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weavemodel_pytorch.py
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weavemodel_pytorch.py
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import numpy as np
from collections.abc import Sequence as SequenceCollection
try:
import torch
import torch.nn as nn
import torch.nn.functional as F
except ModuleNotFoundError:
raise ImportError('These classes require PyTorch to be installed.')
from typing import List, Tuple, Iterable, Optional, Callable, Union, Sequence
from deepchem.data import Dataset
from deepchem.metrics import to_one_hot
from deepchem.utils.typing import OneOrMany, ActivationFn
from deepchem.models.losses import L2Loss, SoftmaxCrossEntropy
from deepchem.models.torch_models.torch_model import TorchModel
import deepchem.models.torch_models.layers as torch_layers
from deepchem.utils.pytorch_utils import get_activation
class Weave(nn.Module):
"""
A graph convolutional network(GCN) for either classification or regression.
The network consists of the following sequence of layers:
- Weave feature modules
- Final convolution
- Weave Gather Layer
- A fully connected layer
- A Softmax layer
Example
--------
>>> import numpy as np
>>> import deepchem as dc
>>> featurizer = dc.feat.WeaveFeaturizer()
>>> X = featurizer(["C", "CC"])
>>> y = np.array([1, 0])
>>> batch_size = 2
>>> weavemodel = dc.models.WeaveModel(n_tasks=1,n_weave=2, fully_connected_layer_sizes=[2000, 1000],mode="classification",batch_size=batch_size)
>>> atom_feat, pair_feat, pair_split, atom_split, atom_to_pair = weavemodel.compute_features_on_batch(X)
>>> model = Weave(n_tasks=1,n_weave=2,fully_connected_layer_sizes=[2000, 1000],mode="classification")
>>> input_data = [atom_feat, pair_feat, pair_split, atom_split, atom_to_pair]
>>> output = model(input_data)
References
----------
.. [1] Kearnes, Steven, et al. "Molecular graph convolutions: moving beyond
fingerprints." Journal of computer-aided molecular design 30.8 (2016):
595-608.
"""
def __init__(
self,
n_tasks: int,
n_atom_feat: OneOrMany[int] = 75,
n_pair_feat: OneOrMany[int] = 14,
n_hidden: int = 50,
n_graph_feat: int = 128,
n_weave: int = 2,
fully_connected_layer_sizes: List[int] = [2000, 100],
conv_weight_init_stddevs: OneOrMany[float] = 0.03,
weight_init_stddevs: OneOrMany[float] = 0.01,
bias_init_consts: OneOrMany[float] = 0.0,
dropouts: OneOrMany[float] = 0.25,
final_conv_activation_fn=F.tanh,
activation_fns: OneOrMany[ActivationFn] = 'relu',
batch_normalize: bool = True,
gaussian_expand: bool = True,
compress_post_gaussian_expansion: bool = False,
mode: str = "classification",
n_classes: int = 2,
batch_size: int = 100,
):
"""
Parameters
----------
n_tasks: int
Number of tasks
n_atom_feat: int, optional (default 75)
Number of features per atom. Note this is 75 by default and should be 78
if chirality is used by `WeaveFeaturizer`.
n_pair_feat: int, optional (default 14)
Number of features per pair of atoms.
n_hidden: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_graph_feat: int, optional (default 128)
Number of output features for each molecule(graph)
n_weave: int, optional (default 2)
The number of weave layers in this model.
fully_connected_layer_sizes: list (default `[2000, 100]`)
The size of each dense layer in the network. The length of
this list determines the number of layers.
conv_weight_init_stddevs: list or float (default 0.03)
The standard deviation of the distribution to use for weight
initialization of each convolutional layer. The length of this lisst
should equal `n_weave`. Alternatively, this may be a single value instead
of a list, in which case the same value is used for each layer.
weight_init_stddevs: list or float (default 0.01)
The standard deviation of the distribution to use for weight
initialization of each fully connected layer. The length of this list
should equal len(layer_sizes). Alternatively this may be a single value
instead of a list, in which case the same value is used for every layer.
bias_init_consts: list or float (default 0.0)
The value to initialize the biases in each fully connected layer. The
length of this list should equal len(layer_sizes).
Alternatively this may be a single value instead of a list, in
which case the same value is used for every layer.
dropouts: list or float (default 0.25)
The dropout probablity to use for each fully connected layer. The length of this list
should equal len(layer_sizes). Alternatively this may be a single value
instead of a list, in which case the same value is used for every layer.
final_conv_activation_fn: Optional[ActivationFn] (default `F.tanh`)
The activation funcntion to apply to the final
convolution at the end of the weave convolutions. If `None`, then no
activate is applied (hence linear).
activation_fns: str (default `relu`)
The activation function to apply to each fully connected layer. The length
of this list should equal len(layer_sizes). Alternatively this may be a
single value instead of a list, in which case the same value is used for
every layer.
batch_normalize: bool, optional (default True)
If this is turned on, apply batch normalization before applying
activation functions on convolutional and fully connected layers.
gaussian_expand: boolean, optional (default True)
Whether to expand each dimension of atomic features by gaussian
histogram
compress_post_gaussian_expansion: bool, optional (default False)
If True, compress the results of the Gaussian expansion back to the
original dimensions of the input.
mode: str (default "classification")
Either "classification" or "regression" for type of model.
n_classes: int (default 2)
Number of classes to predict (only used in classification mode)
batch_size: int (default 100)
Batch size used by this model for training.
"""
super(Weave, self).__init__()
if mode not in ['classification', 'regression']:
raise ValueError(
"mode must be either 'classification' or 'regression'")
if not isinstance(n_atom_feat, SequenceCollection):
n_atom_feat = [n_atom_feat] * n_weave
if not isinstance(n_pair_feat, SequenceCollection):
n_pair_feat = [n_pair_feat] * n_weave
n_layers = len(fully_connected_layer_sizes)
if not isinstance(conv_weight_init_stddevs, SequenceCollection):
conv_weight_init_stddevs = [conv_weight_init_stddevs] * n_weave
if not isinstance(weight_init_stddevs, SequenceCollection):
weight_init_stddevs = [weight_init_stddevs] * n_layers
if not isinstance(bias_init_consts, SequenceCollection):
bias_init_consts = [bias_init_consts] * n_layers
if not isinstance(dropouts, SequenceCollection):
dropouts = [dropouts] * n_layers
if isinstance(
activation_fns,
str) or not isinstance(activation_fns, SequenceCollection):
activation_fns = [activation_fns] * n_layers
self.n_tasks: int = n_tasks
self.n_atom_feat: OneOrMany[int] = n_atom_feat
self.n_pair_feat: OneOrMany[int] = n_pair_feat
self.n_hidden: int = n_hidden
self.n_graph_feat: int = n_graph_feat
self.mode: str = mode
self.n_classes: int = n_classes
self.n_layers: int = n_layers
self.fully_connected_layer_sizes: List[
int] = fully_connected_layer_sizes
self.weight_init_stddevs: OneOrMany[float] = weight_init_stddevs
self.bias_init_consts: OneOrMany[float] = bias_init_consts
self.dropouts: Sequence[float] = dropouts
self.activation_fns: OneOrMany[ActivationFn] = [
get_activation(i) for i in activation_fns
]
self.batch_normalize: bool = batch_normalize
self.n_weave: int = n_weave
torch.manual_seed(22)
self.layers: nn.ModuleList = nn.ModuleList()
for ind in range(n_weave):
n_atom: int = self.n_atom_feat[ind]
n_pair: int = self.n_pair_feat[ind]
if ind < n_weave - 1:
n_atom_next: int = self.n_atom_feat[ind + 1]
n_pair_next: int = self.n_pair_feat[ind + 1]
else:
n_atom_next = n_hidden
n_pair_next = n_hidden
weave_layer = torch_layers.WeaveLayer(
n_atom_input_feat=n_atom,
n_pair_input_feat=n_pair,
n_atom_output_feat=n_atom_next,
n_pair_output_feat=n_pair_next,
batch_normalize=batch_normalize)
nn.init.trunc_normal_(weave_layer.W_AA,
0,
std=conv_weight_init_stddevs[ind])
nn.init.trunc_normal_(weave_layer.W_PA,
0,
std=conv_weight_init_stddevs[ind])
nn.init.trunc_normal_(weave_layer.W_A,
0,
std=conv_weight_init_stddevs[ind])
if weave_layer.update_pair:
nn.init.trunc_normal_(weave_layer.W_AP,
0,
std=conv_weight_init_stddevs[ind])
nn.init.trunc_normal_(weave_layer.W_PP,
0,
std=conv_weight_init_stddevs[ind])
nn.init.trunc_normal_(weave_layer.W_P,
0,
std=conv_weight_init_stddevs[ind])
self.layers.append(weave_layer)
self.dense1: nn.Linear = nn.Linear(n_hidden, self.n_graph_feat)
self.dense1_act = final_conv_activation_fn
self.dense1_bn: nn.BatchNorm1d = nn.BatchNorm1d(
num_features=self.n_graph_feat,
eps=1e-3,
momentum=0.99,
affine=True,
track_running_stats=True)
self.weave_gather = torch_layers.WeaveGather(
batch_size,
n_input=self.n_graph_feat,
gaussian_expand=gaussian_expand,
compress_post_gaussian_expansion=compress_post_gaussian_expansion)
if n_layers > 0:
self.layers2: nn.ModuleList = nn.ModuleList()
in_size = self.n_graph_feat * 11
for ind, layer_size, weight_stddev, bias_const, dropout, activation_fn in zip(
[0, 1], fully_connected_layer_sizes, weight_init_stddevs,
bias_init_consts, dropouts, self.activation_fns):
self.layer: nn.Linear = nn.Linear(in_size, layer_size)
nn.init.trunc_normal_(self.layer.weight, 0, std=weight_stddev)
if self.layer.bias is not None:
self.layer.bias = nn.Parameter(
torch.full(self.layer.bias.shape, bias_const))
self.layer.layer_bn = nn.BatchNorm1d(num_features=layer_size,
eps=1e-3,
momentum=0.99,
affine=True,
track_running_stats=True)
self.layer.weight_stddev = weight_stddev
self.layer.bias_const = bias_const
self.layer.dropout = nn.Dropout(dropout)
self.layer.layer_act = activation_fn
self.layers2.append(self.layer)
in_size = layer_size
n_tasks = self.n_tasks
if self.mode == 'classification':
n_classes = self.n_classes
self.layer_2 = nn.Linear(fully_connected_layer_sizes[1],
n_tasks * n_classes)
else:
self.layer_2 = nn.Linear(fully_connected_layer_sizes[1], n_tasks)
def forward(self, inputs: OneOrMany[torch.Tensor]) -> List[torch.Tensor]:
"""
Parameters
----------
inputs: OneOrMany[torch.Tensor]
Should contain 5 tensors [atom_features, pair_features, pair_split, atom_split, atom_to_pair]
Returns
-------
List[torch.Tensor]
Output as per use case : regression/classification
"""
input1: List[np.ndarray] = [
np.array(inputs[0]),
np.array(inputs[1]),
np.array(inputs[2]),
np.array(inputs[4])
]
for ind in range(self.n_weave):
weave_layer_ind_A, weave_layer_ind_P = self.layers[ind](input1)
input1 = [
weave_layer_ind_A, weave_layer_ind_P,
np.array(inputs[2]),
np.array(inputs[4])
]
dense1: torch.Tensor = self.dense1(weave_layer_ind_A)
dense1 = self.dense1_act(dense1)
if self.batch_normalize:
self.dense1_bn.eval()
dense1 = self.dense1_bn(dense1)
weave_gather: torch.Tensor = self.weave_gather([dense1, inputs[3]])
if self.n_layers > 0:
input_layer: torch.Tensor = weave_gather
for ind, dropout in zip([0, 1], self.dropouts):
dense2 = self.layers2[ind]
layer = self.layers2[ind](input_layer)
if dropout > 0.0:
dense2.dropout.eval()
layer = dense2.dropout(layer)
if self.batch_normalize:
dense2.layer_bn.eval()
layer = dense2.layer_bn(layer)
layer = dense2.layer_act(layer)
input_layer = layer
output: torch.Tensor = input_layer
else:
output = weave_gather
n_tasks = self.n_tasks
if self.mode == 'classification':
n_classes = self.n_classes
logits: torch.Tensor = torch.reshape(self.layer_2(output),
(-1, n_tasks, n_classes))
output = F.softmax(logits, dim=2)
outputs: List[torch.Tensor] = [output, logits]
else:
output = self.layer_2(output)
outputs = [output]
return outputs
class WeaveModel(TorchModel):
"""Implements Google-style Weave Graph Convolutions
This model implements the Weave style graph convolutions
from [1]_.
The biggest difference between WeaveModel style convolutions
and GraphConvModel style convolutions is that Weave
convolutions model bond features explicitly. This has the
side effect that it needs to construct a NxN matrix
explicitly to model bond interactions. This may cause
scaling issues, but may possibly allow for better modeling
of subtle bond effects.
Note that [1]_ introduces a whole variety of different architectures for
Weave models. The default settings in this class correspond to the W2N2
variant from [1]_ which is the most commonly used variant..
Examples
--------
Here's an example of how to fit a `WeaveModel` on a tiny sample dataset.
>>> import numpy as np
>>> import deepchem as dc
>>> featurizer = dc.feat.WeaveFeaturizer()
>>> X = featurizer(["C", "CC"])
>>> y = np.array([1, 0])
>>> dataset = dc.data.NumpyDataset(X, y)
>>> model = dc.models.WeaveModel(n_tasks=1, n_weave=2, fully_connected_layer_sizes=[2000, 1000], mode="classification")
>>> loss = model.fit(dataset)
References
----------
.. [1] Kearnes, Steven, et al. "Molecular graph convolutions: moving beyond
fingerprints." Journal of computer-aided molecular design 30.8 (2016):
595-608.
"""
def __init__(self,
n_tasks: int,
n_atom_feat: OneOrMany[int] = 75,
n_pair_feat: OneOrMany[int] = 14,
n_hidden: int = 50,
n_graph_feat: int = 128,
n_weave: int = 2,
fully_connected_layer_sizes: List[int] = [2000, 100],
conv_weight_init_stddevs: OneOrMany[float] = 0.03,
weight_init_stddevs: OneOrMany[float] = 0.01,
bias_init_consts: OneOrMany[float] = 0.0,
weight_decay_penalty: float = 0.0,
weight_decay_penalty_type: str = "l2",
dropouts: OneOrMany[float] = 0.25,
final_conv_activation_fn: Optional[ActivationFn] = F.tanh,
activation_fns: OneOrMany[ActivationFn] = 'relu',
batch_normalize: bool = True,
gaussian_expand: bool = True,
compress_post_gaussian_expansion: bool = False,
mode: str = "classification",
n_classes: int = 2,
batch_size: int = 100,
**kwargs):
"""
Parameters
----------
n_tasks: int
Number of tasks
n_atom_feat: int, optional (default 75)
Number of features per atom. Note this is 75 by default and should be 78
if chirality is used by `WeaveFeaturizer`.
n_pair_feat: int, optional (default 14)
Number of features per pair of atoms.
n_hidden: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_graph_feat: int, optional (default 128)
Number of output features for each molecule(graph)
n_weave: int, optional (default 2)
The number of weave layers in this model.
fully_connected_layer_sizes: list (default `[2000, 100]`)
The size of each dense layer in the network. The length of
this list determines the number of layers.
conv_weight_init_stddevs: list or float (default 0.03)
The standard deviation of the distribution to use for weight
initialization of each convolutional layer. The length of this lisst
should equal `n_weave`. Alternatively, this may be a single value instead
of a list, in which case the same value is used for each layer.
weight_init_stddevs: list or float (default 0.01)
The standard deviation of the distribution to use for weight
initialization of each fully connected layer. The length of this list
should equal len(layer_sizes). Alternatively this may be a single value
instead of a list, in which case the same value is used for every layer.
bias_init_consts: list or float (default 0.0)
The value to initialize the biases in each fully connected layer. The
length of this list should equal len(layer_sizes).
Alternatively this may be a single value instead of a list, in
which case the same value is used for every layer.
weight_decay_penalty: float (default 0.0)
The magnitude of the weight decay penalty to use
weight_decay_penalty_type: str (default "l2")
The type of penalty to use for weight decay, either 'l1' or 'l2'
dropouts: list or float (default 0.25)
The dropout probablity to use for each fully connected layer. The length of this list
should equal len(layer_sizes). Alternatively this may be a single value
instead of a list, in which case the same value is used for every layer.
final_conv_activation_fn: Optional[ActivationFn] (default `F.tanh`)
The activation funcntion to apply to the final
convolution at the end of the weave convolutions. If `None`, then no
activate is applied (hence linear).
activation_fns: str (default `relu`)
The activation function to apply to each fully connected layer. The length
of this list should equal len(layer_sizes). Alternatively this may be a
single value instead of a list, in which case the same value is used for
every layer.
batch_normalize: bool, optional (default True)
If this is turned on, apply batch normalization before applying
activation functions on convolutional and fully connected layers.
gaussian_expand: boolean, optional (default True)
Whether to expand each dimension of atomic features by gaussian
histogram
compress_post_gaussian_expansion: bool, optional (default False)
If True, compress the results of the Gaussian expansion back to the
original dimensions of the input.
mode: str (default "classification")
Either "classification" or "regression" for type of model.
n_classes: int (default 2)
Number of classes to predict (only used in classification mode)
batch_size: int (default 100)
Batch size used by this model for training.
"""
self.mode: str = mode
self.model = Weave(
n_tasks=n_tasks,
n_atom_feat=n_atom_feat,
n_pair_feat=n_pair_feat,
n_hidden=n_hidden,
n_graph_feat=n_graph_feat,
n_weave=n_weave,
fully_connected_layer_sizes=fully_connected_layer_sizes,
conv_weight_init_stddevs=conv_weight_init_stddevs,
weight_init_stddevs=weight_init_stddevs,
bias_init_consts=bias_init_consts,
dropouts=dropouts,
final_conv_activation_fn=final_conv_activation_fn,
activation_fns=activation_fns,
batch_normalize=batch_normalize,
gaussian_expand=gaussian_expand,
compress_post_gaussian_expansion=compress_post_gaussian_expansion,
mode=mode,
n_classes=n_classes,
batch_size=batch_size)
if mode not in ['classification', 'regression']:
raise ValueError(
"mode must be either 'classification' or 'regression'")
regularization_loss: Optional[Callable]
if weight_decay_penalty != 0.0:
weights = [layer.weight for layer in self.model.layers2]
if weight_decay_penalty_type == 'l1':
regularization_loss = lambda: weight_decay_penalty * torch.sum( # noqa: E731
torch.stack([torch.abs(w).sum() for w in weights]))
else:
regularization_loss = lambda: weight_decay_penalty * torch.sum( # noqa: E731
torch.stack([torch.square(w).sum() for w in weights]))
else:
regularization_loss = None
loss: Union[SoftmaxCrossEntropy, L2Loss]
if self.mode == 'classification':
output_types = ['prediction', 'loss']
loss = SoftmaxCrossEntropy()
else:
output_types = ['prediction']
loss = L2Loss()
super(WeaveModel,
self).__init__(self.model,
loss=loss,
output_types=output_types,
batch_size=batch_size,
regularization_loss=regularization_loss,
**kwargs)
def compute_features_on_batch(self, X_b):
"""Compute tensors that will be input into the model from featurized representation.
The featurized input to `WeaveModel` is instances of `WeaveMol` created by
`WeaveFeaturizer`. This method converts input `WeaveMol` objects into
tensors used by the Keras implementation to compute `WeaveModel` outputs.
Parameters
----------
X_b: np.ndarray
A numpy array with dtype=object where elements are `WeaveMol` objects.
Returns
-------
atom_feat: np.ndarray
Of shape `(N_atoms, N_atom_feat)`.
pair_feat: np.ndarray
Of shape `(N_pairs, N_pair_feat)`. Note that `N_pairs` will depend on
the number of pairs being considered. If `max_pair_distance` is
`None`, then this will be `N_atoms**2`. Else it will be the number
of pairs within the specifed graph distance.
pair_split: np.ndarray
Of shape `(N_pairs,)`. The i-th entry in this array will tell you the
originating atom for this pair (the "source"). Note that pairs are
symmetric so for a pair `(a, b)`, both `a` and `b` will separately be
sources at different points in this array.
atom_split: np.ndarray
Of shape `(N_atoms,)`. The i-th entry in this array will be the molecule
with the i-th atom belongs to.
atom_to_pair: np.ndarray
Of shape `(N_pairs, 2)`. The i-th row in this array will be the array
`[a, b]` if `(a, b)` is a pair to be considered. (Note by symmetry, this
implies some other row will contain `[b, a]`.
"""
atom_feat = []
pair_feat = []
atom_split = []
atom_to_pair = []
pair_split = []
start = 0
for im, mol in enumerate(X_b):
n_atoms = mol.get_num_atoms()
# pair_edges is of shape (2, N)
pair_edges = mol.get_pair_edges()
# number of atoms in each molecule
atom_split.extend([im] * n_atoms)
# index of pair features
C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms))
atom_to_pair.append(pair_edges.T + start)
# Get starting pair atoms
pair_starts = pair_edges.T[:, 0]
# number of pairs for each atom
pair_split.extend(pair_starts + start)
start = start + n_atoms
# atom features
atom_feat.append(mol.get_atom_features())
# pair features
pair_feat.append(mol.get_pair_features())
return (np.concatenate(atom_feat, axis=0),
np.concatenate(pair_feat, axis=0), np.array(pair_split),
np.array(atom_split), np.concatenate(atom_to_pair, axis=0))
def default_generator(
self,
dataset: Dataset,
epochs: int = 1,
mode: str = 'fit',
deterministic: bool = True,
pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
"""Convert a dataset into the tensors needed for learning.
Parameters
----------
dataset: `dc.data.Dataset`
Dataset to convert
epochs: int, optional (Default 1)
Number of times to walk over `dataset`
mode: str, optional (Default 'fit')
Ignored in this implementation.
deterministic: bool, optional (Default True)
Whether the dataset should be walked in a deterministic fashion
pad_batches: bool, optional (Default True)
If true, each returned batch will have size `self.batch_size`.
Returns
-------
Iterator which walks over the batches
"""
for epoch in range(epochs):
for (X_b, y_b, w_b,
ids_b) in dataset.iterbatches(batch_size=self.batch_size,
deterministic=deterministic,
pad_batches=pad_batches):
if y_b is not None:
if self.model.mode == 'classification':
y_b = to_one_hot(y_b.flatten(),
self.model.n_classes).reshape(
-1, self.model.n_tasks,
self.model.n_classes)
inputs = self.compute_features_on_batch(X_b)
yield (inputs, [y_b], [w_b])