Merge pull request #355 from zzz4zzz/mypy_version_upgrade

Upgrade version of mypy

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

@@ -2,9 +2,9 @@
 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
 

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',

eli5/keras/explain_prediction.py

@@ -2,21 +2,21 @@
 from __future__ import absolute_import
 from typing import Union, Optional, Callable, Tuple, List, TYPE_CHECKING
 if TYPE_CHECKING:
-    import PIL # type: ignore
-
-import numpy as np # type: ignore
-import keras # type: ignore
-import keras.backend as K # type: ignore
-from keras.models import Model # type: ignore
-from keras.layers import Layer # type: ignore
-from keras.layers import (  # type: ignore
+    import PIL
+
+import numpy as np
+import keras
+import keras.backend as K
+from keras.models import Model
+from keras.layers import Layer
+from keras.layers import (
     Conv2D,
     MaxPooling2D,
     AveragePooling2D,
     GlobalMaxPooling2D,
     GlobalAveragePooling2D,
 )
-from keras.preprocessing.image import array_to_img # type: ignore
+from keras.preprocessing.image import array_to_img
 
 from eli5.base import Explanation, TargetExplanation
 from eli5.explain import explain_prediction

eli5/keras/gradcam.py

@@ -2,11 +2,11 @@
 from __future__ import absolute_import
 from typing import Union, Optional, Tuple, List
 
-import numpy as np # type: ignore
-import keras # type: ignore
-import keras.backend as K # type: ignore
-from keras.models import Model # type: ignore
-from keras.layers import Layer # type: ignore
+import numpy as np
+import keras
+import keras.backend as K
+from keras.models import Model
+from keras.layers import Layer
 
 
 def gradcam(weights, activations):
@@ -199,7 +199,7 @@ def _validate_target(target, output_shape):
         output_nodes = output_shape[1:][0]
         if not (0 <= target < output_nodes):
             raise ValueError('Prediction target index is ' 
-                             'outside the required range [0, {}). ',
+                             'outside the required range [0, {}). '
                              'Got {}'.format(output_nodes, target))
     else:
         raise TypeError('Prediction target must be int. '

eli5/lightgbm.py

@@ -3,8 +3,8 @@
 from collections import defaultdict
 from typing import DefaultDict, Optional
 
-import numpy as np  # type: ignore
-import lightgbm  # type: ignore
+import numpy as np
+import lightgbm
 
 from eli5.explain import explain_weights, explain_prediction
 from eli5._feature_importances import get_feature_importance_explanation

eli5/lightning.py

@@ -1,9 +1,9 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
 
-from lightning.impl.base import BaseEstimator  # type: ignore
-from lightning import classification, regression   # type: ignore
-from sklearn.multiclass import OneVsRestClassifier   # type: ignore
+from lightning.impl.base import BaseEstimator
+from lightning import classification, regression
+from sklearn.multiclass import OneVsRestClassifier
 
 from eli5.base import Explanation
 from eli5.base_utils import singledispatch

eli5/lime/_vectorizer.py

@@ -2,8 +2,8 @@
 from __future__ import absolute_import
 from typing import Tuple, Callable, Dict, Optional, List
 
-import numpy as np  # type: ignore
-from sklearn.base import BaseEstimator, TransformerMixin  # type: ignore
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
 
 from eli5.base import DocWeightedSpans, FeatureWeights
 from eli5.sklearn.text import _get_feature_weights_dict
@@ -13,8 +13,8 @@
 class SingleDocumentVectorizer(BaseEstimator, TransformerMixin):
     """ Fake vectorizer which converts document just to a vector of ones """
 
-    def __init__(self, token_pattern=None):
-        # type: (Optional[str]) -> None
+    def __init__(self, token_pattern):
+        # type: (str) -> None
         self.token_pattern = token_pattern
 
     def fit(self, X, y=None):

eli5/lime/lime.py

@@ -6,12 +6,12 @@
 from __future__ import absolute_import
 from typing import Any, Callable, Dict, Optional
 
-import numpy as np  # type: ignore
-from sklearn.feature_extraction.text import CountVectorizer  # type: ignore
-from sklearn.linear_model import SGDClassifier  # type: ignore
-from sklearn.model_selection import train_test_split  # type: ignore
-from sklearn.utils import check_random_state  # type: ignore
-from sklearn.base import clone, BaseEstimator  # type: ignore
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.utils import check_random_state
+from sklearn.base import clone, BaseEstimator
 
 import eli5
 from eli5.sklearn.utils import sklearn_version
@@ -148,7 +148,7 @@ def __init__(self,
                  rbf_sigma=None,  # type: float
                  random_state=None,
                  expand_factor=10,  # type: Optional[int]
-                 token_pattern=None,  # type: str
+                 token_pattern=None,  # type: Optional[str]
                  ):
         # type: (...) -> None
         self.n_samples = n_samples
@@ -162,7 +162,7 @@ def __init__(self,
         if char_based is None:
             if token_pattern is None:
                 self.char_based = False  # type: Optional[bool]
-                self.token_pattern = DEFAULT_TOKEN_PATTERN
+                self.token_pattern = DEFAULT_TOKEN_PATTERN  # type: str
             else:
                 self.char_based = None
                 self.token_pattern = token_pattern

eli5/lime/samplers.py

@@ -5,13 +5,13 @@
 from typing import List, Tuple, Any, Union, Dict, Optional
 import six
 
-import numpy as np  # type: ignore
-from scipy.stats import itemfreq  # type: ignore
-from sklearn.base import BaseEstimator, clone  # type: ignore
-from sklearn.neighbors import KernelDensity  # type: ignore
-from sklearn.metrics import pairwise_distances  # type: ignore
-from sklearn.model_selection import GridSearchCV, KFold  # type: ignore
-from sklearn.utils import check_random_state  # type: ignore
+import numpy as np
+from scipy.stats import itemfreq
+from sklearn.base import BaseEstimator, clone
+from sklearn.neighbors import KernelDensity
+from sklearn.metrics import pairwise_distances
+from sklearn.model_selection import GridSearchCV, KFold
+from sklearn.utils import check_random_state
 
 from eli5.utils import vstack
 from eli5.lime.utils import rbf

eli5/lime/textutils.py

@@ -7,8 +7,8 @@
 import math
 from typing import List, Tuple, Union, Optional
 
-import numpy as np  # type: ignore
-from sklearn.utils import check_random_state  # type: ignore
+import numpy as np
+from sklearn.utils import check_random_state
 
 from eli5.utils import indices_to_bool_mask, vstack
 

eli5/lime/utils.py

@@ -2,12 +2,12 @@
 from __future__ import absolute_import
 from typing import List, Any
 
-import numpy as np  # type: ignore
-from scipy.stats import entropy  # type: ignore
-from sklearn.pipeline import Pipeline  # type: ignore
-from sklearn.utils import check_random_state, issparse  # type: ignore
-from sklearn.utils.metaestimators import if_delegate_has_method  # type: ignore
-from sklearn.utils import shuffle as _shuffle  # type: ignore
+import numpy as np
+from scipy.stats import entropy
+from sklearn.pipeline import Pipeline
+from sklearn.utils import check_random_state, issparse
+from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils import shuffle as _shuffle
 
 from eli5.utils import vstack
 from eli5.sklearn.utils import sklearn_version