From 36ec3e0be8590d62c6981505777ef52ce81934c2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 13 Oct 2022 17:42:51 +0000 Subject: [PATCH 01/11] Update many dependencies Bump pymatgen from 2020.8.13 to 2022.9.21 Bumps [pymatgen](https://github.com/materialsproject/pymatgen) from 2020.8.13 to 2022.9.21. - [Release notes](https://github.com/materialsproject/pymatgen/releases) - [Changelog](https://github.com/materialsproject/pymatgen/blob/master/CHANGES.rst) - [Commits](https://github.com/materialsproject/pymatgen/compare/v2020.8.13...v2022.9.21) --- updated-dependencies: - dependency-name: pymatgen dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Bump matminer from 0.6.5 to 0.7.8 Bumps [matminer](https://github.com/hackingmaterials/matminer) from 0.6.5 to 0.7.8. - [Release notes](https://github.com/hackingmaterials/matminer/releases) - [Changelog](https://github.com/hackingmaterials/matminer/blob/main/docs/changelog.html) - [Commits](https://github.com/hackingmaterials/matminer/compare/v0.6.5...v0.7.8) --- updated-dependencies: - dependency-name: matminer dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Update sphinx requirement from ~=4.4 to ~=5.3 Updates the requirements on [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version. - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/master/CHANGES) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v4.4.0...v5.3.0) --- updated-dependencies: - dependency-name: sphinx dependency-type: direct:production ... Signed-off-by: dependabot[bot] Bump scikit-learn from 0.23.2 to 1.1.2 Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 0.23.2 to 1.1.2. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/0.23.2...1.1.2) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Bump scikit-learn from 0.23.2 to 1.2.0 Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 0.23.2 to 1.2.0. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/0.23.2...1.2.0) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Bump numpy from 1.23.4 to 1.23.5 Bumps [numpy](https://github.com/numpy/numpy) from 1.23.4 to 1.23.5. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.23.4...v1.23.5) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Bump pandas from 1.5.0 to 1.5.2 Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.5.0 to 1.5.2. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.5.0...v1.5.2) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Unpin deps in setup.py Bump matminer to 0.8 Update pre-commit hooks --- .pre-commit-config.yaml | 4 ++-- docs/requirements.txt | 2 +- requirements.txt | 10 +++++----- setup.py | 14 +++++++------- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 753957da..6a752f83 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: - id: check-symlinks - id: end-of-file-fixer - - repo: https://gitlab.com/pycqa/flake8 - rev: '3.9.2' + - repo: https://github.com/pycqa/flake8 + rev: '6.0.0' hooks: - id: flake8 diff --git a/docs/requirements.txt b/docs/requirements.txt index cfcee751..a77c0214 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ -sphinx~=4.4 +sphinx~=5.3 sphinx-rtd-theme~=1.0 sphinxcontrib-napoleon~=0.7 diff --git a/requirements.txt b/requirements.txt index 8b477f81..10b00d63 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ tensorflow==2.10.0 tensorflow-probability==0.18.0 -pandas==1.5.0 -pymatgen==2020.8.13 -matminer==0.6.5 -numpy==1.23.4 -scikit-learn==0.23.2 +pandas==1.5.2 +pymatgen==2022.9.21 +matminer==0.8.0 +numpy==1.23.5 +scikit-learn==1.2.0 diff --git a/setup.py b/setup.py index 45d311d3..25659417 100644 --- a/setup.py +++ b/setup.py @@ -34,13 +34,13 @@ include_package_data=True, packages=setuptools.find_packages(), install_requires=[ - "pandas>=0.25.3", - "tensorflow>=2.4", - "tensorflow-probability>=0.12", - "pymatgen>=2020,<2020.9", - "matminer>=0.6.2", - "numpy>=1.18.3", - "scikit-learn>=0.23,<0.24", + "pandas", + "tensorflow", + "tensorflow-probability", + "pymatgen", + "matminer", + "numpy", + "scikit-learn", ], tests_require=tests_require, test_suite="modnet.tests", From 0cafb2460dd35186af8269337d41b7253fe7fe76 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Tue, 18 Oct 2022 00:12:45 +0100 Subject: [PATCH 02/11] Update outdated pymatgen imports and associated tweaks --- .github/workflows/ci.yml | 2 +- .gitignore | 5 +++- README.md | 4 ++-- modnet/featurizers/featurizers.py | 8 +++---- modnet/featurizers/presets/debreuck_2020.py | 25 +++++++++++--------- modnet/preprocessing.py | 13 ++++++----- modnet/tests/test_benchmark.py | 5 +++- modnet/tests/test_model.py | 1 + modnet/tests/test_preprocessing.py | 26 +++++++++++++-------- setup.py | 21 +++++++---------- 10 files changed, 62 insertions(+), 48 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 291a54a8..50042f9a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,4 +44,4 @@ jobs: - name: Run tests with pytest run: | # run tests with pytest, reporting coverage and timings - py.test -rs -vvv --durations=0 --cov=./modnet/ + pytest -m "not slow" -rs -vvv --durations=0 --cov=./modnet/ diff --git a/.gitignore b/.gitignore index f5b186f2..35cf3e19 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,12 @@ +### Custom +modnet/data/ +.mypy_cache + ### Python template # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class -modnet/data/ # Distribution / packaging build/ diff --git a/README.md b/README.md index a08c34ba..a36099a5 100644 --- a/README.md +++ b/README.md @@ -47,10 +47,10 @@ Activate the environment: conda activate modnet ``` -Then, install pymatgen v2020.8.13 with conda, which will bundle several pre-built dependencies (e.g., numpy, scipy): +Then, install pymatgen from conda-forge, which will bundle several pre-built dependencies (e.g., numpy, scipy): ```shell -conda install -c conda-forge pymatgen=2020.8.13 +conda install -c conda-forge pymatgen ``` (you could alternatively do this step with `pip install pymatgen==2020.8.13`). diff --git a/modnet/featurizers/featurizers.py b/modnet/featurizers/featurizers.py index 0835668c..f90e7b6d 100644 --- a/modnet/featurizers/featurizers.py +++ b/modnet/featurizers/featurizers.py @@ -70,7 +70,7 @@ def featurize(self, df: pd.DataFrame) -> pd.DataFrame: Arguments: df: the input dataframe with a `"structure"` column - containing `pymatgen.Structure` objects. + containing pymatgen `Structure` objects. Returns: The featurized DataFrame. @@ -137,7 +137,7 @@ def featurize_composition(self, df: pd.DataFrame) -> pd.DataFrame: Arguments: df: the input dataframe with a `"structure"` column - containing `pymatgen.Structure` objects. + containing pymatgen `Structure` objects. Returns: pandas.DataFrame: the decorated DataFrame, or an empty @@ -184,7 +184,7 @@ def featurize_structure(self, df: pd.DataFrame) -> pd.DataFrame: Arguments: df: the input dataframe with a `"structure"` column - containing `pymatgen.Structure` objects. + containing pymatgen `Structure` objects. Returns: pandas.DataFrame: the decorated DataFrame. @@ -206,7 +206,7 @@ def featurize_site( Arguments: df: the input dataframe with a `"structure"` column - containing `pymatgen.Structure` objects. + containing pymatgen `Structure` objects. aliases: optional dictionary to map matminer output column names to new aliases, mostly used for backwards-compatibility. diff --git a/modnet/featurizers/presets/debreuck_2020.py b/modnet/featurizers/presets/debreuck_2020.py index 8e888e3a..034d8efa 100644 --- a/modnet/featurizers/presets/debreuck_2020.py +++ b/modnet/featurizers/presets/debreuck_2020.py @@ -151,18 +151,21 @@ def featurize_structure(self, df): df = super().featurize_structure(df) - dist = df["RadialDistributionFunction|radial distribution function"].iloc[0][ - "distances" - ][:50] - for i, d in enumerate(dist): - _rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format( - d + if "RadialDistributionFunction|radial distribution function" in df: + dist = df["RadialDistributionFunction|radial distribution function"].iloc[ + 0 + ]["distances"][:50] + for i, d in enumerate(dist): + _rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format( + d + ) + df[_rdf_key] = df[ + "RadialDistributionFunction|radial distribution function" + ].apply(lambda x: x["distribution"][i]) + + df = df.drop( + "RadialDistributionFunction|radial distribution function", axis=1 ) - df[_rdf_key] = df[ - "RadialDistributionFunction|radial distribution function" - ].apply(lambda x: x["distribution"][i]) - - df = df.drop("RadialDistributionFunction|radial distribution function", axis=1) _crystal_system = { "cubic": 1, diff --git a/modnet/preprocessing.py b/modnet/preprocessing.py index 2fb9c686..bbcbb1e4 100644 --- a/modnet/preprocessing.py +++ b/modnet/preprocessing.py @@ -13,11 +13,11 @@ from typing import Dict, List, Union, Optional, Callable, Hashable, Iterable, Tuple from functools import partial -from pymatgen import Structure, Composition +from pymatgen.core import Structure, Composition from sklearn.feature_selection import mutual_info_regression, mutual_info_classif from sklearn.utils import resample -from sklearn.preprocessing import MinMaxScaler +from sklearn.preprocessing import MinMaxScaler, Binarizer import pandas as pd import numpy as np @@ -28,6 +28,7 @@ from modnet import __version__ from modnet.utils import LOG + DATABASE = pd.DataFrame([]) @@ -539,14 +540,14 @@ def merge_ranked(lists: List[List[Hashable]]) -> List[Hashable]: class MODData: - """The MODData class takes takes a list of `pymatgen.Structure` + """The MODData class takes takes a list of pymatgen `Structure` objects and creates a `pandas.DataFrame` that contains many matminer features per structure. It then uses mutual information between features and targets, and between the features themselves, to perform feature selection using relevance-redundancy indices. Attributes: - df_structure (pd.DataFrame): dataframe storing the `pymatgen.Structure` + df_structure (pd.DataFrame): dataframe storing the pymatgen `Structure` representations for each structured, indexed by ID. df_targets (pd.Dataframe): dataframe storing the prediction targets per structure, indexed by ID. @@ -906,12 +907,12 @@ def rebalance(self): @property def structures(self) -> List[Union[Structure, CompositionContainer]]: - """Returns the list of `pymatgen.Structure` objects.""" + """Returns the list of pymatgen `Structure` objects.""" return list(self.df_structure["structure"]) @property def compositions(self) -> List[Union[Structure, CompositionContainer]]: - """Returns the list of materials as`pymatgen.Composition` objects.""" + """Returns the list of materials as pymatgen `Composition` objects.""" return [s.composition for s in self.df_structure["structure"]] @property diff --git a/modnet/tests/test_benchmark.py b/modnet/tests/test_benchmark.py index 8ac6fe58..5492a61b 100644 --- a/modnet/tests/test_benchmark.py +++ b/modnet/tests/test_benchmark.py @@ -2,6 +2,7 @@ import pytest +@pytest.mark.slow def test_train_small_model_benchmark(small_moddata, tf_session): """Tests the `matbench_benchmark()` method with optional arguments.""" from modnet.matbench.benchmark import matbench_benchmark @@ -81,6 +82,7 @@ def test_train_small_ensemblemodel_benchmark(small_moddata, tf_session): assert all(len(results[key]) == 5 for key in expected_keys) +@pytest.mark.slow def test_train_small_model_benchmark_with_extra_args(small_moddata): """Tests the `matbench_benchmark()` method with some extra settings, parallelised over 2 jobs. @@ -130,6 +132,7 @@ def test_train_small_model_benchmark_with_extra_args(small_moddata): assert all(len(results[key]) == 5 for key in expected_keys) +@pytest.mark.slow def test_ga_benchmark(small_moddata, tf_session): """Tests the `matbench_benchmark()` method with the GA strategy.""" from modnet.matbench.benchmark import matbench_benchmark @@ -156,7 +159,7 @@ def test_ga_benchmark(small_moddata, tf_session): "refit": False, }, fast=True, - n_jobs=2, + n_jobs=1, ) expected_keys = ( diff --git a/modnet/tests/test_model.py b/modnet/tests/test_model.py index 73f58c30..dad75a15 100644 --- a/modnet/tests/test_model.py +++ b/modnet/tests/test_model.py @@ -72,6 +72,7 @@ def test_train_small_model_multi_target(subset_moddata, tf_session): model.predict(data) +@pytest.mark.slow def test_train_small_model_presets(subset_moddata, tf_session): """Tests the `fit_preset()` method.""" from modnet.model_presets import gen_presets diff --git a/modnet/tests/test_preprocessing.py b/modnet/tests/test_preprocessing.py index 41ff27ce..e1ce00bb 100644 --- a/modnet/tests/test_preprocessing.py +++ b/modnet/tests/test_preprocessing.py @@ -305,20 +305,26 @@ def test_small_moddata_featurization(small_moddata): new_cols = sorted(new.df_featurized.columns.tolist()) old_cols = sorted(old.df_featurized.columns.tolist()) - for i in range(len(old_cols)): - assert new_cols[i] == old_cols[i] + # for i in range(len(old_cols)): + # assert new_cols[i] == old_cols[i] - np.testing.assert_array_equal(old_cols, new_cols) + # np.testing.assert_array_equal(old_cols, new_cols) # assert relative error below 3 percent for col in new.df_featurized.columns: - assert ( - np.absolute( - (new.df_featurized[col].to_numpy() - old.df_featurized[col].to_numpy()) - / (old.df_featurized[col].to_numpy() + 1e-6) - ).max() - < 0.03 - ) + if col in old.df_featurized.columns: + assert ( + np.max( + np.absolute( + ( + new.df_featurized[col].to_numpy() + - old.df_featurized[col].to_numpy() + ) + # / (old.df_featurized[col].to_numpy() + 1e-6) + ) + ) + < 0.03 + ) def test_small_moddata_composition_featurization(small_moddata_composition): diff --git a/setup.py b/setup.py index 25659417..5f706554 100644 --- a/setup.py +++ b/setup.py @@ -12,11 +12,8 @@ version = re.search('__version__ = "(.*)"', lines).group(1) -tests_require = ["pytest>=6.0", "pytest-cov>=2.10", "flake8>=3.8"] - -dev_require = [ - "pre-commit~=2.11", -] +tests_require = ("pytest>=6.0", "pytest-cov>=2.10", "flake8>=3.8") +dev_require = ("pre-commit~=2.11",) setuptools.setup( name="modnet", @@ -34,13 +31,13 @@ include_package_data=True, packages=setuptools.find_packages(), install_requires=[ - "pandas", - "tensorflow", - "tensorflow-probability", - "pymatgen", - "matminer", - "numpy", - "scikit-learn", + "pandas~=1.5", + "tensorflow~=2.10", + "tensorflow-probability~=0.18", + "pymatgen~=2022.9", + "matminer~=0.7", + "numpy~=1.23", + "scikit-learn~=1.1", ], tests_require=tests_require, test_suite="modnet.tests", From 7e515776d3cc35b5d7f49368ac0bcf35ec4195bd Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Tue, 13 Dec 2022 13:21:28 +0000 Subject: [PATCH 03/11] Add warning to `DeBreuck2020Featurizer` when used with newer matminer --- modnet/featurizers/presets/debreuck_2020.py | 59 ++++++++++++++++++--- 1 file changed, 51 insertions(+), 8 deletions(-) diff --git a/modnet/featurizers/presets/debreuck_2020.py b/modnet/featurizers/presets/debreuck_2020.py index 034d8efa..f50685fd 100644 --- a/modnet/featurizers/presets/debreuck_2020.py +++ b/modnet/featurizers/presets/debreuck_2020.py @@ -1,26 +1,56 @@ """ This submodule contains the DeBreuck2020Featurizer class. """ import numpy as np -from pymatgen.core.periodic_table import Element -from pymatgen.analysis.local_env import VoronoiNN import modnet.featurizers import contextlib +import warnings class DeBreuck2020Featurizer(modnet.featurizers.MODFeaturizer): - """Featurizer presets used for the paper 'Machine learning - materials properties for small datasets' by Pierre-Paul De Breuck, - Geoffroy Hautier & Gian-Marco Rignanese, arXiv:2004.14766 (2020). + """Featurizer presets used for the paper + + **Materials property prediction for limited datasets enabled + by feature selection and joint learning with MODNet**, + Pierre-Paul De Breuck, Geoffroy Hautier & Gian-Marco Rignanese + npj Comp. Mat. 7(1) 1-8 (2021) + 10.1038/s41524-021-00552-2 Uses most of the featurizers implemented by matminer at the time of writing with their default hyperparameters and presets. """ - def __init__(self, fast_oxid=False): - super().__init__() + package_version_requirements = {"matminer": "==0.6.2"} + + def __init__(self, fast_oxid: bool = False): + """Creates the featurizer and imports all featurizer functions. + + Parameters: + fast_oxid: Whether to use the accelerated oxidation state parameters within + pymatgen when constructing features that constrain oxidation states such + that all sites with the same species in a structure will have the same + oxidation state (recommended if featurizing any structure + with large unit cells). + + """ + import matminer + if matminer.__version__ != self.package_version_requirements["matminer"]: + warnings.warn( + f"The {self.__class__.__name__} preset was written for and tested only with matminer{self.package_version_requirements['matminer']}.\n" + "Newer versions of matminer will not work, and older versions may not be compatible with newer MODNet versions due to other conflicts.\n" + "To use this featurizer robustly, please install `modnet==0.1.13` with its pinned dependencies.\n\n" + "This preset will now be initialised without importing matminer featurisers to enable use with existing previously featurized data, " + "but attempts to perform further featurization will result in an error." + ) + else: + super().__init__() + self.load_featurizers() + self.fast_oxid = fast_oxid + + def load_featurizers(self): with contextlib.redirect_stdout(None): + from pymatgen.analysis.local_env import VoronoiNN from matminer.featurizers.composition import ( AtomicOrbitals, AtomicPackingEfficiency, @@ -117,13 +147,13 @@ def __init__(self, fast_oxid=False): OPSiteFingerprint(), VoronoiFingerprint(), ) - self.fast_oxid = fast_oxid def featurize_composition(self, df): """Applies the preset composition featurizers to the input dataframe, renames some fields and cleans the output dataframe. """ + from pymatgen.core.periodic_table import Element df = super().featurize_composition(df) _orbitals = {"s": 1, "p": 2, "d": 3, "f": 4} @@ -213,6 +243,19 @@ def featurize_site(self, df): class CompositionOnlyFeaturizer(DeBreuck2020Featurizer): + """This subclass simply disables structure and site-level features + frm the main `DeBreuck2020Featurizer` class. + + **Materials property prediction for limited datasets enabled + by feature selection and joint learning with MODNet** + Pierre-Paul De Breuck, Geoffroy Hautier & Gian-Marco Rignanese + npj Comp. Mat. 7(1) 1-8 (2021) + 10.1038/s41524-021-00552-2 + + Uses most of the featurizers implemented by matminer at the time of + writing with their default hyperparameters and presets. + + """ def __init__(self): super().__init__() self.oxid_composition_featurizers = () From f1ae91f92562edd3eec72499cf1fb3f909993c50 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Fri, 16 Dec 2022 17:29:28 +0100 Subject: [PATCH 04/11] Add and make default a Matminer2023 featurizer preset - Rename old test moddatas to 2020 and add 2023 versions and update tests - Add same alias to new moddata --- modnet/featurizers/presets/__init__.py | 16 +- modnet/featurizers/presets/debreuck_2020.py | 3 + modnet/featurizers/presets/matminer_2023.py | 231 ++++++++++++++++++ modnet/preprocessing.py | 14 +- modnet/tests/conftest.py | 42 +++- ...8.6_small.zip => MP_2018.6_small_2020.zip} | Bin modnet/tests/data/MP_2018.6_small_2023.zip | Bin 0 -> 220836 bytes ...p => MP_2018.6_small_composition_2020.zip} | Bin .../data/MP_2018.6_small_composition_2023.zip | Bin 0 -> 132141 bytes modnet/tests/test_model.py | 26 +- modnet/tests/test_preprocessing.py | 39 +-- setup.py | 2 +- 12 files changed, 333 insertions(+), 40 deletions(-) create mode 100644 modnet/featurizers/presets/matminer_2023.py rename modnet/tests/data/{MP_2018.6_small.zip => MP_2018.6_small_2020.zip} (100%) create mode 100644 modnet/tests/data/MP_2018.6_small_2023.zip rename modnet/tests/data/{MP_2018.6_small_composition.zip => MP_2018.6_small_composition_2020.zip} (100%) create mode 100644 modnet/tests/data/MP_2018.6_small_composition_2023.zip diff --git a/modnet/featurizers/presets/__init__.py b/modnet/featurizers/presets/__init__.py index 26479a66..f1417fb7 100644 --- a/modnet/featurizers/presets/__init__.py +++ b/modnet/featurizers/presets/__init__.py @@ -1,8 +1,20 @@ -__all__ = ("FEATURIZER_PRESETS",) +__all__ = ( + "FEATURIZER_PRESETS", + "DEFAULT_FEATURIZER", + "DEFAULT_COMPOSITION_ONLY_FEATURIZER", +) +from typing import Dict, Type from .debreuck_2020 import DeBreuck2020Featurizer, CompositionOnlyFeaturizer +from .matminer_2023 import Matminer2023Featurizer, CompositionOnlyMatminer2023Featurizer +from modnet.featurizers import MODFeaturizer -FEATURIZER_PRESETS = { +DEFAULT_FEATURIZER: str = "Matminer2023" +DEFAULT_COMPOSITION_ONLY_FEATURIZER: str = "CompositionOnlyMatminer2023" + +FEATURIZER_PRESETS: Dict[str, Type[MODFeaturizer]] = { "DeBreuck2020": DeBreuck2020Featurizer, "CompositionOnly": CompositionOnlyFeaturizer, + "Matminer2023": Matminer2023Featurizer, + "CompositionOnlyMatminer2023": CompositionOnlyMatminer2023Featurizer, } diff --git a/modnet/featurizers/presets/debreuck_2020.py b/modnet/featurizers/presets/debreuck_2020.py index f50685fd..f672f5fa 100644 --- a/modnet/featurizers/presets/debreuck_2020.py +++ b/modnet/featurizers/presets/debreuck_2020.py @@ -34,6 +34,7 @@ def __init__(self, fast_oxid: bool = False): """ import matminer + if matminer.__version__ != self.package_version_requirements["matminer"]: warnings.warn( f"The {self.__class__.__name__} preset was written for and tested only with matminer{self.package_version_requirements['matminer']}.\n" @@ -154,6 +155,7 @@ def featurize_composition(self, df): """ from pymatgen.core.periodic_table import Element + df = super().featurize_composition(df) _orbitals = {"s": 1, "p": 2, "d": 3, "f": 4} @@ -256,6 +258,7 @@ class CompositionOnlyFeaturizer(DeBreuck2020Featurizer): writing with their default hyperparameters and presets. """ + def __init__(self): super().__init__() self.oxid_composition_featurizers = () diff --git a/modnet/featurizers/presets/matminer_2023.py b/modnet/featurizers/presets/matminer_2023.py new file mode 100644 index 00000000..cf6f491c --- /dev/null +++ b/modnet/featurizers/presets/matminer_2023.py @@ -0,0 +1,231 @@ +""" This submodule contains the `Matminer2023Featurizer` class. """ + +import numpy as np +import modnet.featurizers +import contextlib + + +class Matminer2023Featurizer(modnet.featurizers.MODFeaturizer): + """A "kitchen-sink" featurizer for features implemented in matminer + at time of creation (matminer v0.8.0 from late 2022/early 2023). + + Follows the same philosophy and featurizer list as the `DeBreuck2020Featurizer` + but with with many features changing their underlying matminer implementation, + definition and behaviour since the creation of the former featurizer. + + """ + + def __init__(self, fast_oxid: bool = False): + """Creates the featurizer and imports all featurizer functions. + + Parameters: + fast_oxid: Whether to use the accelerated oxidation state parameters within + pymatgen when constructing features that constrain oxidation states such + that all sites with the same species in a structure will have the same + oxidation state (recommended if featurizing any structure + with large unit cells). + + """ + + super().__init__() + self.load_featurizers() + self.fast_oxid = fast_oxid + + def load_featurizers(self): + with contextlib.redirect_stdout(None): + from pymatgen.analysis.local_env import VoronoiNN + from matminer.featurizers.composition import ( + AtomicOrbitals, + AtomicPackingEfficiency, + BandCenter, + # CohesiveEnergy, - This descriptor was not used in the paper preset + # ElectronAffinity, - This descriptor was not used in the paper preset + ElectronegativityDiff, + ElementFraction, + ElementProperty, + IonProperty, + Miedema, + OxidationStates, + Stoichiometry, + TMetalFraction, + ValenceOrbital, + YangSolidSolution, + ) + from matminer.featurizers.structure import ( + # BagofBonds, - This descriptor was not used in the paper preset + BondFractions, + ChemicalOrdering, + CoulombMatrix, + DensityFeatures, + EwaldEnergy, + GlobalSymmetryFeatures, + MaximumPackingEfficiency, + # PartialRadialDistributionFunction, + RadialDistributionFunction, + SineCoulombMatrix, + StructuralHeterogeneity, + XRDPowderPattern, + ) + + from matminer.featurizers.site import ( + AGNIFingerprints, + AverageBondAngle, + AverageBondLength, + BondOrientationalParameter, + ChemEnvSiteFingerprint, + CoordinationNumber, + CrystalNNFingerprint, + GaussianSymmFunc, + GeneralizedRadialDistributionFunction, + LocalPropertyDifference, + OPSiteFingerprint, + VoronoiFingerprint, + ) + + self.composition_featurizers = ( + AtomicOrbitals(), + AtomicPackingEfficiency(), + BandCenter(), + ElementFraction(), + ElementProperty.from_preset("magpie"), + IonProperty(), + Miedema(), + Stoichiometry(), + TMetalFraction(), + ValenceOrbital(), + YangSolidSolution(), + ) + + self.oxid_composition_featurizers = ( + ElectronegativityDiff(), + OxidationStates(), + ) + + self.structure_featurizers = ( + DensityFeatures(), + GlobalSymmetryFeatures(), + RadialDistributionFunction(), + CoulombMatrix(), + # PartialRadialDistributionFunction(), + SineCoulombMatrix(), + EwaldEnergy(), + BondFractions(), + StructuralHeterogeneity(), + MaximumPackingEfficiency(), + ChemicalOrdering(), + XRDPowderPattern(), + # BagofBonds(), + ) + + # Patch for matminer: see https://github.com/hackingmaterials/matminer/issues/864 + self.structure_featurizers[0].desired_features = None + self.structure_featurizers[1].desired_features = None + + self.site_featurizers = ( + AGNIFingerprints(), + AverageBondAngle(VoronoiNN()), + AverageBondLength(VoronoiNN()), + BondOrientationalParameter(), + ChemEnvSiteFingerprint.from_preset("simple"), + CoordinationNumber(), + CrystalNNFingerprint.from_preset("ops"), + GaussianSymmFunc(), + GeneralizedRadialDistributionFunction.from_preset("gaussian"), + LocalPropertyDifference(), + OPSiteFingerprint(), + VoronoiFingerprint(), + ) + + def featurize_composition(self, df): + """Applies the preset composition featurizers to the input dataframe, + renames some fields and cleans the output dataframe. + + """ + from pymatgen.core.periodic_table import Element + + df = super().featurize_composition(df) + + _orbitals = {"s": 1, "p": 2, "d": 3, "f": 4} + df["AtomicOrbitals|HOMO_character"] = df["AtomicOrbitals|HOMO_character"].map( + _orbitals + ) + df["AtomicOrbitals|LUMO_character"] = df["AtomicOrbitals|LUMO_character"].map( + _orbitals + ) + + df["AtomicOrbitals|HOMO_element"] = df["AtomicOrbitals|HOMO_element"].apply( + lambda x: -1 if not isinstance(x, str) else Element(x).Z + ) + df["AtomicOrbitals|LUMO_element"] = df["AtomicOrbitals|LUMO_element"].apply( + lambda x: -1 if not isinstance(x, str) else Element(x).Z + ) + + return modnet.featurizers.clean_df(df) + + def featurize_structure(self, df): + """Applies the preset structural featurizers to the input dataframe, + renames some fields and cleans the output dataframe. + + """ + + if self.structure_featurizers: + df = super().featurize_structure(df) + + _crystal_system = { + "cubic": 1, + "tetragonal": 2, + "orthorombic": 3, + "hexagonal": 4, + "trigonal": 5, + "monoclinic": 6, + "triclinic": 7, + } + + def _int_map(x): + if x == np.nan: + return 0 + elif x: + return 1 + else: + return 0 + + df["GlobalSymmetryFeatures|crystal_system"] = df[ + "GlobalSymmetryFeatures|crystal_system" + ].map(_crystal_system) + df["GlobalSymmetryFeatures|is_centrosymmetric"] = df[ + "GlobalSymmetryFeatures|is_centrosymmetric" + ].map(_int_map) + + return modnet.featurizers.clean_df(df) + + def featurize_site(self, df): + """Applies the preset site featurizers to the input dataframe, + renames some fields and cleans the output dataframe. + + """ + + # rename some features for backwards compatibility with pretrained models + aliases = { + "GeneralizedRadialDistributionFunction": "GeneralizedRDF", + "AGNIFingerprints": "AGNIFingerPrint", + "BondOrientationalParameter": "BondOrientationParameter", + } + df = super().featurize_site(df, aliases=aliases) + df = df.loc[:, (df != 0).any(axis=0)] + + return modnet.featurizers.clean_df(df) + + +class CompositionOnlyMatminer2023Featurizer(Matminer2023Featurizer): + """This subclass simply disables structure and site-level features + frm the main `Matminer2023Featurizer` class. + + This should yield identical results to the original 2020 version. + + """ + + def __init__(self): + super().__init__() + self.oxid_composition_featurizers = () + self.structure_featurizers = () + self.site_featurizers = () diff --git a/modnet/preprocessing.py b/modnet/preprocessing.py index bbcbb1e4..bdf3bb88 100644 --- a/modnet/preprocessing.py +++ b/modnet/preprocessing.py @@ -17,7 +17,7 @@ from sklearn.feature_selection import mutual_info_regression, mutual_info_classif from sklearn.utils import resample -from sklearn.preprocessing import MinMaxScaler, Binarizer +from sklearn.preprocessing import MinMaxScaler import pandas as pd import numpy as np @@ -601,7 +601,11 @@ def __init__( """ - from modnet.featurizers.presets import FEATURIZER_PRESETS + from modnet.featurizers.presets import ( + FEATURIZER_PRESETS, + DEFAULT_FEATURIZER, + DEFAULT_COMPOSITION_ONLY_FEATURIZER, + ) self.__modnet_version__ = __version__ self.df_featurized = df_featurized @@ -645,9 +649,11 @@ def __init__( self.featurizer = featurizer elif featurizer is None and self.df_featurized is None: if getattr(self, "_composition_only", False): - self.featurizer = FEATURIZER_PRESETS["CompositionOnly"]() + self.featurizer = FEATURIZER_PRESETS[ + DEFAULT_COMPOSITION_ONLY_FEATURIZER + ]() else: - self.featurizer = FEATURIZER_PRESETS["DeBreuck2020"]() + self.featurizer = FEATURIZER_PRESETS[DEFAULT_FEATURIZER]() if self.featurizer is not None: LOG.info(f"Loaded {self.featurizer.__class__.__name__} featurizer.") diff --git a/modnet/tests/conftest.py b/modnet/tests/conftest.py index d855b78c..d5061c90 100644 --- a/modnet/tests/conftest.py +++ b/modnet/tests/conftest.py @@ -9,14 +9,22 @@ "d7d75e646dbde539645c8c0b065fd82cbe93f81d3500809655bd13d0acf2027c" "1786091a73f53985b08868c5be431a3c700f7f1776002df28ebf3a12a79ab1a1" ), - "MP_2018.6_small.zip": ( + "MP_2018.6_small_2020.zip": ( "0efc2ce998faaadc9cf54a25e1db80834c5f53b1298da0e824ee2675124f47c8" "3fce2a86971a92eb3d0a860d29e0eb37683aa47ec80af2b6c8dee879584b1491" ), - "MP_2018.6_small_composition.zip": ( + "MP_2018.6_small_2023.zip": ( + "47e3f34fe31679575b3143ea410d61d73158794018bdb09d362672fd3e048bbc" + "b2d7967bceb664918b1af77e2e6c853f3ae642a42cd8ce47db304c5612cb3aeb" + ), + "MP_2018.6_small_composition_2020.zip": ( "59f8c4e546df005799e3fb7a1e64daa0edfece48fa346ab0d2efe92aa107d0d1" "b14bb16f56bfe3f54e5a9020d088a268536f6ad86134e264ed7547b4fd583c79" ), + "MP_2018.6_small_composition_2023.zip": ( + "519e6bc8c2f7277e8f9d9f8e99d4def3fc088de1978857bfaef2aa0ff2db873e" + "c5c4bb3beeda58f1508d2ea06a98aa4743b80f991fe25007fd8f0bfa11d92edd" + ), } @@ -46,21 +54,43 @@ def subset_moddata(): @pytest.fixture(scope="function") -def small_moddata(): +def small_moddata_2023(): + """Loads the small 5-structure featurized subset of MP.2018.6 for use + in other tests, checking only the hash, updated for 2023. + + """ + return _load_moddata("MP_2018.6_small_2023.zip") + + +small_moddata = small_moddata_2023 +"""Alias for new data.""" + + +@pytest.fixture(scope="function") +def small_moddata_composition_2023(): + """Loads the small 5-structure featurized subset of MP.2018.6 composition only for use + in other tests, checking only the hash, updated for 2023. + + """ + return _load_moddata("MP_2018.6_small_composition_2023.zip") + + +@pytest.fixture(scope="function") +def small_moddata_2020(): """Loads the small 5-structure featurized subset of MP.2018.6 for use in other tests, checking only the hash. """ - return _load_moddata("MP_2018.6_small.zip") + return _load_moddata("MP_2018.6_small_2020.zip") @pytest.fixture(scope="function") -def small_moddata_composition(): +def small_moddata_composition_2020(): """Loads the small 5-structure featurized subset of MP.2018.6 composition only for use in other tests, checking only the hash. 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zbDLuaWO(r7yzIi=+)?j+9tWSmWj?6@as8@Ew@Ow1aHT4_E9(tZvB!1m%KM4R6CkxUn6PZa@kP`#8BL}azMy8WZjQn?Z7;eS~p5AbZ{<3g%)u~|W zRAx}}*}Zpu(^i{b#&nwo6^m@_j2^~ba3+Vy6fYA=iVJ3 aZ=gb60b@)>{kI)C@HGSWUjR#FWd8-1PIJZp literal 0 HcmV?d00001 diff --git a/modnet/tests/test_model.py b/modnet/tests/test_model.py index dad75a15..971bc2a8 100644 --- a/modnet/tests/test_model.py +++ b/modnet/tests/test_model.py @@ -19,7 +19,7 @@ def test_train_small_model_single_target(subset_moddata, tf_session): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) @@ -48,7 +48,7 @@ def is_metal(egap): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) def test_train_small_model_multi_target(subset_moddata, tf_session): @@ -68,7 +68,7 @@ def test_train_small_model_multi_target(subset_moddata, tf_session): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) @@ -81,7 +81,7 @@ def test_train_small_model_presets(subset_moddata, tf_session): modified_presets = gen_presets(100, 100)[:2] for ind, preset in enumerate(modified_presets): - modified_presets[ind]["epochs"] = 5 + modified_presets[ind]["epochs"] = 2 data = subset_moddata # set 'optimal' features manually @@ -128,7 +128,7 @@ def test_model_integration(subset_moddata, tf_session): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.save("test") loaded_model = MODNetModel.load("test") @@ -153,7 +153,7 @@ def test_train_small_bayesian_single_target(subset_moddata, tf_session): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) model.predict(data, return_unc=True) @@ -183,7 +183,7 @@ def is_metal(egap): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) model.predict(data, return_unc=True) @@ -205,7 +205,7 @@ def test_train_small_bayesian_multi_target(subset_moddata, tf_session): n_feat=10, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) model.predict(data, return_unc=True) @@ -229,7 +229,7 @@ def test_train_small_bootstrap_single_target(subset_moddata, tf_session): bootstrap=True, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) model.predict(data, return_unc=True) @@ -261,7 +261,7 @@ def is_metal(egap): bootstrap=True, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data) model.predict(data, return_unc=True) @@ -285,16 +285,18 @@ def test_train_small_bootstrap_multi_target(small_moddata, tf_session): bootstrap=True, ) - model.fit(data, epochs=5) + model.fit(data, epochs=2) model.predict(data, return_unc=True) @pytest.mark.slow def test_train_small_bootstrap_presets(small_moddata, tf_session): """Tests the `fit_preset()` method.""" + import time from modnet.model_presets import gen_presets from modnet.models import EnsembleMODNetModel + start = time.time() modified_presets = gen_presets(100, 100)[:2] for ind, preset in enumerate(modified_presets): @@ -314,6 +316,7 @@ def test_train_small_bootstrap_presets(small_moddata, tf_session): n_models=2, bootstrap=True, ) + print(f"{time.time() - start} elapsed after model creation.") # nested=0/False -> no inner loop, so only 1 model # nested=1/True -> inner loop, but default n_folds so 5 @@ -325,6 +328,7 @@ def test_train_small_bootstrap_presets(small_moddata, tf_session): val_fraction=0.2, n_jobs=2, ) + print(f"{time.time() - start} elapsed nested {num_nested}, {nested_option}") models = results[0] assert len(models) == len(modified_presets) assert len(models[0]) == num_nested diff --git a/modnet/tests/test_preprocessing.py b/modnet/tests/test_preprocessing.py index e1ce00bb..16fafdb1 100644 --- a/modnet/tests/test_preprocessing.py +++ b/modnet/tests/test_preprocessing.py @@ -291,24 +291,28 @@ def test_load_moddata_zip(subset_moddata): assert len(data.df_targets) == 100 -def test_small_moddata_featurization(small_moddata): +def test_small_moddata_featurization(small_moddata_2023): """This test creates a new MODData from the MP 2018.6 structures.""" - old = small_moddata + from modnet.featurizers.presets import Matminer2023Featurizer + + old = small_moddata_2023 structures = old.structures targets = old.targets names = old.names - new = MODData(structures, targets, target_names=names) + new = MODData( + structures, targets, target_names=names, featurizer=Matminer2023Featurizer() + ) new.featurize(fast=False, n_jobs=1) new_cols = sorted(new.df_featurized.columns.tolist()) old_cols = sorted(old.df_featurized.columns.tolist()) - # for i in range(len(old_cols)): - # assert new_cols[i] == old_cols[i] + for i in range(len(old_cols)): + assert new_cols[i] == old_cols[i] - # np.testing.assert_array_equal(old_cols, new_cols) + np.testing.assert_array_equal(old_cols, new_cols) # assert relative error below 3 percent for col in new.df_featurized.columns: @@ -320,20 +324,23 @@ def test_small_moddata_featurization(small_moddata): new.df_featurized[col].to_numpy() - old.df_featurized[col].to_numpy() ) - # / (old.df_featurized[col].to_numpy() + 1e-6) + / (old.df_featurized[col].to_numpy() + 1e-6) ) ) < 0.03 ) -def test_small_moddata_composition_featurization(small_moddata_composition): +def test_small_moddata_composition_featurization(small_moddata_composition_2023): """This test creates a new MODData from the MP 2018.6 structures.""" + from modnet.featurizers.presets import CompositionOnlyMatminer2023Featurizer - reference = small_moddata_composition + reference = small_moddata_composition_2023 compositions = reference.compositions - new = MODData(materials=compositions) + new = MODData( + materials=compositions, featurizer=CompositionOnlyMatminer2023Featurizer() + ) new.featurize(fast=False, n_jobs=1) new_cols = sorted(new.df_featurized.columns.tolist()) @@ -357,7 +364,7 @@ def test_small_moddata_composition_featurization(small_moddata_composition): ) -def test_small_moddata_feature_selection_classif(small_moddata): +def test_small_moddata_feature_selection_classif(small_moddata_2023): """This test creates classifier MODData and test the feature selection method""" x1 = np.array([0] * 500 + [1] * 500 + [2] * 500, dtype="float") @@ -472,13 +479,13 @@ def test_moddata_splits(subset_moddata): break -def test_precomputed_cross_nmi(small_moddata): +def test_precomputed_cross_nmi(small_moddata_2020): new = MODData( - materials=small_moddata.structures, - targets=small_moddata.targets, - target_names=small_moddata.names, - df_featurized=small_moddata.df_featurized, + materials=small_moddata_2020.structures, + targets=small_moddata_2020.targets, + target_names=small_moddata_2020.names, + df_featurized=small_moddata_2020.df_featurized, ) new.feature_selection(5, use_precomputed_cross_nmi=True) diff --git a/setup.py b/setup.py index 5f706554..285ddfbd 100644 --- a/setup.py +++ b/setup.py @@ -35,7 +35,7 @@ "tensorflow~=2.10", "tensorflow-probability~=0.18", "pymatgen~=2022.9", - "matminer~=0.7", + "matminer~=0.8", "numpy~=1.23", "scikit-learn~=1.1", ], From 57ad6b726c5a62dc8685d201208a5a73248be707 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Fri, 16 Dec 2022 19:14:38 +0100 Subject: [PATCH 05/11] Add pip dependency cache --- .github/workflows/ci.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 50042f9a..6b9f4ea1 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,12 +11,22 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 30 steps: + - uses: actions/checkout@v1 + - name: Set up Python 3.8 uses: actions/setup-python@v1 with: python-version: 3.8 + - name: Cache pip deps + uses: actions/cache@v3 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + - name: Install dependencies run: | python -m pip install --upgrade pip From cdd92ea84f2aed55806fd0ceb22eb5b0ec34b495 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Fri, 16 Dec 2022 19:27:08 +0100 Subject: [PATCH 06/11] Bump modnet version to 0.2.0~develop --- modnet/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modnet/__init__.py b/modnet/__init__.py index 3cb7d95e..fe087a7d 100644 --- a/modnet/__init__.py +++ b/modnet/__init__.py @@ -1 +1 @@ -__version__ = "0.1.13" +__version__ = "0.2.0~develop" From e8a528d1c5539edc1d5fb9e03afa581c78f32d4e Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Sun, 18 Dec 2022 23:39:03 +0100 Subject: [PATCH 07/11] Fix CI pip install process --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6b9f4ea1..6e1182cb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,7 +31,7 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - pip install --ignore-installed .[test,dev] + pip install .[test,dev] - name: Run pre-commit run: | From f80ffe32f3adbac4f7111aef0f3552f1421316a7 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Mon, 19 Dec 2022 17:27:22 +0100 Subject: [PATCH 08/11] Simplify CI pip cache --- .github/workflows/ci.yml | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6e1182cb..bd36fb40 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,14 +18,9 @@ jobs: uses: actions/setup-python@v1 with: python-version: 3.8 - - - name: Cache pip deps - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} - restore-keys: | - ${{ runner.os }}-pip- + cache: 'pip' + cache-dependency-path: | + **/requirements*.txt - name: Install dependencies run: | From 31c84915a442ae101a1b1797c2226fbc600e7a65 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Tue, 20 Dec 2022 14:14:57 +0100 Subject: [PATCH 09/11] Slow dependabot down and also configure gh actions updates --- .github/dependabot.yml | 9 ++++++++- .github/workflows/ci.yml | 4 ++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 1c0d5c1a..76d30044 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -3,8 +3,15 @@ updates: - package-ecosystem: pip directory: "/" schedule: - interval: daily + interval: monthly open-pull-requests-limit: 10 target-branch: master labels: - dependency_updates +- package-ecosystem: github-actions + directory: "/" + schedule: + interval: monthly + target-branch: master + labels: + - CI diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index bd36fb40..76f70ebf 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,10 +12,10 @@ jobs: timeout-minutes: 30 steps: - - uses: actions/checkout@v1 + - uses: actions/checkout@v3 - name: Set up Python 3.8 - uses: actions/setup-python@v1 + uses: actions/setup-python@v4 with: python-version: 3.8 cache: 'pip' From 96da75e0a9229b3725f8d168c6e12803f3f698cf Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Tue, 20 Dec 2022 21:48:31 +0100 Subject: [PATCH 10/11] Remove specific pymatgen clause in README --- README.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/README.md b/README.md index a36099a5..15c972ff 100644 --- a/README.md +++ b/README.md @@ -47,14 +47,6 @@ Activate the environment: conda activate modnet ``` -Then, install pymatgen from conda-forge, which will bundle several pre-built dependencies (e.g., numpy, scipy): - -```shell -conda install -c conda-forge pymatgen -``` - -(you could alternatively do this step with `pip install pymatgen==2020.8.13`). - Finally, install MODNet from PyPI with `pip`: ```shell From 4e964247d9188c1ff8e199bd308373e21cd36ee3 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Tue, 20 Dec 2022 21:56:43 +0100 Subject: [PATCH 11/11] Fix some typos and format changes - Fix spelling 'mistake' - Fix another typo - Fix shields.io badge using new format - Fix hopefully final typo --- README.md | 2 +- modnet/featurizers/presets/debreuck_2020.py | 8 +++++--- modnet/featurizers/presets/matminer_2023.py | 2 +- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 15c972ff..3457a3c0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MODNet: Material Optimal Descriptor Network -[![arXiv](https://img.shields.io/badge/arXiv-2004.14766-brightgreen)](https://arxiv.org/abs/2004.14766) [![Build Status](https://img.shields.io/github/workflow/status/ppdebreuck/modnet/Run%20tests?logo=github)](https://github.com/ppdebreuck/modnet/actions?query=branch%3Amaster+) [![Read the Docs](https://img.shields.io/readthedocs/modnet)](https://modnet.readthedocs.io/en/latest/) +[![arXiv](https://img.shields.io/badge/arXiv-2004.14766-brightgreen)](https://arxiv.org/abs/2004.14766) [![Build Status](https://img.shields.io/github/actions/workflow/status/ppdebreuck/modnet/ci.yml?logo=github&branch=main)](https://github.com/ppdebreuck/modnet/actions?query=branch%3Amaster+) [![Read the Docs](https://img.shields.io/readthedocs/modnet)](https://modnet.readthedocs.io/en/latest/) ## Introduction diff --git a/modnet/featurizers/presets/debreuck_2020.py b/modnet/featurizers/presets/debreuck_2020.py index f672f5fa..dd588f14 100644 --- a/modnet/featurizers/presets/debreuck_2020.py +++ b/modnet/featurizers/presets/debreuck_2020.py @@ -35,12 +35,14 @@ def __init__(self, fast_oxid: bool = False): """ import matminer - if matminer.__version__ != self.package_version_requirements["matminer"]: + if matminer.__version__ != self.package_version_requirements[ + "matminer" + ].replace("==", ""): warnings.warn( f"The {self.__class__.__name__} preset was written for and tested only with matminer{self.package_version_requirements['matminer']}.\n" "Newer versions of matminer will not work, and older versions may not be compatible with newer MODNet versions due to other conflicts.\n" "To use this featurizer robustly, please install `modnet==0.1.13` with its pinned dependencies.\n\n" - "This preset will now be initialised without importing matminer featurisers to enable use with existing previously featurized data, " + "This preset will now be initialised without importing matminer featurizers to enable use with existing previously featurized data, " "but attempts to perform further featurization will result in an error." ) @@ -246,7 +248,7 @@ def featurize_site(self, df): class CompositionOnlyFeaturizer(DeBreuck2020Featurizer): """This subclass simply disables structure and site-level features - frm the main `DeBreuck2020Featurizer` class. + from the main `DeBreuck2020Featurizer` class. **Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet** diff --git a/modnet/featurizers/presets/matminer_2023.py b/modnet/featurizers/presets/matminer_2023.py index cf6f491c..14b0f3cf 100644 --- a/modnet/featurizers/presets/matminer_2023.py +++ b/modnet/featurizers/presets/matminer_2023.py @@ -218,7 +218,7 @@ def featurize_site(self, df): class CompositionOnlyMatminer2023Featurizer(Matminer2023Featurizer): """This subclass simply disables structure and site-level features - frm the main `Matminer2023Featurizer` class. + from the main `Matminer2023Featurizer` class. This should yield identical results to the original 2020 version.