Test layers

deckard/base/model/model.py

@@ -202,8 +202,8 @@ def __call__(self, data: list, model: object, library=None):
             else:
                 raise e
         time_dict = {
-            "train_time": (start - end) / 1e9,
-            "train_time_per_sample": end / (len(data[0]) * 1e9),
+            "train_time": (end - start) / 1e9,
+            "train_time_per_sample": (end - start) / (len(data[0]) * 1e9),
             "train_start_time": start_timestamp,
             "train_end_time": end_timestamp,
             "train_device": device,
@@ -589,8 +589,8 @@ def predict(self, data=None, model=None, predictions_file=None):
         return (
             predictions,
             {
-                "predict_time": (start - end) / 1e9,
-                "predict_time_per_sample": (start - end) / (len(data[0]) * 1e9),
+                "predict_time": (end - start) / 1e9,
+                "predict_time_per_sample": (end - start) / (len(data[0]) * 1e9),
                 "predict_start_time": start_timestamp,
                 "predict_stop_time": end_timestamp,
                 "predict_device": device,
@@ -643,8 +643,8 @@ def predict_proba(self, data=None, model=None, probabilities_file=None):
         return (
             predictions,
             {
-                "predict_proba_time": (start - end) / 1e9,
-                "predict_proba_time_per_sample": end / (len(data[0]) * 1e9),
+                "predict_proba_time": (end - start) / 1e9,
+                "predict_proba_time_per_sample": (end - start) / (len(data[0]) * 1e9),
                 "predict_proba_start_time": start_timestamp,
                 "predict_proba_stop_time": end_timestamp,
                 "predict_proba_device": device,
@@ -706,8 +706,9 @@ def predict_log_loss(self, data, model, losses_file=None):
         return (
             predictions,
             {
-                "predict_log_proba_time": (start - end) / 1e9,
-                "predict_log_proba_time_per_sample": end / (len(data[0]) * 1e9),
+                "predict_log_proba_time": (end - start) / 1e9,
+                "predict_log_proba_time_per_sample": (end - start)
+                / (len(data[0]) * 1e9),
                 "predict_log_proba_start_time": start_timestamp,
                 "predict_log_proba_stop_time": end_timestamp,
                 "predict_log_device": device,

deckard/layers/README.md

@@ -11,4 +11,6 @@
 ├── parse.py: Dumps the hydra configuration folder to `params.yaml` as specified by the `defaults` list in the hydra configuration file.
 ├── template.html: Draft webpage for each experiment (work in progress)
 ├── utils.py: Handy utilities for the other scripts
+├── afr.py: For building accelerated failure rate models
+├── plots.py: For generating plots using seaborn
 └── watcher.py: Watches a file, transforms the data, and moves it somewhere else via parallelized scp (work in progress).