Add docstrings and add CompositionFeaturizer to featurization api docs (5a93e166) · Commits · 钟慕尧 / deepchem

deepchem/feat/material_featurizers/composition_featurizer.py

+60 −13

Original line number	Diff line number	Diff line
		@@ -2,9 +2,10 @@ import re
		import numpy as np
		from collections import defaultdict

		from deepchem.utils.typing import PymatgenComposition, DefaultDict
		from deepchem.utils.typing import PymatgenComposition, DefaultDict, Union
		from deepchem.feat import MaterialCompositionFeaturizer


		elements_tl = [
		'H', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Na', 'Mg', 'Al', 'Si', 'P', 'S',
		'Cl', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn',
		@@ -20,13 +21,13 @@ formulare = re.compile(r'([A-Z][a-z])(\d\.\d)')

		class CompositionFeaturizer(MaterialCompositionFeaturizer):
		"""
		Fixed size vector containing raw elemental compositions in the compound.
		Fixed size vector of length 85 containing raw fractional elemental
		compositions in the compound. The 85 chosen elements are based on the
		original implementation at https://github.com/NU-CUCIS/ElemNet.

		Returns a vector containing fractional compositions of each element
		in the compound.

		This featurizer requires the optional dependency pymatgen.

		References
		----------
		.. [1] Jha, D., Ward, L., Paul, A. et al. Sci Rep 8, 17593 (2018).
		@@ -38,16 +39,51 @@ class CompositionFeaturizer(MaterialCompositionFeaturizer):
		>>> comp = mg.Composition("Fe2O3")
		>>> featurizer = CompositionFeaturizer()
		>>> features = featurizer.featurize([comp])

		Notes
		-----
		This class requires Pymatgen to be installed.
		"""

		def get_fractions(self, comp: DefaultDict) -> Union[np.ndarray, None]:
		"""
		Converts a dictionary containing element names and corresponding
		compositional fractions into a vector of fractions.

		Parameters
		----------
		comp: collections.defaultdict object
		Dictionary mapping element names to fractional compositions.

		def get_fractions(self, comp: DefaultDict) -> np.ndarray:
		Returns
		-------
		fractions: np.ndarray
		Vector of fractional compositions of each element.
		"""
		if all(e in elements_tl for e in comp):
		return np.array([comp[e] if e in comp else 0 for e in elements_tl],
		fractions = np.array([comp[e] if e in comp else 0 for e in elements_tl],
		np.float32)
		else:
		return None
		fractions = None
		return fractions

		def parse_fractions(self, form: str) -> str:
		"""
		Convert fractional quantities (ex. 2/3) in the composition string
		into float values.

		Parameters
		----------
		form: str
		String containing elemental composition of the compound
		(Might contain fractional quantities).

		Returns
		-------
		form: str
		String containing elemental composition of compound with fractions converted
		into decimal equivalents.
		"""
		while '/' in form:
		di = form.index('/')
		num1 = [x for x in re.findall(r'\d\.\d*', form[:di]) if x != ''][-1]
		@@ -57,6 +93,20 @@ class CompositionFeaturizer(MaterialCompositionFeaturizer):
		return form

		def parse_formula(self, formula: str) -> DefaultDict:
		"""
		Convert composition string into a dictionary mapping element names
		to corresponding fractions.

		Parameters
		----------
		formula: str
		String containing the reduced elemental composition of the compound.

		Returns
		-------
		res: collections.defaultdict
		Dictionary containing element names and corresponding fractions.
		"""
		stack = []
		curr_str = ''
		i = 0
		@@ -131,7 +181,8 @@ class CompositionFeaturizer(MaterialCompositionFeaturizer):

		def _featurize(self, composition: PymatgenComposition) -> np.ndarray:
		"""
		Calculate composition vector from composition.
		Calculate 85 dimensional vector containing fractional compositions of
		each element in the compound.

		Parameters
		----------
		@@ -141,12 +192,8 @@ class CompositionFeaturizer(MaterialCompositionFeaturizer):
		Returns
		-------
		feats: np.ndarray
		Vector of fractional compositions of each element.
		85 dimensional vector containing fractional compositions of elements.
		"""
		try:
		pretty_comp = composition.reduced_formula
		feats = self.get_fractions(self.parse_formula(pretty_comp))
		except:
		feats = []

		return np.array(feats)

docs/source/api_reference/featurizers.rst

+6 −0

Original line number	Diff line number	Diff line
		@@ -229,6 +229,12 @@ ElementPropertyFingerprint
		.. autoclass:: deepchem.feat.ElementPropertyFingerprint
		:members:

		CompositionFeaturizer
		^^^^^^^^^^^^^^^^^^^^^

		.. autoclass:: deepchem.feat.CompositionFeaturizer
		:members:

		BindingPocketFeaturizer
		-----------------------

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