def match_one_set1_individual_pool(idx, sorted_idx, pool_size):
global local_ctx
set2_size = context_length(local_ctx)
if not set2_size:
raise StopIteration
if set2_size > pool_size:
pool = random.sample(xrange(context_length(local_ctx)), pool_size)
else:
pool = range(set2_size)
sub_local_ctx = context_subset(local_ctx, pool, None)
sub_local_ctx.update((k, set1[k][sorted_idx]) for k in ['id'] + used_variables1)
set2_scores = expr_eval(score_expr, sub_local_ctx)
individual2_pool_idx = np.argmax(set2_scores)
individual2_idx = pool[individual2_pool_idx]
id1 = sub_local_ctx['id']
id2 = local_ctx['__other_id'][individual2_idx]
local_ctx = context_delete(local_ctx, individual2_idx)
result[id_to_rownum[id1]] = id2
result[id_to_rownum[id2]] = id1
def match_one_set1_individual(idx, sorted_idx):
global local_ctx
if not context_length(local_ctx):
raise StopIteration
local_ctx.update((k, set1[k][sorted_idx]) for k in ['id'] + used_variables1)
# pk = tuple(individual1[fname] for fname in pk_names)
# optimized_expr = optimized_exprs.get(pk)
# if optimized_expr is None:
# for name in pk_names:
# fake_set1['__f_%s' % name].value = individual1[name]
# optimized_expr = str(symbolic_expr.simplify())
# optimized_exprs[pk] = optimized_expr
# set2_scores = evaluate(optimized_expr, mm_dict, set2)
set2_scores = expr_eval(score_expr, local_ctx)
individual2_idx = np.argmax(set2_scores)
id1 = local_ctx['id']
id2 = local_ctx['__other_id'][individual2_idx]
local_ctx = context_delete(local_ctx, individual2_idx)
result[id_to_rownum[id1]] = id2
result[id_to_rownum[id2]] = id1
def match_one_set1_individual(idx, sorted_idx):
global local_ctx
if not context_length(local_ctx):
raise StopIteration
local_ctx.update((k, set1[k][sorted_idx]) for k in used_variables1)
set2_scores = expr_eval(score_expr, local_ctx)
# print set2_scores
individual2_idx = np.argmax(set2_scores)
id1 = local_ctx['id']
id2 = local_ctx['__other_id'][individual2_idx]
local_ctx = context_delete(local_ctx, individual2_idx)
result[id_to_rownum[id1]] = id2
result[id_to_rownum[id2]] = id1
def match_cell(idx, sorted_idx, pool_size):
global matching_ctx
set2_size = context_length(matching_ctx)
if not set2_size:
raise StopIteration
if pool_size is not None and set2_size > pool_size:
pool = random.sample(xrange(set2_size), pool_size)
local_ctx = context_subset(matching_ctx, pool)
else:
local_ctx = matching_ctx.copy()
local_ctx.update((k, set1[k][sorted_idx])
for k in {'__ids__'} | used_variables1)
eval_ctx = context.clone(entity_data=local_ctx)
set2_scores = expr_eval(score, eval_ctx)
cell2_idx = set2_scores.argmax()
cell1ids = local_ctx['__ids__']
cell2ids = local_ctx['__other___ids__'][cell2_idx]
if pool_size is not None and set2_size > pool_size:
# transform pool-local index to set/matching_ctx index
cell2_idx = pool[cell2_idx]
cell1size = len(cell1ids)
cell2size = len(cell2ids)
nb_match = min(cell1size, cell2size)
# we could introduce a random choice here but it is not
# much necessary. In that case, it should be done in group_context
ids1 = cell1ids[:nb_match]
ids2 = cell2ids[:nb_match]
result[id_to_rownum[ids1]] = ids2
result[id_to_rownum[ids2]] = ids1
if nb_match == cell2size:
matching_ctx = context_delete(matching_ctx, cell2_idx)
else:
# other variables do not need to be modified since the cell
# only got smaller and was not deleted
matching_ctx['__other___ids__'][cell2_idx] = cell2ids[nb_match:]
# FIXME: the expr gets cached for the full matching_ctx at the
# beginning and then when another women with the same values is
# found, it thinks it can reuse the expr but it breaks because it
# has not the correct length.
# the current workaround is to invalidate the whole cache for the
# current entity but this is not the right way to go.
# * disable the cache for matching?
# * use a local cache so that methods after matching() can use
# what was in the cache before matching(). Shouldn't the cache be
# stored inside the context anyway?
expr_cache.invalidate(context.period, context.entity_name)
if nb_match < cell1size:
set1['__ids__'][sorted_idx] = cell1ids[nb_match:]
match_cell(idx, sorted_idx, pool_size)
