scikit-learn/sklearn/externals/joblib/memory.py

575 lines
22 KiB
Python

"""
A context object for caching a function's return value each time it
is called with the same input arguments.
"""
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2009 Gael Varoquaux
# License: BSD Style, 3 clauses.
from __future__ import with_statement
import os
import shutil
import time
import pydoc
try:
import cPickle as pickle
except ImportError:
import pickle
import functools
import traceback
import warnings
import inspect
try:
# json is in the standard library for Python >= 2.6
import json
except ImportError:
try:
import simplejson as json
except ImportError:
# Not the end of the world: we'll do without this functionality
json = None
# Local imports
from .hashing import hash
from .func_inspect import get_func_code, get_func_name, filter_args
from .logger import Logger, format_time
from . import numpy_pickle
from .disk import mkdirp, rm_subdirs
FIRST_LINE_TEXT = "# first line:"
# TODO: The following object should have a data store object as a sub
# object, and the interface to persist and query should be separated in
# the data store.
#
# This would enable creating 'Memory' objects with a different logic for
# pickling that would simply span a MemorizedFunc with the same
# store (or do we want to copy it to avoid cross-talks?), for instance to
# implement HDF5 pickling.
# TODO: Same remark for the logger, and probably use the Python logging
# mechanism.
def extract_first_line(func_code):
""" Extract the first line information from the function code
text if available.
"""
if func_code.startswith(FIRST_LINE_TEXT):
func_code = func_code.split('\n')
first_line = int(func_code[0][len(FIRST_LINE_TEXT):])
func_code = '\n'.join(func_code[1:])
else:
first_line = -1
return func_code, first_line
class JobLibCollisionWarning(UserWarning):
""" Warn that there might be a collision between names of functions.
"""
###############################################################################
# class `MemorizedFunc`
###############################################################################
class MemorizedFunc(Logger):
""" Callable object decorating a function for caching its return value
each time it is called.
All values are cached on the filesystem, in a deep directory
structure. Methods are provided to inspect the cache or clean it.
Attributes
----------
func: callable
The original, undecorated, function.
cachedir: string
Path to the base cache directory of the memory context.
ignore: list or None
List of variable names to ignore when choosing whether to
recompute.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}
The memmapping mode used when loading from cache
numpy arrays. See numpy.load for the meaning of the
arguments.
compress: boolean
Whether to zip the stored data on disk. Note that compressed
arrays cannot be read by memmapping.
verbose: int, optional
The verbosity flag, controls messages that are issued as
the function is revaluated.
"""
#-------------------------------------------------------------------------
# Public interface
#-------------------------------------------------------------------------
def __init__(self, func, cachedir, ignore=None, mmap_mode=None,
compress=False, verbose=1, timestamp=None):
"""
Parameters
----------
func: callable
The function to decorate
cachedir: string
The path of the base directory to use as a data store
ignore: list or None
List of variable names to ignore.
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.
verbose: int, optional
Verbosity flag, controls the debug messages that are issued
as functions are revaluated. The higher, the more verbose
timestamp: float, optional
The reference time from which times in tracing messages
are reported.
"""
Logger.__init__(self)
self._verbose = verbose
self.cachedir = cachedir
self.func = func
self.mmap_mode = mmap_mode
self.compress = compress
if compress and mmap_mode is not None:
warnings.warn('Compressed results cannot be memmapped',
stacklevel=2)
if timestamp is None:
timestamp = time.time()
self.timestamp = timestamp
if ignore is None:
ignore = []
self.ignore = ignore
mkdirp(self.cachedir)
try:
functools.update_wrapper(self, func)
except:
" Objects like ufunc don't like that "
if inspect.isfunction(func):
doc = pydoc.TextDoc().document(func
).replace('\n', '\n\n', 1)
else:
# Pydoc does a poor job on other objects
doc = func.__doc__
self.__doc__ = 'Memoized version of %s' % doc
def __call__(self, *args, **kwargs):
# Compare the function code with the previous to see if the
# function code has changed
output_dir, argument_hash = self.get_output_dir(*args, **kwargs)
# FIXME: The statements below should be try/excepted
if not (self._check_previous_func_code(stacklevel=3) and
os.path.exists(output_dir)):
if self._verbose > 10:
_, name = get_func_name(self.func)
self.warn('Computing func %s, argument hash %s in '
'directory %s'
% (name, argument_hash, output_dir))
return self.call(*args, **kwargs)
else:
try:
t0 = time.time()
out = self.load_output(output_dir)
if self._verbose > 4:
t = time.time() - t0
_, name = get_func_name(self.func)
msg = '%s cache loaded - %s' % (name, format_time(t))
print max(0, (80 - len(msg))) * '_' + msg
return out
except Exception:
# XXX: Should use an exception logger
self.warn('Exception while loading results for '
'(args=%s, kwargs=%s)\n %s' %
(args, kwargs, traceback.format_exc()))
shutil.rmtree(output_dir, ignore_errors=True)
return self.call(*args, **kwargs)
def __reduce__(self):
""" We don't store the timestamp when pickling, to avoid the hash
depending from it.
In addition, when unpickling, we run the __init__
"""
return (self.__class__, (self.func, self.cachedir, self.ignore,
self.mmap_mode, self.compress, self._verbose))
#-------------------------------------------------------------------------
# Private interface
#-------------------------------------------------------------------------
def _get_func_dir(self, mkdir=True):
""" Get the directory corresponding to the cache for the
function.
"""
module, name = get_func_name(self.func)
module.append(name)
func_dir = os.path.join(self.cachedir, *module)
if mkdir:
mkdirp(func_dir)
return func_dir
def get_output_dir(self, *args, **kwargs):
""" Returns the directory in which are persisted the results
of the function corresponding to the given arguments.
The results can be loaded using the .load_output method.
"""
coerce_mmap = (self.mmap_mode is not None)
argument_hash = hash(filter_args(self.func, self.ignore,
args, kwargs),
coerce_mmap=coerce_mmap)
output_dir = os.path.join(self._get_func_dir(self.func),
argument_hash)
return output_dir, argument_hash
def _write_func_code(self, filename, func_code, first_line):
""" Write the function code and the filename to a file.
"""
func_code = '%s %i\n%s' % (FIRST_LINE_TEXT, first_line, func_code)
with open(filename, 'w') as out:
out.write(func_code)
def _check_previous_func_code(self, stacklevel=2):
"""
stacklevel is the depth a which this function is called, to
issue useful warnings to the user.
"""
# Here, we go through some effort to be robust to dynamically
# changing code and collision. We cannot inspect.getsource
# because it is not reliable when using IPython's magic "%run".
func_code, source_file, first_line = get_func_code(self.func)
func_dir = self._get_func_dir()
func_code_file = os.path.join(func_dir, 'func_code.py')
try:
with open(func_code_file) as infile:
old_func_code, old_first_line = \
extract_first_line(infile.read())
except IOError:
self._write_func_code(func_code_file, func_code, first_line)
return False
if old_func_code == func_code:
return True
# We have differing code, is this because we are refering to
# differing functions, or because the function we are refering as
# changed?
if old_first_line == first_line == -1:
_, func_name = get_func_name(self.func, resolv_alias=False,
win_characters=False)
if not first_line == -1:
func_description = '%s (%s:%i)' % (func_name,
source_file, first_line)
else:
func_description = func_name
warnings.warn(JobLibCollisionWarning(
"Cannot detect name collisions for function '%s'"
% func_description), stacklevel=stacklevel)
# Fetch the code at the old location and compare it. If it is the
# same than the code store, we have a collision: the code in the
# file has not changed, but the name we have is pointing to a new
# code block.
if (not old_first_line == first_line
and source_file is not None
and os.path.exists(source_file)):
_, func_name = get_func_name(self.func, resolv_alias=False)
num_lines = len(func_code.split('\n'))
on_disk_func_code = file(source_file).readlines()[
old_first_line - 1:old_first_line - 1 + num_lines - 1]
on_disk_func_code = ''.join(on_disk_func_code)
if on_disk_func_code.rstrip() == old_func_code.rstrip():
warnings.warn(JobLibCollisionWarning(
'Possible name collisions between functions '
"'%s' (%s:%i) and '%s' (%s:%i)" %
(func_name, source_file, old_first_line,
func_name, source_file, first_line)),
stacklevel=stacklevel)
# The function has changed, wipe the cache directory.
# XXX: Should be using warnings, and giving stacklevel
if self._verbose > 10:
_, func_name = get_func_name(self.func, resolv_alias=False)
self.warn("Function %s (stored in %s) has changed." %
(func_name, func_dir))
self.clear(warn=True)
return False
def clear(self, warn=True):
""" Empty the function's cache.
"""
func_dir = self._get_func_dir(mkdir=False)
if self._verbose and warn:
self.warn("Clearing cache %s" % func_dir)
if os.path.exists(func_dir):
shutil.rmtree(func_dir, ignore_errors=True)
mkdirp(func_dir)
func_code, _, first_line = get_func_code(self.func)
func_code_file = os.path.join(func_dir, 'func_code.py')
self._write_func_code(func_code_file, func_code, first_line)
def call(self, *args, **kwargs):
""" Force the execution of the function with the given arguments and
persist the output values.
"""
start_time = time.time()
output_dir, argument_hash = self.get_output_dir(*args, **kwargs)
if self._verbose:
print self.format_call(*args, **kwargs)
output = self.func(*args, **kwargs)
self._persist_output(output, output_dir)
duration = time.time() - start_time
if self._verbose:
_, name = get_func_name(self.func)
msg = '%s - %s' % (name, format_time(duration))
print max(0, (80 - len(msg))) * '_' + msg
return output
def format_call(self, *args, **kwds):
""" Returns a nicely formatted statement displaying the function
call with the given arguments.
"""
path, signature = self.format_signature(self.func, *args,
**kwds)
msg = '%s\n[Memory] Calling %s...\n%s' % (80 * '_', path, signature)
return msg
# XXX: Not using logging framework
#self.debug(msg)
def format_signature(self, func, *args, **kwds):
# XXX: This should be moved out to a function
# XXX: Should this use inspect.formatargvalues/formatargspec?
module, name = get_func_name(func)
module = [m for m in module if m]
if module:
module.append(name)
module_path = '.'.join(module)
else:
module_path = name
arg_str = list()
previous_length = 0
for arg in args:
arg = self.format(arg, indent=2)
if len(arg) > 1500:
arg = '%s...' % arg[:700]
if previous_length > 80:
arg = '\n%s' % arg
previous_length = len(arg)
arg_str.append(arg)
arg_str.extend(['%s=%s' % (v, self.format(i)) for v, i in
kwds.iteritems()])
arg_str = ', '.join(arg_str)
signature = '%s(%s)' % (name, arg_str)
return module_path, signature
# Make make public
def _persist_output(self, output, dir):
""" Persist the given output tuple in the directory.
"""
try:
mkdirp(dir)
filename = os.path.join(dir, 'output.pkl')
numpy_pickle.dump(output, filename, compress=self.compress)
if self._verbose > 10:
print 'Persisting in %s' % dir
except OSError:
" Race condition in the creation of the directory "
def _persist_input(self, output_dir, *args, **kwargs):
""" Save a small summary of the call using json format in the
output directory.
"""
argument_dict = filter_args(self.func, self.ignore,
args, kwargs)
input_repr = dict((k, repr(v)) for k, v in argument_dict.iteritems())
if json is not None:
# This can fail do to race-conditions with multiple
# concurrent joblibs removing the file or the directory
try:
mkdirp(output_dir)
json.dump(
input_repr,
file(os.path.join(output_dir, 'input_args.json'), 'w'),
)
except:
pass
return input_repr
def load_output(self, output_dir):
""" Read the results of a previous calculation from the directory
it was cached in.
"""
if self._verbose > 1:
t = time.time() - self.timestamp
if self._verbose < 10:
print '[Memory]% 16s: Loading %s...' % (
format_time(t),
self.format_signature(self.func)[0]
)
else:
print '[Memory]% 16s: Loading %s from %s' % (
format_time(t),
self.format_signature(self.func)[0],
output_dir
)
filename = os.path.join(output_dir, 'output.pkl')
return numpy_pickle.load(filename,
mmap_mode=self.mmap_mode)
# XXX: Need a method to check if results are available.
#-------------------------------------------------------------------------
# Private `object` interface
#-------------------------------------------------------------------------
def __repr__(self):
return '%s(func=%s, cachedir=%s)' % (
self.__class__.__name__,
self.func,
repr(self.cachedir),
)
###############################################################################
# class `Memory`
###############################################################################
class Memory(Logger):
""" 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.
see :ref:`memory_reference`
"""
#-------------------------------------------------------------------------
# Public interface
#-------------------------------------------------------------------------
def __init__(self, cachedir, mmap_mode=None, compress=False, verbose=1):
"""
Parameters
----------
cachedir: string 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.
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
Whether to zip the stored data on disk. Note that
compressed arrays cannot be read by memmapping.
verbose: int, optional
Verbosity flag, controls the debug messages that are issued
as functions are revaluated.
"""
# XXX: Bad explaination of the None value of cachedir
Logger.__init__(self)
self._verbose = verbose
self.mmap_mode = mmap_mode
self.timestamp = time.time()
self.compress = compress
if compress and mmap_mode is not None:
warnings.warn('Compressed results cannot be memmapped',
stacklevel=2)
if cachedir is None:
self.cachedir = None
else:
self.cachedir = os.path.join(cachedir, 'joblib')
mkdirp(self.cachedir)
def cache(self, func=None, ignore=None, verbose=None,
mmap_mode=False):
""" 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 :class:`joblib.memory.MemorizedFunc`.
"""
if func is None:
# Partial application, to be able to specify extra keyword
# arguments in decorators
return functools.partial(self.cache, ignore=ignore)
if self.cachedir is None:
return func
if verbose is None:
verbose = self._verbose
if mmap_mode is False:
mmap_mode = self.mmap_mode
if isinstance(func, MemorizedFunc):
func = func.func
return MemorizedFunc(func, cachedir=self.cachedir,
mmap_mode=mmap_mode,
ignore=ignore,
compress=self.compress,
verbose=verbose,
timestamp=self.timestamp)
def clear(self, warn=True):
""" Erase the complete cache directory.
"""
if warn:
self.warn('Flushing completely the cache')
rm_subdirs(self.cachedir)
def eval(self, func, *args, **kwargs):
""" 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.
"""
if self.cachedir is None:
return func(*args, **kwargs)
return self.cache(func)(*args, **kwargs)
#-------------------------------------------------------------------------
# Private `object` interface
#-------------------------------------------------------------------------
def __repr__(self):
return '%s(cachedir=%s)' % (
self.__class__.__name__,
repr(self.cachedir),
)
def __reduce__(self):
""" We don't store the timestamp when pickling, to avoid the hash
depending from it.
In addition, when unpickling, we run the __init__
"""
# We need to remove 'joblib' from the end of cachedir
cachedir = self.cachedir[:-7] if self.cachedir is not None else None
return (self.__class__, (cachedir,
self.mmap_mode, self.compress, self._verbose))