def plot_pr(self, ml_method, classifier, n_pca):
title = compose_configuration(f'PR curves of {ml_method} {classifier}',
self.config['filter_latent'],
self.config['standardization'], n_pca,
self.name)
print(f"Plotting {title}")
plt.figure(figsize=(6.5, 4.3333))
self.plot_pr_class(ml_method, classifier, 'micro', f'micro-average',
':', color_phase_dict['micro'])
self.plot_macro_pr(ml_method, classifier, 'macro-average', ':',
color_phase_dict['macro'])
for class_ in ['immediate-early', 'early', 'late']:
self.plot_pr_class(ml_method, classifier, class_, f'{class_}', '-',
color_phase_dict[class_])
plt.legend()
# plt.title(title)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.tight_layout()
filename = compose_filename(
self.config['output_pr_curves_plot_directory'],
self.config['filter_latent'], self.config['standardization'],
n_pca,
f'PR_{self.MULTI_CLASS_NAME[ml_method]}_{self.CLASSIFIER_NAME[classifier]}',
self.name, '')
plt.savefig(filename, dpi=150)
plt.close()
def plot_average_of_all_pr(self, av_kind):
title = compose_configuration(f'{av_kind} PR curves',
self.config['filter_latent'],
self.config['standardization'], 'no-pca',
self.name)
print(f"Plotting {title}")
plt.figure(figsize=(6, 4.5))
for ml_method, classifiers in self.results.items():
for classifier, result in classifiers.items():
if len(result['fpr']['early']) > 0:
if av_kind == 'macro':
self.plot_macro_pr(
ml_method, classifier,
f'{self.MULTI_CLASS_NAME[ml_method]} {self.CLASSIFIER_NAME[classifier]} {av_kind}-average',
'-', None, 1.5)
else:
self.plot_pr_class(
ml_method, classifier, av_kind,
f'{self.MULTI_CLASS_NAME[ml_method]} {self.CLASSIFIER_NAME[classifier]} {av_kind}-average',
'-', None, 1.5)
plt.legend()
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.tight_layout()
filename = compose_filename(
self.config['output_pr_curves_plot_directory'],
self.config['filter_latent'], self.config['standardization'],
'no-pca', f'PR_{av_kind}_average', self.name, '')
plt.savefig(filename, dpi=150)
plt.close()
def plot_average_of_all_roc(self, av_kind):
title = compose_configuration(f'Micro and Macro average ROC curves',
self.config['filter_latent'],
self.config['standardization'], 'no-pca',
self.name)
print(f"Plotting {title}")
plt.figure(figsize=(6, 4.5))
for ml_method, classifiers in self.results.items():
if ml_method == 'ML':
for classifier, result in classifiers.items():
if len(result['fpr']['early']) > 0:
self.plot_roc_class(
ml_method, classifier, av_kind,
f'{self.CLASSIFIER_NAME[classifier]} {av_kind}-average',
'-', None, 2)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.tight_layout()
filename = compose_filename(
self.config['output_roc_curves_plot_directory'],
self.config['filter_latent'], self.config['standardization'],
'no-pca', f'ROC_{av_kind}_average', self.name, '')
plt.savefig(filename, dpi=150)
plt.close()
def _extract(self, name, n_pca):
print(
compose_configuration('Extracting features', self.filter_phase,
self.standardization, n_pca, name))
if self.filter_phase:
self.filter_original_phases()
features = self.feature_possibilities[name]
if 'length' in features:
self.add_length()
if 'counts' in features:
self.add_aa_counts()
if 'relative-counts' in features:
self.add_relative_counts()
if 'physchem' in features:
self.add_physchem_properties()
if 'mass' in features:
self.add_mass()
if 'pI' in features:
self.add_p_i()
for feature in features:
if feature[2:] == 'windowed':
self.add_windowed(int(feature[0]))
if self.standardization:
self.standardize()
def grid_search(self, ml_method, classifier_name, grid, splits, n_pca):
print(
compose_configuration('Grid Search', self.config['filter_latent'],
self.config['standardization'], n_pca,
self.name))
print(ml_method, classifier_name)
classifier = Classifier(self.config_filepath, ml_method,
classifier_name)
classifier.grid_search(self.name, grid, splits, n_pca)
def _fit_all(self, n_pca):
print(
compose_configuration('Fitting features',
self.config['filter_latent'],
self.config['standardization'], n_pca,
self.name))
for ml_method in self.config['ML-method-options']:
for classifier_name in self.config['Classifier-options']:
classifier = Classifier(self.config_filepath, ml_method,
classifier_name)
scores = classifier.fit(self.name, n_pca)
if ml_method in self.results:
self.results[ml_method][classifier_name] = scores
else:
self.results[ml_method] = {classifier_name: scores}
def plot_permutation_importance_summary(self, classifier, max_n, n_pca):
title = compose_configuration(
f'Summarized Permutation Importances of {classifier}',
self.config['filter_latent'], self.config['standardization'],
n_pca, self.name)
print(f"Plotting {title}")
features = self.results['RR'][classifier]['features']
perm_imps = []
for mc_technique in ['ML', '1vsA', 'RR']:
perm_imps.extend(self.results[mc_technique][classifier]
['permutation_importance'])
boxplot_data = {}
for i, f in enumerate(features):
feature_imps = [perm_imp[i] for perm_imp in perm_imps]
boxplot_data[f] = feature_imps
boxplot_data = {
k: v
for k, v in sorted(
boxplot_data.items(),
key=lambda item: np.median(item[1]) + 0.001 * np.mean(item[1]),
reverse=True)
}
fig, ax = plt.subplots(figsize=(8.5, 4.5))
ax.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=1)
ax.set_axisbelow(True)
ax.boxplot(boxplot_data.values(),
flierprops=dict(markersize=4, markeredgewidth=0.6))
ax.set_xticklabels(boxplot_data.keys())
plt.xticks(rotation=45, rotation_mode='anchor', ha='right')
plt.ylabel('Permutation Importance')
plt.xlabel('Feature')
plt.tight_layout()
filename = compose_filename(self.config['output_pi_plot_directory'],
self.config['filter_latent'],
self.config['standardization'], n_pca,
f'pi_{classifier}', self.name, '')
plt.savefig(filename, dpi=150)
plt.close()
def plot_permutation_importance(self, ml_method, classifier, n_pca):
title = compose_configuration(
f'Permutation Importances of {ml_method} {classifier}',
self.config['filter_latent'], self.config['standardization'],
n_pca, self.name)
print(f"Plotting {title}")
features = self.results[ml_method][classifier]['features']
perm_imps = self.results[ml_method][classifier][
'permutation_importance']
permutation_importances = {}
for i, f in enumerate(features):
feature_imps = [perm_imp[i] for perm_imp in perm_imps]
permutation_importances[f] = (np.mean(feature_imps),
np.std(feature_imps))
permutation_importances = {
k: v
for k, v in sorted(permutation_importances.items(),
key=lambda item: item[1][0],
reverse=True)[:30]
}
x_size = len(permutation_importances) / 2.5
y_size = x_size / 1.375
plt.figure(figsize=(x_size, y_size))
plt.bar(permutation_importances.keys(),
[val[0] for val in permutation_importances.values()],
yerr=[val[1] for val in permutation_importances.values()],
width=1,
capsize=5)
plt.xticks(rotation=45, rotation_mode='anchor', ha='right')
plt.ylabel('Permutation Importance')
plt.xlabel('Feature')
plt.title(title, wrap=True)
plt.tight_layout()
filename = compose_filename(
self.config['output_pi_plot_directory'],
self.config['filter_latent'], self.config['standardization'],
n_pca, f'permutation_importance_{ml_method}_{classifier}',
self.name, '')
plt.savefig(filename)
plt.close()
def plot(self, score_metric, n_pca):
plt.figure()
title = compose_configuration(self.TITLE[score_metric],
self.config['filter_latent'],
self.config['standardization'], n_pca,
self.name)
print(f"Plotting {title}")
max_score = 0
max_configuration = None
bar_width = 0.25
n_groups = len(self.results)
index = np.arange(n_groups)
for i, ml_method in enumerate(self.config["ML-method-options"]):
classifier = self.results[ml_method]
classifier_tuples = list(classifier.items())
score_name = self.SCORE_NAME[score_metric]
x = []
y = []
error = []
for cl, res in classifier_tuples:
if score_name in res:
x.append(self.CLASSIFIER_NAME[cl])
score = np.mean(res.get(score_name, None))
y.append(score)
error.append(np.std(res.get(score_name), None))
if score >= max_score:
max_score = score
max_configuration = (self.MULTI_CLASS_NAME[ml_method],
self.CLASSIFIER_NAME[cl])
if len(x) > 0:
plt.bar(index + (i * bar_width),
y,
yerr=error,
width=bar_width,
label=self.MULTI_CLASS_NAME[ml_method],
capsize=5,
color=color_ml_dict[ml_method])
print(f"\t{self.MULTI_CLASS_NAME[ml_method]}")
for j, mean in enumerate(y):
print(
f"\t\t{x[j]}: {100 * mean:.2f}% +-{100 * error[j]:.2f}%"
)
print(
f"Maximum score: {max_configuration[0]}, {max_configuration[1]}: {100 * max_score:.2f}%"
)
# plt.title(title, wrap=True)
plt.ylim(top=self.YMAX[score_metric])
plt.xticks(index + bar_width, [
self.CLASSIFIER_NAME[cl]
for cl in list(self.results.values())[0].keys()
])
plt.xlabel('Classifier')
plt.ylabel(self.YLABEL[score_metric])
plt.legend(title='Multiclass strategy')
plt.tight_layout()
filename = output_filename(self.config['output_bar_plot_directory'],
self.config['filter_latent'],
self.config['standardization'], n_pca,
self.SAVE_TITLE[score_metric], self.name,
'')
plt.savefig(filename)
plt.close()
print()
