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| import collections | ||
|
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||
| import numpy as np | ||
| import six | ||
| import tensorflow as tf | ||
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| from deepchem.data import NumpyDataset | ||
| from deepchem.feat.graph_features import ConvMolFeaturizer | ||
| from deepchem.feat.mol_graphs import ConvMol | ||
| from deepchem.metrics import to_one_hot | ||
| from deepchem.models.tensorgraph.graph_layers import WeaveGather, \ | ||
| DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, \ | ||
| DAGGather, DTNNExtract, MessagePassing, SetGather | ||
| from deepchem.models.tensorgraph.graph_layers import WeaveLayerFactory | ||
| from deepchem.models.tensorgraph.layers import Dense, SoftMax, \ | ||
| SoftMaxCrossEntropy, GraphConv, BatchNorm, \ | ||
| GraphPool, GraphGather, WeightedError, Dropout, BatchNorm, Stack, Flatten, GraphCNN, GraphCNNPool | ||
| from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature | ||
| from deepchem.models.tensorgraph.tensor_graph import TensorGraph | ||
| from deepchem.trans import undo_transforms | ||
|
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|
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| class PetroskiSuchModel(TensorGraph): | ||
| """ | ||
| Model from Robust Spatial Filtering with Graph Convolutional Neural Networks | ||
| https://arxiv.org/abs/1703.00792 | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| n_tasks, | ||
| max_atoms=200, | ||
| dropout=0.0, | ||
| mode="classification", | ||
| **kwargs): | ||
| """ | ||
| Parameters | ||
| ---------- | ||
| n_tasks: int | ||
| Number of tasks | ||
| mode: str | ||
| Either "classification" or "regression" | ||
| """ | ||
| self.n_tasks = n_tasks | ||
| self.mode = mode | ||
| self.max_atoms = max_atoms | ||
| self.error_bars = True if 'error_bars' in kwargs and kwargs['error_bars'] else False | ||
| self.dropout = dropout | ||
| kwargs['use_queue'] = False | ||
| super(PetroskiSuchModel, self).__init__(**kwargs) | ||
| self.build_graph() | ||
|
|
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| def build_graph(self): | ||
| self.vertex_features = Feature(shape=(None, self.max_atoms, 75)) | ||
| self.adj_matrix = Feature(shape=(None, self.max_atoms, 1, self.max_atoms)) | ||
| self.mask = Feature(shape=(None, self.max_atoms, 1)) | ||
|
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| gcnn1 = BatchNorm( | ||
| GraphCNN( | ||
| num_filters=64, | ||
| in_layers=[self.vertex_features, self.adj_matrix, self.mask])) | ||
| gcnn1 = Dropout(self.dropout, in_layers=gcnn1) | ||
| gcnn2 = BatchNorm( | ||
| GraphCNN(num_filters=64, in_layers=[gcnn1, self.adj_matrix, self.mask])) | ||
| gcnn2 = Dropout(self.dropout, in_layers=gcnn2) | ||
| gc_pool, adj_matrix = GraphCNNPool( | ||
| num_vertices=32, in_layers=[gcnn2, self.adj_matrix, self.mask]) | ||
| gc_pool = BatchNorm(gc_pool) | ||
| gc_pool = Dropout(self.dropout, in_layers=gc_pool) | ||
| gcnn3 = BatchNorm(GraphCNN(num_filters=32, in_layers=[gc_pool, adj_matrix])) | ||
| gcnn3 = Dropout(self.dropout, in_layers=gcnn3) | ||
| gc_pool2, adj_matrix2 = GraphCNNPool( | ||
| num_vertices=8, in_layers=[gcnn3, adj_matrix]) | ||
| gc_pool2 = BatchNorm(gc_pool2) | ||
| gc_pool2 = Dropout(self.dropout, in_layers=gc_pool2) | ||
| flattened = Flatten(in_layers=gc_pool2) | ||
| readout = Dense( | ||
| out_channels=256, activation_fn=tf.nn.relu, in_layers=flattened) | ||
| costs = [] | ||
| self.my_labels = [] | ||
| for task in range(self.n_tasks): | ||
| if self.mode == 'classification': | ||
| classification = Dense( | ||
| out_channels=2, activation_fn=None, in_layers=[readout]) | ||
|
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| softmax = SoftMax(in_layers=[classification]) | ||
| self.add_output(softmax) | ||
|
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| label = Label(shape=(None, 2)) | ||
| self.my_labels.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=[readout]) | ||
| self.add_output(regression) | ||
|
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| label = Label(shape=(None, 1)) | ||
| self.my_labels.append(label) | ||
| cost = L2Loss(in_layers=[label, regression]) | ||
| costs.append(cost) | ||
| if self.mode == "classification": | ||
| entropy = Stack(in_layers=costs, axis=-1) | ||
| elif self.mode == "regression": | ||
| entropy = Stack(in_layers=costs, axis=1) | ||
| self.my_task_weights = Weights(shape=(None, self.n_tasks)) | ||
| loss = WeightedError(in_layers=[entropy, self.my_task_weights]) | ||
| self.set_loss(loss) | ||
|
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||
| def default_generator(self, | ||
| dataset, | ||
| epochs=1, | ||
| predict=False, | ||
| deterministic=True, | ||
| pad_batches=True): | ||
| for epoch in range(epochs): | ||
| if not predict: | ||
| print('Starting epoch %i' % epoch) | ||
| for ind, (X_b, y_b, w_b, ids_b) in enumerate( | ||
| dataset.iterbatches( | ||
| self.batch_size, pad_batches=True, deterministic=deterministic)): | ||
| d = {} | ||
| for index, label in enumerate(self.my_labels): | ||
| if self.mode == 'classification': | ||
| d[label] = to_one_hot(y_b[:, index]) | ||
| if self.mode == 'regression': | ||
| d[label] = np.expand_dims(y_b[:, index], -1) | ||
| d[self.my_task_weights] = w_b | ||
| d[self.adj_matrix] = np.expand_dims(np.array([x[0] for x in X_b]), -2) | ||
| d[self.vertex_features] = np.array([x[1] for x in X_b]) | ||
| mask = np.zeros(shape=(self.batch_size, self.max_atoms, 1)) | ||
| for i in range(self.batch_size): | ||
| mask_size = X_b[i][2] | ||
| mask[i][:mask_size][0] = 1 | ||
| d[self.mask] = mask | ||
| yield d | ||
|
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||
| def predict_proba_on_generator(self, generator, transformers=[]): | ||
| 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: | ||
| feed_dict = { | ||
| self.layers[k.name].out_tensor: v | ||
| for k, v in six.iteritems(feed_dict) | ||
| } | ||
| feed_dict[self._training_placeholder] = 1.0 ## | ||
| 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]) | ||
| if len(transformers) > 0: | ||
| result = undo_transforms(result, transformers) | ||
| results.append(result) | ||
| return np.concatenate(results, axis=0) | ||
|
|
||
| def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False): | ||
| if not self.built: | ||
| self.build() | ||
| return self.evaluate_generator( | ||
| self.default_generator(dataset, predict=True), | ||
| metrics, | ||
| labels=self.my_labels, | ||
| weights=[self.my_task_weights], | ||
| per_task_metrics=per_task_metrics) |
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