Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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280 lines
7.7 KiB
280 lines
7.7 KiB
"""
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Support pre-0.12 series pickle compatibility.
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"""
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import contextlib
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import copy
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import io
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import pickle as pkl
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from typing import TYPE_CHECKING, Optional
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import warnings
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from pandas._libs.tslibs import BaseOffset
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from pandas import Index
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if TYPE_CHECKING:
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from pandas import DataFrame, Series
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def load_reduce(self):
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stack = self.stack
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args = stack.pop()
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func = stack[-1]
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if len(args) and type(args[0]) is type:
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n = args[0].__name__ # noqa
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try:
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stack[-1] = func(*args)
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return
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except TypeError as err:
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# If we have a deprecated function,
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# try to replace and try again.
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msg = "_reconstruct: First argument must be a sub-type of ndarray"
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if msg in str(err):
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try:
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cls = args[0]
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stack[-1] = object.__new__(cls)
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return
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except TypeError:
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pass
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elif args and issubclass(args[0], BaseOffset):
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# TypeError: object.__new__(Day) is not safe, use Day.__new__()
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cls = args[0]
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stack[-1] = cls.__new__(*args)
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return
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raise
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_sparse_msg = """\
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Loading a saved '{cls}' as a {new} with sparse values.
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'{cls}' is now removed. You should re-save this dataset in its new format.
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"""
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class _LoadSparseSeries:
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# To load a SparseSeries as a Series[Sparse]
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# https://github.com/python/mypy/issues/1020
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# error: Incompatible return type for "__new__" (returns "Series", but must return
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# a subtype of "_LoadSparseSeries")
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def __new__(cls) -> "Series": # type: ignore
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from pandas import Series
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warnings.warn(
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_sparse_msg.format(cls="SparseSeries", new="Series"),
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FutureWarning,
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stacklevel=6,
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)
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return Series(dtype=object)
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class _LoadSparseFrame:
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# To load a SparseDataFrame as a DataFrame[Sparse]
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# https://github.com/python/mypy/issues/1020
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# error: Incompatible return type for "__new__" (returns "DataFrame", but must
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# return a subtype of "_LoadSparseFrame")
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def __new__(cls) -> "DataFrame": # type: ignore
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from pandas import DataFrame
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warnings.warn(
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_sparse_msg.format(cls="SparseDataFrame", new="DataFrame"),
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FutureWarning,
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stacklevel=6,
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)
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return DataFrame()
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# If classes are moved, provide compat here.
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_class_locations_map = {
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("pandas.core.sparse.array", "SparseArray"): ("pandas.core.arrays", "SparseArray"),
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# 15477
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("pandas.core.base", "FrozenNDArray"): ("numpy", "ndarray"),
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("pandas.core.indexes.frozen", "FrozenNDArray"): ("numpy", "ndarray"),
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("pandas.core.base", "FrozenList"): ("pandas.core.indexes.frozen", "FrozenList"),
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# 10890
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("pandas.core.series", "TimeSeries"): ("pandas.core.series", "Series"),
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("pandas.sparse.series", "SparseTimeSeries"): (
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"pandas.core.sparse.series",
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"SparseSeries",
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),
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# 12588, extensions moving
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("pandas._sparse", "BlockIndex"): ("pandas._libs.sparse", "BlockIndex"),
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("pandas.tslib", "Timestamp"): ("pandas._libs.tslib", "Timestamp"),
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# 18543 moving period
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("pandas._period", "Period"): ("pandas._libs.tslibs.period", "Period"),
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("pandas._libs.period", "Period"): ("pandas._libs.tslibs.period", "Period"),
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# 18014 moved __nat_unpickle from _libs.tslib-->_libs.tslibs.nattype
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("pandas.tslib", "__nat_unpickle"): (
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"pandas._libs.tslibs.nattype",
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"__nat_unpickle",
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),
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("pandas._libs.tslib", "__nat_unpickle"): (
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"pandas._libs.tslibs.nattype",
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"__nat_unpickle",
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),
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# 15998 top-level dirs moving
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("pandas.sparse.array", "SparseArray"): (
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"pandas.core.arrays.sparse",
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"SparseArray",
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),
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("pandas.sparse.series", "SparseSeries"): (
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"pandas.compat.pickle_compat",
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"_LoadSparseSeries",
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),
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("pandas.sparse.frame", "SparseDataFrame"): (
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"pandas.core.sparse.frame",
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"_LoadSparseFrame",
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),
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("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"),
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("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"),
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("pandas.indexes.numeric", "Int64Index"): (
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"pandas.core.indexes.numeric",
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"Int64Index",
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),
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("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"),
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("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"),
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("pandas.tseries.index", "_new_DatetimeIndex"): (
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"pandas.core.indexes.datetimes",
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"_new_DatetimeIndex",
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),
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("pandas.tseries.index", "DatetimeIndex"): (
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"pandas.core.indexes.datetimes",
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"DatetimeIndex",
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),
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("pandas.tseries.period", "PeriodIndex"): (
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"pandas.core.indexes.period",
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"PeriodIndex",
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),
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# 19269, arrays moving
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("pandas.core.categorical", "Categorical"): ("pandas.core.arrays", "Categorical"),
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# 19939, add timedeltaindex, float64index compat from 15998 move
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("pandas.tseries.tdi", "TimedeltaIndex"): (
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"pandas.core.indexes.timedeltas",
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"TimedeltaIndex",
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),
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("pandas.indexes.numeric", "Float64Index"): (
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"pandas.core.indexes.numeric",
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"Float64Index",
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),
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("pandas.core.sparse.series", "SparseSeries"): (
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"pandas.compat.pickle_compat",
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"_LoadSparseSeries",
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),
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("pandas.core.sparse.frame", "SparseDataFrame"): (
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"pandas.compat.pickle_compat",
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"_LoadSparseFrame",
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),
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}
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# our Unpickler sub-class to override methods and some dispatcher
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# functions for compat and uses a non-public class of the pickle module.
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# error: Name 'pkl._Unpickler' is not defined
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class Unpickler(pkl._Unpickler): # type: ignore
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def find_class(self, module, name):
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# override superclass
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key = (module, name)
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module, name = _class_locations_map.get(key, key)
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return super().find_class(module, name)
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Unpickler.dispatch = copy.copy(Unpickler.dispatch)
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Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce
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def load_newobj(self):
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args = self.stack.pop()
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cls = self.stack[-1]
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# compat
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if issubclass(cls, Index):
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obj = object.__new__(cls)
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else:
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obj = cls.__new__(cls, *args)
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self.stack[-1] = obj
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Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj
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def load_newobj_ex(self):
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kwargs = self.stack.pop()
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args = self.stack.pop()
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cls = self.stack.pop()
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# compat
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if issubclass(cls, Index):
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obj = object.__new__(cls)
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else:
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obj = cls.__new__(cls, *args, **kwargs)
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self.append(obj)
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try:
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Unpickler.dispatch[pkl.NEWOBJ_EX[0]] = load_newobj_ex
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except (AttributeError, KeyError):
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pass
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def load(fh, encoding: Optional[str] = None, is_verbose: bool = False):
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"""
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Load a pickle, with a provided encoding,
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Parameters
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----------
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fh : a filelike object
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encoding : an optional encoding
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is_verbose : show exception output
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"""
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try:
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fh.seek(0)
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if encoding is not None:
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up = Unpickler(fh, encoding=encoding)
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else:
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up = Unpickler(fh)
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up.is_verbose = is_verbose
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return up.load()
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except (ValueError, TypeError):
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raise
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def loads(
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bytes_object: bytes,
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*,
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fix_imports: bool = True,
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encoding: str = "ASCII",
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errors: str = "strict",
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):
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"""
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Analogous to pickle._loads.
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"""
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fd = io.BytesIO(bytes_object)
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return Unpickler(
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fd, fix_imports=fix_imports, encoding=encoding, errors=errors
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).load()
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@contextlib.contextmanager
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def patch_pickle():
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"""
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Temporarily patch pickle to use our unpickler.
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"""
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orig_loads = pkl.loads
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try:
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pkl.loads = loads
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yield
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finally:
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pkl.loads = orig_loads
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