Fix no maximization saving (sustainable-processes#236)

Mu-Mu-github · Feb 10, 2023 · a861b91 · a861b91
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diff --git a/scripts/train_emulators/README.md b/scripts/train_emulators/README.md
@@ -5,13 +5,13 @@ The `train_emulators.py` script will train emulators and create this report.
 This is the data from training of the reizman suzuki benchmark for 1000 epochs with 5 cross-validation folds.
 | case   |   avg_fit_time |   avg_val_r2 |   avg_val_RMSE |   avg_test_r2 |   avg_test_RMSE |
 |:-------|---------------:|-------------:|---------------:|--------------:|----------------:|
-| case_1 |           8.63 |         0.82 |          11.14 |          0.93 |            7.54 |
-| case_2 |           8.8  |         0.61 |           5.38 |          0.66 |            4.99 |
-| case_3 |           8.24 |         0.78 |          12.91 |          0.84 |           11.9  |
-| case_4 |           8.31 |         0.7  |          15.67 |          0.73 |           14.06 |
+| case_1 |           3.52 |         0.81 |          11.21 |          0.93 |            7.66 |
+| case_2 |           3.58 |         0.59 |           5.54 |          0.67 |            4.91 |
+| case_3 |           3.61 |         0.76 |          13.11 |          0.84 |           12.04 |
+| case_4 |           3.6  |         0.7  |          15.99 |          0.74 |           13.8  |
 ## Baumgartner C-N Cross Cross Coupling 
 This is the data from training of the Baumgartner C-N aniline cross-coupling benchmark for 1000 epochs with 5 cross-validation folds.
 | case        |   avg_fit_time |   avg_val_r2 |   avg_val_RMSE |   avg_test_r2 |   avg_test_RMSE |
 |:------------|---------------:|-------------:|---------------:|--------------:|----------------:|
-| one-hot     |           8.17 |         0.8  |           0.18 |          0.89 |            0.13 |
-| descriptors |           8.19 |         0.86 |           0.15 |          0.91 |            0.11 |
+| one-hot     |           3.54 |         0.79 |           0.18 |          0.88 |            0.13 |
+| descriptors |           3.52 |         0.84 |           0.16 |          0.9  |            0.12 |
diff --git a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png
diff --git a/...ts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png b/...ts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png
diff --git a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv
@@ -1,3 +1,3 @@
 case,avg_fit_time,avg_score_time,avg_val_r2,avg_val_neg_root_mean_squared_error,avg_test_r2,avg_test_neg_root_mean_squared_error
-one-hot,8.166110754013062,0.005637454986572266,0.801623251767853,-0.17928863167762757,0.8913133689375095,-0.12660206639121113
-descriptors,8.191894769668579,0.006843852996826172,0.8564572592750457,-0.15195560306310654,0.9149636380225289,-0.11228418010873668
+one-hot,3.535422945022583,0.0018382549285888671,0.7885762443951967,-0.1828158438205719,0.8784990184881403,-0.13178804204825675
+descriptors,3.520027923583984,0.0021105289459228517,0.8410473186245367,-0.16089824438095093,0.9011444270426143,-0.12116910725437406
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_1.png b/scripts/train_emulators/results/reizman_suzuki_case_1.png
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_2.png b/scripts/train_emulators/results/reizman_suzuki_case_2.png
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_3.png b/scripts/train_emulators/results/reizman_suzuki_case_3.png
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_4.png b/scripts/train_emulators/results/reizman_suzuki_case_4.png
diff --git a/scripts/train_emulators/results/reizman_suzuki_scores.csv b/scripts/train_emulators/results/reizman_suzuki_scores.csv
@@ -1,5 +1,5 @@
 case,avg_fit_time,avg_score_time,avg_val_r2,avg_val_neg_root_mean_squared_error,avg_test_r2,avg_test_neg_root_mean_squared_error
-case_1,8.6258864402771,0.005854988098144531,0.8212155526088756,-11.137054443359375,0.9295815380048114,-7.543151896248529
-case_2,8.79561619758606,0.00565180778503418,0.605790245122981,-5.384605264663696,0.6605449615937999,-4.99218215483449
-case_3,8.240145397186279,0.00576167106628418,0.7760751544224982,-12.907710647583007,0.842104321811395,-11.899007938905271
-case_4,8.307093811035156,0.005579137802124023,0.7043271376927565,-15.66837387084961,0.7326339105287581,-14.055312980735687
+case_1,3.524441385269165,0.0019971370697021485,0.8133778193051648,-11.210040664672851,0.9277257528325263,-7.659243144643234
+case_2,3.584489107131958,0.0018500328063964845,0.5875258046769148,-5.542116260528564,0.6731724135929321,-4.914557483011899
+case_3,3.61135573387146,0.0018646240234375,0.7575032073182122,-13.106094932556152,0.8388203899628657,-12.040626679884866
+case_4,3.5974781036376955,0.001851367950439453,0.6979552061032572,-15.986057090759278,0.7417433519970528,-13.803447416357022
diff --git a/scripts/train_emulators/train_emulators.py b/scripts/train_emulators/train_emulators.py
@@ -102,7 +102,6 @@ def _train_baumgartner(use_descriptors=False, show_plots=False, save_plots=True)
         domain,
         dataset=ds,
         regressor=ANNRegressor,
-        output_variable_names=["yield"],
         descriptors_features=["catalyst", "base"] if use_descriptors else [],
     )
     res = exp.train(