SimThresholdSplit - Code Metrics - Inspection of "changed SimThresholdSplit it use fit" - richlewis42/scikit-chem - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 0187d9...d34d24 )

by Rich

created 2016-08-11 21:28 UTC

SimThresholdSplit C

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	350
Duplicated Lines	0 %

Test Coverage

Coverage

69.57%

Importance

Changes	2
Bugs	1	Features	1

Metric	Value
wmc	56
c	2
b	1
f	1
dl	0
loc	350
ccs	96
cts	138
cp	0.6957
rs	5.5555

19 Methods

Rating	Name	Size	Complexity
B	fit()	42	6
B	visualize_similarities()	28	4
A	k_fold()	15	2
A	n_instances_()	4	2
A	_cluster_cumsum()	6	2
A	_pairs_from_fps_mem_intensive()	7	1
A	n_jobs()	4	2
A	_pairs_from_fps()	9	2
B	split()	29	5
A	_cluster()	13	3
A	__init__()	63	3
B	slice_generator()	12	6
F	_pairs_from_fps_mem_opt()	37	10
A	_split_sizes()	5	2
A	f()	4	1
A	_optimal_thresh()	13	2
A	block_width()	4	2
A	_pairs_from_sim_mat()	5	1
B	visualize_space()	31	4

How to fix Complexity

#! /usr/bin/env python
#
# Copyright (C) 2016 Rich Lewis <[email protected]>
# License: 3-clause BSD

"""
## skchem.cross_validation.similarity_threshold

Similarity threshold dataset partitioning functionality.
"""

import logging
import sys


import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import pandas as pd
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.pyplot as plt
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from scipy.spatial.distance import pdist, squareform
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from scipy.sparse import triu
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from scipy.optimize import minimize_scalar
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import multiprocessing
from functools import partial, wraps

from .. import descriptors

LOGGER = logging.getLogger(__name__)


def returns_pairs(func):
    """ Wraps a function that returns a ((i, j), sim) list to return a dataframe. """
    @wraps(func)
    def inner(*args, **kwargs):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        pairs = func(*args, **kwargs)
        return pd.DataFrame([(p[0][0], p[0][1], p[1]) for p in pairs], columns=['i', 'j', 'sim']).sort_values('sim')

    return inner


def _above_minimum(args, X, metric, threshold, size):

    """ finds pairs above a minimum similarity in chunks """
    from scipy.spatial.distance import cdist
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
    from scipy.sparse import dok_matrix
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
    import numpy as np
param = 5

class Foo:
    def __init__(self, param):   # "param" would be flagged here
        self.param = param
    i, j = slice(*args[0]), slice(*args[1])
    x_i, x_j = X[i], X[j]
    C = 1 - cdist(x_i, x_j, metric=metric)

    if i == j:
        C = np.triu(C, k=1)

    C[C <= threshold] = 0
    M = dok_matrix((size, size), dtype=float)

    M[i, j] = C
    return list(M.items())


class SimThresholdSplit(object):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

    def __init__(self,  min_threshold=0.45, largest_cluster_fraction=0.1, fper='morgan',

                 similarity_metric='jaccard', memory_optimized=True, n_jobs=1, block_width=1000,
                 verbose=False):

        """ Threshold similarity split for chemical datasets.

        This class implements a splitting technique that will pool compounds
        with similarity above a theshold into the same splits.  The threshold
        value is decided by specifying the maximum number of compounds to pool
        into a cluster, as the density of compounds varies with dataset.

        Machine learning techniques should be able to extrapolate outside of a
        molecular series, or scaffold, however random splits will result in some
        'easy' test sets that are either *identical* or in the same molecular
        series or share a significant scaffold with training set compounds.

        This splitting technique reduces or eliminates (depending on the
        threshold set) this effect, making the problem harder.

        Args:

            min_threshold (float):
                The minimum similarity threshold.  Lower will be slower.

            largest_cluster_fraction (float):
                The fraction of the total dataset the largest cluster can be. This decided the
                final similarity threshold.

            fper (str or skchem.Fingerprinter):
                The fingerprinting technique to use to generate the similarity
                matrix.

            similarity_metric (str or callable):
                The similarity metric to use.

            memory_optimized (bool):
                Whether to use the memory optimized implementation.

            n_jobs (int):
                If memory_optimized is True, how many processes to run it over.

            block_width (int):
                If memory_optimized, what block length to use.  This is the width of the sub
                matrices that are calculated at a time.

        Notes:
            The splits will not always be exactly the size requested, due to the
            constraint and requirement to maintain random shuffling.
        """

        if isinstance(fper, str):
            fper = descriptors.get(fper)

        self.fper = fper
        self.similarity_metric = similarity_metric
        self.memory_optimized = memory_optimized
        self.n_jobs = n_jobs
        self._block_width = block_width
        self.min_threshold = min_threshold
        self.largest_cluster = largest_cluster_fraction

        if self.fper:
            self.fper.verbose = verbose

    def fit(self, inp, pairs=None):

        """
        Args:
             inp (pd.Series or pd.DataFrame or np.array):
                - `pd.Series` of `skchem.Mol` instances
                - `pd.DataFrame` with `skchm.Mol` instances as a `structure` row.
                - `pd.DataFrame` of fingerprints if `fper` is `None`
                - `pd.DataFrame` of similarity matrix if `similarity_metric` is `None`
                - `np.array` of similarity matrix if `similarity_metric` is `None`

            pairs (list<tuple<tuple(i, j), k>>):
                An optional precalculated list of pairwise distances.
        """

        self.n_instances_ = len(inp)
        self.pairs_ = pairs
class Foo:
    def __init__(self, x=None):
        self.x = x

        if isinstance(inp, (pd.Series, pd.DataFrame)):
            self.index = inp.index
class Foo:
    def __init__(self, x=None):
        self.x = x
        else:
            self.index = pd.RangeIndex(len(inp), name='batch')
class Foo:
    def __init__(self, x=None):
        self.x = x

        if self.similarity_metric is None:
            # we were passed a similarity matrix directly
            self.pairs_ = self._pairs_from_sim_mat(inp)
class Foo:
    def __init__(self, x=None):
        self.x = x

        elif self.fper is None:
            # we were passed fingerprints directly
            self.fps = inp
class Foo:
    def __init__(self, x=None):
        self.x = x
            if self.pairs_ is None:
                self.pairs_ = self._pairs_from_fps(inp)
class Foo:
    def __init__(self, x=None):
        self.x = x

        else:
            # we were passed Mol
            if self.pairs_ is None:
                self.fps = self.fper.transform(inp)
class Foo:
    def __init__(self, x=None):
        self.x = x
                self.pairs_ = self._pairs_from_fps(self.fps)
class Foo:
    def __init__(self, x=None):
        self.x = x

        self.threshold_ = self._optimal_thresh()
class Foo:
    def __init__(self, x=None):
        self.x = x

        return self

    @property
    def n_jobs(self):
        """ The number of processes to use to calculate the distance matrix.  -1 for all available. """

        return self._n_jobs

    @n_jobs.setter
    def n_jobs(self, val):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        if val == -1:
            self._n_jobs = multiprocessing.cpu_count()
class Foo:
    def __init__(self, x=None):
        self.x = x
        else:
            self._n_jobs = val
class Foo:
    def __init__(self, x=None):
        self.x = x

    @property
    def block_width(self):
        """ The width of the subsets of features.  Only used in parallelized. """
        return self._block_width

    @block_width.setter
    def block_width(self, val):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        assert val <= self.n_instances_, 'The block width should be less than or equal to the number of instances'

        self._block_width = val

    @property
    def n_instances_(self):
        """ The number of instances that were used to fit the object. """
        return self._n_instances_

    @n_instances_.setter
    def n_instances_(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        assert val >= self._block_width, 'The block width should be less than or equal to the number of instances'

        self._n_instances_ = val
class Foo:
    def __init__(self, x=None):
        self.x = x

    def _cluster_cumsum(self, shuffled=True):

        nums = self.clusters.value_counts()
        if shuffled:
            nums = nums.ix[np.random.permutation(nums.index)].cumsum()
        return nums

    def split(self, ratio):

        """ Return splits of the data with thresholded similarity according to a
        specified ratio.

        Args:
            ratio (tuple[ints]):
                the ratio to use.
        Returns:
            generator[pd.Series]:
                Generator of boolean split masks for the reqested splits.

        Example:
            st = SimThresholdSplit(ms, fper='morgan', similarity_metric='jaccard')
            train, valid, test = st.split(ratio=(70, 15, 15))
        """

        ratio = self._split_sizes(ratio)
        nums = self._cluster_cumsum()
        res = pd.Series(np.nan, index=nums.index, name='split')

        for i in range(len(ratio)):
            lower = 0 if i == 0 else sum(ratio[:i])
            upper = ratio if i == len(ratio) else sum(ratio[:i + 1])
            res[nums[(nums > lower) & (nums <= upper)].index] = i

        res = res.sort_index()
        res = self.clusters.to_frame().join(res, on='clusters')['split']
        return (res == i for i in range(len(ratio)))

    def k_fold(self, n_folds):

        """ Returns k-fold cross-validated folds with thresholded similarity.

        Args:
            n_folds (int):
                The number of folds to provide.

        Returns:
            generator[(pd.Series, pd.Series)]:
                The splits in series.
        """

        folds = self.split((1,) * n_folds)
        return ((~fold, fold) for fold in folds)

    def _split_sizes(self, ratio):
        """ Calculate the sizes of the splits """

        tot = sum(ratio)
        return [self.n_instances_ * rat / tot for rat in ratio]

    @staticmethod
    @returns_pairs
    def _pairs_from_sim_mat(S):

        S = triu(S, k=1).todok()
        return list(S.items())

    @returns_pairs
    def _pairs_from_fps(self, fps):
        """ Pairs from fps. """
        if self.memory_optimized:
            pairs = self._pairs_from_fps_mem_opt(fps)
        else:
            pairs = self._pairs_from_fps_mem_intensive(fps)

        return pairs

    def _pairs_from_fps_mem_intensive(self, fps):
        """ Fast single process but memory intensive implementation of pairs. """
        LOGGER.debug('Generating pairs using memory intensive technique.')
        D = squareform(pdist(fps, self.similarity_metric))

        S = 1 - D # similarity is 1 - distance

        S[S <= self.min_threshold] = 0
        return self._pairs_from_sim_mat(S)

    def _pairs_from_fps_mem_opt(self, fps):

        """ Fast, multi-processed and memory efficient generation of pairwise distances above a certain threshold."""


        def slice_generator(low, high, width, end=False):
            """ Generator of index of checkerboards for the upper triangle of a matrix. """
            while low < high:
                res = (low, low + width if low + width < high else high)
                if end:
                    yield res
                else:
                    # py2 compat
                    # yield from ((res, j) for j in slice_generator(low, high, width, end=True))
                    for slice_ in ((res, j) for j in slice_generator(low, high, width, end=True)):
                        yield slice_
                low += width

        size = len(fps)

        fps = fps.values
        f = partial(_above_minimum, X=fps, threshold=self.min_threshold, metric=self.similarity_metric, size=size)

        slices = slice_generator(0, len(fps), self.block_width)

        if self.n_jobs == 1:
            # single processed
            LOGGER.debug('Generating pairs using memory optimized technique.')
            return sum((f(slice) for slice in slices), [])
        else:
            # multiprocessed
            LOGGER.debug('Generating pairs using memory optimized technique with %s processes', self.n_jobs)

            # py2 compat
            # with multiprocessing.Pool(self.n_jobs) as p:
            #    return sum(p.map(f, [(i, j) for i, j in slices]), [])
            p = multiprocessing.Pool(self.n_jobs)

            res = sum(p.map(f, [(i, j) for i, j in slices]), [])
            p.close()
            return res

    def _cluster(self, pairs):
        """ Assign instances to clusters. """

        LOGGER.debug('Generating clusters with %s close pairs', len(pairs))
        clustered = np.arange(self.n_instances_)

        for i, j in pairs.values.tolist(): # faster as list
            i_clust, j_clust = clustered[i], clustered[j]
            if i_clust < j_clust:
                clustered[clustered == j_clust] = i_clust
            else:
                clustered[clustered == i_clust] = j_clust
        return clustered

    def _optimal_thresh(self):
        """ Calculate the optimal threshold for the given max pair density. """
        def f(threshold):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
            pairs = self.pairs_.loc[self.pairs_.sim > threshold, ('i', 'j')]

            res = pd.Series(self._cluster(pairs))
            return np.abs(res.value_counts().max() - self.largest_cluster * self.n_instances_)

        self.threshold_ = minimize_scalar(f, bounds=(self.min_threshold, 1), method='bounded').x
class Foo:
    def __init__(self, x=None):
        self.x = x
        LOGGER.info('Optimal threshold: %s', self.threshold_)
        self.clusters = pd.Series(self._cluster(self.pairs_.loc[self.pairs_.sim > self.threshold_, ('i', 'j')]),
class Foo:
    def __init__(self, x=None):
        self.x = x
                                  index=self.index,
                                  name='clusters')
        return self.threshold_

    def visualize_similarities(self, subsample=5000, ax=None):


        """ Plot a histogram of similarities, with the threshold plotted.

        Args:
            subsample (int):
                For a large dataset, subsample the number of compounds to
                consider.
            ax (matplotlib.axis):
                Axis to make the plot on.
        Returns:
            matplotlib.axes
        """

        if not ax:
            ax = plt.gca()

        if subsample and len(self.fps) > subsample:
            fps = self.fps.sample(subsample)
        else:
            fps = self.fps

        dists = 1 - squareform(pdist(fps, self.similarity_metric))
        dists = (dists - np.identity(dists.shape[0])).flatten()
        hist = ax.hist(dists, bins=50)
        ax.vlines(self.threshold_, 0, max(hist[0]))
        ax.set_xlabel('similarity')
        return ax

    def visualize_space(self, dim_reducer='tsne', dim_red_kw={}, subsample=5000, ax=None, c=None):
# Bad:
# If array_param is modified inside the function, the next invocation will
# receive the modified object.
def some_function(array_param=[]):
    # ...

# Better: Create an array on each invocation
def some_function(array_param=None):
    array_param = array_param or []
    # ...

        """ Plot chemical space using a transformer

        Args:
            dim_reducer (str or sklearn object):
                Technique to use to reduce fingerprint space.

            subsample (int):
                for a large dataset, subsample the number of compounds to
                consider.

            ax (matplotlib.axis):
                Axis to make the plot on.
        Returns:
            matplotlib.axes
        """

        if isinstance(dim_reducer, str):
            if dim_reducer not in ('tsne', 'mds'):
                raise NotImplementedError('Dimensionality reducer {} not available'.format(dim_reducer))

            from sklearn.manifold import TSNE, MDS
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
            reducers = {'tsne': TSNE, 'mds': MDS}
            dim_reducer = reducers[dim_reducer](**dim_red_kw)


        two_d = dim_reducer.fit_transform(self.fps)


        if not ax:
            ax = plt.gca()

        return ax.scatter(two_d[:, 0], two_d[:, 1], c=c)





Push — master ( 0187d9...d34d24 )

SimThresholdSplit C

Complexity

Size/Duplication

Test Coverage

Importance

19 Methods

How to fix Complexity

Complex Class

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1		#! /usr/bin/env python
2		#
3		# Copyright (C) 2016 Rich Lewis <[email protected]>
4		# License: 3-clause BSD
5
6	1	"""
7		## skchem.cross_validation.similarity_threshold
8
9		Similarity threshold dataset partitioning functionality.
10		"""
11
12	1	import logging
13	1	import sys
		0 ignored issues – show Unused Code introduced 2016-08-05 16:20 UTC by Report Bug Copy Issue Report The import `sys` seems to be unused. Loading history...
14
15	1	import numpy as np
		0 ignored issues – show Configuration introduced 2016-06-03 13:36 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...
16	1	import pandas as pd
		0 ignored issues – show Configuration introduced 2016-06-03 13:36 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...
17	1	import matplotlib.pyplot as plt
		0 ignored issues – show Configuration introduced 2016-06-12 20:13 UTC by Report Bug Copy Issue Report The import `matplotlib.pyplot` 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...
18	1	from scipy.spatial.distance import pdist, squareform
		0 ignored issues – show Configuration introduced 2016-06-03 13:36 UTC by Report Bug Copy Issue Report The import `scipy.spatial.distance` 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...
19	1	from scipy.sparse import triu
		0 ignored issues – show Configuration introduced 2016-08-05 16:20 UTC by Report Bug Copy Issue Report The import `scipy.sparse` 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...
20	1	from scipy.optimize import minimize_scalar
		0 ignored issues – show Configuration introduced 2016-08-05 16:20 UTC by Report Bug Copy Issue Report The import `scipy.optimize` 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...
21
22	1	import multiprocessing
23	1	from functools import partial, wraps
24
25	1	from .. import descriptors
26
27	1	LOGGER = logging.getLogger(__name__)
28
29
30	1	def returns_pairs(func):
31		""" Wraps a function that returns a ((i, j), sim) list to return a dataframe. """
32	1	@wraps(func)
33		def inner(args, *kwargs):

richlewis42 / scikit-chem

Push — master ( 0187d9...d34d24 )

SimThresholdSplit C

Complexity

Size/Duplication

Test Coverage

Importance

19 Methods

How to fix Complexity

Complex Class

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

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

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2. Missing init.py files

2. Missing init.py files

2. Missing init.py files