Merge branch 'keras-gradcam-text' of github.com:teabolt/eli5 into pyt…

…orch-gradcam

CHANGES.rst

@@ -1,6 +1,18 @@
 Changelog
 =========
 
+0.10.1 (2019-08-29)
+-------------------
+
+* Don't include typing dependency on Python 3.5+
+  to fix installation on Python 3.7
+
+0.10.0 (2019-08-21)
+-------------------
+
+* Keras image classifiers: explaining predictions with Grad-CAM
+  (GSoC-2019 project by @teabolt).
+
 0.9.0 (2019-07-05)
 ------------------
 

eli5/__init__.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
 
-__version__ = '0.9.0'
+__version__ = '0.10.1'
 
 from .formatters import (
     format_as_html,
@@ -96,6 +96,7 @@
 except ImportError:
     # keras is not available
     pass
+<<<<<<< HEAD
 
 
 try:
@@ -104,4 +105,6 @@
     )
 except ImportError:
     # pytorch is not available
-    pass
+    pass
+=======
+>>>>>>> ec0f51c60aaf360327ca18e3e0cdae2222cec6bf

eli5/_feature_names.py

@@ -5,8 +5,8 @@
     Union, Callable, Pattern
 )
 
-import numpy as np  # type: ignore
-import scipy.sparse as sp  # type: ignore
+import numpy as np
+import scipy.sparse as sp
 
 
 class FeatureNames(Sized, Iterable):

eli5/_feature_weights.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
 
-import numpy as np  # type: ignore
+import numpy as np
 
 from eli5.base import FeatureWeights, FeatureWeight
 from .utils import argsort_k_largest_positive, argsort_k_smallest, mask

eli5/_graphviz.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-import graphviz  # type: ignore
+import graphviz
 
 
 def is_supported():

eli5/base.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from typing import Any, List, Tuple, Union, Optional
 
-import numpy as np # type: ignore
+import numpy as np
 
 from .base_utils import attrs
 from .formatters.features import FormattedFeatureName

eli5/base_utils.py

@@ -1,9 +1,9 @@
 import inspect
 
-import attr  # type: ignore
+import attr
 
 try:
-    from functools import singledispatch  # type: ignore
+    from functools import singledispatch
 except ImportError:
     from singledispatch import singledispatch  # type: ignore
 

eli5/catboost.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division
 
-import numpy as np  # type: ignore
-import catboost # type: ignore
+import numpy as np
+import catboost
 
 from eli5.explain import explain_weights
 from eli5._feature_importances import get_feature_importance_explanation

eli5/formatters/as_dataframe.py

@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Optional
 import warnings
 
-import pandas as pd  # type: ignore
+import pandas as pd
 
 import eli5
 from eli5.base import (

eli5/formatters/as_dict.py

@@ -1,7 +1,7 @@
 import six
 
-import attr   # type: ignore
-import numpy as np   # type: ignore
+import attr
+import numpy as np
 
 from .features import FormattedFeatureName
 

eli5/formatters/html.py

@@ -3,8 +3,8 @@
 from itertools import groupby
 from typing import List, Optional, Tuple
 
-import numpy as np  # type: ignore
-from jinja2 import Environment, PackageLoader  # type: ignore
+import numpy as np
+from jinja2 import Environment, PackageLoader
 
 from eli5 import _graphviz
 from eli5.base import (Explanation, TargetExplanation, FeatureWeights,

eli5/formatters/image.py

@@ -2,11 +2,14 @@
 from __future__ import absolute_import
 from typing import Union, Optional, Callable
 
-import numpy as np # type: ignore
-from PIL import Image # type: ignore
-import matplotlib.cm # type: ignore
+import numpy as np
+from PIL import Image
+import matplotlib.cm
 
 from eli5.base import Explanation
+from eli5.nn.gradcam import (
+    _validate_heatmap,
+)
 
 
 def format_as_image(expl, # type: Explanation
@@ -287,14 +290,6 @@ def _validate_image(image):
                         'Got: {}'.format(image))
 
 
-def _validate_heatmap(heatmap):
-    # type: (np.ndarray) -> None
-    """Check that ``heatmap`` has the right type."""
-    if not isinstance(heatmap, np.ndarray):
-        raise TypeError('heatmap must be a numpy.ndarray instance. '
-                        'Got: {}'.format(heatmap))
-
-
 def _needs_normalization(heatmap):
     # type: (np.ndarray) -> bool
     """Return whether ``heatmap`` values are in the interval [0, 1]."""
@@ -311,4 +306,4 @@ def _normalize_heatmap(h, epsilon=1e-07):
     # https://datascience.stackexchange.com/questions/5885/how-to-scale-an-array-of-signed-integers-to-range-from-0-to-1
     # add eps to avoid division by zero in case heatmap is all 0's
     # this also means that lmap max will be slightly less than the 'true' max
-    return (h - h.min()) / (h.max() - h.min() + epsilon)
+    return (h - h.min()) / (h.max() - h.min() + epsilon)

eli5/formatters/text.py

@@ -2,14 +2,16 @@
 from __future__ import absolute_import
 from itertools import chain
 import six
+from tabulate import tabulate
 from typing import List, Optional, Iterator
 
 from eli5.base import Explanation, FeatureImportances
 from . import fields
 from .features import FormattedFeatureName
 from .utils import (
     format_signed, format_value, format_weight, has_any_values_for_weights,
-    replace_spaces, should_highlight_spaces, tabulate)
+    replace_spaces, should_highlight_spaces)
+from .utils import tabulate as eli5_tabulate
 from .trees import tree2text
 
 
@@ -153,7 +155,6 @@ def _decision_tree_lines(explanation):
 
 def _transition_features_lines(explanation):
     # type: (Explanation) -> List[str]
-    from tabulate import tabulate  # type: ignore
     tf = explanation.transition_features
     assert tf is not None
     return [
@@ -203,7 +204,7 @@ def _targets_lines(explanation,  # type: Explanation
 
         w = target.feature_weights
         assert w is not None
-        table = tabulate(
+        table = eli5_tabulate(
             [table_line(fw) for fw in chain(w.pos, reversed(w.neg))],
             header=table_header,
             col_align=col_align,

eli5/formatters/text_helpers.py

@@ -1,7 +1,7 @@
 from collections import Counter
 from typing import List, Optional
 
-import numpy as np  # type: ignore
+import numpy as np
 
 from eli5.base import TargetExplanation, WeightedSpans, DocWeightedSpans
 from eli5.base_utils import attrs

eli5/formatters/trees.py

@@ -24,6 +24,8 @@ def p(*args):
         else:
             assert node.left is not None
             assert node.right is not None
+            assert node.threshold is not None
+
             feat_name = node.feature_name
 
             if depth > 0:

eli5/formatters/utils.py

@@ -2,10 +2,9 @@
 from itertools import chain
 import re
 import six
-from numbers import Real
 from typing import Any, Union, List, Dict, Callable, Match, Optional
 
-import numpy as np  # type: ignore
+import numpy as np
 
 from eli5.base import Explanation
 from .features import FormattedFeatureName
@@ -143,12 +142,12 @@ def tabulate(data,  # type: List[List[Any]]
 
 
 def format_weight(value):
-    # type: (Real) -> str
+    # type: (float) -> str
     return '{:+.3f}'.format(value)
 
 
 def format_value(value):
-    # type: (Optional[Real]) -> str
+    # type: (Optional[float]) -> str
     if value is None:
         return ''
     elif np.isnan(value):

eli5/ipython.py

@@ -3,15 +3,15 @@
 from typing import Any, Dict, Tuple
 import warnings
 
-from IPython.display import HTML, Image  # type: ignore
+from IPython.display import HTML, Image
 
 from .explain import explain_weights, explain_prediction
 from .formatters import format_as_html, fields
 try:
     from .formatters.image import format_as_image
 except ImportError as e:
     # missing dependencies
-    format_as_image = e # type: ignore
+    format_as_image = e  # type: ignore
 
 
 FORMAT_KWARGS = {'include_styles', 'force_weights',