sklearn.utils.Memory

class sklearn.utils.Memory(location=None, backend=’local’, cachedir=None, mmap_mode=None, compress=False, verbose=1, bytes_limit=None, backend_options=None) [source]

A context object for caching a function’s return value each time it is called with the same input arguments.

All values are cached on the filesystem, in a deep directory structure.

Read more in the User Guide.

Parameters:

location: str or None The path of the base directory to use as a data store or None. If None is given, no caching is done and the Memory object is completely transparent. This option replaces cachedir since version 0.12. backend: str, optional Type of store backend for reading/writing cache files. Default: ‘local’. The ‘local’ backend is using regular filesystem operations to manipulate data (open, mv, etc) in the backend. cachedir: str or None, optional mmap_mode: {None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optional The memmapping mode used when loading from cache numpy arrays. See numpy.load for the meaning of the arguments. compress: boolean, or integer, optional Whether to zip the stored data on disk. If an integer is given, it should be between 1 and 9, and sets the amount of compression. Note that compressed arrays cannot be read by memmapping. verbose: int, optional Verbosity flag, controls the debug messages that are issued as functions are evaluated. bytes_limit: int, optional Limit in bytes of the size of the cache. backend_options: dict, optional Contains a dictionnary of named parameters used to configure the store backend.

Attributes:

cachedir

Methods

cache([func, ignore, verbose, mmap_mode])	Decorates the given function func to only compute its return value for input arguments not cached on disk.
clear([warn])	Erase the complete cache directory.
eval(func, *args, **kwargs)	Eval function func with arguments *args and **kwargs, in the context of the memory.
format(obj[, indent])	Return the formatted representation of the object.
reduce_size()	Remove cache elements to make cache size fit in bytes_limit.

debug
warn

__init__(location=None, backend=’local’, cachedir=None, mmap_mode=None, compress=False, verbose=1, bytes_limit=None, backend_options=None) [source]

cache(func=None, ignore=None, verbose=None, mmap_mode=False) [source]

Decorates the given function func to only compute its return value for input arguments not cached on disk.

Parameters:

func: callable, optional The function to be decorated ignore: list of strings A list of arguments name to ignore in the hashing verbose: integer, optional The verbosity mode of the function. By default that of the memory object is used. mmap_mode: {None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optional The memmapping mode used when loading from cache numpy arrays. See numpy.load for the meaning of the arguments. By default that of the memory object is used.

Returns:

decorated_func: MemorizedFunc object The returned object is a MemorizedFunc object, that is callable (behaves like a function), but offers extra methods for cache lookup and management. See the documentation for joblib.memory.MemorizedFunc.

clear(warn=True) [source]

Erase the complete cache directory.

eval(func, *args, **kwargs) [source]

Eval function func with arguments *args and **kwargs, in the context of the memory.

This method works similarly to the builtin apply, except that the function is called only if the cache is not up to date.

format(obj, indent=0) [source]

Return the formatted representation of the object.

reduce_size() [source]

Remove cache elements to make cache size fit in bytes_limit.

Examples using sklearn.utils.Memory