Update requirements.txt and setup.py according to GHSA-977j-xj7q-2jr9

.travis.yml

@@ -3,8 +3,6 @@ notifications:
     email: false
 language: python
 python:
-    - "2.7"
-    - "3.4"
     - "3.5"
     - "3.6"
     - "3.7"

CHANGELOG.md

@@ -9,6 +9,10 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 ### Fixed
 - Fixed an issue with builds failing due to numerical issues (#103)
 
+### Changed
+- Increase minimum version of `tensorflow` to v1.15.2 to fix the security vulnerability reported in https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9 (#101).
+- Dropped support for Python 2.7 and 3.4 (#101).
+
 ## [2.3.0] - 2019-11-22
 
 ### Changed

README.rst

@@ -17,6 +17,8 @@ models implemented in `TensorFlow <https://www.tensorflow.org/>`__
 Installation
 ============
 
+This package currently supports Python 3.5, 3.6, and 3.7.
+
 Installation with ``pip`` is recommended:
 
 .. code:: bash

muffnn/autoencoder/autoencoder.py

@@ -1,8 +1,6 @@
 """
 Autoencoder in scikit-learn style with TensorFlow
 """
-from __future__ import print_function
-from __future__ import division
 
 import logging
 import re

muffnn/autoencoder/tests/test_autoencoder.py

@@ -1,23 +1,17 @@
 """
 Tests for the Autoencoder.
 """
-from __future__ import print_function
-from __future__ import division
 
 import logging
 import pprint
 from io import BytesIO
 import pickle
 import sys
-try:
-    from unittest import mock
-except ImportError:
-    mock = None
+from unittest import mock
 
 import pytest
 import numpy as np
 import scipy.sparse as sp
-import six
 import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
 from sklearn.datasets import load_iris
@@ -27,11 +21,6 @@
 
 from muffnn import Autoencoder
 
-if six.PY2:
-    from mock import patch
-else:
-    from unittest.mock import patch
-
 
 _LOGGER = logging.getLogger(__name__)
 iris = load_iris()
@@ -112,7 +101,7 @@ def __init__(self, hidden_units=(256,), batch_size=128, n_epochs=300,
 # replicate outside of travis, but I was able to get the test to fail locally
 # by changing atol in sklearn.utils.check_methods_subset_invariance from 1e-7
 # to 1e-10. This simply skips that part of check_estimator.
-@patch('sklearn.utils.estimator_checks.check_methods_subset_invariance')
+@mock.patch('sklearn.utils.estimator_checks.check_methods_subset_invariance')
 def test_check_estimator(mock_check_methods_subset_invariance):
     """Check adherence to Estimator API."""
     if sys.version_info.major == 3 and sys.version_info.minor == 7:

muffnn/core.py

@@ -1,80 +1,10 @@
-from __future__ import print_function
-from __future__ import division
-
-import os
+from abc import ABCMeta, abstractmethod
 import glob
+import os
 import tarfile
-from abc import ABCMeta, abstractmethod
-import six
-import tensorflow as tf
-
+from tempfile import TemporaryDirectory
 
-if six.PY3:
-    from tempfile import TemporaryDirectory
-else:
-    # Backport TemporaryDirectory; this was introduced in Python 3.2
-    from tempfile import mkdtemp
-    import shutil as _shutil
-    import sys as _sys
-    import warnings as _warnings
-
-    class ResourceWarning(Warning):
-        pass
-
-    # from civis-python (civis.compat)
-    # copied here to avoid the dependency
-    class TemporaryDirectory(object):
-        """Create and return a temporary directory.  This has the same
-        behavior as mkdtemp but can be used as a context manager.  For
-        example:
-
-            with TemporaryDirectory() as tmpdir:
-                ...
-
-        Upon exiting the context, the directory and everything contained
-        in it are removed.
-
-        This is a port of the Python 3.2+ TemporaryDirectory object,
-        modified slightly to work with Python 2.7. Python 3 docs are at
-        https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory
-        """
-
-        def __init__(self, suffix='', prefix='tmp', dir=None):
-            self._closed = False
-            self.name = None  # Handle mkdtemp raising an exception
-            self.name = mkdtemp(suffix, prefix, dir)
-
-        def __repr__(self):
-            return "<{} {!r}>".format(self.__class__.__name__, self.name)
-
-        def __enter__(self):
-            return self.name
-
-        def __exit__(self, exc, value, tb):
-            self.cleanup()
-
-        def __del__(self):
-            # Issue a ResourceWarning if implicit cleanup needed
-            self.cleanup(_warn=True)
-
-        def cleanup(self, _warn=False):
-            if self.name and not self._closed:
-                try:
-                    _shutil.rmtree(self.name)
-                except (TypeError, AttributeError) as ex:
-                    # Issue #10188: Emit a warning on stderr
-                    # if the directory could not be cleaned
-                    # up due to missing globals
-                    if "None" not in str(ex):
-                        raise
-                    print("ERROR: {!r} while cleaning up "
-                          "{!r}".format(ex, self,),
-                          file=_sys.stderr)
-                    return
-                self._closed = True
-                if _warn:
-                    _warnings.warn("Implicitly cleaning up {!r}".format(self),
-                                   ResourceWarning)
+import tensorflow as tf
 
 
 def affine(input_tensor, output_size, bias=True, bias_start=0.0,
@@ -131,8 +61,7 @@ def affine(input_tensor, output_size, bias=True, bias_start=0.0,
     return t
 
 
-@six.add_metaclass(ABCMeta)
-class TFPicklingBase(object):
+class TFPicklingBase(object, metaclass=ABCMeta):
     """Base class for pickling TensorFlow-based scikit-learn estimators.
 
     This base class defines a few standard attributes to enable fairly

muffnn/fm/fm_classifier.py

@@ -1,6 +1,3 @@
-from __future__ import print_function
-from __future__ import division
-
 import logging
 import re
 

muffnn/fm/fm_regressor.py

@@ -1,6 +1,3 @@
-from __future__ import print_function
-from __future__ import division
-
 import logging
 from warnings import warn
 import re

muffnn/fm/tests/test_fm_classifier.py

@@ -4,16 +4,10 @@
 based in part on sklearn's logistic tests:
 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_logistic.py
 """
-from __future__ import print_function
-from __future__ import division
-
 from io import BytesIO
 import pickle
 import sys
-try:
-    from unittest import mock
-except ImportError:
-    mock = None
+from unittest import mock
 
 import numpy as np
 import pytest

muffnn/fm/tests/test_fm_regressor.py

@@ -4,16 +4,11 @@
 based in part on sklearn's logistic tests:
 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_logistic.py
 """
-from __future__ import print_function
-from __future__ import division
 
 from io import BytesIO
 import pickle
 import sys
-try:
-    from unittest import mock
-except ImportError:
-    mock = None
+from unittest import mock
 
 import numpy as np
 import pytest

muffnn/mlp/base.py

@@ -4,11 +4,8 @@
 
 Similar to sklearn.neural_network.MLPClassifier, but using TensorFlow.
 """
-from __future__ import print_function
-from __future__ import division
 
 from abc import ABCMeta, abstractmethod
-import six
 import logging
 import re
 from warnings import warn
@@ -32,8 +29,7 @@
 _LOGGER = logging.getLogger(__name__)
 
 
-@six.add_metaclass(ABCMeta)
-class MLPBaseEstimator(TFPicklingBase, BaseEstimator):
+class MLPBaseEstimator(TFPicklingBase, BaseEstimator, metaclass=ABCMeta):
     """Base class for multilayer perceptron models
 
     Notes

muffnn/mlp/mlp_classifier.py

@@ -3,8 +3,6 @@
 
 Similar to sklearn.neural_network.MLPClassifier, but using TensorFlow.
 """
-from __future__ import print_function
-from __future__ import division
 
 import logging
 

muffnn/mlp/mlp_regressor.py

@@ -3,8 +3,6 @@
 
 Similar to sklearn.neural_network.MLPRegressor, but using TensorFlow.
 """
-from __future__ import print_function
-from __future__ import division
 
 import logging
 from warnings import warn

muffnn/mlp/tests/test_base.py

@@ -1,22 +1,15 @@
-from __future__ import print_function
-from __future__ import division
-
 from io import BytesIO
 import pickle
 
 import pytest
-import six
 import numpy as np
 import scipy.sparse
 import tensorflow as tf
 from tensorflow.python.ops import nn
 
 import muffnn.mlp.base as base
 
-if six.PY2:
-    import mock
-else:
-    from unittest import mock
+from unittest import mock
 
 
 class SimpleTestEstimator(base.MLPBaseEstimator):

muffnn/mlp/tests/test_mlp_classifier.py

@@ -4,18 +4,13 @@
 based in part on sklearn's logistic tests:
 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_logistic.py
 """
-from __future__ import print_function
-from __future__ import division
 
 from io import BytesIO
 import pickle
 import sys
-try:
-    from unittest import mock
-except ImportError:
-    mock = None
+from unittest import mock
+from unittest.mock import MagicMock
 
-import six
 import numpy as np
 import pytest
 import scipy.sparse as sp
@@ -34,11 +29,6 @@
 from muffnn import MLPClassifier
 from muffnn.mlp.tests.util import assert_sample_weights_work
 
-if six.PY2:
-    from mock import MagicMock, patch
-else:
-    from unittest.mock import MagicMock, patch
-
 
 iris = load_iris()
 X = [[-1, 0], [0, 1], [1, 1]]
@@ -84,7 +74,7 @@ def predict_proba(self, *args, **kwargs):
 # replicate outside of travis, but I was able to get the test to fail locally
 # by changing atol in sklearn.utils.check_methods_subset_invariance from 1e-7
 # to 1e-10. This simply skips that part of check_estimator.
-@patch('sklearn.utils.estimator_checks.check_methods_subset_invariance')
+@mock.patch('sklearn.utils.estimator_checks.check_methods_subset_invariance')
 def test_check_estimator(mock_check_methods_subset_invariance):
     """Check adherence to Estimator API."""
     if sys.version_info.major == 3 and sys.version_info.minor == 7:

muffnn/mlp/tests/test_mlp_regressor.py

@@ -1,14 +1,9 @@
 """
 Tests for MLP Regressor
 """
-from __future__ import print_function
-from __future__ import division
 
 import sys
-try:
-    from unittest import mock
-except ImportError:
-    mock = None
+from unittest import mock
 
 import numpy as np
 import pytest

muffnn/tests/test_tfbase.py

@@ -1,8 +1,6 @@
 """
 Tests for TFPicklingBase
 """
-from __future__ import print_function
-from __future__ import division
 
 import io
 import pickle

requirements.txt

@@ -1,6 +1,4 @@
 numpy~=1.14
 scipy~=1.0
 scikit-learn>=0.19
-six~=1.10
-tensorflow>=1.12.1,<2
-mock==2.0.0 ; python_version < '3.0'
+tensorflow>=1.15.2,<2

setup.py

@@ -5,6 +5,15 @@
 
 _VERSION = '2.3.0'
 
+CLASSIFIERS = [
+    'Programming Language :: Python',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3.5',
+    'Programming Language :: Python :: 3.6',
+    'Programming Language :: Python :: 3.7',
+    'Programming Language :: Python :: 3 :: Only',
+]
+
 setup(
     name='muffnn',
     version=_VERSION,
@@ -20,5 +29,6 @@
     install_requires=['numpy',
                       'scipy',
                       'scikit-learn>=0.19',
-                      'tensorflow>=1.12.1,<2']
+                      'tensorflow>=1.15.2,<2'],
+    classifiers=CLASSIFIERS
 )