Merge pull request #57 from jajcayn/feature/python3.9

Support python 3.9

.github/workflows/pythontesting.yml

@@ -5,11 +5,7 @@ name: pytest
 
 on:
   push:
-    branches:
-      - "*"
   pull_request:
-    branches:
-      - "*"
 
 jobs:
   build:
@@ -18,14 +14,20 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest]
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.6, 3.7, 3.8, 3.9]
 
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+      - name: Install MacOS dependencies
+        if: startsWith(matrix.os, 'macos')
+        run: |
+          set -e
+          brew update
+          brew install hdf5 c-blosc
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

README.md

@@ -1,6 +1,7 @@
 [![Build Status](https://github.com/jajcayn/pygpso/workflows/pytest/badge.svg)](https://github.com/jajcayn/pygpso/actions) ![](https://img.shields.io/github/v/release/jajcayn/pygpso) [![codecov](https://codecov.io/gh/jajcayn/pygpso/branch/master/graph/badge.svg)](https://codecov.io/gh/jajcayn/pygpso) [![PyPI license](https://img.shields.io/pypi/l/pygpso.svg)](https://pypi.python.org/pypi/pygpso/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples) [![DOI](https://zenodo.org/badge/236983676.svg)](https://zenodo.org/badge/latestdoi/236983676) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 # pyGPSO
+
 *Optimise anything (but mainly large-scale biophysical models) using Gaussian Processes surrogate*
 
 `pyGPSO` is a python package for Gaussian-Processes Surrogate Optimisation. GPSO is a Bayesian optimisation method designed to cope with costly, high-dimensional, non-convex problems by switching between exploration of the parameter space (using partition tree) and exploitation of the gathered knowledge (by training the surrogate function using Gaussian Processes regression). The motivation for this method stems from the optimisation of large-scale biophysical models in neuroscience when the modelled data should match the experimental one. This package leverages [`GPFlow`](https://github.com/GPflow/GPflow) for training and predicting the Gaussian Processes surrogate.
@@ -21,17 +22,32 @@ Example of ternary partition tree after optimisation.
 
 ## Installation
 
-`GPSO` package is tested and should run without any problems on python versions 3.6 -- 3.8.
+`GPSO` package is tested and should run without any problems on python versions 3.6 -- 3.9.
+
+### Note on python3.9 with macOS
+
+Installing `pytables` might give you hdf5 errors. If this is the case, please do
+
+```bash
+brew install hdf5 c-blosc
+```
+
+and all should work like a charm afterwards.
 
 ### One-liner
+
 For those who want to optimise right away just
+
 ```bash
 pip install pygpso
 ```
+
 and go ahead! Make sure to check example notebooks in [the **examples** directory](examples/) to see how it works and what it can do. Or, alternatively, you can run interactive notebooks in binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples)
 
 ### Go proper
+
 When you are the type of girl or guy who likes to install packages properly, start by cloning (or forking) this repository, then installing all the dependencies and finally install the package itself
+
 ```bash
 git clone https://github.com/jajcayn/pygpso
 cd pygpso/
@@ -40,15 +56,19 @@ pip install -r requirements.txt
 pip install -r requirements_optional.txt
 pip install .
 ```
+
 Don't forget to test!
+
 ```bash
 pytest
 ```
 
 ## Usage
+
 A guide on how to optimise and what can be done using this package is given as jupyter notebooks in [the **examples** directory](examples/). You can also try them out live thanks to binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jajcayn/pygpso.git/master?filepath=examples).
 
 The basic idea is to initialise the parameter space in which the optimisation is to be run and then iteratively dig deeper and evaluate the objective function when necessary
+
 ```python
 from gpso import ParameterSpace, GPSOptimiser
 
@@ -72,19 +92,23 @@ best_point = opt.run(objective_function)
 The package also offers plotting functions for visualising the results. Again, those are documented and showcased in [the **examples** directory](examples/).
 
 ### Notes
+
 Gaussian Processes regression uses normalised coordinates within the bounds [0, 1]. All normalisation and de-normalisation is done automatically, however when you want to call `predict_y` on GPR model, do not forget to pass normalised coordinates. The normalisation is handled by `sklearn.MinMaxScaler` and `ParameterSpace` instance offers a convenience functions for this: `ParameterSpace.normalise_coords(orig_coords)` and `ParameterSpace.denormalise_coords(normed_coords)`.
 
 Plotting of the ternary tree (`gpso.plotting.plot_ternary_tree()`) requires `igraph` package, whose layout function is exploited. If you want to see the resulting beautiful tree, please install `python-igraph`.
 
 Support of saver (for saving models run, e.g. timeseries along with the optimisation) is provided by `PyTables` (and `pandas` if you're saving results to `DataFrame`s).
 
 ## Known bugs and future improvements
+
 * saving of GP surrogate is now hacky, as `GPFlow` supports only saving model for future prediction but AFAIK they cannot be trained anymore, since the information on kernels and mean-functions are not saved (only the trained weights in the computational graph). Thus, `pyGPSO` still relies on hacky saving to `pkl` files and recreating kernels and mean-function on-the-go when loading from saved.
 
 ## Final notes
+
 When you encounter a bug or have any idea for an improvement, please open an issue and/or contact me.
 
 When using this package in publications, please cite the original Jonathan's paper for the methodology as
+
 ```bibtex
 @article{hadida2018bayesian,
   title={Bayesian Optimisation of Large-Scale Biophysical Networks},
@@ -96,4 +120,5 @@ When using this package in publications, please cite the original Jonathan's pap
   publisher={Elsevier}
 }
 ```
+
 and acknowledge the usage of this software via its DOI: [![DOI](https://zenodo.org/badge/236983676.svg)](https://zenodo.org/badge/latestdoi/236983676). After clicking, you will see citation data.

gpso/callbacks.py

@@ -141,7 +141,7 @@ def run(self, optimiser):
         if self.first_update:
             ckpt = tf.train.Checkpoint(
                 model=optimiser.gp_surr.gpflow_model,
-                evluations=self.n_evals,
+                evaluations=self.n_evals,
             )
             self.manager = tf.train.CheckpointManager(
                 ckpt, self.path, max_to_keep=self.max_to_keep
@@ -150,4 +150,4 @@ def run(self, optimiser):
             self.first_update = False
         else:
             saved_to = self.manager.save()
-        logging.debug(f"Checkppoint saved to {saved_to}")
+        logging.debug(f"Checkpoint saved to {saved_to}")

gpso/param_space.py

@@ -257,7 +257,8 @@ def _replace_normed_coord(self, index, new_coord):
     def ternary_split(self):
         """
         Parcellate this leaf using the ternary split function along the largest
-        dimension. For discussion why ternary is the best choise see the paper.
+        dimension. For the discussion on why ternary is the best choice, see the
+        paper.
 
         Hadida, J., Sotiropoulos, S. N., Abeysuriya, R. G., Woolrich, M. W., &
             Jbabdi, S. (2018). Bayesian Optimisation of Large-Scale Biophysical
@@ -436,7 +437,7 @@ def normalise_coords(self, orig_coords):
 
     def denormalise_coords(self, normed_coords):
         """
-        Deormalise normed coordinates.
+        Denormalise normed coordinates.
 
         :param normed_coords: normed coordinates in the parameter space as
             [n points x ndim]

gpso/plotting.py

@@ -48,7 +48,7 @@ def plot_ternary_tree(
     :type param_space: `gpso.param_space.ParameterSpace`
     :param cmap: colormap for scores
     :type cmap: str
-    :param cmap_limits: limits for the colormapping of the scores, if None will
+    :param cmap_limits: limits for the color mapping of the scores, if None will
         be inferred from the data
     :param center_root_node: whether to center root node within the figure -
         graph might become less readable

gpso/saving_helper.py

@@ -25,7 +25,7 @@ def __init__(self, filename, extras=None):
         :param filename: filename for the HDF file
         :type filename: str
         :param extras: extras to write as a group into HDF file, e.g. default
-            parameters of the model, which are not subject to optimisaion
+            parameters of the model, which are not subject to optimisation
         :type extras: dict|None
         """
         if not filename.endswith(H5_EXT):
@@ -54,7 +54,7 @@ def save_runs(self, result, score, parameters):
         :param result: result(s) of the run, can be single result or multiple
             results (same parameters, different results, typically valid for
             stochastic systems)
-        :type result: any pytables supported + pd.DataFrame|list of thereof
+        :type result: any `pytables` supported + pd.DataFrame|list of thereof
         :param score: score(s) of the run
         :type score: float|list[float]
         :param parameters: parameters for this particular run(s)
@@ -100,7 +100,7 @@ def _write_result(self, group, result, score):
         :param group: group to write to
         :type group: `tables.group.Group`
         :param result: result to write
-        :type result: any pytables supported + pd.DataFrame
+        :type result: any `pytables` supported + pd.DataFrame
         :param score: score to write
         :type score: float
         """
@@ -138,7 +138,7 @@ def table_reader(filename):
 
     :param filename: filename of the HDF file
     :type filename: str
-    :return: results, scores, parameters and extras if present; if mutliple
+    :return: results, scores, parameters and extras if present; if multiple
         runs per parameter set, that item is a list itself
     :rtype: (list,list,list,dict|None)
     """