Experimental emulator

pyproject.toml

@@ -21,13 +21,14 @@ tqdm = "^4.46.1"
 ipywidgets = "^7.5.1"
 gpyopt = "^1.2.6"
 matplotlib = "^3.2.2"
-scikit-learn = "^0.23.1"
 neptune-client = {version= "^0.4.115", optional = true}
 hiplot = {version= "^0.1.12", optional = true}
 neptune-contrib = {extras = ["viz"], version = "^0.22.0", optional=true}
 gryffin = {git = "https://github.com/sustainable-processes/gryffin.git"}
 tensorflow = "^2.2.0"
 tensorflow-probability = "^0.10.1"
+torch = {url = "https://download.pytorch.org/whl/cu101/torch-1.4.0-cp37-cp37m-linux_x86_64.whl"}
+blitz-bayesian-pytorch = "0.2.3"
 
 [tool.poetry.extras]
 neptune = ["neptune-client", "hiplot", "neptune-contrib"]

summit/benchmarks/__init__.py

@@ -1,2 +1,3 @@
 from .snar import SnarBenchmark
 from .test_functions import Himmelblau, Hartmann3D, ThreeHumpCamel
+from .experimental_emulator import ExperimentalEmulator, ReizmanSuzukiEmulator, BaumgartnerCrossCouplingEmulator, BaumgartnerCrossCouplingDescriptorEmulator

summit/benchmarks/experiment_emulator/__init__.py

@@ -0,0 +1,5 @@
+import os, sys
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+
+from .emulator import Emulator
+from .bnn_emulator import BNNEmulator

summit/benchmarks/experiment_emulator/data/README.md

@@ -0,0 +1,15 @@
+# data
+
+This folder contains experimental datasets. The datasets are used in order to build probalistic models that mimic the experiments in order to test the strategies within Summit on these models. This prevents the need to run a large number of experiments but should always be seen in the light of the fact that these models only reproduce experiments (with errors) and do not replace costly experiments.
+
+The "<dataset_name>_train_test.csv" files include the same data points as the original dataset files but are ordered according to training (first) and test set (second). Please refer to our paper for the numbers of data points included in the training and test set, respectively.
+
+Following experimental datasets are included so far:
+
+* **reizman_2016_suzuki - Suzuki-Miyaura cross-coupling reaction** datasets for 4 different cases - by Reizman et al. (2016)
+
+'''
+Reizman, B. J., Wang, Y. M., Buchwald, S. L., & Jensen, K. F. (2016). Suzuki–Miyaura cross-coupling optimization enabled by automated feedback. Reaction chemistry & engineering, 1(6), 658-666
+'''
+
+

summit/benchmarks/experiment_emulator/data/baumgartner_aniline_cn_crosscoupling.csv

@@ -0,0 +1,98 @@
+catalyst,base,base_equivalents,temperature,t_res,yield
+DATA,DATA,DATA,DATA,DATA,DATA
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summit/benchmarks/experiment_emulator/data/baumgartner_aniline_cn_crosscoupling_descriptors.csv

@@ -0,0 +1,98 @@
+catalyst,base,base_equivalents,temperature,t_res,yield
+DATA,DATA,DATA,DATA,DATA,DATA
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+AlPhos,TMG,1.09959019999812,88.5,1393.36269569396,0.939708297977867
+tBuXPhos,TMG,2.18627638191082,63.1,1242.84908723831,0.31536670318976
+tBuXPhos,DBU,1.09796637520676,30,377.934616565704,0.093450374738737
+AlPhos,TEA,2.09120779317256,100,1350.93226861953,0.207359998748906
+AlPhos,BTMG,1.64319372094794,65,341.414527893066,0.9458049572347
+tBuBrettPhos,BTMG,1.10048981292401,100,526.4841132164,1.00080817597436
+tBuBrettPhos,TEA,1.14283782885996,30,75.3073072433471,0.005499146204924
+tBuBrettPhos,BTMG,2.19217570734463,30,1240.71996498107,1.01967151097374
+AlPhos,DBU,1.09145338882669,100,321.263375282287,0.959020869551022
+tBuBrettPhos,TMG,1.10028016249451,47.5,65.7707619667053,0.043402107312367

summit/benchmarks/experiment_emulator/data/reizman_suzuki_case1.csv

@@ -0,0 +1,98 @@
+96,4,,,,
+ Catalyst, tres (s), T  (ºC),Cat. Loading (mol%),TON,Yield (%)
+P1-L3,600,30,0.498,1.1,0.6
+P1-L6,600,30,2.515,0.2,0.6
+P1-L4,60,30,2.508,0.2,0.6
+P1-L1,60,30,0.513,1.1,0.6
+P1-L2,600,30,2.513,0.2,0.6
+P1-L5,60,30,0.508,1.1,0.6
+P1-L7,600,30,0.506,1.1,0.6
+P2-L1,60,30,2.509,0.2,0.6
+P2-L1,600,110,0.496,8.5,4.3
+P1-L4,600,110,0.512,84.7,43.4
+P1-L6,60,110,0.498,1.1,0.6
+P1-L1,600,110,2.509,24,60.2
+P1-L5,600,110,2.512,16.7,42
+P1-L7,60,110,2.499,33.8,84.6
+P1-L2,60,110,0.508,16.9,8.5
+P1-L3,60,110,2.489,21.8,54.4
+P1-L7,189.7,65.3,1.123,0.5,0.6
+P1-L1,189.7,65.3,1.106,22.5,24.9
+P1-L6,600,65.3,2.515,0.2,0.6
+P2-L1,189.7,65.3,2.509,8.4,21.1
+P1-L4,189.7,65.3,2.508,21.2,53.1
+P1-L5,189.7,65.3,1.106,0.5,0.6
+P1-L3,600,65.3,1.106,5.1,5.6
+P1-L2,600,65.3,1.129,6.7,7.5
+P1-L4,600,110,1.106,82.6,91.3
+P1-L1,600,110,2.509,27.4,68.6
+P1-L2,189.7,110,2.513,21.1,52.9
+P1-L6,189.7,110,1.127,6.6,7.4
+P1-L7,600,110,2.499,30,75
+P1-L3,189.7,110,2.489,21.6,53.8
+P1-L5,600,110,2.512,18.9,47.6
+P2-L1,600,110,1.131,15,17
+P1-L4,600,30,0.512,1.1,0.6
+P1-L5,600,30,0.508,1.1,0.6
+P1-L1,600,30,0.513,1.1,0.6
+P2-L1,600,30,0.496,1.1,0.6
+P1-L2,60,110,2.513,16.6,41.9
+P1-L3,60,110,2.489,31.2,77.5
+P1-L6,60,110,2.515,3.4,8.5
+P1-L7,60,110,2.499,34.2,85.4
+P1-L4,60,67.5,2.508,13.4,33.4
+P1-L5,60,66.7,2.512,0.2,0.6
+P1-L3,60,67,2.489,9.8,24.4
+P1-L7,60,66.8,2.499,1.4,3.6
+P1-L1,60,66.3,2.509,4.9,12.4
+P2-L1,60,67,2.509,4.5,11.2
+P1-L7,155.6,110,2.499,32.5,81.3
+P1-L5,109.3,110,2.482,15.8,39.1
+P2-L1,104.5,110,2.509,11.3,28.4
+P1-L1,109.2,110,2.482,29.8,74.1
+P1-L3,166.5,110,2.489,24.5,60.9
+P1-L4,60,110,0.512,20.1,10.3
+P1-L7,60,110,0.506,1.1,0.6
+P1-L3,60,110,0.498,43.7,21.7
+P1-L1,60,110,0.513,40,20.5
+P1-L4,60,110,0.512,17,8.7
+P1-L3,600,110,0.968,24.5,23.7
+P1-L1,600,110,0.971,42.6,41.4
+P1-L5,600,110,0.957,40.6,38.8
+P1-L4,600,110,1.268,72.4,91.8
+P1-L7,600,110,0.814,46.6,38
+P1-L4,161.7,110,2.104,39.9,84
+P1-L1,146.1,110,2.509,27.7,69.6
+P1-L3,185.5,110,2.489,25.2,62.8
+P1-L7,176.5,110,2.499,27.5,68.6
+P1-L1,60,110,2.266,37.1,83.9
+P1-L4,60,110,1.915,34.4,65.7
+P1-L7,60,110,2.303,35,80.7
+P1-L3,60,110,2.323,27.7,64.4
+P1-L4,600,110,2.508,30.8,82.1
+P1-L4,600,110,2.508,31.9,80
+P1-L4,600,67.3,1.214,24.4,29.6
+P1-L4,600,68.7,1.268,16.1,20.4
+P1-L4,199.3,110,1.241,57.8,71.6
+P1-L1,202.8,110,1.592,44.6,70.9
+P1-L4,600,110,1.16,65.1,75.5
+P1-L4,600,110,1.16,71.4,82.7
+P1-L4,600,110,1.106,65.3,72.2
+P1-L4,600,110,1.106,83.9,92.8
+P1-L4,600,110,1.187,60.7,72.1
+P1-L4,600,110,1.106,73,80.7
+P1-L4,600,66.3,0.998,20.2,20.1
+P1-L4,600,67.6,0.998,18,17.9
+P1-L4,600,110,1.241,70.5,87.5
+P1-L4,600,110,1.268,63.2,80.2
+P1-L4,600,110,1.187,65.8,78.1
+P1-L4,189.1,110,2.508,39.4,98.7
+P1-L4,600,110,1.268,65.6,83.1
+P1-L4,199.8,110,2.508,33.5,83.9
+P1-L4,199.6,110,2.508,33.2,83.2
+P1-L4,600,110,1.241,62,76.9
+P1-L4,600,110,1.025,73.3,75
+P1-L4,600,110,1.079,81.2,87.6
+P1-L4,600,110,1.133,67,75.9
+P1-L4,600,110,1.052,65.4,68.7
+P1-L4,600,110,1.106,71.2,78.7