Loading deepchem/data/data_loader.py +2 −2 Original line number Diff line number Diff line Loading @@ -293,7 +293,7 @@ class CSVLoader(DataLoader): >>> import deepchem as dc >>> with dc.utils.UniversalNamedTemporaryFile(mode='w') as tmpfile: ... df.to_csv(tmpfile.name) ... loader = dc.data.CSVFragmentLoader(["task1"], feature_field="smiles", ... loader = dc.data.CSVLoader(["task1"], feature_field="smiles", ... featurizer=dc.feat.CircularFingerprint()) ... dataset = loader.create_dataset(tmpfile.name) >>> len(dataset) Loading Loading @@ -522,7 +522,7 @@ class CSVFragmentLoader(CSVLoader, FragmentLoader): >>> import deepchem as dc >>> with dc.utils.UniversalNamedTemporaryFile(mode='w') as tmpfile: ... df.to_csv(tmpfile.name) ... loader = dc.data.CSVLoader([], feature_field="smiles", ... loader = dc.data.CSVFragmentLoader([], feature_field="smiles", ... featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True)) ... dataset = loader.create_dataset(tmpfile.name) >>> len(dataset) # equals sum of all fragments from molecules, that is 0 + 3 Loading deepchem/feat/graph_features.py +13 −3 Original line number Diff line number Diff line Loading @@ -630,6 +630,15 @@ class ConvMolFeaturizer(MolecularFeaturizer): Duvenaud graph convolutions [1]_ construct a vector of descriptors for each atom in a molecule. The featurizer computes that vector of local descriptors. Examples --------- Using ConvMolFeaturizer to create featurized fragments derived from molecules of interest >>> import deepchem as dc >>> smiles = ["C", "CCC"] >>> featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True) >>> f = featurizer.featurize(smiles) >>> len(f) # contains 2 lists with featurized fragments from 2 mols References --------- Loading Loading @@ -672,9 +681,10 @@ class ConvMolFeaturizer(MolecularFeaturizer): name of the molecule level property in mol where the solvent accessible surface area of atom 0 would be stored. per_atom_fragmentation: Boolean If True, then multiple "atom-deprived" featurizations will be possible to do for each molecule. It will be possible to remove atoms one by one, and then, featurize each atom-deprived molecule. Thus, applying featurize method will produce a set of ConvMol objects for each molecule. If True, then multiple "atom-depleted" featurizations will be possible to do for each molecule. It will be possible to remove atoms one by one, and then, featurize each atom-depleted molecule. Thus, applying featurize method will produce a set of ConvMol objects for each molecule. This is useful for subsequent model interpretation: finding atoms favorable/unfavorable for (modelled) activity. Since ConvMol is an object and not a numpy array, need to set dtype to object. Loading Loading
deepchem/data/data_loader.py +2 −2 Original line number Diff line number Diff line Loading @@ -293,7 +293,7 @@ class CSVLoader(DataLoader): >>> import deepchem as dc >>> with dc.utils.UniversalNamedTemporaryFile(mode='w') as tmpfile: ... df.to_csv(tmpfile.name) ... loader = dc.data.CSVFragmentLoader(["task1"], feature_field="smiles", ... loader = dc.data.CSVLoader(["task1"], feature_field="smiles", ... featurizer=dc.feat.CircularFingerprint()) ... dataset = loader.create_dataset(tmpfile.name) >>> len(dataset) Loading Loading @@ -522,7 +522,7 @@ class CSVFragmentLoader(CSVLoader, FragmentLoader): >>> import deepchem as dc >>> with dc.utils.UniversalNamedTemporaryFile(mode='w') as tmpfile: ... df.to_csv(tmpfile.name) ... loader = dc.data.CSVLoader([], feature_field="smiles", ... loader = dc.data.CSVFragmentLoader([], feature_field="smiles", ... featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True)) ... dataset = loader.create_dataset(tmpfile.name) >>> len(dataset) # equals sum of all fragments from molecules, that is 0 + 3 Loading
deepchem/feat/graph_features.py +13 −3 Original line number Diff line number Diff line Loading @@ -630,6 +630,15 @@ class ConvMolFeaturizer(MolecularFeaturizer): Duvenaud graph convolutions [1]_ construct a vector of descriptors for each atom in a molecule. The featurizer computes that vector of local descriptors. Examples --------- Using ConvMolFeaturizer to create featurized fragments derived from molecules of interest >>> import deepchem as dc >>> smiles = ["C", "CCC"] >>> featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True) >>> f = featurizer.featurize(smiles) >>> len(f) # contains 2 lists with featurized fragments from 2 mols References --------- Loading Loading @@ -672,9 +681,10 @@ class ConvMolFeaturizer(MolecularFeaturizer): name of the molecule level property in mol where the solvent accessible surface area of atom 0 would be stored. per_atom_fragmentation: Boolean If True, then multiple "atom-deprived" featurizations will be possible to do for each molecule. It will be possible to remove atoms one by one, and then, featurize each atom-deprived molecule. Thus, applying featurize method will produce a set of ConvMol objects for each molecule. If True, then multiple "atom-depleted" featurizations will be possible to do for each molecule. It will be possible to remove atoms one by one, and then, featurize each atom-depleted molecule. Thus, applying featurize method will produce a set of ConvMol objects for each molecule. This is useful for subsequent model interpretation: finding atoms favorable/unfavorable for (modelled) activity. Since ConvMol is an object and not a numpy array, need to set dtype to object. Loading
