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56
README.md
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@ -1,10 +1,62 @@
# Information Modelling & Analysis: Project 2 # Information Modelling & Analysis: Project 2
Student: enter your name here Student: Claudio Maggioni
Please follow the instructions provided in the project slides Please follow the instructions provided in the project slides
and consider the submission instructions available on iCorsi. and consider the submission instructions available on iCorsi.
For your convencience, I the following resources are available in the `resources` folder: 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 ...` - **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 pvalue = wilcoxon(series_a, series_b).pvalue
df_stats.loc[i, 'pvalue'] = pvalue df_stats.loc[i, 'pvalue'] = pvalue
i += 1
if metric not in observations: if metric not in observations:
observations[metric] = [] observations[metric] = []

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models/boxplot.svg
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models/model_stats.csv
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@ -1,2 +1,46 @@
,classifier_a,classifier_b,metric,pvalue ,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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@ -21,7 +21,7 @@ expected info, you'll be fine.-->
The code and result files, part of this submission, can be found at: 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) - 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 # Data Pre-Processing

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