def assess_model(y_pred, y_true, labels, target_names, dataset_type, stats_graph_folder, epoch_number, evaluation_mode='BIO', verbose=False):
'''
INPUT:
- y_pred is the list of predicted labels
- y_true is the list of gold labels
- dataset_type is either 'train' or 'test'
- epoch_number is the epoch number
'''
results = {}
assert len(y_true) == len(y_pred)
# Classification report
classification_report = sklearn.metrics.classification_report(y_true, y_pred, labels=labels, target_names=target_names, sample_weight=None, digits=4)
utils_plots.plot_classification_report(classification_report, title='Classification report for epoch {0} in {1} ({2} evaluation)\n'.format(epoch_number, dataset_type, evaluation_mode),
cmap='RdBu')
plt.savefig(os.path.join(stats_graph_folder, 'classification_report_for_epoch_{0:04d}_in_{1}_{2}_evaluation.png'.format(epoch_number, dataset_type, evaluation_mode)), dpi=300, format='png', bbox_inches='tight') # use
plt.close()
results['classification_report'] = classification_report
# F1 scores
results['f1_score'] = {}
for f1_average_style in ['weighted', 'micro', 'macro']:
results['f1_score'][f1_average_style] = sklearn.metrics.f1_score(y_true, y_pred, average=f1_average_style, labels=labels)
results['f1_score']['per_label'] = sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, average=None, labels=labels)[2].tolist()
confusion_matrix = sklearn.metrics.confusion_matrix(y_true, y_pred, labels=labels)
results['confusion_matrix'] = confusion_matrix.tolist()
title = 'Confusion matrix for epoch {0} in {1} ({2} evaluation)\n'.format(epoch_number, dataset_type, evaluation_mode)
xlabel = 'Predicted'
ylabel = 'True'
xticklabels = yticklabels = labels
utils_plots.heatmap(confusion_matrix, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=True)
plt.savefig(os.path.join(stats_graph_folder, 'confusion_matrix_for_epoch_{0:04d}_in_{1}_{2}_evaluation.png'.format(epoch_number, dataset_type, evaluation_mode)), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures
plt.close()
results['accuracy_score'] = sklearn.metrics.accuracy_score(y_true, y_pred)
return results
def assess_model(y_pred, y_true, labels, target_names, labels_with_o, target_names_with_o, dataset_type, stats_graph_folder, epoch_number, parameters,
evaluation_mode='bio', verbose=False):
results = {}
assert len(y_true) == len(y_pred)
# Classification report
classification_report = sklearn.metrics.classification_report(y_true, y_pred, labels=labels, target_names=target_names, sample_weight=None, digits=4)
utils_plots.plot_classification_report(classification_report,
title='Classification report for epoch {0} in {1} ({2} evaluation)\n'.format(epoch_number, dataset_type,
evaluation_mode),
cmap='RdBu')
plt.savefig(os.path.join(stats_graph_folder, 'classification_report_for_epoch_{0:04d}_in_{1}_{2}_evaluation.{3}'.format(epoch_number, dataset_type,
evaluation_mode, parameters['plot_format'])),
dpi=300, format=parameters['plot_format'], bbox_inches='tight')
plt.close()
results['classification_report'] = classification_report
# F1 scores
results['f1_score'] = {}
for f1_average_style in ['weighted', 'micro', 'macro']:
results['f1_score'][f1_average_style] = sklearn.metrics.f1_score(y_true, y_pred, average=f1_average_style, labels=labels)*100
results['f1_score']['per_label'] = [x*100 for x in sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, average=None, labels=labels)[2].tolist()]
confusion_matrix = sklearn.metrics.confusion_matrix(y_true, y_pred, labels=labels_with_o)
results['confusion_matrix'] = confusion_matrix.tolist()
title = 'Confusion matrix for epoch {0} in {1} ({2} evaluation)\n'.format(epoch_number, dataset_type, evaluation_mode)
xlabel = 'Predicted'
ylabel = 'True'
xticklabels = yticklabels = target_names_with_o
utils_plots.heatmap(confusion_matrix, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=True, fmt="%d",
remove_diagonal=True)
plt.savefig(os.path.join(stats_graph_folder, 'confusion_matrix_for_epoch_{0:04d}_in_{1}_{2}_evaluation.{3}'.format(epoch_number, dataset_type,
evaluation_mode, parameters['plot_format'])),
dpi=300, format=parameters['plot_format'], bbox_inches='tight')
plt.close()
# Accuracy
results['accuracy_score'] = sklearn.metrics.accuracy_score(y_true, y_pred)*100
return results
def assess_model(dataset,
model_options,
f_pred_prob,
pred_probs,
all_y_true,
dataset_type,
stats_graph_folder,
epoch,
update,
verbose=False,
multilabel_prediction=False,
save_proba=False):
'''
INPUT:
- dataset is the full data set
- model_options are all options in the models
- data is a list of (x, y) pairs
- dataset_type is either 'train' or 'test'
- iterator indicates the batches when reading data
- f_pred_prob is a function that takes x as input and output y_proba (i.e. the probabilities for each label)
- epoch is the epoch number
- update is the update number
http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics
http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
'''
results = {}
print('Generating plots for the {0} set'.format(dataset_type))
y_true = all_y_true
y_true_monolabel = all_y_true
y_pred_monolabel = y_pred = pred_probs
#print('y_pred[0:10]: {0}'.format(y_pred[0:10]))
assert len(y_true) == len(y_pred)
#print('y_true[0:10]: {0}'.format(y_true[0:10]))
#print('y_pred[0:10]: {0}'.format(y_pred[0:10]))
# Classification report
classification_report = sklearn.metrics.classification_report(
y_true,
y_pred,
labels=dataset.unique_label_indices_of_interest,
target_names=dataset.unique_labels_of_interest,
sample_weight=None,
digits=4)
utils_plots.plot_classification_report(
classification_report,
title='Classification report for epoch {0} update {2} in {1}\n'.format(
epoch, dataset_type, update),
cmap='RdBu')
plt.savefig(os.path.join(
stats_graph_folder,
'classification_report_for_epoch_{0:04d}_update_{2:05d}in_{1}.png'.
format(epoch, dataset_type, update)),
dpi=300,
format='png',
bbox_inches='tight'
) # use format='svg' or 'pdf' for vectorial pictures
plt.close()
#print(classification_report)
results['classification_report'] = classification_report
if not multilabel_prediction:
# for monolabel
classification_report_monolabel = sklearn.metrics.classification_report(
y_true_monolabel,
y_pred_monolabel,
labels=dataset.unique_label_indices_of_interest,
target_names=dataset.unique_labels_of_interest,
sample_weight=None,
digits=4)
#print('monolabel')
#print(classification_report_monolabel)
results[
'classification_report_monolabel'] = classification_report_monolabel
# F1 scores
results['f1_score'] = {}
for f1_average_style in ['weighted', 'micro', 'macro']:
results['f1_score'][f1_average_style] = sklearn.metrics.f1_score(
y_true,
y_pred,
average=f1_average_style,
labels=dataset.unique_label_indices_of_interest)
results['f1_score'][
'per_label'] = sklearn.metrics.precision_recall_fscore_support(
y_true,
y_pred,
average=None,
labels=dataset.unique_label_indices_of_interest)[2].tolist()
if not multilabel_prediction:
# for monolabel
results['f1_score_monolabel'] = {}
for f1_average_style in ['weighted', 'micro', 'macro']:
results['f1_score_monolabel'][
f1_average_style] = sklearn.metrics.f1_score(
y_true_monolabel,
y_pred_monolabel,
average=f1_average_style,
labels=dataset.unique_label_indices_of_interest)
results['f1_score_monolabel'][
'per_label'] = sklearn.metrics.precision_recall_fscore_support(
y_true_monolabel,
y_pred_monolabel,
average=None,
labels=dataset.unique_label_indices_of_interest)[2].tolist()
# Confusion matrix
if multilabel_prediction:
results['confusion_matrix'] = 0
else:
confusion_matrix = sklearn.metrics.confusion_matrix(
y_true_monolabel,
y_pred_monolabel,
labels=dataset.unique_label_indices_of_interest)
results['confusion_matrix'] = confusion_matrix.tolist()
#print(confusion_matrix)
title = 'Confusion matrix for epoch {0} update {2} in {1}\n'.format(
epoch, dataset_type, update)
xlabel = 'Predicted'
ylabel = 'True'
xticklabels = yticklabels = dataset.unique_labels_of_interest #range(model_options['ydim'])
utils_plots.heatmap(confusion_matrix,
title,
xlabel,
ylabel,
xticklabels,
yticklabels,
figure_width=40,
figure_height=20,
correct_orientation=True)
plt.savefig(os.path.join(
stats_graph_folder,
'confusion_matrix_for_epoch_{0:04d}_update_{2:05d}in_{1}.png'.
format(epoch, dataset_type, update)),
dpi=300,
format='png',
bbox_inches='tight'
) # use format='svg' or 'pdf' for vectorial pictures
plt.close()
# Accuracy
results['accuracy_score'] = sklearn.metrics.accuracy_score(y_true, y_pred)
if not multilabel_prediction:
results['accuracy_score_monolabel'] = sklearn.metrics.accuracy_score(
y_true_monolabel, y_pred_monolabel)
return results
def save_results(all_y_true, all_y_pred, stats_graph_folder, name, epoch):
output_filepath = os.path.join(
stats_graph_folder,
'classification_report_for_epoch_{0:04d}_in_{1}.txt'.format(
epoch, name))
plot_format = 'pdf'
unique_labels = [0, 1]
# classification_report = sklearn.metrics.classification_report(labels, predictions, digits=4,
# labels=unique_labels)
classification_report = sklearn.metrics.classification_report(all_y_true,
all_y_pred,
digits=4)
acc = sklearn.metrics.accuracy_score(all_y_true, all_y_pred)
lines = classification_report.split('\n')
classification_report = [
'Accuracy: {:05.2f}%'.format(
sklearn.metrics.accuracy_score(all_y_true, all_y_pred) * 100)
]
for line in lines[2:(len(lines) - 1)]:
new_line = []
t = line.strip().replace(' avg', '-avg').split()
if len(t) < 2: continue
new_line.append((' ' if t[0].isdigit() else '') + t[0])
new_line += [
'{:05.2f}'.format(float(x) * 100) for x in t[1:len(t) - 1]
]
new_line.append(t[-1])
classification_report.append('\t'.join(new_line))
classification_report = '\n'.join(classification_report)
print('\n\n' + classification_report + '\n', flush=True)
#with open(output_filepath + '_evaluation.txt', 'a', encoding='utf-8') as fp:
with open(output_filepath, 'a', encoding='utf-8') as fp:
fp.write(classification_report)
output_filepath_acc = os.path.join(stats_graph_folder,
'{0}_accuracy.txt'.format(name))
with open(output_filepath_acc, 'a', encoding='utf-8') as f:
f.write("{:.2f}\n".format(acc * 100))
# save confusion matrix and generate plots
confusion_matrix = sklearn.metrics.confusion_matrix(all_y_true, all_y_pred)
#results['confusion_matrix'] = confusion_matrix.tolist()
title = 'Confusion matrix for epoch {0} in {1}\n'.format(epoch, name)
xlabel = 'Predicted'
ylabel = 'True'
xticklabels = yticklabels = unique_labels
utils_plots.heatmap(confusion_matrix,
title,
xlabel,
ylabel,
xticklabels,
yticklabels,
figure_width=40,
figure_height=20,
correct_orientation=True,
fmt="%d",
remove_diagonal=True)
plt.savefig(os.path.join(
stats_graph_folder,
'confusion_matrix_for_epoch_{0:04d}_in_{1}_{2}.{2}'.format(
epoch, name, plot_format)),
dpi=300,
format=plot_format,
bbox_inches='tight')
plt.close()
