Merge pull request #66 from jakirkham/dist_more_metrics

Support more metrics in cdist and pdist

dask_distance/__init__.py

@@ -53,12 +53,15 @@ def cdist(XA, XB, metric="euclidean", **kwargs):
         "hamming": hamming,
         "jaccard": jaccard,
         "kulsinski": kulsinski,
+        "mahalanobis": mahalanobis,
         "minkowski": minkowski,
         "rogerstanimoto": rogerstanimoto,
         "russellrao": russellrao,
         "sokalmichener": sokalmichener,
         "sokalsneath": sokalsneath,
+        "seuclidean": seuclidean,
         "sqeuclidean": sqeuclidean,
+        "wminkowski": wminkowski,
         "yule": yule,
     }
 
@@ -93,8 +96,22 @@ def cdist(XA, XB, metric="euclidean", **kwargs):
 
         metric = func_mappings[metric]
 
-        if metric == minkowski:
-            kwargs["p"] = kwargs.get("p", 2)
+        if metric == mahalanobis:
+            if "VI" not in kwargs:
+                kwargs["VI"] = (
+                    dask.array.linalg.inv(
+                        dask.array.cov(dask.array.vstack([XA, XB]).T)
+                    ).T
+                )
+        elif metric == minkowski:
+            kwargs.setdefault("p", 2)
+        elif metric == seuclidean:
+            if "V" not in kwargs:
+                kwargs["V"] = (
+                    dask.array.var(dask.array.vstack([XA, XB]), axis=0, ddof=1)
+                )
+        elif metric == wminkowski:
+            kwargs.setdefault("p", 2)
 
         result = metric(XA, XB, **kwargs)
 
@@ -124,6 +141,13 @@ def pdist(X, metric="euclidean", **kwargs):
         other tradeoffs.
     """
 
+    if metric == "mahalanobis":
+        if "VI" not in kwargs:
+            kwargs["VI"] = dask.array.linalg.inv(dask.array.cov(X.T)).T
+    elif metric == "seuclidean":
+        if "V" not in kwargs:
+            kwargs["V"] = dask.array.var(X, axis=0, ddof=1)
+
     result = cdist(X, X, metric, **kwargs)
 
     result = dask.array.triu(result, 1)

tests/test_dask_distance.py

@@ -106,9 +106,15 @@ def test_1d_dist(funcname, kw, seed, size, chunks):
         ("correlation", {}),
         ("cosine", {}),
         ("euclidean", {}),
+        ("mahalanobis", {"VI": None}),
+        ("mahalanobis", {}),
         ("minkowski", {}),
         ("minkowski", {"p": 3}),
+        ("seuclidean", {"V": None}),
+        ("seuclidean", {}),
         ("sqeuclidean", {}),
+        ("wminkowski", {}),
+        ("wminkowski", {"p": 1.6}),
         (lambda u, v: (abs(u - v) ** 3).sum() ** (1.0 / 3.0), {}),
     ]
 )
@@ -133,6 +139,19 @@ def test_2d_cdist(metric, kw, seed, u_shape, u_chunks, v_shape, v_chunks):
     d_u = da.from_array(a_u, chunks=u_chunks)
     d_v = da.from_array(a_v, chunks=v_chunks)
 
+    if metric == "mahalanobis":
+        if "VI" not in kw:
+            kw["VI"] = 2 * np.random.random(2 * u_shape[-1:]) - 1
+        elif kw["VI"] is None:
+            kw.pop("VI")
+    elif metric == "seuclidean":
+        if "V" not in kw:
+            kw["V"] = 2 * np.random.random(u_shape[-1:]) - 1
+        elif kw["V"] is None:
+            kw.pop("V")
+    elif metric == "wminkowski":
+        kw["w"] = np.random.random(u_shape[-1:])
+
     a_r = spdist.cdist(a_u, a_v, metric, **kw)
     d_r = dask_distance.cdist(d_u, d_v, metric, **kw)
 
@@ -148,9 +167,15 @@ def test_2d_cdist(metric, kw, seed, u_shape, u_chunks, v_shape, v_chunks):
         ("correlation", {}),
         ("cosine", {}),
         ("euclidean", {}),
+        ("mahalanobis", {"VI": None}),
+        ("mahalanobis", {}),
         ("minkowski", {}),
         ("minkowski", {"p": 3}),
+        ("seuclidean", {"V": None}),
+        ("seuclidean", {}),
         ("sqeuclidean", {}),
+        ("wminkowski", {}),
+        ("wminkowski", {"p": 1.6}),
         (lambda u, v: (abs(u - v) ** 3).sum() ** (1.0 / 3.0), {}),
     ]
 )
@@ -172,6 +197,19 @@ def test_2d_pdist(metric, kw, seed, u_shape, u_chunks):
     a_u = 2 * np.random.random(u_shape) - 1
     d_u = da.from_array(a_u, chunks=u_chunks)
 
+    if metric == "mahalanobis":
+        if "VI" not in kw:
+            kw["VI"] = 2 * np.random.random(2 * u_shape[-1:]) - 1
+        elif kw["VI"] is None:
+            kw.pop("VI")
+    elif metric == "seuclidean":
+        if "V" not in kw:
+            kw["V"] = 2 * np.random.random(u_shape[-1:]) - 1
+        elif kw["V"] is None:
+            kw.pop("V")
+    elif metric == "wminkowski":
+        kw["w"] = np.random.random(u_shape[-1:])
+
     a_r = spdist.pdist(a_u, metric, **kw)
     d_r = dask_distance.pdist(d_u, metric, **kw)