Optimising chemical reactions using machine learning

Last update: Dec 14, 2022

Overview

Summit

Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions.

What is Summit?

Currently, reaction optimisation in the fine chemicals industry is done by intuition or design of experiments. Both scale poorly with the complexity of the problem.

Summit uses recent advances in machine learning to make the process of reaction optimisation faster. Essentially, it applies algorithms that learn which conditions (e.g., temperature, stoichiometry, etc.) are important to maximising one or more objectives (e.g., yield, enantiomeric excess). This is achieved through an iterative cycle.

Summit has two key features:

Strategies: Optimisation algorithms designed to find the best conditions with the least number of iterations. Summit has eight strategies implemented.
Benchmarks: Simulations of chemical reactions that can be used to test strategies. We have both mechanistic and data-driven benchmarks.

To get started, see the Quick Start below or follow our tutorial.

Installation

To install summit, use the following command:

pip install summit

News

Kobi (@marcosfelt) will give a tutorial on Summit at the online Autonomous Discovery Symposium on Wednesday 21 April 2021. Register now for free!

Quick Start

Below, we show how to use the Nelder-Mead strategy to optimise a benchmark representing a nucleophlic aromatic substitution (SnAr) reaction.

# Import summit
from summit.benchmarks import SnarBenchmark
from summit.strategies import NelderMead, MultitoSingleObjective
from summit.run import Runner

# Instantiate the benchmark
exp = SnarBenchmark()

# Since the Snar benchmark has two objectives and Nelder-Mead is single objective, we need a multi-to-single objective transform
transform = MultitoSingleObjective(
    exp.domain, expression="-sty/1e4+e_factor/100", maximize=False
)

# Set up the strategy, passing in the optimisation domain and transform
nm = NelderMead(exp.domain, transform=transform)

# Use the runner to run closed loop experiments
r = Runner(
    strategy=nm, experiment=exp,max_iterations=50
)
r.run()

Documentation

The documentation for summit can be found here.

Issues?

Submit an issue or send an email to [email protected].

Citing

If you find this project useful, we encourage you to

Star this repository ⭐
Cite our paper.

@article{Felton2021,
author = "Kobi Felton and Jan Rittig and Alexei Lapkin",
title = "{Summit: Benchmarking Machine Learning Methods for Reaction Optimisation}",
year = "2021",
month = "2",
url = "https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmtd.202000051",
journal = "Chemistry Methods"
}

Comments

Lack of max python version in Summit 0.8.4

Operating System: Arch
Python version: 3.10.6
summit version used: 0.8.4

Description

I installed Summit via pip and the system installed 0.8.4 even if 0.8.6 is the latest release. I suppose this happened due to the fact that in the sources of 0.8.4 there is no indication that Python 3.10 is not supported yet

https://github.com/sustainable-processes/summit/blob/a1cecdd41df8119005173b46ac45fb22472628d6/pyproject.toml#L14

while this is present in the sources of 0.8.5 and 0.8.6. This caused the installation of incompatible versions of BoTorch and GPyTorch resulting in messages like this:

Requirement already satisfied: botorch<0.6.0,>=0.5.0 in /usr/lib/python3.10/site-packages (from summit) (0.5.1)
Requirement already satisfied: gpytorch<2.0.0,>=1.3.0 in /usr/lib/python3.10/site-packages (from summit) (1.9.0)
[...]
Python 3.10.6 (main, Aug  3 2022, 17:39:45) [GCC 12.1.1 20220730] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from summit.benchmarks import SnarBenchmark
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.10/site-packages/summit/__init__.py", line 19, in <module>
    from summit.run import *
  File "/usr/lib/python3.10/site-packages/summit/run.py", line 1, in <module>
    from summit.strategies import Strategy, strategy_from_dict
  File "/usr/lib/python3.10/site-packages/summit/strategies/__init__.py", line 9, in <module>
    from .multitask import MTBO, STBO
  File "/usr/lib/python3.10/site-packages/summit/strategies/multitask.py", line 6, in <module>
    from botorch.acquisition import ExpectedImprovement as EI
  File "/usr/lib/python3.10/site-packages/botorch/__init__.py", line 7, in <module>
    from botorch import (
  File "/usr/lib/python3.10/site-packages/botorch/acquisition/__init__.py", line 7, in <module>
    from botorch.acquisition.acquisition import (
  File "/usr/lib/python3.10/site-packages/botorch/acquisition/acquisition.py", line 18, in <module>
    from botorch.models.model import Model
  File "/usr/lib/python3.10/site-packages/botorch/models/__init__.py", line 7, in <module>
    from botorch.models.cost import AffineFidelityCostModel
  File "/usr/lib/python3.10/site-packages/botorch/models/cost.py", line 16, in <module>
    from botorch.models.deterministic import DeterministicModel
  File "/usr/lib/python3.10/site-packages/botorch/models/deterministic.py", line 20, in <module>
    from botorch.models.model import Model
  File "/usr/lib/python3.10/site-packages/botorch/models/model.py", line 19, in <module>
    from botorch.models.utils import fantasize as fantasize_flag
  File "/usr/lib/python3.10/site-packages/botorch/models/utils.py", line 23, in <module>
    from gpytorch.utils.broadcasting import _mul_broadcast_shape
ImportError: cannot import name '_mul_broadcast_shape' from 'gpytorch.utils.broadcasting' (/usr/lib/python3.10/site-packages/gpytorch/utils/broadcasting.py)

The incompatibility is mentioned here: https://github.com/pytorch/botorch/issues/1370

Adding a maximum Python version in the old Summit releases should fix.

Thanks for your work.

opened by ilario 12

Add neldermead

I added the Nelder-Mead Simplex algorithm to strategies (#18). Therefore, I've adapted the Nelder-Mead code from the scipy-optmize package to make it usable for sequential requests.

Also, the modifications proposed by Cortes-Borda et al. in terms of reflecting points, dimension reduction and dimension recovery are implemented. These modifications increase the chance of finding optimal conditions that are close to the predefined bounds of the variables.

Constraints are treated within the suggest_experiments method of the Nelder-Mead strategy. Suggested points that violate the constraints are penalized with a function value of ininity (for minimization problems) and the algorithm is called again. Note that if more than one point is suggested by the algorithm, only the points that meet the constraints are returned. Points that violate the constraints are then stored temporarily and read in (again with an associated infinity value) the next time the algorithm is called.

Note that the number of suggested points can vary for different iterations of the algorithm and one iteration requires at least 2 squential function evalutions, hence one call of the Nelder-Mead algorithm does not correspond to one iteration.

One 2D (Himmelblau function) and one 3D (Hartmann function) application of the Nelder-Mead strategy are implemented in tests/test_strategies.py. The results are plotted, whereby each point corresponds to a suggested point that meets the constraints and connected points indicate the resulting simplex of one iteration.

opened by jangerit 8
Snobfit fix bugs
I've fixed issues #28 and #29. This includes:

return correct number of suggested experiments, also for small number of requested experiments per batch;

transform maximization problems to minimization problems within SNOBFIT.

Update (13/05/2020):

r̵e̵t̵u̵r̵n̵ ̵c̵o̵r̵r̵e̵c̵t̵ ̵n̵u̵m̵b̵e̵r̵ ̵o̵f̵ ̵s̵u̵g̵g̵e̵s̵t̵e̵d̵ ̵e̵x̵p̵e̵r̵i̵m̵e̵n̵t̵s̵,̵ ̵a̵l̵s̵o̵ ̵f̵o̵r̵ ̵s̵m̵a̵l̵l̵ ̵n̵u̵m̵b̵e̵r̵ ̵o̵f̵ ̵r̵e̵q̵u̵e̵s̵t̵e̵d̵ ̵e̵x̵p̵e̵r̵i̵m̵e̵n̵t̵s̵ ̵p̵e̵r̵ ̵b̵a̵t̵c̵h̵;̵

adjust snobfit strategy to return varying number of suggested experiments;

add more tests for different <number_experiments> of requested experiments (1,2,4) and make <num_iter> and <max_stop> a function of <number_experiments>;

transform maximization problems to minimization problems within SNOBFIT.
opened by jangerit 7

run.py avoid writing to disk by default

Operating System: Arch Linux
Python version: 3.10.8
summit version used: 0.8.8

Description

The default location for saving stuff is not writable, on my system. And the default behavior is to save stuff on disk. In my opinion, by default, no stuff should be saved, neither temporarily, unless it is actually unavoidable.

This causes this error on my system:

PermissionError                           Traceback (most recent call last)
Cell In[37], line 4
      2 rNMadaptive_20220301 = Runner(strategy=strategyNMadaptive, experiment=emul_constr_20220301, max_iterations=20)
      3 rNMadaptive_20220301.reset()
----> 4 rNMadaptive_20220301.run(prev_res=starting_points_20220301)

File /home/ilario/.venv/lib/python3.10/site-packages/summit/run.py:145, in Runner.run(self, **kwargs)
    143 save_dir = pathlib.Path(save_dir) / "runner" / str(self.uuid_val)
    144 if not os.path.isdir(save_dir):
--> 145     os.makedirs(save_dir)
    146 save_at_end = kwargs.get("save_at_end", True)
    148 n_objs = len(self.experiment.domain.output_variables)

File /usr/lib/python3.10/os.py:215, in makedirs(name, mode, exist_ok)
    213 if head and tail and not path.exists(head):
    214     try:
--> 215         makedirs(head, exist_ok=exist_ok)
    216     except FileExistsError:
    217         # Defeats race condition when another thread created the path
    218         pass

File /usr/lib/python3.10/os.py:215, in makedirs(name, mode, exist_ok)
    213 if head and tail and not path.exists(head):
...
    227     # Cannot rely on checking for EEXIST, since the operating system
    228     # could give priority to other errors like EACCES or EROFS
    229     if not exist_ok or not path.isdir(name):

PermissionError: [Errno 13] Permission denied: '/usr/bin/.summit'

A possible solution is to specify the save_dir path in the run command like:

rNM_20220301.run(prev_res=starting_points_20220301, save_dir="~")

But what I would expect is that if save_freq is unset it does not save any file at all, rather than creating it and deleting it.

In order to do so, the lines to be changed are these ones:

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L144-L145

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L178-L181

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L202-L204

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L343-L344

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L402-L408

https://github.com/sustainable-processes/summit/blob/a5e4d0ca848bf3b4c794f2975bc1790e21b0343d/summit/run.py#L430-L435

What I Did

python -m venv .venv     
source .venv/bin/activate
pip install -U summit

Open VSCodium and indicate to use .venv as an environment.

Run some summit stuff.

opened by ilario 5

Fix TSEMO
This is a PR for tracking improvements to TSEMO (issue #2). It is a work in progress.

I've made one change to the hypervolume reference, but I think it's still not the same as the Bradford paper.

[x] Test functions (DTLZ2, vmlop)

[x] Correct reference based on Bradford et al.

[x] Set up spectral sampling

[x] Do some ablation experiments on fixed vs ideal-anti-ideal difference and random rate.
opened by marcosfelt 5
Non-existing module entmoot.optimizer
Summit 0.8.7

Testing ENTMOOT I observed this error:

ModuleNotFoundError: No module named 'entmoot'

caused by this line:

https://github.com/sustainable-processes/summit/blob/249751d57c3603fda99437b35656a1d78e2f0371/summit/strategies/entmoot.py#L253
opened by ilario 4
Experimental emulator
This PR adds an experimental emulator (data-driven virtual experiment) to Summit.

The emulator is a BNN predicting experimental outcomes (e.g. yield) given the inputs (i.e., the conditions) of an experiment. It is included in benchmarks.

The following workflow describes the way to build such an emulator:

In the folder summit/benchmarks/experiment_emulator:

upload dataset to the ./data folder with real experimental data

create a loading procedure in experimental_datasets.py that returns inputs and outcomes of the experiments in the uploaded dataset (c.f. load_reizman_suzuki())

set up BNN parameters (e.g. dataset, input dimension, prediction objective, hyperparameters, saving location) and data transformation (e.g.transform discrete variables to one-hot vectors) in bnn.py

run python bnn.py -> The final BNN model parameters will be saved in ./trained_models and the emulator training is finished.

In the folder summit/benchmarks: Create a benchmark with the inputs and outputs of the dataset the BNN emulator was trained on. Thereby, set up a BNN model similar to the trained BNN and load the parameters of the trained model (c.f. reizman_suzuki_emulator.py). -> The emulator is now ready for use and acts like a virtual experiment.

Further regression techniques, like ANNs, can be implemented analogously according to the workflow described above.

Emulators included in this PR are based on the data for a Suzuki-Miyaura cross coupling reaction (#25) obtained from the SI of the paper published by Reizman et al. (2016).
opened by jangerit 4
Numpy issue in strategies
Operating System: Windows 10 Home Version 21H2 Build 19044.1645

Python version: 3.10

summit version used: 0.8.4

Using pycharm

Description

Was testing out the strategies module and ran into issues with numpy

What I Did

scratch.py

from summit import * import numpy as np

domain = Domain()

domain += ContinuousVariable(name="x_0", description="reaction temperature in celsius", bounds=[50, 100])

domain += ContinuousVariable(name="x_1", description="flow of reactant a in mL/min", bounds=[0.1, 0.5])

domain += ContinuousVariable(name="y_0", description="yield", bounds=[0.1, 0.5], is_objective=True, maximize=True)

domain += ContinuousVariable(name="y_1", description="cost", bounds=[0.1, 0.5], is_objective=True, maximize=True)

strategy = LHS(domain, random_state=np.random.RandomState(3))

results = strategy.suggest_experiments(5)

strategy_lhs = LHS(domain, random_state=np.random.RandomState(3)) results = strategy.suggest_experiments(5) print(results)

strategy_tsemo = TSEMO(domain) experiments = strategy_tsemo.suggest_experiments(5) print(experiments)

Error message:

"C:\Users\njose\Dropbox (Cambridge CARES)\Python\idmt_codeshare\pyflab_CURRENT\venv\Scripts\python.exe" "C:/Users/njose/Dropbox (Cambridge CARES)/Python/idmt_codeshare/pyflab_CURRENT/scratch.py" C:\Users\njose\Dropbox (Cambridge CARES)\Python\idmt_codeshare\pyflab_CURRENT\venv\lib\site-packages\torch_masked_init_.py:223: UserWarning: Failed to initialize NumPy: module compiled against API version 0xf but this version of numpy is 0xe (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:68.) example_input = torch.tensor([[-3, -2, -1], [0, 1, 2]]) Traceback (most recent call last): File "C:\Users\njose\Dropbox (Cambridge CARES)\Python\idmt_codeshare\pyflab_CURRENT\scratch.py", line 25, in strategy = LHS(domain, random_state=np.random.RandomState(3)) NameError: name 'np' is not defined

Process finished with exit code 1
opened by njoseGIT 3

pip install summit fails with python 3.10 due to requirements

Operating System: Windows 10
Python version: 3.10
summit version used: pypi version (0.8.3)

Description

Was trying to install summit through anaconda for python 3.10 but recieved a ResolutionImpossible error. Succeeded when installing summit with python version 3.7.

What I Did

conda create -n test_summit_310 python=3.10
conda activate test_summit_310
pip install summit

....

ERROR: Cannot install summit==0.5.0, summit==0.6.0, summit==0.8.0, summit==0.8.1, summit==0.8.2 and summit==0.8.3 because these package versions have conflicting dependencies.

The conflict is caused by:
    summit 0.8.3 depends on scikit-learn<0.25.0 and >=0.24.1
    summit 0.8.2 depends on scikit-learn<0.25.0 and >=0.24.1
    summit 0.8.1 depends on scikit-learn<0.25.0 and >=0.24.1
    summit 0.8.0 depends on scikit-learn<0.25.0 and >=0.24.1
    summit 0.6.0 depends on gryffin<0.2.0 and >=0.1.1
    summit 0.5.0 depends on numpy==1.18.0

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies

opened by ddceruti 3

TSEMO error
Operating System: Windows 10

Python version: Python 3.7.10 through anaconda

summit version used: 0.8.0rc1

Description

I'm trying to optimize a chemical rection with 8 inputs and 9 objectives through the TSEMO strategy. However when I use the runner to run closed loop optimisation obtained an error.

or when I applied the transform the issue I got was

Do you know how i can solve this issue? Thank you very much
opened by AFilipaAlmeida 3

Error encountered when running multitask bayesian optimization

Operating System: Ubuntu 16.04
Python version: 3.7
summit version used: 0.8.0

Description

Hi @marcosfelt, thanks for sharing the awesome work! I have encountered an error when I was trying to deploy multitask bayesian optimization on my own task.

Basically when I tried to call the suggest_experiments function, it raises error like this:

  File "obj_funcs/mtbo_transfer_summit.py", line 184, in <module>
    mtbo_transfer(100, pretrain_data)
  File "obj_funcs/mtbo_transfer_summit.py", line 161, in mtbo_transfer
    result = mtbo.suggest_experiments(num_experiments=1, prev_res=prev_res)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/summit/strategies/multitask.py", line 138, in suggest_experiments
    fit_gpytorch_model(mll)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/botorch/fit.py", line 126, in fit_gpytorch_model
    mll, _ = optimizer(mll, track_iterations=False, **kwargs)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/botorch/optim/fit.py", line 247, in fit_gpytorch_scipy
    callback=cb,
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/_minimize.py", line 620, in minimize
    callback=callback, **options)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py", line 308, in _minimize_lbfgsb
    finite_diff_rel_step=finite_diff_rel_step)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/optimize.py", line 262, in _prepare_scalar_function
    finite_diff_rel_step, bounds, epsilon=epsilon)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py", line 136, in __init__
    self._update_fun()
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py", line 226, in _update_fun
    self._update_fun_impl()
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py", line 133, in update_fun
    self.f = fun_wrapped(self.x)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py", line 130, in fun_wrapped
    return fun(x, *args)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/optimize.py", line 74, in __call__
    self._compute_if_needed(x, *args)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/scipy/optimize/optimize.py", line 68, in _compute_if_needed
    fg = self.fun(x, *args)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/botorch/optim/utils.py", line 219, in _scipy_objective_and_grad
    raise e  # pragma: nocover
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/botorch/optim/utils.py", line 212, in _scipy_objective_and_grad
    output = mll.model(*train_inputs)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/models/exact_gp.py", line 257, in __call__
    res = super().__call__(*inputs, **kwargs)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/module.py", line 28, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/botorch/models/multitask.py", line 167, in forward
    covar = covar_x.mul(covar_i)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py", line 1162, in mul
    return self._mul_matrix(lazify(other))
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py", line 506, in _mul_matrix
    return NonLazyTensor(self.evaluate() * other.evaluate())
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/utils/memoize.py", line 59, in g
    return _add_to_cache(self, cache_name, method(self, *args, **kwargs), *args, kwargs_pkl=kwargs_pkl)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py", line 906, in evaluate
    res = self.matmul(eye)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/lazy/interpolated_lazy_tensor.py", line 402, in matmul
    right_interp_res = left_t_interp(self.right_interp_indices, self.right_interp_values, tensor, base_size)
  File "/home/bizon/anaconda3/envs/primitive/lib/python3.7/site-packages/gpytorch/utils/interpolation.py", line 230, in left_t_interp
    summing_matrix = cls(summing_matrix_indices, summing_matrix_values, size)
RuntimeError: size is inconsistent with indices: for dim 1, size is 1 but found index 1

Here is how I call the function:

mtbo = MTBO(
        domain=domain,
        pretraining_data=pretraining_data,
        task=1,
        )

result = mtbo.suggest_experiments(num_experiments=1, prev_res=prev_res)

The pretraining_data and prev_res are all wrapped as DataSet format. Here is what the concatenation of pretraining_data and prev_res looks like:

new data: 
NAME approach_stiffness_trans approach_stiffness_ang  ... strategy     task
TYPE                     DATA                   DATA  ... METADATA METADATA
0                   37.500000             112.500000  ...      LHS        1
1                  112.500000              37.500000  ...      LHS        1
0                  109.855384             146.133033  ...     MTBO        1
1                   17.365006              95.320634  ...     MTBO        1
2                   88.126421              49.029255  ...     MTBO        1
..                        ...                    ...  ...      ...      ...
495                  1.076072             137.851873  ...     MTBO        1
496                 34.013880             108.785283  ...     MTBO        1
497                 30.227277             112.787455  ...     MTBO        1
498                 79.603186             126.381992  ...     MTBO        1
499                 54.544665             103.928718  ...     MTBO        1

I'm wondering if that's the correct way to construct the previous results and pretraining data from other tasks. Could you share some insights on how to debug this? Thank you very much!

opened by wuzheng-sjtu 3

Bump setuptools from 65.3.0 to 65.5.1
Bumps setuptools from 65.3.0 to 65.5.1.

Changelog

Sourced from setuptools's changelog.

v65.5.1

Misc ^^^^

#3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok

#3659: Fixed REDoS vector in package_index.

v65.5.0

Changes ^^^^^^^

#3624: Fixed editable install for multi-module/no-package src-layout projects.

#3626: Minor refactorings to support distutils using stdlib logging module.

Documentation changes ^^^^^^^^^^^^^^^^^^^^^

#3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

Misc ^^^^

#3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).

#3576: Updated version of validate_pyproject.

v65.4.1

Misc ^^^^

#3613: Fixed encoding errors in expand.StaticModule when system default encoding doesn't match expectations for source files.

#3617: Merge with pypa/[email protected] including fix for pypa/distutils#181.

v65.4.0

Changes ^^^^^^^

#3609: Merge with pypa/[email protected] including support for DIST_EXTRA_CONFIG in pypa/distutils#177.

Commits

a462cb5 Bump version: 65.5.0 → 65.5.1

de35d8b Merge pull request #3656 from bmorris3/typos

58e23de Update changelog. Ref #3659.

43a9c9b Limit the amount of whitespace to search/backtrack. Fixes #3659.

5791343 Add test capturing failed expectation. Ref #3659.

1f97905 ⚫ Fade to black.

6254567 Remove workaround for emacs.

729b180 ⚫ Fade to black.

c068081 Typo corrections

f777a40 Suppress deprecation warning in --rsyncdir. Workaround for #3655.

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dependencies
opened by dependabot[bot] 0
Bump wheel from 0.37.1 to 0.38.1
Bumps wheel from 0.37.1 to 0.38.1.

Changelog

Sourced from wheel's changelog.

Release Notes

UNRELEASED

Updated vendored packaging to 22.0

0.38.4 (2022-11-09)

Fixed PKG-INFO conversion in bdist_wheel mangling UTF-8 header values in METADATA (PR by Anderson Bravalheri)

0.38.3 (2022-11-08)

Fixed install failure when used with --no-binary, reported on Ubuntu 20.04, by removing setup_requires from setup.cfg

0.38.2 (2022-11-05)

Fixed regression introduced in v0.38.1 which broke parsing of wheel file names with multiple platform tags

0.38.1 (2022-11-04)

Removed install dependency on setuptools

The future-proof fix in 0.36.0 for converting PyPy's SOABI into a abi tag was faulty. Fixed so that future changes in the SOABI will not change the tag.

0.38.0 (2022-10-21)

Dropped support for Python < 3.7

Updated vendored packaging to 21.3

Replaced all uses of distutils with setuptools

The handling of license_files (including glob patterns and default values) is now delegated to setuptools>=57.0.0 (#466). The package dependencies were updated to reflect this change.

Fixed potential DoS attack via the WHEEL_INFO_RE regular expression

Fixed ValueError: ZIP does not support timestamps before 1980 when using SOURCE_DATE_EPOCH=0 or when on-disk timestamps are earlier than 1980-01-01. Such timestamps are now changed to the minimum value before packaging.

0.37.1 (2021-12-22)

Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)

Fixed parsing of file names containing commas in RECORD (PR by Hood Chatham)

0.37.0 (2021-08-09)

Added official Python 3.10 support

Updated vendored packaging library to v20.9

... (truncated)

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9ec2016 Removed install dependency on setuptools (#483)

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dependencies
opened by dependabot[bot] 0

SNOBFIT randomly fails

Operating System: Arch Linux
Python version: 3.10.8
summit version used: 0.8.8

Description

It did not use to happen, likely it started due to the new version of everything that I am using.

When I ask for suggested_experiments to SNOBFIT, it some times gives me the answers and some times it gives an error, and this happens repeating exactly this same code:

strategySNOBFIT = SNOBFIT(domain_constr)
strategySNOBFIT.reset()
strategySNOBFIT.suggest_experiments(2, prev_res=prev_res_extreme)

the more experiments I require, the more likely it is to fail.

This is the error:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[75], line 2
      1 strategySNOBFIT.reset()
----> 2 strategySNOBFIT.suggest_experiments(2, prev_res=prev_res_extreme)

File /home/ilario/.venv/lib/python3.10/site-packages/summit/strategies/snobfit.py:142, in SNOBFIT.suggest_experiments(self, num_experiments, prev_res, **kwargs)
    140 while not valid_next_experiments and c_iter < inner_iter_tol:
    141     valid_next_experiments = False
--> 142     next_experiments, xbest, fbest, param = self._inner_suggest_experiments(
    143         num_experiments=num_experiments,
    144         prev_res=prev_res,
    145         prev_param=inner_prev_param,
    146     )
    147     # Invalid experiments hidden from data returned to user but stored internally elswehere
    148     invalid_experiments = next_experiments.loc[
    149         next_experiments[("constraint", "DATA")] == False
    150     ]

File /home/ilario/.venv/lib/python3.10/site-packages/summit/strategies/snobfit.py:337, in SNOBFIT._inner_suggest_experiments(self, num_experiments, prev_res, prev_param)
    334 dx = (bounds[:, 1] - bounds[:, 0]) * self._dx_dim
    336 # Run SNOBFIT for one iteration
--> 337 request, xbest, fbest, param = self.snobfit(x0, y0, config, dx, prev_param)
    339 # Generate DataSet object with variable values of next experiments
    340 next_experiments = {}

File /home/ilario/.venv/lib/python3.10/site-packages/summit/strategies/snobfit.py:609, in SNOBFIT.snobfit(self, x, f, config, dx, prev_param)
    607     fnan = numpy.concatenate((fnan, ind.flatten()))
    608 if fnan.size > 0:
--> 609     f = snobnan(fnan, f, near, inew)
    611 fbest, jbest = min_(f[:, 0])
    612 xbest = x[jbest]

File /home/ilario/.venv/lib/python3.10/site-packages/SQSnobFit/_snobnan.py:78, in snobnan(fnan, f, near, inew)
     75          ind1 = numpy.concatenate((ind1, [i]), 0)
     77 if ind1.size > 0:
---> 78     ind = numpy.delete(ind, ind1, 0)
     80 if ind.size <= 0:
     81     f[l,0] = fmx + 1.e-3*(fmx-fmn)

File <__array_function__ internals>:180, in delete(*args, **kwargs)

File /home/ilario/.venv/lib/python3.10/site-packages/numpy/lib/function_base.py:5128, in delete(arr, obj, axis)
   5126 else:
   5127     keep = ones(N, dtype=bool)
-> 5128     keep[obj,] = False
   5130 slobj[axis] = keep
   5131 new = arr[tuple(slobj)]

IndexError: arrays used as indices must be of integer (or boolean) type

What I Did

python -m venv .venv     
source .venv/bin/activate
pip install -U summit

This installed these versions of the Python packages:

GPy-1.10.0 SQCommon-0.3.2 SQSnobFit-0.4.5 attrs-22.1.0 autograd-1.5 botorch-0.7.3 cma-2.7.0 contourpy-1.0.6 cycler-0.11.0 cython-0.29.32 decorator-5.1.1 fastprogress-0.2.6 fonttools-4.38.0 future-0.18.2 gpyopt-1.2.6 gpytorch-1.9.0 h5py-3.7.0 iniconfig-1.1.1 joblib-1.2.0 kiwisolver-1.4.4 linear-operator-0.2.0 llvmlite-0.38.1 matplotlib-3.6.2 multipledispatch-0.6.0 numba-0.55.2 numpy-1.22.4 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 opt-einsum-3.3.0 packaging-21.3 pandas-1.5.2 paramz-0.9.5 pillow-9.3.0 pluggy-1.0.0 py-1.11.0 pymoo-0.4.2.2 pyparsing-3.0.9 pyrff-2.0.2 pyro-api-0.1.2 pyro-ppl-1.8.3 pytest-6.2.5 python-dateutil-2.8.2 pytz-2022.6 scikit-learn-1.1.3 scipy-1.9.3 six-1.16.0 skorch-0.9.0 summit-0.8.8 tabulate-0.9.0 threadpoolctl-3.1.0 toml-0.10.2 torch-1.13.0 tqdm-4.64.1 typing-extensions-4.4.0 wheel-0.38.4

Open VSCodium and indicate to use .venv as an environment.

Run some summit stuff.

opened by ilario 1

Update botorch

See if that gets rid of these warnings

../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14
   /Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/cholesky.py:14: DeprecationWarning:gpytorch.utils.cholesky.psd_safe_cholesky is deprecated. Use linear_operator.utils.cholesky.psd_safe_cholesky instead.

../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/errors.py:15
../../../Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/errors.py:15
   /Users/Kobi/Library/Caches/pypoetry/virtualenvs/summit-TfmmV07p-py3.8/lib/python3.8/site-packages/gpytorch/utils/errors.py:15: DeprecationWarning:gpytorch.utils.errors.NotPSDError has been deprecated. Use linear_operator.utils.error.NotPSDError instead.

opened by marcosfelt 0

Update skorch
Operating System:

Python version:

summit version used:

Description

Describe what you were trying to get done. Tell us what happened, what went wrong, and what you expected to happen.

What I Did

Paste the command(s) you ran and the output.
opened by marcosfelt 0
Get optimal point and estimated values at arbitrary points
Thanks for the very nice software!

We are using it for the optimization of the synthesis of a catalyst (the objective is the activity of the catalyst).

I was wondering if two things are possible:

get the estimated optimum position from a model. I managed to get the software to suggest new experiments, but I cannot get the estimated location of the best point.

probe the model at arbitrary points (for plotting the model as heatmaps, for example, see the plots in Figure 8 here: https://pubs.acs.org/doi/10.1021/acsnano.8b04726 ).

In order to have that, I suppose that there could be a function returning the model, so that the user can interact with it directly and fetch this information.
opened by ilario 4

Releases(0.8.8)

0.8.8(Dec 2, 2022)
What's Changed

Fix SOBO issue by @marcosfelt in https://github.com/sustainable-processes/summit/pull/204

Update README example by @marcosfelt in https://github.com/sustainable-processes/summit/pull/202

Bump oauthlib from 3.2.0 to 3.2.1 by @dependabot in https://github.com/sustainable-processes/summit/pull/206

Format parity_plot correctly by @marcosfelt in https://github.com/sustainable-processes/summit/pull/210

Bump joblib from 1.1.0 to 1.2.0 by @dependabot in https://github.com/sustainable-processes/summit/pull/209

Small edits in the docs by @ilario in https://github.com/sustainable-processes/summit/pull/211

Check emulator objectives by @marcosfelt in https://github.com/sustainable-processes/summit/pull/205

Bump protobuf from 3.20.1 to 3.20.2 by @dependabot in https://github.com/sustainable-processes/summit/pull/208

Updating pandas and numpy API usage by @marcosfelt in https://github.com/sustainable-processes/summit/pull/215

TSEMO no samples by @marcosfelt in https://github.com/sustainable-processes/summit/pull/218

Improve TSEMO categorical by @marcosfelt in https://github.com/sustainable-processes/summit/pull/220

Bump version to 0.8.8 by @marcosfelt in https://github.com/sustainable-processes/summit/pull/221

New Contributors

@ilario made their first contribution in https://github.com/sustainable-processes/summit/pull/211

Full Changelog: https://github.com/sustainable-processes/summit/compare/0.8.7...0.8.8
Source code(tar.gz)
Source code(zip)
0.8.7(Sep 8, 2022)
What's Changed

Bug Fixes 🐛

Upgrading the version of pytorch to 1.11 and the version of botorch to 0.7.0 (#199)

Source code(tar.gz)
Source code(zip)
0.8.6(Jul 30, 2022)
What's Changed

Bug Fixes 🐛

Fix bug in SnAr benchmark (#187) - thanks @Yujikaiya for the issue

Fix issue with sklearn imports (#188)

Source code(tar.gz)
Source code(zip)
0.8.5(Apr 26, 2022)
What's Changed

Bug Fixes 🐛

Fix issue with MTBO rounding errors (#164)

Remove support for python 3.10 until pytorch supports python 3.10

Source code(tar.gz)
Source code(zip)
0.8.3(Jul 18, 2021)

Source code(tar.gz)
Source code(zip)
0.8.1(Apr 29, 2021)

Source code(tar.gz)
Source code(zip)
0.8.0(Apr 17, 2021)
This verison comes with new optimization strategies as well as improvements to existing functionality. You can install it using pip:

pip install --upgrade summit

Below are some highlights!

Multitask Bayesian Optimization Strategy

Multitask models have been shown to improve performance of things like drug activity and site selectivity. We extended this concept to accelerate reaction optimization in a paper published in the NeurIPs ML4 Molecules workshop last year (see the code for the paper here). This functionality is encapsulated in the MTBO strategy. The strategy works by taking data from one reaction optimization and using it to help with another.

ENTMOOT Strategy

ENTMOOT is a technique that uses gradient boosted tree models inside a bayesian optimization loop. @jezsadler of Ruth Misener's research group kindly contributed a new strategy based on their original code. It is currently an experimental feature.

Improvements to TSEMO

TSEMO is the best performing strategy in Summit for multiobjective optimization, but it previously had issues with robustness. We changed from GPy to GPytorch for the implementation of gaussian processes (GPs), which resolved this issue. Additionally, TSEMO documentation was improved and more metadata about the GP hyperparameters were added to the return of suggest_experiments.

Overhaul of the Experimental Emulator

The ExperimentalEmulator enables you to create new benchmarks based on experimental data. Underneath the hood, a machine learning model is trained, which predicts the outcomes of a reaction given the reaction conditions. The code for ExperimentalEmulator was simplified using Skorch, an extension to scikit-learn that works with pytorch. See this tutorial to learn how to create your own benchmark.

Deprecation of Gryffin

Gryffin is a strategy for optimization mixed categorical-continuous domains. This enables things like selecting catalysts when descriptors are not available. Unfortunately, there were repeated issues with installing Gryffin, so we removed it. Similar functionality can be achieved with the SOBO or MTBO strategy.

Other performance improvements and bug fixes

Some imports were inlined to improve startup performance of Summit

The dependency list was trimmed. We hope to improve this further by removing the need for GPy and GPyOpt and relying solely on GPytorch and BOtorch.

and many more!

Source code(tar.gz)
Source code(zip)
0.8.0rc1(Mar 8, 2021)
Replace GPy with GpyTorch (#94)

Improve documentation of TSEMO (#93) and the ExperimentalEmulator (#101)

Add the ability to use descriptors in the ExperimentalEmulator (#100 and #101)

Source code(tar.gz)
Source code(zip)
0.8.0rc0(Feb 19, 2021)
This is a pre-release of Denali, our newest update to Summit. Key features include:

New Multitask strategy as in Multi-task Bayesian Optimization of Chemical Reactions (see #80)

New ENTMOOT optimization strategy from this paper (#77)

A refactor of the ExperimentalEmulator to use skorch (see #89)

Deprecation of Gryffin (this is not final and might change before the full release)

Trimming down of dependencies and faster imports due to better dependency management (see #87)

The docs still need to be updated to include the two new strategies and properly explain the changes to ExperimentalEmulator.
Source code(tar.gz)
Source code(zip)
0.7.0(Jan 26, 2021)

Source code(tar.gz)
Source code(zip)
0.6.0(Sep 21, 2020)

Source code(tar.gz)
Source code(zip)
0.5.0(Sep 6, 2020)

This release has all of the strategies, benchmarks and experiments for our upcoming paper implemented.
Source code(tar.gz)
Source code(zip)
0.4.0(Jun 10, 2020)
Implemented Bayesian optimization, Nelder-Mead and Snobfit strategies

Added several benchmarks

Source code(tar.gz)
Source code(zip)
0.3.0(Apr 17, 2020)

Source code(tar.gz)
Source code(zip)
0.2.2(Oct 23, 2019)

Demos for version 0.2.2. This includes the formulation and nanosilica demos.
Source code(tar.gz)
Source code(zip)
demos.zip(225.79 KB)
0.2.0(Oct 22, 2019)

This demos of the capabilities of summit for formulation optimization.
Source code(tar.gz)
Source code(zip)
formulation.zip(129.63 KB)
0.1-alpha(Aug 20, 2019)

This is an early release with some basic features.
Source code(tar.gz)
Source code(zip)

Optimising chemical reactions using machine learning

Related tags

Overview

Summit

What is Summit?

Installation

News

Quick Start

Documentation

Issues?

Citing

Comments

Description

Description

What I Did

Description

What I Did

Description

What I Did

Description

Description

v65.5.1

v65.5.0

v65.4.1

v65.4.0

Release Notes

Description

What I Did

Description

What I Did

Releases(0.8.8)

0.8.8(Dec 2, 2022)

What's Changed

New Contributors

0.8.7(Sep 8, 2022)

What's Changed

Bug Fixes 🐛

0.8.6(Jul 30, 2022)

What's Changed

Bug Fixes 🐛

0.8.5(Apr 26, 2022)

What's Changed

Bug Fixes 🐛

0.8.3(Jul 18, 2021)

0.8.1(Apr 29, 2021)

0.8.0(Apr 17, 2021)

Multitask Bayesian Optimization Strategy

ENTMOOT Strategy

Improvements to TSEMO

Overhaul of the Experimental Emulator

Deprecation of Gryffin

Other performance improvements and bug fixes

0.8.0rc1(Mar 8, 2021)

0.8.0rc0(Feb 19, 2021)

0.7.0(Jan 26, 2021)

0.6.0(Sep 21, 2020)

0.5.0(Sep 6, 2020)

0.4.0(Jun 10, 2020)

0.3.0(Apr 17, 2020)

0.2.2(Oct 23, 2019)

0.2.0(Oct 22, 2019)

0.1-alpha(Aug 20, 2019)

Owner

Sustainable Reaction Engineering Group

Pytorch Lightning Distributed Accelerators using Ray

[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

Ranger deep learning optimizer rewrite to use newest components

3D Human Pose Machines with Self-supervised Learning

This code provides various models combining dilated convolutions with residual networks

Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CCT)

Official implementation of particle-based models (GNS and DPI-Net) on the Physion dataset.

Revisting Open World Object Detection

Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

A community run, 5-day PyTorch Deep Learning Bootcamp

NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch

A different spin on dataclasses.

Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch

TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How