User iteritems() and itervalues() for iterating over dictionaries.

Author: Feras A. Saad

src/backends/cgpm_backend.py

@@ -955,7 +955,7 @@ def _json_ready_model(self, state, name_map):
  view_indices = sorted(state.views.keys())
  # state.Zv() is a column partition given as {colnum: viewnum, ...}
  column_partition = [
- [name_map[colno] for colno, current_view_index in state.Zv().items()
+ [name_map[colno] for colno, current_view_index in state.Zv().iteritems()
  if current_view_index==view_index]
  for view_index in view_indices
  ]
@@ -975,10 +975,10 @@ def _json_ready_model(self, state, name_map):
  [
  sorted([
  rowid
- for (rowid, current_cluster_index) in state.views[view_index].Zr().items()
+ for (rowid, current_cluster_index) in state.views[view_index].Zr().iteritems()
  if current_cluster_index==cluster_index
  ])
- for cluster_index in sorted(set(state.views[view_index].Zr().values()))
+ for cluster_index in sorted(set(state.views[view_index].Zr().itervalues()))
  ]
  for view_index in view_indices
  ]
@@ -1000,7 +1000,7 @@ def _json_ready_model(self, state, name_map):
  def _json_ready_categories(self, bdb, population_id, generator_id, stattypes):
  name_map = {
  k:v
- for k, v in core.bayesdb_colno_to_variable_names(bdb, population_id, generator_id).items()
+ for k, v in core.bayesdb_colno_to_variable_names(bdb, population_id, generator_id).iteritems()
  if stattypes[v] == 'nominal'
  }
  # All categories for all categorical variables
@@ -1011,7 +1011,7 @@ def _json_ready_categories(self, bdb, population_id, generator_id, stattypes):
  # Collate categories by variable
  return {
  name: [item[2] for item in raw_categories if item[0]==colno]
- for colno,name in name_map.items()
+ for colno,name in name_map.iteritems()
  }
 
  def _unique_rowid(self, rowids):