Fix elfi.Distance to support scipy 1.0.0 (#249)

CHANGELOG.rst

@@ -8,6 +8,7 @@ dev
 - Implemented the Two Stage Procedure, a method of summary-statistics diagnostics  
 - Added the MaxVar acquisition method
 - Added the RandMaxVar acquisition method
+- Fix elfi.Distance to support scipy 1.0.0
 
 0.6.3 (2017-09-28)
 ------------------

elfi/model/elfi_model.py

@@ -968,7 +968,7 @@ def __init__(self, discrepancy, *parents, **kwargs):
 class Distance(Discrepancy):
     """A convenience class for the discrepancy node."""
 
-    def __init__(self, distance, *summaries, p=None, w=None, V=None, VI=None, **kwargs):
+    def __init__(self, distance, *summaries, **kwargs):
         """Initialize a distance node of an ELFI graph.
 
         This class contains many common distance implementations through scipy.
@@ -983,18 +983,12 @@ def __init__(self, distance, *summaries, p=None, w=None, V=None, VI=None, **kwar
             that contains the observed values (summaries). The callable should return
             a vector of distances between the simulated summaries and the observed
             summaries.
-        summaries
-            summary nodes of the model
-        p : double, optional
-            The p-norm to apply Only for distance Minkowski (`'minkowski'`), weighted
-            and unweighted. Default: 2.
-        w : ndarray, optional
-            The weight vector. Only for weighted Minkowski (`'wminkowski'`). Mandatory.
-        V : ndarray, optional
-            The variance vector. Only for standardized Euclidean (`'seuclidean'`).
-            Mandatory.
-        VI : ndarray, optional
-            The inverse of the covariance matrix. Only for Mahalanobis. Mandatory.
+        *summaries
+            Summary nodes of the model.
+        **kwargs
+            Additional parameters may be required depending on the chosen distance.
+            See the scipy documentation. (The support is not exhaustive.)
+            ELFI-related kwargs are passed on to elfi.Discrepancy.
 
         Examples
         --------
@@ -1021,16 +1015,23 @@ def __init__(self, distance, *summaries, p=None, w=None, V=None, VI=None, **kwar
             raise ValueError("This node requires that at least one parent is specified.")
 
         if isinstance(distance, str):
-            if distance == 'wminkowski' and w is None:
+            cdist_kwargs = dict(metric=distance)
+            if distance == 'wminkowski' and 'w' not in kwargs.keys():
                 raise ValueError('Parameter w must be specified for distance=wminkowski.')
-            if distance == 'seuclidean' and V is None:
+            elif distance == 'seuclidean' and 'V' not in kwargs.keys():
                 raise ValueError('Parameter V must be specified for distance=seuclidean.')
-            if distance == 'mahalanobis' and VI is None:
+            elif distance == 'mahalanobis' and 'VI' not in kwargs.keys():
                 raise ValueError('Parameter VI must be specified for distance=mahalanobis.')
-            cdist_kwargs = dict(metric=distance, p=p, w=w, V=V, VI=VI)
+
+            # extract appropriate keyword arguments (depends on distance, not exhaustive!)
+            for key in ['p', 'w', 'V', 'VI']:
+                if key in kwargs.keys():
+                    cdist_kwargs[key] = kwargs.pop(key)
+
             dist_fn = partial(scipy.spatial.distance.cdist, **cdist_kwargs)
         else:
             dist_fn = distance
+
         discrepancy = partial(distance_as_discrepancy, dist_fn)
         super(Distance, self).__init__(discrepancy, *summaries, **kwargs)
         # Store the original passed distance