Source code for conkit.misc.selectalg
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"""Energy function templates for restraint generation"""
__author__ = "Felix Simkovic"
__date__ = "13 Aug 2018"
__version__ = "0.13.3"
import inspect
import numpy as np
[docs]class SubselectionAlgorithm(object):
"""A class to collect all subselection algorithms"""
@classmethod
def _numpify(cls, data):
"""Convert a Python array to a :obj:`~numpy.ndarray`"""
return np.asarray(data)
[docs] @classmethod
def cutoff(cls, data, cutoff=0.287):
"""A cutoff-defined subselection algorithm
This algorithm removes a decoy, if its score is less
than the cutoff.
Parameters
----------
data : list, tuple
A 1D array of scores
cutoff : float, optional
The cutoff of keeping decoys
Returns
-------
list
The decoy indices to keep
list
The decoy indices to throw
"""
data = cls._numpify(data)
keep = np.where(data >= cutoff)[0]
throw = np.where(data < cutoff)[0]
return keep.tolist(), throw.tolist()
[docs] @classmethod
def linear(cls, data, cutoff=0.5):
"""A linearly-defined subselection algorithm
This algorithm removes the worst ``x``% decoys, where ``x``
is defined by ``cutoff``.
Parameters
----------
data : list, tuple
A 1D array of scores
cutoff : float, optional
The porportion of the total number of decoys to keep
Returns
-------
list
The decoy indices to keep
list
The decoy indices to throw
"""
sorted_indices = cls._numpify(data).argsort()[::-1]
pivot = np.ceil(sorted_indices.shape[0] * cutoff).astype(np.int)
keep = sorted_indices[:pivot]
throw = sorted_indices[pivot:]
return keep.tolist(), throw.tolist()
[docs] @classmethod
def scaled(cls, data, cutoff=0.5):
"""A scaling-defined subselection algorithm
This algorithm removes a decoy, if its scaled score
is less than 0.5. The scaled score is calculated by
dividing the precision score by the average of the
set.
Parameters
----------
data : list, tuple
A 1D array of scores
cutoff : float, optional
The cutoff of keeping decoys
Returns
-------
list
The decoy indices to keep
list
The decoy indices to throw
"""
data = cls._numpify(data)
data_scaled = data / np.mean(data)
return SubselectionAlgorithm.cutoff(data_scaled, cutoff=cutoff)
[docs] @classmethod
def ignore(cls, data):
""""A subselection algorithm to keep all
This algorithm doesn't do anything except mimic others.
It will not discard any decoys and keep all!!
Parameters
----------
data : list, tuple
A 1D array of scores
Returns
-------
list
The decoy indices to keep
list
The decoy indices to throw
"""
data = cls._numpify(data)
return cls.cutoff(data, cutoff=0)
SUBSELECTION_ALGORITHMS = [
func_name for func_name, _ in inspect.getmembers(SubselectionAlgorithm) if not func_name.startswith("_")
]