Incremental learning is a method of machine learning in which input data is continuously used to
extend the existing model’s knowledge. It can be applied when training data becomes available
gradually over time or its size is out of system memory limits. In this assignment, you will
implement a Naive Bayes classifier which can learn incrementally (i.e. without seeing all the
instances at once).
1. Homework Description
Please use Python 3 to implement your homework assignment.
In this assignment, you are given Blackbox31.pyc instead of ready-made data, and you need
to generate your own data from that blackbox to simulate data stream.
Example of getting data from Blackbox31.pyc, in your NaiveBayes.py:
Each time you ask the blackbox, it will randomly return a tuple (X, y) back. As you can see in
the above image, X is a list and y is an integer, together X and y form one single sample.
Observations X has 3 attributes, all attributes have continuous value and are in the range [0.0,
1.0), and target y has 3 possible values: 0, 1 and 2.
Your python script submitted to Vocareum must import both blackbox31 and blackbox32 even
though blackbox32 is not provided to you.
Remember that your code will be run with 2 different boxes. One way to parse the input argument is
and then use bb as data source. When you develop your algorithm, you only need to care about
blackbox31, but you also need to import and add additional parsing logic for blackbox32 since
blackbox32 will be used to test your code on Vocareum.
1.2 Noisy data
This time the data includes noises, that means even the features of a sample(X) shows that the
sample should belongs to class Ci(y), it might still return a wrong class Cj when you ask the
blackbox. However, Bayesian model is not quite sensitive to noises, so that should not be a big
program (hopefully). You are also encouraged to try other models like decision tree and Neural
Network to compare the differences.
1.3 Task Description
Your task is to classify this categorical data into 3 classes incrementally and keep track of the
testing accuracy statistics.
You need to hold out 200 samples as testing data, and then repeatedly generate training data one at
a time during the learning process for 1000 times. Note that not all training data examples are known
at once, it comes one at a time.
The logic of your program should be something like this:
# hold out 200 examples to estimate accuracy
Repeat 200 times:
Ask the blackbox for one data point
# do incremental training
Repeat 1000 times:
X, y = blackbox31.ask()
adapt model to this new X, y
accumulate test accuracy stats per 10 samples
# output accuracy stats
Your program will be run in the following way:
python3 NaiveBayes.py blackbox31
When we grade your model with hidden blackbox32, it should be run:
python3 NaiveBayes.py blackbox32
The results.txt contains accuracy stats, it should have the following format:
The first column indicates the number of training data that have been seen so far, and the
second column is the corresponding test accuracy (rounded to 3 decimals).
Since the data will be randomly generated, it is acceptable that sometimes your classifier does
not give very ideal results and you will not lose points for that.
In your implementation, please do not use any existing machine learning library call. You
must implement the algorithm yourself. Please develop your code yourself and do not copy from
other students or from the Internet.
When we grade your algorithm, we will use a different blackbox. Your code will be autograded
for technical correctness. Please name your file correctly, or you will wreak havoc on the
autograder. The maximum running time is 3 minutes.
2.1 Submit your code to Vocareum
● Submit NaiveBayes.py to Vocareum
● After your submission, Vocareum would run two scripts to test your code, a submission
script and a grading script. The submission script will test your code with only
blackbox31, while the grading script will test your code with another blackbox32.
● The program will terminate and fail if it exceeds the 3 minutes time limit.
● After the submission script/grading script finishes, you can view your submission report
immediately to see if your code works, while the grading report will be released after the
deadline of the homework.
● You don’t have to keep the page open while the scripts are running.
2.2 Submit your report to Blackboard
● Create a single .zip (Firstname_Lastname_HW3.zip) which contains:
○ Report.pdf, a brief report contains your testing accuracy graph for
blackbox31. Example graph:
● Submit your zipped file to the blackboard.
100 points in total
○ Program correctness(60 points): program always works correctly and meets the
specifications within the time limit
○ Documentation(20 points): code is well commented and submitted graph is reasonable
○ Performance (20 points): the classifier has reasonable performance