maggicl/ima02
Archived
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

report work

Browse source
This commit is contained in:
Claudio Maggioni 2023-05-24 18:05:44 +02:00
parent 39a6c59e2c
commit 185bee2933
4 changed files with 288 additions and 35 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

30
grid_search_table.py Executable file
View file

@ -0,0 +1,30 @@
#!/usr/bin/env python3
import os
import pandas as pd
from train_classifiers import get_classifiers
def main():
i = 0
df = pd.DataFrame(columns=['Classifier', 'Parameter', 'Values'])
for clazz, grid in get_classifiers():
for name, values in grid.items():
df.loc[i, 'Classifier'] = type(clazz).__name__
df.loc[i, 'Parameter'] = name
df.loc[i, 'Values'] = ', '.join([str(x) for x in values])
i += 1
n1 = '5, 10, 15, ..., 100'
n2 = ', '.join([str(x) for x in range(15, 101, 15)])
n3 = ', '.join([str(x) for x in range(20, 101, 20)])
df.loc[(df.Classifier == 'MLPClassifier') & (df.Parameter == 'hidden_layer_sizes'), 'Values'] = f'$[{n1}]$, $[{n2}]^2$, $[{n3}]^3$'
for i in set(df['Classifier']):
print(i)
print(df.loc[df.Classifier == i, ['Parameter', 'Values']].to_markdown(index=False))
print()
if __name__ == '__main__':
main()

272
report/main.md
View file

@ -1,7 +1,7 @@
---
author: Claudio Maggioni
title: Information Modelling & Analysis -- Project 2
geometry: margin=2cm,bottom=3cm
geometry: margin=2cm
---
<!--The following shows a minimal submission report for project 2. If you
@ -18,7 +18,7 @@ expected info, you'll be fine.-->
# Code Repository
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)
- Commit ID: **TBD**
@ -37,8 +37,8 @@ and used the code in the following subfolder for the project:
./resources/defects4j-checkout-closure-1f/src/com/google/javascript/jscomp/
```
relative to the root folder of the repository. The resulting csv of extracted, labelled feature vectors can be found in
the repository at the following path:
relative to the root folder of the repository. The resulting CSV of extracted, labelled feature vectors can be found in
the repository at the path:
```
./metrics/feature_vectors_labeled.csv
@ -46,6 +46,11 @@ the repository at the following path:
relative to the root folder of the repository.
Unlabeled feature vectors can be computed by running the script `./extract_feature_vectors.py`.
The resulting CSV of unlabeled feature vectors is located in `./metrics/feature_vectors.csv`.
Labels for feature vectors can be computed by running the script `./label_feature_vectors.py`.
## Feature Vector Extraction
I extracted **291** feature vectors in total. Aggregate metrics
@ -54,24 +59,25 @@ code metric, can be found in Table [1](#tab:metrics){reference-type="ref"
reference="tab:metrics"}.
::: {#tab:metrics}
| **Metric** | **Min** | **Average** | **Max** |
|:----|------:|-----------:|-------:|
| BCM | 0 | 13.4124 | 221 |
| CPX | 0 | 5.8247 | 96 |
| DCM | 0 | 4.8652 | 176.2 |
| EX | 0 | 0.1134 | 2 |
| FLD | 0 | 6.5773 | 167 |
| INT | 0 | 0.6667 | 3 |
| MTH | 0 | 11.6529 | 209 |
| NML | 0 | 13.5622 | 28 |
| RET | 0 | 3.6735 | 86 |
| RFC | 0 | 107.2710 | 882 |
| SZ | 0 | 18.9966 | 347 |
| WRD | 0 | 314.4740 | 3133 |
| **Metric** | **Minimum** | **Average** | **Maximum** |
|:------|-:|---------:|-----:|
| `BCM` |0| 13.4124 | 221 |
| `CPX` |0| 5.8247 | 96 |
| `DCM` |0| 4.8652 |176.2 |
| `EX` |0| 0.1134 | 2 |
| `FLD` |0| 6.5773 | 167 |
| `INT` |0| 0.6667 | 3 |
| `MTH` |0| 11.6529 | 209 |
| `NML` |0| 13.5622 | 28 |
| `RET` |0| 3.6735 | 86 |
| `RFC` |0| 107.2710 | 882 |
| `SZ` |0| 18.9966 | 347 |
| `WRD` |0| 314.4740 | 3133 |
: Distribution of values for each extracted code metric.
:::
## Feature Vector Labelling
After feature vectors are labeled, I determine that the dataset contains
@ -79,25 +85,221 @@ After feature vectors are labeled, I determine that the dataset contains
# Classifiers
In every subsection below, describe in a concise way which different
<!--In every subsection below, describe in a concise way which different
hyperparameters you tried for the corresponding classifier, and report
the corresponding precision, recall and F1 values (for example in a
table or an [itemize]{.smallcaps}-environment). Furthermore, for every
type of classifiers, explicitly mention which hyperparameter
configuration you chose (based on above reported results) to be used in
further steps, and (in one or two sentences), explain why these
hyperparameters may outperform the other ones you tested..
hyperparameters may outperform the other ones you tested..-->
In this section I explain how I define and perform training for each classifier.
Since the dataset has an unbalanced number of feature vectors of each class, in order
to increase classification performance I upsample the dataset by performing sampling with
replacement over the least frequent class until the number of feature vectors matches the
most frequest class[^1].
[^1]: Upsampling due to unbalanced classes was suggested by *Michele Cattaneo*, who is attending this class.
Other than for the `GaussianNB` (Naive Bayes) classifier, the classifiers chosen for the
project offer to select hyperparameter values. In order to choose them, I perform a grid
search over each classifier. The hyperparameter values I have considered in the grid search
for each classifier are the following:
- For *DecisionTreeClassifier*:
| **Parameter** | **Values** |
|:------------|:--------------|
| criterion | gini, entropy |
| splitter | best, random |
- For *SVC*:
| **Parameter** | **Values** |
|:------------|:---------------------------|
| kernel | linear, poly, rbf, sigmoid |
| gamma | scale, auto |
- For *MLPClassifier*:
| **Parameter** | **Values** |
|:------------|:---------------------------|
| max_iter | 500000 |
| hidden_layer_sizes | $[5, 10, 15, ..., 100]$, $[15, 30, 45, 60, 75, 90]^2$, $[20, 40, 60, 80, 100]^3$ |
| activation | identity, logistic, tanh, relu |
| solver | lbfgs, sgd, adam |
| learning_rate | constant, invscaling, adaptive |
Note that the $[...]^2$ denotes a cartesian product of the array with itself, and $[...]^3$
denotes the cartesian product of $[...]^2$ with the array (i.e. $[...]^3 = [...]^2 \times [...] = ([...] \times [...]) \times [...]$).
Note also the high upper bound on iterations (500000). This is to allow convergence of the less optimal hyperparameter configurations and avoid `ConvergenceWarning` errors.
- For *RandomForestClassifier*:
| **Parameter** | **Values** |
|:-------------|:-----------------------------|
| criterion | gini, entropy |
| max_features | sqrt, log2 |
| class_weight | balanced, balanced_subsample |
The script `./train_classifiers.py`, according to the random seed $3735924759$, performs upscaling of the dataset and the grid search training, by recording precision, accuracy, recall and the F1 score of each configuration of hyperparameters. These metrics are then collected and stored in `./models/models.csv`.
The metrics for each classifier and each hyperparameter configuration in decreasing order of
accuracy are reported in the following sections.
For each classifier, I then choose the hyperparameter configuration with highest accuracy.
## Decision Tree (DT)
| criterion | splitter | precision | accuracy | recall | f1 |
|:------------|:-----------|------------:|-----------:|---------:|---------:|
| gini | best | 0.788462 | 0.850575 | 0.953488 | 0.863158 |
| gini | random | 0.784314 | 0.83908 | 0.930233 | 0.851064 |
| entropy | random | 0.736842 | 0.816092 | 0.976744 | 0.84 |
| entropy | best | 0.745455 | 0.816092 | 0.953488 | 0.836735 |
## Naive Bayes (NB)
| precision | accuracy | recall | f1 |
|------------:|-----------:|---------:|---------:|
| 0.8 | 0.678161 | 0.465116 | 0.588235 |
## Support Vector Machine (SVP)
| gamma | kernel | precision | accuracy | recall | f1 |
|:--------|:---------|------------:|-----------:|---------:|---------:|
| scale | rbf | 0.717391 | 0.735632 | 0.767442 | 0.741573 |
| scale | linear | 0.75 | 0.735632 | 0.697674 | 0.722892 |
| auto | linear | 0.75 | 0.735632 | 0.697674 | 0.722892 |
| auto | rbf | 0.702128 | 0.724138 | 0.767442 | 0.733333 |
| scale | sigmoid | 0.647059 | 0.678161 | 0.767442 | 0.702128 |
| auto | sigmoid | 0.647059 | 0.678161 | 0.767442 | 0.702128 |
| auto | poly | 0.772727 | 0.643678 | 0.395349 | 0.523077 |
| scale | poly | 0.833333 | 0.597701 | 0.232558 | 0.363636 |
## Multi-Layer Perceptron (MLP)
For sake of brevity, only the top 100 results by accuracy are shown.
| activation | hidden_layer_sizes | learning_rate | max_iter | solver | precision | accuracy | recall | f1 |
|:-------------|:---------------------|:----------------|-----------:|:---------|------------:|-----------:|---------:|---------:|
| logistic | (60, 80, 100) | constant | 500000 | lbfgs | 0.895833 | 0.942529 | 1 | 0.945055 |
| logistic | (40, 80, 100) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (40, 80, 100) | invscaling | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (60, 100, 80) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (100, 60, 20) | constant | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| tanh | (100, 80, 80) | constant | 500000 | adam | 0.86 | 0.91954 | 1 | 0.924731 |
| relu | (75, 30) | adaptive | 500000 | lbfgs | 0.86 | 0.91954 | 1 | 0.924731 |
| logistic | (20, 40, 60) | adaptive | 500000 | lbfgs | 0.875 | 0.91954 | 0.976744 | 0.923077 |
| logistic | (40, 60, 80) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| logistic | (80, 40, 20) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 30 | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 60 | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | 85 | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (30, 30) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (45, 45) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 60) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (75, 45) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (75, 75) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (90, 90) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (20, 40, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (20, 100, 20) | invscaling | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 20, 100) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 80, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (40, 80, 100) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 20, 40) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 60, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (60, 80, 80) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 20, 40) | adaptive | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 40, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| tanh | (80, 60, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 20, 80) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 40, 100) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 60, 20) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 60, 100) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 100, 20) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (20, 100, 40) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (40, 20, 80) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (40, 80, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (60, 20, 100) | constant | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (80, 20, 60) | constant | 500000 | lbfgs | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (80, 60, 20) | adaptive | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| relu | (100, 20, 60) | invscaling | 500000 | adam | 0.843137 | 0.908046 | 1 | 0.914894 |
| logistic | (20, 60, 80) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| logistic | (60, 20, 20) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (15, 45) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (45, 90) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (90, 30) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (20, 80, 100) | invscaling | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (20, 80, 100) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (40, 40, 40) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (40, 60, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (60, 80, 60) | constant | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (100, 40, 60) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| tanh | (100, 80, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (30, 30) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (20, 20, 40) | adaptive | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (20, 40, 40) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (40, 20, 100) | adaptive | 500000 | adam | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| relu | (60, 80, 20) | invscaling | 500000 | lbfgs | 0.857143 | 0.908046 | 0.976744 | 0.913043 |
| logistic | (40, 80, 60) | adaptive | 500000 | lbfgs | 0.87234 | 0.908046 | 0.953488 | 0.911111 |
| logistic | 35 | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (15, 60) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (45, 45) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (20, 20, 60) | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (60, 60, 80) | adaptive | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (80, 40, 100) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| logistic | (100, 100, 100) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | 60 | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (15, 15) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (15, 45) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (30, 30) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (30, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 90) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (75, 15) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (75, 45) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (90, 15) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (90, 45) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 40, 20) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 40, 40) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 60, 20) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 80) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (20, 80, 100) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 20, 60) | invscaling | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 60, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 60, 60) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 80, 20) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (40, 100, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 20) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 40) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 40, 80) | constant | 500000 | lbfgs | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 60, 20) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 80, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 80, 80) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 20) | invscaling | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 40) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 60) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 60) | adaptive | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (60, 100, 80) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
| tanh | (80, 40, 40) | constant | 500000 | adam | 0.826923 | 0.896552 | 1 | 0.905263 |
## Random Forest (RF)
| criterion | class_weight | max_features | precision | accuracy | recall | f1 |
|:------------|:-------------------|:---------------|------------:|-----------:|---------:|---------:|
| gini | balanced | sqrt | 0.836735 | 0.885057 | 0.953488 | 0.891304 |
| entropy | balanced | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| gini | balanced_subsample | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| entropy | balanced_subsample | sqrt | 0.807692 | 0.873563 | 0.976744 | 0.884211 |
| gini | balanced | log2 | 0.82 | 0.873563 | 0.953488 | 0.88172 |
| entropy | balanced | log2 | 0.82 | 0.873563 | 0.953488 | 0.88172 |
| gini | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
| entropy | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
# Evaluation
## Output Distributions
@ -121,11 +323,11 @@ subsubsections:
::: {#tab:precision}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
| BiasedClassifier | 0 | 0 | 0 | 0 | 0 |
| DecisionTreeClassifier | -- | 0.0893 | 0.4012 | 0 | 0 |
| GaussianNB | -- | -- | 0.0348 | 0 | 0 |
| MLPClassifier | -- | -- | -- | 0 | 0 |
| RandomForestClassifier | -- | -- | -- | -- | 0 |
| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0893 | 0.4012 | 0.0000 |0.0000|
| GaussianNB | -- | -- | 0.0348 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0000 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
: Pairwise Wilcoxon test for precision for each combination of classifiers.
:::
@ -133,22 +335,22 @@ subsubsections:
::: {#tab:recall}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
| BiasedClassifier | 0 | 0 | 0 | 0 | 0 |
| DecisionTreeClassifier | -- | 0 | 0.0118 | 0.3276 | 0 |
| GaussianNB | -- | -- | 0 | 0 | 0 |
| MLPClassifier | -- | -- | -- | 0.0001 | 0 |
| RandomForestClassifier | -- | -- | -- | -- | 0 |
| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0000 | 0.0118 | 0.3276 |0.0000|
| GaussianNB | -- | -- | 0.0000 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0001 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
: Pairwise Wilcoxon test for recall for each combination of classifiers.
:::
::: {#tab:f1}
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
| BiasedClassifier | 0 | 0 | 0 | 0 | 0 |
| DecisionTreeClassifier | -- | 0 | 0.4711 | 0 | 0 |
| GaussianNB | -- | -- | 0 | 0 | 0 |
| MLPClassifier | -- | -- | -- | 0 | 0 |
| RandomForestClassifier | -- | -- | -- | -- | 0 |
| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000|
| DecisionTreeClassifier | -- | 0.0000 | 0.4711 | 0.0000 |0.0000|
| GaussianNB | -- | -- | 0.0000 | 0.0000 |0.0000|
| MLPClassifier | -- | -- | -- | 0.0000 |0.0000|
| RandomForestClassifier | -- | -- | -- | -- |0.0000|
: Pairwise Wilcoxon test for the F1 score metric for each combination of classifiers.
:::

BIN
report/main.pdf
View file

Binary file not shown.

21
train_classifiers.py
View file

@ -165,6 +165,27 @@ def main():
else:
df = pd.read_csv(OUT_DIR + '/models.csv')
for clazz in set(df['classifier']):
dfc = df.loc[df.classifier == clazz, :].copy()
dfc = dfc[dfc.columns.drop(list(df.filter(regex='^(mean_)|(std_)|(rank_)|(params$)|(classifier$)')))]
dfc = dfc.rename(columns={
"split0_test_precision": "precision",
"split0_test_accuracy": "accuracy",
"split0_test_recall": "recall",
"split0_test_f1": "f1"
})
dfc = dfc.reindex(
[x for x in dfc.columns if x.startswith('param_')] + \
[x for x in dfc.columns if not x.startswith('param_')], \
axis=1)
dfc = dfc.rename(columns=dict([(c, c.replace('param_', '')) for c in dfc.columns]))
dfc = dfc.loc[:, dfc.notna().any(axis=0)]
print(clazz)
print(dfc.head(100).to_markdown(index=False))
print()
find_best_and_save(df)