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

[ENH] Added to array and probas to preds castings in metrics #75

Merged
merged 1 commit into from
Jul 17, 2024
Merged

[ENH] Added to array and probas to preds castings in metrics #75

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
23 changes: 23 additions & 0 deletions dlordinal/metrics/metrics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,9 @@ def ranked_probability_score(y_true, y_proba):
>>> ranked_probability_score(y_true, y_pred)
0.506875
"""
y_true = np.array(y_true)
y_proba = np.array(y_proba)

y_oh = np.zeros(y_proba.shape)
y_oh[np.arange(len(y_true)), y_true] = 1

Expand Down Expand Up @@ -72,6 +75,13 @@ def minimum_sensitivity(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> minimum_sensitivity(y_true, y_pred)
0.5
"""
y_true = np.array(y_true)
y_pred = np.array(y_pred)

if len(y_true.shape) > 1:
y_true = np.argmax(y_true, axis=1)
if len(y_pred.shape) > 1:
y_pred = np.argmax(y_pred, axis=1)

sensitivities = recall_score(y_true, y_pred, average=None)
return np.min(sensitivities)
Expand Down Expand Up @@ -101,6 +111,8 @@ def accuracy_off1(y_true: np.ndarray, y_pred: np.ndarray, labels=None) -> float:
>>> accuracy_off1(y_true, y_pred)
1.0
"""
y_true = np.array(y_true)
y_pred = np.array(y_pred)

if len(y_true.shape) > 1:
y_true = np.argmax(y_true, axis=1)
Expand Down Expand Up @@ -141,6 +153,13 @@ def gmsec(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> gmec(y_true, y_pred)
0.5
"""
y_true = np.array(y_true)
y_pred = np.array(y_pred)

if len(y_true.shape) > 1:
y_true = np.argmax(y_true, axis=1)
if len(y_pred.shape) > 1:
y_pred = np.argmax(y_pred, axis=1)

sensitivities = recall_score(y_true, y_pred, average=None)
return np.sqrt(sensitivities[0] * sensitivities[-1])
Expand All @@ -161,6 +180,8 @@ def amae(y_true: np.ndarray, y_pred: np.ndarray):
amae : float
Average mean absolute error.
"""
y_true = np.array(y_true)
y_pred = np.array(y_pred)

if len(y_true.shape) > 1:
y_true = np.argmax(y_true, axis=1)
Expand Down Expand Up @@ -193,6 +214,8 @@ def mmae(y_true: np.ndarray, y_pred: np.ndarray):
mmae : float
Maximum mean absolute error.
"""
y_true = np.array(y_true)
y_pred = np.array(y_pred)

if len(y_true.shape) > 1:
y_true = np.argmax(y_true, axis=1)
Expand Down
Loading