from py_stringsimjoin.filter.overlap_filter import OverlapFilter
from py_stringsimjoin.utils.validation import validate_tokenizer
[docs]def overlap_join(ltable, rtable,
l_key_attr, r_key_attr,
l_join_attr, r_join_attr,
tokenizer, threshold, comp_op='>=',
allow_missing=False,
l_out_attrs=None, r_out_attrs=None,
l_out_prefix='l_', r_out_prefix='r_',
out_sim_score=True, n_jobs=1, show_progress=True):
"""Join two tables using overlap measure.
For two sets X and Y, the overlap between them is given by:
:math:`overlap(X, Y) = |X \\cap Y|`
Finds tuple pairs from left table and right table such that the overlap
between the join attributes satisfies the condition on input threshold. For
example, if the comparison operator is '>=', finds tuple pairs whose
overlap between the strings that are the values of the join attributes is
greater than or equal to the input threshold, as specified in "threshold".
Args:
ltable (DataFrame): left input table.
rtable (DataFrame): right input table.
l_key_attr (string): key attribute in left table.
r_key_attr (string): key attribute in right table.
l_join_attr (string): join attribute in left table.
r_join_attr (string): join attribute in right table.
tokenizer (Tokenizer): tokenizer to be used to tokenize join
attributes.
threshold (float): overlap threshold to be satisfied.
comp_op (string): comparison operator. Supported values are '>=', '>'
and '=' (defaults to '>=').
allow_missing (boolean): flag to indicate whether tuple pairs with
missing value in at least one of the join attributes should be
included in the output (defaults to False). If this flag is set to
True, a tuple in ltable with missing value in the join attribute
will be matched with every tuple in rtable and vice versa.
l_out_attrs (list): list of attribute names from the left table to be
included in the output table (defaults to None).
r_out_attrs (list): list of attribute names from the right table to be
included in the output table (defaults to None).
l_out_prefix (string): prefix to be used for the attribute names coming
from the left table, in the output table (defaults to 'l\_').
r_out_prefix (string): prefix to be used for the attribute names coming
from the right table, in the output table (defaults to 'r\_').
out_sim_score (boolean): flag to indicate whether similarity score
should be included in the output table (defaults to True). Setting
this flag to True will add a column named '_sim_score' in the
output table. This column will contain the similarity scores for the
tuple pairs in the output.
n_jobs (int): number of parallel jobs to use for the computation
(defaults to 1). If -1 is given, all CPUs are used. If 1 is given,
no parallel computing code is used at all, which is useful for
debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used
(where n_cpus is the total number of CPUs in the machine). Thus for
n_jobs = -2, all CPUs but one are used. If (n_cpus + 1 + n_jobs)
becomes less than 1, then no parallel computing code will be used
(i.e., equivalent to the default).
show_progress (boolean): flag to indicate whether task progress should
be displayed to the user (defaults to True).
Returns:
An output table containing tuple pairs that satisfy the join
condition (DataFrame).
"""
# check if the input tokenizer is valid
validate_tokenizer(tokenizer)
# set return_set flag of tokenizer to be True, in case it is set to False
revert_tokenizer_return_set_flag = False
if not tokenizer.get_return_set():
tokenizer.set_return_set(True)
revert_tokenizer_return_set_flag = True
# use overlap filter to perform the join.
overlap_filter = OverlapFilter(tokenizer, threshold, comp_op, allow_missing)
output_table = overlap_filter.filter_tables(ltable, rtable,
l_key_attr, r_key_attr,
l_join_attr, r_join_attr,
l_out_attrs, r_out_attrs,
l_out_prefix, r_out_prefix,
out_sim_score, n_jobs,
show_progress)
# revert the return_set flag of tokenizer, in case it was modified.
if revert_tokenizer_return_set_flag:
tokenizer.set_return_set(False)
return output_table