From 3f99c060dd84dbd52b11fe191be9116e753a9c49 Mon Sep 17 00:00:00 2001
From: Charlie Meyers <cmeyers@cs.umu.se>
Date: Thu, 30 Nov 2023 21:00:12 +0000
Subject: [PATCH] fixed train time bug

---
 deckard/base/model/model.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py
index 5e96557a..c4f4d643 100644
--- a/deckard/base/model/model.py
+++ b/deckard/base/model/model.py
@@ -130,7 +130,7 @@ def __call__(self, data: list, model: object, library=None):
             start = process_time_ns()
             start_timestamp = time()
             model.fit(data[0], data[2], **trainer)
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         except np.AxisError:  # pragma: no cover
             from art.utils import to_categorical
@@ -139,7 +139,7 @@ def __call__(self, data: list, model: object, library=None):
             start = process_time_ns()
             start_timestamp = time()
             model.fit(data[0], data[2], **trainer)
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         except ValueError as e:  # pragma: no cover
             if "Shape of labels" in str(e):
@@ -150,7 +150,7 @@ def __call__(self, data: list, model: object, library=None):
                 start = process_time_ns()
                 start_timestamp = time()
                 model.fit(data[0], data[2], **trainer)
-                end = process_time_ns() - start
+                end = process_time_ns()
                 end_timestamp = time()
             else:
                 raise e
@@ -162,7 +162,7 @@ def __call__(self, data: list, model: object, library=None):
                 start = process_time_ns()
                 start_timestamp = time()
                 model.fit(data[0], data[2], **trainer)
-                end = process_time_ns() - start
+                end = process_time_ns()
                 end_timestamp = time()
             except Exception as e:
                 raise e
@@ -174,7 +174,7 @@ def __call__(self, data: list, model: object, library=None):
                 start = process_time_ns()
                 start_timestamp = time()
                 model.fit(data[0], data[2], **trainer)
-                end = process_time_ns() - start
+                end = process_time_ns()
                 end_timestamp = time()
             elif "should be the same" in str(e).lower():
                 import torch
@@ -194,7 +194,7 @@ def __call__(self, data: list, model: object, library=None):
                 start = process_time_ns()
                 start_timestamp = time()
                 model.fit(data[0], data[2], **trainer)
-                end = process_time_ns() - start
+                end = process_time_ns()
                 end_timestamp = time()
             else:
                 raise e
@@ -561,7 +561,7 @@ def predict(self, data=None, model=None, predictions_file=None):
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         except NotFittedError as e:  # pragma: no cover
             logger.warning(e)
@@ -579,7 +579,7 @@ def predict(self, data=None, model=None, predictions_file=None):
         except Exception as e:  # pragma: no cover
             logger.error(e)
             raise e
-        end = process_time_ns() - start
+        end = process_time_ns()
         end_timestamp = time()
         if predictions_file is not None:
             self.data.save(predictions, predictions_file)
@@ -627,13 +627,13 @@ def predict_proba(self, data=None, model=None, probabilities_file=None):
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict_proba(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         else:
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         if probabilities_file is not None:
             self.data.save(predictions, probabilities_file)
@@ -680,19 +680,19 @@ def predict_log_loss(self, data, model, losses_file=None):
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict_log_proba(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         elif hasattr(model, "predict_proba"):
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict_proba(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         elif hasattr(model, "predict"):
             start = process_time_ns()
             start_timestamp = time()
             predictions = model.predict(data[1])
-            end = process_time_ns() - start
+            end = process_time_ns()
             end_timestamp = time()
         else:  # pragma: no cover
             raise ValueError(