cluster/hierarchical: Remove scipy=<0.18 compatibility

Author: ales-erjavec

Orange/clustering/hierarchical.py

@@ -3,7 +3,6 @@
 from collections import namedtuple, deque, defaultdict
 from operator import attrgetter
 from itertools import count
-from distutils.version import LooseVersion as _LooseVersion
 
 import heapq
 import numpy
@@ -23,17 +22,6 @@
 WEIGHTED = "weighted"
 WARD = "ward"
 
-# Does scipy implement a O(n**2) NN chain algorithm?
-_HAS_NN_CHAIN = hasattr(scipy.cluster.hierarchy, "_hierarchy") and \
-                hasattr(scipy.cluster.hierarchy._hierarchy, "nn_chain")
-
-# Prior to 0.18 scipy.cluster.hierarchical's python interface disallowed
-# ward clustering from a precomputed distance matrix even though it's cython
-# implementation allowed it and was documented to support it (scipy issue 5220)
-_HAS_WARD_LINKAGE_FROM_DIST = \
-    _LooseVersion(scipy.__version__) >= _LooseVersion("0.18") and \
-    _HAS_NN_CHAIN
-
 
 def condensedform(X, mode="upper"):
     X = numpy.asarray(X)
@@ -105,23 +93,7 @@ def dist_matrix_linkage(matrix, linkage=AVERAGE):
     """
     # Extract compressed upper triangular distance matrix.
     distances = condensedform(matrix)
-    if linkage == WARD and not _HAS_WARD_LINKAGE_FROM_DIST:
-        # Avoid `scipy.cluster.hierarchy.linkage` and dispatch to it's
-        # cython implementation directly.
-        # This the core of the scipy.cluster.hierarchy.linkage in
-        # scipy 0.16, 0.17. Assuming the branches are in bug fix mode
-        # only so this interface will not change.
-        y = numpy.asarray(distances, dtype=float)
-        scipy.spatial.distance.is_valid_y(y, throw=True)
-        N = scipy.spatial.distance.num_obs_y(y)
-        # allocate the output linkage matrix
-        Z = numpy.zeros((N - 1, 4))
-        # retrieve the correct method flag
-        method = scipy.cluster.hierarchy._cpy_euclid_methods["ward"]
-        scipy.cluster.hierarchy._hierarchy.linkage(y, Z, int(N), int(method))
-        return Z
-    else:
-        return scipy.cluster.hierarchy.linkage(distances, method=linkage)
+    return scipy.cluster.hierarchy.linkage(distances, method=linkage)
 
 
 def dist_matrix_clustering(matrix, linkage=AVERAGE):

Orange/widgets/unsupervised/owdistancemap.py

@@ -285,11 +285,7 @@ class Outputs:
     graph_name = "grid_widget"
 
     # Disable clustering for inputs bigger than this
-    if hierarchical._HAS_NN_CHAIN:
-        _MaxClustering = 25000
-    else:
-        _MaxClustering = 3000
-
+    _MaxClustering = 25000
     # Disable cluster leaf ordering for inputs bigger than this
     _MaxOrderedClustering = 1000