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all done but practical usefulness

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Claudio Maggioni 2023-05-27 22:39:27 +02:00
parent 5841b222ee
commit a6662bc950
6 changed files with 282 additions and 261 deletions
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52
evaluate_classifiers.py
View file

@ -4,6 +4,7 @@ import pandas as pd
import glob import glob
import re import re
import itertools import itertools
import statsmodels.stats.power as pw
import numpy as np import numpy as np
from train_classifiers import perform_grid_search, load_dataset from train_classifiers import perform_grid_search, load_dataset
import seaborn as sns import seaborn as sns
@ -110,6 +111,17 @@ def main():
df_stats = pd.DataFrame(columns=['classifier_a', 'classifier_b', 'metric', 'pvalue']) df_stats = pd.DataFrame(columns=['classifier_a', 'classifier_b', 'metric', 'pvalue'])
short_names = {
'MLPClassifier': "MLP",
'GaussianNB': "NB",
'SVC': "SVP",
'DecisionTreeClassifier': "DT",
'RandomForestClassifier': "RF",
'BiasedClassifier': 'Biased'
}
observations = {}
i = 1 i = 1
for classifier_a in classifier_list: for classifier_a in classifier_list:
for classifier_b in classifier_list: for classifier_b in classifier_list:
@ -128,9 +140,43 @@ def main():
df_stats.loc[i, 'classifier_a'] = classifier_a df_stats.loc[i, 'classifier_a'] = classifier_a
df_stats.loc[i, 'classifier_b'] = classifier_b df_stats.loc[i, 'classifier_b'] = classifier_b
df_stats.loc[i, 'metric'] = metric df_stats.loc[i, 'metric'] = metric
df_stats.loc[i, 'pvalue'] = wilcoxon(series_a, series_b).pvalue pvalue = wilcoxon(series_a, series_b).pvalue
df_stats.loc[i, 'pvalue'] = pvalue
i += 1 if metric not in observations:
observations[metric] = []
sa = short_names[classifier_a]
sb = short_names[classifier_b]
meana = np.round(np.mean(series_a), decimals=4)
meanb = np.round(np.mean(series_b), decimals=4)
pvalue = np.round(pvalue, decimals=4)
s = f"- Mean *{metric}* for *{sa}*: {meana},"
s += f" mean *{metric}* for *{sb}*: {meanb} $\\Rightarrow$ "
if pvalue < 0.05:
better = sa if meana > meanb else sb
worse = sa if better == sb else sb
s += f"*{better}* is better than *{worse}* (*p-value* $= {pvalue}$)"
else:
eff_size = (np.mean(series_a) - np.mean(series_b)) / np.sqrt((np.std(series_a) ** 2 + np.std(series_b) ** 2) / 2.0)
power = pw.FTestAnovaPower().solve_power(effect_size=eff_size, nobs=len(series_a) + len(series_b), alpha=0.05)
power = np.round(power, decimals=4)
if power >= 0.8:
s += f"*{sa}* is as effective as *{sb}* (*p-value* $= {pvalue}$, *5% corrected ANOVA power* $= {power}$)"
else:
s += f"statistical test inconclusive (*p-value* $= {pvalue}$, *5% corrected ANOVA power* $= {power}$)"
observations[metric].append(s)
for metric in metric_list:
if metric == 'accuracy':
continue
print(metric + ":")
print("\n".join(observations[metric]))
df_stats.to_csv(OUT_DIR + '/model_stats.csv') df_stats.to_csv(OUT_DIR + '/model_stats.csv')
@ -169,6 +215,8 @@ def main():
dftab.columns = [x[1] if x[0] == 'value' else x[0] for x in dftab.columns] dftab.columns = [x[1] if x[0] == 'value' else x[0] for x in dftab.columns]
print(dftab.to_markdown(index=False)) print(dftab.to_markdown(index=False))
print()
if __name__ == '__main__': if __name__ == '__main__':
main() main()

334
models/boxplot.svg
View file

@ -6,7 +6,7 @@
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46
models/model_stats.csv
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@ -1,46 +1,2 @@
,classifier_a,classifier_b,metric,pvalue ,classifier_a,classifier_b,metric,pvalue
1,DecisionTreeClassifier,GaussianNB,precision,0.08929133280531223 1,BiasedClassifier,SVC,f1,4.337936940303975e-17
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 DecisionTreeClassifier BiasedClassifier GaussianNB SVC precision f1 0.08929133280531223 4.337936940303975e-17
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

108
report/main.md
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@ -308,10 +308,19 @@ For sake of brevity, only the top 100 results by accuracy are shown.
| gini | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 | | gini | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
| entropy | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 | | entropy | balanced_subsample | log2 | 0.803922 | 0.862069 | 0.953488 | 0.87234 |
# Evaluation # Evaluation
To evaluate the performance of each selected classifier, each model has trained 100 times by repeating a 5-fold
cross validation procedure 20 times. A graphical and statistical analysis to compare the distribution of
performance metrics (precision, recall and F1 score) follows.
Numeric tables, statistical analysis conclusions and the boxplot diagram shown in this section are obtained by
running the script:
```
./evaluate_classifiers.py
```
## Output Distributions ## Output Distributions
A boxplot chart to show the distribution of each of precision, recall, and F1 score A boxplot chart to show the distribution of each of precision, recall, and F1 score
@ -348,58 +357,65 @@ table summing up mean and standard deviation of each metric.
## Comparison and Significance ## Comparison and Significance
For every combination of two classifiers and every performance metric Given the distribution of metrics presented in the previous section, I perform a statistical analysis
(precision, recall, f1) compare which algorithm performs better, by how using the Wilixcon paired test to determine for each pair of classifiers which one performs better
much, and report the corresponding p-value in the following according to each performance metric. When the *p-value* is too high, *ANOVA* power analysis (corrected
subsubsections: by *alpha* = $= 0.05$) is performed to determine if the metrics are equally distributed or if the statistical
test is inconclusive.
::: {#tab:precision} # F1 Values
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
| 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. - Mean *F1* for *DT*: 0.8881, mean *F1* for *NB*: 0.5495 $\Rightarrow$ *DT* is better than *NB* (*p-value* $= 0.0$)
::: - Mean *F1* for *DT*: 0.8881, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.4711$, *5% corrected ANOVA power* $= 0.2987$)
- Mean *F1* for *DT*: 0.8881, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *DT* (*p-value* $= 0.0$)
::: {#tab:recall} - Mean *F1* for *DT*: 0.8881, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC | - Mean *F1* for *NB*: 0.5495, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0$)
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:| - Mean *F1* for *NB*: 0.5495, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
| BiasedClassifier | 0.0000 | 0.0000 | 0.0000 | 0.0000 |0.0000| - Mean *F1* for *NB*: 0.5495, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *NB* (*p-value* $= 0.0$)
| DecisionTreeClassifier | -- | 0.0000 | 0.0118 | 0.3276 |0.0000| - Mean *F1* for *MLP*: 0.8848, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0$)
| GaussianNB | -- | -- | 0.0000 | 0.0000 |0.0000| - Mean *F1* for *MLP*: 0.8848, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
| MLPClassifier | -- | -- | -- | 0.0001 |0.0000| - Mean *F1* for *RF*: 0.9108, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
| RandomForestClassifier | -- | -- | -- | -- |0.0000| - Mean *F1* for *Biased*: 0.6662, mean *F1* for *DT*: 0.8881 $\Rightarrow$ *DT* is better than *Biased* (*p-value* $= 0.0$)
: Pairwise Wilcoxon test for recall for each combination of classifiers. - Mean *F1* for *Biased*: 0.6662, mean *F1* for *NB*: 0.5495 $\Rightarrow$ *Biased* is better than *NB* (*p-value* $= 0.0$)
::: - Mean *F1* for *Biased*: 0.6662, mean *F1* for *MLP*: 0.8848 $\Rightarrow$ *MLP* is better than *Biased* (*p-value* $= 0.0$)
- Mean *F1* for *Biased*: 0.6662, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *Biased* (*p-value* $= 0.0$)
::: {#tab:f1} - Mean *F1* for *Biased*: 0.6662, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *Biased* (*p-value* $= 0.0$)
| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC |
|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:|
| 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.
:::
### F1 Values
-
- \...
### Precision ### Precision
(same as for F1 above) - Mean *precision* for *DT*: 0.8327, mean *precision* for *NB*: 0.8209 $\Rightarrow$ *DT* is as effective as *NB* (*p-value* $= 0.0893$, *5% corrected ANOVA power* $= 0.8498$)
- Mean *precision* for *DT*: 0.8327, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.4012$, *5% corrected ANOVA power* $= 0.2196$)
- Mean *precision* for *DT*: 0.8327, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *DT* (*p-value* $= 0.0$)
- Mean *precision* for *DT*: 0.8327, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
- Mean *precision* for *NB*: 0.8209, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0348$)
- Mean *precision* for *NB*: 0.8209, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
- Mean *precision* for *NB*: 0.8209, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *NB* is better than *SVP* (*p-value* $= 0.0$)
- Mean *precision* for *MLP*: 0.8365, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0$)
- Mean *precision* for *MLP*: 0.8365, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
- Mean *precision* for *RF*: 0.8707, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *DT*: 0.8327 $\Rightarrow$ *DT* is better than *Biased* (*p-value* $= 0.0$)
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *NB*: 0.8209 $\Rightarrow$ *NB* is better than *Biased* (*p-value* $= 0.0$)
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *MLP*: 0.8365 $\Rightarrow$ *MLP* is better than *Biased* (*p-value* $= 0.0$)
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *RF*: 0.8707 $\Rightarrow$ *RF* is better than *Biased* (*p-value* $= 0.0$)
- Mean *precision* for *Biased*: 0.4995, mean *precision* for *SVP*: 0.7557 $\Rightarrow$ *SVP* is better than *Biased* (*p-value* $= 0.0$)
### Recall ### Recall
(same as for F1 above) - Mean *recall* for *DT*: 0.9533, mean *recall* for *NB*: 0.4189 $\Rightarrow$ *DT* is better than *NB* (*p-value* $= 0.0$)
- Mean *recall* for *DT*: 0.9533, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *DT* is better than *MLP* (*p-value* $= 0.0118$)
- Mean *recall* for *DT*: 0.9533, mean *recall* for *RF*: 0.9567 $\Rightarrow$ statistical test inconclusive (*p-value* $= 0.3276$, *5% corrected ANOVA power* $= 0.2558$)
- Mean *recall* for *DT*: 0.9533, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$)
- Mean *recall* for *NB*: 0.4189, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *MLP* is better than *NB* (*p-value* $= 0.0$)
- Mean *recall* for *NB*: 0.4189, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *RF* is better than *NB* (*p-value* $= 0.0$)
- Mean *recall* for *NB*: 0.4189, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *SVP* is better than *NB* (*p-value* $= 0.0$)
- Mean *recall* for *MLP*: 0.9418, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0001$)
- Mean *recall* for *MLP*: 0.9418, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$)
- Mean *recall* for *RF*: 0.9567, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$)
- Mean *recall* for *Biased*: 1.0, mean *recall* for *DT*: 0.9533 $\Rightarrow$ *Biased* is better than *DT* (*p-value* $= 0.0$)
- Mean *recall* for *Biased*: 1.0, mean *recall* for *NB*: 0.4189 $\Rightarrow$ *Biased* is better than *NB* (*p-value* $= 0.0$)
- Mean *recall* for *Biased*: 1.0, mean *recall* for *MLP*: 0.9418 $\Rightarrow$ *Biased* is better than *MLP* (*p-value* $= 0.0$)
- Mean *recall* for *Biased*: 1.0, mean *recall* for *RF*: 0.9567 $\Rightarrow$ *Biased* is better than *RF* (*p-value* $= 0.0$)
- Mean *recall* for *Biased*: 1.0, mean *recall* for *SVP*: 0.7547 $\Rightarrow$ *Biased* is better than *SVP* (*p-value* $= 0.0$)
## Practical Usefulness ## Practical Usefulness

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requirements.txt
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@ -4,3 +4,4 @@ scikit_learn==1.2.1
tabulate==0.9.0 tabulate==0.9.0
scipy==1.24.2 scipy==1.24.2
seaborn==0.12.2 seaborn==0.12.2
statsmodels==0.14.0