Fix NaNs in --quick-test scores

.gitignore

@@ -6,3 +6,4 @@ rampwf.egg-info
 .coverage
 coverage.xml
 ramp_workflow.egg-info
+.cache

rampwf/score_types/detection/base.py

@@ -1,6 +1,7 @@
 from __future__ import division
 
 from ..base import BaseScoreType
+from .util import _filter_y_pred
 
 
 class DetectionBaseScoreType(BaseScoreType):
@@ -16,10 +17,8 @@ class implements `detection_score`.
     def __call__(self, y_true, y_pred, conf_threshold=None):
         if conf_threshold is None:
             conf_threshold = self.conf_threshold
-        y_pred_above_confidence = [
-            [detected_object[1:] for detected_object in single_detection
-             if detected_object[0] > conf_threshold]
-            for single_detection in y_pred]
+        y_pred_above_confidence = _filter_y_pred(y_pred, conf_threshold)
+
         return self.detection_score(y_true, y_pred_above_confidence)
 
 

rampwf/score_types/detection/precision_recall.py

@@ -155,7 +155,13 @@ def mad_radius(y_true, y_pred, matches=None, iou_threshold=0.5,
     loc_true, loc_pred = _locate_matches(
         y_true, y_pred, matches, iou_threshold=iou_threshold)
 
-    return np.abs((loc_pred[:, 2] - loc_true[:, 2]) / loc_true[:, 2]).mean()
+    _, _, rad_true = loc_true.T
+    _, _, rad_pred = loc_pred.T
+
+    if not rad_true:
+        return np.inf
+
+    return np.abs((rad_pred - rad_true) / rad_true).mean()
 
 
 def mad_center(y_true, y_pred, matches=None, iou_threshold=0.5,
@@ -189,7 +195,12 @@ def mad_center(y_true, y_pred, matches=None, iou_threshold=0.5,
     loc_true, loc_pred = _locate_matches(
         y_true, y_pred, matches, iou_threshold=iou_threshold)
 
-    d = np.sqrt((loc_pred[:, 0] - loc_true[:, 0]) ** 2 + (
-        loc_pred[:, 1] - loc_true[:, 1]) ** 2)
+    x_true, y_true, rad_true = loc_true.T
+    x_pred, y_pred, _ = loc_pred.T
+
+    if not x_true:
+        return np.inf
+
+    d = np.sqrt((x_pred - x_true) ** 2 + (y_pred - y_true) ** 2)
 
-    return np.abs(d / loc_true[:, 2]).mean()
+    return np.abs(d / rad_true).mean()

rampwf/score_types/detection/util.py

@@ -168,7 +168,22 @@ def _locate_matches(y_true, y_pred, matches, iou_threshold=0.5):
 
 
 def _filter_y_pred(y_pred, conf_threshold):
-    return [[detected_object[1:] for detected_object in y_pred_patch
+    """
+    Given a list of list of predicted craters return those
+    with a confidence value above given threshold
+
+    Parameters
+    ----------
+    y_pred : list of list of tuples
+    conf_threshold : float
+
+    Returns
+    -------
+    y_pred_filtered : list of list of tuples
+
+    """
+    return [[detected_object[1:]
+             for detected_object in y_pred_patch
              if detected_object[0] > conf_threshold]
             for y_pred_patch in y_pred]
 
@@ -182,7 +197,7 @@ def mask_detection_curve(y_true, y_pred, conf_thresholds):
     y_true : list of list of tuples
         Tuples are of form (x, y, radius).
     y_pred : list of list of tuples
-        Tuples are of form (x, y, radius, confidence).
+        Tuples are of form (confidence, x, y, radius).
     conf_thresholds : array-like
         The confidence threshold for which to calculate the
         precision and recall.
@@ -212,7 +227,7 @@ def ospa_curve(y_true, y_pred, conf_thresholds):
     y_true : list of list of tuples
         Tuples are of form (x, y, radius).
     y_pred : list of list of tuples
-        Tuples are of form (x, y, radius, confidence).
+        Tuples are of form (confidence, x, y, radius).
     conf_thresholds : array-like
         The confidence threshold for which to calculate the
         precision and recall.

rampwf/utils/testing.py

@@ -106,9 +106,10 @@ def assert_submission(ramp_kit_dir='.', ramp_data_dir='.',
     X_train, y_train, X_test, y_test = assert_data(ramp_kit_dir, ramp_data_dir)
     cv = assert_cv(ramp_kit_dir, ramp_data_dir)
     score_types = assert_score_types(ramp_kit_dir)
-    print('Training {}/submissions/{} ...'.format(
-        ramp_kit_dir, submission))
+
     module_path = join(ramp_kit_dir, 'submissions', submission)
+    print('Training {} ...'.format(module_path))
+
     train_train_scoress = np.empty((len(cv), len(score_types)))
     train_valid_scoress = np.empty((len(cv), len(score_types)))
     test_scoress = np.empty((len(cv), len(score_types)))
@@ -164,23 +165,24 @@ def assert_submission(ramp_kit_dir='.', ramp_data_dir='.',
                 score_type.name, round(score, score_type.precision)))
 
     print('----------------------------')
-    means = train_train_scoress.mean(axis=0)
-    stds = train_train_scoress.std(axis=0)
-    for mean, std, score_type in zip(means, stds, score_types):
-        print('train {} = {} ± {}'.format(
-            score_type.name, round(mean, score_type.precision),
-            round(std, score_type.precision + 1)))
 
-    means = train_valid_scoress.mean(axis=0)
-    stds = train_valid_scoress.std(axis=0)
-    for mean, std, score_type in zip(means, stds, score_types):
-        print('valid {} = {} ± {}'.format(
-            score_type.name, round(mean, score_type.precision),
-            round(std, score_type.precision + 1)))
+    _print_result(train_train_scoress, score_types, 'train')
+    _print_result(train_valid_scoress, score_types, 'valid')
+    _print_result(test_scoress, score_types, 'test')
+
 
-    means = test_scoress.mean(axis=0)
-    stds = test_scoress.std(axis=0)
+def _print_result(scores, score_types, step):
+    means = scores.mean(axis=0)
+    stds = scores.std(axis=0)
     for mean, std, score_type in zip(means, stds, score_types):
-        print('test {} = {} ± {}'.format(
-            score_type.name, round(mean, score_type.precision),
-            round(std, score_type.precision + 1)))
+        # If std is a NaN
+        if std != std:
+            result = '{step} {name} = {val}'.format(
+                step=step, name=score_type.name, val=mean)
+        else:
+            result = '{step} {name} = {val} ± {std}'.format(
+                step=step,
+                name=score_type.name,
+                val=round(mean, score_type.precision),
+                std=round(std, score_type.precision + 1))
+        print(result)