Loading deepchem/splits/splitters.py +20 −20 Original line number Diff line number Diff line Loading @@ -398,15 +398,15 @@ class RandomGroupSplitter(Splitter): An array indicating the group of each item. The length is equals to `len(dataset.X)` Notes ----- Note ---- The examples of groups is the following. groups : 3 2 2 0 1 1 2 4 3 dataset.X : 0 1 2 3 4 5 6 7 8 | groups : 3 2 2 0 1 1 2 4 3 | dataset.X : 0 1 2 3 4 5 6 7 8 groups : a b b e q x a a r dataset.X : 0 1 2 3 4 5 6 7 8 | groups : a b b e q x a a r | dataset.X : 0 1 2 3 4 5 6 7 8 """ self.groups = groups Loading Loading @@ -488,8 +488,8 @@ class RandomStratifiedSplitter(Splitter): sparse multitask datasets it usually manages to produces a fairly accurate division of the actives for each task. Notes ----- Note ---- This splitter is primarily designed for boolean labeled data. It considers only whether a label is zero or non-zero. When labels can take on multiple non-zero values, it does not try to give each split a proportional fraction Loading Loading @@ -873,8 +873,8 @@ class MolecularWeightSplitter(Splitter): """ Class for doing data splits by molecular weight. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -946,8 +946,8 @@ class MaxMinSplitter(Splitter): Furthermore, the validation set is comprised of diverse compounds under the test set. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1044,8 +1044,8 @@ class ButinaSplitter(Splitter): """Class for doing data splits based on the butina clustering of a bulk tanimoto fingerprint matrix. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1166,8 +1166,8 @@ def _generate_scaffold(smiles: str, include_chirality: bool = False) -> str: .. [1] Bemis, Guy W., and Mark A. Murcko. "The properties of known drugs. 1. Molecular frameworks." Journal of medicinal chemistry 39.15 (1996): 2887-2893. Notes ----- Note ---- This function requires RDKit to be installed. """ try: Loading @@ -1184,8 +1184,8 @@ def _generate_scaffold(smiles: str, include_chirality: bool = False) -> str: class ScaffoldSplitter(Splitter): """Class for doing data splits based on the scaffold of small molecules. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1285,8 +1285,8 @@ class FingerprintSplitter(Splitter): """Class for doing data splits based on the fingerprints of small molecules O(N**2) algorithm. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading deepchem/trans/transformers.py +59 −24 Original line number Diff line number Diff line Loading @@ -306,7 +306,8 @@ class MinMaxTransformer(Transformer): Raises ------ `ValueError` if `transform_X` and `transform_y` are both set. ValueError if `transform_X` and `transform_y` are both set. """ def __init__(self, transform_X=False, transform_y=False, dataset=None): Loading Loading @@ -454,7 +455,8 @@ class NormalizationTransformer(Transformer): Raises ------ `ValueError` if `transform_X` and `transform_y` are both set. ValueError if `transform_X` and `transform_y` are both set. """ def __init__(self, Loading Loading @@ -659,7 +661,8 @@ class ClippingTransformer(Transformer): Raises ------ `ValueError` if `transform_w` is set. ValueError if `transform_w` is set. """ super(ClippingTransformer, self).__init__( transform_X=transform_X, transform_y=transform_y, dataset=dataset) Loading Loading @@ -737,8 +740,8 @@ class LogTransformer(Transformer): Raises ------ `ValueError` if `transform_w` is set or `transform_X` and `transform_y` are both set. ValueError if `transform_w` is set or `transform_X` and `transform_y` are both set. """ def __init__(self, Loading Loading @@ -901,8 +904,8 @@ class BalancingTransformer(Transformer): Raises ------ `ValueError` if `transform_X` or `transform_y` are set. Also raises `ValueError` if `y` or `w` aren't of shape `(N,)` or `(N, n_tasks)`. ValueError if `transform_X` or `transform_y` are set. Also raises or if `y` or `w` aren't of shape `(N,)` or `(N, n_tasks)`. """ def __init__(self, dataset: Dataset): Loading Loading @@ -1026,7 +1029,6 @@ class CDFTransformer(Transformer): >>> dataset = dc.data.NumpyDataset(X, y) >>> cdftrans = dc.trans.CDFTransformer(transform_y=True, dataset=dataset, bins=n_bins) >>> dataset = cdftrans.transform(dataset) """ def __init__(self, Loading Loading @@ -1178,7 +1180,6 @@ class PowerTransformer(Transformer): >>> dataset = dc.data.NumpyDataset(X, y) >>> trans = dc.trans.PowerTransformer(transform_y=True, dataset=dataset, powers=powers) >>> dataset = trans.transform(dataset) """ def __init__(self, Loading Loading @@ -1265,9 +1266,8 @@ class PowerTransformer(Transformer): class CoulombFitTransformer(Transformer): """Performs randomization and binarization operations on batches of Coulomb Matrix features during fit. Example ------- Examples -------- >>> n_samples = 10 >>> n_features = 3 >>> n_tasks = 1 Loading @@ -1288,8 +1288,8 @@ class CoulombFitTransformer(Transformer): Parameters ---------- dataset: dc.data.Dataset object dataset: dc.data.Dataset Dataset object to be transformed. """ X = dataset.X num_atoms = X.shape[1] Loading Loading @@ -1342,7 +1342,6 @@ class CoulombFitTransformer(Transformer): ------- X: np.ndarray Normalized features """ return (X - self.mean) / self.std Loading Loading @@ -1577,6 +1576,7 @@ class IRVTransformer(Transformer): Returns ------- DiskDataset or NumpyDataset `Dataset` object that is transformed. """ X_length = dataset.X.shape[0] Loading Loading @@ -1674,6 +1674,7 @@ class DAGTransformer(Transformer): Returns ------- List List of parent adjacency matrices """ # list of calculation orders for DAGs Loading Loading @@ -2068,6 +2069,11 @@ class DataTransforms(object): Height of the images w: int Width of the images Returns ------- np.ndarray The scaled image. """ from PIL import Image return Image.fromarray(self.Image).resize((h, w)) Loading @@ -2079,6 +2085,11 @@ class DataTransforms(object): ---------- direction: str "lr" denotes left-right flip and "ud" denotes up-down flip. Returns ------- np.ndarray The flipped image. """ if direction == "lr": return np.fliplr(self.Image) Loading @@ -2099,7 +2110,8 @@ class DataTransforms(object): Returns ------- The rotated input array np.ndarray The rotated image. """ return scipy.ndimage.rotate(self.Image, angle) Loading @@ -2110,6 +2122,11 @@ class DataTransforms(object): ---------- sigma: float Std dev. of the gaussian distribution Returns ------- np.ndarray The image added gaussian noise. """ return scipy.ndimage.gaussian_filter(self.Image, sigma) Loading @@ -2125,8 +2142,8 @@ class DataTransforms(object): Returns ------- The center cropped input array np.ndarray The center cropped image. """ y = self.Image.shape[0] x = self.Image.shape[1] Loading @@ -2150,7 +2167,8 @@ class DataTransforms(object): Returns ------- The cropped input array np.ndarray The cropped image. """ y = self.Image.shape[0] x = self.Image.shape[1] Loading @@ -2161,6 +2179,7 @@ class DataTransforms(object): Returns ------- np.ndarray The grayscale image. """ return np.dot(self.Image[..., :3], [0.2989, 0.5870, 0.1140]) Loading @@ -2180,6 +2199,11 @@ class DataTransforms(object): ‘constant’ order: int The order of the spline interpolation, default is 3. The order has to be in the range 0-5. Returns ------- np.ndarray The shifted image. """ if len(self.Image.shape) == 2: return scipy.ndimage.shift( Loading @@ -2197,6 +2221,11 @@ class DataTransforms(object): Mean of gaussian. std: float Standard deviation of gaussian. Returns ------- np.ndarray The image added gaussian noise. """ x = self.Image Loading @@ -2214,6 +2243,11 @@ class DataTransforms(object): value of salt noise. pepper: float value of pepper noise. Returns ------- np.ndarray The image added salt and pepper noise. """ noise = np.random.random(size=self.Image.shape) Loading @@ -2232,6 +2266,7 @@ class DataTransforms(object): Returns ------- np.ndarray The median filtered image. """ from PIL import Image, ImageFilter Loading docs/source/api_reference/splitters.rst +44 −17 Original line number Diff line number Diff line Loading @@ -15,78 +15,105 @@ learning models more rigorously than standard deep models since we're looking for the ability to generalize to new domains. Some of the implemented splitters here may help. Splitter -------- The :code:`dc.splits.Splitter` class is the abstract parent class for all splitters. This class should never be directly instantiated. .. contents:: Contents :local: .. autoclass:: deepchem.splits.Splitter :members: General Splitters ----------------- RandomSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomSplitter :members: :inherited-members: :exclude-members: __init__ IndexSplitter ------------- ^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.IndexSplitter :members: :inherited-members: :exclude-members: __init__ SpecifiedSplitter ----------------- ^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.SpecifiedSplitter :members: :inherited-members: RandomGroupSplitter ------------------- ^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomGroupSplitter :members: :inherited-members: RandomStratifiedSplitter ------------------------ ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomStratifiedSplitter :members: :inherited-members: :exclude-members: __init__ SingletaskStratifiedSplitter ---------------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.SingletaskStratifiedSplitter :members: :inherited-members: TaskSplitter ^^^^^^^^^^^^ .. autoclass:: deepchem.splits.TaskSplitter :members: :inherited-members: Molecule Splitters ------------------ MolecularWeightSplitter ----------------------- ^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.MolecularWeightSplitter :members: :inherited-members: :exclude-members: __init__ MaxMinSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.MaxMinSplitter :members: :inherited-members: :exclude-members: __init__ ButinaSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.ButinaSplitter :members: :inherited-members: ScaffoldSplitter ---------------- ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.ScaffoldSplitter :members: :inherited-members: :exclude-members: __init__ FingeprintSplitter ------------------ ^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.FingerprintSplitter :members: :inherited-members: :exclude-members: __init__ docs/source/api_reference/transformers.rst +46 −37 Original line number Diff line number Diff line Loading @@ -8,101 +8,110 @@ distribution. Real data of course is wild and hard to control. What do you do if you have a crazy dataset and need to bring its statistics to heel? Fear not for you have :code:`Transformer` objects. Transformer ----------- The :code:`dc.trans.Transformer` class is the abstract parent class for all transformers. This class should never be directly initialized, but contains a number of useful method implementations. .. contents:: Contents :local: .. autoclass:: deepchem.trans.Transformer :members: General Transformers -------------------- MinMaxTransformer ----------------- ^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.MinMaxTransformer :members: :inherited-members: NormalizationTransformer ------------------------ ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.NormalizationTransformer :members: :inherited-members: ClippingTransformer ------------------- ^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ClippingTransformer :members: :inherited-members: LogTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.LogTransformer :members: :inherited-members: BalancingTransformer -------------------- ^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.BalancingTransformer :members: :inherited-members: DuplicateBalancingTransformer ----------------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.DuplicateBalancingTransformer :members: :inherited-members: CDFTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.CDFTransformer :members: :inherited-members: PowerTransformer ---------------- ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.PowerTransformer :members: :inherited-members: ImageTransformer ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ImageTransformer :members: :inherited-members: FeaturizationTransformer ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.FeaturizationTransformer :members: :inherited-members: Specified Usecase Transformers ------------------------------ CoulombFitTransformer --------------------- ^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.CoulombFitTransformer :members: :inherited-members: IRVTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.IRVTransformer :members: :inherited-members: DAGTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.DAGTransformer :members: ImageTransformer ---------------- .. autoclass:: deepchem.trans.ImageTransformer :members: :inherited-members: ANITransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ANITransformer :members: FeaturizationTransformer ------------------------ .. autoclass:: deepchem.trans.FeaturizationTransformer :members: DataTransforms -------------- .. autoclass:: deepchem.trans.DataTransforms :members: :inherited-members: Loading
deepchem/splits/splitters.py +20 −20 Original line number Diff line number Diff line Loading @@ -398,15 +398,15 @@ class RandomGroupSplitter(Splitter): An array indicating the group of each item. The length is equals to `len(dataset.X)` Notes ----- Note ---- The examples of groups is the following. groups : 3 2 2 0 1 1 2 4 3 dataset.X : 0 1 2 3 4 5 6 7 8 | groups : 3 2 2 0 1 1 2 4 3 | dataset.X : 0 1 2 3 4 5 6 7 8 groups : a b b e q x a a r dataset.X : 0 1 2 3 4 5 6 7 8 | groups : a b b e q x a a r | dataset.X : 0 1 2 3 4 5 6 7 8 """ self.groups = groups Loading Loading @@ -488,8 +488,8 @@ class RandomStratifiedSplitter(Splitter): sparse multitask datasets it usually manages to produces a fairly accurate division of the actives for each task. Notes ----- Note ---- This splitter is primarily designed for boolean labeled data. It considers only whether a label is zero or non-zero. When labels can take on multiple non-zero values, it does not try to give each split a proportional fraction Loading Loading @@ -873,8 +873,8 @@ class MolecularWeightSplitter(Splitter): """ Class for doing data splits by molecular weight. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -946,8 +946,8 @@ class MaxMinSplitter(Splitter): Furthermore, the validation set is comprised of diverse compounds under the test set. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1044,8 +1044,8 @@ class ButinaSplitter(Splitter): """Class for doing data splits based on the butina clustering of a bulk tanimoto fingerprint matrix. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1166,8 +1166,8 @@ def _generate_scaffold(smiles: str, include_chirality: bool = False) -> str: .. [1] Bemis, Guy W., and Mark A. Murcko. "The properties of known drugs. 1. Molecular frameworks." Journal of medicinal chemistry 39.15 (1996): 2887-2893. Notes ----- Note ---- This function requires RDKit to be installed. """ try: Loading @@ -1184,8 +1184,8 @@ def _generate_scaffold(smiles: str, include_chirality: bool = False) -> str: class ScaffoldSplitter(Splitter): """Class for doing data splits based on the scaffold of small molecules. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading Loading @@ -1285,8 +1285,8 @@ class FingerprintSplitter(Splitter): """Class for doing data splits based on the fingerprints of small molecules O(N**2) algorithm. Notes ----- Note ---- This class requires RDKit to be installed. """ Loading
deepchem/trans/transformers.py +59 −24 Original line number Diff line number Diff line Loading @@ -306,7 +306,8 @@ class MinMaxTransformer(Transformer): Raises ------ `ValueError` if `transform_X` and `transform_y` are both set. ValueError if `transform_X` and `transform_y` are both set. """ def __init__(self, transform_X=False, transform_y=False, dataset=None): Loading Loading @@ -454,7 +455,8 @@ class NormalizationTransformer(Transformer): Raises ------ `ValueError` if `transform_X` and `transform_y` are both set. ValueError if `transform_X` and `transform_y` are both set. """ def __init__(self, Loading Loading @@ -659,7 +661,8 @@ class ClippingTransformer(Transformer): Raises ------ `ValueError` if `transform_w` is set. ValueError if `transform_w` is set. """ super(ClippingTransformer, self).__init__( transform_X=transform_X, transform_y=transform_y, dataset=dataset) Loading Loading @@ -737,8 +740,8 @@ class LogTransformer(Transformer): Raises ------ `ValueError` if `transform_w` is set or `transform_X` and `transform_y` are both set. ValueError if `transform_w` is set or `transform_X` and `transform_y` are both set. """ def __init__(self, Loading Loading @@ -901,8 +904,8 @@ class BalancingTransformer(Transformer): Raises ------ `ValueError` if `transform_X` or `transform_y` are set. Also raises `ValueError` if `y` or `w` aren't of shape `(N,)` or `(N, n_tasks)`. ValueError if `transform_X` or `transform_y` are set. Also raises or if `y` or `w` aren't of shape `(N,)` or `(N, n_tasks)`. """ def __init__(self, dataset: Dataset): Loading Loading @@ -1026,7 +1029,6 @@ class CDFTransformer(Transformer): >>> dataset = dc.data.NumpyDataset(X, y) >>> cdftrans = dc.trans.CDFTransformer(transform_y=True, dataset=dataset, bins=n_bins) >>> dataset = cdftrans.transform(dataset) """ def __init__(self, Loading Loading @@ -1178,7 +1180,6 @@ class PowerTransformer(Transformer): >>> dataset = dc.data.NumpyDataset(X, y) >>> trans = dc.trans.PowerTransformer(transform_y=True, dataset=dataset, powers=powers) >>> dataset = trans.transform(dataset) """ def __init__(self, Loading Loading @@ -1265,9 +1266,8 @@ class PowerTransformer(Transformer): class CoulombFitTransformer(Transformer): """Performs randomization and binarization operations on batches of Coulomb Matrix features during fit. Example ------- Examples -------- >>> n_samples = 10 >>> n_features = 3 >>> n_tasks = 1 Loading @@ -1288,8 +1288,8 @@ class CoulombFitTransformer(Transformer): Parameters ---------- dataset: dc.data.Dataset object dataset: dc.data.Dataset Dataset object to be transformed. """ X = dataset.X num_atoms = X.shape[1] Loading Loading @@ -1342,7 +1342,6 @@ class CoulombFitTransformer(Transformer): ------- X: np.ndarray Normalized features """ return (X - self.mean) / self.std Loading Loading @@ -1577,6 +1576,7 @@ class IRVTransformer(Transformer): Returns ------- DiskDataset or NumpyDataset `Dataset` object that is transformed. """ X_length = dataset.X.shape[0] Loading Loading @@ -1674,6 +1674,7 @@ class DAGTransformer(Transformer): Returns ------- List List of parent adjacency matrices """ # list of calculation orders for DAGs Loading Loading @@ -2068,6 +2069,11 @@ class DataTransforms(object): Height of the images w: int Width of the images Returns ------- np.ndarray The scaled image. """ from PIL import Image return Image.fromarray(self.Image).resize((h, w)) Loading @@ -2079,6 +2085,11 @@ class DataTransforms(object): ---------- direction: str "lr" denotes left-right flip and "ud" denotes up-down flip. Returns ------- np.ndarray The flipped image. """ if direction == "lr": return np.fliplr(self.Image) Loading @@ -2099,7 +2110,8 @@ class DataTransforms(object): Returns ------- The rotated input array np.ndarray The rotated image. """ return scipy.ndimage.rotate(self.Image, angle) Loading @@ -2110,6 +2122,11 @@ class DataTransforms(object): ---------- sigma: float Std dev. of the gaussian distribution Returns ------- np.ndarray The image added gaussian noise. """ return scipy.ndimage.gaussian_filter(self.Image, sigma) Loading @@ -2125,8 +2142,8 @@ class DataTransforms(object): Returns ------- The center cropped input array np.ndarray The center cropped image. """ y = self.Image.shape[0] x = self.Image.shape[1] Loading @@ -2150,7 +2167,8 @@ class DataTransforms(object): Returns ------- The cropped input array np.ndarray The cropped image. """ y = self.Image.shape[0] x = self.Image.shape[1] Loading @@ -2161,6 +2179,7 @@ class DataTransforms(object): Returns ------- np.ndarray The grayscale image. """ return np.dot(self.Image[..., :3], [0.2989, 0.5870, 0.1140]) Loading @@ -2180,6 +2199,11 @@ class DataTransforms(object): ‘constant’ order: int The order of the spline interpolation, default is 3. The order has to be in the range 0-5. Returns ------- np.ndarray The shifted image. """ if len(self.Image.shape) == 2: return scipy.ndimage.shift( Loading @@ -2197,6 +2221,11 @@ class DataTransforms(object): Mean of gaussian. std: float Standard deviation of gaussian. Returns ------- np.ndarray The image added gaussian noise. """ x = self.Image Loading @@ -2214,6 +2243,11 @@ class DataTransforms(object): value of salt noise. pepper: float value of pepper noise. Returns ------- np.ndarray The image added salt and pepper noise. """ noise = np.random.random(size=self.Image.shape) Loading @@ -2232,6 +2266,7 @@ class DataTransforms(object): Returns ------- np.ndarray The median filtered image. """ from PIL import Image, ImageFilter Loading
docs/source/api_reference/splitters.rst +44 −17 Original line number Diff line number Diff line Loading @@ -15,78 +15,105 @@ learning models more rigorously than standard deep models since we're looking for the ability to generalize to new domains. Some of the implemented splitters here may help. Splitter -------- The :code:`dc.splits.Splitter` class is the abstract parent class for all splitters. This class should never be directly instantiated. .. contents:: Contents :local: .. autoclass:: deepchem.splits.Splitter :members: General Splitters ----------------- RandomSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomSplitter :members: :inherited-members: :exclude-members: __init__ IndexSplitter ------------- ^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.IndexSplitter :members: :inherited-members: :exclude-members: __init__ SpecifiedSplitter ----------------- ^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.SpecifiedSplitter :members: :inherited-members: RandomGroupSplitter ------------------- ^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomGroupSplitter :members: :inherited-members: RandomStratifiedSplitter ------------------------ ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.RandomStratifiedSplitter :members: :inherited-members: :exclude-members: __init__ SingletaskStratifiedSplitter ---------------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.SingletaskStratifiedSplitter :members: :inherited-members: TaskSplitter ^^^^^^^^^^^^ .. autoclass:: deepchem.splits.TaskSplitter :members: :inherited-members: Molecule Splitters ------------------ MolecularWeightSplitter ----------------------- ^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.MolecularWeightSplitter :members: :inherited-members: :exclude-members: __init__ MaxMinSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.MaxMinSplitter :members: :inherited-members: :exclude-members: __init__ ButinaSplitter -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.ButinaSplitter :members: :inherited-members: ScaffoldSplitter ---------------- ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.ScaffoldSplitter :members: :inherited-members: :exclude-members: __init__ FingeprintSplitter ------------------ ^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.splits.FingerprintSplitter :members: :inherited-members: :exclude-members: __init__
docs/source/api_reference/transformers.rst +46 −37 Original line number Diff line number Diff line Loading @@ -8,101 +8,110 @@ distribution. Real data of course is wild and hard to control. What do you do if you have a crazy dataset and need to bring its statistics to heel? Fear not for you have :code:`Transformer` objects. Transformer ----------- The :code:`dc.trans.Transformer` class is the abstract parent class for all transformers. This class should never be directly initialized, but contains a number of useful method implementations. .. contents:: Contents :local: .. autoclass:: deepchem.trans.Transformer :members: General Transformers -------------------- MinMaxTransformer ----------------- ^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.MinMaxTransformer :members: :inherited-members: NormalizationTransformer ------------------------ ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.NormalizationTransformer :members: :inherited-members: ClippingTransformer ------------------- ^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ClippingTransformer :members: :inherited-members: LogTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.LogTransformer :members: :inherited-members: BalancingTransformer -------------------- ^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.BalancingTransformer :members: :inherited-members: DuplicateBalancingTransformer ----------------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.DuplicateBalancingTransformer :members: :inherited-members: CDFTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.CDFTransformer :members: :inherited-members: PowerTransformer ---------------- ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.PowerTransformer :members: :inherited-members: ImageTransformer ^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ImageTransformer :members: :inherited-members: FeaturizationTransformer ^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.FeaturizationTransformer :members: :inherited-members: Specified Usecase Transformers ------------------------------ CoulombFitTransformer --------------------- ^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.CoulombFitTransformer :members: :inherited-members: IRVTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.IRVTransformer :members: :inherited-members: DAGTransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.DAGTransformer :members: ImageTransformer ---------------- .. autoclass:: deepchem.trans.ImageTransformer :members: :inherited-members: ANITransformer -------------- ^^^^^^^^^^^^^^ .. autoclass:: deepchem.trans.ANITransformer :members: FeaturizationTransformer ------------------------ .. autoclass:: deepchem.trans.FeaturizationTransformer :members: DataTransforms -------------- .. autoclass:: deepchem.trans.DataTransforms :members: :inherited-members:
