Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Show the number of not classified instances for multi-label models (#3964) #4150

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Show the number of not classified instances for multi-label models (#3964) #4150

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 10 additions & 3 deletions bugbug/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -120,7 +120,7 @@ def classification_report_imbalanced_values(
return result


def print_labeled_confusion_matrix(confusion_matrix, labels, is_multilabel=False):
def print_labeled_confusion_matrix(confusion_matrix, labels, is_multilabel=False, test_classes=None):
confusion_matrix_table = confusion_matrix.tolist()

# Don't show the Not classified row in the table output
Expand All @@ -144,8 +144,13 @@ def print_labeled_confusion_matrix(confusion_matrix, labels, is_multilabel=False
if table_labels[i] != "__NOT_CLASSIFIED__"
else "Not classified"
)
if is_multilabel and test_classes is not None:
confusion_matrix_header.append("Not classified")
for i in range(len(table)):
table[i].insert(0, f"{table_labels[i]} (Actual)")
if is_multilabel and test_classes is not None:
y_true_count = (test_classes[:, num].tolist()).count(i)
table[i].append(y_true_count - sum(table[i][1:]))
Comment on lines +152 to +153
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The number of "Not classified" was originally part of the table but dropped earlier:

    # Don't show the Not classified row in the table output
    if "__NOT_CLASSIFIED__" in labels and not is_multilabel:
        confusion_matrix_table.pop(labels.index("__NOT_CLASSIFIED__"))

It may be cleaner to avoid dropping it in the first place.

print(
tabulate(table, headers=confusion_matrix_header, tablefmt="fancy_grid"),
end="\n\n",
Expand Down Expand Up @@ -499,8 +504,9 @@ def train(self, importance_cutoff=0.15, limit=None):

tracking_metrics["report"] = report

# no confidence threshold - no need to handle 'Not classified' instances
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think this comment is helpful in the context of reviewing this PR. However, without this context, the comment feels out of place (after merging the PR).

print_labeled_confusion_matrix(
confusion_matrix, self.class_names, is_multilabel=is_multilabel
confusion_matrix, self.class_names, is_multilabel=is_multilabel, test_classes=None,
)

tracking_metrics["confusion_matrix"] = confusion_matrix.tolist()
Expand Down Expand Up @@ -567,8 +573,9 @@ def train(self, importance_cutoff=0.15, limit=None):
labels=confidence_class_names,
)
)
# with confidence threshold - handle 'Not classified' instances by passing y_test
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The same case as the comment above.

print_labeled_confusion_matrix(
confusion_matrix, confidence_class_names, is_multilabel=is_multilabel
confusion_matrix, confidence_class_names, is_multilabel=is_multilabel, test_classes=y_test
)

self.evaluation()
Expand Down