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Adding TargetValue and MolecularWeight Sampler (#172)
This PR supersedes #165 and #170 and resolves #168. #165 implemented a sampler which would divide molecules based on their molecular weight (either ascending or descending), and #170 added basically the same thing but for arbitrary target values. This PR just refactors #170 as if #165 where already implemented, reducing code duplication quite a bit. There is also some small internal cleanup, namely 00eeca6, 98b3efd, and 3b8bca1 (as well as some long-overdue CI updates).
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| name: Continuous Integration | ||
| on: | ||
| schedule: | ||
| - cron: "0 8 * * 1-5" | ||
| push: | ||
| branches: [main] | ||
| pull_request: | ||
| branches: [main] | ||
| workflow_dispatch: | ||
|
|
||
| concurrency: | ||
| group: actions-id-${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ||
| cancel-in-progress: true | ||
|
|
||
| jobs: | ||
| check-formatting: | ||
| name: Check Build and Formatting Errors | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - uses: actions/checkout@v3 | ||
| - name: Install Dependencies | ||
| run: | | ||
| python -m pip install pycodestyle isort | ||
| - name: Check Build | ||
| run: | | ||
| python -m pip install . | ||
| - name: Run pycodestyle | ||
| run: | | ||
| pycodestyle --statistics --count --max-line-length=150 --show-source --ignore=E203 . | ||
| - name: Check Import Ordering Errors | ||
| run: | | ||
| isort --check-only --verbose . | ||
| build-and-test: | ||
| needs: check-formatting | ||
| continue-on-error: true | ||
| strategy: | ||
| fail-fast: false | ||
| matrix: | ||
| python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] | ||
| os: [ubuntu-latest, windows-latest, macos-latest] | ||
|
|
||
| runs-on: ${{ matrix.os }} | ||
| defaults: | ||
| run: | ||
| shell: bash -el {0} | ||
| name: ${{ matrix.os }} Python ${{ matrix.python-version }} Subtest | ||
| steps: | ||
| - uses: actions/checkout@v3 | ||
| - uses: mamba-org/setup-micromamba@main | ||
| with: | ||
| environment-name: temp | ||
| condarc: | | ||
| channels: | ||
| - defaults | ||
| - conda-forge | ||
| channel_priority: flexible | ||
| create-args: | | ||
| python=${{ matrix.python-version }} | ||
| - name: Install Dependencies | ||
| run: | | ||
| python -m pip install -e .[molecules] | ||
| python -m pip install coverage pytest | ||
| - name: Run Tests | ||
| run: | | ||
| coverage run --source=. --omit=astartes/__init__.py,setup.py,test/* -m pytest -v | ||
| - name: Show Coverage | ||
| run: | | ||
| coverage report -m | ||
| ipynb-ci: | ||
| needs: check-formatting | ||
| strategy: | ||
| fail-fast: false | ||
| matrix: | ||
| nb-file: | ||
| ["barrier_prediction_with_RDB7/RDB7_barrier_prediction_example", "train_val_test_split_sklearn_example/train_val_test_split_example", "split_comparisons/split_comparisons", "mlpds_2023_astartes_demonstration/mlpds_2023_demo"] | ||
| runs-on: ubuntu-latest | ||
| defaults: | ||
| run: | ||
| shell: bash -el {0} | ||
| name: Check ${{ matrix.nb-file }} Notebook Execution | ||
| steps: | ||
| - uses: actions/checkout@v3 | ||
| - uses: mamba-org/setup-micromamba@main | ||
| with: | ||
| environment-name: temp | ||
| condarc: | | ||
| channels: | ||
| - defaults | ||
| - conda-forge | ||
| channel_priority: flexible | ||
| create-args: | | ||
| python=3.11 | ||
| - name: Install dependencies | ||
| run: | | ||
| python -m pip install -e .[molecules,demos] | ||
| python -m pip install notebook | ||
| - name: Test Execution | ||
| run: | | ||
| cd examples/$(dirname ${{ matrix.nb-file }}) | ||
| jupyter nbconvert --to script $(basename ${{ matrix.nb-file }}).ipynb | ||
| ipython $(basename ${{ matrix.nb-file }}).py | ||
| coverage-check: | ||
| if: contains(github.event.pull_request.labels.*.name, 'PR Ready for Review') | ||
| needs: [build-and-test, ipynb-ci] | ||
| runs-on: ubuntu-latest | ||
| defaults: | ||
| run: | ||
| shell: bash -el {0} | ||
| steps: | ||
| - uses: actions/checkout@v3 | ||
| - uses: conda-incubator/setup-miniconda@v2 | ||
| with: | ||
| auto-update-conda: true | ||
| python-version: "3.10" | ||
| - name: Install Dependencies | ||
| run: | | ||
| python -m pip install -e .[molecules] | ||
| python -m pip install coverage | ||
| - name: Run Tests | ||
| run: | | ||
| coverage run --source=. --omit=astartes/__init__.py,setup.py,test/*,astartes/samplers/sampler.py -m unittest discover -v | ||
| - name: Show Coverage | ||
| run: | | ||
| coverage report -m > temp.txt | ||
| cat temp.txt | ||
| python .github/workflows/coverage_helper.py | ||
| echo "COVERAGE_PERCENT=$(cat temp2.txt)" >> $GITHUB_ENV | ||
| - name: Request Changes via Review | ||
| if: ${{ env.COVERAGE_PERCENT < 90 }} | ||
| uses: andrewmusgrave/[email protected] | ||
| with: | ||
| repo-token: ${{ secrets.GITHUB_TOKEN }} | ||
| event: REQUEST_CHANGES | ||
| body: "Increase test coverage from ${{ env.COVERAGE_PERCENT }}% to at least 90% before merging." | ||
|
|
||
| - name: Approve PR if Coverage Sufficient | ||
| if: ${{ env.COVERAGE_PERCENT > 89 }} | ||
| uses: andrewmusgrave/[email protected] | ||
| with: | ||
| repo-token: ${{ secrets.GITHUB_TOKEN }} | ||
| event: APPROVE | ||
| body: "Test coverage meets or exceeds 90% threshold (currently ${{ env.COVERAGE_PERCENT }}%)." | ||
|
|
||
| ci-report-status: | ||
| name: report CI status | ||
| needs: [build-and-test, ipynb-ci] | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - run: | | ||
| result_1="${{ needs.build-and-test.result }}" | ||
| result_2="${{ needs.ipynb-ci.result }}" | ||
| if test $result_1 == "success" && test $result_2 == "success"; then | ||
| exit 0 | ||
| else | ||
| exit 1 | ||
| fi | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,8 @@ | ||
| from .dbscan import DBSCAN | ||
| from .kmeans import KMeans | ||
| from .molecular_weight import MolecularWeight | ||
| from .optisim import OptiSim | ||
| from .scaffold import Scaffold | ||
| from .sphere_exclusion import SphereExclusion | ||
| from .target_property import TargetProperty | ||
| from .time_based import TimeBased |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| """ | ||
| This sampler partitions the data based on molecular weight. It first sorts the | ||
| molecules by molecular weight and then places the smallest molecules in the training set, | ||
| the next smallest in the validation set if applicable, and finally the largest molecules | ||
| in the testing set. | ||
| """ | ||
|
|
||
| import numpy as np | ||
|
|
||
| try: | ||
| from astartes.utils.aimsim_featurizer import featurize_molecules | ||
| except ImportError: | ||
| # this is in place so that the import of this from parent directory will work | ||
| # if it fails, it is caught in molecules instead and the error is more helpful | ||
| NO_MOLECULES = True | ||
|
|
||
| from .scaffold import Scaffold | ||
| from .target_property import TargetProperty | ||
|
|
||
|
|
||
| # inherit sample method from TargetProperty | ||
| class MolecularWeight(TargetProperty): | ||
| def _before_sample(self): | ||
| # check for invalid data types using the method in the Scaffold sampler | ||
| Scaffold._validate_input(self.X) | ||
| # calculate the average molecular weight of the molecule | ||
| self.y_backup = self.y | ||
| self.y = featurize_molecules((Scaffold.str_to_mol(i) for i in self.X), "mordred:MW", fprints_hopts={}) | ||
|
|
||
| def _after_sample(self): | ||
| # restore the original y values | ||
| self.y = self.y_backup |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| """ | ||
| This sampler partitions the data based on the regression target y. It first sorts the | ||
| data by y value and then constructs the training set to have either the smallest (largest) | ||
| y values, the validation set to have the next smallest (largest) set of y values, and the | ||
| testing set to have the largest (smallest) y values. | ||
| """ | ||
|
|
||
| import numpy as np | ||
|
|
||
| from astartes.samplers import AbstractSampler | ||
|
|
||
|
|
||
| class TargetProperty(AbstractSampler): | ||
| def _sample(self): | ||
| """ | ||
| Implements the target property sampler to create an extrapolation split. | ||
| """ | ||
| data = [(y, idx) for y, idx in zip(self.y, np.arange(len(self.y)))] | ||
|
|
||
| # by default, the smallest property values are placed in the training set | ||
| sorted_list = sorted(data, reverse=self.get_config("descending", False)) | ||
|
|
||
| self._samples_idxs = np.array([idx for time, idx in sorted_list], dtype=int) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,51 @@ | ||
| import warnings | ||
|
|
||
| import numpy as np | ||
|
|
||
| from astartes.utils.exceptions import MoleculesNotInstalledError | ||
|
|
||
| try: | ||
| """ | ||
| aimsim depends on sklearn_extra, which uses a version checking technique that is due to | ||
| be deprecated in a version of Python after 3.11, so it is throwing a deprecation warning | ||
| We ignore this warning since we can't do anything about it (sklearn_extra seems to be | ||
| abandonware) and in the future it will become an error that we can deal with. | ||
| """ | ||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter("ignore", category=DeprecationWarning) | ||
| from aimsim.chemical_datastructures import Molecule | ||
| from aimsim.exceptions import LoadingError | ||
| except ImportError: # pragma: no cover | ||
| raise MoleculesNotInstalledError("""To use molecule featurizer, install astartes with pip install astartes[molecules].""") | ||
|
|
||
|
|
||
| def featurize_molecules(molecules, fingerprint, fprints_hopts): | ||
| """Call AIMSim's Molecule to featurize the molecules according to the arguments. | ||
| Args: | ||
| molecules (np.array): SMILES strings or RDKit molecule objects. | ||
| fingerprint (str): The molecular fingerprint to be used. | ||
| fprints_hopts (dict): Hyperparameters for AIMSim. | ||
| Returns: | ||
| np.array: X array (featurized molecules) | ||
| """ | ||
| X = [] | ||
| for molecule in molecules: | ||
| try: | ||
| if type(molecule) in (np.str_, str): | ||
| mol = Molecule(mol_smiles=molecule) | ||
| else: | ||
| mol = Molecule(mol_graph=molecule) | ||
| except LoadingError as le: | ||
| raise RuntimeError( | ||
| "Unable to featurize molecules using '{:s}' with this configuration: fprint_hopts={:s}" | ||
| "\nCheck terminal output for messages from the RDkit logger. ".format(fingerprint, repr(fprints_hopts)) | ||
| ) from le | ||
| mol.descriptor.make_fingerprint( | ||
| mol.mol_graph, | ||
| fingerprint, | ||
| fingerprint_params=fprints_hopts, | ||
| ) | ||
| X.append(mol.descriptor.to_numpy()) | ||
| return np.array(X) |
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165 changes: 165 additions & 0 deletions
165
test/unit/samplers/extrapolative/test_molecular_weight.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,165 @@ | ||
| import unittest | ||
|
|
||
| import numpy as np | ||
|
|
||
| from astartes import train_test_split | ||
| from astartes.samplers import MolecularWeight | ||
|
|
||
|
|
||
| class Test_MolecularWeight(unittest.TestCase): | ||
| """ | ||
| Test the various functionalities of MolecularWeight. | ||
| """ | ||
|
|
||
| @classmethod | ||
| def setUpClass(self): | ||
| """Convenience attributes for later tests.""" | ||
| self.X = np.array( | ||
| [ | ||
| "C", | ||
| "CC", | ||
| "CCC", | ||
| "CCCC", | ||
| "CCCCC", | ||
| "CCCCCC", | ||
| "CCCCCCC", | ||
| "CCCCCCCC", | ||
| "CCCCCCCCC", | ||
| "CCCCCCCCCC", | ||
| ] | ||
| ) | ||
| self.X_inchi = np.array( | ||
| [ | ||
| "InChI=1S/CH4/h1H4", | ||
| "InChI=1S/C2H6/c1-2/h1-2H3", | ||
| "InChI=1S/C3H8/c1-3-2/h3H2,1-2H3", | ||
| "InChI=1S/C4H10/c1-3-4-2/h3-4H2,1-2H3", | ||
| "InChI=1S/C5H12/c1-3-5-4-2/h3-5H2,1-2H3", | ||
| "InChI=1S/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3", | ||
| "InChI=1S/C7H16/c1-3-5-7-6-4-2/h3-7H2,1-2H3", | ||
| "InChI=1S/C8H18/c1-3-5-7-8-6-4-2/h3-8H2,1-2H3", | ||
| "InChI=1S/C9H20/c1-3-5-7-9-8-6-4-2/h3-9H2,1-2H3", | ||
| "InChI=1S/C10H22/c1-3-5-7-9-10-8-6-4-2/h3-10H2,1-2H3", | ||
| ] | ||
| ) | ||
| self.y = np.arange(len(self.X)) | ||
| self.labels = np.array( | ||
| [ | ||
| "methane", | ||
| "ethane", | ||
| "propane", | ||
| "butane", | ||
| "pentane", | ||
| "hexane", | ||
| "heptane", | ||
| "octane", | ||
| "nonane", | ||
| "decane", | ||
| ] | ||
| ) | ||
|
|
||
| def test_molecular_weight_sampling(self): | ||
| """Use MolecularWeight in the train_test_split and verify results.""" | ||
| ( | ||
| X_train, | ||
| X_test, | ||
| y_train, | ||
| y_test, | ||
| labels_train, | ||
| labels_test, | ||
| ) = train_test_split( | ||
| self.X, | ||
| self.y, | ||
| labels=self.labels, | ||
| test_size=0.2, | ||
| train_size=0.8, | ||
| sampler="molecular_weight", | ||
| hopts={}, | ||
| ) | ||
|
|
||
| # test that the known arrays equal the result from above | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_train, | ||
| self.X[:8], # X was already sorted by ascending molecular weight | ||
| ), | ||
| "Train X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_test, | ||
| self.X[8:], # X was already sorted by ascending molecular weight | ||
| ), | ||
| "Test X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_train, | ||
| self.y[:8], # y was already sorted by ascending molecular weight | ||
| ), | ||
| "Train y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_test, | ||
| self.y[8:], # y was already sorted by ascending molecular weight | ||
| ), | ||
| "Test y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_train, | ||
| self.labels[:8], # labels was already sorted by ascending molecular weight | ||
| ), | ||
| "Train labels incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_test, | ||
| self.labels[8:], # labels was already sorted by ascending molecular weight | ||
| ), | ||
| "Test labels incorrect.", | ||
| ) | ||
|
|
||
| def test_molecular_weight(self): | ||
| """Directly instantiate and test MolecularWeight.""" | ||
| molecular_weight_instance = MolecularWeight( | ||
| self.X, | ||
| self.y, | ||
| self.labels, | ||
| {}, | ||
| ) | ||
| self.assertIsInstance( | ||
| molecular_weight_instance, | ||
| MolecularWeight, | ||
| "Failed instantiation.", | ||
| ) | ||
| self.assertFalse( | ||
| len(molecular_weight_instance.get_clusters()), | ||
| "Clusters was set when it should not have been.", | ||
| ) | ||
| self.assertTrue( | ||
| len(molecular_weight_instance._samples_idxs), | ||
| "Sample indices not set.", | ||
| ) | ||
|
|
||
| def test_incorrect_input(self): | ||
| """Calling with something other than SMILES, InChI, or RDKit Molecule should raise TypeError""" | ||
| with self.assertRaises(TypeError): | ||
| train_test_split( | ||
| np.array([[1], [2]]), | ||
| sampler="molecular_weight", | ||
| ) | ||
|
|
||
| def test_mol_from_inchi(self): | ||
| """Ability to load data from InChi inputs""" | ||
| MolecularWeight( | ||
| self.X_inchi, | ||
| None, | ||
| None, | ||
| {}, | ||
| ) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |
194 changes: 194 additions & 0 deletions
194
test/unit/samplers/extrapolative/test_target_property.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,194 @@ | ||
| import unittest | ||
|
|
||
| import numpy as np | ||
|
|
||
| from astartes import train_test_split | ||
| from astartes.samplers import TargetProperty | ||
|
|
||
|
|
||
| class Test_TargetProperty(unittest.TestCase): | ||
| """ | ||
| Test the various functionalities of TargetProperty. | ||
| """ | ||
|
|
||
| @classmethod | ||
| def setUpClass(self): | ||
| """Convenience attributes for later tests.""" | ||
| self.X = np.array( | ||
| [ | ||
| "C", | ||
| "CC", | ||
| "CCC", | ||
| "CCCC", | ||
| "CCCCC", | ||
| "CCCCCC", | ||
| "CCCCCCC", | ||
| "CCCCCCCC", | ||
| "CCCCCCCCC", | ||
| "CCCCCCCCCC", | ||
| ] | ||
| ) | ||
|
|
||
| self.y = np.arange(len(self.X)) | ||
| self.labels = np.array( | ||
| [ | ||
| "methane", | ||
| "ethane", | ||
| "propane", | ||
| "butane", | ||
| "pentane", | ||
| "hexane", | ||
| "heptane", | ||
| "octane", | ||
| "nonane", | ||
| "decane", | ||
| ] | ||
| ) | ||
|
|
||
| def test_target_property_sampling_ascending(self): | ||
| """Use TargetProperty in the train_test_split and verify results.""" | ||
| ( | ||
| X_train, | ||
| X_test, | ||
| y_train, | ||
| y_test, | ||
| labels_train, | ||
| labels_test, | ||
| ) = train_test_split( | ||
| self.X, | ||
| self.y, | ||
| labels=self.labels, | ||
| test_size=0.2, | ||
| train_size=0.8, | ||
| sampler="target_property", | ||
| hopts={"descending": False}, | ||
| ) | ||
|
|
||
| # test that the known arrays equal the result from above | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_train, | ||
| self.X[:8], # X was already sorted by ascending target value | ||
| ), | ||
| "Train X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_test, | ||
| self.X[8:], # X was already sorted by ascending target value | ||
| ), | ||
| "Test X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_train, | ||
| self.y[:8], # y was already sorted by ascending target value | ||
| ), | ||
| "Train y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_test, | ||
| self.y[8:], # y was already sorted by ascending target value | ||
| ), | ||
| "Test y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_train, | ||
| self.labels[:8], # labels was already sorted by ascending target value | ||
| ), | ||
| "Train labels incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_test, | ||
| self.labels[8:], # labels was already sorted by ascending target value | ||
| ), | ||
| "Test labels incorrect.", | ||
| ) | ||
|
|
||
| def test_target_property_sampling_descending(self): | ||
| """Use TargetProperty in the train_test_split and verify results.""" | ||
| ( | ||
| X_train, | ||
| X_test, | ||
| y_train, | ||
| y_test, | ||
| labels_train, | ||
| labels_test, | ||
| ) = train_test_split( | ||
| self.X, | ||
| self.y, | ||
| labels=self.labels, | ||
| test_size=0.2, | ||
| train_size=0.8, | ||
| sampler="target_property", | ||
| hopts={"descending": True}, | ||
| ) | ||
|
|
||
| # test that the known arrays equal the result from above | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_train, | ||
| np.flip(self.X)[:8], | ||
| ), | ||
| "Train X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| X_test, | ||
| np.flip(self.X)[8:], | ||
| ), | ||
| "Test X incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_train, | ||
| np.flip(self.y)[:8], | ||
| ), | ||
| "Train y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| y_test, | ||
| np.flip(self.y)[8:], | ||
| ), | ||
| "Test y incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_train, | ||
| np.flip(self.labels)[:8], | ||
| ), | ||
| "Train labels incorrect.", | ||
| ) | ||
| self.assertIsNone( | ||
| np.testing.assert_array_equal( | ||
| labels_test, | ||
| np.flip(self.labels)[8:], | ||
| ), | ||
| "Test labels incorrect.", | ||
| ) | ||
|
|
||
| def test_target_property(self): | ||
| """Directly instantiate and test TargetProperty.""" | ||
| target_property_instance = TargetProperty( | ||
| self.X, | ||
| self.y, | ||
| self.labels, | ||
| {}, | ||
| ) | ||
| self.assertIsInstance( | ||
| target_property_instance, | ||
| TargetProperty, | ||
| "Failed instantiation.", | ||
| ) | ||
| self.assertFalse( | ||
| len(target_property_instance.get_clusters()), | ||
| "Clusters was set when it should not have been.", | ||
| ) | ||
| self.assertTrue( | ||
| len(target_property_instance._samples_idxs), | ||
| "Sample indices not set.", | ||
| ) |
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