basic programming experience is required
 Install RStudio on your system.
KNearest neighbor algorithm implement in R Language from scratch
We are going to follow the below workflow for implementing the knn algorithm in R:
 Getting Data
 Train & Test Data Split
 Euclidean Distance Calculation
 KNN prediction function
 Accuracy calculation
Let’s get our hands dirty and start the coding stuff.
Getting Data in R
For any programmatic implementation on the dataset, we first need to import it. Using read.csv(), we are importing dataset into knn.df dataframe. Since dataset has no header so, we are using header= FALSE. sep parameter is to define the literal which separates values our document.
knn.df is a dataframe. A dataframe is a table or 2D array, in which each column contains measurements on one variable, and each row contains one record.
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knn.df < read.csv('i_data_sample_30.csv', header = FALSE, sep = ',') 
For checking dimensions of the dataset, we can call dim() method and be passing data frame as a parameter.
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dim(knn.df) [1] 30 6 
It shows that the data frame consists of 30 records and 6 columns.
To check summary of our dataset, we can use summary() method.
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summary(knn.df) V1 V2 V3 V4 V5 Min. :1.0000 Min. :1.0000 Min. :1.0000 Min. :1.0000 Min. :1.0000 1st Qu.:0.1079 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.6477 Median : 0.0000 Median : 0.8099 Median : 0.4059 Median : 0.5891 Median : 0.0000 Mean :0.0137 Mean : 0.5611 Mean : 0.3246 Mean : 0.2769 Mean :0.0633 3rd Qu.: 0.1336 3rd Qu.: 1.0000 3rd Qu.: 0.9730 3rd Qu.: 0.8269 3rd Qu.: 0.2464 Max. : 1.0000 Max. : 1.0000 Max. : 1.0000 Max. : 1.0000 Max. : 1.0000 V6 b:15 g:15 
It shows that records with bad class and good class are 15 each.
Train & Test Data split in R
Before Train & Test data split, we need to distribute it randomly. In R, we can use sample() method. It helps to randomize all the records of dataframe.
Please use set.seed(2), seed() method is used to produce reproducible results. In the next line we are passing sample() method inside dataframe. This is to randomize all 30 records of knn.df. Now, we are ready for a split. For dividing train, test data we are splitting them in 70:30 ratio i.e., 70% of data will be considered as train set & 30% as the test set.
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set.seed(2) knn.df< knn.df[sample(nrow(knn.df)),] train.df < knn.df[1:as.integer(0.7*30),] test.df < knn.df[as.integer(0.7*30 +1):30,] 
Euclidean Distance Calculation in R
Below snippet consists of a function defined in R to calculate Euclidean distance between 2 points a & b. The formula of Euclidean distance is:
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euclideanDist < function(a, b){ d = 0 for(i in c(1:(length(a)1) )) { d = d + (a[[i]]b[[i]])^2 } d = sqrt(d) return(d) } 
KNN prediction function in R
This function is the core part of this tutorial. We are writing a function knn_predict. It takes 3 arguments: test data, train data & value of K. It loops over all the records of test data and train data. It returns the predicted class labels of test data.
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knn_predict < function(test_data, train_data, k_value){ pred < c() #empty pred vector #LOOP1 for(i in c(1:nrow(test_data))){ #looping over each record of test data eu_dist =c() #eu_dist & eu_char empty vector eu_char = c() good = 0 #good & bad variable initialization with 0 value bad = 0 #LOOP2looping over train data for(j in c(1:nrow(train_data))){ #adding euclidean distance b/w test data point and train data to eu_dist vector eu_dist < c(eu_dist, euclideanDist(test_data[i,], train_data[j,])) #adding class variable of training data in eu_char eu_char < c(eu_char, as.character(train_data[j,][[6]])) } eu < data.frame(eu_char, eu_dist) #eu dataframe created with eu_char & eu_dist columns eu < eu[order(eu$eu_dist),] #sorting eu dataframe to gettop K neighbors eu < eu[1:k_value,] #eu dataframe with top K neighbors #Loop 3: loops over eu and counts classes of neibhors. for(k in c(1:nrow(eu))){ if(as.character(eu[k,"eu_char"]) == "g"){ good = good + 1 } else bad = bad + 1 } # Compares the no. of neighbors with class label good or bad if(good > bad){ #if majority of neighbors are good then put "g" in pred vector pred < c(pred, "g") } else if(good < bad){ #if majority of neighbors are bad then put "b" in pred vector pred < c(pred, "b") } } return(pred) #return pred vector } 
It returns a vector with predicted classes of test dataset. These predictions can be used to calculate accuracy metric.
Accuracy Calculation in R
The accuracy metric calculates the ratio of the number of correctly predicted class labels to the total number of predicted labels.
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accuracy < function(test_data){ correct = 0 for(i in c(1:nrow(test_data))){ if(test_data[i,6] == test_data[i,7]){ correct = correct+1 } } accu = correct/nrow(test_data) * 100 return(accu) } 
KNN Algorithm accuracy print: In this code snippet we are joining all our functions. We are calling the knn_predict function with train and test dataframes that we split earlier and K value as 5.
We are appending the prediction vector as the 7th column in our test dataframe and then using accuracy() method we are printing accuracy of our KNN model.
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K = 5 predictions < knn_predict(test.df, train.df, K) #calling knn_predict() test.df[,7] < predictions #Adding predictions in test data as 7th column print(accuracy(test.df)) 
Script Output:
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Accuracy of our KNN model is 77.77778 
It prints accuracy of our knn model. Here our accuracy is 77.78%. That’s pretty good 🙂 for our randomly selected dummy dataset.
You can download the RMD file of this code from our GitHub repository.
Finally, we have implemented our KNN model in R programming without using any specific R packages.Hope you enjoyed learning it.
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