代写program编程、代写Python程序

日期：2022-04-15 10:59

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.