RDKFingerprinter.__init__() - Code Metrics - Inspection of "added filters" - richlewis42/scikit-chem - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 3371cb...5cb87e )

by Rich

created 2016-05-15 17:11 UTC

RDKFingerprinter.init() B

↳ Parent: RDKFingerprinter

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rs	8.8571

#! /usr/bin/env python
#
# Copyright (C) 2007-2009 Rich Lewis <[email protected]>
# License: 3-clause BSD

"""
## skchem.descriptors.fingerprints

Fingerprinting classes and associated functions are defined.
"""

from functools import wraps

import pandas as pd
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from rdkit.Chem import DataStructs, GetDistanceMatrix
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from rdkit.Chem.rdMolDescriptors import (GetMorganFingerprint,
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
                                         GetHashedMorganFingerprint,
                                         GetAtomPairFingerprint,
                                         GetHashedAtomPairFingerprint,
                                         GetTopologicalTorsionFingerprint,
                                         GetHashedTopologicalTorsionFingerprint,
                                         GetMACCSKeysFingerprint,
                                         GetFeatureInvariants,
                                         GetConnectivityInvariants)
from rdkit.Chem.rdReducedGraphs import GetErGFingerprint
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from rdkit.Chem.rdmolops import RDKFingerprint
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import skchem

def skchemize(func, columns=None, *args, **kwargs):
    """

    transform an RDKit fingerprinting function to work well with pandas

    >>> from rdkit import Chem
    >>> import skchem
    >>> from skchem.descriptors import skchemize
    >>> from skchem.core import Mol
    >>> f = skchemize(Chem.RDKFingerprint)
    >>> m = Mol.from_smiles('c1ccccc1')
    >>> f(m)
    0       0
    1       0
    2       0
    3       0
    4       0
    5       0
    6       0
    7       0
    8       0
    9       0
    10      0
    11      0
    12      0
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    15      0
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    20      0
    21      0
    22      0
    23      0
    24      0
    25      0
    26      0
    27      0
    28      0
    29      0
           ..
    2018    0
    2019    0
    2020    0
    2021    0
    2022    0
    2023    0
    2024    0
    2025    0
    2026    0
    2027    0
    2028    0
    2029    0
    2030    0
    2031    0
    2032    0
    2033    0
    2034    0
    2035    0
    2036    0
    2037    0
    2038    0
    2039    0
    2040    0
    2041    0
    2042    0
    2043    0
    2044    0
    2045    0
    2046    0
    2047    0
    dtype: int64
    >>> from skchem.data import resource
    >>> df = skchem.read_sdf(resource('test_sdf', 'multi_molecule-simple.sdf'))
    >>> df.structure.apply(f)
          0     1     2     3     4     5     6     7     8     9     ...   2038
    name                                                              ...
    297      0     0     0     0     0     0     0     0     0     0  ...      0
    6324     0     0     0     0     0     0     0     0     0     0  ...      0
    6334     0     0     0     0     0     0     0     0     0     0  ...      0
    <BLANKLINE>
          2039  2040  2041  2042  2043  2044  2045  2046  2047
    name
    297      0     0     0     0     0     0     0     0     0
    6324     0     0     0     0     0     0     0     0     0
    6334     0     0     0     0     0     0     0     0     0
    <BLANKLINE>
    [3 rows x 2048 columns]

    """
    @wraps(func)
    def func_wrapper(m):


        """ Function that wraps an rdkit function allowing it to produce dataframes. """

        arr = np.array(0)
        DataStructs.ConvertToNumpyArray(func(m, *args, **kwargs), arr)


        return pd.Series(arr, index=columns)

    return func_wrapper


class Fingerprinter(object):

    """ Fingerprinter Base class. """

    def __init__(self, func, name=None):

        """ A generic fingerprinter.  Create with a function.

        Args:
            func (callable):
                A fingerprinting function that takes an skchem.Mol argument, and
                returns an iterable of values.
            name (str):
                The name of the fingerprints that are being calculated"""

        self.NAME = name

        self.func = func

    def __call__(self, obj):

        """ Call the fingerprinter directly.

        This is a shorthand for transform. """

        return self.transform(obj)

    def __add__(self, other):

        """ Add fingerprinters together to create a fusion fingerprinter.

        Fusion featurizers will transform molecules to series with all
        features from all component featurizers.
        """

        fpers = []
        for fper in (self, other):
            if isinstance(fper, FusionFingerprinter):
                fpers += fper.fingerprinters

            else:
                fpers.append(fper)

        return FusionFingerprinter(fpers)

    def fit(self, X, y):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self

    def transform(self, obj):

        """ calculate the fingerprint for the given object. """

        if isinstance(obj, skchem.Mol):
            return self._transform(obj)

        elif isinstance(obj, pd.DataFrame):
            return obj.structure.apply(self.transform)

        elif isinstance(obj, pd.Series):
            return obj.apply(self.transform)

        else:
            raise NotImplementedError

    def _transform(self, mol):

        """ Calculate the fingerprint on a molecule. """

        return pd.Series(list(self.func(mol)), name=mol.name)


class FusionFingerprinter(Fingerprinter):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

    def __init__(self, fingerprinters):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)

        self.fingerprinters = fingerprinters

    def transform(self, obj):

        if isinstance(obj, skchem.Mol):
            return pd.concat([fp.transform(obj) for fp in self.fingerprinters],
                             keys=[fp.NAME for fp in self.fingerprinters])

        elif isinstance(obj, pd.DataFrame):
            return pd.concat([fp.transform(obj) for fp in self.fingerprinters],
                             keys=[fp.NAME for fp in self.fingerprinters],
                             axis=1)

        elif isinstance(obj, pd.Series):
            return pd.concat([fp.transform(obj.structure) \
                                for fp in self.fingerprinters],
                             keys=[fp.NAME for fp in self.fingerprinters],
                             axis=1)

        else:
            raise NotImplementedError

    def _transform(self, mol):

        return pd.concat([fp.transform(mol) for fp in self.fingerprinters])

class MorganFingerprinter(Fingerprinter):

    """ Morgan Fingerprint Transformer. """

    NAME = 'morgan'

    def __init__(self, radius=2, n_feats=2048, as_bits=False,
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
                 use_features=False, use_bond_types=True, use_chirality=False):

        """
        Args:
            radius (int):
                The maximum radius for atom environments.
                Default is `2`.
            n_feats (int):
                The number of features to which to fold the fingerprint down.
                For unfolded, use `-1`.
                Default is `2048`.
            as_bits (bool):
                Whether to return bits (`True`) or counts (`False`).
                Default is `True`.
            use_features (bool):
                Whether to use map atom types to generic features (FCFP analog).
                Default is `False`.
            use_bond_types (bool):
                Whether to use bond types to differentiate environments.
                Default is `False`.
            use_chirality (bool):
                Whether to use chirality to differentiate environments.
                Default is `False`.
        """

        self.radius = radius
        self.n_feats = n_feats
        self.as_bits = as_bits
        self.use_features = use_features
        self.use_bond_types = use_bond_types
        self.use_chirality = use_chirality

    def _transform(self, mol):

        """Private method to transform a skchem molecule.

        Use transform` for the public method, which genericizes the argument to
        iterables of mols.

        Args:
            mol (skchem.Mol): Molecule to calculate fingerprint for.

        Returns:
            pandas.Series:
                Fingerprint as a series.
        """

        if self.n_feats < 0:

            res = GetMorganFingerprint(mol, self.radius,
                                       useFeatures=self.use_features,
                                       useBondTypes=self.use_bond_types,
                                       useChirality=self.use_chirality).GetNonzeroElements()
            idx = pd.Index(list(res.keys()), name='features')

        else:
            res = list(GetHashedMorganFingerprint(mol, self.radius,
                                        nBits=self.n_feats,

                                        useFeatures=self.use_features,

                                        useBondTypes=self.use_bond_types,

                                        useChirality=self.use_chirality))

            idx = pd.Index(range(self.n_feats), name='features')

        res = pd.Series(res, name=mol.name, index=idx).astype(int)

        if self.as_bits:
            res = (res > 0).astype(np.uint8) #smallest memory size

        return res

    def grad(self, mol):

        """ Calculate the pseudo gradient with resepect to the atoms.

        The pseudo gradient is the number of times the atom set that particular
        bit.

        Args:
            mol (skchem.Mol):
                The molecule for which to calculate the pseudo gradient.

        Returns:
            pandas.DataFrame:
                Dataframe of pseudogradients, with columns corresponding to
                atoms, and rows corresponding to features of the fingerprint.
        """

        cols = pd.Index(list(range(len(mol.atoms))), name='atoms')
        dist = GetDistanceMatrix(mol)

        info = {}

        if self.n_feats < 0:

            res = GetMorganFingerprint(mol, self.radius,
                                       useFeatures=self.use_features,
                                       useBondTypes=self.use_bond_types,
                                       useChirality=self.use_chirality,
                                       bitInfo=info).GetNonzeroElements()
            idx_list = list(res.keys())
            idx = pd.Index(idx_list, name='features')
            grad = np.zeros((len(idx), len(cols)))
            for bit in info:
                for atom_idx, radius in info[bit]:
                    grad[idx_list.index(bit)] += (dist <= radius)[atom_idx]

        else:

            res = list(GetHashedMorganFingerprint(mol, self.radius,
                                        nBits=self.n_feats,

                                        useFeatures=self.use_features,

                                        useBondTypes=self.use_bond_types,

                                        useChirality=self.use_chirality,

                                        bitInfo=info))

            idx = pd.Index(range(self.n_feats), name='features')
            grad = np.zeros((len(idx), len(cols)))

            for bit in info:
                for atom_idx, radius in info[bit]:
                    grad[bit] += (dist <= radius)[atom_idx]

        grad = pd.DataFrame(grad, index=idx, columns=cols)

        if self.as_bits:
            grad = (grad > 0)

        return grad.astype(int)

class AtomPairFingerprinter(Fingerprinter):

    """ Atom Pair Tranformer. """

    NAME = 'atom_pair'

    def __init__(self, min_length=1, max_length=30, n_feats=2048, as_bits=False,
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
                 use_chirality=False):

        """ Instantiate an atom pair fingerprinter.

        Args:
            min_length (int):
                The minimum length of paths between pairs.
                Default is `1`, i.e. pairs can be bonded together.
            max_length (int):
                The maximum length of paths between pairs.
                Default is `30`.
            n_feats (int):
                The number of features to which to fold the fingerprint down.
                For unfolded, use `-1`.
                Default is `2048`.
            as_bits (bool):
                Whether to return bits (`True`) or counts (`False`).
                Default is `False`.
            use_chirality (bool):
                Whether to use chirality to differentiate environments.
                Default is `False`.
        """

        self.min_length = min_length
        self.max_length = max_length
        self.n_feats = n_feats
        self.as_bits = as_bits
        self.use_chirality = use_chirality

    def _transform(self, mol):

        """Private method to transform a skchem molecule.

        Use transform` for the public method, which genericizes the argument to
        iterables of mols.

        Args:
            mol (skchem.Mol): Molecule to calculate fingerprint for.

        Returns:
            pandas.Series:
                Fingerprint as a series.
        """

        if self.n_feats == -1:

            res = GetAtomPairFingerprint(mol, minLength=self.min_length,
                                         maxLength=self.max_length,
                                         includeChirality=self.use_chirality)
        else:
            res = list(GetHashedAtomPairFingerprint(mol,
                                        minLength=self.min_length,

                                         maxLength=self.max_length,

                                         nBits=self.n_feats,

                                         includeChirality=self.use_chirality))


        res = pd.Series(res, name=mol.name)

        if self.as_bits:
            return (res > 0).astype(int)
        else:
            return res

class TopologicalTorsionFingerprinter(Fingerprinter):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

    NAME = 'topological_torsion'

    def __init__(self, target_size=4, n_feats=2048, as_bits=False,
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
                 use_chirality=False):

        """
        Args:
            target_size (int):
                # TODO
            n_feats (int):
                The number of features to which to fold the fingerprint down.
                For unfolded, use `-1`.
                Default is `2048`.
            as_bits (bool):
                Whether to return bits (`True`) or counts (`False`).
                Default is `False`.
            use_chirality (bool):
                Whether to use chirality to differentiate environments.
                Default is `False`.
        """

        self.target_size = target_size
        self.n_feats = n_feats
        self.as_bits = as_bits
        self.use_chirality = use_chirality

    def _transform(self, mol):

        if self.n_feats == -1:

            res = GetTopologicalTorsionFingerprint(mol,
                                        targetSize=self.targetSize,

                                        includeChirality=self.use_chirality)


        else:
            res = list(GetHashedTopologicalTorsionFingerprint(mol,
                                        targetSize=self.targetSize,

                                        nBits=self.n_feats))


        res = pd.Series(res, name=mol.name)

        if self.as_bits:
            return (res > 0).astype(int)
        else:
            return res


class MACCSKeysFingerprinter(Fingerprinter):

    """ MACCS Keys Fingerprints """

    NAME = 'maccs'

    def __init__(self):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        pass

    def _transform(self, mol):

        return pd.Series(list(GetMACCSKeysFingerprint(mol)))

class ErGFingerprinter(Fingerprinter):

    """ ErG Fingerprints """

    NAME = 'erg'

    def __init__(self):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        pass

    def _transform(self, mol):

        return pd.Series(GetErGFingerprint(mol))

class FeatureInvariantsFingerprinter(Fingerprinter):

    """ Feature invariants fingerprints. """

    NAME = 'feat_inv'

    def __init__(self):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        pass

    def _transform(self, mol):

        return pd.Series(GetFeatureInvariants(mol))

class ConnectivityInvariantsFingerprinter(Fingerprinter):

    """ Connectivity invariants fingerprints """

    NAME = 'conn_inv'

    def __init__(self):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        pass

    def _transform(self, mol):

        return pd.Series(GetConnectivityInvariants(mol))

class RDKFingerprinter(Fingerprinter):


    """ RDKit fingerprint """

    NAME = 'rdk'

    def __init__(self, min_path=1, max_path=7, n_feats=2048, n_bits_per_hash=2,
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
                 use_hs=True, target_density=0.0, min_size=128,

                 branched_paths=True, use_bond_types=True):


        """ RDK fingerprints

        # TODO

        Args:
            min_path (int):

            max_path (int):

            n_feats (int):
                The number of features to which to fold the fingerprint down.
                For unfolded, use `-1`.
                Default is `2048`.

            n_bits_per_hash (int)

            use_hs (bool):

            target_density (float):

            min_size (int):

            branched_paths (bool):

            use_bond_types (bool):
        """

        self.min_path = 1
        self.max_path = 7
        self.n_feats = 2048
        self.n_bits_per_hash = 2
        self.use_hs = True
        self.target_density = 0.0
        self.min_size = 128
        self.branched_paths = True
        self.use_bond_types = True

    def _transform(self, mol):

        return pd.Series(list(RDKFingerprint(mol, minPath=self.min_path,
                                             maxPath=self.max_path,
                                             fpSize=self.n_feats,
                                             nBitsPerHash=self.n_bits_per_hash,
                                             useHs=self.use_hs,
                                             tgtDensity=self.target_density,
                                             minSize=self.min_size,
                                             branchedPaths=self.branched_paths,
                                             useBondOrder=self.use_bond_types)),
                        name=mol.name)



Push — master ( 3371cb...5cb87e )

RDKFingerprinter.init() B

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1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

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2. Missing init.py files

1. Missing Dependencies

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1. Missing Dependencies

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1			#! /usr/bin/env python
2			#
3			# Copyright (C) 2007-2009 Rich Lewis <[email protected]>
4			# License: 3-clause BSD
5
6			"""
7			## skchem.descriptors.fingerprints
8
9			Fingerprinting classes and associated functions are defined.
10			"""
11
12			from functools import wraps
13
14			import pandas as pd
			0 ignored issues – show Configuration introduced 2016-01-19 16:21 UTC by Report Bug Copy Issue Report The import `pandas` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
15			from rdkit.Chem import DataStructs, GetDistanceMatrix
			0 ignored issues – show Configuration introduced 2016-05-15 17:12 UTC by Report Bug Copy Issue Report The import `rdkit.Chem` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
16			from rdkit.Chem.rdMolDescriptors import (GetMorganFingerprint,
			0 ignored issues – show Configuration introduced 2016-04-14 16:48 UTC by Report Bug Copy Issue Report The import `rdkit.Chem.rdMolDescriptors` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
17			GetHashedMorganFingerprint,
18			GetAtomPairFingerprint,
19			GetHashedAtomPairFingerprint,
20			GetTopologicalTorsionFingerprint,
21			GetHashedTopologicalTorsionFingerprint,
22			GetMACCSKeysFingerprint,
23			GetFeatureInvariants,
24			GetConnectivityInvariants)
25			from rdkit.Chem.rdReducedGraphs import GetErGFingerprint
			0 ignored issues – show Configuration introduced 2016-04-14 16:48 UTC by Report Bug Copy Issue Report The import `rdkit.Chem.rdReducedGraphs` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
26			from rdkit.Chem.rdmolops import RDKFingerprint
			0 ignored issues – show Configuration introduced 2016-04-14 16:48 UTC by Report Bug Copy Issue Report The import `rdkit.Chem.rdmolops` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
27
28			import numpy as np
			0 ignored issues – show Configuration introduced 2016-01-19 16:21 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
29			import skchem
30
31			def skchemize(func, columns=None, args, *kwargs):
32			"""
33
34			transform an RDKit fingerprinting function to work well with pandas

richlewis42 / scikit-chem

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1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

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