/
base.py
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/
base.py
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"""
Base classes for datasets.
"""
import gzip
import pickle
import logging
import multiprocessing as mp
from collections import defaultdict
from abc import ABCMeta, abstractmethod
import inspect
from six import with_metaclass
from .results import MetaResult
LGR = logging.getLogger(__name__)
class NiMAREBase(with_metaclass(ABCMeta)):
"""
Base class for NiMARE.
"""
def __init__(self):
"""
TODO: Actually write/refactor class methods. They mostly come directly from sklearn
https://github.com/scikit-learn/scikit-learn/blob/2a1e9686eeb203f5fddf44fd06414db8ab6a554a/sklearn/base.py#L141
"""
pass
def _check_ncores(self, n_cores):
"""
Check number of cores used for method.
"""
if n_cores == -1:
n_cores = mp.cpu_count()
elif n_cores > mp.cpu_count():
LGR.warning(
'Desired number of cores ({0}) greater than number '
'available ({1}). Setting to {1}.'.format(n_cores,
mp.cpu_count()))
n_cores = mp.cpu_count()
return n_cores
@classmethod
def _get_param_names(cls):
"""Get parameter names for the estimator"""
# fetch the constructor or the original constructor before
# deprecation wrapping if any
init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
if init is object.__init__:
# No explicit constructor to introspect
return []
# introspect the constructor arguments to find the model parameters
# to represent
init_signature = inspect.signature(init)
# Consider the constructor parameters excluding 'self'
parameters = [p for p in init_signature.parameters.values()
if p.name != 'self' and p.kind != p.VAR_KEYWORD]
for p in parameters:
if p.kind == p.VAR_POSITIONAL:
raise RuntimeError("scikit-learn estimators should always "
"specify their parameters in the signature"
" of their __init__ (no varargs)."
" %s with constructor %s doesn't "
" follow this convention."
% (cls, init_signature))
# Extract and sort argument names excluding 'self'
return sorted([p.name for p in parameters])
def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
----------
deep : boolean, optional
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key, None)
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Returns
-------
self
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition('__')
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self))
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
valid_params[key].set_params(**sub_params)
return self
def save(self, filename, compress=True):
"""
Pickle the class instance to the provided file.
Parameters
----------
filename : :obj:`str`
File to which object will be saved.
compress : :obj:`bool`, optional
If True, the file will be compressed with gzip. Otherwise, the
uncompressed version will be saved. Default = True.
"""
if compress:
with gzip.GzipFile(filename, 'wb') as file_object:
pickle.dump(self, file_object)
else:
with open(filename, 'wb') as file_object:
pickle.dump(self, file_object)
@classmethod
def load(cls, filename, compressed=True):
"""
Load a pickled class instance from file.
Parameters
----------
filename : :obj:`str`
Name of file containing object.
compressed : :obj:`bool`, optional
If True, the file is assumed to be compressed and gzip will be used
to load it. Otherwise, it will assume that the file is not
compressed. Default = True.
Returns
-------
obj : class object
Loaded class object.
"""
if compressed:
try:
with gzip.GzipFile(filename, 'rb') as file_object:
obj = pickle.load(file_object)
except UnicodeDecodeError:
# Need to try this for python3
with gzip.GzipFile(filename, 'rb') as file_object:
obj = pickle.load(file_object, encoding='latin')
else:
try:
with open(filename, 'rb') as file_object:
obj = pickle.load(file_object)
except UnicodeDecodeError:
# Need to try this for python3
with open(filename, 'rb') as file_object:
obj = pickle.load(file_object, encoding='latin')
if not isinstance(obj, cls):
raise IOError('Pickled object must be {0}, '
'not {1}'.format(cls, type(obj)))
return obj
class Transformer(NiMAREBase):
"""Transformers take in Datasets and return Datasets
Initialize with hyperparameters.
"""
def __init__(self):
pass
@abstractmethod
def transform(self, dataset):
"""Add stuff to transformer.
"""
if not hasattr(dataset, 'slice'):
raise ValueError('Argument "dataset" must be a valid Dataset '
'object, not a {0}'.format(type(dataset)))
class Estimator(NiMAREBase):
"""Estimators take in Datasets and return MetaResults
"""
# Inputs that must be available in input Dataset. Keys are names of
# attributes to set; values are strings indicating location in Dataset.
_required_inputs = {}
def _validate_input(self, dataset):
"""
Search for, and validate, required inputs as necessary.
"""
if not hasattr(dataset, 'slice'):
raise ValueError('Argument "dataset" must be a valid Dataset '
'object, not a {0}'.format(type(dataset)))
if self._required_inputs:
data = dataset.get(self._required_inputs)
self.inputs_ = {}
for k, v in data.items():
if not v:
raise ValueError(
"Estimator {0} requires input dataset to contain {1}, but "
"none were found.".format(self.__class__.__name__, k))
self.inputs_[k] = v
def _preprocess_input(self, dataset):
"""
Perform any additional preprocessing steps on data in self.input_
"""
pass
def fit(self, dataset):
"""
Fit Estimator to Dataset.
Parameters
----------
dataset : :obj:`nimare.dataset.Dataset`
Dataset object to analyze.
Returns
-------
:obj:`nimare.results.MetaResult`
Results of Estimator fitting.
"""
self._validate_input(dataset)
self._preprocess_input(dataset)
maps = self._fit(dataset)
if hasattr(self, 'masker') and self.masker is not None:
masker = self.masker
else:
masker = dataset.masker
self.results = MetaResult(self, masker, maps)
return self.results
@abstractmethod
def _fit(self, dataset):
"""
Apply estimation to dataset and output results. Must return a
dictionary of results, where keys are names of images and values are
ndarrays.
"""
pass