def generate_main_results():
"""Generate the main results of the experiment."""
wide_optimal = sort_tbl(
calculate_wide_optimal(results),
ovrs_order=OVRS_NAMES, clfs_order=CLFS_NAMES
)\
.set_index(['Dataset', 'Classifier', 'Metric'])\
.apply(lambda row: make_bold(row, num_decimals=3), axis=1)\
.reset_index()
wide_optimal['Dataset'] = wide_optimal['Dataset'].apply(
lambda x: x.title()
if len(x.split(' ')) == 1
else ''.join([w[0] for w in x.split(' ')])
)
mean_sem_scores = sort_tbl(
generate_mean_std_tbl_bold(*calculate_mean_sem_scores(results), maximum=True, decimals=3),
ovrs_order=OVRS_NAMES, clfs_order=CLFS_NAMES
)
mean_sem_perc_diff_scores = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_perc_diff_scores(results, ['SMOTE', 'K-SMOTE'])),
ovrs_order=OVRS_NAMES, clfs_order=CLFS_NAMES
)
mean_sem_ranking = sort_tbl(
generate_mean_std_tbl_bold(*calculate_mean_sem_ranking(results), maximum=False),
ovrs_order=OVRS_NAMES, clfs_order=CLFS_NAMES
)
main_results_names = ('wide_optimal', 'mean_sem_scores', 'mean_sem_perc_diff_scores', 'mean_sem_ranking')
return zip(main_results_names, (wide_optimal, mean_sem_scores, mean_sem_perc_diff_scores, mean_sem_ranking))
def generate_main_results(data_path, results_path):
"""Generate the main results of the experiment."""
# Load dataset
dataset = load_datasets(data_dir=data_path)[0]
# Load results
results = []
for name in RESULTS_NAMES:
file_path = join(results_path, f'{name}.pkl')
results.append(pd.read_pickle(file_path))
# Combine and select results
results = combine_results(*results)
results = select_results(results, oversamplers_names=OVRS_NAMES, classifiers_names=CLFS_NAMES)
# Extract metrics names
metrics_names, *_ = zip(*METRICS_MAPPING.items())
# Dataset description
dataset_description = describe_dataset(dataset)
# Scores
wide_optimal = calculate_wide_optimal(results).drop(columns='Dataset')
# Ranking
ranking = calculate_ranking(results).drop(columns='Dataset')
ranking.iloc[:, 2:] = ranking.iloc[:, 2:].astype(int)
# Percentage difference
perc_diff_scores = []
for oversampler in BASELINE_OVRS:
perc_diff_scores_ovs = calculate_mean_sem_perc_diff_scores(results, [oversampler, 'G-SMOTE'])[0]
perc_diff_scores_ovs = perc_diff_scores_ovs[['Difference']].rename(columns={'Difference': oversampler})
perc_diff_scores.append(perc_diff_scores_ovs)
perc_diff_scores = sort_tbl(pd.concat([ranking[['Classifier', 'Metric']], pd.concat(perc_diff_scores, axis=1)], axis=1), clfs_order=CLFS_NAMES, ovrs_order=OVRS_NAMES, metrics_order=metrics_names)
perc_diff_scores.iloc[:, 2:] = round(perc_diff_scores.iloc[:, 2:], 2)
# Wilcoxon test
pvalues = []
for ovr in OVRS_NAMES[:-1]:
mask = (wide_optimal['Metric'] != 'accuracy') if ovr == 'NONE' else np.repeat(True, len(wide_optimal))
pvalues.append(wilcoxon(wide_optimal.loc[mask, ovr], wide_optimal.loc[mask, 'G-SMOTE']).pvalue)
wilcoxon_results = pd.DataFrame({'Oversampler': OVRS_NAMES[:-1], 'p-value': pvalues, 'Significance': np.array(pvalues) < ALPHA})
# Format results
main_results = [(MAIN_RESULTS_NAMES[0], dataset_description)]
for name, result in zip(MAIN_RESULTS_NAMES[1:], (wide_optimal, ranking, perc_diff_scores, wilcoxon_results)):
if name != 'wilcoxon_results':
result = sort_tbl(result, clfs_order=CLFS_NAMES, ovrs_order=OVRS_NAMES, metrics_order=metrics_names)
result['Metric'] = result['Metric'].apply(lambda metric: METRICS_MAPPING[metric])
if name == 'wide_optimal':
result.iloc[:, 2:] = result.iloc[:, 2:].apply(lambda row: make_bold(row, True, 3), axis=1)
elif name == 'ranking':
result.iloc[:, 2:] = result.iloc[:, 2:].apply(lambda row: make_bold(row, False, 0), axis=1)
elif name == 'wilcoxon_results':
wilcoxon_results = generate_pvalues_tbl(wilcoxon_results)
main_results.append((name, result))
return main_results
def generate_statistical_results():
"""Generate the statistical results of the experiment."""
# Combine experiments objects
results = []
for ratio in UNDERSAMPLING_RATIOS:
# Generate results
partial_results = generate_results(ratio)
# Extract results
cols = partial_results.columns
partial_results = partial_results.reset_index()
partial_results['Dataset'] = partial_results['Dataset'].apply(lambda name: f'{name}({ratio})')
partial_results.set_index(['Dataset', 'Oversampler', 'Classifier', 'params'], inplace=True)
partial_results.columns = cols
results.append(partial_results)
# Combine results
results = combine_results(*results)
# Calculate statistical results
friedman_test = sort_tbl(generate_pvalues_tbl(apply_friedman_test(results)), ovrs_order=OVERSAMPLERS_NAMES, clfs_order=CLASSIFIERS_NAMES)
holms_test = sort_tbl(generate_pvalues_tbl(apply_holms_test(results, control_oversampler='G-SMOTE')), ovrs_order=OVERSAMPLERS_NAMES[:-1], clfs_order=CLASSIFIERS_NAMES)
statistical_results_names = ('friedman_test', 'holms_test')
statistical_results = zip(statistical_results_names, (friedman_test, holms_test))
return statistical_results
def generate_main_results():
"""Generate the main results of the experiment."""
main_results = {}
for ratio in UNDERSAMPLING_RATIOS:
# Generate results
results = generate_results(ratio)
# Calculate results
mean_sem_scores = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_scores(results)),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
mean_sem_perc_diff_scores = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_perc_diff_scores(
results, ['NO OVERSAMPLING', 'G-SMOTE'])),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
mean_sem_ranking = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_ranking(results)),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
# Populate main results
main_results_names = ('mean_sem_scores', 'mean_sem_perc_diff_scores',
'mean_sem_ranking')
main_results[ratio] = zip(
main_results_names,
(mean_sem_scores, mean_sem_perc_diff_scores, mean_sem_ranking))
return main_results
def generate_statistical_results():
"""Generate the statistical results of the experiment."""
friedman_test = sort_tbl(generate_pvalues_tbl(apply_friedman_test(results)), ovrs_order=OVRS_NAMES, clfs_order=CLFS_NAMES)
holms_test = sort_tbl(generate_pvalues_tbl_bold(apply_holms_test(results, control_oversampler='K-SMOTE')), ovrs_order=OVRS_NAMES[:-1], clfs_order=CLFS_NAMES)
statistical_results_names = ('friedman_test', 'holms_test')
statistical_results = zip(statistical_results_names, (friedman_test, holms_test))
return statistical_results
def generate_statistical_results():
"""Generate the statistical results of the experiment."""
# Generate results
results = generate_results()
# Calculate statistical results
friedman_test = sort_tbl(generate_pvalues_tbl(
apply_friedman_test(results)),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
holms_test = sort_tbl(generate_pvalues_tbl(
apply_holms_test(results, control_oversampler='G-SOMO')),
ovrs_order=OVERSAMPLERS_NAMES[:-1],
clfs_order=CLASSIFIERS_NAMES)
# Generate statistical results
statistical_results_names = ('friedman_test', 'holms_test')
statistical_results = zip(statistical_results_names,
(friedman_test, holms_test))
return statistical_results
def generate_main_results():
"""Generate the main results of the experiment."""
# Generate results
results = generate_results()
# Calculate results
mean_sem_scores = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_scores(results)),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
keys = mean_sem_scores[['Classifier', 'Metric']]
mean_sem_perc_diff_scores = []
for oversampler in ('SMOTE', 'K-MEANS SMOTE', 'SOMO', 'G-SMOTE'):
perc_diff_scores = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_perc_diff_scores(
results, [oversampler, 'G-SOMO'])),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
perc_diff_scores = perc_diff_scores.rename(columns={
'Difference': oversampler
}).drop(columns=['Classifier', 'Metric'])
mean_sem_perc_diff_scores.append(perc_diff_scores)
mean_sem_perc_diff_scores = pd.concat(
[keys, pd.concat(mean_sem_perc_diff_scores, axis=1)], axis=1)
mean_sem_ranking = sort_tbl(
generate_mean_std_tbl(*calculate_mean_sem_ranking(results)),
ovrs_order=OVERSAMPLERS_NAMES,
clfs_order=CLASSIFIERS_NAMES)
# Generate main results
main_results_names = ('mean_sem_scores', 'mean_sem_perc_diff_scores',
'mean_sem_ranking')
main_results = zip(
main_results_names,
(mean_sem_scores, mean_sem_perc_diff_scores, mean_sem_ranking))
return main_results