def predict(self,
table,
exclude_attrs=None,
target_attr=None,
append=False,
inplace=True,
nchunks=1,
scheduler=threaded.get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
candset_splitted = delayed(candsplit_df)(table, nchunks)
results = []
for i in xrange(nchunks):
result = delayed(self._predict_table_part)(candset_splitted[i],
exclude_attrs,
target_attr, append,
inplace)
results.append(result)
feat_vecs = delayed(concat_df)(results)
if compute:
feat_vecs = exec_dag(feat_vecs, num_workers, cache_size, scheduler,
show_progress)
return feat_vecs
pass
def block_candset(self, candset, ltable, rtable, fk_ltable, fk_rtable, l_key,
r_key, l_block_attr, r_block_attr, rem_stop_words=False, q_val=None,
word_level=True, overlap_size=1,
nchunks=1, scheduler=threaded.get,
num_workers=None, cache_size=1e9, compute=False,
show_progress=True):
cand_splitted = delayed(candsplit_df)(candset, nchunks)
# l_proj_attrs = (get_lattrs_to_project)(l_key, l_block_attr)
# r_proj_attrs = (get_rattrs_to_project)(r_key, r_block_attr)
#
# ltbl = (lproj_df)(ltable, l_proj_attrs)
# rtbl = (rproj_df)(rtable, r_proj_attrs)
if word_level == True:
tokenizer = WhiteSpaceTokenizer()
else:
tokenizer = QgramTokenizer(q_val=q_val)
results = []
for i in xrange(nchunks):
result = delayed(self._block_candset_part)(cand_splitted[i], ltable, rtable,
fk_ltable,
fk_rtable, l_key, r_key,
l_block_attr,
r_block_attr, rem_stop_words,
tokenizer, overlap_size)
results.append(result)
valid_candset = delayed(concat_df)(results)
if compute:
valid_candset = exec_dag(valid_candset, num_workers, cache_size, scheduler,
show_progress)
return valid_candset
def extract_feature_vecs(candset,
ltable,
rtable,
key,
fk_ltable,
fk_rtable,
l_key,
r_key,
attrs_before=None,
feature_table=None,
attrs_after=None,
nchunks=1,
scheduler=get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
# candset_splitted = delayed(candsplit_df)(candset, nchunks)
results = []
# for i in xrange(nchunks):
for i in range(candset.npartitions):
result = delayed(_extract_feature_vecs_part)(
candset.get_partition(i), ltable, rtable, key, fk_ltable,
fk_rtable, l_key, r_key, attrs_before, feature_table, attrs_after)
results.append(result)
feat_vecs = delayed(concat_df)(results)
if compute:
feat_vecs = exec_dag(feat_vecs, num_workers, cache_size, scheduler,
show_progress)
return feat_vecs
def block_candset(self,
candset,
ltable,
rtable,
fk_ltable,
fk_rtable,
l_key,
r_key,
nchunks=1,
scheduler=threaded.get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
# candset_splitted = delayed(candsplit_df)(candset, nchunks)
# l_proj_attrs = (get_lattrs_to_project)(l_key, self.ltable_attrs)
# r_proj_attrs = (get_rattrs_to_project)(r_key, self.rtable_attrs)
#
# ltbl = (lproj_df)(ltable, l_proj_attrs)
# rtbl = (rproj_df)(rtable, r_proj_attrs)
results = []
# for i in xrange(nchunks):
for i in range(candset.npartitions):
result = delayed(self._block_candset_part)(
candset_splitted.get_partition(i), ltable, rtable, fk_ltable,
fk_rtable, l_key, r_key)
results.append(result)
valid_candset = delayed(concat_df)(results)
if compute:
valid_candset = exec_dag(valid_candset, num_workers, cache_size,
scheduler, show_progress)
return valid_candset
def block_tables(self,
ltable,
rtable,
l_key,
r_key,
l_block_attr,
r_block_attr,
l_output_attrs=None,
r_output_attrs=None,
l_output_prefix='l_',
r_output_prefix='r_',
nltable_chunks=1,
nrtable_chunks=1,
scheduler=threaded.get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
# @todo validate inputs
# @todo need to handle missing values.
ltable_splitted = (lsplit_df)(ltable, nltable_chunks)
rtable_splitted = (rsplit_df)(rtable, nrtable_chunks)
# l_proj_attrs = (get_lattrs_to_project)(l_key, l_block_attr, l_output_attrs)
# r_proj_attrs = (get_rattrs_to_project)(r_key, r_block_attr, r_output_attrs)
# list ot accomodate results
results = []
for i in xrange(nltable_chunks):
# ltbl = (lproj_df)(ltable_splitted[i], l_proj_attrs)
for j in xrange(nrtable_chunks):
# rtbl = (rproj_df)(rtable_splitted[j], r_proj_attrs)
res = delayed(self._block_table_part)(
ltable_splitted[i], rtable_splitted[j], l_key, r_key,
l_block_attr, r_block_attr, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix)
results.append(res)
candset = delayed(concat_df)(results)
candset = delayed(add_id)(candset)
if compute:
candset = exec_dag(candset, num_workers, cache_size, scheduler,
show_progress)
return candset
def block_tables(self,
ltable,
rtable,
l_key,
r_key,
l_output_attrs=None,
r_output_attrs=None,
l_output_prefix='l_',
r_output_prefix='r_',
nltable_chunks=1,
nrtable_chunks=1,
scheduler=threaded.get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
ltable_splitted = (lsplit_df)(ltable, nltable_chunks)
rtable_splitted = (rsplit_df)(rtable, nrtable_chunks)
# l_proj_attrs = (get_lattrs_to_project)(l_key, self.ltable_attrs,
# l_output_attrs)
# # needs to be modified as self.ltable_attrs can be None.
# r_proj_attrs = (get_rattrs_to_project)(r_key, self.rtable_attrs,
# r_output_attrs)
results = []
# for i in xrange(nltable_chunks):
for i in range(ltable.npartitions):
# ltbl = (lproj_df)(ltable_splitted[i], l_proj_attrs)
# for j in xrange(nrtable_chunks):
for j in range(rtable.npartitions):
# rtbl = (rproj_df)(rtable_splitted[j], r_proj_attrs)
result = delayed(self._block_tables_part)(
ltable.get_partition(i), rtable.get_partition(j), l_key,
r_key, l_output_attrs, r_output_attrs, l_output_prefix,
r_output_prefix)
results.append(result)
candset = delayed(concat_df)(results)
# candset = delayed(add_id)(candset)
if compute:
candset = exec_dag(candset, num_workers, cache_size, scheduler,
show_progress)
return candset
def select_matcher(matchers,
x=None,
y=None,
table=None,
exclude_attrs=None,
target_attr=None,
metric='precision',
k=5,
random_state=None,
scheduler=threaded.get,
num_workers=None,
cache_size=1e9,
compute=False,
show_progress=True):
x, y = _get_xy_data(x, y, table, exclude_attrs, target_attr)
scores = []
for m in matchers:
score = (cross_validation)(m, x, y, metric, k, random_state)
scores.append(score)
res = delayed(process_scores)(matchers, scores, k)
if compute:
res = exec_dag(res, num_workers, cache_size, scheduler, show_progress)
return res