581 lines
23 KiB
Python
581 lines
23 KiB
Python
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"""Utilities for fast persistence of big data, with optional compression."""
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# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
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# Copyright (c) 2009 Gael Varoquaux
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# License: BSD Style, 3 clauses.
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import pickle
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import os
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import sys
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import warnings
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try:
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from pathlib import Path
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except ImportError:
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Path = None
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from .numpy_pickle_utils import _COMPRESSORS
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from .numpy_pickle_utils import BinaryZlibFile
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from .numpy_pickle_utils import Unpickler, Pickler
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from .numpy_pickle_utils import _read_fileobject, _write_fileobject
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from .numpy_pickle_utils import _read_bytes, BUFFER_SIZE
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from .numpy_pickle_compat import load_compatibility
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from .numpy_pickle_compat import NDArrayWrapper
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# For compatibility with old versions of joblib, we need ZNDArrayWrapper
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# to be visible in the current namespace.
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# Explicitly skipping next line from flake8 as it triggers an F401 warning
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# which we don't care.
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from .numpy_pickle_compat import ZNDArrayWrapper # noqa
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from ._compat import _basestring, PY3_OR_LATER
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from .backports import make_memmap
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###############################################################################
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# Utility objects for persistence.
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class NumpyArrayWrapper(object):
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"""An object to be persisted instead of numpy arrays.
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This object is used to hack into the pickle machinery and read numpy
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array data from our custom persistence format.
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More precisely, this object is used for:
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* carrying the information of the persisted array: subclass, shape, order,
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dtype. Those ndarray metadata are used to correctly reconstruct the array
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with low level numpy functions.
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* determining if memmap is allowed on the array.
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* reading the array bytes from a file.
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* reading the array using memorymap from a file.
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* writing the array bytes to a file.
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Attributes
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----------
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subclass: numpy.ndarray subclass
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Determine the subclass of the wrapped array.
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shape: numpy.ndarray shape
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Determine the shape of the wrapped array.
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order: {'C', 'F'}
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Determine the order of wrapped array data. 'C' is for C order, 'F' is
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for fortran order.
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dtype: numpy.ndarray dtype
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Determine the data type of the wrapped array.
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allow_mmap: bool
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Determine if memory mapping is allowed on the wrapped array.
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Default: False.
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"""
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def __init__(self, subclass, shape, order, dtype, allow_mmap=False):
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"""Constructor. Store the useful information for later."""
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self.subclass = subclass
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self.shape = shape
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self.order = order
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self.dtype = dtype
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self.allow_mmap = allow_mmap
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def write_array(self, array, pickler):
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"""Write array bytes to pickler file handle.
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This function is an adaptation of the numpy write_array function
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available in version 1.10.1 in numpy/lib/format.py.
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"""
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# Set buffer size to 16 MiB to hide the Python loop overhead.
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buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)
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if array.dtype.hasobject:
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# We contain Python objects so we cannot write out the data
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# directly. Instead, we will pickle it out with version 2 of the
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# pickle protocol.
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pickle.dump(array, pickler.file_handle, protocol=2)
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else:
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for chunk in pickler.np.nditer(array,
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flags=['external_loop',
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'buffered',
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'zerosize_ok'],
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buffersize=buffersize,
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order=self.order):
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pickler.file_handle.write(chunk.tostring('C'))
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def read_array(self, unpickler):
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"""Read array from unpickler file handle.
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This function is an adaptation of the numpy read_array function
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available in version 1.10.1 in numpy/lib/format.py.
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"""
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if len(self.shape) == 0:
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count = 1
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else:
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count = unpickler.np.multiply.reduce(self.shape)
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# Now read the actual data.
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if self.dtype.hasobject:
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# The array contained Python objects. We need to unpickle the data.
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array = pickle.load(unpickler.file_handle)
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else:
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if (not PY3_OR_LATER and
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unpickler.np.compat.isfileobj(unpickler.file_handle)):
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# In python 2, gzip.GzipFile is considered as a file so one
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# can use numpy.fromfile().
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# For file objects, use np.fromfile function.
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# This function is faster than the memory-intensive
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# method below.
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array = unpickler.np.fromfile(unpickler.file_handle,
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dtype=self.dtype, count=count)
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else:
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# This is not a real file. We have to read it the
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# memory-intensive way.
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# crc32 module fails on reads greater than 2 ** 32 bytes,
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# breaking large reads from gzip streams. Chunk reads to
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# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
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# of the read. In non-chunked case count < max_read_count, so
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# only one read is performed.
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max_read_count = BUFFER_SIZE // min(BUFFER_SIZE,
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self.dtype.itemsize)
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array = unpickler.np.empty(count, dtype=self.dtype)
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for i in range(0, count, max_read_count):
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read_count = min(max_read_count, count - i)
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read_size = int(read_count * self.dtype.itemsize)
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data = _read_bytes(unpickler.file_handle,
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read_size, "array data")
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array[i:i + read_count] = \
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unpickler.np.frombuffer(data, dtype=self.dtype,
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count=read_count)
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del data
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if self.order == 'F':
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array.shape = self.shape[::-1]
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array = array.transpose()
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else:
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array.shape = self.shape
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return array
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def read_mmap(self, unpickler):
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"""Read an array using numpy memmap."""
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offset = unpickler.file_handle.tell()
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if unpickler.mmap_mode == 'w+':
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unpickler.mmap_mode = 'r+'
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marray = make_memmap(unpickler.filename,
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dtype=self.dtype,
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shape=self.shape,
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order=self.order,
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mode=unpickler.mmap_mode,
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offset=offset)
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# update the offset so that it corresponds to the end of the read array
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unpickler.file_handle.seek(offset + marray.nbytes)
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return marray
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def read(self, unpickler):
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"""Read the array corresponding to this wrapper.
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Use the unpickler to get all information to correctly read the array.
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Parameters
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----------
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unpickler: NumpyUnpickler
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Returns
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-------
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array: numpy.ndarray
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"""
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# When requested, only use memmap mode if allowed.
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if unpickler.mmap_mode is not None and self.allow_mmap:
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array = self.read_mmap(unpickler)
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else:
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array = self.read_array(unpickler)
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# Manage array subclass case
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if (hasattr(array, '__array_prepare__') and
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self.subclass not in (unpickler.np.ndarray,
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unpickler.np.memmap)):
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# We need to reconstruct another subclass
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new_array = unpickler.np.core.multiarray._reconstruct(
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self.subclass, (0,), 'b')
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return new_array.__array_prepare__(array)
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else:
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return array
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###############################################################################
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# Pickler classes
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class NumpyPickler(Pickler):
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"""A pickler to persist big data efficiently.
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The main features of this object are:
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* persistence of numpy arrays in a single file.
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* optional compression with a special care on avoiding memory copies.
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Attributes
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----------
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fp: file
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File object handle used for serializing the input object.
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protocol: int
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Pickle protocol used. Default is pickle.DEFAULT_PROTOCOL under
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python 3, pickle.HIGHEST_PROTOCOL otherwise.
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"""
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dispatch = Pickler.dispatch.copy()
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def __init__(self, fp, protocol=None):
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self.file_handle = fp
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self.buffered = isinstance(self.file_handle, BinaryZlibFile)
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# By default we want a pickle protocol that only changes with
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# the major python version and not the minor one
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if protocol is None:
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protocol = (pickle.DEFAULT_PROTOCOL if PY3_OR_LATER
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else pickle.HIGHEST_PROTOCOL)
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Pickler.__init__(self, self.file_handle, protocol=protocol)
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# delayed import of numpy, to avoid tight coupling
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try:
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import numpy as np
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except ImportError:
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np = None
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self.np = np
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def _create_array_wrapper(self, array):
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"""Create and returns a numpy array wrapper from a numpy array."""
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order = 'F' if (array.flags.f_contiguous and
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not array.flags.c_contiguous) else 'C'
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allow_mmap = not self.buffered and not array.dtype.hasobject
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wrapper = NumpyArrayWrapper(type(array),
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array.shape, order, array.dtype,
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allow_mmap=allow_mmap)
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return wrapper
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def save(self, obj):
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"""Subclass the Pickler `save` method.
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This is a total abuse of the Pickler class in order to use the numpy
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persistence function `save` instead of the default pickle
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implementation. The numpy array is replaced by a custom wrapper in the
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pickle persistence stack and the serialized array is written right
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after in the file. Warning: the file produced does not follow the
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pickle format. As such it can not be read with `pickle.load`.
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"""
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if self.np is not None and type(obj) in (self.np.ndarray,
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self.np.matrix,
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self.np.memmap):
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if type(obj) is self.np.memmap:
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# Pickling doesn't work with memmapped arrays
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obj = self.np.asanyarray(obj)
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# The array wrapper is pickled instead of the real array.
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wrapper = self._create_array_wrapper(obj)
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Pickler.save(self, wrapper)
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# A framer was introduced with pickle protocol 4 and we want to
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# ensure the wrapper object is written before the numpy array
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# buffer in the pickle file.
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# See https://www.python.org/dev/peps/pep-3154/#framing to get
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# more information on the framer behavior.
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if self.proto >= 4:
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self.framer.commit_frame(force=True)
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# And then array bytes are written right after the wrapper.
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wrapper.write_array(obj, self)
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return
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return Pickler.save(self, obj)
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class NumpyUnpickler(Unpickler):
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"""A subclass of the Unpickler to unpickle our numpy pickles.
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Attributes
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----------
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mmap_mode: str
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The memorymap mode to use for reading numpy arrays.
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file_handle: file_like
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File object to unpickle from.
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filename: str
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Name of the file to unpickle from. It should correspond to file_handle.
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This parameter is required when using mmap_mode.
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np: module
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Reference to numpy module if numpy is installed else None.
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"""
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dispatch = Unpickler.dispatch.copy()
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def __init__(self, filename, file_handle, mmap_mode=None):
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# The next line is for backward compatibility with pickle generated
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# with joblib versions less than 0.10.
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self._dirname = os.path.dirname(filename)
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self.mmap_mode = mmap_mode
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self.file_handle = file_handle
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# filename is required for numpy mmap mode.
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self.filename = filename
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self.compat_mode = False
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Unpickler.__init__(self, self.file_handle)
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try:
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import numpy as np
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except ImportError:
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np = None
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self.np = np
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def load_build(self):
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"""Called to set the state of a newly created object.
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We capture it to replace our place-holder objects, NDArrayWrapper or
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NumpyArrayWrapper, by the array we are interested in. We
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replace them directly in the stack of pickler.
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NDArrayWrapper is used for backward compatibility with joblib <= 0.9.
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"""
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Unpickler.load_build(self)
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# For backward compatibility, we support NDArrayWrapper objects.
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if isinstance(self.stack[-1], (NDArrayWrapper, NumpyArrayWrapper)):
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if self.np is None:
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raise ImportError("Trying to unpickle an ndarray, "
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"but numpy didn't import correctly")
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array_wrapper = self.stack.pop()
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# If any NDArrayWrapper is found, we switch to compatibility mode,
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# this will be used to raise a DeprecationWarning to the user at
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# the end of the unpickling.
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if isinstance(array_wrapper, NDArrayWrapper):
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self.compat_mode = True
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self.stack.append(array_wrapper.read(self))
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# Be careful to register our new method.
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if PY3_OR_LATER:
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dispatch[pickle.BUILD[0]] = load_build
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else:
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dispatch[pickle.BUILD] = load_build
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###############################################################################
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# Utility functions
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||
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def dump(value, filename, compress=0, protocol=None, cache_size=None):
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|
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"""Persist an arbitrary Python object into one file.
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Parameters
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-----------
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value: any Python object
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The object to store to disk.
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filename: str or pathlib.Path
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The path of the file in which it is to be stored. The compression
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method corresponding to one of the supported filename extensions ('.z',
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'.gz', '.bz2', '.xz' or '.lzma') will be used automatically.
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compress: int from 0 to 9 or bool or 2-tuple, optional
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Optional compression level for the data. 0 or False is no compression.
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Higher value means more compression, but also slower read and
|
||
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write times. Using a value of 3 is often a good compromise.
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See the notes for more details.
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If compress is True, the compression level used is 3.
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|
If compress is a 2-tuple, the first element must correspond to a string
|
||
|
|
between supported compressors (e.g 'zlib', 'gzip', 'bz2', 'lzma'
|
||
|
|
'xz'), the second element must be an integer from 0 to 9, corresponding
|
||
|
|
to the compression level.
|
||
|
|
protocol: positive int
|
||
|
|
Pickle protocol, see pickle.dump documentation for more details.
|
||
|
|
cache_size: positive int, optional
|
||
|
|
This option is deprecated in 0.10 and has no effect.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
filenames: list of strings
|
||
|
|
The list of file names in which the data is stored. If
|
||
|
|
compress is false, each array is stored in a different file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
joblib.load : corresponding loader
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Memmapping on load cannot be used for compressed files. Thus
|
||
|
|
using compression can significantly slow down loading. In
|
||
|
|
addition, compressed files take extra extra memory during
|
||
|
|
dump and load.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
if Path is not None and isinstance(filename, Path):
|
||
|
|
filename = str(filename)
|
||
|
|
|
||
|
|
is_filename = isinstance(filename, _basestring)
|
||
|
|
is_fileobj = hasattr(filename, "write")
|
||
|
|
|
||
|
|
compress_method = 'zlib' # zlib is the default compression method.
|
||
|
|
if compress is True:
|
||
|
|
# By default, if compress is enabled, we want to be using 3 by default
|
||
|
|
compress_level = 3
|
||
|
|
elif isinstance(compress, tuple):
|
||
|
|
# a 2-tuple was set in compress
|
||
|
|
if len(compress) != 2:
|
||
|
|
raise ValueError(
|
||
|
|
'Compress argument tuple should contain exactly 2 elements: '
|
||
|
|
'(compress method, compress level), you passed {}'
|
||
|
|
.format(compress))
|
||
|
|
compress_method, compress_level = compress
|
||
|
|
else:
|
||
|
|
compress_level = compress
|
||
|
|
|
||
|
|
if compress_level is not False and compress_level not in range(10):
|
||
|
|
# Raising an error if a non valid compress level is given.
|
||
|
|
raise ValueError(
|
||
|
|
'Non valid compress level given: "{}". Possible values are '
|
||
|
|
'{}.'.format(compress_level, list(range(10))))
|
||
|
|
|
||
|
|
if compress_method not in _COMPRESSORS:
|
||
|
|
# Raising an error if an unsupported compression method is given.
|
||
|
|
raise ValueError(
|
||
|
|
'Non valid compression method given: "{}". Possible values are '
|
||
|
|
'{}.'.format(compress_method, _COMPRESSORS))
|
||
|
|
|
||
|
|
if not is_filename and not is_fileobj:
|
||
|
|
# People keep inverting arguments, and the resulting error is
|
||
|
|
# incomprehensible
|
||
|
|
raise ValueError(
|
||
|
|
'Second argument should be a filename or a file-like object, '
|
||
|
|
'%s (type %s) was given.'
|
||
|
|
% (filename, type(filename))
|
||
|
|
)
|
||
|
|
|
||
|
|
if is_filename and not isinstance(compress, tuple):
|
||
|
|
# In case no explicit compression was requested using both compression
|
||
|
|
# method and level in a tuple and the filename has an explicit
|
||
|
|
# extension, we select the corresponding compressor.
|
||
|
|
if filename.endswith('.z'):
|
||
|
|
compress_method = 'zlib'
|
||
|
|
elif filename.endswith('.gz'):
|
||
|
|
compress_method = 'gzip'
|
||
|
|
elif filename.endswith('.bz2'):
|
||
|
|
compress_method = 'bz2'
|
||
|
|
elif filename.endswith('.lzma'):
|
||
|
|
compress_method = 'lzma'
|
||
|
|
elif filename.endswith('.xz'):
|
||
|
|
compress_method = 'xz'
|
||
|
|
else:
|
||
|
|
# no matching compression method found, we unset the variable to
|
||
|
|
# be sure no compression level is set afterwards.
|
||
|
|
compress_method = None
|
||
|
|
|
||
|
|
if compress_method in _COMPRESSORS and compress_level == 0:
|
||
|
|
# we choose a default compress_level of 3 in case it was not given
|
||
|
|
# as an argument (using compress).
|
||
|
|
compress_level = 3
|
||
|
|
|
||
|
|
if not PY3_OR_LATER and compress_method in ('lzma', 'xz'):
|
||
|
|
raise NotImplementedError("{} compression is only available for "
|
||
|
|
"python version >= 3.3. You are using "
|
||
|
|
"{}.{}".format(compress_method,
|
||
|
|
sys.version_info[0],
|
||
|
|
sys.version_info[1]))
|
||
|
|
|
||
|
|
if cache_size is not None:
|
||
|
|
# Cache size is deprecated starting from version 0.10
|
||
|
|
warnings.warn("Please do not set 'cache_size' in joblib.dump, "
|
||
|
|
"this parameter has no effect and will be removed. "
|
||
|
|
"You used 'cache_size={}'".format(cache_size),
|
||
|
|
DeprecationWarning, stacklevel=2)
|
||
|
|
|
||
|
|
if compress_level != 0:
|
||
|
|
with _write_fileobject(filename, compress=(compress_method,
|
||
|
|
compress_level)) as f:
|
||
|
|
NumpyPickler(f, protocol=protocol).dump(value)
|
||
|
|
elif is_filename:
|
||
|
|
with open(filename, 'wb') as f:
|
||
|
|
NumpyPickler(f, protocol=protocol).dump(value)
|
||
|
|
else:
|
||
|
|
NumpyPickler(filename, protocol=protocol).dump(value)
|
||
|
|
|
||
|
|
# If the target container is a file object, nothing is returned.
|
||
|
|
if is_fileobj:
|
||
|
|
return
|
||
|
|
|
||
|
|
# For compatibility, the list of created filenames (e.g with one element
|
||
|
|
# after 0.10.0) is returned by default.
|
||
|
|
return [filename]
|
||
|
|
|
||
|
|
|
||
|
|
def _unpickle(fobj, filename="", mmap_mode=None):
|
||
|
|
"""Internal unpickling function."""
|
||
|
|
# We are careful to open the file handle early and keep it open to
|
||
|
|
# avoid race-conditions on renames.
|
||
|
|
# That said, if data is stored in companion files, which can be
|
||
|
|
# the case with the old persistence format, moving the directory
|
||
|
|
# will create a race when joblib tries to access the companion
|
||
|
|
# files.
|
||
|
|
unpickler = NumpyUnpickler(filename, fobj, mmap_mode=mmap_mode)
|
||
|
|
obj = None
|
||
|
|
try:
|
||
|
|
obj = unpickler.load()
|
||
|
|
if unpickler.compat_mode:
|
||
|
|
warnings.warn("The file '%s' has been generated with a "
|
||
|
|
"joblib version less than 0.10. "
|
||
|
|
"Please regenerate this pickle file."
|
||
|
|
% filename,
|
||
|
|
DeprecationWarning, stacklevel=3)
|
||
|
|
except UnicodeDecodeError as exc:
|
||
|
|
# More user-friendly error message
|
||
|
|
if PY3_OR_LATER:
|
||
|
|
new_exc = ValueError(
|
||
|
|
'You may be trying to read with '
|
||
|
|
'python 3 a joblib pickle generated with python 2. '
|
||
|
|
'This feature is not supported by joblib.')
|
||
|
|
new_exc.__cause__ = exc
|
||
|
|
raise new_exc
|
||
|
|
# Reraise exception with Python 2
|
||
|
|
raise
|
||
|
|
|
||
|
|
return obj
|
||
|
|
|
||
|
|
|
||
|
|
def load(filename, mmap_mode=None):
|
||
|
|
"""Reconstruct a Python object from a file persisted with joblib.dump.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
-----------
|
||
|
|
filename: str or pathlib.Path
|
||
|
|
The path of the file from which to load the object
|
||
|
|
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
|
||
|
|
If not None, the arrays are memory-mapped from the disk. This
|
||
|
|
mode has no effect for compressed files. Note that in this
|
||
|
|
case the reconstructed object might not longer match exactly
|
||
|
|
the originally pickled object.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
result: any Python object
|
||
|
|
The object stored in the file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
joblib.dump : function to save an object
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
This function can load numpy array files saved separately during the
|
||
|
|
dump. If the mmap_mode argument is given, it is passed to np.load and
|
||
|
|
arrays are loaded as memmaps. As a consequence, the reconstructed
|
||
|
|
object might not match the original pickled object. Note that if the
|
||
|
|
file was saved with compression, the arrays cannot be memmaped.
|
||
|
|
"""
|
||
|
|
if Path is not None and isinstance(filename, Path):
|
||
|
|
filename = str(filename)
|
||
|
|
|
||
|
|
if hasattr(filename, "read"):
|
||
|
|
fobj = filename
|
||
|
|
filename = getattr(fobj, 'name', '')
|
||
|
|
with _read_fileobject(fobj, filename, mmap_mode) as fobj:
|
||
|
|
obj = _unpickle(fobj)
|
||
|
|
else:
|
||
|
|
with open(filename, 'rb') as f:
|
||
|
|
with _read_fileobject(f, filename, mmap_mode) as fobj:
|
||
|
|
if isinstance(fobj, _basestring):
|
||
|
|
# if the returned file object is a string, this means we
|
||
|
|
# try to load a pickle file generated with an version of
|
||
|
|
# Joblib so we load it with joblib compatibility function.
|
||
|
|
return load_compatibility(fobj)
|
||
|
|
|
||
|
|
obj = _unpickle(fobj, filename, mmap_mode)
|
||
|
|
|
||
|
|
return obj
|