Merge pull request #17 from nwschurink/dev

BUG: Fix Python 3 compatibility

misvm/kernel.py

@@ -117,7 +117,7 @@ def set_kernel(k, normalizer=no_norm):
     def K(X, Y):
         if type(X) == list:
             norm = lambda x: normalizer(x, k)
-            x_norm = matrix(map(norm, X))
+            x_norm = matrix(list(map(norm, X)))
             if id(X) == id(Y):
                 # Optimize for symmetric case
                 norms = x_norm.T * x_norm
@@ -134,11 +134,11 @@ def K(X, Y):
                     diag = np.array([np.sum(k(x, x)) for x in X])
                     raw_kernel = upper + upper.T + spdiag(diag)
             else:
-                y_norm = matrix(map(norm, Y))
+                y_norm = matrix(list(map(norm, Y)))
                 norms = x_norm.T * y_norm
                 raw_kernel = k(vstack(X), vstack(Y))
-                lensX = map(len, X)
-                lensY = map(len, Y)
+                lensX = list(map(len, X))
+                lensY = list(map(len, Y))
                 if any(l != 1 for l in lensX):
                     raw_kernel = vstack([np.sum(raw_kernel[i:j, :], axis=0)
                                          for i, j in slices(lensX)])

misvm/mica.py

@@ -55,7 +55,7 @@ def fit(self, bags, y):
                       object containing m instances with k features
         @param y : an array-like object of length n containing -1/+1 labels
         """
-        self._bags = map(np.asmatrix, bags)
+        self._bags = list(map(np.asmatrix, bags))
         bs = BagSplitter(self._bags,
                          np.asmatrix(y).reshape((-1, 1)))
         self._X = bs.instances
@@ -235,4 +235,4 @@ def t(list_of_lists):
     Transpose a list of lists, since 'sparse'
     takes arguments in column-major order.
     """
-    return map(list, zip(*list_of_lists))
+    return list(map(list, zip(*list_of_lists)))

misvm/misssvm.py

@@ -48,7 +48,7 @@ def fit(self, bags, y):
                       object containing m instances with k features
         @param y : an array-like object of length n containing -1/+1 labels
         """
-        self._bags = map(np.asmatrix, bags)
+        self._bags = list(map(np.asmatrix, bags))
         bs = BagSplitter(self._bags,
                          np.asmatrix(y).reshape((-1, 1)))
         self._X = np.vstack([bs.pos_instances,

misvm/nsk.py

@@ -44,7 +44,7 @@ def fit(self, bags, y):
                       object containing m instances with k features
         @param y : an array-like object of length n containing -1/+1 labels
         """
-        self._bags = map(np.asmatrix, bags)
+        self._bags = list(map(np.asmatrix, bags))
         self._y = np.asmatrix(y).reshape((-1, 1))
         if self.scale_C:
             C = self.C / float(len(self._bags))
@@ -93,5 +93,5 @@ def predict(self, bags):
             return np.zeros(len(bags))
         else:
             kernel = kernel_by_name(self.kernel, p=self.p, gamma=self.gamma)
-            K = kernel(map(np.asmatrix, bags), self._sv_bags)
+            K = kernel(list(map(np.asmatrix, bags)), self._sv_bags)
             return np.array(self._b + K * spdiag(self._sv_y) * self._sv_alphas).reshape((-1,))

misvm/sil.py

@@ -79,4 +79,4 @@ def get_params(self, deep=True):
 
 def _inst_to_bag_preds(inst_preds, bags):
     return np.array([np.max(inst_preds[slice(*bidx)])
-                     for bidx in slices(map(len, bags))])
+                     for bidx in slices(list(map(len, bags)))])

misvm/smil.py

@@ -41,7 +41,7 @@ def fit(self, bags, y):
                       object containing m instances with k features
         @param y : an array-like object of length n containing -1/+1 labels
         """
-        bs = BagSplitter(map(np.asmatrix, bags),
+        bs = BagSplitter(list(map(np.asmatrix, bags)),
                          np.asmatrix(y).reshape((-1, 1)))
         self._bags = bs.neg_inst_as_bags + bs.pos_bags
         self._y = np.matrix(np.vstack([-np.ones((bs.L_n, 1)),