Loading deepchem/models/tensorgraph/models/graph_models.py +97 −148 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ import numpy as np import six import tensorflow as tf from deepchem.data import NumpyDataset from deepchem.data import NumpyDataset, pad_features from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.mol_graphs import ConvMol from deepchem.metrics import to_one_hot Loading Loading @@ -54,6 +54,7 @@ class WeaveModel(TensorGraph): n_hidden=50, n_graph_feat=128, mode="classification", n_classes=2, **kwargs): """ Parameters Loading @@ -70,13 +71,18 @@ class WeaveModel(TensorGraph): Number of output features for each molecule(graph) mode: str Either "classification" or "regression" for type of model. n_classes: int Number of classes to predict (only used in classification mode) """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat self.n_hidden = n_hidden self.n_graph_feat = n_graph_feat self.mode = mode self.n_classes = n_classes super(WeaveModel, self).__init__(**kwargs) self.build_graph() Loading Loading @@ -118,35 +124,29 @@ class WeaveModel(TensorGraph): gaussian_expand=True, in_layers=[batch_norm1, self.atom_split]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[weave_gather]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[weave_gather]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Stack(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=weave_gather, out_channels=n_tasks * n_classes) ]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=weave_gather, out_channels=n_tasks)]) self.add_output(output) weighted_loss = ReduceSum(L2Loss(in_layers=[labels, output, weights])) self.set_loss(weighted_loss) def default_generator(self, dataset, Loading @@ -165,13 +165,15 @@ class WeaveModel(TensorGraph): feed_dict = dict() if y_b is not None: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] if self.mode == 'classification': feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape( -1, self.n_tasks, self.n_classes) else: feed_dict[self.labels[0]] = y_b if w_b is not None: feed_dict[self.weights] = w_b feed_dict[self.task_weights[0]] = w_b atom_feat = [] pair_feat = [] Loading Loading @@ -207,17 +209,6 @@ class WeaveModel(TensorGraph): feed_dict[self.atom_to_pair] = np.concatenate(atom_to_pair, axis=0) yield feed_dict def predict_on_generator(self, generator, transformers=[], outputs=None): out = super(WeaveModel, self).predict_on_generator( generator, transformers=[], outputs=outputs) if outputs is None: outputs = self.outputs if len(outputs) > 1: out = np.stack(out, axis=1) out = undo_transforms(out, transformers) return out class DTNNModel(TensorGraph): Loading Loading @@ -250,6 +241,8 @@ class DTNNModel(TensorGraph): mode: str Either "classification" or "regression" for type of model. """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_embedding = n_embedding self.n_hidden = n_hidden Loading Loading @@ -427,6 +420,8 @@ class DAGModel(TensorGraph): if True, include extra outputs and loss terms to enable the uncertainty in outputs to be predicted """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.max_atoms = max_atoms self.n_atom_feat = n_atom_feat Loading Loading @@ -568,17 +563,6 @@ class DAGModel(TensorGraph): feed_dict[self.n_atoms] = n_atoms yield feed_dict def predict_on_generator(self, generator, transformers=[], outputs=None): out = super(DAGModel, self).predict_on_generator( generator, transformers=[], outputs=outputs) if outputs is None: outputs = self.outputs if len(outputs) > 1: out = np.stack(out, axis=1) out = undo_transforms(out, transformers) return out class GraphConvModel(TensorGraph): Loading @@ -586,7 +570,7 @@ class GraphConvModel(TensorGraph): n_tasks, graph_conv_layers=[64, 64], dense_layer_size=128, dropouts=0.0, dropout=0.0, mode="classification", number_atom_features=75, n_classes=2, Loading @@ -601,7 +585,7 @@ class GraphConvModel(TensorGraph): Width of channels for the Graph Convolution Layers dense_layer_size: int Width of channels for Atom Level Dense Layer before GraphPool dropouts: list or float dropout: list or float the dropout probablity to use for each layer. The length of this list should equal len(graph_conv_layers)+1 (one value for each convolution layer, and one for the dense layer). Alternatively this may be a single value instead of a list, in which Loading @@ -628,15 +612,15 @@ class GraphConvModel(TensorGraph): self.number_atom_features = number_atom_features self.n_classes = n_classes self.uncertainty = uncertainty if not isinstance(dropouts, collections.Sequence): dropouts = [dropouts] * (len(graph_conv_layers) + 1) if len(dropouts) != len(graph_conv_layers) + 1: if not isinstance(dropout, collections.Sequence): dropout = [dropout] * (len(graph_conv_layers) + 1) if len(dropout) != len(graph_conv_layers) + 1: raise ValueError('Wrong number of dropout probabilities provided') self.dropouts = dropouts self.dropout = dropout if uncertainty: if mode != "regression": raise ValueError("Uncertainty is only supported in regression mode") if any(d == 0.0 for d in dropouts): if any(d == 0.0 for d in dropout): raise ValueError( 'Dropout must be included in every layer to predict uncertainty') super(GraphConvModel, self).__init__(**kwargs) Loading @@ -655,7 +639,7 @@ class GraphConvModel(TensorGraph): deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32) self.deg_adjs.append(deg_adj) in_layer = self.atom_features for layer_size, dropout in zip(self.graph_conv_layers, self.dropouts): for layer_size, dropout in zip(self.graph_conv_layers, self.dropout): gc1_in = [in_layer, self.degree_slice, self.membership] + self.deg_adjs gc1 = GraphConv(layer_size, activation_fn=tf.nn.relu, in_layers=gc1_in) batch_norm1 = BatchNorm(in_layers=[gc1]) Loading @@ -668,8 +652,8 @@ class GraphConvModel(TensorGraph): activation_fn=tf.nn.relu, in_layers=[in_layer]) batch_norm3 = BatchNorm(in_layers=[dense]) if self.dropouts[-1] > 0.0: batch_norm3 = Dropout(self.dropouts[-1], in_layers=batch_norm3) if self.dropout[-1] > 0.0: batch_norm3 = Dropout(self.dropout[-1], in_layers=batch_norm3) readout = GraphGather( batch_size=self.batch_size, activation_fn=tf.nn.tanh, Loading @@ -677,16 +661,16 @@ class GraphConvModel(TensorGraph): self.deg_adjs) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) weights = Weights(shape=(None, n_tasks)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=readout, out_channels=n_tasks * n_classes) ]) logits = TrimGraphOutput([logits, labels]) logits = TrimGraphOutput([logits, weights]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) Loading @@ -694,17 +678,16 @@ class GraphConvModel(TensorGraph): self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) weights = Weights(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=readout, out_channels=n_tasks)]) output = TrimGraphOutput([output, labels]) output = TrimGraphOutput([output, weights]) self.add_output(output) if self.uncertainty: log_var = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=readout, out_channels=n_tasks)]) log_var = TrimGraphOutput([log_var, labels]) log_var = TrimGraphOutput([log_var, weights]) var = Exp(log_var) self.add_variance(var) diff = labels - output Loading Loading @@ -780,6 +763,7 @@ class MPNNModel(TensorGraph): T=5, M=10, mode="regression", n_classes=2, **kwargs): """ Parameters Loading @@ -794,8 +778,12 @@ class MPNNModel(TensorGraph): Number of units(convolution depths) in corresponding hidden layer n_graph_feat: int, optional Number of output features for each molecule(graph) n_classes: int the number of classes to predict (only used in classification mode) """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat Loading @@ -803,6 +791,7 @@ class MPNNModel(TensorGraph): self.T = T self.M = M self.mode = mode self.n_classes = n_classes super(MPNNModel, self).__init__(**kwargs) self.build_graph() Loading Loading @@ -832,35 +821,32 @@ class MPNNModel(TensorGraph): out_channels=2 * self.n_hidden, activation_fn=tf.nn.relu, in_layers=[mol_embeddings]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[dense1]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[dense1]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Stack(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=dense1, out_channels=n_tasks * n_classes) ]) logits = TrimGraphOutput([logits, weights]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=dense1, out_channels=n_tasks)]) output = TrimGraphOutput([output, weights]) self.add_output(output) weighted_loss = ReduceSum(L2Loss(in_layers=[labels, output, weights])) self.set_loss(weighted_loss) def default_generator(self, dataset, Loading @@ -875,18 +861,20 @@ class MPNNModel(TensorGraph): for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): pad_batches=False): X_b = pad_features(self.batch_size, X_b) feed_dict = dict() if y_b is not None: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] # w_b act as the indicator of unique samples in the batch if self.mode == 'classification': feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape( -1, self.n_tasks, self.n_classes) else: feed_dict[self.labels[0]] = y_b if w_b is not None: feed_dict[self.weights] = w_b feed_dict[self.task_weights[0]] = w_b atom_feat = [] pair_feat = [] Loading Loading @@ -921,45 +909,6 @@ class MPNNModel(TensorGraph): feed_dict[self.atom_to_pair] = np.concatenate(atom_to_pair, axis=0) yield feed_dict def predict(self, dataset, transformers=[], batch_size=None): # MPNN only accept padded input generator = self.default_generator(dataset, predict=True, pad_batches=True) return self.predict_on_generator(generator, transformers) def predict_proba(self, dataset, transformers=[], batch_size=None): # MPNN only accept padded input generator = self.default_generator(dataset, predict=True, pad_batches=True) return self.predict_proba_on_generator(generator, transformers) def predict_proba_on_generator(self, generator, transformers=[]): """ Returns: y_pred: numpy ndarray of shape (n_samples, n_classes*n_tasks) """ if not self.built: self.build() with self._get_tf("Graph").as_default(): out_tensors = [x.out_tensor for x in self.outputs] results = [] for feed_dict in generator: # Extract number of unique samples in the batch from w_b n_valid_samples = len(np.nonzero(np.sum(feed_dict[self.weights], 1))[0]) feed_dict = { self.layers[k.name].out_tensor: v for k, v in six.iteritems(feed_dict) } feed_dict[self._training_placeholder] = 0.0 result = np.array(self.session.run(out_tensors, feed_dict=feed_dict)) if len(result.shape) == 3: result = np.transpose(result, axes=[1, 0, 2]) result = undo_transforms(result, transformers) # Only fetch the first set of unique samples results.append(result[:n_valid_samples]) return np.concatenate(results, axis=0) def predict_on_generator(self, generator, transformers=[]): return self.predict_proba_on_generator(generator, transformers) #################### Deprecation warnings for renamed TensorGraph models #################### Loading deepchem/models/tensorgraph/models/test_graph_models.py +71 −5 Original line number Diff line number Diff line Loading @@ -4,11 +4,10 @@ import numpy as np import deepchem as dc from deepchem.data import NumpyDataset from deepchem.models import GraphConvModel, DAGModel from deepchem.models import GraphConvModel, DAGModel, WeaveModel, MPNNModel from deepchem.models import TensorGraph from deepchem.molnet import load_bace_classification, load_delaney from deepchem.models.tensorgraph.layers import ReduceSum, L2Loss from deepchem.models import WeaveModel from deepchem.feat import ConvMolFeaturizer from nose.plugins.attrib import attr Loading Loading @@ -39,7 +38,7 @@ class TestGraphModels(unittest.TestCase): metric = dc.metrics.Metric( dc.metrics.mean_absolute_error, mode="regression") ds = NumpyDataset(train.X[:10], y, w, train.ids[:10]) ds = NumpyDataset(train.X[:data_points], y, w, train.ids[:data_points]) return tasks, ds, transformers, metric Loading @@ -53,7 +52,7 @@ class TestGraphModels(unittest.TestCase): model.fit(dataset, nb_epoch=10) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] > 0.95 assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) Loading Loading @@ -87,7 +86,7 @@ class TestGraphModels(unittest.TestCase): len(tasks), batch_size=batch_size, mode='regression', dropouts=0.1, dropout=0.1, uncertainty=True) model.fit(dataset, nb_epoch=100) Loading Loading @@ -171,6 +170,39 @@ class TestGraphModels(unittest.TestCase): model2.restore() model2.fit(dataset, nb_epoch=1, submodel=module) @attr("slow") def test_weave_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'Weave') model = WeaveModel(len(tasks), mode='classification') model.fit(dataset, nb_epoch=50) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean-roc_auc_score'], scores2['mean-roc_auc_score']) def test_weave_regression_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'regression', 'Weave') model = WeaveModel(len(tasks), mode='regression') model.fit(dataset, nb_epoch=70) scores = model.evaluate(dataset, [metric], transformers) assert all(s < 0.1 for s in scores['mean_absolute_error']) model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean_absolute_error'], scores2['mean_absolute_error']) def test_dag_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'GraphConv') Loading Loading @@ -246,3 +278,37 @@ class TestGraphModels(unittest.TestCase): assert mean_error < 0.5 * mean_value assert mean_std > 0.5 * mean_error assert mean_std < mean_value @attr("slow") def test_mpnn_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'Weave') model = MPNNModel(len(tasks), mode='classification', n_hidden=75, n_atom_feat=75, n_pair_feat=14, T=1, M=1) model.fit(dataset, nb_epoch=20) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean-roc_auc_score'], scores2['mean-roc_auc_score']) @attr("slow") def test_mpnn_regression_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'regression', 'Weave') model = MPNNModel(len(tasks), mode='regression', n_hidden=75, n_atom_feat=75, n_pair_feat=14, T=1, M=1) model.fit(dataset, nb_epoch=50) scores = model.evaluate(dataset, [metric], transformers) assert all(s < 0.1 for s in scores['mean_absolute_error']) model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean_absolute_error'], scores2['mean_absolute_error']) Loading
deepchem/models/tensorgraph/models/graph_models.py +97 −148 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ import numpy as np import six import tensorflow as tf from deepchem.data import NumpyDataset from deepchem.data import NumpyDataset, pad_features from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.mol_graphs import ConvMol from deepchem.metrics import to_one_hot Loading Loading @@ -54,6 +54,7 @@ class WeaveModel(TensorGraph): n_hidden=50, n_graph_feat=128, mode="classification", n_classes=2, **kwargs): """ Parameters Loading @@ -70,13 +71,18 @@ class WeaveModel(TensorGraph): Number of output features for each molecule(graph) mode: str Either "classification" or "regression" for type of model. n_classes: int Number of classes to predict (only used in classification mode) """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat self.n_hidden = n_hidden self.n_graph_feat = n_graph_feat self.mode = mode self.n_classes = n_classes super(WeaveModel, self).__init__(**kwargs) self.build_graph() Loading Loading @@ -118,35 +124,29 @@ class WeaveModel(TensorGraph): gaussian_expand=True, in_layers=[batch_norm1, self.atom_split]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[weave_gather]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[weave_gather]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Stack(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=weave_gather, out_channels=n_tasks * n_classes) ]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=weave_gather, out_channels=n_tasks)]) self.add_output(output) weighted_loss = ReduceSum(L2Loss(in_layers=[labels, output, weights])) self.set_loss(weighted_loss) def default_generator(self, dataset, Loading @@ -165,13 +165,15 @@ class WeaveModel(TensorGraph): feed_dict = dict() if y_b is not None: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] if self.mode == 'classification': feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape( -1, self.n_tasks, self.n_classes) else: feed_dict[self.labels[0]] = y_b if w_b is not None: feed_dict[self.weights] = w_b feed_dict[self.task_weights[0]] = w_b atom_feat = [] pair_feat = [] Loading Loading @@ -207,17 +209,6 @@ class WeaveModel(TensorGraph): feed_dict[self.atom_to_pair] = np.concatenate(atom_to_pair, axis=0) yield feed_dict def predict_on_generator(self, generator, transformers=[], outputs=None): out = super(WeaveModel, self).predict_on_generator( generator, transformers=[], outputs=outputs) if outputs is None: outputs = self.outputs if len(outputs) > 1: out = np.stack(out, axis=1) out = undo_transforms(out, transformers) return out class DTNNModel(TensorGraph): Loading Loading @@ -250,6 +241,8 @@ class DTNNModel(TensorGraph): mode: str Either "classification" or "regression" for type of model. """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_embedding = n_embedding self.n_hidden = n_hidden Loading Loading @@ -427,6 +420,8 @@ class DAGModel(TensorGraph): if True, include extra outputs and loss terms to enable the uncertainty in outputs to be predicted """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.max_atoms = max_atoms self.n_atom_feat = n_atom_feat Loading Loading @@ -568,17 +563,6 @@ class DAGModel(TensorGraph): feed_dict[self.n_atoms] = n_atoms yield feed_dict def predict_on_generator(self, generator, transformers=[], outputs=None): out = super(DAGModel, self).predict_on_generator( generator, transformers=[], outputs=outputs) if outputs is None: outputs = self.outputs if len(outputs) > 1: out = np.stack(out, axis=1) out = undo_transforms(out, transformers) return out class GraphConvModel(TensorGraph): Loading @@ -586,7 +570,7 @@ class GraphConvModel(TensorGraph): n_tasks, graph_conv_layers=[64, 64], dense_layer_size=128, dropouts=0.0, dropout=0.0, mode="classification", number_atom_features=75, n_classes=2, Loading @@ -601,7 +585,7 @@ class GraphConvModel(TensorGraph): Width of channels for the Graph Convolution Layers dense_layer_size: int Width of channels for Atom Level Dense Layer before GraphPool dropouts: list or float dropout: list or float the dropout probablity to use for each layer. The length of this list should equal len(graph_conv_layers)+1 (one value for each convolution layer, and one for the dense layer). Alternatively this may be a single value instead of a list, in which Loading @@ -628,15 +612,15 @@ class GraphConvModel(TensorGraph): self.number_atom_features = number_atom_features self.n_classes = n_classes self.uncertainty = uncertainty if not isinstance(dropouts, collections.Sequence): dropouts = [dropouts] * (len(graph_conv_layers) + 1) if len(dropouts) != len(graph_conv_layers) + 1: if not isinstance(dropout, collections.Sequence): dropout = [dropout] * (len(graph_conv_layers) + 1) if len(dropout) != len(graph_conv_layers) + 1: raise ValueError('Wrong number of dropout probabilities provided') self.dropouts = dropouts self.dropout = dropout if uncertainty: if mode != "regression": raise ValueError("Uncertainty is only supported in regression mode") if any(d == 0.0 for d in dropouts): if any(d == 0.0 for d in dropout): raise ValueError( 'Dropout must be included in every layer to predict uncertainty') super(GraphConvModel, self).__init__(**kwargs) Loading @@ -655,7 +639,7 @@ class GraphConvModel(TensorGraph): deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32) self.deg_adjs.append(deg_adj) in_layer = self.atom_features for layer_size, dropout in zip(self.graph_conv_layers, self.dropouts): for layer_size, dropout in zip(self.graph_conv_layers, self.dropout): gc1_in = [in_layer, self.degree_slice, self.membership] + self.deg_adjs gc1 = GraphConv(layer_size, activation_fn=tf.nn.relu, in_layers=gc1_in) batch_norm1 = BatchNorm(in_layers=[gc1]) Loading @@ -668,8 +652,8 @@ class GraphConvModel(TensorGraph): activation_fn=tf.nn.relu, in_layers=[in_layer]) batch_norm3 = BatchNorm(in_layers=[dense]) if self.dropouts[-1] > 0.0: batch_norm3 = Dropout(self.dropouts[-1], in_layers=batch_norm3) if self.dropout[-1] > 0.0: batch_norm3 = Dropout(self.dropout[-1], in_layers=batch_norm3) readout = GraphGather( batch_size=self.batch_size, activation_fn=tf.nn.tanh, Loading @@ -677,16 +661,16 @@ class GraphConvModel(TensorGraph): self.deg_adjs) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) weights = Weights(shape=(None, n_tasks)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=readout, out_channels=n_tasks * n_classes) ]) logits = TrimGraphOutput([logits, labels]) logits = TrimGraphOutput([logits, weights]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) Loading @@ -694,17 +678,16 @@ class GraphConvModel(TensorGraph): self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) weights = Weights(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=readout, out_channels=n_tasks)]) output = TrimGraphOutput([output, labels]) output = TrimGraphOutput([output, weights]) self.add_output(output) if self.uncertainty: log_var = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=readout, out_channels=n_tasks)]) log_var = TrimGraphOutput([log_var, labels]) log_var = TrimGraphOutput([log_var, weights]) var = Exp(log_var) self.add_variance(var) diff = labels - output Loading Loading @@ -780,6 +763,7 @@ class MPNNModel(TensorGraph): T=5, M=10, mode="regression", n_classes=2, **kwargs): """ Parameters Loading @@ -794,8 +778,12 @@ class MPNNModel(TensorGraph): Number of units(convolution depths) in corresponding hidden layer n_graph_feat: int, optional Number of output features for each molecule(graph) n_classes: int the number of classes to predict (only used in classification mode) """ if mode not in ['classification', 'regression']: raise ValueError("mode must be either 'classification' or 'regression'") self.n_tasks = n_tasks self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat Loading @@ -803,6 +791,7 @@ class MPNNModel(TensorGraph): self.T = T self.M = M self.mode = mode self.n_classes = n_classes super(MPNNModel, self).__init__(**kwargs) self.build_graph() Loading Loading @@ -832,35 +821,32 @@ class MPNNModel(TensorGraph): out_channels=2 * self.n_hidden, activation_fn=tf.nn.relu, in_layers=[mol_embeddings]) costs = [] self.labels_fd = [] for task in range(self.n_tasks): if self.mode == "classification": classification = Dense( out_channels=2, activation_fn=None, in_layers=[dense1]) softmax = SoftMax(in_layers=[classification]) self.add_output(softmax) label = Label(shape=(None, 2)) self.labels_fd.append(label) cost = SoftMaxCrossEntropy(in_layers=[label, classification]) costs.append(cost) if self.mode == "regression": regression = Dense( out_channels=1, activation_fn=None, in_layers=[dense1]) self.add_output(regression) label = Label(shape=(None, 1)) self.labels_fd.append(label) cost = L2Loss(in_layers=[label, regression]) costs.append(cost) if self.mode == "classification": all_cost = Stack(in_layers=costs, axis=1) elif self.mode == "regression": all_cost = Stack(in_layers=costs, axis=1) self.weights = Weights(shape=(None, self.n_tasks)) loss = WeightedError(in_layers=[all_cost, self.weights]) self.set_loss(loss) n_tasks = self.n_tasks weights = Weights(shape=(None, n_tasks)) if self.mode == 'classification': n_classes = self.n_classes labels = Label(shape=(None, n_tasks, n_classes)) logits = Reshape( shape=(None, n_tasks, n_classes), in_layers=[ Dense(in_layers=dense1, out_channels=n_tasks * n_classes) ]) logits = TrimGraphOutput([logits, weights]) output = SoftMax(logits) self.add_output(output) loss = SoftMaxCrossEntropy(in_layers=[labels, logits]) weighted_loss = WeightedError(in_layers=[loss, weights]) self.set_loss(weighted_loss) else: labels = Label(shape=(None, n_tasks)) output = Reshape( shape=(None, n_tasks), in_layers=[Dense(in_layers=dense1, out_channels=n_tasks)]) output = TrimGraphOutput([output, weights]) self.add_output(output) weighted_loss = ReduceSum(L2Loss(in_layers=[labels, output, weights])) self.set_loss(weighted_loss) def default_generator(self, dataset, Loading @@ -875,18 +861,20 @@ class MPNNModel(TensorGraph): for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): pad_batches=False): X_b = pad_features(self.batch_size, X_b) feed_dict = dict() if y_b is not None: for index, label in enumerate(self.labels_fd): if self.mode == "classification": feed_dict[label] = to_one_hot(y_b[:, index]) if self.mode == "regression": feed_dict[label] = y_b[:, index:index + 1] # w_b act as the indicator of unique samples in the batch if self.mode == 'classification': feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape( -1, self.n_tasks, self.n_classes) else: feed_dict[self.labels[0]] = y_b if w_b is not None: feed_dict[self.weights] = w_b feed_dict[self.task_weights[0]] = w_b atom_feat = [] pair_feat = [] Loading Loading @@ -921,45 +909,6 @@ class MPNNModel(TensorGraph): feed_dict[self.atom_to_pair] = np.concatenate(atom_to_pair, axis=0) yield feed_dict def predict(self, dataset, transformers=[], batch_size=None): # MPNN only accept padded input generator = self.default_generator(dataset, predict=True, pad_batches=True) return self.predict_on_generator(generator, transformers) def predict_proba(self, dataset, transformers=[], batch_size=None): # MPNN only accept padded input generator = self.default_generator(dataset, predict=True, pad_batches=True) return self.predict_proba_on_generator(generator, transformers) def predict_proba_on_generator(self, generator, transformers=[]): """ Returns: y_pred: numpy ndarray of shape (n_samples, n_classes*n_tasks) """ if not self.built: self.build() with self._get_tf("Graph").as_default(): out_tensors = [x.out_tensor for x in self.outputs] results = [] for feed_dict in generator: # Extract number of unique samples in the batch from w_b n_valid_samples = len(np.nonzero(np.sum(feed_dict[self.weights], 1))[0]) feed_dict = { self.layers[k.name].out_tensor: v for k, v in six.iteritems(feed_dict) } feed_dict[self._training_placeholder] = 0.0 result = np.array(self.session.run(out_tensors, feed_dict=feed_dict)) if len(result.shape) == 3: result = np.transpose(result, axes=[1, 0, 2]) result = undo_transforms(result, transformers) # Only fetch the first set of unique samples results.append(result[:n_valid_samples]) return np.concatenate(results, axis=0) def predict_on_generator(self, generator, transformers=[]): return self.predict_proba_on_generator(generator, transformers) #################### Deprecation warnings for renamed TensorGraph models #################### Loading
deepchem/models/tensorgraph/models/test_graph_models.py +71 −5 Original line number Diff line number Diff line Loading @@ -4,11 +4,10 @@ import numpy as np import deepchem as dc from deepchem.data import NumpyDataset from deepchem.models import GraphConvModel, DAGModel from deepchem.models import GraphConvModel, DAGModel, WeaveModel, MPNNModel from deepchem.models import TensorGraph from deepchem.molnet import load_bace_classification, load_delaney from deepchem.models.tensorgraph.layers import ReduceSum, L2Loss from deepchem.models import WeaveModel from deepchem.feat import ConvMolFeaturizer from nose.plugins.attrib import attr Loading Loading @@ -39,7 +38,7 @@ class TestGraphModels(unittest.TestCase): metric = dc.metrics.Metric( dc.metrics.mean_absolute_error, mode="regression") ds = NumpyDataset(train.X[:10], y, w, train.ids[:10]) ds = NumpyDataset(train.X[:data_points], y, w, train.ids[:data_points]) return tasks, ds, transformers, metric Loading @@ -53,7 +52,7 @@ class TestGraphModels(unittest.TestCase): model.fit(dataset, nb_epoch=10) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] > 0.95 assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) Loading Loading @@ -87,7 +86,7 @@ class TestGraphModels(unittest.TestCase): len(tasks), batch_size=batch_size, mode='regression', dropouts=0.1, dropout=0.1, uncertainty=True) model.fit(dataset, nb_epoch=100) Loading Loading @@ -171,6 +170,39 @@ class TestGraphModels(unittest.TestCase): model2.restore() model2.fit(dataset, nb_epoch=1, submodel=module) @attr("slow") def test_weave_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'Weave') model = WeaveModel(len(tasks), mode='classification') model.fit(dataset, nb_epoch=50) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean-roc_auc_score'], scores2['mean-roc_auc_score']) def test_weave_regression_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'regression', 'Weave') model = WeaveModel(len(tasks), mode='regression') model.fit(dataset, nb_epoch=70) scores = model.evaluate(dataset, [metric], transformers) assert all(s < 0.1 for s in scores['mean_absolute_error']) model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean_absolute_error'], scores2['mean_absolute_error']) def test_dag_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'GraphConv') Loading Loading @@ -246,3 +278,37 @@ class TestGraphModels(unittest.TestCase): assert mean_error < 0.5 * mean_value assert mean_std > 0.5 * mean_error assert mean_std < mean_value @attr("slow") def test_mpnn_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'classification', 'Weave') model = MPNNModel(len(tasks), mode='classification', n_hidden=75, n_atom_feat=75, n_pair_feat=14, T=1, M=1) model.fit(dataset, nb_epoch=20) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.9 model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean-roc_auc_score'], scores2['mean-roc_auc_score']) @attr("slow") def test_mpnn_regression_model(self): tasks, dataset, transformers, metric = self.get_dataset( 'regression', 'Weave') model = MPNNModel(len(tasks), mode='regression', n_hidden=75, n_atom_feat=75, n_pair_feat=14, T=1, M=1) model.fit(dataset, nb_epoch=50) scores = model.evaluate(dataset, [metric], transformers) assert all(s < 0.1 for s in scores['mean_absolute_error']) model.save() model = TensorGraph.load_from_dir(model.model_dir) scores2 = model.evaluate(dataset, [metric], transformers) assert np.allclose(scores['mean_absolute_error'], scores2['mean_absolute_error'])
