def _block_candset_part(self, candset, ltable, rtable, fk_ltable,
fk_rtable, l_key, r_key, l_block_attr,
r_block_attr):
# 1. create dummy column names to contain the values pulled from ltable and rtable
# based on the fk's
if isinstance(candset, pd.DataFrame) and len(candset):
l_proj_attrs = (get_lattrs_to_project)(l_key, l_block_attr)
r_proj_attrs = (get_rattrs_to_project)(r_key, r_block_attr)
ltable = (lproj_df)(ltable, l_proj_attrs)
rtable = (rproj_df)(rtable, r_proj_attrs)
l_prefix, r_prefix = '__blk_a_', '__blk_b_'
# add attrs
cdf = add_attributes(candset, ltable, rtable, fk_ltable, fk_rtable,
l_key, r_key, [l_block_attr], [r_block_attr],
l_prefix, r_prefix)
l_chk, r_chk = l_prefix + l_block_attr, r_prefix + r_block_attr
res = candset[cdf[l_chk] == cdf[r_chk]]
if not isinstance(res, pd.DataFrame):
print('Returning {0}'.format(res))
return res
else:
return candset
def _block_table_part(self, ltable, rtable, l_key, r_key, l_block_attr,
r_block_attr,
tokenizer, threshold, rem_stop_words, l_output_attrs,
r_output_attrs, l_output_prefix, r_output_prefix):
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)
ltable = (lproj_df)(ltable, l_proj_attrs)
rtable = (rproj_df)(rtable, r_proj_attrs)
ltbl = ltable[~ltable[l_block_attr].isnull()]
rtbl = rtable[~rtable[r_block_attr].isnull()]
l_strings = self._preprocess_table(ltbl, l_key, l_block_attr, rem_stop_words)
l_tokens = tokenize_strings(l_strings, tokenizer)
inv_index = build_inv_index([l_tokens])
r_strings = self._preprocess_table(rtbl, r_key, r_block_attr, rem_stop_words)
r_tokens = tokenize_strings(r_strings, tokenizer)
candset = self._probe(r_tokens, inv_index, threshold)
fk_ltable, fk_rtable = l_output_prefix + l_key, r_output_prefix + r_key
candset = pd.DataFrame(candset.get_pairids(), columns=[fk_ltable, fk_rtable])
candset = add_attributes(candset, ltbl, rtbl, fk_ltable, fk_rtable, l_key,
r_key, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix)
if not isinstance(candset, pd.DataFrame):
print('Returning {0}'.format(candset))
return candset
def _block_table_part(self, ltable, rtable, l_key, r_key, l_block_attr,
r_block_attr, l_out_attrs, r_out_attrs, l_prefix,
r_prefix):
l_proj_attrs = (get_lattrs_to_project)(l_key, l_block_attr,
l_out_attrs)
r_proj_attrs = (get_rattrs_to_project)(r_key, r_block_attr,
r_out_attrs)
ltbl = (lproj_df)(ltable, l_proj_attrs)
rtbl = (rproj_df)(rtable, r_proj_attrs)
# join the tables
# ltbl = lproj_df(ltable, [l_key, l_block_attr])
# rtbl = rproj_df(rtable, [r_key, r_block_attr])
res = ltbl.merge(rtbl, left_on=l_block_attr, right_on=r_block_attr)
# get the cols to project & project
lcol, rcol = l_key + '_x', r_key + '_y'
res = candproj_df(res, [lcol, rcol])
# rename_cols the fk columns to conform with given prefix
lcol, rcol = l_prefix + l_key, r_prefix + r_key
res = rename_cols(res, [lcol, rcol])
# add the required output attrs.
res = add_attributes(res, ltable, rtable, lcol, rcol, l_key, r_key,
l_out_attrs, r_out_attrs, l_prefix, r_prefix)
# finally return the result.
if not isinstance(res, pd.DataFrame):
print('Returning {0}'.format(res))
return res
def _block_tables_part(self, ltable, rtable, l_key, r_key, l_output_attrs,
r_output_attrs, l_output_prefix, r_output_prefix):
fk_ltable, fk_rtable = l_output_prefix + l_key, r_output_prefix + r_key
candset, rule_applied = self._block_tables_with_filters(
ltable, rtable, l_key, r_key)
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)
ltable = lproj_df(ltable, l_proj_attrs)
rtable = rproj_df(rtable, r_proj_attrs)
if candset is None:
candset = self._block_tables_without_filters(
ltable, rtable, l_key, r_key, fk_ltable, fk_rtable)
elif len(self.rules) > 1:
candset = self._block_candset_excluding_rule(
candset, ltable, rtable, fk_ltable, fk_rtable, l_key, r_key,
rule_applied)
# candset = pd.DataFrame(candset, )candset
candset = add_attributes(candset, ltable, rtable, fk_ltable, fk_rtable,
l_key, r_key, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix)
return candset
def _block_tables_part(self, ltable, rtable, l_key, r_key, l_output_attrs,
r_output_attrs, l_output_prefix, r_output_prefix):
l_proj_attrs = (get_lattrs_to_project)(l_key, self.ltable_attrs,
l_output_attrs)
r_proj_attrs = (get_rattrs_to_project)(r_key, self.rtable_attrs,
r_output_attrs)
ltbl = (lproj_df)(ltable, l_proj_attrs)
rtbl = (rproj_df)(rtable, r_proj_attrs)
ltbl.set_index(l_key, inplace=True, drop=False)
rtbl.set_index(r_key, inplace=True, drop=False)
l_dict = ltbl.T.to_dict()
r_dict = rtbl.T.to_dict()
valid_pairs = []
for l_id in l_dict.keys():
ltuple = l_dict[l_id]
for r_id in r_dict.keys():
rtuple = r_dict[r_id]
res = self.black_box_function(ltuple, rtuple)
if not res:
valid_pairs.append([l_id, r_id])
fk_ltable, fk_rtable = l_output_prefix + l_key, r_output_prefix + r_key
candset = pd.DataFrame(valid_pairs, columns=[fk_ltable, fk_rtable])
candset = add_attributes(candset, ltable, rtable, fk_ltable, fk_rtable,
l_key, r_key, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix)
# if not isinstance(candset, pd.DataFrame):
# print('Returning {0}'.format(candset))
return candset
def block_candset_part(candset, ltable, rtable, fk_ltable, fk_rtable, l_key,
r_key, l_block_attr, r_block_attr):
# 1. create dummy column names to contain the values pulled from ltable and rtable
# based on the fk's
l_prefix, r_prefix = '__blk_a_', '__blk_b_'
# add attrs
cdf = add_attributes(candset, ltable, rtable, fk_ltable, fk_rtable, l_key,
r_key, [l_block_attr], [r_block_attr], l_prefix,
r_prefix)
l_chk, r_chk = l_prefix + l_block_attr, r_prefix + r_block_attr
res = candset[cdf[l_chk] == cdf[r_chk]]
return res
def block_table_chunks(ldf, rdf, l_key, r_key, l_attr, r_attr, tokenizer,
threshold, stopwords, l_out, r_out, l_prefix, r_prefix):
ldf = ldf[~ldf[l_attr].isnull()]
rdf = rdf[~rdf[r_attr].isnull()]
lstrings = preprocess_table(ldf, l_attr, l_key, stopwords)
ltokens = tokenize_strings(lstrings, tokenizer)
invindex = build_inv_index([ltokens])
rstrings = preprocess_table(rdf, r_attr, r_key, stopwords)
rtokens = tokenize_strings(rstrings, tokenizer)
res = probe(rtokens, invindex, threshold)
lcol, rcol = l_prefix + l_key, r_prefix + r_key
res = pd.DataFrame(res.get_pairids(), columns=[lcol, rcol])
if len(res):
res = add_attributes(res, ldf, rdf, lcol, rcol, l_key, r_key, l_out,
r_out, l_prefix, r_prefix)
return res
def _block_candset_part(self, candset, ltable, rtable, fk_ltable,
fk_rtable, l_key, r_key, l_block_attr,
r_block_attr, rem_stop_words, tokenizer,
threshold):
if isinstance(candset, pd.DataFrame) and len(candset):
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)
ltable = (lproj_df)(ltable, l_proj_attrs)
rtable = (rproj_df)(rtable, r_proj_attrs)
ltbl = ltable[
~ltable[l_block_attr].isnull()] # this might be redundant
rtbl = rtable[
~rtable[r_block_attr].isnull()] # this might be redundant
l_prefix, r_prefix = '__blk_a_', '__blk_b_'
temp_candset = add_attributes(candset, ltbl, rtbl, fk_ltable,
fk_rtable, l_key, r_key,
[l_block_attr], [r_block_attr],
l_prefix, r_prefix)
l_chk, r_chk = l_prefix + l_block_attr, r_prefix + r_block_attr
x = self._process_column(temp_candset[l_chk], rem_stop_words)
x = x.map(str2bytes).map(tokenizer.tokenize)
y = self._process_column(temp_candset[r_chk], rem_stop_words)
y = y.map(str2bytes).map(tokenizer.tokenize)
overlap_fn = partial(self._compute_overlap, threshold=threshold)
tmp = pd.DataFrame()
tmp['x'] = x
tmp['y'] = y
# print(tmp)
valid = tmp.apply(overlap_fn, raw=True, axis=1)
# print(len(valid))
valid_candset = candset[valid.values]
return valid_candset
else:
candset
def block_table_part(ltable, rtable, l_key, r_key, l_block_attr, r_block_attr,
l_out_attrs, r_out_attrs, l_prefix, r_prefix):
# join the tables
ltbl = proj_df(ltable, [l_key, l_block_attr])
rtbl = proj_df(rtable, [r_key, r_block_attr])
res = ltbl.merge(rtbl, left_on=l_block_attr, right_on=r_block_attr)
# get the cols to project & project
lcol, rcol = l_key + '_x', r_key + '_y'
res = proj_df(res, [lcol, rcol])
# rename_cols the fk columns to conform with given prefix
lcol, rcol = l_prefix + l_key, r_prefix + r_key
res = rename_cols(res, [lcol, rcol])
# add the required output attrs.
res = add_attributes(res, ltable, rtable, lcol, rcol, l_key, r_key,
l_out_attrs, r_out_attrs, l_prefix, r_prefix)
# finally return the result.
return res
def block_candset_chunks(candset, ldf, rdf, fk_ltable, fk_rtable, l_key, r_key,
l_attr, r_attr, tokenizer, threshold, stopwords):
#ldf = ldf.dropna()
#rdf = rdf.dropna()
ldf = ldf[~ldf[l_attr].isnull()]
rdf = rdf[~rdf[r_attr].isnull()]
tmp = pd.DataFrame()
l_prefix, r_prefix = '__blk_a_', '__blk_b_'
cdf = add_attributes(candset, ldf, rdf, fk_ltable, fk_rtable, l_key, r_key,
[l_attr], [r_attr], l_prefix, r_prefix)
l_chk, r_chk = l_prefix + l_attr, r_prefix + r_attr
x = process_col(cdf[l_chk], stopwords)
x = x.map(str2bytes).map(tokenizer.tokenize)
y = process_col(cdf[r_chk], stopwords)
y = y.map(str2bytes).map(tokenizer.tokenize)
overlap_fn = partial(compute_overlap, threshold=threshold)
tmp['x'] = x
tmp['y'] = y
valid = tmp.apply(overlap_fn, raw=True, axis=1)
res = candset[valid.values]
return res