[SOLVED] Pa 01: knn classification cs535/ee514
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part. You have to implement your own k-NN classifier from scratch. You may use
Pandas, NumPy, Matplotlib, and other standard Python libraries.Problem:
The purpose of this assignment is to get you familiar with the k nearest neighbor classification. You are given the Apple Sentiment Tweets dataset that contains around 1630
tweets about Apple labeled as positive, neutral and negative in the form of 1, 0, and -1
respectively. Your task is to implement the k nearest classifier and use it for predicting
the sentiments of the tweets about Apple.Dataset:
The dataset contains around 1,630 tweets. There are only two columns in the dataset:
Text: Contains the text of the tweet.
Sentiment: Contains the sentiment of the tweet which is divided into three classes:1 (positive), -1 (negative), and 0 (neutral).
Divide this dataset into training and testing sets based on an 80-20 split using python.
Preprocessing:
In the preprocessing step, you’re required to remove the stop words, punctuation marks,
numbers, unwanted symbols, hyperlinks, and usernames from the tweets and convert
them to lower case. You may find the string and regex module useful for this purpose. A
stop word list is provided within the assignment.Feature Extraction:
In the feature extraction step, you’ll represent each tweet as a bag-of-words (BoW), that
is, an unordered set of words with their position ignored, keeping only their frequency in
the tweet.For example, consider the below tweets:
T1 = Welcome to machine learning!
T2 = kNN is a powerful machine learning algorithm.
2
The bag-of-words representation (ignoring numbers, case, and punctuation) for the
above tweets are:
Vocabulary welcome to machine learning knn is a powerful algorithm
T1 1 1 1 1 0 0 0 0 0
T2 0 0 1 1 1 1 1 1 1Note: We only use the training set to construct the vocabulary for the BoW representation.After you have preprocessed the data and created a bag of words, you have to implement
the following tasks:
1. Create your own k-Nearest Neighbors classifier function by performing the following
tasks:
– For a test data point, find its distance from all training instances.
– Sort the calculated distances in ascending order based on distance values.
– Choose k training samples with minimum distances from the test data point.
– Return the most frequent class of these samples. (Your function should work
with Euclidean distance as well as Manhattan distance. Pass the distance
metric as a parameter in the k-NN classifier function. Your function should
also be general enough to work with any value of k.)2. Implement an evaluation function that calculates the classification accuracy, F1
score, and the confusion matrix of your classifier on the test set.3. Use 5- fold cross-validation on your training data. (In cross-validation, you divide
the training data set into 5 parts. 4 parts will be used for training and 1 part
will be used for validation. Then you will take a different part of your data as a
validation data set and train your algorithm on the rest of the data set.) Run your
k-NN function for this data for the values of k = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Do
this for both the Euclidean distance and the Manhattan distance for each value of
k.Formulas for both are listed below:
Euclidean Distance:
d(~p, ~q) = p
(p1 − q1)
2 + (p2 − q2)
2 + (p3 − q3)
2 + … + (pn − qn)
2
Manhattan Distance:
d(~p, ~q) = |(p1 − q1)| + |(p2 − q2)| + |(p3 − q3)| + … + |(pn − qn)|
4. For the even values of k given in the above task, break ties by backing off to the k-1
value. (For example, if you have k = 6 nearest neighbors and three of them havethe label ‘positive’ and three have the label ‘negative, then you will break off the
tie by taking k=5 nearest neighbors. On the other hand, let’s say if you have k = 6
nearest neighbors where two have the label ‘positive’, two have the label ‘negative’,
and two have the label ‘neutral’. In that case, k =5 will still lead to two labels
having a draw in which case you will continue decreasing k until there is a clear
winner.)5. Run your evaluation function for each value of k for both distance metrics, Report
classification accuracy, F1 score, and confusion matrix.6. Present the results as a graph with k values on the x-axis and classification accuracy
on the y-axis. Use a single plot to compare the two versions of the classifier (one
using Euclidean and the other using Manhattan distance metric). Make another
graph but with the F1 score on the y-axis this time. The graphs should be properly
labeled.7. Comment on the best value of k you have found for both distance metrics using
cross-validation.8. Finally, use the best value of k for both distance metrics and run it on the test
data set. Find the F1 score, classification accuracy, and confusion matrix and print
them.In this part, you have to use scikit-learn’s k-NN implementation to train and test your
classifier on the dataset used in Part 1. Repeat the tasks you have done in Part 1
but this time using scikit-learn. Use your bag of words to do cross-validation and run
the k-NN classifier for values of k = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 using both Euclidean
and Manhattan distance.Use scikit-learn’s accuracy_score function to calculate the
accuracy, classification_report to calculate macro-average (precision, recall, and F1),
and confusion_matrix function to calculate confusion matrix for test data. Also present
the results as a graph with k values on the x-axis and performance measures on the y-axis
just like you did in part 1.Use a single plot to compare the two versions of the classifier
(one using Euclidean and the other using Manhattan distance metric). Finally, print the
best values of k for both distance metrics. Then use this value of k on the test data set
and print the evaluation scores.