From 0b02cee54e806c17b50fb731de65e2d005892e7c Mon Sep 17 00:00:00 2001
From: Tjark Miener <tjark.miener4692@googlemail.com>
Date: Fri, 5 Jul 2019 16:27:47 +0200
Subject: [PATCH] added a script to calculate the auc and acc of the
 predictions (using sklearn)

---
 scripts/get_prediction_acc_auc.py | 49 +++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 scripts/get_prediction_acc_auc.py

diff --git a/scripts/get_prediction_acc_auc.py b/scripts/get_prediction_acc_auc.py
new file mode 100644
index 00000000..9d06a02c
--- /dev/null
+++ b/scripts/get_prediction_acc_auc.py
@@ -0,0 +1,49 @@
+import argparse
+import numpy as np
+import sklearn.metrics
+
+parser = argparse.ArgumentParser(
+    description=("Get acc and auc from the predictions csv file."))
+parser.add_argument('predictions_list_file',
+        help='list of paths to predictions csv file')
+args = parser.parse_args()
+
+# Predictions list has the format: predicted_class,proton,gamma,tel_id,event_number,run_number,class_label
+labels = []
+predictions = []
+gamma_predictions = []
+with open(args.predictions_list_file) as f:
+    for line in f:
+        if not line or line[0] == '#': continue
+        predicted_class,proton,gamma,tel_id,event_number,run_number,class_label = line.split(',')
+        labels.append(class_label.strip())
+        gamma_predictions.append(gamma.strip())
+        predictions.append(predicted_class.strip())
+    labels = np.array(labels[1:]).astype(np.int)
+    gamma_predictions = np.array(gamma_predictions[1:]).astype(np.float)
+    predictions = np.array(predictions[1:]).astype(np.int)
+
+fpr, tpr, thresholds = sklearn.metrics.roc_curve(labels,gamma_predictions, pos_label=1)
+auc = sklearn.metrics.auc(fpr, tpr)
+print("auc = {}".format(auc))
+
+acc = sklearn.metrics.accuracy_score(labels,predictions)
+print("acc = {}%".format(acc*100))
+
+
+
+'''
+labels = tf.convert_to_tensor(labels, dtype=tf.float32)
+predictions = tf.convert_to_tensor(predictions, dtype=tf.float32)
+
+#acc, update_op = tf.metrics.accuracy(labels,predictions)
+
+auc, update_op = tf.metrics.auc(labels,predictions)
+print(auc)
+
+sess = tf.Session()
+
+result = sess.run(auc)
+print(result)
+
+'''