Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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199 lines
6.4 KiB
199 lines
6.4 KiB
""" pickle compat """
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import pickle
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from typing import Any, Optional
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import warnings
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from pandas._typing import FilePathOrBuffer
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from pandas.compat import pickle_compat as pc
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from pandas.io.common import get_filepath_or_buffer, get_handle
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def to_pickle(
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obj: Any,
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filepath_or_buffer: FilePathOrBuffer,
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compression: Optional[str] = "infer",
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protocol: int = pickle.HIGHEST_PROTOCOL,
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):
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"""
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Pickle (serialize) object to file.
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Parameters
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----------
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obj : any object
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Any python object.
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filepath_or_buffer : str, path object or file-like object
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File path, URL, or buffer where the pickled object will be stored.
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.. versionchanged:: 1.0.0
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Accept URL. URL has to be of S3 or GCS.
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compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
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If 'infer' and 'path_or_url' is path-like, then detect compression from
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the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
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compression) If 'infer' and 'path_or_url' is not path-like, then use
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None (= no decompression).
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protocol : int
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Int which indicates which protocol should be used by the pickler,
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default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
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values for this parameter depend on the version of Python. For Python
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2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
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For Python >= 3.4, 4 is a valid value. A negative value for the
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protocol parameter is equivalent to setting its value to
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HIGHEST_PROTOCOL.
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.. [1] https://docs.python.org/3/library/pickle.html
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See Also
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--------
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read_pickle : Load pickled pandas object (or any object) from file.
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DataFrame.to_hdf : Write DataFrame to an HDF5 file.
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DataFrame.to_sql : Write DataFrame to a SQL database.
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DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
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Examples
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--------
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>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
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>>> original_df
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> pd.to_pickle(original_df, "./dummy.pkl")
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>>> unpickled_df = pd.read_pickle("./dummy.pkl")
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>>> unpickled_df
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> import os
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>>> os.remove("./dummy.pkl")
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"""
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fp_or_buf, _, compression, should_close = get_filepath_or_buffer(
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filepath_or_buffer, compression=compression, mode="wb"
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)
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if not isinstance(fp_or_buf, str) and compression == "infer":
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compression = None
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f, fh = get_handle(fp_or_buf, "wb", compression=compression, is_text=False)
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if protocol < 0:
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protocol = pickle.HIGHEST_PROTOCOL
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try:
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f.write(pickle.dumps(obj, protocol=protocol))
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finally:
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f.close()
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for _f in fh:
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_f.close()
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if should_close:
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try:
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fp_or_buf.close()
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except ValueError:
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pass
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def read_pickle(
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filepath_or_buffer: FilePathOrBuffer, compression: Optional[str] = "infer"
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):
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"""
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Load pickled pandas object (or any object) from file.
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.. warning::
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Loading pickled data received from untrusted sources can be
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unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
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Parameters
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----------
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filepath_or_buffer : str, path object or file-like object
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File path, URL, or buffer where the pickled object will be loaded from.
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.. versionchanged:: 1.0.0
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Accept URL. URL is not limited to S3 and GCS.
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compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
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If 'infer' and 'path_or_url' is path-like, then detect compression from
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the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
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compression) If 'infer' and 'path_or_url' is not path-like, then use
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None (= no decompression).
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Returns
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-------
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unpickled : same type as object stored in file
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See Also
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--------
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DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
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Series.to_pickle : Pickle (serialize) Series object to file.
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read_hdf : Read HDF5 file into a DataFrame.
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read_sql : Read SQL query or database table into a DataFrame.
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read_parquet : Load a parquet object, returning a DataFrame.
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Notes
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-----
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read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3.
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Examples
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--------
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>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
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>>> original_df
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> pd.to_pickle(original_df, "./dummy.pkl")
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>>> unpickled_df = pd.read_pickle("./dummy.pkl")
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>>> unpickled_df
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> import os
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>>> os.remove("./dummy.pkl")
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"""
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fp_or_buf, _, compression, should_close = get_filepath_or_buffer(
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filepath_or_buffer, compression=compression
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)
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if not isinstance(fp_or_buf, str) and compression == "infer":
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compression = None
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f, fh = get_handle(fp_or_buf, "rb", compression=compression, is_text=False)
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# 1) try standard library Pickle
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# 2) try pickle_compat (older pandas version) to handle subclass changes
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# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
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try:
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excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError, TypeError)
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# TypeError for Cython complaints about object.__new__ vs Tick.__new__
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try:
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with warnings.catch_warnings(record=True):
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# We want to silence any warnings about, e.g. moved modules.
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warnings.simplefilter("ignore", Warning)
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return pickle.load(f)
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except excs_to_catch:
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# e.g.
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# "No module named 'pandas.core.sparse.series'"
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# "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
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return pc.load(f, encoding=None)
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except UnicodeDecodeError:
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# e.g. can occur for files written in py27; see GH#28645 and GH#31988
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return pc.load(f, encoding="latin-1")
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finally:
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f.close()
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for _f in fh:
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_f.close()
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if should_close:
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try:
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fp_or_buf.close()
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except ValueError:
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pass
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