From a6662bc950f7f6bd5198daa119311b4de0cd624d Mon Sep 17 00:00:00 2001 From: Claudio Maggioni Date: Sat, 27 May 2023 22:39:27 +0200 Subject: [PATCH] all done but practical usefulness --- evaluate_classifiers.py | 52 ++++++- models/boxplot.svg | 334 ++++++++++++++++++++-------------------- models/model_stats.csv | 46 +----- report/main.md | 108 +++++++------ report/main.pdf | Bin 355866 -> 362615 bytes requirements.txt | 3 +- 6 files changed, 282 insertions(+), 261 deletions(-) diff --git a/evaluate_classifiers.py b/evaluate_classifiers.py index 1ce8422..f35d7ce 100755 --- a/evaluate_classifiers.py +++ b/evaluate_classifiers.py @@ -4,6 +4,7 @@ import pandas as pd import glob import re import itertools +import statsmodels.stats.power as pw import numpy as np from train_classifiers import perform_grid_search, load_dataset import seaborn as sns @@ -110,6 +111,17 @@ def main(): 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 for classifier_a 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_b'] = classifier_b 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 + + 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* $= {power}$)" - i += 1 + 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') @@ -169,6 +215,8 @@ def main(): dftab.columns = [x[1] if x[0] == 'value' else x[0] for x in dftab.columns] print(dftab.to_markdown(index=False)) + print() + if __name__ == '__main__': main() diff --git a/models/boxplot.svg b/models/boxplot.svg index 5efd8e9..02dc86b 100644 --- a/models/boxplot.svg +++ b/models/boxplot.svg @@ -6,7 +6,7 @@ - 2023-05-24T18:24:16.947257 + 2023-05-27T22:19:21.168970 image/svg+xml @@ -44,7 +44,7 @@ L 106.5216 360.413793 L 87.0784 360.413793 L 87.0784 366.786207 z -" clip-path="url(#p5256a55a9d)" style="fill: #3274a1; 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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 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 -For every combination of two classifiers and every performance metric -(precision, recall, f1) compare which algorithm performs better, by how -much, and report the corresponding p-value in the following -subsubsections: +Given the distribution of metrics presented in the previous section, I perform a statistical analysis +using the Wilixcon paired test to determine for each pair of classifiers which one performs better +according to each performance metric. When the *p-value* is too high, *ANOVA* power analysis (corrected +by *alpha* = $= 0.05$) is performed to determine if the metrics are equally distributed or if the statistical +test is inconclusive. -::: {#tab:precision} -| | 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| +# F1 Values - : Pairwise Wilcoxon test for precision for each combination of classifiers. -::: - -::: {#tab:recall} -| | DecisionTreeClassifier | GaussianNB | MLPClassifier | RandomForestClassifier | SVC | -|:-----------------------|:-------------------------|:-------------|:----------------|:-------------------------|------:| -| 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 | 0.0000 | 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 - -- - -- \... +- 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* $= 0.0$) +- Mean *F1* for *DT*: 0.8881, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *DT* is better than *SVP* (*p-value* $= 0.0$) +- 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* $= 0.0$) +- Mean *F1* for *NB*: 0.5495, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *NB* (*p-value* $= 0.0$) +- Mean *F1* for *MLP*: 0.8848, mean *F1* for *RF*: 0.9108 $\Rightarrow$ *RF* is better than *MLP* (*p-value* $= 0.0$) +- Mean *F1* for *MLP*: 0.8848, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *MLP* is better than *SVP* (*p-value* $= 0.0$) +- Mean *F1* for *RF*: 0.9108, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *RF* is better than *SVP* (*p-value* $= 0.0$) +- Mean *F1* for *Biased*: 0.6662, mean *F1* for *DT*: 0.8881 $\Rightarrow$ *DT* is better than *Biased* (*p-value* $= 0.0$) +- 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* $= 0.0$) +- Mean *F1* for *Biased*: 0.6662, mean *F1* for *SVP*: 0.7527 $\Rightarrow$ *SVP* is better than *Biased* (*p-value* $= 0.0$) ### 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 -(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 diff --git a/report/main.pdf b/report/main.pdf index a45ddae4a22882ab3a9181a83e3c6950da47b1af..a670252bd93ad0c07a0776ba2a0c1df8181c1911 100644 GIT binary patch delta 63766 zcmZs?Q*fXSv@9BHV%tt8HYdi!wrxB4;!JFNV%xTDCllM&`S-oGpU!#d>h-XyA6Iu* zt&B~K=Qzw-Au0$KW-iuL5&}qSph-h2c0(Mq`?+Q}9*n;IX{Z<9hzgN1jf-_(+({BF zNVZNJf?ScLtoif$xWY>;Jjt?yz#uP3qo#bj8*_q?CFRp&VE$X56aSljKb<4wmZd=n zBj>epedptgYJy|+oyIuik;9uv{)+Wb*AQO7eQhK2d$Y5%%kxXCd=hILfJxli&Q*lf zl&q@XkbUyjbDdIfm-W`DDw?OQaWmcN@$NxosL2eoeIZh_HVqknJ`8hzrpsA5bT~UB zRH*XsyErR9B?H^TnV2Z&aW#EJc`0P&cAMC;Yg(3>_{gdG>ij%C3fq8uZT*R#xN;{{ zY@ZsNF8og~={ldAEw8!_n7$s@^(;M`va(HDc&+wDu3$Hx9?il)-0Ank5U5%dH=5v$ zDOyqD4%5X*opz)P#5ORQ;%;VFEJsq*)b)n{7V;a}`p!HF^tgUo(AB?+tzE2!5(ZsB z(M&C6IUNeFN11$))TCDkZ7H?KayKD$Bxb~iNHF-x6j0^UFX=A;M5l@&v4~^HMc79p 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