Pylint: Add pylint ignores to more human-friendly formatted matrices

Author: pavlin-policar

Orange/statistics/util.py

@@ -55,7 +55,7 @@ def sparse_zero_weights(x, weights):
         return weights[zero_indices]
     else:
         # Can easily be implemented using a coo_matrix
-        raise NotImplemented(
+        raise NotImplementedError(
             'Computing zero weights on ndimensinal weight matrix is not implemented'
         )
 

Orange/tests/test_distribution.py

@@ -353,6 +353,7 @@ def assert_dist_and_unknowns(computed, goal_dist):
             [data.DiscreteVariable("d%i" % i, values=list("abc")) for i in range(10)] +
             [data.ContinuousVariable("c%i" % i) for i in range(10)])
 
+        # pylint: disable=bad-whitespace
         X = sp.csr_matrix(
             # 0  1  2  3       4       5       6  7  8  9 10 11 12   13 14 15 16      17 18 19
             # --------------------------------------------------------------------------------
@@ -433,20 +434,23 @@ def test_compute_distributions_metas(self):
 
 class TestContinuous(unittest.TestCase):
     def test_mean(self):
+        # pylint: disable=bad-whitespace
         x = np.array([[0, 5, 10],
                       [9, 0,  1]])
         dist = distribution.Continuous(x)
 
         self.assertEqual(dist.mean(), np.mean(([0] * 9) + [10]))
 
     def test_variance(self):
+        # pylint: disable=bad-whitespace
         x = np.array([[0, 5, 10],
                       [9, 0,  1]])
         dist = distribution.Continuous(x)
 
         self.assertEqual(dist.variance(), np.var(([0] * 9) + [10]))
 
     def test_standard_deviation(self):
+        # pylint: disable=bad-whitespace
         x = np.array([[0, 5, 10],
                       [9, 0,  1]])
         dist = distribution.Continuous(x)

Orange/tests/test_normalize.py

@@ -97,12 +97,14 @@ def test_normalize_transform_by_span_zero_class(self):
 
     def test_normalize_sparse(self):
         domain = Domain([ContinuousVariable(str(i)) for i in range(3)])
+        # pylint: disable=bad-whitespace
         X = sp.csr_matrix(np.array([
             [0, -1, -2],
             [0,  1,  2],
         ]))
         data = Table.from_numpy(domain, X)
 
+        # pylint: disable=bad-whitespace
         solution = sp.csr_matrix(np.array([
             [0, -1, -1],
             [0,  1,  1],

Orange/tests/test_statistics.py

@@ -1,8 +1,11 @@
 import unittest
 import warnings
-
 from functools import wraps
 from itertools import chain
+try:
+    from typing import Callable
+except:  # pylint: disable=bare-except
+    pass
 
 import numpy as np
 import scipy as sp
@@ -12,7 +15,7 @@
     nanmin, nanmax, unique, nanunique, mean, nanmean, digitize, var
 
 
-def dense_sparse(test_case):
+def dense_sparse(test_case: Callable) -> Callable:
     """Run a single test case on both dense and sparse data."""
     @wraps(test_case)
     def _wrapper(self):
@@ -182,6 +185,7 @@ def test_var(self):
 
 class TestDigitize(unittest.TestCase):
     def setUp(self):
+        # pylint: disable=bad-whitespace
         self.data = [
             np.array([
                 [0., 1.,     0., np.nan, 3.,     5.],
@@ -323,6 +327,7 @@ def test_1d_weights_with_axis_1(self, array):
 
     @dense_sparse
     def test_2d_weights(self, array):
+        # pylint: disable=bad-whitespace
         x = array([[np.nan, np.nan, 1,      1 ],
                    [     0, np.nan, 2, np.nan ]])
         w = np.array([[1, 2, 3, 4],