Handling merge conflicts (84ea2a9b) · Commits · 钟慕尧 / deepchem

datasets/gbd3k.pkl.gz

0 → 100644

+405 KiB

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deepchem/featurizers/featurize.py

+14 −10

Original line number	Diff line number	Diff line
		@@ -114,7 +114,8 @@ class DataFeaturizer(object):
		self.verbose = verbose
		self.log_every_n = log_every_n

		def featurize(self, input_file, feature_dir, samples_dir, shard_size=128, worker_pool=None):
		def featurize(self, input_file, feature_dir, samples_dir,
		shard_size=128, worker_pool=None):
		"""Featurize provided file and write to specified location."""
		input_type = _get_input_type(input_file)

		@@ -141,15 +142,14 @@ class DataFeaturizer(object):
		raw_df_shard = raw_df.iloc[range(interval_points[j], interval_points[j+1])]

		df = self._standardize_df(raw_df_shard)
		log("Aggregating User-Specified Features", self.verbose)

		for compound_featurizer in self.compound_featurizers:
		log("Currently feauturizing feature_type: %s"
		log("Currently featurizing feature_type: %s"
		% compound_featurizer.__class__.__name__, self.verbose)
		self._featurize_compounds(df, compound_featurizer, worker_pool=worker_pool)

		for complex_featurizer in self.complex_featurizers:
		log("Currently feauturizing feature_type: %s"
		log("Currently featurizing feature_type: %s"
		% complex_featurizer.__class__.__name__, self.verbose)
		self._featurize_complexes(df, complex_featurizer, worker_pool=worker_pool)

		@@ -159,8 +159,7 @@ class DataFeaturizer(object):

		featurizers = self.compound_featurizers + self.complex_featurizers
		samples = FeaturizedSamples(samples_dir=samples_dir, featurizers=featurizers,
		dataset_files=shard_files,
		reload_data=False)
		dataset_files=shard_files, reload_data=False)

		return samples

		@@ -190,6 +189,9 @@ class DataFeaturizer(object):
		df["smiles"] = ori_df[[self.smiles_field]]
		for task in self.tasks:
		df[task] = ori_df[[task]]
		if self.user_specified_features is not None:
		for feature in self.user_specified_features:
		df[feature] = ori_df[[feature]]
		if self.split_field is not None:
		df["split"] = ori_df[[self.split_field]]
		if self.protein_pdb_field is not None:
		@@ -261,22 +263,24 @@ class DataFeaturizer(object):
		into final features dataframe
		"""
		if self.user_specified_features is not None:
		log("Adding user-defined features.", self.verbose)
		log("Aggregating User-Specified Features", self.verbose)
		#log("Adding user-defined features.", self.verbose)
		features_data = []
		for ind, row in ori_df.iterrows():
		# pandas rows are tuples (row_num, row_data)
		feature_list = []
		for feature_name in self.user_specified_features:
		feature_list.append(row[feature_name])
		features_data.append({"user-specified-features": np.array(feature_list)})
		df["user-specified-features"] = pd.DataFrame(features_data)
		features_data.append(np.array(feature_list))
		df["user-specified-features"] = features_data


		def map_function(data_tuple, featurizer):
		featurizer = NNScoreComplexFeaturizer()
		ind, ligand_pdb, protein_pdb = data_tuple
		print("Mapping on ind %d" % ind)
		print("ind, type(ligand_pdb), type(protein_pdb): %s " % str((ind, type(ligand_pdb), type(protein_pdb))))
		print("ind, type(ligand_pdb), type(protein_pdb): %s " %
		str((ind, type(ligand_pdb), type(protein_pdb))))
		return featurizer.featurize_complexes([ligand_pdb], [protein_pdb])

		class FeaturizedSamples(object):

deepchem/featurizers/nnscore_utils.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -79,8 +79,8 @@ def hydrogenate_and_compute_partial_charges(input_file, input_format,
		hyd_conversion.SetInAndOutFormats(str(input_format), str("pdb"))
		mol = openbabel.OBMol()
		hyd_conversion.ReadFile(mol, str(input_file))
		# AddHydrogens(polaronly, correctForPH, pH)
		mol.AddHydrogens(True, True, 7.4)
		# AddHydrogens(not-polaronly, correctForPH, pH)
		mol.AddHydrogens(False, True, 7.4)
		hyd_conversion.WriteFile(mol, str(hyd_output))

		if verbose:

deepchem/models/init.py

+2 −18

Original line number	Diff line number	Diff line
		@@ -80,23 +80,6 @@ class Model(object):
		else:
		return "regression"

		#@staticmethod
		#def load(model_dir):
		# """Dispatcher function for loading."""
		# params = load_from_disk(Model.get_params_filename(model_dir))
		# model_class = params["model_class"]
		# if model_class in Model.registered_model_classes:
		# model = Model.registered_model_classes[model_class](
		# task_types=params["task_types"],
		# model_params=params["model_params"])
		# model.load(model_dir)
		# else:
		# model = Model.registered_model_classes["SklearnModel"](model_instance=model_class,
		# task_types=params["task_types"],
		# model_params=params["model_params"])
		# model.load(model_dir)
		# return model

		def save(self, out_dir):
		"""Dispatcher function for saving."""
		params = {"model_params" : self.model_params,
		@@ -149,7 +132,8 @@ class Model(object):
		y_preds = []
		for j in range(len(interval_points)-1):
		indices = range(interval_points[j], interval_points[j+1])
		y_pred_on_batch = self.predict_on_batch(X[indices, :]).reshape((len(indices),len(task_names)))
		y_pred_on_batch = self.predict_on_batch(X[indices, :]).reshape(
		(len(indices),len(task_names)))
		y_preds.append(y_pred_on_batch)

		y_pred = np.concatenate(y_preds)

deepchem/utils/dataset.py

+5 −1

Original line number	Diff line number	Diff line
		@@ -30,9 +30,13 @@ class Dataset(object):
		os.makedirs(data_dir)
		self.data_dir = data_dir

		if featurizers is not None:
		feature_types = [featurizer.__class__.__name__ for featurizer in featurizers]
		else:
		feature_types = None

		if use_user_specified_features:
		feature_types += ["user-specified-features"]
		feature_types = ["user-specified-features"]

		if samples is not None and feature_types is not None:
		if not isinstance(feature_types, list):

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