Homework 6 Rubric

Submission and Other (10 Points)

Code	Points	Description
A1	2	Submission is zip containing mlmodel/engi1006 folders with python files
A2	2	Zip is named after uni, unzips to folder with uni-hw6
A3	3	Python files have correct names/imports
A4	3	Python files have good style

Part 1 - OOP (30 points)

Code	Points	Description
B1	15	All charts and outputs working
B2	2	`Assignment.registerGrade`
B3	2	`Course.register`
B4	2	`Course.assign`
B5	2	`Student.giveGrade`
B6	2	`Student.doAssignment`
B7	2	`Teacher.grade`
B8	3	Good Style (No globals, comments, well named vars)

Tester script: test_engi1006_grading.py in solutions

Output (modulus randomness):

Class Average: ~80
Teacher Pay: 3150

Part 2 - ML Model (60 points)

Code	Points	Description
C1	5	`readCSV` drops `id` column (watch for chatgpt - wrong column name)
C2	5	`datasetInfo` is `{'rows': 569, 'columns': 31, 'benign': 357, 'malignant': 212}`
C3	10	`advancedStats` close to below
C4	10	`scatterMatrix` output matches homework, see guidelines below
C5	5	`correlationHeatmap` output matches homework, see guidelines below
C6	20	`splitDataset` close to below
C7	5	Good Style (No globals, comments, well named vars)

Tester script: test_mlmodel_grading.py in solutions

Advanced Stats:

numerical differences ok
small formatting changes ok
warnings from pandas ok
5 for skew/kurt and 5 for describe
ensure diagnosis column omitted (-3)

Column 1 statistics:
	Skewness:0.9423795716730992	Kurtosis:0.8455216229065377
Column 2 statistics:
	Skewness:0.6504495420828159	Kurtosis:0.7583189723727752
Column 3 statistics:
	Skewness:0.9906504253930081	Kurtosis:0.9722135477110654
Column 4 statistics:
	Skewness:1.6457321756240424	Kurtosis:3.6523027623507582
Column 5 statistics:
	Skewness:0.45632376481955844	Kurtosis:0.8559749303632245
Column 6 statistics:
	Skewness:1.1901230311980404	Kurtosis:1.650130467219256
Column 7 statistics:
	Skewness:1.4011797389486722	Kurtosis:1.9986375291042124
Column 8 statistics:
	Skewness:1.1711800812336282	Kurtosis:1.066555702965477
Column 9 statistics:
	Skewness:0.7256089733641999	Kurtosis:1.2879329922294565
Column 10 statistics:
	Skewness:1.3044888125755076	Kurtosis:3.0058921201694933
Column 11 statistics:
	Skewness:3.0886121663847574	Kurtosis:17.686725966164644
Column 12 statistics:
	Skewness:1.646443808753053	Kurtosis:5.349168692469973
Column 13 statistics:
	Skewness:3.443615202194899	Kurtosis:21.40190492588045
Column 14 statistics:
	Skewness:5.447186284898394	Kurtosis:49.20907650724119
Column 15 statistics:
	Skewness:2.314450056636759	Kurtosis:10.469839532360393
Column 16 statistics:
	Skewness:1.9022207096378565	Kurtosis:5.10625248342338
Column 17 statistics:
	Skewness:5.110463049043661	Kurtosis:48.8613953017919
Column 18 statistics:
	Skewness:1.4446781446974786	Kurtosis:5.1263019430439565
Column 19 statistics:
	Skewness:2.1951328995478216	Kurtosis:7.896129827528971
Column 20 statistics:
	Skewness:3.923968620227413	Kurtosis:26.280847486373336
Column 21 statistics:
	Skewness:1.1031152059604372	Kurtosis:0.9440895758772196
Column 22 statistics:
	Skewness:0.49832130948716474	Kurtosis:0.22430186846478772
Column 23 statistics:
	Skewness:1.1281638713683722	Kurtosis:1.070149666654432
Column 24 statistics:
	Skewness:1.8593732724433467	Kurtosis:4.396394828992138
Column 25 statistics:
	Skewness:0.4154259962824678	Kurtosis:0.5178251903311124
Column 26 statistics:
	Skewness:1.4735549003297956	Kurtosis:3.0392881719200657
Column 27 statistics:
	Skewness:1.1502368219460262	Kurtosis:1.6152532975830205
Column 28 statistics:
	Skewness:0.49261552688550875	Kurtosis:-0.5355351225188589
Column 29 statistics:
	Skewness:1.433927765189328	Kurtosis:4.444559517846582
Column 30 statistics:
	Skewness:1.6625792663955146	Kurtosis:5.244610555815004

Dataframe statistics:        radius_mean   radius_se  radius_worst  texture_mean  texture_se  texture_worst  perimeter_mean  ...  concavepoints_worst  symmetry_mean  symmetry_se  symmetry_worst  fractaldimension_mean  fractaldimension_se  fractaldimension_worst
count   569.000000  569.000000    569.000000    569.000000  569.000000     569.000000      569.000000  ...           569.000000     569.000000   569.000000      569.000000             569.000000           569.000000              569.000000
mean     14.127292   19.289649     91.969033    654.889104    0.096360       0.104341        0.088799  ...           880.583128       0.132369     0.254265        0.272188               0.114606             0.290076                0.083946
std       3.524049    4.301036     24.298981    351.914129    0.014064       0.052813        0.079720  ...           569.356993       0.022832     0.157336        0.208624               0.065732             0.061867                0.018061
min       6.981000    9.710000     43.790000    143.500000    0.052630       0.019380        0.000000  ...           185.200000       0.071170     0.027290        0.000000               0.000000             0.156500                0.055040
25%      11.700000   16.170000     75.170000    420.300000    0.086370       0.064920        0.029560  ...           515.300000       0.116600     0.147200        0.114500               0.064930             0.250400                0.071460
50%      13.370000   18.840000     86.240000    551.100000    0.095870       0.092630        0.061540  ...           686.500000       0.131300     0.211900        0.226700               0.099930             0.282200                0.080040
75%      15.780000   21.800000    104.100000    782.700000    0.105300       0.130400        0.130700  ...          1084.000000       0.146000     0.339100        0.382900               0.161400             0.317900                0.092080
max      28.110000   39.280000    188.500000   2501.000000    0.163400       0.345400        0.426800  ...          4254.000000       0.222600     1.058000        1.252000               0.291000             0.663800                0.207500

Scatter Matrix:

5 columns (not 4) (-5)
Colored by diagnosis (doesn’t need to match)
No title, extra decoration (chatgpt) (-3)
rocket colorscheme not coolwarm (chatgpt) (-3)
Extra blank windows due to seaborn bugs are ok

Heatmap:

Might display every other / every third label, thats ok
annot=False (chatgpt) (-3)
No title, extra decoration (chatgpt) (-3)
rocket colorscheme not coolwarm (chatgpt) (-3)
Extra blank windows due to seaborn bugs are ok

Split Dataset:

returns ALL pandas series or ALL numpy array (5)
train.shape in ((456, 30), (455, 30)) (6)
test.shape in ((113, 30), (114, 30)) (5)
set(train_labels) == set(["B", "M"]) (different columns from lecture) (2)
set(test_labels) == set(["B", "M"]) (different columns from lecture) (2)