Update pytorch plots (#179)

* fix bug in scoring failure

* update power example to support bit-depth search

* update result directories

* revert changes to example/power

* add bit depth example

* revert directory changes for power example

* fixed directory for mnist in power example

* fixed directory for mnist in power example

* use the latest  torch torchvision torchaudio

* update workflow to push on PR

* removed double stage folders in log folder

* + more epochs for cifar100

* changed intervals from uniform to log uniform, made learning rate range larger

* strip whitespace, covert o numeric in compile script

* update git ignores

* suport nb_epoch as defence choice

* remove adv_success from requirements

* add "NaN" to nones

* update afr script

* update mnist .dvc cache

* updat cifar10 plots

* uncomment paretoset in plotting

* fix default defence bug and realtive pathing in compile script

* moved plots to subfolder

* better configuration support

* fix compile script bug

* update compile and plots yaml for power example

* fix compile bug

* update plots

* include plot files in dvc

* update afr to read from conf file

* linting

* linting

* update pytorch example

* update pytorch afr.yaml (not working)

* split cleaning from plotting, but only working for examples/pytorch/mnist

* working cleaning script

* fix pytorch examples with new clean script

* remove debug check from parse_results

* make deckard a depedendency of the parsing script

* made models.sh easier to read

* update afr for pytorch example

* update power example

* update dvc.lock for pytorch example

* update pytorch/cifar100

* update power/plots (not working)

* add docstrings to plots.py

* update power example with merge script

* add power data

* update configs

* add combined plots

* update afr models

* added support for dummy variables in afr

* ++combined_plots.py and fix afr bug

* add cifar100 l4 power data with commenting everything else

* add varepsilon to attack params

* add dummy variables

* fix rounding bug

* update to newest plots

* newest plots for power example

* linting

* removed old afr file

* linting

* Merge branch 'fix-compile-script' of github.com:simplymathematics/deckard into fix-compile-script

* update conf

* fixed kepler script bug

* linting

* linting

* linting

* linting

* linting

* linting

* linting

* linting

* linting

* add dvc.yaml

* add query_kepler

* update layers

* update confs

* fix post-merge scripts

* linting

* linting

* update setup scripts

* update gitignore

* update gitignore

* model.py layer

* update gitignore

* update gitignore

* update gitignore

* update gitignore

* update gitignore

* update gitignore

* refactor fit method out of plot method, add support for dummy variables

* linting

* linting

* add optional summary when fitting

* small refactor

* add xlim, ylim support

* removed cox

* moved plots to root directory of plots folder

* add plots for power example

* linting

* linting

* linting

* add csvs

* removed test data

* remove data and plots from git

* add plot summary plot to afr models

* add plot summary plot to afr models

* fix formatting bugs

* fix pred time bug

* update afr plot labels

* update combined conf

* update configuration

* linting

* update confs

* remove time normalization

* fix timing bug

* fix timing bug

* tweak font formatting

* tweak result formatting

* add partial effect of accuracy plot

* update plot confs

* refactor plot summary

* update plotting in AFR model, add penalizer to all models

* re-ran afr plotting

* fix sample size bug

* add support for multivariate QQ plot

* linting

* linting

* linting

* bug fix

* re-run

* linting

* linting

* linting

* linting

* linting

* re-ran for IEEE Cloud

---------

Co-authored-by: Mohammad Reza Saleh <[email protected]>
Co-authored-by: salehsedghpour <[email protected]>

deckard/base/model/model.py

@@ -565,7 +565,7 @@ def predict(self, data=None, model=None, predictions_file=None):
             predictions,
             {
                 "predict_time": (end - start) / 1e9,
-                "predict_time_per_sample": (end - start) / (len(data[0]) * 1e9),
+                "predict_time_per_sample": (end - start) / (len(data[1]) * 1e9),
                 "predict_start_time": start_timestamp,
                 "predict_end_time": end_timestamp,
                 "predict_device": device,
@@ -619,7 +619,7 @@ def predict_proba(self, data=None, model=None, probabilities_file=None):
             predictions,
             {
                 "predict_proba_time": (end - start) / 1e9,
-                "predict_proba_time_per_sample": (end - start) / (len(data[0]) * 1e9),
+                "predict_proba_time_per_sample": (end - start) / (len(data[1]) * 1e9),
                 "predict_proba_start_time": start_timestamp,
                 "predict_proba_end_time": end_timestamp,
                 "predict_proba_device": device,
@@ -683,7 +683,7 @@ def predict_log_loss(self, data, model, losses_file=None):
             {
                 "predict_log_proba_time": (end - start) / 1e9,
                 "predict_log_proba_time_per_sample": (end - start)
-                / (len(data[0]) * 1e9),
+                / (len(data[1]) * 1e9),
                 "predict_log_proba_start_time": start_timestamp,
                 "predict_log_proba_end_time": end_timestamp,
                 "predict_log_device": device,