Loading deepchem/feat/one_hot.py +5 −4 Original line number Diff line number Diff line Loading @@ -14,7 +14,7 @@ class OneHotFeaturizer(Featurizer): NOTE(LESWING) Not Thread Safe in initialization of charset """ def __init__(self, charset, padlength=120): def __init__(self, charset=None, padlength=120): """ Parameters ---------- Loading Loading @@ -44,7 +44,7 @@ class OneHotFeaturizer(Featurizer): """ smiles = [Chem.MolToSmiles(mol) for mol in mols] if self.charset is None: self.charset = self._create_charset(mols) self.charset = self._create_charset(smiles) return np.array([self.one_hot_encoded(smile) for smile in smiles]) def one_hot_array(self, i): Loading Loading @@ -143,5 +143,6 @@ class OneHotFeaturizer(Featurizer): """ s = set() for smile in smiles: s.union(list(smile)) return sorted(list(s)) for c in smile: s.add(c) return [' '] + sorted(list(s)) deepchem/metrics/__init__.py +4 −3 Original line number Diff line number Diff line Loading @@ -14,7 +14,7 @@ from sklearn.metrics import precision_score from scipy.stats import pearsonr def to_one_hot(y): def to_one_hot(y, axis=-1): """Transforms label vector into one-hot encoding. Turns y into vector of shape [n_samples, 2] (assuming binary labels). Loading @@ -23,7 +23,8 @@ def to_one_hot(y): A vector of shape [n_samples, 1] """ n_samples = np.shape(y)[0] y_hot = np.zeros((n_samples, 2)) n_classes = np.max(y, axis=axis) y_hot = np.zeros((n_samples, n_classes)) y_hot[np.arange(n_samples), y.astype(np.int64)] = 1 return y_hot Loading deepchem/models/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -22,3 +22,4 @@ from deepchem.models.tensorflow_models.lr import TensorflowLogisticRegression from deepchem.models.tensorflow_models.progressive_multitask import ProgressiveMultitaskRegressor from deepchem.models.tensorflow_models.progressive_joint import ProgressiveJointRegressor from deepchem.models.tensorflow_models.IRV import TensorflowMultiTaskIRVClassifier from deepchem.models.tensorgraph.tensor_graph import TensorGraph, MultiTaskTensorGraph No newline at end of file deepchem/models/tensorgraph/__init__.py 0 → 100644 +1 −0 Original line number Diff line number Diff line from deepchem.models.tensorgraph.tensor_graph import TensorGraph No newline at end of file deepchem/models/tensorgraph/layers.py 0 → 100644 +239 −0 Original line number Diff line number Diff line import random import string import tensorflow as tf class Layer(object): def __init__(self, **kwargs): if "name" not in kwargs: self.name = "%s%s" % (self.__class__.__name__, self._random_name()) else: self.name = kwargs['name'] if "tensorboard" not in kwargs: self.tensorboard = False else: self.tensorboard = kwargs['tensorboard'] def _random_name(self): return ''.join( random.choice(string.ascii_uppercase + string.digits) for _ in range(4)) class Conv1DLayer(Layer): def __init__(self, width, out_channels, **kwargs): self.width = width self.out_channels = out_channels self.out_tensor = None super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to conv1D over") parent = parents[0] if len(parent.out_tensor.get_shape()) != 3: raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.out_tensor.get_shape() parent_channel_size = parent_shape[2].value f = tf.Variable( tf.random_normal([self.width, parent_channel_size, self.out_channels])) b = tf.Variable(tf.random_normal([self.out_channels])) t = tf.nn.conv1d(parent.out_tensor, f, stride=1, padding="SAME") t = tf.nn.bias_add(t, b) self.out_tensor = tf.nn.relu(t) return self.out_tensor class Dense(Layer): def __init__(self, out_channels, activation_fn=None, **kwargs): self.out_channels = out_channels self.out_tensor = None self.activation_fn = activation_fn super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to Dense over") parent = parents[0] if len(parent.out_tensor.get_shape()) != 2: raise ValueError("Parent tensor must be (batch, width)") self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, num_outputs=self.out_channels, activation_fn=self.activation_fn, scope=self.name, trainable=True) return self.out_tensor class Flatten(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to conv1D over") parent = parents[0] parent_shape = parent.out_tensor.get_shape() vector_size = 1 for i in range(1, len(parent_shape)): vector_size *= parent_shape[i].value parent_tensor = parent.out_tensor self.out_tensor = tf.reshape(parent_tensor, shape=(-1, vector_size)) return self.out_tensor class Reshape(Layer): def __init__(self, shape, **kwargs): self.shape = shape super().__init__(**kwargs) def __call__(self, *parents): parent_tensor = parents[0].out_tensor self.out_tensor = tf.reshape(parent_tensor, self.shape) class CombineMeanStd(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 2: raise ValueError("Must have two parents") mean_parent, std_parent = parents[0], parents[1] mean_parent_tensor, std_parent_tensor = mean_parent.out_tensor, std_parent.out_tensor sample_noise = tf.random_normal( mean_parent_tensor.get_shape(), 0, 1, dtype=tf.float32) self.out_tensor = mean_parent_tensor + (std_parent_tensor * sample_noise) class Repeat(Layer): def __init__(self, n_times, **kwargs): self.n_times = n_times super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor t = tf.expand_dims(parent_tensor, 1) pattern = tf.stack([1, self.n_times, 1]) self.out_tensor = tf.tile(t, pattern) class GRU(Layer): def __init__(self, n_hidden, out_channels, batch_size, **kwargs): self.n_hidden = n_hidden self.out_channels = out_channels self.batch_size = batch_size super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor gru_cell = tf.nn.rnn_cell.GRUCell(self.n_hidden) initial_gru_state = gru_cell.zero_state(self.batch_size, tf.float32) rnn_outputs, rnn_states = tf.nn.dynamic_rnn( gru_cell, parent_tensor, initial_state=initial_gru_state, scope=self.name) projection = lambda x: tf.contrib.layers.linear(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) self.out_tensor = tf.map_fn(projection, rnn_outputs) class TimeSeriesDense(Layer): def __init__(self, out_channels, **kwargs): self.out_channels = out_channels super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) self.out_tensor = tf.map_fn(dense_fn, parent_tensor) class Input(Layer): def __init__(self, shape, **kwargs): self.t_shape = shape super().__init__(**kwargs) def __call__(self, *parents): self.out_tensor = tf.placeholder(tf.float32, shape=self.t_shape) class LossLayer(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): guess, label = parents[0], parents[1] self.out_tensor = tf.reduce_mean( tf.square(guess.out_tensor - label.out_tensor)) return self.out_tensor class SoftMax(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must only Softmax single parent") parent = parents[0] self.out_tensor = tf.contrib.layers.softmax(parent.out_tensor) return self.out_tensor class Concat(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) == 1: self.out_tensor = parents[0].out_tensor return self.out_tensor out_tensors = [x.out_tensor for x in parents] self.out_tensor = tf.concat(out_tensors, 1) return self.out_tensor class SoftMaxCrossEntropy(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 2: raise ValueError() labels, logits = parents[0].out_tensor, parents[1].out_tensor self.out_tensor = tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=labels) self.out_tensor = tf.reshape(self.out_tensor, [-1, 1]) return self.out_tensor class ReduceMean(Layer): def __call__(self, *parents): parent_tensor = parents[0].out_tensor self.out_tensor = tf.reduce_mean(parent_tensor) return self.out_tensor Loading
deepchem/feat/one_hot.py +5 −4 Original line number Diff line number Diff line Loading @@ -14,7 +14,7 @@ class OneHotFeaturizer(Featurizer): NOTE(LESWING) Not Thread Safe in initialization of charset """ def __init__(self, charset, padlength=120): def __init__(self, charset=None, padlength=120): """ Parameters ---------- Loading Loading @@ -44,7 +44,7 @@ class OneHotFeaturizer(Featurizer): """ smiles = [Chem.MolToSmiles(mol) for mol in mols] if self.charset is None: self.charset = self._create_charset(mols) self.charset = self._create_charset(smiles) return np.array([self.one_hot_encoded(smile) for smile in smiles]) def one_hot_array(self, i): Loading Loading @@ -143,5 +143,6 @@ class OneHotFeaturizer(Featurizer): """ s = set() for smile in smiles: s.union(list(smile)) return sorted(list(s)) for c in smile: s.add(c) return [' '] + sorted(list(s))
deepchem/metrics/__init__.py +4 −3 Original line number Diff line number Diff line Loading @@ -14,7 +14,7 @@ from sklearn.metrics import precision_score from scipy.stats import pearsonr def to_one_hot(y): def to_one_hot(y, axis=-1): """Transforms label vector into one-hot encoding. Turns y into vector of shape [n_samples, 2] (assuming binary labels). Loading @@ -23,7 +23,8 @@ def to_one_hot(y): A vector of shape [n_samples, 1] """ n_samples = np.shape(y)[0] y_hot = np.zeros((n_samples, 2)) n_classes = np.max(y, axis=axis) y_hot = np.zeros((n_samples, n_classes)) y_hot[np.arange(n_samples), y.astype(np.int64)] = 1 return y_hot Loading
deepchem/models/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -22,3 +22,4 @@ from deepchem.models.tensorflow_models.lr import TensorflowLogisticRegression from deepchem.models.tensorflow_models.progressive_multitask import ProgressiveMultitaskRegressor from deepchem.models.tensorflow_models.progressive_joint import ProgressiveJointRegressor from deepchem.models.tensorflow_models.IRV import TensorflowMultiTaskIRVClassifier from deepchem.models.tensorgraph.tensor_graph import TensorGraph, MultiTaskTensorGraph No newline at end of file
deepchem/models/tensorgraph/__init__.py 0 → 100644 +1 −0 Original line number Diff line number Diff line from deepchem.models.tensorgraph.tensor_graph import TensorGraph No newline at end of file
deepchem/models/tensorgraph/layers.py 0 → 100644 +239 −0 Original line number Diff line number Diff line import random import string import tensorflow as tf class Layer(object): def __init__(self, **kwargs): if "name" not in kwargs: self.name = "%s%s" % (self.__class__.__name__, self._random_name()) else: self.name = kwargs['name'] if "tensorboard" not in kwargs: self.tensorboard = False else: self.tensorboard = kwargs['tensorboard'] def _random_name(self): return ''.join( random.choice(string.ascii_uppercase + string.digits) for _ in range(4)) class Conv1DLayer(Layer): def __init__(self, width, out_channels, **kwargs): self.width = width self.out_channels = out_channels self.out_tensor = None super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to conv1D over") parent = parents[0] if len(parent.out_tensor.get_shape()) != 3: raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.out_tensor.get_shape() parent_channel_size = parent_shape[2].value f = tf.Variable( tf.random_normal([self.width, parent_channel_size, self.out_channels])) b = tf.Variable(tf.random_normal([self.out_channels])) t = tf.nn.conv1d(parent.out_tensor, f, stride=1, padding="SAME") t = tf.nn.bias_add(t, b) self.out_tensor = tf.nn.relu(t) return self.out_tensor class Dense(Layer): def __init__(self, out_channels, activation_fn=None, **kwargs): self.out_channels = out_channels self.out_tensor = None self.activation_fn = activation_fn super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to Dense over") parent = parents[0] if len(parent.out_tensor.get_shape()) != 2: raise ValueError("Parent tensor must be (batch, width)") self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, num_outputs=self.out_channels, activation_fn=self.activation_fn, scope=self.name, trainable=True) return self.out_tensor class Flatten(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Only One Parent to conv1D over") parent = parents[0] parent_shape = parent.out_tensor.get_shape() vector_size = 1 for i in range(1, len(parent_shape)): vector_size *= parent_shape[i].value parent_tensor = parent.out_tensor self.out_tensor = tf.reshape(parent_tensor, shape=(-1, vector_size)) return self.out_tensor class Reshape(Layer): def __init__(self, shape, **kwargs): self.shape = shape super().__init__(**kwargs) def __call__(self, *parents): parent_tensor = parents[0].out_tensor self.out_tensor = tf.reshape(parent_tensor, self.shape) class CombineMeanStd(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 2: raise ValueError("Must have two parents") mean_parent, std_parent = parents[0], parents[1] mean_parent_tensor, std_parent_tensor = mean_parent.out_tensor, std_parent.out_tensor sample_noise = tf.random_normal( mean_parent_tensor.get_shape(), 0, 1, dtype=tf.float32) self.out_tensor = mean_parent_tensor + (std_parent_tensor * sample_noise) class Repeat(Layer): def __init__(self, n_times, **kwargs): self.n_times = n_times super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor t = tf.expand_dims(parent_tensor, 1) pattern = tf.stack([1, self.n_times, 1]) self.out_tensor = tf.tile(t, pattern) class GRU(Layer): def __init__(self, n_hidden, out_channels, batch_size, **kwargs): self.n_hidden = n_hidden self.out_channels = out_channels self.batch_size = batch_size super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor gru_cell = tf.nn.rnn_cell.GRUCell(self.n_hidden) initial_gru_state = gru_cell.zero_state(self.batch_size, tf.float32) rnn_outputs, rnn_states = tf.nn.dynamic_rnn( gru_cell, parent_tensor, initial_state=initial_gru_state, scope=self.name) projection = lambda x: tf.contrib.layers.linear(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) self.out_tensor = tf.map_fn(projection, rnn_outputs) class TimeSeriesDense(Layer): def __init__(self, out_channels, **kwargs): self.out_channels = out_channels super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must have one parent") parent_tensor = parents[0].out_tensor dense_fn = lambda x: tf.contrib.layers.fully_connected(x, num_outputs=self.out_channels, activation_fn=tf.nn.sigmoid) self.out_tensor = tf.map_fn(dense_fn, parent_tensor) class Input(Layer): def __init__(self, shape, **kwargs): self.t_shape = shape super().__init__(**kwargs) def __call__(self, *parents): self.out_tensor = tf.placeholder(tf.float32, shape=self.t_shape) class LossLayer(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): guess, label = parents[0], parents[1] self.out_tensor = tf.reduce_mean( tf.square(guess.out_tensor - label.out_tensor)) return self.out_tensor class SoftMax(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 1: raise ValueError("Must only Softmax single parent") parent = parents[0] self.out_tensor = tf.contrib.layers.softmax(parent.out_tensor) return self.out_tensor class Concat(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) == 1: self.out_tensor = parents[0].out_tensor return self.out_tensor out_tensors = [x.out_tensor for x in parents] self.out_tensor = tf.concat(out_tensors, 1) return self.out_tensor class SoftMaxCrossEntropy(Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, *parents): if len(parents) != 2: raise ValueError() labels, logits = parents[0].out_tensor, parents[1].out_tensor self.out_tensor = tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=labels) self.out_tensor = tf.reshape(self.out_tensor, [-1, 1]) return self.out_tensor class ReduceMean(Layer): def __call__(self, *parents): parent_tensor = parents[0].out_tensor self.out_tensor = tf.reduce_mean(parent_tensor) return self.out_tensor
