Description
In this programming assignment, you are given a dataset of experimental speed dating events, and
your task is to predict whether the participant of a date decides to give his or her partner a second
date after the speed dating event (i.e., the “decision” column in the dataset). You will implement algorithms to learn and apply some naive Bayes classification (NBC) models to make such predictions.
More specifically, the dataset dating-full.csv is to be used for this assignment. This .csv file
contains information for 6744 speed dating events in the comma-separated format. The file fieldmeaning.pdf contains the complete description for the meaning of each column of the dataset.
You are asked to implement your algorithms in Python. Note that although there are many data
mining algorithms available online, for this assignment (as well as the next few programming assignments) you must design and implement your own versions of the algorithm. DO NOT use
any publicly available code including libraries such as sklearn. Your code will be checked against
public implementations. In addition, we will not provide separate testing data to you. You are
asked to design your own tests to ensure that your code runs correctly and meets the specifications
below. Note: You may use the pandas, numpy, scipy libraries for data processing purposes.
The only restriction is that you have to write your own version of data mining algorithms; you
can not use any built-in functions for your algorithm. This is a general rule for this assignment
and all the upcoming ones as well.
To make it easier to refer to a few sets of columns in the dataset, we will use the following terms
(usages will be italicized):
1. preference scores of participant: [attractive important, sincere important, intelligence important,
funny important, ambition important, shared interests important]
2. preference scores of partner : [pref o attractive, pref o sincere, pref o intelligence, pref o funny
pref o ambitious, pref o shared interests]
3. continuous valued columns: All columns other than [gender, race, race o, samerace, field,
decision].
4. rating of partner from participant: [attractive partner, sincere partner, intelligence partner,
funny partner, ambition partner, shared interests partner]
In the following, we specify a number of steps you are asked to complete for this assignment. Note
that all results in sample outputs are fictitious and for representation only for this
assignment and all upcoming assignments as well.
1 Preprocessing (4 pts)
Write a Python script named preprocess.py which reads the file dating-full.csv as input and
performs the following operations to output a new file dating.csv:
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(i) The format of values in some columns of the dataset is not unified. Strip the surrounding
quotes in the values for columns race, race o and field (e.g., ‘Asian/Pacific Islander/AsianAmerican’ → Asian/Pacific Islander/Asian-American), count how many cells are changed
after this pre-processing step, and output this number.
• Expected output line: Quotes removed from [count-of-changed-cells] cells.
(ii) Convert all the values in the column field to lowercase if they are not already in lowercases
(e.g., Law → law). Count the number of cells that are changed after this pre-processing step,
and output this number.
• Expected output line: Standardized [count-of-changed-cells] cells to lower case.
(iii) Use label encoding to convert the categorical values in columns gender, race, race o and
field to numeric values start from 0. The process of label encoding works by mapping
each categorical value of an attribute to an integer number between 0 and nvalues − 1 where
nvalues is the number of distinct values for that attribute. Sort the values of each categorical
attribute lexicographically/alphabetically before you start the encoding process. You
are then asked to output the mapped numeric values for ‘male’ in the gender column, for
‘European/Caucasian-American’ in the race column, for ‘Latino/Hispanic American’ in the
race o column, and for ‘law’ in the field column.
• Expected output lines:
Value assigned for male in column gender: [value-for-male].
Value assigned for European/Caucasian-American in column race: [valuefor-European/Caucasian-American].
Value assigned for Latino/Hispanic American in column race o: [value-forLatino/Hispanic American].
Value assigned for law in column field: [value-for-law].
(iv) Normalization: As the speed dating experiments are conducted in several different batches,
the instructions participants received across different batches vary slightly. For example, in
some batches of experiments participants are asked to allocate a total of 100 points among the
six attributes (i.e., attractiveness, sincerity, intelligence, fun, ambition, shared interests) to
indicate how much they value each of these attributes in their romantic partner—that is, the
values in preference scores of participant columns of a row should sum up to 100 (similarly,
values in preference scores of partner columns of a row should also sum up to 100)—while in
some other batches of experiments, participants are not explicitly instructed to do so.
To deal with this problem, let’s conduct one more pre-process step for values in preference scores of participant and preference scores of partner columns. For each row, let’s first
sum up all the values in the six columns that belong to the set preference scores of participant
(denote the sum value as total), and then transform the value for each column in the set preference scores of participant in that row as follows: new value=old value/total. We then conduct
similar transformation for values in the set preference scores of partner.
Finally, you are asked to output the mean values for each column in these two sets after
the transformation.
• Expected output lines:
Mean of attractive important: [mean-rounded-to-2-digits].
2
…
Mean of shared interests important: [mean-rounded-to-2-digits].
Mean of pref o attractive: [mean-rounded-to-2-digits].
…
Mean of pref o shared interests: [mean-rounded-to-2-digits].
In summary, below are the sample inputs and outputs we expect to see. We expect 18 lines
of outputs in total (the numbers are ficititious):
$python preprocess.py dating-full.csv dating.csv
Quotes removed from 123 cells.
Standardized 456 cells to lower case.
Value assigned for male in column gender: 0.
Value assigned for European/Caucasian-American in column race: 1.
Value assigned for Latino/Hispanic American in column race o: 4.
Value assigned for law in column field: 2.
Mean of attractive important: 0.12.
…
Mean of shared interests important: 0.34.
Mean of pref o attractive: 0.45.
…
Mean of pref o shared interests: 0.56.
2 Visualizing interesting trends in data (6 pts)
(i) First, let’s explore how males and females differ in terms of what are the attributes they value
the most in their romantic partners. Please perform the following task on dating.csv and
include your visualization code in a file named 2 1.py.
(a) Divide the dataset into two sub-datasets by the gender of participant
(b) Within each sub-dataset, compute the mean values for each column in the set preference scores of participant
(c) Use a single barplot to contrast how females and males value the six attributes in their
romantic partners differently. Please use color of the bars to indicate gender.
What do you observe from this visualization? What characteristics do males favor in their
romantic partners? How does this differ from what females prefer?
(ii) Next, let’s explore how a participant’s rating to their partner on each of the six attributes
relate to how likely he/she will decide to give the partner a second date. Please perform the
following task on dating.csv and include your visualization code in a file named 2 2.py.
(a) Given an attribute in the set rating of partner from participant (e.g., attractive partner),
determine the number of distinct values for this attribute.
(b) Given a particular value for the chosen attribute (e.g., a value of 10 for attribute ‘attractive partner’), compute the fraction of participants who decide to give the partner a
second date among all participants whose rating of the partner on the chosen attribute
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(e.g., attractive partner) is the given value (e.g., 10). We refer to this probability as
the success rate for the group of partners whose rating on the chosen attribute is the
specified value.
(c) Repeat the above process for all distinct values on each of the six attributes in the set
rating of partner from participant.
(d) For each of the six attributes in the set rating of partner from participant, draw a scatter
plot using the information computed above. Specifically, for the scatter plot of a particular attribute (e.g., attractive partner), use x-axis to represent different values on that
attribute and y-axis to represent the success rate. We expect 6 scatter plots in total.
What do you observe from these scatter plots?
3 Convert continuous attributes to categorical attributes (3 pts)
Write a Python script named discretize.py to discretize all columns in continuous valued columns
by splitting them into 5 bins of equal-width in the range of values for that column (check fieldmeaning.pdf for the range of each column; for those columns that you’ve finished pre-processing
in Question 1(iv), the range should be considered as [0, 1]). If you encounter any values that lie
outside the specified range of a certain column, please treat that value as the max value specified for that column. The script reads dating.csv as input and produces dating-binned.csv
as output. As an output of your scripts, please print the number of items in each of the 5 bins.
Bins should be sorted from small value ranges to large value ranges for each column in
continuous valued columns.
The sample inputs and outputs are as follows. We expect 47 lines of output, and the order of the
attributes in the output should be the same as the order they occur in the dataset:
$python discretize.py dating.csv dating-binned.csv
age: [3203 1188 1110 742 511]
age o: [2151 1292 1233 1383 685]
importance same race: [1282 4306 1070 58 28]
…
like: [119 473 2258 2804 1090]
4 Training-Test Split (2 pts)
Use the sample function from pandas with the parameters initialized as random state = 47,
frac = 0.2 to take a random 20% sample from the entire dataset. This sample will serve as your
test dataset, and the rest will be your training dataset. (Note: The use of the random state will
ensure all students have the same training and test datasets; incorrect or no initialization of this
parameter will lead to non-reproducible results). Create a new script called split.py that takes
dating-binned.csv as input and outputs trainingSet.csv and testSet.csv.
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5 Implement a Naive Bayes Classifier (15 pts)
• Learn a NBC model using the data in the training dataset, and then apply the learned model
to the test dataset.
• Evaluate the accuracy of your learned model and print out the model’s accuracy on both the
training dataset and the test dataset as specified below.
Code Specification:
Write a function named nbc(t frac) to train your NBC which takes a parameter t frac that
represents the fraction of the training data to sample from the original training set. Use the
sample function from pandas with the parameters initialized as random state = 47, frac =
t frac to generate random samples of training data of different sizes.
1. Use all the attributes and all training examples in trainingSet.csv to train the NBC by
calling your nbc(t frac) function with t frac = 1. After get the learned model, apply it on all
examples in the training dataset (i.e., trainingSet.csv) and test dataset (i.e., testSet.csv)
and compute the accuracy respectively. Please put your code for this question in a file called
5 1.py.
• Expected output lines:
Training Accuracy: [training-accuracy-rounded-to-2-decimals]
Testing Accuracy: [testing-accuracy-rounded-to-2-decimals]
The sample inputs and outputs are as follows:
$python 5 1.py
Training Accuracy: 0.71
Testing Accuracy: 0.68
2. Examine the effects of varying the number of bins for continuous attributes during the discretization step. Please put your code for this question parts(ii, iii, iv) in a file called 5 2.py.
(i) Given the number of bins b ∈ B = {2, 5, 10, 50, 100, 200}, perform discretization for all
columns in set continuous valued columns by splitting the values in each column into b
bins of equal width within its range. For this task, you can re-use your discretize.py
code to perform the binning procedure, now taking the number of bins as a parameter
and using dating.csv as input as earlier.)
(ii) Repeat the train-test split as described in Question 4 for the obtained dataset after
discretizing each continuous attribute into b bins.
(iii) For each value of b, train the NBC on the corresponding new training dataset by calling your nbc(t frac) function with t frac = 1, and apply the learned model on the
corresponding new test dataset.
(iv) Draw a plot to show how the value of b affects the learned NBC model’s performance
on the training dataset and the test dataset, with x-axis representing the value of b and
y-axis representing the model accuracy. Comment on what you observe in the plot.
The sample inputs and outputs are as follows:
$python 5 2.py
Bin size: 2
5
Training Accuracy: 0.34
Testing Accuracy: 0.12
Bin size: 5
Training Accuracy: 0.78
Testing Accuracy: 0.56
.
.
Bin size: 200
Training Accuracy: 0.90
Testing Accuracy: 0.88
3. Plot the learning curve. Please put your code for this question in a file called 5 3.py.
(i) For each f in F = {0.01, 0.1, 0.2, 0.5, 0.6, 0.75, 0.9, 1}, randomly sample a fraction of the
training data in trainingSet.csv with our fixed seed (i.e., random state=47).
(ii) Train a NBC model on the selected f fraction of the training dataset (You can call your
nbc(t frac) function with t frac = f). Evaluate the performance of the learned model
on all examples in the selected samples of training data as well as all examples in the
test dataset (i.e., testSet.csv), and compute the accuracy respectively. Do so for all
f ∈ F.
(iii) Draw one plot of learning curves where the x-axis representing the values of f and
the y-axis representing the corresponding model’s accuracy on training/test dataset.
Comment on what you observe in this plot.
Submission Instructions:
Submit through BrightSpace
1. Make a directory named yourF irstN ame yourLastN ame HW2 and copy all of your files to
this directory.
2. DO NOT put the datasets into your directory.
3. Make sure you compress your directory into a zip folder with the same name as described
above, and then upload your zip folder to BrightSpace.
Keep in mind that old submissions are overwritten with new ones whenever you re-upload.
Your submission should include the following files:
1. The source code in python.
2. Your evaluation & analysis in .pdf format. Note that your analysis should include visualization
plots as well as a discussion of results, as described in details in the questions above.
3. A README file containing your name, instructions to run your code and anything you would
like us to know about your program (like errors, special conditions, etc).
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