AttributeError with -w and MatplotlibDeprecationWarning

[NekoAlosama]

Using py -3 easylist_pac.py -w on Windows 10 produces an error (Python version 3.7.8, Windows version 10.0.18362.1016):

Traceback (most recent call last):
  File "easylist_pac.py", line 2270, in <module>
    res = EasyListPAC()
  File "easylist_pac.py", line 63, in __init__
    self.prioritize_rules()
  File "easylist_pac.py", line 252, in prioritize_rules
    self.logreg_priorities()
  File "easylist_pac.py", line 312, in logreg_priorities
    if self.sliding_window: self.logreg_sliding_window()
  File "easylist_pac.py", line 432, in logreg_sliding_window
    p.start()
  File "C:\Program Files\Python37\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)
  File "C:\Program Files\Python37\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Program Files\Python37\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Program Files\Python37\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Program Files\Python37\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'EasyListPAC.logreg_sliding_window.<locals>.training_op'

Then there is the MatplotlibDeprecationWarning (Matplotlib version 1.15.0):

easylist_pac.py:50: MatplotlibDeprecationWarning: Support for setting the 'text.latex.preamble' or 'pgf.preamble' rcParam to a list of strings is deprecated since 3.3 and will be removed two minor releases later; set it to a single string instead.
  mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath,sfmath} \\boldmath']

I'm not familiar with using Matplotlib. @essandess

[NekoAlosama]

@essandess

[NekoAlosama]

A fix for the MatplotlibDeprecationWarning is a now a pull request.

[NekoAlosama]

@essandess Did you ever get -w/--sliding-window to work for you?

[NekoAlosama]

I'm using my own fork of this repo, but I'm just going to say that there are a lot of deprecation warnings from NumPy 1.20:
easylist_pac.py:242: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.good_signal = np.array([self.good_class_test(x,opts) for (x,opts,f) in zip(self.good_rules,self.good_opts,self.good_rules_include_flag) if f], dtype=np.int) easylist_pac.py:243: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.bad_signal = np.array([self.bad_class_test(x,opts) for (x,opts,f) in zip(self.bad_rules,self.bad_opts,self.bad_rules_include_flag) if f], dtype=np.int) Performing logistic regression on rule sets. This will take a few minutes…easylist_pac.py:1407: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations fv_mat = sps.coo_matrix((vals,(cols,rows)),shape=(len(rules),len(feature_vector)),dtype=np.float).tocsr() easylist_pac.py:299: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.good_X_all = StandardScaler(with_mean=False).fit_transform(self.good_fv_mat.astype(np.float)) easylist_pac.py:300: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.good_y_all = np.array([self.good_class_test(x,opts) for (x,opts) in zip(self.good_rules, self.good_opts)], dtype=np.int) easylist_pac.py:302: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.bad_X_all = StandardScaler(with_mean=False).fit_transform(self.bad_fv_mat.astype(np.float)) easylist_pac.py:303: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations self.bad_y_all = np.array([self.bad_class_test(x,opts) for (x,opts) in zip(self.bad_rules, self.bad_opts)], dtype=np.int) done. easylist_pac.py:257: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if isinstance(self.good_rule_max,(int,np.int)) else np.count_nonzero(self.good_signal > 0) easylist_pac.py:259: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if isinstance(self.bad_rule_max,(int,np.int)) else np.count_nonzero(self.bad_signal > 0) easylist_pac.py:1202: UserWarning: Truncating regex alternatives rule set 'bad_da_hostpath_regex' from 836 to 499. warnings.warn("Truncating regex alternatives rule set '{}' from {:d} to {:d}.".format(array_name,len(arr),self.truncate_alternatives_max)) easylist_pac.py:1202: UserWarning: Truncating regex alternatives rule set 'bad_url_parts' from 1550 to 499. warnings.warn("Truncating regex alternatives rule set '{}' from {:d} to {:d}.".format(array_name,len(arr),self.truncate_alternatives_max))