From 483ecf11c8c3a0d99f85351dc40127e83901cfa4 Mon Sep 17 00:00:00 2001
From: Starbeamrainbowlabs <sbrl@starbeamrainbowlabs.com>
Date: Fri, 3 Mar 2023 19:35:20 +0000
Subject: [PATCH] add specificity metric

---
 .../lib/ai/components/MetricSpecificity.py    | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 aimodel/src/lib/ai/components/MetricSpecificity.py

diff --git a/aimodel/src/lib/ai/components/MetricSpecificity.py b/aimodel/src/lib/ai/components/MetricSpecificity.py
new file mode 100644
index 0000000..d0a9805
--- /dev/null
+++ b/aimodel/src/lib/ai/components/MetricSpecificity.py
@@ -0,0 +1,46 @@
+import math
+
+import tensorflow as tf
+
+
+def specificity(y_pred, y_true):
+	"""
+	@source https://datascience.stackexchange.com/a/40746/86851
+	param:
+	y_pred - Predicted labels
+	y_true - True labels 
+	Returns:
+	Specificity score
+	"""
+	neg_y_true = 1 - y_true
+	neg_y_pred = 1 - y_pred
+	fp = K.sum(neg_y_true * y_pred)
+	tn = K.sum(neg_y_true * neg_y_pred)
+	specificity = tn / (tn + fp + K.epsilon())
+	return specificity
+
+
+class MetricSpecificity(tf.keras.metrics.Metric):
+	"""An implementation of the sensitivity.
+	@source 
+	Args:
+		smooth (float): The batch size (currently unused).
+	"""
+
+	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)
+
+		return specificity(ground_truth, prediction)
+
+	def get_config(self):
+		config = super(MetricSpecificity, self).get_config()
+		config.update({
+			"smooth": self.param_smooth,
+		})
+		return config