Added convertion to sparse format in category dataset

fluents/dataset.py

@@ -273,6 +273,19 @@ class CategoryDataset(Dataset):
     def __init__(self, array, identifiers=None, name='C'):
         Dataset.__init__(self, array, identifiers=identifiers, name=name)
 
+    def asspmatrix(self):
+        if isinstance(self._array, sparse.spmatrix):
+            return self._array
+        else:
+            arr = self.asarray()
+            return sparse.csr_matrix(arr.astype('i'))
+
+    def tospmatrix(self):
+        if isinstance(self._array, sparse.spmatrix):
+            self._array = self._array.tocsr()
+        else:
+            self._array = sparse.scr_matrix(self._array)
+
     def as_dict_lists(self):
         """Returns data as dict of identifiers along first dim.
 
@@ -423,7 +436,7 @@ class Selection(dict):
     def select(self, axis, labels):
         self[axis] = labels
 
-def write_ftsv(fd, ds, decimals=7, sep='\t', fmt=None):
+def write_ftsv(fd, ds, decimals=7, sep='\t', fmt=None, sp_format=True):
     """Writes a dataset in fluents tab separated values (ftsv) form.
     
     @param fd: An open file descriptor to the output file.
@@ -466,7 +479,10 @@ def write_ftsv(fd, ds, decimals=7, sep='\t', fmt=None):
     print >> fd, "# name: %s" % ds.get_name()
 
     # Write data
-    m = ds.asarray()
+    if hasattr(ds, "asspmatrix") and sp_format == True:
+        m = ds.asspmatrix()
+    else:
+        m = ds.asarray()
     if isinstance(m, sparse.spmatrix):
         _write_sparse_elements(fd, m, fmt, sep)
     else: