cleanup

class ChemCeption(KerasModel):

  """

  Implements the ChemCeption model that leverages the

  representational capacities of convolutional neural networks

  (CNNs) to predict molecular properties.

  The model is based on the description in Goh et al.,

  "Chemception: A Deep Neural Network with Minimal Chemistry

  Knowledge Matches the Performance of Expert-developed

  QSAR/QSPR Models" (https://arxiv.org/pdf/1706.06689.pdf).  The

  authors use an image based representation of the molecule,

  where pixels encode different atomic and bond properties. More

  details on the image repres- entations can be found at

  https://arxiv.org/abs/1710.02238

  The model consists of a Stem Layer that reduces the image

  resolution for the layers to follow. The output of the Stem

  Layer is followed by a series of Inception-Resnet blocks & a

  Reduction layer. Layers in the Inception-Resnet blocks process

  image tensors at multiple resolutions and use a ResNet style

  skip-connection, combining features from different

  resolutions. The Reduction layers reduce the spatial extent of

  the image by max-pooling and 2-strided convolutions. More

  details on these layers can be found in the ChemCeption paper

  referenced above. The output of the final Reduction layer is

  subject to a Global Average Pooling, and a fully-connected

  layer maps the features to downstream outputs.

  In the ChemCeption paper, the authors perform real-time image

  augmentation by rotating images between 0 to 180 degrees. This

  can be done during model training by setting the augment

  argument to True.

  Implements the ChemCeption model that leverages the representational capacities

  of convolutional neural networks (CNNs) to predict molecular properties.

  The model is based on the description in Goh et al., "Chemception: A Deep

  Neural Network with Minimal Chemistry Knowledge Matches the Performance of

  Expert-developed QSAR/QSPR Models" (https://arxiv.org/pdf/1706.06689.pdf).

  The authors use an image based representation of the molecule, where pixels

  encode different atomic and bond properties. More details on the image repres-

  entations can be found at https://arxiv.org/abs/1710.02238

  The model consists of a Stem Layer that reduces the image resolution for the

  layers to follow. The output of the Stem Layer is followed by a series of

  Inception-Resnet blocks & a Reduction layer. Layers in the Inception-Resnet

  blocks process image tensors at multiple resolutions and use a ResNet style

  skip-connection, combining features from different resolutions. The Reduction

  layers reduce the spatial extent of the image by max-pooling and 2-strided

  convolutions. More details on these layers can be found in the ChemCeption

  paper referenced above. The output of the final Reduction layer is subject to

  a Global Average Pooling, and a fully-connected layer maps the features to

  downstream outputs.

  In the ChemCeption paper, the authors perform real-time image augmentation by

  rotating images between 0 to 180 degrees. This can be done during model

  training by setting the augment argument to True.

  """

  def __init__(self,

"""Utilities for handling datasets."""

import numpy as np

import deepchem as dc

def datasetify(data_like):

  """

  This utility function attempts to intelligently convert it's

  input into a DeepChem dataset object. Here are the classes of

  common sense transformations it attempts to apply:

  - `dc.data.Dataset`: If the input is already a

  `dc.data.Dataset`, just return unmodified.

  - List of strings: The strings are assumed to be unique identifiers. They are packaged into `dc.data.NumpyDataset`, as follows.

  >>> import deepchem as dc

  >>> import numpy as np

  >>> l = ["C", "CC"]

  >>> dc.data.NumpyDataset(X=np.array(l), ids=np.array(l))

  The double packaging as `X` and `ids` is awkward, but it's

  currently not feasible to create a `dc.data.NumpyDataset`

  without `X` specified.

  - Numpy array: This array is assumed to be the `X` feature array. This is packaged as follows

  >>> import deepchem as dc

  >>> import numpy as np

  >>> X = np.random.rand(5, 5)

  >>> dc.data.NumpyDataset(X)

  Parameters

  ----------

  data_like: object

    Some object which will attempt to be converted to a

    `dc.data.Dataset` object.

  Returns

  -------

  If successful in conversion, returns `dc.data.NumpyDataset`

  object. Else raises `ValueError`.

  """

  if isinstance(data_like, dc.data.Dataset):

    return data_like

  elif isinstance(data_like, list):

    if len(data_like) > 0 and isinstance(data_like[0], str):

      return dc.data.NumpyDataset(X=np.array(data_like), ids=np.array(data_like))

  elif isinstance(data_like, np.ndarray):

    return dc.data.NumpyDataset(data_like)

  else:

    raise ValueError("Cannot convert into Dataset object.")