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56
README.md
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@ -1,10 +1,62 @@
# Information Modelling & Analysis: Project 2
Student: enter your name here
Student: Claudio Maggioni
Please follow the instructions provided in the project slides
and consider the submission instructions available on iCorsi.
For your convencience, I the following resources are available in the `resources` folder:
- **defects4j-checkout-closure-1f**: The output of the command `defects4j checkout -p Closure -v 1f -w ...`
- **modified_classes** The list of buggy classes in: `framework/projects/Closure/modified_classes/`
- **modified_classes** The list of buggy classes in: `framework/projects/Closure/modified_classes/`
## Setup
To install the required libraries run:
```shell
python3 -m venv .env
source .env/bin/activate
pip install -r requirements.txt
```
## Data pre-processing
To extract and label the feature vectors run:
```shell
python3 ./extract_feature_vectors.py
python3 ./label_feature_vectors.py
```
The labeled feature vectors are stored in file
`./metrics/feature_vectors_labeled.csv` from the repository root.
## Training
To train the classifiers with the grid search procedure defined in the report
to later extract the optimal combination of hyperparameters run:
```shell
python3 ./train_classifiers.py
```
and answer `y` to run again training when prompted. Answering `n` simply
computes again data about the best hyperparameter configuration from the metrics
produced by a previous training.
Raw cross validation training metrics are stored in `./models/models.csv`. The
optimal hyperparameter configurations found are stored in `./models/best.csv`.
## Evaluation
To run the 20-times 5-fold cross validation procedure delete the file
`./models/evaluation.csv` and run:
```shell
python3 ./evaluate_classifiers.py
```
Raw data from the repeated cross validation procedure is stored in
`./models/evaluation.csv`. P-values for each metric of each classifier pair are
stored in `./models/model_stats.csv`.

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evaluate_classifiers.py
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@ -143,6 +143,8 @@ def main():
pvalue = wilcoxon(series_a, series_b).pvalue
df_stats.loc[i, 'pvalue'] = pvalue
i += 1
if metric not in observations:
observations[metric] = []

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46
models/model_stats.csv
View file

@ -1,2 +1,46 @@
,classifier_a,classifier_b,metric,pvalue
1,BiasedClassifier,SVC,f1,4.337936940303975e-17
1,DecisionTreeClassifier,GaussianNB,precision,0.08929133280531223
2,DecisionTreeClassifier,GaussianNB,recall,3.877480505802584e-18
3,DecisionTreeClassifier,GaussianNB,f1,3.896340037647931e-18
4,DecisionTreeClassifier,MLPClassifier,precision,0.4012348497407896
5,DecisionTreeClassifier,MLPClassifier,recall,0.011820059675817408
6,DecisionTreeClassifier,MLPClassifier,f1,0.4710651684151138
7,DecisionTreeClassifier,RandomForestClassifier,precision,8.283473187323235e-12
8,DecisionTreeClassifier,RandomForestClassifier,recall,0.3276029575034267
9,DecisionTreeClassifier,RandomForestClassifier,f1,1.4515097813437996e-10
10,DecisionTreeClassifier,SVC,precision,6.472995016722292e-16
11,DecisionTreeClassifier,SVC,recall,3.864155888689142e-18
12,DecisionTreeClassifier,SVC,f1,3.896559845095909e-18
13,GaussianNB,MLPClassifier,precision,0.03476088049603166
14,GaussianNB,MLPClassifier,recall,3.873544128513129e-18
15,GaussianNB,MLPClassifier,f1,3.896120241954008e-18
16,GaussianNB,RandomForestClassifier,precision,5.027978595522601e-10
17,GaussianNB,RandomForestClassifier,recall,3.8656827355135645e-18
18,GaussianNB,RandomForestClassifier,f1,3.896120241954008e-18
19,GaussianNB,SVC,precision,7.361006463422299e-13
20,GaussianNB,SVC,recall,3.881639684405151e-18
21,GaussianNB,SVC,f1,4.265842540306607e-18
22,MLPClassifier,RandomForestClassifier,precision,2.9302015489842885e-09
23,MLPClassifier,RandomForestClassifier,recall,0.00010909237805840521
24,MLPClassifier,RandomForestClassifier,f1,1.1542838431590428e-11
25,MLPClassifier,SVC,precision,3.6744416439536415e-16
26,MLPClassifier,SVC,recall,5.645631221640026e-18
27,MLPClassifier,SVC,f1,5.112831740936498e-18
28,RandomForestClassifier,SVC,precision,4.0161556854627e-18
29,RandomForestClassifier,SVC,recall,3.8584897469079895e-18
30,RandomForestClassifier,SVC,f1,3.896559845095909e-18
31,BiasedClassifier,DecisionTreeClassifier,precision,3.881858705649312e-18
32,BiasedClassifier,DecisionTreeClassifier,recall,1.0267247842714985e-14
33,BiasedClassifier,DecisionTreeClassifier,f1,3.881858705649312e-18
34,BiasedClassifier,GaussianNB,precision,3.876167958900271e-18
35,BiasedClassifier,GaussianNB,recall,3.7861845925093915e-18
36,BiasedClassifier,GaussianNB,f1,4.22499456189738e-16
37,BiasedClassifier,MLPClassifier,precision,3.887338045617697e-18
38,BiasedClassifier,MLPClassifier,recall,2.2417567344517687e-15
39,BiasedClassifier,MLPClassifier,f1,3.886680137925553e-18
40,BiasedClassifier,RandomForestClassifier,precision,3.8798879361158955e-18
41,BiasedClassifier,RandomForestClassifier,recall,4.647240654275836e-14
42,BiasedClassifier,RandomForestClassifier,f1,3.8798879361158955e-18
43,BiasedClassifier,SVC,precision,3.890848669938197e-18
44,BiasedClassifier,SVC,recall,3.803119001919453e-18
45,BiasedClassifier,SVC,f1,4.337936940303975e-17

1 classifier_a classifier_b metric pvalue
2 1 BiasedClassifier DecisionTreeClassifier SVC GaussianNB f1 precision 4.337936940303975e-17 0.08929133280531223
3 2 DecisionTreeClassifier GaussianNB recall 3.877480505802584e-18
4 3 DecisionTreeClassifier GaussianNB f1 3.896340037647931e-18
5 4 DecisionTreeClassifier MLPClassifier precision 0.4012348497407896
6 5 DecisionTreeClassifier MLPClassifier recall 0.011820059675817408
7 6 DecisionTreeClassifier MLPClassifier f1 0.4710651684151138
8 7 DecisionTreeClassifier RandomForestClassifier precision 8.283473187323235e-12
9 8 DecisionTreeClassifier RandomForestClassifier recall 0.3276029575034267
10 9 DecisionTreeClassifier RandomForestClassifier f1 1.4515097813437996e-10
11 10 DecisionTreeClassifier SVC precision 6.472995016722292e-16
12 11 DecisionTreeClassifier SVC recall 3.864155888689142e-18
13 12 DecisionTreeClassifier SVC f1 3.896559845095909e-18
14 13 GaussianNB MLPClassifier precision 0.03476088049603166
15 14 GaussianNB MLPClassifier recall 3.873544128513129e-18
16 15 GaussianNB MLPClassifier f1 3.896120241954008e-18
17 16 GaussianNB RandomForestClassifier precision 5.027978595522601e-10
18 17 GaussianNB RandomForestClassifier recall 3.8656827355135645e-18
19 18 GaussianNB RandomForestClassifier f1 3.896120241954008e-18
20 19 GaussianNB SVC precision 7.361006463422299e-13
21 20 GaussianNB SVC recall 3.881639684405151e-18
22 21 GaussianNB SVC f1 4.265842540306607e-18
23 22 MLPClassifier RandomForestClassifier precision 2.9302015489842885e-09
24 23 MLPClassifier RandomForestClassifier recall 0.00010909237805840521
25 24 MLPClassifier RandomForestClassifier f1 1.1542838431590428e-11
26 25 MLPClassifier SVC precision 3.6744416439536415e-16
27 26 MLPClassifier SVC recall 5.645631221640026e-18
28 27 MLPClassifier SVC f1 5.112831740936498e-18
29 28 RandomForestClassifier SVC precision 4.0161556854627e-18
30 29 RandomForestClassifier SVC recall 3.8584897469079895e-18
31 30 RandomForestClassifier SVC f1 3.896559845095909e-18
32 31 BiasedClassifier DecisionTreeClassifier precision 3.881858705649312e-18
33 32 BiasedClassifier DecisionTreeClassifier recall 1.0267247842714985e-14
34 33 BiasedClassifier DecisionTreeClassifier f1 3.881858705649312e-18
35 34 BiasedClassifier GaussianNB precision 3.876167958900271e-18
36 35 BiasedClassifier GaussianNB recall 3.7861845925093915e-18
37 36 BiasedClassifier GaussianNB f1 4.22499456189738e-16
38 37 BiasedClassifier MLPClassifier precision 3.887338045617697e-18
39 38 BiasedClassifier MLPClassifier recall 2.2417567344517687e-15
40 39 BiasedClassifier MLPClassifier f1 3.886680137925553e-18
41 40 BiasedClassifier RandomForestClassifier precision 3.8798879361158955e-18
42 41 BiasedClassifier RandomForestClassifier recall 4.647240654275836e-14
43 42 BiasedClassifier RandomForestClassifier f1 3.8798879361158955e-18
44 43 BiasedClassifier SVC precision 3.890848669938197e-18
45 44 BiasedClassifier SVC recall 3.803119001919453e-18
46 45 BiasedClassifier SVC f1 4.337936940303975e-17

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report/main.md
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@ -21,7 +21,7 @@ expected info, you'll be fine.-->
The code and result files, part of this submission, can be found at:
- Repository: [https://github.com/infoMA2023/project-02-bug-prediction-maggicl](https://github.com/infoMA2023/project-02-bug-prediction-maggicl)
- Commit ID: **TBD**
- Commit ID: **5f30b3b71b50b28f4c870ff114a760ece1005531**
# Data Pre-Processing

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report/main.pdf
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