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School of Computer Science
The University of Adelaide
Artificial Intelligence
Assignment 2
Semester 1 2022
Due 11:59pm Saturday 7 May
1 Wine Quality Prediction with Decision Tree
Wine experts evaluate the quality of wine based on sensory data. We could also collect
the features of wine from objective tests, thus the objective features could be used to
predict the expert’s judgement, which is the quality rating of the wine. This could be
formed as a supervised learning problem with the objective features as the data features
and wine quality rating as the data labels. In this assignment, we provide objective
features obtained from physicochemical statistics for each white wine sample and its
corresponding rating provided by wine experts. You are expect to implement decision
tree learning (DTL), and use the training set to train your decision tree, then provide
wine quality prediction on the test set.
Wine quality rating is measured in the range of 0-9. In our dataset, we only keep
the samples for quality ratings 5, 6 and 7. The 11 objective features are listed as follows
[1]:
• f acid: fixed acidity
• v acid: volatile acidity
• c acid: citric acid
• res sugar: residual sugar
• chlorides: chlorides
• fs dioxide: free sulfur dioxide
• ts dioxide: total sulfur dioxide
• density: density
• pH: pH
• sulphates: sulphates
• alcohol: alcohol
Explanation of the Data.
train: The first 11 columns represent the 11 features and the 12th column is the wine
quality. A sample is depicted as follows:
f_acid v_acid c_acid res_sugar chlorides fs_dioxide ts_dioxide density pH sulphates alcohol quality
8.10 0.270 0.41 1.45 0.033 11.0 63.0 0.99080 2.99 0.56 12.0 5
8.60 0.230 0.40 4.20 0.035 17.0 109.0 0.99470 3.14 0.53 9.7 5
7.90 0.180 0.37 1.20 0.040 16.0 75.0 0.99200 3.18 0.63 10.8 5
8.30 0.420 0.62 19.25 0.040 41.0 172.0 1.00020 2.98 0.67 9.7 5
6.50 0.310 0.14 7.50 0.044 34.0 133.0 0.99550 3.22 0.50 9.5 5
test: Similar to train, but without the 12th colum as they are the values your model
will predict. A sample is depicted as follows:
f_acid v_acid c_acid res_sugar chlorides fs_dioxide ts_dioxide density pH sulphates alcohol
7.0 0.360 0.14 11.60 0.043 35.0 228.0 0.99770 3.13 0.51 8.900000
6.3 0.270 0.18 7.70 0.048 45.0 186.0 0.99620 3.23 0.47 9.000000
7.2 0.290 0.20 7.70 0.046 51.0 174.0 0.99582 3.16 0.52 9.500000
7.1 0.140 0.35 1.40 0.039 24.0 128.0 0.99212 2.97 0.68 10.400000
7.6 0.480 0.28 10.40 0.049 57.0 205.0 0.99748 3.24 0.45 9.300000
1.1 Decision Tree Learning
From the given training data, our goal is to learn a function that can predict the wine
quality rating of a wine sample, based on the objective features. In this assignment, the
predictor function will be constructed as a decision tree. Since the attributes (objective
features) are continuous valued, you shall apply the DTL algorithm for continuous data,
as outlined in Algorithms 1 and 2. Once the tree is constructed, Algorithm 3 to predict
the wine quality to a new wine sample.
Algorithm 1 DTL(data, minleaf )
Require: data in the form of N input-output pairs {xi
, yi}
N
i=1, minleaf ≥ 1
1: if (N ≤ minleaf) or (yi = yj for all i, j) or (xi = xj for all i, j) then
2: Create new leaf node n.
3: if there is a unique mode (most frequent value) in {yi}
N
i=1 then
4: n.label ← mode in {yi}
N
i=1
5: else
6: n.label ← unknown
7: end if
8: return n
9: end if
10: [attr, splitval] ← ChooseSplit(data) =⇒ Algorithm 2
11: Create new node n.
12: n.attr ← attr
13: n.splitval ← splitval
14: n.lef t ← DTL(data with xi
[attr] ≤ splitval, minleaf)
15: n.right ← DTL(data with xi
[attr] > splitval, minleaf)
16: return n
Algorithm 2 ChooseSplit(data)
Require: data in the form of N input-output pairs {xi
, yi}
N
i=1.
1: bestgain ← 0
2: for each attr in data do
3: Sort the array x1[attr], x2[attr], ..., xN [attr].
4: for i = 1, 2, ...N − 1 do
5: splitval ← 0.5(xi
[attr] + xi+1[attr])
6: gain ← Information gain of (attr, splitval) // See lecture slides.
7: if gain > bestgain then
8: bestattr ← attr and bestsplitval ← splitval and bestgain ← gain
9: end if
10: end for
11: end for
12: return (bestattr, bestsplitval)
Algorithm 3 Predict DTL(n, data)
Require: Decision tree root node n, data in the form of attribute values x.
1: while n is not a leaf node do
2: if x[n.attr] ≤ n.splitval then
3: n ← n.lef t
4: else
5: n ← n.right
6: end if
7: end while
8: return n.label
1.2 Deliverable
Write your decision tree learning program in Python 3.6.9 in a file called winequality.py.
Your program must be able to run as follows:
$ python winequality.py [train] [test] [minleaf]
The inputs/options to the program are as follows:
• [train] specifies the path to a set of training data file.
• [test] specifies the path to a set of testing data file.
• [minleaf] is an integer greater than zero which specifies the second input parameter
to the DTL algorithm (Algorithm 1).
Given the inputs, your program must learn a decision tree (following the prescribed
algorithms) using the training data, then predict the quality rating of each of the wine
sample in the testing data. Your program must then print to standard output (i.e.,
the command prompt) the list of predicted wine quality ratings, vertically based on the
order in which the testing cases appear in [test].
1.2.1 Python libraries
You are allowed to use the Python standard library to write your decision tree learning
program (see https://docs.python.org/3/library/ for the components that make
up the Python v3.6.9 standard library). In addition to the standard library, you are
allowed to use NumPy. Note that the marking program will not be able to run your
program to completion if other third party libraries are used.
1.3 Submission
You must submit your program files on Gradescope. Instructions on accessing Gradescope
and submitting assignments are provided at https://help.gradescope.com/
article/5d3ifaeqi4-student-canvas. Please use the course code X3ZJZE to enrol
into the course. For undergraduates, please submit your decision tree learning program
(winequality.py) to Assignment 2 - Undergraduates. If there are any questions
or issues with Gradescope, please contact Andrew via email at andrew.du@adelaide.edu.au.
1.4 Expected run time
Your program must be able to terminate within 300 seconds on the sample data given.
1.5 Debugging Suggestions
Step-by-step debugging by checking intermediate values/results will help you to identify
the problems of your code. This function is enabled by most of the Python IDE. If not
in your case, you could also print the intermediate values out. The values that are worth
checking when debugging are (but not limited to):
• bestsplitval, bestgain, bestattr when splitting;
• n.splitval, n.attr, n.label, when creating nodes and prediction
You could use sample data or create data in the same format for debugging.
1.6 Assessment
I will compile and run your code on several test problems. If it passes all tests, you will
get 15% (undergrads) or 12% (postgrads) of the overall course mark. For undergraduates,
bonus marks of 3% will be awarded if Section 2 is completed correctly.
There will be no further manual inspection/grading of your program to award marks
on the basis of coding style, commenting or “amount” of code written.
1.7 Using other source code
You may not use other source code for this assignment. All submitted code must be
your own work written from scratch. Only by writing the solution yourself will you fully
understand the concept.
1.8 Due date and late submission policy
This assignment is due by 11:59pm Saturday 7 May. If your submission is late, the
maximum mark you can obtain will be reduced by 25% per day (or part thereof) past
the due date or any extension you are granted.
Continues next page for postgraduate section.
2 Wine Quality Prediction with Random Forest
For postgraduate students, completing this section will give you the remaining 3% of
the assignment marks.
Random forest is an ensemble method which combines predictions of multiple decision
trees. In this task, you will extend your knowledge learnt from decision tree learning to
random forest learning (RFL). The process for a simplified RFL given N input-output
pairs is:
(1) Randomly select a set of N samples via bootstrap sampling (see explianation later).
This dataset is used for training a decision tree (i.e., the root node of the decision
tree).
(2) Build a decision tree on the dataset from (1) and apply Algorithm 1.
(3) Repeat (1) and (2) until reaching the maximum number of trees.
This process is also shown in Algorithm 4. In random forest learning, a sample set is
used to train a decision tree. That is to say, different trees in the forest could have
different root data. For prediction, the random forest will choose the most voted label
as its prediction.
For the wine quality prediction task, you shall apply Algorithm 4 for random forest
learning and apply Algorithm 5 to predict the wine quality for a new wine sample.
Bootstrap sampling. It is a random sampling with replacement (i.e., replace the sampled
data back to the original dataset). For example, if N = 5, so we have {x1, x2, ..., x5}
(omit yi for simplicity). To perform bootstrap sampling, we randomly select one data
point from the five, let’s say x3, to be the first sample. Then we put x3 back to the
dataset and draw another sample from the five data points. This time, the sample could
also be x3 or another one. We repeat this process until we get N samples and form
a sample set. In this way, the sample set could contain repeated data points. So the
following cases are possible:
{x3, x3, x3, x3, x3}, when all the data samples are the same.
{x1, x3, x4, x2, x5}, when all the data samples are different.
{x3, x1, x3, x2, x1}, when some samples are repeated.
...
2.1 Deliverables
Write your random forest program in Python 3.6.9 in a file called winequalityRFL.py.
Your program must be able to run as follows:
$ python winequalityRFL.py [train] [test] [minleaf] [n_trees] [random_seed]
The inputs/options to the program are as follows:
• [train] specifies the path to a set of training data file.
Algorithm 4 RFL(data, minleaf, n trees, rand seed)
Require: data in the form of N input-output pairs {xi
, yi}
N
i=1, n trees > 1, minleaf ≥ 1.
1: forest ← []
2: sample indexes ← N ∗ n trees random integers with value in [0, N) generated by rand seed
3: count ← 0
4: for count < n trees do
5: sampled data ← N data pairs selected by N indexes from sample indexes sequentially
6: n=DTL(sampled data, minleaf ) =⇒ Algorithm 1
7: forest.append(n)
8: end for
9: return forest
Algorithm 5 Predict RFL(forest, data)
Require: forest is a list of tree roots, data in the form of attribute values x.
1: labels ← []
2: for Each tree n in the forest do
3: label ← Predict DTL (n, data) =⇒ Algorithm 3
4: labels.append(n)
5: end for
6: return the most voted label in labels
• [test] specifies the path to a set of testing data file.
• [minleaf] is an integer greater than zero which specifies the second input parameter
to the RFL algorithm (Algorithm 4).
• [n_trees] is an integer greater than one which specifies the third input parameter
to the RFL algorithm (Algorithm 4).
• [random_seed] is the seed value generate random values. Please use import random
package, use random.seed(rand seed) to set seed value and random.randint(0,
M) to create a random value in [0, M], repeat random.randint to get enough
random values.
Given the inputs, your program must learn a random forest (following the prescribed
algorithms) using the training data, then predict the quality rating of each wine sample
in the testing data. Your program must then print to standard output (i.e., the
command prompt) the list of predicted wine quality ratings, vertically based on the
order in which the testing cases appear in [test].
Submit your program in the same way as the submission for Sec. 1. For postgraduates,
please submit your learning programs (winequality.py and winequalityRFL.py)
to Assignment 2 - Postgraduates. The due date, late submission policy and code
reuse policy are also the same as in Sec. 1.
2.2 Expected run time
Your program must be able to terminate within 300 seconds on the sample data given.
2.3 Debugging Suggestions
In addition to Sec. 1.5, another value worth checking when debugging is (but not limited
to):
• the sampleindexes – by setting random seed, the indexes should be the same each
time you run the code
2.4 Assessment
I will compile and run your code on a single test case. If it passes, you will get 3% of
the overall course mark.
∼∼∼ The End ∼∼∼
References
[1] Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J. Modeling
wine preferences by data mining from physicochemical properties. Decision support
systems 47, 4 (2009), 547–553.

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