From c5afddeac61fc69de0b97f80edd259f851399963 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 08:27:03 +0100 Subject: [PATCH 01/15] refactor: remove commented out code Which was described on GitHub --- scikit_mol/conversions.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/scikit_mol/conversions.py b/scikit_mol/conversions.py index 0f66858..0488f39 100644 --- a/scikit_mol/conversions.py +++ b/scikit_mol/conversions.py @@ -53,9 +53,7 @@ def transform(self, X_smiles_list, y=None): n_chunks = n_processes*2 if n_processes is not None else multiprocessing.cpu_count()*2 #TODO, tune the number of chunks per child process with get_context(self.start_method).Pool(processes=n_processes) as pool: x_chunks = np.array_split(X_smiles_list, n_chunks) - #x_chunks = [x.reshape(-1, 1) for x in x_chunks] Why the reshape? it doesn't exist on things like e.g. Pandas Arrays or Series arrays = pool.map(self._transform, x_chunks) #is the helper function a safer way of handling the picklind and child process communication - arr = np.concatenate(arrays) return arr From b747f47ba4d22646b3f4c2684c058ef1ea4a0590 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 09:09:08 +0100 Subject: [PATCH 02/15] test: set minor python version for pandas output and skip pandas_output tests --- scikit_mol/core.py | 28 ++++++++++++++++++++++++++++ tests/test_desctransformer.py | 5 ++++- tests/test_smilestomol.py | 5 +++++ tests/test_transformers.py | 3 +++ 4 files changed, 40 insertions(+), 1 deletion(-) diff --git a/scikit_mol/core.py b/scikit_mol/core.py index 9ecdf41..8a9f1c1 100644 --- a/scikit_mol/core.py +++ b/scikit_mol/core.py @@ -6,10 +6,38 @@ """ import functools +from dataclasses import dataclass +from typing import Optional import numpy as np import pandas as pd +@dataclass +class PythonVersion: + """ + Simple class to represent a Python version. + """ + major: int + minor: int + patch: Optional[int] = None + + def __str__(self): + """The string representation of the Python version.""" + if self.patch is not None: + return f"{self.major}.{self.minor}.{self.patch}" + else: + return f"{self.major}.{self.minor}" + + def as_tuple(self): + """The Python version as a tuple.""" + if self.patch is not None: + return (self.major, self.minor, self.patch) + else: + return (self.major, self.minor) + +MIN_PYTHON_FOR_PANDAS_OUT = PythonVersion(3, 8) + + def _validate_transform_input(X): """Validate and adapt the input of the _transform method""" try: diff --git a/tests/test_desctransformer.py b/tests/test_desctransformer.py index 200adf2..69cf55a 100644 --- a/tests/test_desctransformer.py +++ b/tests/test_desctransformer.py @@ -1,17 +1,18 @@ import time +import sys import pytest import numpy as np import pandas as pd from rdkit.Chem import Descriptors from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer +from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT from fixtures import mols_list, smiles_list, mols_container, smiles_container from sklearn import clone from sklearn.pipeline import Pipeline import joblib - @pytest.fixture def default_descriptor_transformer(): return MolecularDescriptorTransformer() @@ -74,6 +75,7 @@ def test_descriptor_transformer_parallel(mols_list, default_descriptor_transform assert(len(features2[0]) == len(Descriptors._descList)) +@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output(mols_container, default_descriptor_transformer, selected_descriptor_transformer, pandas_output): for transformer in [default_descriptor_transformer, selected_descriptor_transformer]: features = transformer.transform(mols_container) @@ -81,6 +83,7 @@ def test_descriptor_transformer_pandas_output(mols_container, default_descriptor assert features.shape[0] == len(mols_container) assert features.columns.tolist() == transformer.selected_descriptors +@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output_pipeline(smiles_container, default_descriptor_transformer, pandas_output): pipeline = Pipeline([("s2m", SmilesToMolTransformer()), ("desc", default_descriptor_transformer)]) features = pipeline.fit_transform(smiles_container) diff --git a/tests/test_smilestomol.py b/tests/test_smilestomol.py index 3f14392..157f934 100644 --- a/tests/test_smilestomol.py +++ b/tests/test_smilestomol.py @@ -1,9 +1,11 @@ +import sys import pytest import numpy as np import pandas as pd from sklearn import clone from rdkit import Chem from scikit_mol.conversions import SmilesToMolTransformer +from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT from fixtures import smiles_list, invalid_smiles_list, smiles_container @@ -39,6 +41,9 @@ def test_descriptor_transformer_parallel(smiles_container, smilestomol_transform expected_smiles = smiles_container assert all([ a == b for a, b in zip(expected_smiles, [Chem.MolToSmiles(mol) for mol in mol_list.flatten()])]) +# TODO: see if it is possible to define this skipif condition +# as its own decorator / fixture, to avoid duplication, even though it is simple. +@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason="requires Python 3.8 or higher") def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output): mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) diff --git a/tests/test_transformers.py b/tests/test_transformers.py index b700918..1b8f7bb 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -6,12 +6,14 @@ # pytest tests/test_transformers.py --> tests/test_transformers.py::test_transformer PASSED +import sys import pytest import pandas as pd import sklearn from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestRegressor from scikit_mol.conversions import SmilesToMolTransformer +from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ MHFingerprintTransformer, AvalonFingerprintTransformer @@ -61,6 +63,7 @@ def test_transformer(SLC6A4_subset): assert len(failed_FP) == 0, f"the following FP have failed {failed_FP}" +@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason="requires Python 3.6 or higher") def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # load some toy data for quick testing on a small number of samples X_smiles = SLC6A4_subset.SMILES From c54a9c5a62ab47ca2e03118d42cee46a2b77f43a Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 09:16:43 +0100 Subject: [PATCH 03/15] test: add tests for pandas container with different names and no name So we test that everything works, regardless of the specific name of the pandas container. --- tests/fixtures.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/tests/fixtures.py b/tests/fixtures.py index 96bae40..38f27d3 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -24,9 +24,18 @@ def smiles_list(): lambda x: x, lambda x: np.array(x), lambda x: np.array(x).reshape(-1, 1), - lambda x: pd.Series(x), - lambda x: pd.DataFrame({"hello": x}), ] +_names_to_test = [ + "molecule", + "mol", + "smiles", + "ROMol", + "hello", + None, +] +for name in _names_to_test: + _CONTAINER_CREATORS.append(lambda x: pd.Series(x, name=name)) + _CONTAINER_CREATORS.append(lambda x: pd.DataFrame({name: x}) if name else pd.DataFrame(x)) @pytest.fixture(params=[container(_CANONICAL_SMILES_LIST) for container in _CONTAINER_CREATORS] ) From 3928593ace866304955d512231723ec235df74bc Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 09:18:39 +0100 Subject: [PATCH 04/15] build: include github test runs for Python versions <= 3.12 I executed the code locally with 3.12.2, and it worked. --- .github/workflows/run_pytests.yaml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.github/workflows/run_pytests.yaml b/.github/workflows/run_pytests.yaml index c5237ab..af03347 100644 --- a/.github/workflows/run_pytests.yaml +++ b/.github/workflows/run_pytests.yaml @@ -15,6 +15,12 @@ jobs: python-version: ["3.10"] include: # test python version compatibility on linux only + - os: ubuntu-latest + python-version: 3.12 + - os: ubuntu-latest + python-version: 3.11 + - os: ubuntu-latest + python-version: 3.10 - os: ubuntu-latest python-version: 3.9 - os: ubuntu-latest From 47efbe177a43b091f5fefe4e2956cc94d992208d Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 23:03:54 +0100 Subject: [PATCH 05/15] refactor: use better Version class to skip tests based on versions NOTE: this include an additional mandatory dependency, which is very small and pure-python: the `packaging` package. It's a package from the Python Packaging Authority, and it's the best tool to handle version information. It wasn't strictly necessary for this simple version check. But in the future, more complex version checks may be needed, and this package will help a lot. --- scikit_mol/core.py | 28 ++-------------------------- setup.cfg | 1 + tests/test_desctransformer.py | 4 ++-- tests/test_smilestomol.py | 2 +- tests/test_transformers.py | 2 +- 5 files changed, 7 insertions(+), 30 deletions(-) diff --git a/scikit_mol/core.py b/scikit_mol/core.py index 8a9f1c1..b7fd2d9 100644 --- a/scikit_mol/core.py +++ b/scikit_mol/core.py @@ -6,36 +6,12 @@ """ import functools -from dataclasses import dataclass -from typing import Optional import numpy as np import pandas as pd +from packaging.version import Version -@dataclass -class PythonVersion: - """ - Simple class to represent a Python version. - """ - major: int - minor: int - patch: Optional[int] = None - - def __str__(self): - """The string representation of the Python version.""" - if self.patch is not None: - return f"{self.major}.{self.minor}.{self.patch}" - else: - return f"{self.major}.{self.minor}" - - def as_tuple(self): - """The Python version as a tuple.""" - if self.patch is not None: - return (self.major, self.minor, self.patch) - else: - return (self.major, self.minor) - -MIN_PYTHON_FOR_PANDAS_OUT = PythonVersion(3, 8) +MIN_PYTHON_FOR_PANDAS_OUT = Version("3.8") def _validate_transform_input(X): diff --git a/setup.cfg b/setup.cfg index 2bf9f11..0e0e008 100644 --- a/setup.cfg +++ b/setup.cfg @@ -34,6 +34,7 @@ install_requires = numpy pandas scikit-learn + packaging [options.packages.find] exclude = diff --git a/tests/test_desctransformer.py b/tests/test_desctransformer.py index 69cf55a..9c7c08b 100644 --- a/tests/test_desctransformer.py +++ b/tests/test_desctransformer.py @@ -75,7 +75,7 @@ def test_descriptor_transformer_parallel(mols_list, default_descriptor_transform assert(len(features2[0]) == len(Descriptors._descList)) -@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") +@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output(mols_container, default_descriptor_transformer, selected_descriptor_transformer, pandas_output): for transformer in [default_descriptor_transformer, selected_descriptor_transformer]: features = transformer.transform(mols_container) @@ -83,7 +83,7 @@ def test_descriptor_transformer_pandas_output(mols_container, default_descriptor assert features.shape[0] == len(mols_container) assert features.columns.tolist() == transformer.selected_descriptors -@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") +@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output_pipeline(smiles_container, default_descriptor_transformer, pandas_output): pipeline = Pipeline([("s2m", SmilesToMolTransformer()), ("desc", default_descriptor_transformer)]) features = pipeline.fit_transform(smiles_container) diff --git a/tests/test_smilestomol.py b/tests/test_smilestomol.py index 157f934..ce2aba9 100644 --- a/tests/test_smilestomol.py +++ b/tests/test_smilestomol.py @@ -43,7 +43,7 @@ def test_descriptor_transformer_parallel(smiles_container, smilestomol_transform # TODO: see if it is possible to define this skipif condition # as its own decorator / fixture, to avoid duplication, even though it is simple. -@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason="requires Python 3.8 or higher") +@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason="requires Python 3.8 or higher") def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output): mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) diff --git a/tests/test_transformers.py b/tests/test_transformers.py index 1b8f7bb..06c463d 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -63,7 +63,7 @@ def test_transformer(SLC6A4_subset): assert len(failed_FP) == 0, f"the following FP have failed {failed_FP}" -@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.as_tuple(), reason="requires Python 3.6 or higher") +@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason="requires Python 3.6 or higher") def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # load some toy data for quick testing on a small number of samples X_smiles = SLC6A4_subset.SMILES From 99cf3668840bbc488a58fb242a81ff17d142bdc3 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 23:20:30 +0100 Subject: [PATCH 06/15] test: skip tests based on sklearn version It's not Python < 3.8 that doesn't support the sklearn set_output API: it's a sklearn version < 1.2. A sklearn version < 1.2 could be present in an environment with Python > 3.8. NOTE: should we raise a warning if the get_feature_names methods are used with sklearn < 1.2? In theory that method could still be used manually, but it would not be used authomatically by the sklearn set_output machinery. --- scikit_mol/core.py | 2 +- tests/test_desctransformer.py | 9 +++++---- tests/test_smilestomol.py | 7 ++++--- tests/test_transformers.py | 6 +++--- 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/scikit_mol/core.py b/scikit_mol/core.py index b7fd2d9..66514c4 100644 --- a/scikit_mol/core.py +++ b/scikit_mol/core.py @@ -11,7 +11,7 @@ import pandas as pd from packaging.version import Version -MIN_PYTHON_FOR_PANDAS_OUT = Version("3.8") +SKLEARN_VERSION_PANDAS_OUT = Version("1.2") def _validate_transform_input(X): diff --git a/tests/test_desctransformer.py b/tests/test_desctransformer.py index 9c7c08b..b4e7215 100644 --- a/tests/test_desctransformer.py +++ b/tests/test_desctransformer.py @@ -1,12 +1,13 @@ import time -import sys import pytest import numpy as np import pandas as pd from rdkit.Chem import Descriptors +import sklearn +from packaging.version import Version from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer -from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT from fixtures import mols_list, smiles_list, mols_container, smiles_container from sklearn import clone from sklearn.pipeline import Pipeline @@ -75,7 +76,7 @@ def test_descriptor_transformer_parallel(mols_list, default_descriptor_transform assert(len(features2[0]) == len(Descriptors._descList)) -@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") +@pytest.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires Python {SKLEARN_VERSION_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output(mols_container, default_descriptor_transformer, selected_descriptor_transformer, pandas_output): for transformer in [default_descriptor_transformer, selected_descriptor_transformer]: features = transformer.transform(mols_container) @@ -83,7 +84,7 @@ def test_descriptor_transformer_pandas_output(mols_container, default_descriptor assert features.shape[0] == len(mols_container) assert features.columns.tolist() == transformer.selected_descriptors -@pytest.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason=f"requires Python {MIN_PYTHON_FOR_PANDAS_OUT} or higher") +@pytest.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires Python {SKLEARN_VERSION_PANDAS_OUT} or higher") def test_descriptor_transformer_pandas_output_pipeline(smiles_container, default_descriptor_transformer, pandas_output): pipeline = Pipeline([("s2m", SmilesToMolTransformer()), ("desc", default_descriptor_transformer)]) features = pipeline.fit_transform(smiles_container) diff --git a/tests/test_smilestomol.py b/tests/test_smilestomol.py index ce2aba9..cba3229 100644 --- a/tests/test_smilestomol.py +++ b/tests/test_smilestomol.py @@ -1,11 +1,12 @@ -import sys import pytest import numpy as np import pandas as pd +from packaging.version import Version from sklearn import clone from rdkit import Chem +import sklearn from scikit_mol.conversions import SmilesToMolTransformer -from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT from fixtures import smiles_list, invalid_smiles_list, smiles_container @@ -43,7 +44,7 @@ def test_descriptor_transformer_parallel(smiles_container, smilestomol_transform # TODO: see if it is possible to define this skipif condition # as its own decorator / fixture, to avoid duplication, even though it is simple. -@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason="requires Python 3.8 or higher") +@pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason="requires Python 3.8 or higher") def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output): mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) diff --git a/tests/test_transformers.py b/tests/test_transformers.py index 06c463d..9e34f0c 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -6,14 +6,14 @@ # pytest tests/test_transformers.py --> tests/test_transformers.py::test_transformer PASSED -import sys import pytest import pandas as pd +from packaging.version import Version import sklearn from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestRegressor from scikit_mol.conversions import SmilesToMolTransformer -from scikit_mol.core import MIN_PYTHON_FOR_PANDAS_OUT +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ MHFingerprintTransformer, AvalonFingerprintTransformer @@ -63,7 +63,7 @@ def test_transformer(SLC6A4_subset): assert len(failed_FP) == 0, f"the following FP have failed {failed_FP}" -@pytest.mark.skipif(sys.version_info < MIN_PYTHON_FOR_PANDAS_OUT.release, reason="requires Python 3.6 or higher") +@pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason="requires Python 3.6 or higher") def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # load some toy data for quick testing on a small number of samples X_smiles = SLC6A4_subset.SMILES From 11e0988a594053446478a1e6da07ac17925fcdea Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Fri, 8 Mar 2024 23:28:02 +0100 Subject: [PATCH 07/15] refactor: define the skip pandas out tests decorator Also, improve the message. --- tests/fixtures.py | 8 +++++++- tests/test_desctransformer.py | 6 +++--- tests/test_smilestomol.py | 6 ++---- tests/test_transformers.py | 4 ++-- 4 files changed, 14 insertions(+), 10 deletions(-) diff --git a/tests/fixtures.py b/tests/fixtures.py index 38f27d3..d412ff8 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -4,6 +4,10 @@ import pandas as pd from rdkit import Chem from rdkit.Chem import rdMolDescriptors +from packaging.version import Version +import sklearn + +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT #TODO these should really go into the conftest.py, so that they are automatically imported in the tests @@ -77,4 +81,6 @@ def fingerprint(mols_list): def SLC6A4_subset(): file_path = os.path.realpath(__file__) data = pd.read_csv(f"{os.path.split(file_path)[0]}/data/SLC6A4_active_excapedb_subset.csv") - return data \ No newline at end of file + return data + +skip_pandas_output_test = pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher") \ No newline at end of file diff --git a/tests/test_desctransformer.py b/tests/test_desctransformer.py index b4e7215..959f9fc 100644 --- a/tests/test_desctransformer.py +++ b/tests/test_desctransformer.py @@ -8,7 +8,7 @@ from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT -from fixtures import mols_list, smiles_list, mols_container, smiles_container +from fixtures import mols_list, smiles_list, mols_container, smiles_container, skip_pandas_output_test from sklearn import clone from sklearn.pipeline import Pipeline import joblib @@ -76,7 +76,7 @@ def test_descriptor_transformer_parallel(mols_list, default_descriptor_transform assert(len(features2[0]) == len(Descriptors._descList)) -@pytest.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires Python {SKLEARN_VERSION_PANDAS_OUT} or higher") +@skip_pandas_output_test def test_descriptor_transformer_pandas_output(mols_container, default_descriptor_transformer, selected_descriptor_transformer, pandas_output): for transformer in [default_descriptor_transformer, selected_descriptor_transformer]: features = transformer.transform(mols_container) @@ -84,7 +84,7 @@ def test_descriptor_transformer_pandas_output(mols_container, default_descriptor assert features.shape[0] == len(mols_container) assert features.columns.tolist() == transformer.selected_descriptors -@pytest.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires Python {SKLEARN_VERSION_PANDAS_OUT} or higher") +@skip_pandas_output_test def test_descriptor_transformer_pandas_output_pipeline(smiles_container, default_descriptor_transformer, pandas_output): pipeline = Pipeline([("s2m", SmilesToMolTransformer()), ("desc", default_descriptor_transformer)]) features = pipeline.fit_transform(smiles_container) diff --git a/tests/test_smilestomol.py b/tests/test_smilestomol.py index cba3229..829ef30 100644 --- a/tests/test_smilestomol.py +++ b/tests/test_smilestomol.py @@ -7,7 +7,7 @@ import sklearn from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT -from fixtures import smiles_list, invalid_smiles_list, smiles_container +from fixtures import smiles_list, invalid_smiles_list, smiles_container, skip_pandas_output_test @pytest.fixture @@ -42,9 +42,7 @@ def test_descriptor_transformer_parallel(smiles_container, smilestomol_transform expected_smiles = smiles_container assert all([ a == b for a, b in zip(expected_smiles, [Chem.MolToSmiles(mol) for mol in mol_list.flatten()])]) -# TODO: see if it is possible to define this skipif condition -# as its own decorator / fixture, to avoid duplication, even though it is simple. -@pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason="requires Python 3.8 or higher") +@skip_pandas_output_test def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output): mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) diff --git a/tests/test_transformers.py b/tests/test_transformers.py index 9e34f0c..e57554b 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -19,7 +19,7 @@ MHFingerprintTransformer, AvalonFingerprintTransformer -from fixtures import SLC6A4_subset +from fixtures import SLC6A4_subset, skip_pandas_output_test def test_transformer(SLC6A4_subset): # load some toy data for quick testing on a small number of samples @@ -63,7 +63,7 @@ def test_transformer(SLC6A4_subset): assert len(failed_FP) == 0, f"the following FP have failed {failed_FP}" -@pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason="requires Python 3.6 or higher") +@skip_pandas_output_test def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # load some toy data for quick testing on a small number of samples X_smiles = SLC6A4_subset.SMILES From c27d19b2603dfbcd07fafaa0d9c6479167790a2b Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sat, 9 Mar 2024 17:21:13 +0100 Subject: [PATCH 08/15] tests: make sure pandas containers are created with different names Previously, they were created with a single name (the last in the list of names!). --- tests/fixtures.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/fixtures.py b/tests/fixtures.py index d412ff8..2bcb6f7 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -38,8 +38,8 @@ def smiles_list(): None, ] for name in _names_to_test: - _CONTAINER_CREATORS.append(lambda x: pd.Series(x, name=name)) - _CONTAINER_CREATORS.append(lambda x: pd.DataFrame({name: x}) if name else pd.DataFrame(x)) + _CONTAINER_CREATORS.append(lambda x, name=name: pd.Series(x, name=name)) + _CONTAINER_CREATORS.append(lambda x, name=name: pd.DataFrame({name: x}) if name else pd.DataFrame(x)) @pytest.fixture(params=[container(_CANONICAL_SMILES_LIST) for container in _CONTAINER_CREATORS] ) From 01e2cfe881a4dfc04a8af85951ed74e53e8ff259 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sat, 9 Mar 2024 17:28:41 +0100 Subject: [PATCH 09/15] refactor: use a centrally defined default mol column name --- scikit_mol/conversions.py | 4 ++-- scikit_mol/core.py | 2 ++ tests/fixtures.py | 4 ++-- tests/test_smilestomol.py | 4 ++-- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/scikit_mol/conversions.py b/scikit_mol/conversions.py index 0488f39..123e655 100644 --- a/scikit_mol/conversions.py +++ b/scikit_mol/conversions.py @@ -6,7 +6,7 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin -from scikit_mol.core import check_transform_input +from scikit_mol.core import check_transform_input, DEFAULT_MOL_COLUMN_NAME class SmilesToMolTransformer(BaseEstimator, TransformerMixin): @@ -16,7 +16,7 @@ def __init__(self, parallel: Union[bool, int] = False): self.start_method = None #TODO implement handling of start_method def get_feature_names_out(self, input_features=None): - prefix = "ROMol" + prefix = DEFAULT_MOL_COLUMN_NAME if input_features is not None: return np.array([f'{prefix}_{name}' for name in input_features]) else: diff --git a/scikit_mol/core.py b/scikit_mol/core.py index 66514c4..0948f0d 100644 --- a/scikit_mol/core.py +++ b/scikit_mol/core.py @@ -13,6 +13,8 @@ SKLEARN_VERSION_PANDAS_OUT = Version("1.2") +DEFAULT_MOL_COLUMN_NAME = "ROMol" + def _validate_transform_input(X): """Validate and adapt the input of the _transform method""" diff --git a/tests/fixtures.py b/tests/fixtures.py index 2bcb6f7..9a92166 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -7,7 +7,7 @@ from packaging.version import Version import sklearn -from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT, DEFAULT_MOL_COLUMN_NAME #TODO these should really go into the conftest.py, so that they are automatically imported in the tests @@ -33,7 +33,7 @@ def smiles_list(): "molecule", "mol", "smiles", - "ROMol", + DEFAULT_MOL_COLUMN_NAME, "hello", None, ] diff --git a/tests/test_smilestomol.py b/tests/test_smilestomol.py index 829ef30..e01af52 100644 --- a/tests/test_smilestomol.py +++ b/tests/test_smilestomol.py @@ -6,7 +6,7 @@ from rdkit import Chem import sklearn from scikit_mol.conversions import SmilesToMolTransformer -from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT +from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT, DEFAULT_MOL_COLUMN_NAME from fixtures import smiles_list, invalid_smiles_list, smiles_container, skip_pandas_output_test @@ -47,4 +47,4 @@ def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output) mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) assert mols.shape[0] == len(smiles_container) - assert mols.columns.tolist() == ["ROMol"] \ No newline at end of file + assert mols.columns.tolist() == [DEFAULT_MOL_COLUMN_NAME] \ No newline at end of file From 8caff2f622c08d9c54299cdf0860e767d6839655 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sat, 9 Mar 2024 17:45:04 +0100 Subject: [PATCH 10/15] refactor: use decorator for get_feature_names that return default mol column name --- scikit_mol/conversions.py | 9 +++------ scikit_mol/core.py | 14 ++++++++++++++ 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/scikit_mol/conversions.py b/scikit_mol/conversions.py index 123e655..1c75ba5 100644 --- a/scikit_mol/conversions.py +++ b/scikit_mol/conversions.py @@ -6,7 +6,7 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin -from scikit_mol.core import check_transform_input, DEFAULT_MOL_COLUMN_NAME +from scikit_mol.core import check_transform_input, feature_names_default_mol ,DEFAULT_MOL_COLUMN_NAME class SmilesToMolTransformer(BaseEstimator, TransformerMixin): @@ -15,12 +15,9 @@ def __init__(self, parallel: Union[bool, int] = False): self.parallel = parallel self.start_method = None #TODO implement handling of start_method + @feature_names_default_mol def get_feature_names_out(self, input_features=None): - prefix = DEFAULT_MOL_COLUMN_NAME - if input_features is not None: - return np.array([f'{prefix}_{name}' for name in input_features]) - else: - return np.array([prefix]) + return input_features def fit(self, X=None, y=None): """Included for scikit-learn compatibility, does nothing""" diff --git a/scikit_mol/core.py b/scikit_mol/core.py index 0948f0d..66685a6 100644 --- a/scikit_mol/core.py +++ b/scikit_mol/core.py @@ -46,4 +46,18 @@ def wrapper(obj, X): # must be changed depending on the initial type of X, do it here. return result + return wrapper + +def feature_names_default_mol(method): + """ + Decorator that returns the default feature names for the mol object + """ + @functools.wraps(method) + def wrapper(obj, input_features=None): + prefix = DEFAULT_MOL_COLUMN_NAME + if input_features is not None: + return np.array([f'{prefix}_{name}' for name in input_features]) + else: + return np.array([prefix]) + return wrapper \ No newline at end of file From dd807d00b72341f21fda9715a40f3408ce572aea Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sat, 9 Mar 2024 17:55:50 +0100 Subject: [PATCH 11/15] feat: make standardizer compatible with sklearn pandas output NOTE: there are no tests for the Standardizer class! I wanted to add a test about the sklearn pandas output for the Standardizer class, but there are no tests for it. Should we open an issue and a PR to add tests for it? --- scikit_mol/standardizer.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/scikit_mol/standardizer.py b/scikit_mol/standardizer.py index f819f21..5277f20 100644 --- a/scikit_mol/standardizer.py +++ b/scikit_mol/standardizer.py @@ -8,7 +8,7 @@ from rdkit.rdBase import BlockLogs import numpy as np -from scikit_mol.core import check_transform_input +from scikit_mol.core import check_transform_input, feature_names_default_mol class Standardizer(BaseEstimator, TransformerMixin): @@ -42,7 +42,11 @@ def _transform(self, X): del block # Release logging block to previous state return np.array(arr).reshape(-1,1) - + + @feature_names_default_mol + def get_feature_names_out(self, input_features=None): + return input_features + @check_transform_input def transform(self, X, y=None): if not self.parallel: From 62950b2762a6779b93e14011d316458a6644280d Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sun, 10 Mar 2024 00:02:42 +0100 Subject: [PATCH 12/15] test: add test that pandas output preserves values With all transformer classes that compute features (descriptors and fingerprints) --- tests/fixtures.py | 21 ++++++++++++++++++++- tests/test_transformers.py | 24 ++++++++++++++++++++++-- 2 files changed, 42 insertions(+), 3 deletions(-) diff --git a/tests/fixtures.py b/tests/fixtures.py index 9a92166..5c7193b 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -6,6 +6,10 @@ from rdkit.Chem import rdMolDescriptors from packaging.version import Version import sklearn +from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ + TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ + MHFingerprintTransformer, AvalonFingerprintTransformer +from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT, DEFAULT_MOL_COLUMN_NAME @@ -83,4 +87,19 @@ def SLC6A4_subset(): data = pd.read_csv(f"{os.path.split(file_path)[0]}/data/SLC6A4_active_excapedb_subset.csv") return data -skip_pandas_output_test = pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher") \ No newline at end of file +skip_pandas_output_test = pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher") + +_FEATURIZER_CLASSES = [ + MACCSKeysFingerprintTransformer, + RDKitFingerprintTransformer, + AtomPairFingerprintTransformer, + TopologicalTorsionFingerprintTransformer, + MorganFingerprintTransformer, + SECFingerprintTransformer, + MHFingerprintTransformer, + AvalonFingerprintTransformer, + MolecularDescriptorTransformer, + ] +@pytest.fixture(params=_FEATURIZER_CLASSES) +def featurizer(request): + return request.param() diff --git a/tests/test_transformers.py b/tests/test_transformers.py index e57554b..57cd863 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -10,6 +10,7 @@ import pandas as pd from packaging.version import Version import sklearn +import numpy as np from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestRegressor from scikit_mol.conversions import SmilesToMolTransformer @@ -17,9 +18,9 @@ from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ MHFingerprintTransformer, AvalonFingerprintTransformer +from scikit_mol.descriptors import MolecularDescriptorTransformer - -from fixtures import SLC6A4_subset, skip_pandas_output_test +from fixtures import SLC6A4_subset, skip_pandas_output_test, mols_container, featurizer def test_transformer(SLC6A4_subset): # load some toy data for quick testing on a small number of samples @@ -106,6 +107,25 @@ def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # overall result assert len(failed_FP) == 0, f"the following FP have failed pandas transformation {failed_FP}" +@skip_pandas_output_test +def test_pandas_out_same_values(featurizer, mols_container): + featurizer_default = sklearn.base.clone(featurizer) + featurizer_default.set_output(transform="default") + featurizer_pandas = sklearn.base.clone(featurizer) + featurizer_pandas.set_output(transform="pandas") + result_default = featurizer_default.fit_transform(mols_container) + result_pandas = featurizer_pandas.fit_transform(mols_container) + assert isinstance(result_default, np.ndarray) + assert isinstance(result_pandas, pd.DataFrame) + assert result_default.shape == result_pandas.shape + featurizer_class_with_nan = MolecularDescriptorTransformer + if isinstance(featurizer, featurizer_class_with_nan): + assert (pd.isna(result_default) == pd.isna(result_pandas.values)).all(), "NaN values are not in the same positions in the default and pandas output" + nan_replacement = 0. + result_default = np.nan_to_num(result_default, nan=nan_replacement) + result_pandas = result_pandas.fillna(nan_replacement) + else: + assert (result_default == result_pandas.values).all() From 3d74eeeacc8b61d7d25b006fef25a851247c0374 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sun, 10 Mar 2024 13:15:57 +0100 Subject: [PATCH 13/15] feat: do not prefix fingerprints with zeros Having fingerprints column names with the same length is better for display purposes: fp_morgan_0001...fp_morgan_0200...fp_morgan_2048. But this may make it harder to test different fingerprint configurations: the column names will be based on the number of bits! For instance, if nBits is 512, the first fingerprint is called fp_morgan_001; if nBits is 1024, the first fingerprint is fp_morgan_0001. Now we will always call the first fingerprint fp_morgan_1. This will simplify processing the transformer outputs in production workflows. An helper method that returns the nice zero-prefixed column names is included. --- scikit_mol/fingerprints.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/scikit_mol/fingerprints.py b/scikit_mol/fingerprints.py index f32ad36..767bfc6 100644 --- a/scikit_mol/fingerprints.py +++ b/scikit_mol/fingerprints.py @@ -45,11 +45,26 @@ def _get_column_prefix(self) -> str: def _get_n_digits_column_suffix(self) -> int: return len(str(self.nBits)) - def get_feature_names_out(self, input_features=None): + def get_display_feature_names_out(self, input_features=None): + """Get feature names for display purposes + + All feature names will have the same length, + since the different elements will be prefixed with zeros + depending on the number of bits. + """ prefix = self._get_column_prefix() n_digits = self._get_n_digits_column_suffix() return np.array([f"{prefix}_{str(i).zfill(n_digits)}" for i in range(1, self.nBits + 1)]) + def get_feature_names_out(self, input_features=None): + """Get feature names for fingerprint transformers + + This method is used by the scikit-learn set_output API + to get the column names of the transformed dataframe. + """ + prefix = self._get_column_prefix() + return np.array([f"{prefix}_{i}" for i in range(1, self.nBits + 1)]) + @abstractmethod def _mol2fp(self, mol): """Generate descriptor from mol From 3dfc1946c460b37b31c2188a3cb54ee4ac92e279 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sun, 10 Mar 2024 14:15:27 +0100 Subject: [PATCH 14/15] docs: add sample notebook to show pandas output Also, add a test dataset with CDDD pre-computed features that can be used with the notebook. TODO: create a test to demonstrate that ColumnTransformer can now be used. --- notebooks/10_pipeline_pandas_output.ipynb | 5004 +++++++++++++++++ .../CDDD_SLC6A4_active_excapedb_subset.csv.gz | Bin 0 -> 470267 bytes 2 files changed, 5004 insertions(+) create mode 100644 notebooks/10_pipeline_pandas_output.ipynb create mode 100644 tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz diff --git a/notebooks/10_pipeline_pandas_output.ipynb b/notebooks/10_pipeline_pandas_output.ipynb new file mode 100644 index 0000000..ddfedb2 --- /dev/null +++ b/notebooks/10_pipeline_pandas_output.ipynb @@ -0,0 +1,5004 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preserving feature information in DataFrames\n", + "\n", + "This notebook highlights the ability of scikit-mol transformers to return data in DataFrames with meaningful column names. Some use-cases of this feature are illustrated.\n", + "\n", + "***NOTE***: The goal of this notebook is to highlight the advantages of storing transformer output in DataFrames with meaningful column names. This notebook should *not* be considered a set of good practices for training and evaluating QSAR pipelines. The performance metrics of the resulting pipelines are pretty bad: the dataset they have been trained on is pretty small. Tuning the hyperparameters of the Random Forest regressor model (maximum depth of the trees, maximum features to consider when splitting...) can be beneficial. Also including dimensionality reduction / feature selection techniques can be beneficial, since pipelines use an high number of features for a small number of samples. Of course, to further reduce the risk of overfitting, the best hyperparameters and preprocessing techniques should be chosen in cross validation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "from sklearn.preprocessing import FunctionTransformer\n", + "from scikit_mol.conversions import SmilesToMolTransformer\n", + "from sklearn.compose import make_column_selector, make_column_transformer\n", + "from scikit_mol.standardizer import Standardizer\n", + "from scikit_mol.descriptors import MolecularDescriptorTransformer\n", + "from scikit_mol.fingerprints import MorganFingerprintTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file = Path(\"../tests/data/SLC6A4_active_excapedb_subset.csv\")\n", + "assert csv_file.is_file()\n", + "data = pd.read_csv(csv_file)\n", + "data.drop_duplicates(subset=\"Ambit_InchiKey\", inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's split the dataset in training and test, so we will be able to use the test set to evaluate the performance of models trained on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "column_smiles = \"SMILES\"\n", + "column_target = \"pXC50\"\n", + "\n", + "smis_train = data_train.loc[:, column_smiles]\n", + "target_train = data_train.loc[:, column_target]\n", + "smis_test = data_test.loc[:, column_smiles]\n", + "target_test = data_test.loc[:, column_target]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Descriptors pipeline that returns DataFrames\n", + "\n", + "Define a pipeline that:\n", + "\n", + "- converts SMILES strings to Mol objects\n", + "- standardizes the molecules\n", + "- computes molecular descriptors\n", + "\n", + "Then, we will configure the pipeline to return output in Pandas DataFrames.\n", + "The column names will correspond to the descriptor names." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
+       "                ('standardizer', Standardizer()),\n",
+       "                ('moleculardescriptortransformer',\n",
+       "                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
+       "                                                           'MaxEStateIndex',\n",
+       "                                                           'MinAbsEStateIndex',\n",
+       "                                                           'MinEStateIndex',\n",
+       "                                                           'qed', 'SPS',\n",
+       "                                                           'MolWt',\n",
+       "                                                           'HeavyAtomMolWt',\n",
+       "                                                           'ExactMolWt',\n",
+       "                                                           'NumValenceElectrons',\n",
+       "                                                           'NumRadicalElectrons',\n",
+       "                                                           'MaxPartialCharge',\n",
+       "                                                           'MinPartialCharge',\n",
+       "                                                           'MaxAbsPartialCharge',\n",
+       "                                                           'MinAbsPartialCharge',\n",
+       "                                                           'FpDensityMorgan1',\n",
+       "                                                           'FpDensityMorgan2',\n",
+       "                                                           'FpDensityMorgan3',\n",
+       "                                                           'BCUT2D_MWHI',\n",
+       "                                                           'BCUT2D_MWLOW',\n",
+       "                                                           'BCUT2D_CHGHI',\n",
+       "                                                           'BCUT2D_CHGLO',\n",
+       "                                                           'BCUT2D_LOGPHI',\n",
+       "                                                           'BCUT2D_LOGPLOW',\n",
+       "                                                           'BCUT2D_MRHI',\n",
+       "                                                           'BCUT2D_MRLOW',\n",
+       "                                                           'AvgIpc', 'BalabanJ',\n",
+       "                                                           'BertzCT', 'Chi0', ...]))])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", + " ('standardizer', Standardizer()),\n", + " ('moleculardescriptortransformer',\n", + " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", + " 'MaxEStateIndex',\n", + " 'MinAbsEStateIndex',\n", + " 'MinEStateIndex',\n", + " 'qed', 'SPS',\n", + " 'MolWt',\n", + " 'HeavyAtomMolWt',\n", + " 'ExactMolWt',\n", + " 'NumValenceElectrons',\n", + " 'NumRadicalElectrons',\n", + " 'MaxPartialCharge',\n", + " 'MinPartialCharge',\n", + " 'MaxAbsPartialCharge',\n", + " 'MinAbsPartialCharge',\n", + " 'FpDensityMorgan1',\n", + " 'FpDensityMorgan2',\n", + " 'FpDensityMorgan3',\n", + " 'BCUT2D_MWHI',\n", + " 'BCUT2D_MWLOW',\n", + " 'BCUT2D_CHGHI',\n", + " 'BCUT2D_CHGLO',\n", + " 'BCUT2D_LOGPHI',\n", + " 'BCUT2D_LOGPLOW',\n", + " 'BCUT2D_MRHI',\n", + " 'BCUT2D_MRLOW',\n", + " 'AvgIpc', 'BalabanJ',\n", + " 'BertzCT', 'Chi0', ...]))])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "descriptors_pipeline = make_pipeline(\n", + " SmilesToMolTransformer(),\n", + " Standardizer(),\n", + " MolecularDescriptorTransformer(),\n", + ")\n", + "descriptors_pipeline.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "                ('standardizer', Standardizer()),\n",
+       "                ('morganfingerprinttransformer',\n",
+       "                 MorganFingerprintTransformer())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", + " ('standardizer', Standardizer()),\n", + " ('morganfingerprinttransformer',\n", + " MorganFingerprintTransformer())])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fingerprints_pipeline = make_pipeline(\n", + " SmilesToMolTransformer(),\n", + " Standardizer(),\n", + " MorganFingerprintTransformer(),\n", + ")\n", + "fingerprints_pipeline.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
+       "                ('standardizer', Standardizer()),\n",
+       "                ('moleculardescriptortransformer',\n",
+       "                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
+       "                                                           'MaxEStateIndex',\n",
+       "                                                           'MinAbsEStateIndex',\n",
+       "                                                           'MinEStateIndex',\n",
+       "                                                           'qed', 'SPS',\n",
+       "                                                           'MolWt',\n",
+       "                                                           'HeavyAtomMolWt',\n",
+       "                                                           'ExactMolWt',\n",
+       "                                                           'NumValenceElectrons',\n",
+       "                                                           'NumRadicalElectrons',\n",
+       "                                                           'MaxPa...\n",
+       "                                                           'MaxAbsPartialCharge',\n",
+       "                                                           'MinAbsPartialCharge',\n",
+       "                                                           'FpDensityMorgan1',\n",
+       "                                                           'FpDensityMorgan2',\n",
+       "                                                           'FpDensityMorgan3',\n",
+       "                                                           'BCUT2D_MWHI',\n",
+       "                                                           'BCUT2D_MWLOW',\n",
+       "                                                           'BCUT2D_CHGHI',\n",
+       "                                                           'BCUT2D_CHGLO',\n",
+       "                                                           'BCUT2D_LOGPHI',\n",
+       "                                                           'BCUT2D_LOGPLOW',\n",
+       "                                                           'BCUT2D_MRHI',\n",
+       "                                                           'BCUT2D_MRLOW',\n",
+       "                                                           'AvgIpc', 'BalabanJ',\n",
+       "                                                           'BertzCT', 'Chi0', ...])),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", + " ('standardizer', Standardizer()),\n", + " ('moleculardescriptortransformer',\n", + " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", + " 'MaxEStateIndex',\n", + " 'MinAbsEStateIndex',\n", + " 'MinEStateIndex',\n", + " 'qed', 'SPS',\n", + " 'MolWt',\n", + " 'HeavyAtomMolWt',\n", + " 'ExactMolWt',\n", + " 'NumValenceElectrons',\n", + " 'NumRadicalElectrons',\n", + " 'MaxPa...\n", + " 'MaxAbsPartialCharge',\n", + " 'MinAbsPartialCharge',\n", + " 'FpDensityMorgan1',\n", + " 'FpDensityMorgan2',\n", + " 'FpDensityMorgan3',\n", + " 'BCUT2D_MWHI',\n", + " 'BCUT2D_MWLOW',\n", + " 'BCUT2D_CHGHI',\n", + " 'BCUT2D_CHGLO',\n", + " 'BCUT2D_LOGPHI',\n", + " 'BCUT2D_LOGPLOW',\n", + " 'BCUT2D_MRHI',\n", + " 'BCUT2D_MRLOW',\n", + " 'AvgIpc', 'BalabanJ',\n", + " 'BertzCT', 'Chi0', ...])),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor', RandomForestRegressor(max_depth=5))])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params_random_forest = {\n", + " \"max_depth\": 5, # Setting a low maximum depth to avoid overfitting\n", + "}\n", + "\n", + "regression_pipeline = make_pipeline(\n", + " SmilesToMolTransformer(),\n", + " Standardizer(),\n", + " MolecularDescriptorTransformer(),\n", + " StandardScaler(), # Scale the descriptors\n", + " RandomForestRegressor(**params_random_forest),\n", + ")\n", + "regression_pipeline.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "regression_pipeline.fit(smis_train, target_train)\n", + "pred_test = regression_pipeline.predict(smis_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a simple function to compute regression metrics, and use it to evaluate the test set performance of the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/enrico/anaconda3/envs/scikit-mol/lib/python3.12/site-packages/sklearn/metrics/_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "{'RMSE': 0.8743529843232205,\n", + " 'MAE': 0.7266287431246283,\n", + " 'R2': 0.12081778342203131}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def compute_metrics(y_true, y_pred):\n", + " result = {\n", + " \"RMSE\": mean_squared_error(y_true=y_true, y_pred=y_pred, squared=False),\n", + " \"MAE\": mean_absolute_error(y_true=y_true, y_pred=y_pred),\n", + " \"R2\": r2_score(y_true=y_true, y_pred=y_pred),\n", + " }\n", + " return result\n", + "\n", + "performance = compute_metrics(y_true=target_test, y_pred=pred_test)\n", + "performance" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor(max_depth=5)
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" + ], + "text/plain": [ + "RandomForestRegressor(max_depth=5)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regressor = regression_pipeline[-1]\n", + "regressor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we used `set_output(transform=\"pandas\")` on the pipeline, the last step of the pipeline (the regression model) has the descriptor names in the `feature_names_in_` attribute. We can use them and the `feature_importances_` attribute to easily analyze the feature importances." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " feature importance\n", + "0 MaxAbsEStateIndex 0.003033\n", + "1 MaxEStateIndex 0.001598\n", + "2 MinAbsEStateIndex 0.006049\n", + "3 MinEStateIndex 0.005194\n", + "4 qed 0.006795\n", + ".. ... ...\n", + "205 fr_thiazole 0.000000\n", + "206 fr_thiocyan 0.000000\n", + "207 fr_thiophene 0.002249\n", + "208 fr_unbrch_alkane 0.000425\n", + "209 fr_urea 0.000000\n", + "\n", + "[210 rows x 2 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_importance = pd.DataFrame({\"feature\": regressor.feature_names_in_, \"importance\": regressor.feature_importances_})\n", + "df_importance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sort the features by most to least important:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featureimportance
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" + ], + "text/plain": [ + " feature importance\n", + "0 PEOE_VSA6 0.172691\n", + "1 VSA_EState5 0.072088\n", + "2 MaxAbsPartialCharge 0.046360\n", + "3 VSA_EState6 0.030461\n", + "4 SlogP_VSA6 0.029400\n", + ".. ... ...\n", + "205 fr_azo 0.000000\n", + "206 fr_azide 0.000000\n", + "207 SlogP_VSA9 0.000000\n", + "208 fr_amidine 0.000000\n", + "209 fr_urea 0.000000\n", + "\n", + "[210 rows x 2 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_importance.sort_values(by=\"importance\", ascending=False, inplace=True, ignore_index=True)\n", + "df_importance" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 5 most important features are:\n", + "PEOE_VSA6\n", + "VSA_EState5\n", + "MaxAbsPartialCharge\n", + "VSA_EState6\n", + "SlogP_VSA6\n" + ] + } + ], + "source": [ + "n_top_features = 5\n", + "top_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\n", + "print(f\"The {n_top_features} most important features are:\")\n", + "for feature in top_features:\n", + " print(feature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Including external features\n", + "\n", + "The ability to keep the results of scikit-learn transformers in DataFrames with meaningful column names simplifies the task of analyzing the resulting models.\n", + "\n", + "Another good use-case is when we want to combine cheminformatics features from some other tool (QM packages, Deep Learning embeddings...) with the traditional cheminformatics features available in scikit-mol. It will be easier to keep track of the features that come from scikit-mol and the features that come from other tools, if they are stored in DataFrames with meaningful column names.\n", + "\n", + "Let's include features from the popular [CDDD](https://github.com/jrwnter/cddd) tool. CDDD is a Variational AutoEncoder Deep Learning model, and the CDDD features are the inner latent space representations of the SMILES. For additional details, have a look at the original CDDD paper:\n", + "\n", + "> R. Winter, F. Montanari, F. Noé, and D.-A. Clevert, “Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations,” Chem. Sci., vol. 10, no. 6, pp. 1692–1701, Feb. 2019, [doi: 10.1039/C8SC04175J](https://doi.org/10.1039/C8SC04175J).\n", + "\n", + "We have precomputed these features and stored them in a file:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Ambit_InchiKey cddd_1 cddd_2 cddd_3 cddd_4 \\\n", + "0 RBCQCVSMIQCOMN-PCQZLOAONA-N 0.034484 0.288955 -0.038534 0.485996 \n", + "1 ALZTYVXVRZIERJ-UHFFFAOYNA-N -0.784729 0.146379 0.442466 0.187816 \n", + "2 MOEMPBAHOJKXBG-MRXNPFEDNA-N -0.751528 -0.506115 0.412968 0.341948 \n", + "3 HEKGBDCRHYILPL-QWOVJGMINA-N -0.757406 0.000328 0.670389 0.856043 \n", + "4 SNNRWIBSGBMYRF-UKRRQHHQNA-N -0.477663 -0.129261 -0.332024 -0.591108 \n", + ".. ... ... ... ... ... \n", + "194 PIKWEFAACQLYMF-UHFFFAOYNA-N -0.354424 0.037133 0.261493 0.191034 \n", + "195 AUZWJAMWJZUPHQ-UHFFFAOYNA-N -0.647606 -0.604185 -0.070398 0.109305 \n", + "196 JCEWQICHOLLRDL-WUFINQPMNA-N -0.681951 -0.346629 0.387501 -0.760321 \n", + "197 NGRIUVQYFBDXMT-JYAVWHMHNA-N -0.622850 -0.760069 -0.175192 0.306767 \n", + "198 ZWLWOTHDIGRTNE-UHFFFAOYNA-N -0.740121 -0.462145 0.351089 0.104331 \n", + "\n", + " cddd_5 cddd_6 cddd_7 cddd_8 cddd_9 ... cddd_503 \\\n", + "0 0.398606 -0.077340 0.417946 -0.114399 0.340925 ... -0.442085 \n", + "1 0.042911 -0.007460 0.012899 0.170997 -0.322217 ... -0.434862 \n", + "2 0.822811 -0.713795 0.159594 -0.453231 0.053278 ... 0.530237 \n", + "3 0.002886 0.064478 0.181017 -0.229966 0.598979 ... 0.670772 \n", + "4 0.786510 -0.007520 -0.171381 -0.048844 0.565125 ... 0.342538 \n", + ".. ... ... ... ... ... ... ... \n", + "194 0.203483 -0.718652 0.481088 -0.077800 -0.359651 ... 0.465482 \n", + "195 0.667468 -0.239701 -0.332139 -0.490862 0.302364 ... 0.268934 \n", + "196 0.003585 0.173832 0.584196 0.314204 -0.375850 ... -0.401828 \n", + "197 0.828635 -0.251226 0.095201 0.029581 -0.098511 ... -0.428120 \n", + "198 0.579055 -0.524488 -0.763961 -0.339895 0.693571 ... -0.461685 \n", + "\n", + " cddd_504 cddd_505 cddd_506 cddd_507 cddd_508 cddd_509 cddd_510 \\\n", + "0 -0.122121 -0.755012 0.580011 -0.999539 -0.466382 0.378511 0.579342 \n", + "1 0.216556 -0.687221 -0.103207 -0.999198 -0.335400 0.136468 0.550440 \n", + "2 -0.131153 -0.007292 -0.065849 -0.978371 -0.653190 0.404358 -0.079914 \n", + "3 -0.372262 -0.571060 -0.443543 -0.986363 0.118407 -0.077974 -0.097596 \n", + "4 0.680307 0.662410 -0.493203 -0.987440 -0.731436 0.016999 -0.503085 \n", + ".. ... ... ... ... ... ... ... \n", + "194 0.181667 0.008707 0.374962 -0.998080 -0.015004 -0.071801 -0.205790 \n", + "195 0.103272 -0.120228 -0.133202 -0.981479 -0.683975 0.748666 -0.171097 \n", + "196 0.187382 0.632996 0.507790 -0.999535 0.041612 0.090283 -0.432323 \n", + "197 0.510929 -0.112762 0.072157 -0.974629 -0.724549 0.754821 0.580699 \n", + "198 -0.582436 0.441268 0.113779 -0.919631 -0.569133 0.647876 -0.348418 \n", + "\n", + " cddd_511 cddd_512 \n", + "0 0.753847 0.812943 \n", + "1 -0.019943 -0.173729 \n", + "2 0.711537 0.445195 \n", + "3 0.283461 -0.099510 \n", + "4 -0.066302 -0.377198 \n", + ".. ... ... \n", + "194 -0.394928 0.386006 \n", + "195 0.053143 0.144776 \n", + "196 -0.191279 0.136006 \n", + "197 0.437276 0.079424 \n", + "198 0.395181 0.272382 \n", + "\n", + "[199 rows x 513 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_cddd_features = Path(\"../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\")\n", + "assert file_cddd_features.is_file()\n", + "df_cddd = pd.read_csv(file_cddd_features)\n", + "df_cddd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CDDD features are stored in columns `cddd_1`, `cddd_2`, ..., `cddd_512`. The file has the identifier column `Ambit_InchiKey` that we can use to combine the CDDD features with the rest of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def combine_datasets(data, cddd):\n", + " data_combined = pd.merge(\n", + " left=data,\n", + " right=cddd,\n", + " on=\"Ambit_InchiKey\",\n", + " how=\"left\",\n", + " validate=\"one_to_one\",\n", + " )\n", + " return data_combined\n", + "\n", + "data_combined_train = combine_datasets(data_train, df_cddd)\n", + "data_combined_test = combine_datasets(data_test, df_cddd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can define a pipeline that uses the original SMILES column to compute the descriptors available in scikit-mol, then concatenates them with the pre-computed CDDD features, and uses all of them to train the regression model. We will need a slightly more complex pipeline with column selectors and transformers. For more details on this technique, please refer to the [official documentation](https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html).\n", + "\n", + "Since we will keep everything in DataFrames, it will be easier to understand the effect of the CDDD features and the traditional descriptors available in scikit-mol." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ColumnTransformer(transformers=[('pipeline-1',\n",
+       "                                 Pipeline(steps=[('smilestomoltransformer',\n",
+       "                                                  SmilesToMolTransformer()),\n",
+       "                                                 ('standardizer',\n",
+       "                                                  Standardizer()),\n",
+       "                                                 ('moleculardescriptortransformer',\n",
+       "                                                  MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
+       "                                                                                            'MaxEStateIndex',\n",
+       "                                                                                            'MinAbsEStateIndex',\n",
+       "                                                                                            'MinEStateIndex',\n",
+       "                                                                                            'qed',\n",
+       "                                                                                            'SPS',\n",
+       "                                                                                            'MolWt',\n",
+       "                                                                                            'HeavyAtomMolWt',\n",
+       "                                                                                            'ExactMolWt',\n",
+       "                                                                                            'Num...\n",
+       "                                                                                            'BCUT2D_LOGPHI',\n",
+       "                                                                                            'BCUT2D_LOGPLOW',\n",
+       "                                                                                            'BCUT2D_MRHI',\n",
+       "                                                                                            'BCUT2D_MRLOW',\n",
+       "                                                                                            'AvgIpc',\n",
+       "                                                                                            'BalabanJ',\n",
+       "                                                                                            'BertzCT',\n",
+       "                                                                                            'Chi0', ...]))]),\n",
+       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7f2998e19850>),\n",
+       "                                ('pipeline-2',\n",
+       "                                 Pipeline(steps=[('functiontransformer',\n",
+       "                                                  FunctionTransformer())]),\n",
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" + ], + "text/plain": [ + "ColumnTransformer(transformers=[('pipeline-1',\n", + " Pipeline(steps=[('smilestomoltransformer',\n", + " SmilesToMolTransformer()),\n", + " ('standardizer',\n", + " Standardizer()),\n", + " ('moleculardescriptortransformer',\n", + " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", + " 'MaxEStateIndex',\n", + " 'MinAbsEStateIndex',\n", + " 'MinEStateIndex',\n", + " 'qed',\n", + " 'SPS',\n", + " 'MolWt',\n", + " 'HeavyAtomMolWt',\n", + " 'ExactMolWt',\n", + " 'Num...\n", + " 'BCUT2D_LOGPHI',\n", + " 'BCUT2D_LOGPLOW',\n", + " 'BCUT2D_MRHI',\n", + " 'BCUT2D_MRLOW',\n", + " 'AvgIpc',\n", + " 'BalabanJ',\n", + " 'BertzCT',\n", + " 'Chi0', ...]))]),\n", + " ),\n", + " ('pipeline-2',\n", + " Pipeline(steps=[('functiontransformer',\n", + " FunctionTransformer())]),\n", + " )])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A pipeline to compute scikit-mol descriptors\n", + "descriptors_pipeline = make_pipeline(\n", + " SmilesToMolTransformer(),\n", + " Standardizer(),\n", + " MolecularDescriptorTransformer(),\n", + ")\n", + "# A pipeline that just passes the input data.\n", + "# We will use it to preserve the CDDD features and pass them to downstream steps.\n", + "identity_pipeline = make_pipeline(\n", + " FunctionTransformer(),\n", + ")\n", + "combined_transformer = make_column_transformer(\n", + " (descriptors_pipeline, make_column_selector(pattern=\"SMILES\")),\n", + " (identity_pipeline, make_column_selector(pattern=r\"^cddd_\\d+$\")),\n", + " remainder=\"drop\",\n", + ")\n", + "combined_transformer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(transformers=[('pipeline-1',\n",
+       "                                                  Pipeline(steps=[('smilestomoltransformer',\n",
+       "                                                                   SmilesToMolTransformer()),\n",
+       "                                                                  ('standardizer',\n",
+       "                                                                   Standardizer()),\n",
+       "                                                                  ('moleculardescriptortransformer',\n",
+       "                                                                   MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
+       "                                                                                                             'MaxEStateIndex',\n",
+       "                                                                                                             'MinAbsEStateIndex',\n",
+       "                                                                                                             'MinEStateIndex',\n",
+       "                                                                                                             'qed',\n",
+       "                                                                                                             'SPS',\n",
+       "                                                                                                             'MolW...\n",
+       "                                                                                                             'Chi0', ...]))]),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7f2998e19850>),\n",
+       "                                                 ('pipeline-2',\n",
+       "                                                  Pipeline(steps=[('functiontransformer',\n",
+       "                                                                   FunctionTransformer())]),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7f2998e1b770>)])),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "Pipeline(steps=[('columntransformer',\n", + " ColumnTransformer(transformers=[('pipeline-1',\n", + " Pipeline(steps=[('smilestomoltransformer',\n", + " SmilesToMolTransformer()),\n", + " ('standardizer',\n", + " Standardizer()),\n", + " ('moleculardescriptortransformer',\n", + " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", + " 'MaxEStateIndex',\n", + " 'MinAbsEStateIndex',\n", + " 'MinEStateIndex',\n", + " 'qed',\n", + " 'SPS',\n", + " 'MolW...\n", + " 'Chi0', ...]))]),\n", + " ),\n", + " ('pipeline-2',\n", + " Pipeline(steps=[('functiontransformer',\n", + " FunctionTransformer())]),\n", + " )])),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor', RandomForestRegressor(max_depth=5))])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_combined = make_pipeline(\n", + " combined_transformer,\n", + " StandardScaler(),\n", + " RandomForestRegressor(**params_random_forest),\n", + ")\n", + "pipeline_combined.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/enrico/anaconda3/envs/scikit-mol/lib/python3.12/site-packages/sklearn/metrics/_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "{'RMSE': 0.8084831947083874,\n", + " 'MAE': 0.6737009532344713,\n", + " 'R2': 0.24829524829300897}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_combined.fit(data_combined_train, target_train)\n", + "pred_combined_test = pipeline_combined.predict(data_combined_test)\n", + "performance_combined = compute_metrics(y_true=target_test, y_pred=pred_combined_test)\n", + "performance_combined" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's combine the performance metrics obtained using only the scikit-mol descriptors as input features, and the performance metrics obtained using also the CDDD features:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RMSEMAER2
descriptors0.8743530.7266290.120818
combined0.8084830.6737010.248295
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" + ], + "text/plain": [ + " RMSE MAE R2\n", + "descriptors 0.874353 0.726629 0.120818\n", + "combined 0.808483 0.673701 0.248295" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_performance = pd.DataFrame([performance, performance_combined], index=[\"descriptors\", \"combined\"])\n", + "df_performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All performance metrics were improved by the includion of the CDDD features.\n", + "Let's analyze the feature importances of the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featureimportance
0pipeline-1__PEOE_VSA60.086083
1pipeline-2__cddd_1020.057354
2pipeline-1__VSA_EState50.039388
3pipeline-2__cddd_3780.033585
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.........
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722 rows × 2 columns

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" + ], + "text/plain": [ + " feature importance\n", + "0 pipeline-1__PEOE_VSA6 0.086083\n", + "1 pipeline-2__cddd_102 0.057354\n", + "2 pipeline-1__VSA_EState5 0.039388\n", + "3 pipeline-2__cddd_378 0.033585\n", + "4 pipeline-2__cddd_415 0.032666\n", + ".. ... ...\n", + "717 pipeline-1__fr_hdrzine 0.000000\n", + "718 pipeline-1__fr_hdrzone 0.000000\n", + "719 pipeline-1__SMR_VSA1 0.000000\n", + "720 pipeline-1__fr_imidazole 0.000000\n", + "721 pipeline-1__fr_Al_OH_noTert 0.000000\n", + "\n", + "[722 rows x 2 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regressor = pipeline_combined[-1]\n", + "df_importance = pd.DataFrame({\"feature\": regressor.feature_names_in_, \"importance\": regressor.feature_importances_}).sort_values(by=\"importance\", ascending=False, ignore_index=True)\n", + "df_importance" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 5 most important features are:\n", + "pipeline-1__PEOE_VSA6\n", + "pipeline-2__cddd_102\n", + "pipeline-1__VSA_EState5\n", + "pipeline-2__cddd_378\n", + "pipeline-2__cddd_415\n" + ] + } + ], + "source": [ + "top_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\n", + "print(f\"The {n_top_features} most important features are:\")\n", + "for feature in top_features:\n", + " print(feature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, some CDDD features are among the most important features for the regression model.\n", + "\n", + "Note that since the pipeline is a combination of two pipelines, the column names were prefixed by `pipeline-1` (the scikit-mol descriptors pipeline) and `pipeline-2` (the pipeline that selects and preserves pre-computed CDDD features)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "scikit-mol", + "language": "python", 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zVUlb*;k+A&jIzg%B0#<_Vh19v@hLCcjFuf0-tX2|rDhD;QWbI`sgQ=ZpQOclpOVz%>w z?1a5*TN76^0vvLvk$cQO`ZU)Vj$F^?9-=^e&pWIE{+I~@w!RUy_}_2ob0V51Z?a0f z5U*tT>IF%oh(fA47{TH@GGlTlh!t}`Yc-F@Y3x)#u)6bZ0EvFLLW{|NPp6;}p=_;` zEya)RnGq^r?YoXs7#5Na6FGBCE{b3t4=C9VPR~}R zts4LO8?6eUYWA8+5UeQ12c4Sq?b0I4QIwzvE|A;hiVa9?LXwF)gBrWXfW2vK$@u%a zTWc%01(Fex%VX^z^=|^uN9MRN$I}!<6U?f7jhaaXb47B32fnFso$-E4m_l)&iAs%% x$12hb4cyS0s?L9f5pTE*I-?r$hOS8)F7|0$<3GOu009600|0Z=XQzHK5dc&b)wKWs literal 0 HcmV?d00001 From 0027c13db02203a8a597f59cb60edc5098e8d703 Mon Sep 17 00:00:00 2001 From: Enrico Gandini Date: Sun, 10 Mar 2024 22:59:27 +0100 Subject: [PATCH 15/15] test: test combined transformer with column transformer --- tests/fixtures.py | 40 +++++++++++++++++++++++++++++++++++--- tests/test_transformers.py | 21 +++++++++++++++++--- 2 files changed, 55 insertions(+), 6 deletions(-) diff --git a/tests/fixtures.py b/tests/fixtures.py index 5c7193b..8cf0751 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -1,4 +1,5 @@ import os +from pathlib import Path import pytest import numpy as np import pandas as pd @@ -6,11 +7,15 @@ from rdkit.Chem import rdMolDescriptors from packaging.version import Version import sklearn +from sklearn.preprocessing import FunctionTransformer +from sklearn.pipeline import make_pipeline +from sklearn.compose import make_column_selector, make_column_transformer from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ MHFingerprintTransformer, AvalonFingerprintTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer - +from scikit_mol.conversions import SmilesToMolTransformer +from scikit_mol.standardizer import Standardizer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT, DEFAULT_MOL_COLUMN_NAME #TODO these should really go into the conftest.py, so that they are automatically imported in the tests @@ -81,10 +86,20 @@ def chiral_mols_list(chiral_smiles_list): def fingerprint(mols_list): return rdMolDescriptors.GetHashedMorganFingerprint(mols_list[0],2,nBits=1000) +_DIR_DATA = Path(__file__).parent / "data" +_FILE_SLC6A4 = _DIR_DATA / "SLC6A4_active_excapedb_subset.csv" +_FILE_SLC6A4_WITH_CDDD = _DIR_DATA / "CDDD_SLC6A4_active_excapedb_subset.csv.gz" + @pytest.fixture def SLC6A4_subset(): - file_path = os.path.realpath(__file__) - data = pd.read_csv(f"{os.path.split(file_path)[0]}/data/SLC6A4_active_excapedb_subset.csv") + data = pd.read_csv(_FILE_SLC6A4) + return data + +@pytest.fixture +def SLC6A4_subset_with_cddd(SLC6A4_subset): + data = SLC6A4_subset.copy().drop_duplicates(subset="Ambit_InchiKey") + cddd = pd.read_csv(_FILE_SLC6A4_WITH_CDDD, index_col="Ambit_InchiKey") + data = data.merge(cddd, left_on="Ambit_InchiKey", right_index=True, how="inner", validate="one_to_one") return data skip_pandas_output_test = pytest.mark.skipif(Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher") @@ -103,3 +118,22 @@ def SLC6A4_subset(): @pytest.fixture(params=_FEATURIZER_CLASSES) def featurizer(request): return request.param() + +@pytest.fixture +def combined_transformer(featurizer): + descriptors_pipeline = make_pipeline( + SmilesToMolTransformer(), + Standardizer(), + featurizer, + ) + # A pipeline that just passes the input data. + # We will use it to preserve the CDDD features and pass them to downstream steps. + identity_pipeline = make_pipeline( + FunctionTransformer(), + ) + transformer = make_column_transformer( + (descriptors_pipeline, make_column_selector(pattern="SMILES")), + (identity_pipeline, make_column_selector(pattern=r"^cddd_\d+$")), + remainder="drop", + ) + return transformer \ No newline at end of file diff --git a/tests/test_transformers.py b/tests/test_transformers.py index 57cd863..143ecd3 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -15,12 +15,12 @@ from sklearn.ensemble import RandomForestRegressor from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT -from scikit_mol.fingerprints import MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ +from scikit_mol.fingerprints import FpsTransformer, MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, \ TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, \ MHFingerprintTransformer, AvalonFingerprintTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer -from fixtures import SLC6A4_subset, skip_pandas_output_test, mols_container, featurizer +from fixtures import SLC6A4_subset, SLC6A4_subset_with_cddd, skip_pandas_output_test, mols_container, featurizer, combined_transformer def test_transformer(SLC6A4_subset): # load some toy data for quick testing on a small number of samples @@ -127,7 +127,22 @@ def test_pandas_out_same_values(featurizer, mols_container): else: assert (result_default == result_pandas.values).all() - +@skip_pandas_output_test +def test_combined_transformer_pandas_out(combined_transformer, SLC6A4_subset_with_cddd, pandas_output): + result = combined_transformer.fit_transform(SLC6A4_subset_with_cddd) + assert isinstance(result, pd.DataFrame) + assert result.shape[0] == SLC6A4_subset_with_cddd.shape[0] + n_cddd_features = SLC6A4_subset_with_cddd.columns.str.match(r"^cddd_\d+$").sum() + pipeline_skmol = combined_transformer.named_transformers_["pipeline-1"] + featurizer_skmol = pipeline_skmol[-1] + if isinstance(featurizer_skmol, FpsTransformer): + n_skmol_features = featurizer_skmol.nBits + elif isinstance(featurizer_skmol, MolecularDescriptorTransformer): + n_skmol_features = len(featurizer_skmol.desc_list) + else: + raise ValueError(f"Unexpected featurizer type {type(featurizer_skmol)}") + expected_n_features = n_cddd_features + n_skmol_features + assert result.shape[1] == expected_n_features