def test_overlap_join_using_tokenizer_with_return_set_false(self):
A = pd.DataFrame([{'id':1, 'attr':'hello'}])
B = pd.DataFrame([{'id':1, 'attr':'he ll'}])
qg2_tok = QgramTokenizer(2)
assert_equal(qg2_tok.get_return_set(), False)
c = overlap_join(A, B, 'id', 'id', 'attr', 'attr', qg2_tok, 1)
assert_equal(len(c), 1)
assert_equal(qg2_tok.get_return_set(), False)
def test_overlap_join_using_tokenizer_with_return_set_false(self):
A = pd.DataFrame([{'id': 1, 'attr': 'hello'}])
B = pd.DataFrame([{'id': 1, 'attr': 'he ll'}])
qg2_tok = QgramTokenizer(2)
assert_equal(qg2_tok.get_return_set(), False)
c = overlap_join(A, B, 'id', 'id', 'attr', 'attr', qg2_tok, 1)
assert_equal(len(c), 1)
assert_equal(qg2_tok.get_return_set(), False)
def test_overlap_join_invalid_tokenizer(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
[], self.threshold)
def test_overlap_join_numeric_r_join_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.int_attr',
self.tokenizer, self.threshold)
def test_overlap_join_invalid_r_join_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.invalid_attr',
self.tokenizer, self.threshold)
def test_overlap_join_invalid_rtable(self):
overlap_join(self.A, [], 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold)
def time_overlap_delim_1(self):
overlap_join(self.ltable, self.rtable, self.l_id_attr, self.r_id_attr,
self.l_join_attr, self.r_join_attr, self.delim_tok, 1)
def test_overlap_join_invalid_l_out_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold, '>=', True, False,
['A.invalid_attr'], ['B.attr'])
def test_overlap_join_invalid_comp_op_le(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold, '<=')
def test_overlap_join_invalid_threshold_zero(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, 0)
def test_overlap_join_invalid_tokenizer(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr', [],
self.threshold)
def test_overlap_join_numeric_r_join_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.int_attr',
self.tokenizer, self.threshold)
def test_overlap_join_invalid_r_join_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr',
'B.invalid_attr', self.tokenizer, self.threshold)
def test_overlap_join_invalid_rtable(self):
overlap_join(self.A, [], 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold)
def test_overlap_join_invalid_threshold_zero(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, 0)
def test_overlap_join_invalid_comp_op_le(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold, '<=')
def time_overlap_delim_1(self):
overlap_join(self.ltable, self.rtable,
self.l_id_attr, self.r_id_attr,
self.l_join_attr, self.r_join_attr,
self.delim_tok, 1)
def time_overlap_delim_1(self):
overlap_join(self.ltable, self.rtable, 'id', 'id', 'attr', 'attr',
self.delim_tok, 1)
def block_tables(self, ltable, rtable, l_overlap_attr, r_overlap_attr,
rem_stop_words=False, q_val=None, word_level=True,
overlap_size=1,
l_output_attrs=None, r_output_attrs=None,
l_output_prefix='ltable_', r_output_prefix='rtable_',
allow_missing=False, verbose=False, show_progress=True,
n_jobs=1):
"""
Blocks two tables based on the overlap of token sets of attribute
values.
Finds tuple pairs from left and right tables such that the overlap
between (a) the set of tokens obtained by tokenizing the value of
attribute l_overlap_attr of a tuple from the left table, and (b) the
set of tokens obtained by tokenizing the value of attribute
r_overlap_attr of a tuple from the right table, is above a certain
threshold.
Args:
ltable (DataFrame): The left input table.
rtable (DataFrame): The right input table.
l_overlap_attr (string): The overlap attribute in left table.
r_overlap_attr (string): The overlap attribute in right table.
rem_stop_words (boolean): A flag to indicate whether stop words
(e.g., a, an, the) should be removed from the token sets of the
overlap attribute values (defaults to False).
q_val (int): The value of q to use if the overlap attributes
values are to be tokenized as qgrams (defaults to None).
word_level (boolean): A flag to indicate whether the overlap
attributes should be tokenized as words (i.e, using whitespace
as delimiter) (defaults to True).
overlap_size (int): The minimum number of tokens that must
overlap (defaults to 1).
l_output_attrs (list): A list of attribute names from the left
table to be included in the output candidate set (defaults
to None).
r_output_attrs (list): A list of attribute names from the right
table to be included in the output candidate set (defaults
to None).
l_output_prefix (string): The prefix to be used for the attribute names
coming from the left table in the output
candidate set (defaults to 'ltable\_').
r_output_prefix (string): The prefix to be used for the attribute names
coming from the right table in the output
candidate set (defaults to 'rtable\_').
allow_missing (boolean): A flag to indicate whether tuple pairs
with missing value in at least one of the
blocking attributes should be included in
the output candidate set (defaults to
False). If this flag is set to True, a
tuple in ltable with missing value in the
blocking attribute will be matched with
every tuple in rtable and vice versa.
verbose (boolean): A flag to indicate whether the debug
information should be logged (defaults to False).
show_progress (boolean): A flag to indicate whether progress should
be displayed to the user (defaults to True).
n_jobs (int): The number of parallel jobs to be used for computation
(defaults to 1). If -1 all CPUs are used. If 0 or 1,
no parallel computation 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) is less than 1, then no parallel
computation is used (i.e., equivalent to the default).
Returns:
A candidate set of tuple pairs that survived blocking (DataFrame).
Raises:
AssertionError: If `ltable` is not of type pandas
DataFrame.
AssertionError: If `rtable` is not of type pandas
DataFrame.
AssertionError: If `l_overlap_attr` is not of type string.
AssertionError: If `r_overlap_attr` is not of type string.
AssertionError: If `l_output_attrs` is not of type of
list.
AssertionError: If `r_output_attrs` is not of type of
list.
AssertionError: If the values in `l_output_attrs` is not of type
string.
AssertionError: If the values in `r_output_attrs` is not of type
string.
AssertionError: If `l_output_prefix` is not of type
string.
AssertionError: If `r_output_prefix` is not of type
string.
AssertionError: If `q_val` is not of type int.
AssertionError: If `word_level` is not of type boolean.
AssertionError: If `overlap_size` is not of type int.
AssertionError: If `verbose` is not of type
boolean.
AssertionError: If `allow_missing` is not of type boolean.
AssertionError: If `show_progress` is not of type
boolean.
AssertionError: If `n_jobs` is not of type
int.
AssertionError: If `l_overlap_attr` is not in the ltable
columns.
AssertionError: If `r_block_attr` is not in the rtable columns.
AssertionError: If `l_output_attrs` are not in the ltable.
AssertionError: If `r_output_attrs` are not in the rtable.
SyntaxError: If `q_val` is set to a valid value and
`word_level` is set to True.
SyntaxError: If `q_val` is set to None and
`word_level` is set to False.
Examples:
>>> import py_entitymatching as em
>>> A = em.read_csv_metadata('path_to_csv_dir/table_A.csv', key='ID')
>>> B = em.read_csv_metadata('path_to_csv_dir/table_B.csv', key='ID')
>>> ob = em.OverlapBlocker()
# Use word-level tokenizer
>>> C1 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], word_level=True, overlap_size=1)
# Use q-gram tokenizer
>>> C2 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], word_level=False, q_val=2)
# Include all possible missing values
>>> C3 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], allow_missing=True)
# Use all the cores in the machine
>>> C3 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], n_jobs=-1)
"""
# validate data types of standard input parameters
self.validate_types_params_tables(ltable, rtable,
l_output_attrs, r_output_attrs,
l_output_prefix,
r_output_prefix, verbose, n_jobs)
# validate data types of input parameters specific to overlap blocker
self.validate_types_other_params(l_overlap_attr, r_overlap_attr,
rem_stop_words, q_val,
word_level, overlap_size)
# validate data type of allow_missing
self.validate_allow_missing(allow_missing)
# validate data type of show_progress
self.validate_show_progress(show_progress)
# validate overlap attributes
self.validate_overlap_attrs(ltable, rtable, l_overlap_attr,
r_overlap_attr)
# validate output attributes
self.validate_output_attrs(ltable, rtable, l_output_attrs,
r_output_attrs)
# get and validate required metadata
log_info(logger, 'Required metadata: ltable key, rtable key', verbose)
# # get metadata
l_key, r_key = cm.get_keys_for_ltable_rtable(ltable, rtable, logger,
verbose)
# # validate metadata
cm._validate_metadata_for_table(ltable, l_key, 'ltable', logger,
verbose)
cm._validate_metadata_for_table(rtable, r_key, 'rtable', logger,
verbose)
# validate word_level and q_val
self.validate_word_level_qval(word_level, q_val)
# do blocking
# # do projection before merge
l_proj_attrs = self.get_attrs_to_project(l_key, l_overlap_attr,
l_output_attrs)
l_df = ltable[l_proj_attrs]
r_proj_attrs = self.get_attrs_to_project(r_key, r_overlap_attr,
r_output_attrs)
r_df = rtable[r_proj_attrs]
# # case the column to string if required.
l_df.is_copy, r_df.is_copy = False, False # to avoid setwithcopy warning
ssj.dataframe_column_to_str(l_df, l_overlap_attr, inplace=True)
ssj.dataframe_column_to_str(r_df, r_overlap_attr, inplace=True)
# # cleanup the tables from non-ascii characters, punctuations, and stop words
l_dummy_overlap_attr = '@#__xx__overlap_ltable__#@'
r_dummy_overlap_attr = '@#__xx__overlap_rtable__#@'
l_df[l_dummy_overlap_attr] = l_df[l_overlap_attr]
r_df[r_dummy_overlap_attr] = r_df[r_overlap_attr]
if not l_df.empty:
self.cleanup_table(l_df, l_dummy_overlap_attr, rem_stop_words)
if not r_df.empty:
self.cleanup_table(r_df, r_dummy_overlap_attr, rem_stop_words)
# # determine which tokenizer to use
if word_level == True:
# # # create a whitespace tokenizer
tokenizer = WhitespaceTokenizer(return_set=True)
else:
# # # create a qgram tokenizer
tokenizer = QgramTokenizer(qval=q_val, return_set=True)
# # perform overlap similarity join
candset = overlap_join(l_df, r_df, l_key, r_key, l_dummy_overlap_attr,
r_dummy_overlap_attr, tokenizer, overlap_size,
'>=',
allow_missing, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix, False, n_jobs,
show_progress)
# # retain only the required attributes in the output candidate set
retain_cols = self.get_attrs_to_retain(l_key, r_key, l_output_attrs,
r_output_attrs,
l_output_prefix, r_output_prefix)
candset = candset[retain_cols]
# update metadata in the catalog
key = get_name_for_key(candset.columns)
candset = add_key_column(candset, key)
cm.set_candset_properties(candset, key, l_output_prefix + l_key,
r_output_prefix + r_key, ltable, rtable)
# return the candidate set
return candset
def block_tables(self,
ltable,
rtable,
l_overlap_attr,
r_overlap_attr,
rem_stop_words=False,
q_val=None,
word_level=True,
overlap_size=1,
l_output_attrs=None,
r_output_attrs=None,
l_output_prefix='ltable_',
r_output_prefix='rtable_',
allow_missing=False,
verbose=False,
show_progress=True,
n_jobs=1):
"""
Blocks two tables based on the overlap of token sets of attribute
values.
Finds tuple pairs from left and right tables such that the overlap
between (a) the set of tokens obtained by tokenizing the value of
attribute l_overlap_attr of a tuple from the left table, and (b) the
set of tokens obtained by tokenizing the value of attribute
r_overlap_attr of a tuple from the right table, is above a certain
threshold.
Args:
ltable (DataFrame): The left input table.
rtable (DataFrame): The right input table.
l_overlap_attr (string): The overlap attribute in left table.
r_overlap_attr (string): The overlap attribute in right table.
rem_stop_words (boolean): A flag to indicate whether stop words
(e.g., a, an, the) should be removed from the token sets of the
overlap attribute values (defaults to False).
q_val (int): The value of q to use if the overlap attributes
values are to be tokenized as qgrams (defaults to None).
word_level (boolean): A flag to indicate whether the overlap
attributes should be tokenized as words (i.e, using whitespace
as delimiter) (defaults to True).
overlap_size (int): The minimum number of tokens that must
overlap (defaults to 1).
l_output_attrs (list): A list of attribute names from the left
table to be included in the output candidate set (defaults
to None).
r_output_attrs (list): A list of attribute names from the right
table to be included in the output candidate set (defaults
to None).
l_output_prefix (string): The prefix to be used for the attribute names
coming from the left table in the output
candidate set (defaults to 'ltable\_').
r_output_prefix (string): The prefix to be used for the attribute names
coming from the right table in the output
candidate set (defaults to 'rtable\_').
allow_missing (boolean): A flag to indicate whether tuple pairs
with missing value in at least one of the
blocking attributes should be included in
the output candidate set (defaults to
False). If this flag is set to True, a
tuple in ltable with missing value in the
blocking attribute will be matched with
every tuple in rtable and vice versa.
verbose (boolean): A flag to indicate whether the debug
information should be logged (defaults to False).
show_progress (boolean): A flag to indicate whether progress should
be displayed to the user (defaults to True).
n_jobs (int): The number of parallel jobs to be used for computation
(defaults to 1). If -1 all CPUs are used. If 0 or 1,
no parallel computation 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) is less than 1, then no parallel
computation is used (i.e., equivalent to the default).
Returns:
A candidate set of tuple pairs that survived blocking (DataFrame).
Raises:
AssertionError: If `ltable` is not of type pandas
DataFrame.
AssertionError: If `rtable` is not of type pandas
DataFrame.
AssertionError: If `l_overlap_attr` is not of type string.
AssertionError: If `r_overlap_attr` is not of type string.
AssertionError: If `l_output_attrs` is not of type of
list.
AssertionError: If `r_output_attrs` is not of type of
list.
AssertionError: If the values in `l_output_attrs` is not of type
string.
AssertionError: If the values in `r_output_attrs` is not of type
string.
AssertionError: If `l_output_prefix` is not of type
string.
AssertionError: If `r_output_prefix` is not of type
string.
AssertionError: If `q_val` is not of type int.
AssertionError: If `word_level` is not of type boolean.
AssertionError: If `overlap_size` is not of type int.
AssertionError: If `verbose` is not of type
boolean.
AssertionError: If `allow_missing` is not of type boolean.
AssertionError: If `show_progress` is not of type
boolean.
AssertionError: If `n_jobs` is not of type
int.
AssertionError: If `l_overlap_attr` is not in the ltable
columns.
AssertionError: If `r_block_attr` is not in the rtable columns.
AssertionError: If `l_output_attrs` are not in the ltable.
AssertionError: If `r_output_attrs` are not in the rtable.
SyntaxError: If `q_val` is set to a valid value and
`word_level` is set to True.
SyntaxError: If `q_val` is set to None and
`word_level` is set to False.
Examples:
>>> import py_entitymatching as em
>>> A = em.read_csv_metadata('path_to_csv_dir/table_A.csv', key='ID')
>>> B = em.read_csv_metadata('path_to_csv_dir/table_B.csv', key='ID')
>>> ob = em.OverlapBlocker()
# Use word-level tokenizer
>>> C1 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], word_level=True, overlap_size=1)
# Use q-gram tokenizer
>>> C2 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], word_level=False, q_val=2)
# Include all possible missing values
>>> C3 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], allow_missing=True)
# Use all the cores in the machine
>>> C3 = ob.block_tables(A, B, 'address', 'address', l_output_attrs=['name'], r_output_attrs=['name'], n_jobs=-1)
"""
# validate data types of standard input parameters
self.validate_types_params_tables(ltable, rtable, l_output_attrs,
r_output_attrs, l_output_prefix,
r_output_prefix, verbose, n_jobs)
# validate data types of input parameters specific to overlap blocker
self.validate_types_other_params(l_overlap_attr, r_overlap_attr,
rem_stop_words, q_val, word_level,
overlap_size)
# validate data type of allow_missing
self.validate_allow_missing(allow_missing)
# validate data type of show_progress
self.validate_show_progress(show_progress)
# validate overlap attributes
self.validate_overlap_attrs(ltable, rtable, l_overlap_attr,
r_overlap_attr)
# validate output attributes
self.validate_output_attrs(ltable, rtable, l_output_attrs,
r_output_attrs)
# get and validate required metadata
log_info(logger, 'Required metadata: ltable key, rtable key', verbose)
# # get metadata
l_key, r_key = cm.get_keys_for_ltable_rtable(ltable, rtable, logger,
verbose)
# # validate metadata
cm._validate_metadata_for_table(ltable, l_key, 'ltable', logger,
verbose)
cm._validate_metadata_for_table(rtable, r_key, 'rtable', logger,
verbose)
# validate word_level and q_val
self.validate_word_level_qval(word_level, q_val)
# do blocking
# # do projection before merge
l_proj_attrs = self.get_attrs_to_project(l_key, l_overlap_attr,
l_output_attrs)
l_df = ltable[l_proj_attrs]
r_proj_attrs = self.get_attrs_to_project(r_key, r_overlap_attr,
r_output_attrs)
r_df = rtable[r_proj_attrs]
# # case the column to string if required.
l_df.is_copy, r_df.is_copy = False, False # to avoid setwithcopy warning
ssj.dataframe_column_to_str(l_df, l_overlap_attr, inplace=True)
ssj.dataframe_column_to_str(r_df, r_overlap_attr, inplace=True)
# # cleanup the tables from non-ascii characters, punctuations, and stop words
l_dummy_overlap_attr = '@#__xx__overlap_ltable__#@'
r_dummy_overlap_attr = '@#__xx__overlap_rtable__#@'
l_df[l_dummy_overlap_attr] = l_df[l_overlap_attr]
r_df[r_dummy_overlap_attr] = r_df[r_overlap_attr]
if not l_df.empty:
self.cleanup_table(l_df, l_dummy_overlap_attr, rem_stop_words)
if not r_df.empty:
self.cleanup_table(r_df, r_dummy_overlap_attr, rem_stop_words)
# # determine which tokenizer to use
if word_level == True:
# # # create a whitespace tokenizer
tokenizer = WhitespaceTokenizer(return_set=True)
else:
# # # create a qgram tokenizer
tokenizer = QgramTokenizer(qval=q_val, return_set=True)
# # perform overlap similarity join
candset = overlap_join(l_df, r_df, l_key, r_key, l_dummy_overlap_attr,
r_dummy_overlap_attr, tokenizer, overlap_size,
'>=', allow_missing, l_output_attrs,
r_output_attrs, l_output_prefix,
r_output_prefix, False, n_jobs, show_progress)
# # retain only the required attributes in the output candidate set
retain_cols = self.get_attrs_to_retain(l_key, r_key, l_output_attrs,
r_output_attrs, l_output_prefix,
r_output_prefix)
candset = candset[retain_cols]
# update metadata in the catalog
key = get_name_for_key(candset.columns)
candset = add_key_column(candset, key)
cm.set_candset_properties(candset, key, l_output_prefix + l_key,
r_output_prefix + r_key, ltable, rtable)
# return the candidate set
return candset
def test_overlap_join_invalid_r_out_attr(self):
overlap_join(self.A, self.B, 'A.id', 'B.id', 'A.attr', 'B.attr',
self.tokenizer, self.threshold, '>=', True, False,
['A.attr'], ['B.invalid_attr'])