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"""Toolbox for imbalanced dataset in machine learning.
``imbalanced-learn`` is a set of python methods to deal with imbalanced
datset in machine learning and pattern recognition.
Subpackages
-----------
combine
Module which provides methods based on over-sampling and under-sampling.
ensemble
Module which provides methods generating an ensemble of
under-sampled subsets.
exceptions
Module including custom warnings and error clases used across
imbalanced-learn.
keras
Module which provides custom generator, layers for deep learning using
keras.
metrics
Module which provides metrics to quantified the classification performance
with imbalanced dataset.
over_sampling
Module which provides methods to over-sample a dataset.
tensorflow
Module which provides custom generator, layers for deep learning using
tensorflow.
under-sampling
Module which provides methods to under-sample a dataset.
utils
Module including various utilities.
pipeline
Module which allowing to create pipeline with scikit-learn estimators.
"""
import importlib
import sys
import types
try:
# This variable is injected in the __builtins__ by the build
# process. It is used to enable importing subpackages of sklearn when
# the binaries are not built
# mypy error: Cannot determine type of '__SKLEARN_SETUP__'
__IMBLEARN_SETUP__ # type: ignore
except NameError:
__IMBLEARN_SETUP__ = False
if __IMBLEARN_SETUP__:
sys.stderr.write("Partial import of imblearn during the build process.\n")
# We are not importing the rest of scikit-learn during the build
# process, as it may not be compiled yet
else:
from . import (
combine,
ensemble,
exceptions,
metrics,
over_sampling,
pipeline,
tensorflow,
under_sampling,
utils,
)
from ._version import __version__
from .base import FunctionSampler
from .utils._show_versions import show_versions # noqa: F401
# FIXME: When we get Python 3.7 as minimal version, we will need to switch to
# the following solution:
# https://snarky.ca/lazy-importing-in-python-3-7/
class LazyLoader(types.ModuleType):
"""Lazily import a module, mainly to avoid pulling in large dependencies.
Adapted from TensorFlow:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/
python/util/lazy_loader.py
"""
def __init__(self, local_name, parent_module_globals, name, warning=None):
self._local_name = local_name
self._parent_module_globals = parent_module_globals
self._warning = warning
super(LazyLoader, self).__init__(name)
def _load(self):
"""Load the module and insert it into the parent's globals."""
# Import the target module and insert it into the parent's namespace
module = importlib.import_module(self.__name__)
self._parent_module_globals[self._local_name] = module
# Update this object's dict so that if someone keeps a reference to the
# LazyLoader, lookups are efficient (__getattr__ is only called on
# lookups that fail).
self.__dict__.update(module.__dict__)
return module
def __getattr__(self, item):
module = self._load()
return getattr(module, item)
def __dir__(self):
module = self._load()
return dir(module)
# delay the import of keras since we are going to import either tensorflow
# or keras
keras = LazyLoader("keras", globals(), "imblearn.keras")
__all__ = [
"combine",
"ensemble",
"exceptions",
"keras",
"metrics",
"over_sampling",
"tensorflow",
"under_sampling",
"utils",
"pipeline",
"FunctionSampler",
"__version__",
]
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