From 6f33bb460d184a627c0554a398a520028cdadbd6 Mon Sep 17 00:00:00 2001 From: Kobi Felton Date: Wed, 14 Apr 2021 15:06:50 +0100 Subject: [PATCH] Fix MTBO one-hot encoding bug + improve docs (#116) --- docs/source/strategies.rst | 16 +- .../baumgartner_primary_amine.json | 1 + .../baumgartner_primary_amine_predictor_0.pt | Bin 0 -> 26391 bytes .../baumgartner_primary_amine_predictor_1.pt | Bin 0 -> 26391 bytes .../baumgartner_primary_amine_predictor_2.pt | Bin 0 -> 26391 bytes .../baumgartner_primary_amine_predictor_3.pt | Bin 0 -> 26391 bytes .../baumgartner_primary_amine_predictor_4.pt | Bin 0 -> 26391 bytes .../mtbo_cn_repeat_0.json | 2 +- .../mtbo_cn_repeat_1.json | 2 +- .../mtbo_cn_repeat_2.json | 2 +- .../mtbo_cn_repeat_3.json | 2 +- .../mtbo_cn_repeat_4.json | 2 +- .../mtbo_cn_repeat_5.json | 2 +- .../mtbo_cn_repeat_6.json | 2 +- .../mtbo_cn_repeat_7.json | 2 +- .../mtbo_cn_repeat_8.json | 2 +- .../mtbo_cn_repeat_9.json | 2 +- .../stbo_cn_noise_repeat_0.json | 2 +- 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| 1 + .../stbo_cn_noise_repeat_7.json | 1 + .../stbo_cn_noise_repeat_8.json | 1 + .../stbo_cn_noise_repeat_9.json | 1 + ...o_case1-3_noise_10-pre-train_repeat_0.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_1.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_2.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_3.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_4.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_5.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_6.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_7.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_8.json | 2 +- ...o_case1-3_noise_10-pre-train_repeat_9.json | 2 +- ...bo_case1-3_noise_5-pre-train_repeat_0.json | 1 + ...bo_case1-3_noise_5-pre-train_repeat_1.json | 1 + ...bo_case1-3_noise_5-pre-train_repeat_2.json | 1 + ...bo_case1-3_noise_5-pre-train_repeat_3.json | 1 + ...bo_case1-3_noise_5-pre-train_repeat_4.json | 1 + ...bo_case1-3_noise_5-pre-train_repeat_5.json | 1 + 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+- .../stbo_case1-3_noise_repeat_6.json | 2 +- .../stbo_case1-3_noise_repeat_7.json | 2 +- .../stbo_case1-3_noise_repeat_8.json | 2 +- .../stbo_case1-3_noise_repeat_9.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_0.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_1.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_2.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_3.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_4.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_5.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_6.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_7.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_8.json | 2 +- ...o_case1-2_noise_10-pre-train_repeat_9.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_0.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_1.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_2.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_3.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_4.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_5.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_6.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_7.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_8.json | 2 +- ...bo_case1-2_noise_5-pre-train_repeat_9.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_0.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_1.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_2.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_3.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_4.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_5.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_6.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_7.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_8.json | 2 +- ...o_case1-2_noise_50-pre-train_repeat_9.json | 2 +- .../stbo_case1-2_noise_repeat_0.json | 2 +- .../stbo_case1-2_noise_repeat_1.json | 2 +- .../stbo_case1-2_noise_repeat_2.json | 2 +- .../stbo_case1-2_noise_repeat_3.json | 2 +- .../stbo_case1-2_noise_repeat_4.json | 2 +- .../stbo_case1-2_noise_repeat_5.json | 2 +- .../stbo_case1-2_noise_repeat_6.json | 2 +- .../stbo_case1-2_noise_repeat_7.json | 2 +- .../stbo_case1-2_noise_repeat_8.json | 2 +- .../stbo_case1-2_noise_repeat_9.json | 2 +- .../stbo_mtbo_cn_different-updated.png | Bin 0 -> 93996 bytes .../figures/stbo_mtbo_cn_different.png | Bin 82092 -> 93996 bytes .../figures/stbo_mtbo_cn_similar.png | Bin 0 -> 97440 bytes ...mtbo_kinetics_case1-2_noise_comparison.png | Bin 107581 -> 137667 bytes ...mtbo_kinetics_case1-3_noise_comparison.png | Bin 102370 -> 141852 bytes .../multitask/multitask_paper_results.ipynb | 951 +++++++++--------- summit/benchmarks/experimental_emulator.py | 1 + summit/strategies/multitask.py | 137 ++- tests/test_strategies.py | 14 +- 136 files changed, 720 insertions(+), 610 deletions(-) create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine.json create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine_predictor_0.pt create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine_predictor_1.pt create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine_predictor_2.pt create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine_predictor_3.pt create mode 100644 experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine_predictor_4.pt create mode 100644 experiments/multitask/data/cross_coupling_similar/mtbo_pre-train_repeat_0.json create mode 100644 experiments/multitask/data/cross_coupling_similar/mtbo_pre-train_repeat_1.json create mode 100644 experiments/multitask/data/cross_coupling_similar/mtbo_pre-train_repeat_2.json create mode 100644 experiments/multitask/data/cross_coupling_similar/mtbo_pre-train_repeat_3.json create mode 100644 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experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_3.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_4.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_5.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_6.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_7.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_8.json create mode 100644 experiments/multitask/data/kinetics_different/mtbo_case1-3_noise_5-pre-train_repeat_9.json create mode 100644 experiments/multitask/figures/stbo_mtbo_cn_different-updated.png create mode 100644 experiments/multitask/figures/stbo_mtbo_cn_similar.png diff --git a/docs/source/strategies.rst b/docs/source/strategies.rst index 0b3d0f58..d9f323f9 100644 --- a/docs/source/strategies.rst +++ b/docs/source/strategies.rst @@ -21,14 +21,15 @@ With the updated model, we use one of two classes of techniques to select our ne :height: 300 px :figclass: align-center - Illustration of how acquisition functions eanble BO strategies to reduce uncertainty and maximise objective simulataneously. Dotted line is actual objective and solid line is posterior of surrogate model. Acquisition function is high where objective to be optimal (exploration) and where there is high uncertainty (exploitation). Adapted from `Shahriari et al. `_ + Illustration of how acquisition functions eanble BO strategies to reduce uncertainty and maximise objective simulataneously. Dotted line is actual objective and solid line is posterior of surrogate model. Acquisition function is high where objective to be optimal (exploration) and where there is high uncertainty (exploitation). Adapted from `Shahriari et al. `_ -To learn more about BO, we suggest reading the review by `Shahriari et al. `_ +To learn more about BO, we suggest reading the review by `Shahriari et al. `_ The BO strategies available in Summit are: * TSEMO_ * SOBO_ +* MTBO_ * ENTMOOT_ @@ -49,6 +50,16 @@ SOBO .. autoclass:: summit.strategies.sobo.SOBO :members: + +MTBO +^^^^ + +.. _MTBO: + +.. autoclass:: summit.strategies.MTBO + :members: + :inherited-members: + ENTMOOT ^^^^^^^ @@ -57,6 +68,7 @@ ENTMOOT .. autoclass:: summit.strategies.ENTMOOT :members: + Reinforcement Learning ---------------------- diff --git a/experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine.json b/experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine.json new file mode 100644 index 00000000..0ceb16bc --- /dev/null +++ b/experiments/multitask/baumgartner_phenethylamine_emulator/baumgartner_primary_amine.json @@ -0,0 +1 @@ +{"domain": [{"type": "CategoricalVariable", "is_objective": false, "name": "catalyst", "description": "Catalyst type", "units": null, "levels": ["tBuXPhos", "tBuBrettPhos", "AlPhos"], "ds": {"index": ["tBuXPhos", "tBuBrettPhos", "AlPhos"], "columns": [["area_cat", "DATA"], ["M2_cat", "DATA"]], "data": [[460.7543, 67.2057], [518.8408, 89.8738], [819.933, 129.0808]]}}, {"type": "CategoricalVariable", "is_objective": false, "name": "base", "description": "Base", "units": null, "levels": ["BTTP", "BTMG", "DBU", "MTBD"], "ds": null}, {"type": "ContinuousVariable", "is_objective": false, "name": "base_equivalents", "description": "Base equivalents", "units": null, "bounds": [1.0, 2.5]}, {"type": "ContinuousVariable", "is_objective": false, "name": "temperature", "description": "Temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 100.0]}, {"type": "ContinuousVariable", "is_objective": false, "name": "t_res", "description": "residence time in seconds (s)", "units": null, "bounds": [60.0, 1800.0]}, {"type": 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ut)$fQ?@1T`JqVS&?f=c=4gX(9Aya;LqHSosAlOB2QRs-XZRO$97ykocvwD62 diff --git a/experiments/multitask/multitask_paper_results.ipynb b/experiments/multitask/multitask_paper_results.ipynb index bf868cf4..1bb50468 100644 --- a/experiments/multitask/multitask_paper_results.ipynb +++ b/experiments/multitask/multitask_paper_results.ipynb @@ -32,6 +32,7 @@ " MIT_case4,\n", " MIT_case5,\n", " BaumgartnerCrossCouplingEmulator,\n", + " get_pretrained_baumgartner_cc_emulator,\n", " ExperimentalEmulator\n", ")\n", "from summit.utils.dataset import DataSet\n", @@ -43,12 +44,13 @@ "import matplotlib.pyplot as plt\n", "from typing import List\n", "from IPython.display import clear_output\n", - "from copy import deepcopy" + "from copy import deepcopy\n", + "import pathlib" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -130,23 +132,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
NameTypeDescriptionValues
conc_catcontinuous, inputcatalyst concentration[0.000835,0.004175]
tcontinuous, inputreaction time[60,600]
cat_indexcategorical, inputChoice of catalyst8 levels
temperaturecontinuous, inputReactor temperature in degress celsius[30,110]
ycontinuous, maximize objectiveyield (%)[0,100]
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "exp_pt = MIT_case1(noise_level=1)\n", "exp_pt.domain" @@ -168,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -210,46 +198,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Repeat 9\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Multi-Task Bayesian Optimization\n", "for n, dataset in zip(n_aux, aux_datasets):\n", @@ -270,22 +221,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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"outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "stbo_results = [summit.Runner.load(f\"data/kinetics_different/stbo_case1-3_noise_repeat_{i}.json\") \n", " for i in range(10)]\n", @@ -469,9 +318,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Your version of xlrd is 1.2.0. In xlrd >= 2.0, only the xls format is supported. As a result, the openpyxl engine will be used if it is installed and the engine argument is not specified. Install openpyxl instead.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], "source": [ "b_df = pd.read_excel(\"data/baumgartner_data.xlsx\", sheet_name=\"Reaction data\")\n", "# Just the columns we want\n", @@ -489,7 +347,7 @@ " \"Base equivalents\": \"base_equivalents\",\n", " \"Temperature (degC)\": \"temperature\",\n", " \"Residence Time Actual (s)\": \"t_res\",\n", - " \"Reaction Yield\": \"yld\"\n", + " \"Reaction Yield\": \"yield\"\n", "}\n", "b_df = b_df.rename(columns=columns)\n", "\n", @@ -508,6 +366,102 @@ " for nucleophile in nucleophiles}" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "inputHidden": false, + "jupyter": { + "outputs_hidden": false + }, + "outputHidden": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1,2, figsize=(10,5))\n", + "fig.subplots_adjust(wspace=0.2)\n", + "# Counts of different catalysts grouped by nucleophile\n", + "(b_df.\n", + " groupby(\"nucleophile\").\n", + " catalyst.\n", + " value_counts().\n", + " unstack(0).\n", + " plot.bar(ax=axes[0])\n", + ")\n", + "# Counts of different bases grouped by nucleophile\n", + "(b_df.\n", + " groupby(\"nucleophile\").\n", + " base.\n", + " value_counts().\n", + " unstack(0).\n", + " plot.bar(ax=axes[1])\n", + ")\n", + "for ax in axes:\n", + " ax.set_ylabel(\"Counts\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "inputHidden": false, + "jupyter": { + "outputs_hidden": false + }, + "outputHidden": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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"/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -662,6 +590,60 @@ "fig.savefig(\"figures/stbo_mtbo_cn_different.png\", bbox_inches='tight', dpi=300)" ] }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tBuXPhos 20\n", + "tBuBrettPhos 1\n", + "Name: (catalyst, DATA), dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtbo_results[0].experiment.data.catalyst.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stbo_results" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BTMG 11\n", + "TEA 8\n", + "TMG 2\n", + "Name: (base, DATA), dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtbo_results[6].experiment.data.base.value_counts()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -680,19 +662,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
NameTypeDescriptionValues
catalystcategorical, inputCatalyst type3 levels
basecategorical, inputBase4 levels
base_equivalentscontinuous, inputBase equivalents[1.0,2.5]
temperaturecontinuous, inputTemperature in degrees Celsius (ºC)[30,100]
t_rescontinuous, inputresidence time in seconds (s)[60,1800]
yldcontinuous, maximize objectiveYield[0.0,1.0]
" + "
NameTypeDescriptionValues
catalystcategorical, inputCatalyst type3 levels
basecategorical, inputBase4 levels
base_equivalentscontinuous, inputBase equivalents[1.0,2.5]
temperaturecontinuous, inputTemperature in degrees Celsius (ºC)[30,100]
t_rescontinuous, inputresidence time in seconds (s)[60,1800]
yieldcontinuous, maximize objectiveYield[0.0,1.0]
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -702,8 +684,8 @@ "bases_primary = pd.unique(datasets[\"Phenethylamine\"][\"base\"])\n", "bases_secondary = pd.unique(datasets[\"Morpholine\"][\"base\"])\n", "assert bases_primary.all() == bases_secondary.all()\n", - "exp = BaumgartnerCrossCouplingEmulator()\n", - "new_domain = deepcopy(exp.domain)\n", + "domain = BaumgartnerCrossCouplingEmulator.setup_domain()\n", + "new_domain = deepcopy(domain)\n", "bases = list(pd.unique(datasets[\"Morpholine\"][\"base\"]))\n", "new_domain[\"base\"] = CategoricalVariable(name=\"base\", description=\"Base\", levels=bases)\n", "new_domain" @@ -711,66 +693,26 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: The file /Users/Kobi/Documents/Research/summit/summit/benchmarks/experiment_emulator/trained_models/BNN/baumgartner_primary_amine_yld_1_BNN_model.pt already exist.\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Do you want to overwrite this file? If yes, type 'y' or 'yes', else type 'n' or 'no': y\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: The file /Users/Kobi/Documents/Research/summit/summit/benchmarks/experiment_emulator/trained_models/BNN/baumgartner_primary_amine_yld_2_BNN_model.pt already exist.\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Do you want to overwrite this file? If yes, type 'y' or 'yes', else type 'n' or 'no': y\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluation based on training and test set.\n" - ] - }, { "data": { "text/plain": [ - "({'yld': {'Train': {'MAE': 0.38840436935424805,\n", - " 'RMSE': 0.40859803557395935,\n", - " 'r2': -0.018294166047013727},\n", - " 'Test': {'MAE': 0.37007373571395874,\n", - " 'RMSE': 0.3907167613506317,\n", - " 'r2': -0.04729970682817819}}},\n", - " [(
,\n", - " )])" + "(
,\n", + " array([],\n", + " dtype=object))" ] }, - "execution_count": 14, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -780,108 +722,41 @@ } ], "source": [ - "# Train primary amine benchmark model\n", - "exp_amine = ExperimentalEmulator(domain=new_domain, model_name=\"baumgartner_primary_amine\")\n", - "training_ds = datasets[\"Phenethylamine\"]\n", - "training_ds = training_ds.replace(\"≥90%\", 0.9)\n", - "exp_amine.train(dataset=datasets[\"Phenethylamine\"], cv_fold=2, test_size=0.25, verbose=False)\n", - "exp_amine.validate(parity_plots=True)" + "# Load or train primary amine benchmark model\n", + "save_dir = pathlib.Path(\"baumgartner_phenethylamine_emulator/\")\n", + "if save_dir.exists():\n", + " exp_amine = ExperimentalEmulator.load(\"baumgartner_primary_amine\", save_dir)\n", + "else:\n", + " exp_amine = ExperimentalEmulator(\n", + " domain=new_domain,\n", + " model_name=\"baumgartner_primary_amine\",\n", + " dataset=datasets[\"Phenethylamine\"].replace(\"≥90%\", 0.9)\n", + " )\n", + " exp_amine.train(max_epochs=1000, cv_fold=2, test_size=0.25, verbose=False)\n", + " exp_amine.save(\"baumgartner_phenethylamine_emulator\")\n", + "exp_amine.parity_plot()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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catalystbasebase_equivalentstemperaturet_resyldcomputation_texperiment_tstrategy
0tBuXPhosBTTP2.12582.51365.00.4630600.00.030719LHS
1AlPhosMTBD1.37547.5495.00.4628610.00.034983LHS
\n", - "
" - ], - "text/plain": [ - "NAME catalyst base base_equivalents temperature t_res yld \\\n", - "TYPE DATA DATA DATA DATA DATA DATA \n", - "0 tBuXPhos BTTP 2.125 82.5 1365.0 0.463060 \n", - "1 AlPhos MTBD 1.375 47.5 495.0 0.462861 \n", - "\n", - "NAME computation_t experiment_t strategy \n", - "TYPE METADATA METADATA METADATA \n", - "0 0.0 0.030719 LHS \n", - "1 0.0 0.034983 LHS " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "exp_amine.reset()\n", - "lhs = LHS(exp_amine.domain)\n", - "conditions = lhs.suggest_experiments(2)\n", - "exp_amine.run_experiments(conditions)" + "#Single-Task Bayesian Optimization\n", + "for i in range(N_REPEATS):\n", + " print(f\"Repeat {i}\")\n", + " result = run_stbo(exp_amine, max_iterations=MAX_ITERATIONS)\n", + " result.save(f\"data/cross_coupling_similar/stbo_cn_noise_repeat_{i}.json\")\n", + " clear_output(wait=True) " ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 25, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -908,7 +783,7 @@ " }\n", " \n", " \n", - " 100.00% [20/20 00:15<00:00]\n", + " 100.00% [20/20 01:36<00:00]\n", " \n", " " ], @@ -918,99 +793,224 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/pandas/core/generic.py:4152: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", + " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n" + ] } ], "source": [ - "#Single-Task Bayesian Optimization\n", + "#Multi-Task Bayesian Optimization\n", + "pt_data = datasets[\"Morpholine\"].copy()\n", + "pt_data[(\"task\", \"METADATA\")] = 0\n", + "pt_data = pt_data.replace(\"≥90%\", 0.9)\n", "for i in range(N_REPEATS):\n", - " print(f\"Repeat {i}\")\n", - " result = run_stbo(exp_amine, max_iterations=MAX_ITERATIONS)\n", - " result.save(f\"data/cross_coupling_similar/stbo_cn_noise_repeat_{i}.json\")\n", + " print(f\"Repeat {i}\") \n", + " exp_amine.reset()\n", + " result = run_mtbo(exp_amine, pt_data, max_iterations=MAX_ITERATIONS)\n", + " result.save(f\"data/cross_coupling_similar/mtbo_pre-train_repeat_{i}.json\")\n", " clear_output(wait=True)" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 26, + "metadata": { + "scrolled": true + }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Repeat 9\n" + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.7/lib/python3.7/site-packages/sklearn/metrics/_regression.py:682: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n" ] }, { "data": { - "text/html": [ - "\n", - "
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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#Multi-Task Bayesian Optimization\n", - "for i in range(N_REPEATS):\n", - " print(f\"Repeat {i}\")\n", - " exp = MIT_case3(noise_level=1)\n", - " result = run_mtbo(exp_amine, pt_data, max_iterations=MAX_ITERATIONS)\n", - " result.save(f\"data/cross_coupling_similar/mtbo_case1-3_noise_{n}-pre-train_repeat_{i}.json\")\n", - " clear_output(wait=True)" + "stbo_results = [summit.Runner.load(f\"data/cross_coupling_similar/stbo_cn_noise_repeat_{i}.json\") \n", + " for i in range(N_REPEATS)]\n", + "mtbo_results_list = [summit.Runner.load(f\"data/cross_coupling_similar/mtbo_pre-train_repeat_{i}.json\") \n", + " for i in range(N_REPEATS)]\n", + "fig, ax = make_comparison_plot(\n", + " dict(results=stbo_results, label=\"STBO\"),\n", + " dict(results=mtbo_results_list,label=f\"\"\"MTBO, n={datasets[\"Morpholine\"].shape[0]}\"\"\")\n", + ")\n", + "fig.savefig(\"figures/stbo_mtbo_cn_similar.png\", bbox_inches='tight', dpi=300)" ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
NameTypeDescriptionValues
catalystcategorical, inputCatalyst type3 levels
basecategorical, inputBase4 levels
base_equivalentscontinuous, inputBase equivalents[1.0,2.5]
temperaturecontinuous, inputTemperature in degrees Celsius (ºC)[30,100]
t_rescontinuous, inputresidence time in seconds (s)[60,1800]
yldcontinuous, maximize objectiveYield[0.0,1.0]
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "kernel_info": { + "name": "python37364bitsummittfmmv07ppy37venv6fc212842bc44e839a51e6623a646abd" + }, "kernelspec": { "display_name": "Python 3.7.3 64-bit ('summit-TfmmV07p-py3.7': venv)", "language": "python", @@ -1028,6 +1028,9 @@ "pygments_lexer": "ipython3", "version": "3.7.3" }, + "nteract": { + "version": "0.12.3" + }, "toc-autonumbering": true }, "nbformat": 4, diff --git a/summit/benchmarks/experimental_emulator.py b/summit/benchmarks/experimental_emulator.py index 796a09a3..d9d2a88b 100644 --- a/summit/benchmarks/experimental_emulator.py +++ b/summit/benchmarks/experimental_emulator.py @@ -636,6 +636,7 @@ def load_regressor(self, save_dir): net = predictor.regressor_.named_steps.net net.initialize() net.load_params(f_params=save_dir / f"{self.model_name}_predictor_{i}.pt") + predictor.regressor_.named_steps.net = net def save(self, save_dir): """Save all the essential parameters of the ExperimentalEmulator to disk diff --git a/summit/strategies/multitask.py b/summit/strategies/multitask.py index f1ad6023..7476a8bd 100644 --- a/summit/strategies/multitask.py +++ b/summit/strategies/multitask.py @@ -3,10 +3,10 @@ from summit.domain import * from summit.utils.dataset import DataSet -# from botorch.models.model import Model -# from botorch.acquisition.objective import ScalarizedObjective from botorch.acquisition import ExpectedImprovement as EI +# from botorch.acquisition import qExpectedImprovement as qEI + import numpy as np from typing import Type, Tuple, Union, Optional @@ -25,12 +25,12 @@ class MTBO(Strategy): domain : :class:`~summit.domain.Domain` The domain of the optimization + pretraining_data : :class:`~summit.utils.data.DataSet`, optional + A DataSet with pretraining data. Must contain a metadata column named "task" + that specfies the task for all data. transform : :class:`~summit.strategies.base.Transform`, optional A transform object. By default, no transformation will be done on the input variables or objectives. - pretraining_data : :class:`~summit.utils.data.DataSet` - A DataSet with pretraining data. Must contain a metadata column named "task" - that specfies the task for all data. task : int, optional The index of the task being optimized. Defaults to 1. categorical_method : str, optional @@ -41,40 +41,43 @@ class MTBO(Strategy): Notes ----- + This strategy is based on a paper from the NIPs2020 ML4Molecules workshop by Felton_. See + Swersky_ for more information on multitask Bayesian optimization. References ---------- + .. [Felton] K. Felton, et al, in `ML4 Molecules 2020 workshop `_. .. [Swersky] K. Swersky et al., in `NIPS Proceedings `_, 2013, pp. 2004–2012. + Examples -------- - - >>> from summit.domain import Domain, ContinuousVariable - >>> from summit.strategies import NelderMead - >>> domain = Domain() - >>> domain += ContinuousVariable(name='temperature', description='reaction temperature in celsius', bounds=[0, 1]) - >>> domain += ContinuousVariable(name='flowrate_a', description='flow of reactant a in mL/min', bounds=[0, 1]) - >>> domain += ContinuousVariable(name='yield', description='relative conversion to xyz', bounds=[0,100], is_objective=True, maximize=True) - >>> strategy = NelderMead(domain) - >>> next_experiments = strategy.suggest_experiments() - >>> print(next_experiments) - NAME temperature flowrate_a strategy - TYPE DATA DATA METADATA - 0 0.500 0.500 Nelder-Mead Simplex - 1 0.625 0.500 Nelder-Mead Simplex - 2 0.500 0.625 Nelder-Mead Simplex + >>> from summit.benchmarks import MIT_case1, MIT_case2 + >>> from summit.strategies import LHS, MTBO + >>> from summit import Runner + >>> # Get pretraining data + >>> exp_pt = MIT_case1(noise_level=1) + >>> lhs = LHS(exp_pt.domain) + >>> conditions = lhs.suggest_experiments(10) + >>> pt_data = exp_pt.run_experiments((conditions)) + >>> pt_data[("task", "METADATA")] = 0 + >>> # Use MTBO on a new mechanism + >>> exp = MIT_case2(noise_level=1) + >>> strategy = MTBO(exp.domain,pretraining_data=pt_data, categorical_method="one-hot",task=1) + >>> r = Runner(strategy=strategy, experiment=exp, max_iterations=2) + >>> r.run(progress_bar=False) """ def __init__( self, domain: Domain, - pretraining_data=None, + pretraining_data: DataSet = None, transform: Transform = None, task: int = 1, categorical_method: str = "one-hot", - **kwargs + **kwargs, ): Strategy.__init__(self, domain, transform, **kwargs) self.pretraining_data = pretraining_data @@ -87,6 +90,42 @@ def __init__( self.reset() def suggest_experiments(self, num_experiments, prev_res: DataSet = None, **kwargs): + """Suggest experiments using MTBO + + Parameters + ---------- + num_experiments : int + The number of experiments (i.e., samples) to generate + prev_res : :class:`~summit.utils.data.DataSet`, optional + Dataset with data from previous experiments. + If no data is passed, then latin hypercube sampling will + be used to suggest an initial design. + + Returns + ------- + next_experiments : :class:`~summit.utils.data.DataSet` + A Dataset object with the suggested experiments + + Examples + -------- + >>> from summit.benchmarks import MIT_case1, MIT_case2 + >>> from summit.strategies import LHS, MTBO + >>> from summit import Runner + >>> # Get pretraining data + >>> exp_pt = MIT_case1(noise_level=1) + >>> lhs = LHS(exp_pt.domain) + >>> conditions = lhs.suggest_experiments(10) + >>> pt_data = exp_pt.run_experiments(conditions) + >>> pt_data["task", "METADATA"] = 0 + >>> # Use MTBO on a new mechanism + >>> exp = MIT_case2(noise_level=1) + >>> new_conditions = lhs.suggest_experiments(10) + >>> data = exp.run_experiments(new_conditions) + >>> data[("task", "METADATA")] = 1 + >>> strategy = MTBO(exp.domain,pretraining_data=pt_data, categorical_method="one-hot",task=1) + >>> res = strategy.suggest_experiments(1, prev_res=data) + + """ from botorch.models import MultiTaskGP from botorch.fit import fit_gpytorch_model from botorch.optim import optimize_acqf @@ -94,6 +133,11 @@ def suggest_experiments(self, num_experiments, prev_res: DataSet = None, **kwarg ExactMarginalLogLikelihood, ) + if num_experiments != 1: + raise NotImplementedError( + "Multitask does not support batch optimization yet. See https://github.com/sustainable-processes/summit/issues/119#" + ) + # Suggest lhs initial design or append new experiments to previous experiments if prev_res is None: lhs = LHS(self.domain) @@ -109,6 +153,10 @@ def suggest_experiments(self, num_experiments, prev_res: DataSet = None, **kwarg self.iterations += 1 # Combine pre-training and experiment data + if "task" not in self.pretraining_data.metadata_columns: + raise ValueError( + """The pretraining data must have a METADATA column called "task" with the task number.""" + ) data = self.all_experiments.append(self.pretraining_data) # Get inputs (decision variables) and outputs (objectives) @@ -120,7 +168,9 @@ def suggest_experiments(self, num_experiments, prev_res: DataSet = None, **kwarg ) # Add column to inputs indicating task - task_data = data["task"].to_numpy() + task_data = data["task"].dropna().to_numpy() + if data.shape[0] != data.shape[0]: + raise ValueError("Pretraining data must have a task for every row.") task_data = np.atleast_2d(task_data).T inputs_task = np.append(inputs.data_to_numpy(), task_data, axis=1).astype( np.float @@ -146,7 +196,7 @@ def suggest_experiments(self, num_experiments, prev_res: DataSet = None, **kwarg maximize = False ei = CategoricalEI(self.domain, model, best_f=fbest_scaled, maximize=maximize) - # Optimize acquisitio function + # Optimize acquisition function results, _ = optimize_acqf( acq_function=ei, bounds=self._get_bounds(), @@ -222,8 +272,9 @@ def __init__( best_f, objective=None, maximize: bool = True, + **kwargs, ) -> None: - super().__init__(model, best_f, objective, maximize) + super().__init__(model, best_f, objective, maximize, **kwargs) self._domain = domain def forward(self, X): @@ -233,17 +284,29 @@ def forward(self, X): @staticmethod def round_to_one_hot(X, domain: Domain): """Round all categorical variables to a one-hot encoding""" - c = 0 - for v in domain.input_variables: - if isinstance(v, CategoricalVariable): - n_levels = len(v.levels) - levels_selected = X[:, :, c : c + n_levels].argmax(axis=1) - X[:, :, c : c + n_levels] = 0 - for j, l in enumerate(levels_selected): - X[j, :, l] = 1 - c += n_levels - else: - c += 1 + num_experiments = X.shape[1] + X = X.clone() + for q in range(num_experiments): + c = 0 + for v in domain.input_variables: + if isinstance(v, CategoricalVariable): + n_levels = len(v.levels) + levels_selected = X[:, q, c : c + n_levels].argmax(axis=1) + X[:, q, c : c + n_levels] = 0 + for j, l in zip(range(X.shape[0]), levels_selected): + X[j, q, int(c + l)] = 1 + + check = int(X[:, q, c : c + n_levels].sum()) == X.shape[0] + if not check: + raise ValueError( + ( + f"Rounding to a one-hot encoding is not properly working. Please report this bug at " + f"https://github.com/sustainable-processes/summit/issues. Tensor: \n {X[:, :, c : c + n_levels]}" + ) + ) + c += n_levels + else: + c += 1 return X @@ -305,7 +368,7 @@ def __init__( domain: Domain, transform: Transform = None, categorical_method: str = "one-hot", - **kwargs + **kwargs, ): Strategy.__init__(self, domain, transform, **kwargs) self.categorical_method = categorical_method diff --git a/tests/test_strategies.py b/tests/test_strategies.py index 3592c788..019be6d9 100644 --- a/tests/test_strategies.py +++ b/tests/test_strategies.py @@ -638,17 +638,16 @@ def test_sobo( @pytest.mark.parametrize( - "batch_size, max_num_exp, maximize, constraint", + "max_num_exp, maximize, constraint", [ - [1, 1, True, True], - [1, 200, True, True], - [4, 200, False, True], - [1, 200, True, False], - [4, 200, False, False], + [1, True, True], + [200, True, True], + [200, False, True], + [200, True, False], + [200, False, False], ], ) def test_mtbo( - batch_size, max_num_exp, maximize, constraint, @@ -664,6 +663,7 @@ def test_mtbo( results = hartmann3D.run_experiments(conditions) results["task", "METADATA"] = 0 strategy = MTBO(domain=hartmann3D.domain, pretraining_data=results) + batch_size = 1 # run SOBO loop for fixed number of iteration num_iter = max_num_exp // batch_size # maximum number of iterations