Loading deepchem/models/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -26,7 +26,7 @@ from deepchem.models.tensorgraph.models.gan import GAN, WGAN from deepchem.models.tensorgraph.models.text_cnn import TextCNNModel from deepchem.models.tensorgraph.sequential import Sequential from deepchem.models.tensorgraph.models.sequence_dnn import SequenceDNN from deepchem.models.tensorgraph.models.ontology import OntologyModel, OntologyNode from deepchem.models.tensorgraph.models.ontology import OntologyModel, OntologyNode, create_gene_ontology #################### Compatibility imports for renamed TensorGraph models. Remove below with DeepChem 3.0. #################### Loading deepchem/models/tensorgraph/models/ontology.py +203 −9 Original line number Diff line number Diff line Loading @@ -3,16 +3,10 @@ from deepchem.models import TensorGraph from deepchem.models.tensorgraph import layers from deepchem.metrics import to_one_hot from deepchem.utils import get_data_dir, download_url import tensorflow as tf class OntologyNode(object): def __init__(self, id, n_outputs, feature_ids=[], children=[]): self.id = id self.n_outputs = n_outputs self.feature_ids = feature_ids self.children = children import math import os class OntologyModel(TensorGraph): Loading @@ -28,6 +22,26 @@ class OntologyModel(TensorGraph): First, incorporating prior knowledge can sometimes lead to much more accurate predictions for a fixed model size. Second, it makes the model's results much easier to interpret. To use this model, you must provide an ontology represented as a tree of OntologyNode objects. Each node corresponds to a category in the ontology. It defines the list of features (e.g. genes) that correspond to that category, as well as its child nodes (subcategories). In addition, every feature and every node has a unique string identifier that can be used to refer to it. As an alternative to building the ontology yourself, you can use the create_gene_ontology() function to build a representation of the GO hierarchy. It downloads a definition of the hierarchy from the GO website, parses it, builds OntologyNodes for all the categories, and returns a root node that you can pass to the OntologyModel constructor. An important feature of this model is that the outputs of its internal layers are meaningful. During training, it tries to make each category independently predict the labels. By default, predict() returns the predictions for the root node of the hierarchy. You can use the prediction_for_node field to get the output layer corresponding to a particular category: prediction = model.predict(dataset, outputs=model.prediction_for_node[node_id]) """ def __init__(self, Loading @@ -39,6 +53,32 @@ class OntologyModel(TensorGraph): intermediate_loss_weight=0.3, weight_decay_penalty=0.0, **kwargs): """Create an OntologyModel. In addition to the following arguments, this class also accepts all the keyword arguments from TensorGraph. Parameters ---------- n_tasks: int the number of tasks this model predicts feature_ids: list of str the unique identifiers for the features this model generates predictions based on. These strings must match the feature IDs in the OntologyNodes. The first element of this list must correspond to the first feature in the data, the second element to the second feature, etc. root_node: OntologyNode the root node of the ontology that defines this models mode: str the type of model to create, either "regression" or "classification" n_classes: int for classification models, the number of classes to predict. This is ignored for regression models. intermediate_loss_weight: float the weight to multiply the loss from intermediate (non-root) categories by weight_decay_penalty: float the magnitude of the weight decay penalty to use for normalization """ super(OntologyModel, self).__init__(**kwargs) self.n_tasks = n_tasks self.feature_ids = feature_ids Loading Loading @@ -183,3 +223,157 @@ class OntologyModel(TensorGraph): tensors[self.labels[0]] = tf.one_hot( tf.cast(labels, tf.int32), self.n_classes) return tensors class OntologyNode(object): """An OntologyNode represents a category within an ontology.""" def __init__(self, node_id=None, n_outputs=10, feature_ids=None, children=None, name=None): """Create a OntologyNode representing a category in an ontology. Parameters ---------- node_id: str a unique identifier for this category. If this is omitted, an identifier is generated automatically. n_outputs: int the number of output values the corresponding layer of the OntologyModel should produce feature_ids: list of str the unique IDs of all features that belong to this category (not including ones that belong to child nodes) children: list of OntologyNode the list of nodes defining subcategories name: str a descriptive name for this category. Ir this is omitted, the name is set to the ID. """ self.id = node_id self.n_outputs = n_outputs self.feature_ids = (feature_ids if feature_ids is not None else []) self.children = (children if children is not None else []) self.name = (name if name is not None else id) def create_gene_ontology(feature_mapping, outputs_per_feature=0.3, min_outputs=20, min_node_features=6, omit_redundant_nodes=True, ontology_file=None): """Create a tree of OntologyNodes describing the Gene Ontology classification. See http://geneontology.org/ for details about the Gene Ontology classification. Parameters ---------- feature_mapping: dict defines the mapping of features to GO categories. Each key should be a feature ID. The corresponding value should be a list of strings, giving the unique identifiers of all GO categories that feature belongs to. outputs_per_feature: float the number of outputs for each node is set to this value times the total number of features the node contains (including all subnodes) min_outputs: int the minimum number of outputs for any node min_node_features: int the minimum number of features corresponding to a node (including all its subnodes). If a category has fewer features than this, no node is create for it. Instead, its features are added directly to its parent node. omit_redundant_nodes: bool if True, a node will be omitted if it has only one child node and does not directly directly correspond to any features ontology_file: str the path to a Gene Ontology OBO file defining the ontology. If this is omitted, the most recent version of the ontology is downloaded from the GO website. """ # If necessary, download the file defining the ontology. if ontology_file is None: ontology_file = os.path.join(get_data_dir(), 'go-basic.obo') if not os.path.isfile(ontology_file): download_url('http://purl.obolibrary.org/obo/go/go-basic.obo') # Parse the ontology definition and create a list of terms. terms = [] term = None with open(ontology_file) as input: for line in input: if line.startswith('[Term]'): if term is not None: terms.append(term) term = {'parents': []} elif line.startswith('[Typedef]'): if term is not None: terms.append(term) term = None elif line.startswith('id:') and term is not None: term['id'] = line.split()[1] elif line.startswith('name:') and term is not None: term['name'] = line[5:].strip() elif line.startswith('is_a:') and term is not None: term['parents'].append(line.split()[1]) elif line.startswith('is_obsolete:'): if line.split()[1] == 'true': term = None if term is not None: terms.append(term) # Create OntologyNode objects for all the terms. nodes = {} for term in terms: nodes[term['id']] = OntologyNode(term['id'], 0, name=term['name']) # Assign parent-child relationships between nodes, and identify root nodes. roots = [] for term in terms: node = nodes[term['id']] for parent in term['parents']: nodes[parent].children.append(node) if len(term['parents']) == 0: roots.append(node) # Create a single root node that combines the three GO roots. root = OntologyNode('GO', 0, name='Gene Ontology Root Node', children=roots) # Assign features to nodes. for feature_id in feature_mapping: for node_id in feature_mapping[feature_id]: nodes[node_id].feature_ids.append(feature_id) # Count the number of features within each node. Eliminate nodes with too few # features and set the number of outputs for each one. def count_features(node): self_features = set(node.feature_ids) all_features = set(node.feature_ids) for i, child in enumerate(node.children[:]): child_features = count_features(child) all_features.update(child_features) if len(child_features) < min_node_features: node.children.remove(child) self_features.update(child.feature_ids) if omit_redundant_nodes and len( node.children) == 1 and len(self_features) == 0: self_features = node.children[0].feature_ids node.children = node.children[0].children n_features = len(self_features) if n_features > len(node.feature_ids): node.feature_ids = list(self_features) node.n_outputs = max(min_outputs, math.ceil(outputs_per_feature * n_features)) return all_features count_features(root) return root deepchem/models/tensorgraph/tests/test_ontology.py +29 −0 Original line number Diff line number Diff line Loading @@ -105,3 +105,32 @@ class TestOntology(test_util.TensorFlowTestCase): assert leaf2_error[1] == 0 assert np.mean( [leaf1_error[1], leaf1_error[2], leaf2_error[0], leaf2_error[2]]) > 0.01 def test_create_gene_ontology(self): """Test creating OntologyNodes for the Gene Ontology classification.""" # Here are mappings for just a few yeast genes. mapping = {} mapping['STE7'] = ['GO:0000187'] mapping['PBS2'] = ['GO:0000187'] mapping['NOP8'] = [ 'GO:0003676', 'GO:0003723', 'GO:0042254', 'GO:0005634', 'GO:0005730' ] # Build the ontology, then see if it looks correct. root = dc.models.tensorgraph.models.ontology.create_gene_ontology( mapping, min_node_features=1) assert len(root.feature_ids) == 0 def find_features(node, features): features.update(node.feature_ids) for child in node.children: find_features(child, features) all_features = set() find_features(root, all_features) assert len(all_features) == 3 for key in mapping: assert key in all_features Loading
deepchem/models/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -26,7 +26,7 @@ from deepchem.models.tensorgraph.models.gan import GAN, WGAN from deepchem.models.tensorgraph.models.text_cnn import TextCNNModel from deepchem.models.tensorgraph.sequential import Sequential from deepchem.models.tensorgraph.models.sequence_dnn import SequenceDNN from deepchem.models.tensorgraph.models.ontology import OntologyModel, OntologyNode from deepchem.models.tensorgraph.models.ontology import OntologyModel, OntologyNode, create_gene_ontology #################### Compatibility imports for renamed TensorGraph models. Remove below with DeepChem 3.0. #################### Loading
deepchem/models/tensorgraph/models/ontology.py +203 −9 Original line number Diff line number Diff line Loading @@ -3,16 +3,10 @@ from deepchem.models import TensorGraph from deepchem.models.tensorgraph import layers from deepchem.metrics import to_one_hot from deepchem.utils import get_data_dir, download_url import tensorflow as tf class OntologyNode(object): def __init__(self, id, n_outputs, feature_ids=[], children=[]): self.id = id self.n_outputs = n_outputs self.feature_ids = feature_ids self.children = children import math import os class OntologyModel(TensorGraph): Loading @@ -28,6 +22,26 @@ class OntologyModel(TensorGraph): First, incorporating prior knowledge can sometimes lead to much more accurate predictions for a fixed model size. Second, it makes the model's results much easier to interpret. To use this model, you must provide an ontology represented as a tree of OntologyNode objects. Each node corresponds to a category in the ontology. It defines the list of features (e.g. genes) that correspond to that category, as well as its child nodes (subcategories). In addition, every feature and every node has a unique string identifier that can be used to refer to it. As an alternative to building the ontology yourself, you can use the create_gene_ontology() function to build a representation of the GO hierarchy. It downloads a definition of the hierarchy from the GO website, parses it, builds OntologyNodes for all the categories, and returns a root node that you can pass to the OntologyModel constructor. An important feature of this model is that the outputs of its internal layers are meaningful. During training, it tries to make each category independently predict the labels. By default, predict() returns the predictions for the root node of the hierarchy. You can use the prediction_for_node field to get the output layer corresponding to a particular category: prediction = model.predict(dataset, outputs=model.prediction_for_node[node_id]) """ def __init__(self, Loading @@ -39,6 +53,32 @@ class OntologyModel(TensorGraph): intermediate_loss_weight=0.3, weight_decay_penalty=0.0, **kwargs): """Create an OntologyModel. In addition to the following arguments, this class also accepts all the keyword arguments from TensorGraph. Parameters ---------- n_tasks: int the number of tasks this model predicts feature_ids: list of str the unique identifiers for the features this model generates predictions based on. These strings must match the feature IDs in the OntologyNodes. The first element of this list must correspond to the first feature in the data, the second element to the second feature, etc. root_node: OntologyNode the root node of the ontology that defines this models mode: str the type of model to create, either "regression" or "classification" n_classes: int for classification models, the number of classes to predict. This is ignored for regression models. intermediate_loss_weight: float the weight to multiply the loss from intermediate (non-root) categories by weight_decay_penalty: float the magnitude of the weight decay penalty to use for normalization """ super(OntologyModel, self).__init__(**kwargs) self.n_tasks = n_tasks self.feature_ids = feature_ids Loading Loading @@ -183,3 +223,157 @@ class OntologyModel(TensorGraph): tensors[self.labels[0]] = tf.one_hot( tf.cast(labels, tf.int32), self.n_classes) return tensors class OntologyNode(object): """An OntologyNode represents a category within an ontology.""" def __init__(self, node_id=None, n_outputs=10, feature_ids=None, children=None, name=None): """Create a OntologyNode representing a category in an ontology. Parameters ---------- node_id: str a unique identifier for this category. If this is omitted, an identifier is generated automatically. n_outputs: int the number of output values the corresponding layer of the OntologyModel should produce feature_ids: list of str the unique IDs of all features that belong to this category (not including ones that belong to child nodes) children: list of OntologyNode the list of nodes defining subcategories name: str a descriptive name for this category. Ir this is omitted, the name is set to the ID. """ self.id = node_id self.n_outputs = n_outputs self.feature_ids = (feature_ids if feature_ids is not None else []) self.children = (children if children is not None else []) self.name = (name if name is not None else id) def create_gene_ontology(feature_mapping, outputs_per_feature=0.3, min_outputs=20, min_node_features=6, omit_redundant_nodes=True, ontology_file=None): """Create a tree of OntologyNodes describing the Gene Ontology classification. See http://geneontology.org/ for details about the Gene Ontology classification. Parameters ---------- feature_mapping: dict defines the mapping of features to GO categories. Each key should be a feature ID. The corresponding value should be a list of strings, giving the unique identifiers of all GO categories that feature belongs to. outputs_per_feature: float the number of outputs for each node is set to this value times the total number of features the node contains (including all subnodes) min_outputs: int the minimum number of outputs for any node min_node_features: int the minimum number of features corresponding to a node (including all its subnodes). If a category has fewer features than this, no node is create for it. Instead, its features are added directly to its parent node. omit_redundant_nodes: bool if True, a node will be omitted if it has only one child node and does not directly directly correspond to any features ontology_file: str the path to a Gene Ontology OBO file defining the ontology. If this is omitted, the most recent version of the ontology is downloaded from the GO website. """ # If necessary, download the file defining the ontology. if ontology_file is None: ontology_file = os.path.join(get_data_dir(), 'go-basic.obo') if not os.path.isfile(ontology_file): download_url('http://purl.obolibrary.org/obo/go/go-basic.obo') # Parse the ontology definition and create a list of terms. terms = [] term = None with open(ontology_file) as input: for line in input: if line.startswith('[Term]'): if term is not None: terms.append(term) term = {'parents': []} elif line.startswith('[Typedef]'): if term is not None: terms.append(term) term = None elif line.startswith('id:') and term is not None: term['id'] = line.split()[1] elif line.startswith('name:') and term is not None: term['name'] = line[5:].strip() elif line.startswith('is_a:') and term is not None: term['parents'].append(line.split()[1]) elif line.startswith('is_obsolete:'): if line.split()[1] == 'true': term = None if term is not None: terms.append(term) # Create OntologyNode objects for all the terms. nodes = {} for term in terms: nodes[term['id']] = OntologyNode(term['id'], 0, name=term['name']) # Assign parent-child relationships between nodes, and identify root nodes. roots = [] for term in terms: node = nodes[term['id']] for parent in term['parents']: nodes[parent].children.append(node) if len(term['parents']) == 0: roots.append(node) # Create a single root node that combines the three GO roots. root = OntologyNode('GO', 0, name='Gene Ontology Root Node', children=roots) # Assign features to nodes. for feature_id in feature_mapping: for node_id in feature_mapping[feature_id]: nodes[node_id].feature_ids.append(feature_id) # Count the number of features within each node. Eliminate nodes with too few # features and set the number of outputs for each one. def count_features(node): self_features = set(node.feature_ids) all_features = set(node.feature_ids) for i, child in enumerate(node.children[:]): child_features = count_features(child) all_features.update(child_features) if len(child_features) < min_node_features: node.children.remove(child) self_features.update(child.feature_ids) if omit_redundant_nodes and len( node.children) == 1 and len(self_features) == 0: self_features = node.children[0].feature_ids node.children = node.children[0].children n_features = len(self_features) if n_features > len(node.feature_ids): node.feature_ids = list(self_features) node.n_outputs = max(min_outputs, math.ceil(outputs_per_feature * n_features)) return all_features count_features(root) return root
deepchem/models/tensorgraph/tests/test_ontology.py +29 −0 Original line number Diff line number Diff line Loading @@ -105,3 +105,32 @@ class TestOntology(test_util.TensorFlowTestCase): assert leaf2_error[1] == 0 assert np.mean( [leaf1_error[1], leaf1_error[2], leaf2_error[0], leaf2_error[2]]) > 0.01 def test_create_gene_ontology(self): """Test creating OntologyNodes for the Gene Ontology classification.""" # Here are mappings for just a few yeast genes. mapping = {} mapping['STE7'] = ['GO:0000187'] mapping['PBS2'] = ['GO:0000187'] mapping['NOP8'] = [ 'GO:0003676', 'GO:0003723', 'GO:0042254', 'GO:0005634', 'GO:0005730' ] # Build the ontology, then see if it looks correct. root = dc.models.tensorgraph.models.ontology.create_gene_ontology( mapping, min_node_features=1) assert len(root.feature_ids) == 0 def find_features(node, features): features.update(node.feature_ids) for child in node.children: find_features(child, features) all_features = set() find_features(root, all_features) assert len(all_features) == 3 for key in mapping: assert key in all_features
