dlr: add sensitivity (aka recall) and specificity metrics

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -19,6 +19,8 @@ import tensorflow as tf
 from lib.dataset.dataset_mono import dataset_mono
 from lib.ai.components.LossCrossEntropyDice import LossCrossEntropyDice
 from lib.ai.components.MetricDice import MetricDice
+from lia.ai.components.MetricSensitivity import MetricSensitivity
+from lib.ai.components.MetricSpecificity import MetricSpecificity
 
 time_start = datetime.now()
 logger.info(f"Starting at {str(datetime.now().isoformat())}")
@@ -160,7 +162,9 @@ else:
 	model = tf.keras.models.load_model(PATH_CHECKPOINT, custom_objects={
 		# Tell Tensorflow about our custom layers so that it can deserialise models that use them
 		"LossCrossEntropyDice": LossCrossEntropyDice,
-		"MetricDice": MetricDice
+		"MetricDice": MetricDice,
+		"MetricSensitivity": MetricSensitivity,
+		"MetricSpecificity": MetricSpecificity
 	})
 
 
@@ -186,7 +190,9 @@ if PATH_CHECKPOINT is None:
 		metrics=[
 			"accuracy",
 			MetricDice(),
-			tf.keras.metrics.MeanIoU(num_classes=2)
+			tf.keras.metrics.MeanIoU(num_classes=2),
+			MetricSensitivity(), # How many true positives were accurately predicted
+			MetricSpecificity() # How many true negatives were accurately predicted?
 			# TODO: Add IoU, F1, Precision, Recall,  here. 
 		],
 	)

aimodel/src/lib/ai/components/MetricSensitivity.py

@@ -0,0 +1,31 @@
+import math
+
+import tensorflow as tf
+
+
+
+class MetricSensitivity(tf.keras.metrics.Metric):
+	"""An implementation of the sensitivity.
+	Also known as Recall. In other words, how many of the true positives were accurately predicted.
+	@source 
+	Args:
+		smooth (float): The batch size (currently unused).
+	"""
+
+	def __init__(self, name="sensitivity", **kwargs):
+		super(MetricSensitivity, self).__init__(name=name)
+		
+		self.recall = tf.keras.metrics.Recall(**kwargs)
+
+	def call(self, y_true, y_pred):
+		ground_truth = tf.cast(y_true, dtype=tf.float32)
+		prediction = tf.cast(y_pred, dtype=tf.float32)
+		
+		return self.recall(y_true, y_pred)
+
+	def get_config(self):
+		config = super(MetricSensitivity, self).get_config()
+		config.update({
+			
+		})
+		return config

aimodel/src/lib/ai/components/MetricSpecificity.py

@@ -21,7 +21,8 @@ def specificity(y_pred, y_true):
 
 
 class MetricSpecificity(tf.keras.metrics.Metric):
-	"""An implementation of the sensitivity.
+	"""An implementation of the specificity.
+	In other words, a measure of how many of the true negatives were accurately predicted
 	@source 
 	Args:
 		smooth (float): The batch size (currently unused).
@@ -30,8 +31,6 @@ class MetricSpecificity(tf.keras.metrics.Metric):
 	def __init__(self, name="specificity", **kwargs):
 		super(MetricSpecificity, self).__init__(name=name, **kwargs)
 
-		self.param_smooth = smooth
-
 	def call(self, y_true, y_pred):
 		ground_truth = tf.cast(y_true, dtype=tf.float32)
 		prediction = tf.cast(y_pred, dtype=tf.float32)
@@ -41,6 +40,6 @@ class MetricSpecificity(tf.keras.metrics.Metric):
 	def get_config(self):
 		config = super(MetricSpecificity, self).get_config()
 		config.update({
-			"smooth": self.param_smooth,
+			
 		})
 		return config