From 5472729f5ed66075ac51b3cace2c45a4761ba663 Mon Sep 17 00:00:00 2001
From: Starbeamrainbowlabs <sbrl@starbeamrainbowlabs.com>
Date: Fri, 3 Mar 2023 22:37:36 +0000
Subject: [PATCH] dlr: fixup argmax & y_true/y_pred

---
 .../src/lib/ai/components/MetricMeanIoU.py    |  1 -
 .../lib/ai/components/MetricSensitivity.py    | 27 -------------------
 .../lib/ai/components/MetricSpecificity.py    |  3 +--
 3 files changed, 1 insertion(+), 30 deletions(-)

diff --git a/aimodel/src/lib/ai/components/MetricMeanIoU.py b/aimodel/src/lib/ai/components/MetricMeanIoU.py
index b1b2dfb..1fa2947 100644
--- a/aimodel/src/lib/ai/components/MetricMeanIoU.py
+++ b/aimodel/src/lib/ai/components/MetricMeanIoU.py
@@ -14,7 +14,6 @@ def one_hot_mean_iou(y_true, y_pred, classes=2):
 	"""
 	
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)
 	
diff --git a/aimodel/src/lib/ai/components/MetricSensitivity.py b/aimodel/src/lib/ai/components/MetricSensitivity.py
index ce9f359..be435e9 100644
--- a/aimodel/src/lib/ai/components/MetricSensitivity.py
+++ b/aimodel/src/lib/ai/components/MetricSensitivity.py
@@ -4,36 +4,9 @@ import tensorflow as tf
 
 def sensitivity(y_true, y_pred):
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)
 	
 	recall = tf.keras.metrics.Recall()
 	recall.update_state(y_true, y_pred)
 	return recall.result()
-
-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
diff --git a/aimodel/src/lib/ai/components/MetricSpecificity.py b/aimodel/src/lib/ai/components/MetricSpecificity.py
index e2fa021..e5fb9c8 100644
--- a/aimodel/src/lib/ai/components/MetricSpecificity.py
+++ b/aimodel/src/lib/ai/components/MetricSpecificity.py
@@ -3,7 +3,7 @@ import math
 import tensorflow as tf
 
 
-def specificity(y_pred, y_true):
+def specificity(y_true, y_pred):
 	"""An implementation of the specificity.
 	In other words, a measure of how many of the true negatives were accurately predicted
 	@source https://datascience.stackexchange.com/a/40746/86851
@@ -14,7 +14,6 @@ def specificity(y_pred, y_true):
 	Specificity score
 	"""
 	y_pred = tf.math.argmax(y_pred, axis=-1)
-	y_true = tf.math.argmax(y_true, axis=-1)
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)