def sample_table(table, size, replace=False):
"""
Sample MTable
Parameters
----------
table : MTable, input table to be sampled
size : int, number of samples
replace : boolean, whether sampling should be done with replacement.
By default, it is set to False.
Returns
-------
sampled_table: MTable, sampled table
"""
if len(table) == 0:
raise AttributeError('size of table is 0')
if len(table) < size:
raise AttributeError('sample size is larger than input table size')
s_indices = np.random.choice(len(table), size, replace=replace)
# sort the indices - just to have an order
s_indices = sorted(s_indices)
sampled_table = table.iloc[list(s_indices)]
#print sampled_table.properties
sampled_table = MTable(sampled_table, key=table.get_key())
sampled_table.properties = table.properties
return sampled_table
def read_csv_(*args, **kwargs):
"""
Read CSV (comma-separated) file into MTable
Parameters
----------
args : arguments to pandas read_csv command
kwargs : arguments to pandas read_csv command along with optional "key" parameter.
If key parameter is given, then it will be set as key, else a new attribute ("_id")
is added and set as key
Returns
-------
result : MTable
"""
# if kwargs.has_key('key') is False:
# raise AttributeError('Key is not specified')
key = kwargs.pop('key', None)
df = pd.read_csv(*args, **kwargs)
if key is not None:
return MTable(df, key=key)
else:
df = MTable(df)
#key_name=df._get_name_for_key(df.columns)
#df.add_key(key_name)
return df
def sample_table(table, size, replace=False):
"""
Sample MTable
Parameters
----------
table : MTable, input table to be sampled
size : int, number of samples
replace : boolean, whether sampling should be done with replacement.
By default, it is set to False.
Returns
-------
sampled_table: MTable, sampled table
"""
if len(table) == 0:
raise AttributeError('size of table is 0')
if len(table) < size:
raise AttributeError('sample size is larger than input table size')
s_indices = np.random.choice(len(table), size, replace=replace)
# sort the indices - just to have an order
s_indices = sorted(s_indices)
sampled_table = table.iloc[list(s_indices)]
#print sampled_table.properties
sampled_table = MTable(sampled_table, key=table.get_key())
sampled_table.properties = table.properties
return sampled_table
def block_tables(self, ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs=None, r_output_attrs=None):
"""
Block tables based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
ltable, rtable : MTable
Input MTables
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
l_output_attrs, r_output_attrs : list (of strings), defaults to None
attribute names to be included in the output table
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
l_output_attrs, r_output_attrs = self.check_attrs(ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs, r_output_attrs)
# remove nans
l_df = self.rem_nan(ltable, l_block_attr)
r_df = self.rem_nan(rtable, r_block_attr)
candset = pd.merge(l_df, r_df, left_on=l_block_attr, right_on=r_block_attr,
suffixes=('_ltable', '_rtable'))
# get output columns
retain_cols, final_cols = self.output_columns(ltable.get_key(), rtable.get_key(), list(candset.columns),
l_output_attrs, r_output_attrs)
candset = candset[retain_cols]
candset.columns = final_cols
candset = MTable(candset)
# set metadata
candset.set_property('ltable', ltable)
candset.set_property('rtable', rtable)
candset.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
candset.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
return candset
def down_sample(ltable, rtable, size, y):
s_table, b_table, is_swapped = _order_tables(ltable, rtable)
s_inv_index = _inv_index(s_table)
b_sample_size = min(math.floor(size/y), len(b_table))
b_tbl_indices = np.random.choice(len(b_table), b_sample_size, replace=False)
s_tbl_indices = _probe_index(b_table.ix[b_tbl_indices], y,
len(s_table), s_inv_index)
if is_swapped:
s_tbl_indices, b_tbl_indices = b_tbl_indices, s_tbl_indices
l_sampled = MTable(ltable.iloc[list(s_tbl_indices)], ltable.get_key())
l_sampled.properties = ltable.properties
r_sampled = MTable(rtable.iloc[list(b_tbl_indices)], rtable.get_key())
r_sampled.properties = rtable.properties
return l_sampled, r_sampled
def block_union_combine(candset_list):
ltable, rtable = lr_tables(candset_list)
key_l = 'ltable.' + ltable.get_key()
key_r = 'rtable.' + rtable.get_key()
# get the set of id tuples
id_set = set([(r[key_l], r[key_r]) for c in candset_list for i, r in c.iterrows()])
# get the union set of columns
col_set = set([x for c in candset_list for x in c.columns])
col_l, col_r = lr_cols(col_set)
dict_list = [get_dict(ltable.ix[x[0]], rtable.ix[x[1]], col_l, col_r) for x in id_set]
table = pd.DataFrame(dict_list)
col_f = fin_cols(col_l, col_r, ltable.get_key(), rtable.get_key())
table = MTable(table[col_f])
table._add_key('_id')
table.set_property('ltable', ltable)
table.set_property('rtable', rtable)
return table
def create_mtable(table, key=None, ltable=None, rtable=None, foreign_key_ltable=None, foreign_key_rtable=None):
"""
Create mtable from dataframe
"""
out_table = MTable(table, key=key)
truth_vals = [ltable is not None, rtable is not None, foreign_key_ltable is not None,
foreign_key_rtable is not None]
if all(truth_vals) == True:
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable', foreign_key_ltable)
out_table.set_property('foreign_key_rtable', foreign_key_rtable)
else:
if any(truth_vals) == True:
logging.getLogger(__name__).warning('Not all the properties for vtable are given; so not setting '
'any of them')
return out_table
def label_table(tbl, col_name, replace=True):
"""
Label training data
Parameters
----------
tbl : MTable, Table to be labeled
col_name : String, Name of the label column
replace : Boolean, specifies whether the column with the given 'col_name' must be overwritten, if it already exists.
[This option is currently experimental].
Returns
-------
result : MTable, Table with labels
Notes
-----
The label value is expected to be only 0 or 1.
"""
from magellan.gui.mtable_gui import edit
table = tbl.copy()
if col_name in table.columns:
if replace == True:
logging.getLogger(__name__).warning(
'Input table already contains column %s. '
'' % col_name)
table[col_name] = 0
else:
table[col_name] = 0
mg.edit(table)
table[col_name] = table[col_name].astype(int)
# check if the table contains only 0s and 1s
c1 = table[col_name] == 1
c2 = table[col_name] == 0
c = sum(c1 | c2)
assert c == len(
table), 'The label column contains values other than 0 and 1'
table = MTable(table, key=tbl.get_key())
table.properties = tbl.properties
return table
def label_table(tbl, col_name, replace=True):
"""
Label training data
Parameters
----------
tbl : MTable, Table to be labeled
col_name : String, Name of the label column
replace : Boolean, specifies whether the column with the given 'col_name' must be overwritten, if it already exists.
[This option is currently experimental].
Returns
-------
result : MTable, Table with labels
Notes
-----
The label value is expected to be only 0 or 1.
"""
from magellan.gui.mtable_gui import edit
table = tbl.copy()
if col_name in table.columns:
if replace == True:
logging.getLogger(__name__).warning('Input table already contains column %s. '
'' %col_name)
table[col_name] = 0
else:
table[col_name] = 0
mg.edit(table)
table[col_name] = table[col_name].astype(int)
# check if the table contains only 0s and 1s
c1 = table[col_name] == 1
c2 = table[col_name] == 0
c = sum(c1|c2)
assert c == len(table), 'The label column contains values other than 0 and 1'
table = MTable(table, key=tbl.get_key())
table.properties = tbl.properties
return table
def create_mtable(table,
key=None,
ltable=None,
rtable=None,
foreign_key_ltable=None,
foreign_key_rtable=None):
"""
Create mtable from dataframe
"""
out_table = MTable(table, key=key)
truth_vals = [
ltable is not None, rtable is not None, foreign_key_ltable is not None,
foreign_key_rtable is not None
]
if all(truth_vals) == True:
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable', foreign_key_ltable)
out_table.set_property('foreign_key_rtable', foreign_key_rtable)
else:
if any(truth_vals) == True:
logging.getLogger(__name__).warning(
'Not all the properties for vtable are given; so not setting '
'any of them')
return out_table
def down_sample(s_table, b_table, size, y):
if len(b_table) < size:
print 'Warning!! size of table B is less than b_size parameter - using entire table B'
size = len(b_table)
t1 = time.time()
s_inv_index = _inv_index(s_table)
print 'Inverted Index Time: '
print int(time.time() - t1)
b_sample_size = min(math.floor(size/y), len(b_table))
b_tbl_indices = list(np.random.choice(len(b_table), b_sample_size, replace=False))
t1 = time.time()
s_tbl_indices = _probe_index(b_table.ix[b_tbl_indices], y,
len(s_table), s_inv_index)
print 'Probe Index Time: '
print int(time.time() - t1)
s_tbl_indices = list(s_tbl_indices)
l_sampled = MTable(s_table.iloc[list(s_tbl_indices)], key=s_table.get_key())
l_sampled.properties = s_table.properties
r_sampled = MTable(b_table.iloc[list(b_tbl_indices)], key=b_table.get_key())
r_sampled.properties = b_table.properties
return l_sampled, r_sampled
def down_sample(ltable, rtable, size, y):
s_table, b_table, is_swapped = _order_tables(ltable, rtable)
s_inv_index = _inv_index(s_table)
b_sample_size = min(math.floor(size / y), len(b_table))
b_tbl_indices = list(
np.random.choice(len(b_table), b_sample_size, replace=False))
s_tbl_indices = _probe_index(b_table.ix[b_tbl_indices], y, len(s_table),
s_inv_index)
s_tbl_indices = list(s_tbl_indices)
if is_swapped:
s_tbl_indices, b_tbl_indices = b_tbl_indices, s_tbl_indices
l_sampled = MTable(ltable.iloc[list(s_tbl_indices)], key=ltable.get_key())
l_sampled.properties = ltable.properties
r_sampled = MTable(rtable.iloc[list(b_tbl_indices)], key=rtable.get_key())
r_sampled.properties = rtable.properties
return l_sampled, r_sampled
def block_tables(self, ltable, rtable, ltable_block_attribute, rtable_block_attribute,
ltable_output_colnames=None, rtable_output_colnames=None):
# integrity checks
ltable_output_colnames, rtable_output_colnames = check_columns(ltable, rtable, ltable_block_attribute,
rtable_block_attribute, ltable_output_colnames,
rtable_output_colnames)
# remove rows with nan values in block attribute column
m_ltable, m_rtable = rem_nans(ltable, rtable, ltable_block_attribute, rtable_block_attribute)
candset = pd.merge(m_ltable, m_rtable, left_on=ltable_block_attribute, right_on=rtable_block_attribute,
suffixes=('_ltable', '_rtable'), copy=False)
ret_cols, fin_cols = out_cols(ltable.get_key(), rtable.get_key(), list(candset.columns),
ltable_output_colnames, rtable_output_colnames)
candset = MTable(candset[ret_cols])
candset._add_key('_id')
candset.columns = fin_cols
candset.set_property('ltable', ltable)
candset.set_property('rtable', rtable)
return candset
def read_csv(file_path, **kwargs):
"""
Read CSV (comma-separated) file into MTable
Parameters
----------
args : arguments to pandas read_csv command
kwargs : arguments to pandas read_csv command along with optional "key" if its MTable
or "key", "ltable", "rtable", "foreign_key_ltable", "foreign_key_rtable" if its VTable
Returns
-------
result : MTable
Note
----
read_csv can read in the meta data mentioned at the beginning of the file like this:
#key=id
A user can override or supply metadata as key-value args to the function
"""
properties, num_lines = get_properties_from_file(file_path)
properties, kwargs = update_properties(properties, **kwargs)
check_properties(properties)
kwargs['skiprows']=num_lines
df = pd.read_csv(file_path, **kwargs)
# get key
key = properties.pop('key', None)
if key is not None:
df = MTable(df, key=key)
else:
df = MTable(df)
for k, v in properties.iteritems():
df.set_property(k, v)
return df
def block_candset(self, vtable, l_block_attr, r_block_attr):
"""
Block candidate set (virtual MTable) based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
vtable : MTable
Input candidate set
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
ltable = vtable.get_property('ltable')
rtable = vtable.get_property('rtable')
self.check_attrs(ltable, rtable, l_block_attr, r_block_attr, None,
None)
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
# convert to dataframes
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
# set index for convenience
l_df.set_index(ltable.get_key(), inplace=True)
r_df.set_index(rtable.get_key(), inplace=True)
if mg._verbose:
count = 0
per_count = math.ceil(mg._percent / 100.0 * len(vtable))
print per_count
elif mg._progbar:
bar = pyprind.ProgBar(len(vtable))
# keep track of valid ids
valid = []
# iterate candidate set and process each row
for idx, row in vtable.iterrows():
if mg._verbose:
count += 1
if count % per_count == 0:
print str(mg._percent * count /
per_count) + ' percentage done !!!'
elif mg._progbar:
bar.update()
# get the value of block attribute from ltuple
l_val = l_df.ix[row[l_key], l_block_attr]
r_val = r_df.ix[row[r_key], r_block_attr]
if l_val != np.NaN and r_val != np.NaN:
if l_val == r_val:
valid.append(True)
else:
valid.append(False)
else:
valid.append(False)
# should be modified
if len(vtable) > 0:
out_table = MTable(vtable[valid], key=vtable.get_key())
else:
out_table = MTable(columns=vtable.columns, key=vtable.get_key())
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable',
'ltable.' + ltable.get_key())
out_table.set_property('foreign_key_rtable',
'rtable.' + rtable.get_key())
return out_table
def block_tables(self,
ltable,
rtable,
l_block_attr,
r_block_attr,
l_output_attrs=None,
r_output_attrs=None):
"""
Block tables based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
ltable, rtable : MTable
Input MTables
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
l_output_attrs, r_output_attrs : list (of strings), defaults to None
attribute names to be included in the output table
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
l_output_attrs, r_output_attrs = self.check_attrs(
ltable, rtable, l_block_attr, r_block_attr, l_output_attrs,
r_output_attrs)
# remove nans
l_df = self.rem_nan(ltable, l_block_attr)
r_df = self.rem_nan(rtable, r_block_attr)
candset = pd.merge(l_df,
r_df,
left_on=l_block_attr,
right_on=r_block_attr,
suffixes=('_ltable', '_rtable'))
# get output columns
retain_cols, final_cols = self.output_columns(ltable.get_key(),
rtable.get_key(),
list(candset.columns),
l_output_attrs,
r_output_attrs)
candset = candset[retain_cols]
candset.columns = final_cols
candset = MTable(candset)
# set metadata
candset.set_property('ltable', ltable)
candset.set_property('rtable', rtable)
candset.set_property('foreign_key_ltable',
'ltable.' + ltable.get_key())
candset.set_property('foreign_key_rtable',
'rtable.' + rtable.get_key())
return candset
def extract_feature_vecs(s, attrs_before=None, feature_table=None, attrs_after=None):
"""
Extract feature vectors
Parameters
----------
s : MTable,
labeled virtual MTable or combined blocker output
attrs_before : list, defaults to None
List of attribute names from "s" to be included in output table before the feature vector
feat_table : pandas DataFrame, defaults to None
List of features to be applied (also see: mg.get_features_for_blocking)
attrs_after : list, defaults to None
List of attribute names from "s" to be included in output table after the feature vector
Returns
-------
feature_vectors : MTable,
Containing features values (obtained by applying feature fns in feat_table) and attributes as
mentioned in the input
"""
# basic checks
assert isJVMStarted(), 'JVM should be started using init_jvm to compute features'
ltable = s.get_property('ltable')
rtable = s.get_property('rtable')
assert ltable is not None, 'Left table is not set'
assert rtable is not None, 'Right table is not set'
if feature_table is None:
feature_table = mg.get_features_for_blocking(ltable, rtable)
l_key, r_key = s.get_property('foreign_key_ltable'), s.get_property('foreign_key_rtable')
start = time.time()
id_list = [(r[l_key], r[r_key]) for i, r in s.iterrows()]
end = time.time()
logging.getLogger(__name__).info('Iterating rows (%d) took %f secs' %(len(s), end - start))
# compute feature values
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
l_df.set_index(ltable.get_key(), inplace=True, drop=False)
r_df.set_index(rtable.get_key(), inplace=True, drop=False)
start = time.time()
feat_vals = [apply_feat_fns(l_df.ix[x[0]], r_df.ix[x[1]], feature_table) for x in id_list]
end = time.time()
logging.getLogger(__name__).info('Applying feature functions took : %f secs' % (end - start))
table = pd.DataFrame(feat_vals, index=s.index.values)
# get the feature names and re-arrange columns in that order
feat_names = list(feature_table['feature_name'])
table = table[feat_names]
# insert attrs_before
if attrs_before:
if not isinstance(attrs_before, list):
attrs_before = [attrs_before]
attrs_before.reverse()
for a in attrs_before:
table.insert(0, a, s[a])
table.insert(0, r_key, s[r_key])
table.insert(0, l_key, s[l_key])
# insert attrs after
if attrs_after:
if not isinstance(attrs_after, list):
attrs_after = [attrs_after]
attrs_after.reverse()
for a in attrs_after:
table.insert(len(table.columns), a, s[a])
# reset the table index
table.reset_index(inplace=True, drop=True)
feature_vectors = MTable(table)
if s.get_key() not in feature_vectors.columns:
feature_vectors.add_key(s.get_key())
# metadata
feature_vectors._metadata = s._metadata
feature_vectors.properties = s.properties
return feature_vectors
def block_tables_skd(self, ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs=None, r_output_attrs=None):
"""
Block tables based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
ltable, rtable : MTable
Input MTables
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
l_output_attrs, r_output_attrs : list (of strings), defaults to None
attribute names to be included in the output table
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
l_output_attrs, r_output_attrs = self.check_attrs(ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs, r_output_attrs)
# remove nans
l_df = self.rem_nan(ltable, l_block_attr)
r_df = self.rem_nan(rtable, r_block_attr)
#print 'cpu_count() = %d\n' % multiprocessing.cpu_count()
cpu_count = multiprocessing.cpu_count()
m = int(math.sqrt(cpu_count)) # no. of splits of l_df
n = cpu_count/m # no. of splits of r_df
print "m: ", m, ", n: ", n
t0 = time.time()
l_splits = np.array_split(l_df, m)
t1 = time.time()
r_splits = np.array_split(r_df, n)
t2 = time.time()
l_key = ltable.get_key()
r_key = rtable.get_key()
lr_splits = [(l, r, l_block_attr, r_block_attr, l_key, r_key, l_output_attrs, r_output_attrs) for l in l_splits for r in r_splits]
t3 = time.time()
#pool = Pool(4)
pool = mp.ProcessingPool(processes=cpu_count, maxtasksperchild=1)
t4 = time.time()
c_splits = pool.map(self.block_data_frames_skd, lr_splits)
t5 = time.time()
pool.close()
t6 = time.time()
pool.join()
t7 = time.time()
candset = pd.concat(c_splits, ignore_index=True)
#candset = c_splits[0].append(c_splits[1], ignore_index=True)
t8 = time.time()
print "Time taken to split table A:", (t1 - t0)
print "Time taken to split table B:", (t2 - t1)
print "Time taken to get AB splits:", (t3 - t2)
print "Time taken to start workers:", (t4 - t3)
print "Time taken to get C splits:", (t5 - t4)
print "Time taken to close pool:", (t6 - t5)
print "Time taken to join pool:", (t7 - t6)
print "Time taken to combine splits:", (t8 - t7)
final_cols = self.get_final_cols(l_key, r_key,
l_output_attrs, r_output_attrs)
candset.columns = final_cols
candset = MTable(candset)
# set metadata
candset.set_property('ltable', ltable)
candset.set_property('rtable', rtable)
candset.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
candset.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
return candset
def block_candset_opt_1(self, vtable, l_block_attr, r_block_attr):
"""
Block candidate set (virtual MTable) based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
vtable : MTable
Input candidate set
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
start_time = time.time()
# do integrity checks
ltable = vtable.get_property('ltable')
rtable = vtable.get_property('rtable')
self.check_attrs(ltable, rtable, l_block_attr, r_block_attr, None, None)
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
t000 = time.time()
print "Time taken to do integrity checks:", (t000 - start_time)
# convert to dataframes
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
t001 = time.time()
print "Time taken to convert tables A and B to data frames:", (t001 - t000)
# set index for convenience
l_df.set_index(ltable.get_key(), inplace=True)
r_df.set_index(rtable.get_key(), inplace=True)
t002 = time.time()
print "Time taken to set indexes for tables A and B:", (t002 - t001)
if mg._verbose:
count = 0
per_count = math.ceil(mg._percent/100.0*len(vtable))
print per_count
elif mg._progbar:
bar = pyprind.ProgBar(len(vtable))
column_names = list(vtable.columns)
#lid_idx = column_names.index(l_key)
#rid_idx = column_names.index(r_key)
l_block_attr_idx = column_names.index('ltable.' + l_block_attr)
r_block_attr_idx = column_names.index('rtable.' + r_block_attr)
# create look up table for quick access of rows
#l_dict = {}
#r_dict = {}
# keep track of valid ids
valid = []
# iterate candidate set and process each row
for row in vtable.itertuples(index=False):
if mg._verbose:
count += 1
if count%per_count == 0:
print str(mg._percent*count/per_count) + ' percentage done !!!'
elif mg._progbar:
bar.update()
# get the value of block attribute from ltuple
#row_lid = row[lid_idx]
#if row_lid not in l_dict:
# l_dict[row_lid] = row[l_block_attr_idx]
#l_val = l_dict[row_lid]
l_val = row[l_block_attr_idx]
# get the value of block attribute from rtuple
#row_rid = row[rid_idx]
#if row_rid not in r_dict:
# r_dict[row_rid] = row[r_block_attr_idx]
#r_val = r_dict[row_rid]
r_val = row[r_block_attr_idx]
if l_val != np.NaN and r_val != np.NaN:
if l_val == r_val:
valid.append(True)
else:
valid.append(False)
else:
valid.append(False)
t6 = time.time()
print "Time taken to get valid ids:", (t6 - t002)
# should be modified
if len(vtable) > 0:
out_table = MTable(vtable[valid], key=vtable.get_key())
else:
out_table = MTable(columns=vtable.columns, key=vtable.get_key())
t7 = time.time()
print "Time taken to create mtable for candset:", (t7 - t6)
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
out_table.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
end_time = time.time()
print "Time taken to set properties of candset: ", (end_time - t7)
print "Total time to block candset: ", (end_time - start_time)
return out_table
def combine_block_outputs_via_union(blocker_output_list):
"""
Combine blocker outputs by unioning ltable, rtable ids in candidate set
Parameters
----------
blocker_output_list : list
List of blocker outputs
Returns
-------
combined_blocker_output : MTable
With combined blocker outputs
Notes
-----
Combined_blocker_output contains the following attributes
* _id
* combined id pairs (ltable.id, rtabled.id) from list of blocker outputs
* union of non-id attributes from each of blocker output
"""
ltable, rtable = lr_tables(blocker_output_list)
# get the attribute names in blocker output that represents ltable, rtable
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
# get the union of attribute names from blocker output list
col_set = set([x for c in blocker_output_list for x in c.columns])
l_col, r_col = lr_cols(col_set)
l_col = list(l_col)
r_col = list(r_col)
l_df = l_df[l_col] # minimally the projection must contain id column
r_df = r_df[r_col]
col_names = ['ltable.'+c for c in l_df.columns]
l_df.columns = col_names
col_names = ['rtable.'+c for c in r_df.columns]
r_df.columns = col_names
l_df.set_index(l_key, inplace=True, drop=False)
r_df.set_index(r_key, inplace=True, drop=False)
# get id pairs
id_set = []
for c in blocker_output_list:
lfid_idx = c.get_attr_names().index(c.get_property('foreign_key_ltable'))
rfid_idx = c.get_attr_names().index(c.get_property('foreign_key_rtable'))
for r in c.itertuples(index=False):
id_set.append((r[lfid_idx], r[rfid_idx]))
id_set = list(set(id_set))
f_cols = fin_cols(l_col, r_col, ltable.get_key(), rtable.get_key())
if len(id_set) > 0:
id_df = pd.DataFrame(id_set)
l_consol_table = l_df.ix[id_df[0]]
r_consol_table = r_df.ix[id_df[1]]
l_consol_table.reset_index(inplace=True, drop=True)
r_consol_table.reset_index(inplace=True, drop=True)
table = pd.concat([l_consol_table, r_consol_table], axis=1)
table.sort([l_key, r_key], inplace=True)
table.reset_index(inplace=True, drop=True)
table = MTable(table[f_cols])
else:
table = MTable([], columns=f_cols)
# project df and convert to MTable
table.set_property('ltable', ltable)
table.set_property('rtable', rtable)
table.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
table.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
return table
def extract_feature_vecs(s,
attrs_before=None,
feature_table=None,
attrs_after=None):
"""
Extract feature vectors
Parameters
----------
s : MTable,
labeled virtual MTable or combined blocker output
attrs_before : list, defaults to None
List of attribute names from "s" to be included in output table before the feature vector
feat_table : pandas DataFrame, defaults to None
List of features to be applied (also see: mg.get_features_for_blocking)
attrs_after : list, defaults to None
List of attribute names from "s" to be included in output table after the feature vector
Returns
-------
feature_vectors : MTable,
Containing features values (obtained by applying feature fns in feat_table) and attributes as
mentioned in the input
"""
# basic checks
assert isJVMStarted(
), 'JVM should be started using init_jvm to compute features'
ltable = s.get_property('ltable')
rtable = s.get_property('rtable')
assert ltable is not None, 'Left table is not set'
assert rtable is not None, 'Right table is not set'
if feature_table is None:
feature_table = mg.get_features_for_blocking(ltable, rtable)
l_key, r_key = s.get_property('foreign_key_ltable'), s.get_property(
'foreign_key_rtable')
start = time.time()
id_list = [(r[l_key], r[r_key]) for i, r in s.iterrows()]
end = time.time()
logging.getLogger(__name__).info('Iterating rows (%d) took %f secs' %
(len(s), end - start))
# compute feature values
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
l_df.set_index(ltable.get_key(), inplace=True, drop=False)
r_df.set_index(rtable.get_key(), inplace=True, drop=False)
start = time.time()
feat_vals = [
apply_feat_fns(l_df.ix[x[0]], r_df.ix[x[1]], feature_table)
for x in id_list
]
end = time.time()
logging.getLogger(__name__).info(
'Applying feature functions took : %f secs' % (end - start))
table = pd.DataFrame(feat_vals, index=s.index.values)
# get the feature names and re-arrange columns in that order
feat_names = list(feature_table['feature_name'])
table = table[feat_names]
# insert attrs_before
if attrs_before:
if not isinstance(attrs_before, list):
attrs_before = [attrs_before]
attrs_before.reverse()
for a in attrs_before:
table.insert(0, a, s[a])
table.insert(0, r_key, s[r_key])
table.insert(0, l_key, s[l_key])
# insert attrs after
if attrs_after:
if not isinstance(attrs_after, list):
attrs_after = [attrs_after]
attrs_after.reverse()
for a in attrs_after:
table.insert(len(table.columns), a, s[a])
# reset the table index
table.reset_index(inplace=True, drop=True)
feature_vectors = MTable(table)
if s.get_key() not in feature_vectors.columns:
feature_vectors.add_key(s.get_key())
# metadata
feature_vectors._metadata = s._metadata
feature_vectors.properties = s.properties
return feature_vectors
def block_candset_skd(self, vtable, l_block_attr, r_block_attr):
"""
Block candidate set (virtual MTable) based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
vtable : MTable
Input candidate set
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
start_time = time.time()
# do integrity checks
ltable = vtable.get_property('ltable')
rtable = vtable.get_property('rtable')
self.check_attrs(ltable, rtable, l_block_attr, r_block_attr, None, None)
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
t000 = time.time()
print "Time taken to do integrity checks:", (t000 - start_time)
# convert to dataframes
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
t001 = time.time()
print "Time taken to convert tables A and B to data frames:", (t001 - t000)
# set index for convenience
l_df.set_index(ltable.get_key(), inplace=True)
r_df.set_index(rtable.get_key(), inplace=True)
t002 = time.time()
print "Time taken to set indexes for tables A and B:", (t002 - t001)
cpu_count = multiprocessing.cpu_count()
#pool = Pool(4)
t00 = time.time()
pool = mp.ProcessingPool(processes=cpu_count, maxtasksperchild=1)
t01 = time.time()
print "Time taken to initialize the pool of workers:", (t01 - t00)
c_df = vtable.to_dataframe()
t0 = time.time()
print "Time taken to convert mtable to data frame:", (t0 - t01)
c_splits = np.array_split(c_df, cpu_count)
t1 = time.time()
print "Time taken to split table C:", (t1 - t0)
args_splits = [(c, l_df, r_df, l_key, r_key, l_block_attr, r_block_attr) for c in c_splits]
t2 = time.time()
print "Time taken to get args splits:", (t2 - t1)
valid_splits = pool.map(self.get_valid_ids, args_splits)
t3 = time.time()
print "Time taken to get valid splits:", (t3 - t2)
pool.close()
t4 = time.time()
print "Time taken to close pool:", (t4 - t3)
pool.join()
t5 = time.time()
print "Time taken to join pool:", (t5 - t4)
#valid = pd.concat(valid_splits, ignore_index=True)
#valid = list(chain(valid_splits))
valid = sum(valid_splits, [])
t6 = time.time()
print "Time taken to combine valid splits:", (t6 - t5)
# should be modified
if len(vtable) > 0:
out_table = MTable(vtable[valid], key=vtable.get_key())
else:
out_table = MTable(columns=vtable.columns, key=vtable.get_key())
t7 = time.time()
print "Time taken to create mtable from data frame:", (t7 - t6)
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
out_table.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
end_time = time.time()
print "Time taken to set properties of mtable:", (end_time - t7)
print "Time taken to block candset:", (end_time - start_time)
return out_table
def _combine_block_outputs_via_union(blocker_output_list):
"""
Combine blocker outputs by unioning ltable, rtable ids in candidate set
Parameters
----------
blocker_output_list : list
List of blocker outputs
Returns
-------
combined_blocker_output : MTable
With combined blocker outputs
Notes
-----
Combined_blocker_output contains the following attributes
* _id
* combined id pairs (ltable.id, rtabled.id) from list of blocker outputs
* union of non-id attributes from each of blocker output
"""
ltable, rtable = lr_tables(blocker_output_list)
# get the attribute names in blocker output that represents ltable, rtable
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
# get the set of id pairs from all blocker output list
id_set = set([(r[l_key], r[r_key]) for c in blocker_output_list for i, r in c.iterrows()])
# get the union of attribute names from blocker output list
col_set = set([x for c in blocker_output_list for x in c.columns])
l_col, r_col = lr_cols(col_set)
# convert ltable, rtable to dfs and set index
l_df = ltable.to_dataframe()
l_df.set_index(ltable.get_key(), inplace=True, drop=False)
r_df = rtable.to_dataframe()
r_df.set_index(rtable.get_key(), inplace=True, drop=False)
# get the l_col, r_col from ltable and rtable respectively
dict_list = [get_dict(l_df.ix[x[0]], r_df.ix[x[1]], l_col, r_col) for x in id_set]
# convert list of dicts to dataframe
table = pd.DataFrame(dict_list)
# get the final column names for output table
f_cols = fin_cols(l_col, r_col, ltable.get_key(), rtable.get_key())
if len(table) > 0:
table.sort([l_key, r_key], inplace=True)
table.reset_index(inplace=True, drop=True)
table = MTable(table[f_cols])
else:
table = MTable(table, columns=f_cols)
# project df and convert to MTable
table.set_property('ltable', ltable)
table.set_property('rtable', rtable)
table.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
table.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
return table
def get_filtered_table(ltable, rtable, corres_list):
ltable_cols = [col_pair[0] for col_pair in corres_list]
rtable_cols = [col_pair[1] for col_pair in corres_list]
l_mtable = MTable(ltable[ltable_cols], key=ltable.get_key())
r_mtable = MTable(rtable[rtable_cols], key=rtable.get_key())
return l_mtable, r_mtable
def block_candset(self, vtable, l_block_attr, r_block_attr):
"""
Block candidate set (virtual MTable) based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
vtable : MTable
Input candidate set
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
ltable = vtable.get_property('ltable')
rtable = vtable.get_property('rtable')
self.check_attrs(ltable, rtable, l_block_attr, r_block_attr, None, None)
l_key = 'ltable.' + ltable.get_key()
r_key = 'rtable.' + rtable.get_key()
# convert to dataframes
l_df = ltable.to_dataframe()
r_df = rtable.to_dataframe()
# set index for convenience
l_df.set_index(ltable.get_key(), inplace=True)
r_df.set_index(rtable.get_key(), inplace=True)
if mg._verbose:
count = 0
per_count = math.ceil(mg._percent/100.0*len(vtable))
print per_count
elif mg._progbar:
bar = pyprind.ProgBar(len(vtable))
# keep track of valid ids
valid = []
# iterate candidate set and process each row
for idx, row in vtable.iterrows():
if mg._verbose:
count += 1
if count%per_count == 0:
print str(mg._percent*count/per_count) + ' percentage done !!!'
elif mg._progbar:
bar.update()
# get the value of block attribute from ltuple
l_val = l_df.ix[row[l_key], l_block_attr]
r_val = r_df.ix[row[r_key], r_block_attr]
if l_val != np.NaN and r_val != np.NaN:
if l_val == r_val:
valid.append(True)
else:
valid.append(False)
else:
valid.append(False)
# should be modified
if len(vtable) > 0:
out_table = MTable(vtable[valid], key=vtable.get_key())
else:
out_table = MTable(columns=vtable.columns, key=vtable.get_key())
out_table.set_property('ltable', ltable)
out_table.set_property('rtable', rtable)
out_table.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
out_table.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
return out_table
def block_tables_opt(self, ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs=None, r_output_attrs=None):
"""
Block tables based on l_block_attr, r_block_attr equivalence (similar to equi-join)
Parameters
----------
ltable, rtable : MTable
Input MTables
l_block_attr, r_block_attr : string,
attribute names in ltable, rtable
l_output_attrs, r_output_attrs : list (of strings), defaults to None
attribute names to be included in the output table
Returns
-------
blocked_table : MTable
Containing tuple pairs whose l_block_attr and r_block_attr values are same
Notes
-----
Output MTable contains the following three attributes
* _id
* id column from ltable
* id column from rtable
Also, the properties of blocked table is updated with following key-value pairs
* ltable - ref to ltable
* rtable - ref to rtable
* key
* foreign_key_ltable - string, ltable's id attribute name
* foreign_key_rtable - string, rtable's id attribute name
"""
# do integrity checks
l_output_attrs, r_output_attrs = self.check_attrs(ltable, rtable, l_block_attr, r_block_attr,
l_output_attrs, r_output_attrs)
# remove nans
l_df = self.rem_nan(ltable, l_block_attr)
r_df = self.rem_nan(rtable, r_block_attr)
t00 = time.time()
lk = ltable.get_key()
l_output_attrs_1 = l_output_attrs;
if lk not in l_output_attrs_1:
l_output_attrs_1.append(lk)
if l_block_attr not in l_output_attrs_1:
l_output_attrs_1.append(l_block_attr)
r_output_attrs_1 = r_output_attrs
rk = rtable.get_key()
if rk not in r_output_attrs_1:
r_output_attrs_1.append(rk)
if r_block_attr not in r_output_attrs_1:
r_output_attrs_1.append(r_block_attr)
print l_output_attrs_1
print r_output_attrs_1
l_df_1 = l_df[l_output_attrs_1]
r_df_1 = r_df[r_output_attrs_1]
#l_df_1.set_index(l_block_attr)
#r_df_1.set_index(r_block_attr)
t0 = time.time()
candset = pd.merge(l_df_1, r_df_1, left_on=l_block_attr, right_on=r_block_attr,
suffixes=('_ltable', '_rtable'))
print list(candset)
t1 = time.time()
# get output columns
retain_cols, final_cols = self.output_columns(ltable.get_key(), rtable.get_key(), list(candset.columns),
l_output_attrs, r_output_attrs)
print "retain_cols: ", retain_cols
print "final_cols: ", final_cols
t2 = time.time()
candset = candset[retain_cols]
t3 = time.time()
candset.columns = final_cols
t4 = time.time()
candset = MTable(candset)
t5 = time.time()
# set metadata
candset.set_property('ltable', ltable)
candset.set_property('rtable', rtable)
candset.set_property('foreign_key_ltable', 'ltable.'+ltable.get_key())
candset.set_property('foreign_key_rtable', 'rtable.'+rtable.get_key())
t6 = time.time()
print "Time taken to project A and B:", (t0 - t00)
print "Time taken to merge A and B:", (t1 - t0)
print "Time taken to get output cols:", (t2 - t1)
print "Time taken to project C cols:", (t3 - t2)
print "Time taken to set C final cols:", (t4 - t3)
print "Time taken to create table C:", (t5 - t4)
print "Time taken to set props for C:", (t6 - t5)
return candset
def debug_blocker(ltable, rtable, candidate_set, pred_list_size=200, field_corres_list=None):
"""
Debug the blocker. The basic idea is trying to suggest the user a list of record pairs
out of the candidate set with high (document) jaccard similarity. The object of similarity
measurement (document) is generated based on a string concatenation method with field
selection. Given the suggestion list, the user should go through the pairs and determine if
there are, and how many true matches in it. And based on this information, the user can
determine if further improvement on the blocking step is necessary (Ex. if there are many
true matches in the list, the user may conclude that the blocking step is flawed, and should
revise it to produce a better candidate set).
Parameters
----------
ltable, rtable : MTable
Input MTables
candidate_set : MTable
The candidate set table after performing blocking on ltable and rtable
pred_list_size : int
The size of the output suggestion list
field_corres_list : list (of tuples), defaults to None
The list of field pairs from ltable and rtable. Each pair indicates a field correspondence
between two tables. Since ltable and rtable can have different schemas, it' necessary to
have this parameter to build the field correspondence to make sure the string concatenation
algorithm runs correctly.
Note each pair in the list should be represented as a tuple in the following format:
(some_ltable_field, some_rtable_field)
Returns
-------
suggestion_table : MTable
Contains a list of pair suggestions with high jaccard similarity. The output MTable contains
the following fields:
* _id
* similarity (of the record pair)
* ltable record key value
* rtable record key value
* field pairs from filtered corres_list (removing the numeric types)
ltable_field_1
rtable_field_1 (corresponding to ltable_field_1)
ltable_field_2
rtable_field_2 (corresponding to ltable_field_2)
.
.
ltable_field_k
rtable_field_k (corresponding to ltable_field_k)
"""
# Basic checks.
if len(ltable) == 0:
raise StandardError('Error: ltable is empty!')
if len(rtable) == 0:
raise StandardError('Error: rtable is empty!')
if pred_list_size <= 0:
raise StandardError('The input parameter: \'pred_list_size\' is less than or equal to 0. Nothing needs to be done!')
logging.info('\nPreparing for debugging blocker')
# Check the user input field correst list (if exists) and get the raw version of
# our internal correst list.
check_input_field_correspondence_list(ltable, rtable, field_corres_list)
corres_list = get_field_correspondence_list(ltable, rtable, field_corres_list)
# Build the (col_name: col_index) dict to speed up locating a field in the schema.
ltable_col_dict = build_col_name_index_dict(ltable)
rtable_col_dict = build_col_name_index_dict(rtable)
# Filter correspondence list to remove numeric types. We only consider string types
# for document concatenation.
filter_corres_list(ltable, rtable, ltable_col_dict, rtable_col_dict, corres_list)
# logging.info('\nFiltered field correspondence list:\n' + str(corres_list))
# Get field filtered new table.
ltable_filtered, rtable_filtered = get_filtered_table(ltable, rtable, corres_list)
# Select features.
"""TODO(hanli): currently we don't select the key fields even if they have the largest score.
# This is because the values of the key field could be simply domain-specific serial numbers,
# which might be meaningless or even harmful (when two tables use different key formats).
# Modify it if this ituition is not proper."""
feature_list = select_features(ltable_filtered, rtable_filtered)
if len(feature_list) == 0:
raise StandardError('\nError: the selected field list is empty, nothing could be done! ' +
'Please check if all table fields are numeric types.')
# logging.info('\nSelected fields for concatenation:\n' + str([(ltable_filtered.columns[i],
# rtable_filtered.columns[i]) for i in feature_list]))
# Get each table kgram dict.
ltable_kgram_dict = get_kgram_dict(ltable_filtered, ltable_filtered.get_key(), feature_list, 3)
rtable_kgram_dict = get_kgram_dict(rtable_filtered, rtable_filtered.get_key(), feature_list, 3)
# Build inverted index on ltable kgrams to speed up debugging.
inverted_index = build_inverted_index(ltable_kgram_dict)
ltable_key = candidate_set.get_property('foreign_key_ltable')
rtable_key = candidate_set.get_property('foreign_key_rtable')
indexed_candidate_set = candidate_set.set_index([rtable_key, ltable_key], drop=False)
candidate_index_key_set = set(indexed_candidate_set[rtable_key])
# logging.info('\nCandidate set size: %d' %(len(indexed_candidate_set)))
rtable_len = len(rtable_filtered)
progress_dict = {}
for i in range(10):
progress_dict[int((i + 1) * 1.0 / 10 * rtable_len)] = (i + 1) * 1.0 / 10
logging.info('\nStart debugging blocker')
pred_index_list = []
count = 0
for rkey in rtable_kgram_dict:
count += 1
# if count == 500:
# print pred_index_list
# break
if rtable_len <= 10:
logging.info('\nDebugging %s' %('{percent:.2%}'.format(percent=count * 1.0 / rtable_len)))
else:
if count in progress_dict:
logging.info('\nDebugging %s' %('{percent:.2%}'.format(percent=progress_dict[count])))
rkgram_set = rtable_kgram_dict[rkey]
if len(rkgram_set) == 0:
continue
cand_set = {}
if rkey in candidate_index_key_set:
cand_set = indexed_candidate_set.ix[rkey].index.values
ltable_index_set = get_potential_match_set(rkgram_set, inverted_index)
for lkey in ltable_index_set:
if lkey in cand_set:
continue
jac_sim = jaccard_kgram_sim(ltable_kgram_dict[lkey], rkgram_set)
if len(pred_index_list) == pred_list_size:
hq.heappushpop(pred_index_list, (jac_sim, lkey, rkey))
else:
hq.heappush(pred_index_list, (jac_sim, lkey, rkey))
ret_data_frame = generate_prediction_table(ltable_filtered, rtable_filtered, pred_index_list)
"""This print is for debugging"""
#print ret_data_frame
ret_mtable = MTable(ret_data_frame)
ret_mtable.set_property('foreign_key_ltable', ltable_key)
ret_mtable.set_property('foreign_key_rtable', rtable_key)
ret_mtable.set_property('ltable', ltable)
ret_mtable.set_property('rtable', rtable)
logging.info('\nFinish debugging blocker')
return ret_mtable
def debug_blocker(ltable,
rtable,
candidate_set,
output_size=200,
attr_corres=None):
"""
Debug the blocker. The basic idea is trying to suggest the user a list of record pairs
out of the candidate set with high (document) jaccard similarity. The object of similarity
measurement (document) is generated based on a string concatenation method with field
selection. Given the suggestion list, the user should go through the pairs and determine if
there are, and how many true matches in it. And based on this information, the user can
determine if further improvement on the blocking step is necessary (Ex. if there are many
true matches in the list, the user may conclude that the blocking step is flawed, and should
revise it to produce a better candidate set).
Parameters
----------
ltable, rtable : MTable
Input MTables
candidate_set : MTable
The candidate set table after performing blocking on ltable and rtable
pred_list_size : int
The size of the output suggestion list
field_corres_list : list (of tuples), defaults to None
The list of field pairs from ltable and rtable. Each pair indicates a field correspondence
between two tables. Since ltable and rtable can have different schemas, it' necessary to
have this parameter to build the field correspondence to make sure the string concatenation
algorithm runs correctly.
Note each pair in the list should be represented as a tuple in the following format:
(some_ltable_field, some_rtable_field)
Returns
-------
suggestion_table : MTable
Contains a list of pair suggestions with high jaccard similarity. The output MTable contains
the following fields:
* _id
* similarity (of the record pair)
* ltable record key value
* rtable record key value
* field pairs from filtered corres_list (removing the numeric types)
ltable_field_1
rtable_field_1 (corresponding to ltable_field_1)
ltable_field_2
rtable_field_2 (corresponding to ltable_field_2)
.
.
ltable_field_k
rtable_field_k (corresponding to ltable_field_k)
"""
# Basic checks.
if len(ltable) == 0:
raise StandardError('Error: ltable is empty!')
if len(rtable) == 0:
raise StandardError('Error: rtable is empty!')
if output_size <= 0:
raise StandardError(
'The input parameter: \'pred_list_size\' is less than or equal to 0. Nothing needs to be done!'
)
# logging.info('\nPreparing for debugging blocker')
# Check the user input field correst list (if exists) and get the raw version of
# our internal correst list.
check_input_field_correspondence_list(ltable, rtable, attr_corres)
corres_list = get_field_correspondence_list(ltable, rtable, attr_corres)
# Build the (col_name: col_index) dict to speed up locating a field in the schema.
ltable_col_dict = build_col_name_index_dict(ltable)
rtable_col_dict = build_col_name_index_dict(rtable)
# Filter correspondence list to remove numeric types. We only consider string types
# for document concatenation.
filter_corres_list(ltable, rtable, ltable_col_dict, rtable_col_dict,
corres_list)
#print('\nFiltered field correspondence list:\n' + str(corres_list))
# Get field filtered new table.
ltable_filtered, rtable_filtered = get_filtered_table(
ltable, rtable, corres_list)
# Select features.
"""TODO: currently we don't select the key fields even if they have the largest score.
# This is because the values of the key field could be simply domain-specific serial numbers,
# which might be meaningless or even harmful (when two tables use different key formats).
# Modify it if this ituition is not proper."""
feature_list = select_features(ltable_filtered, rtable_filtered)
if len(feature_list) == 0:
raise StandardError(
'\nError: the selected field list is empty, nothing could be done! '
+ 'Please check if all table fields are numeric types.')
#print('\nSelected fields for concatenation:\n' + str([(ltable_filtered.columns[i], rtable_filtered.columns[i]) for i in feature_list]))
# Get each table kgram dict.
ltable_kgram_dict = get_kgram_dict(ltable_filtered,
ltable_filtered.get_key(), feature_list,
3)
rtable_kgram_dict = get_kgram_dict(rtable_filtered,
rtable_filtered.get_key(), feature_list,
3)
# Build inverted index on ltable kgrams to speed up debugging.
inverted_index = build_inverted_index(ltable_kgram_dict)
ltable_key = candidate_set.get_property('foreign_key_ltable')
rtable_key = candidate_set.get_property('foreign_key_rtable')
indexed_candidate_set = candidate_set.set_index([rtable_key, ltable_key],
drop=False)
candidate_index_key_set = set(indexed_candidate_set[rtable_key])
#print('\nCandidate set size: %d' %(len(indexed_candidate_set)))
rtable_len = len(rtable_filtered)
progress_dict = {}
for i in range(10):
progress_dict[int(
(i + 1) * 1.0 / 10 * rtable_len)] = (i + 1) * 1.0 / 10
#print('\nStart debugging blocker')
# print('Start debugging blocker')
pred_index_list = []
count = 0
if mg._verbose:
count_ = 0
per_count = math.ceil(mg._percent / 100.0 * len(rtable))
elif mg._progbar:
bar = pyprind.ProgBar(len(rtable))
for rkey in rtable_kgram_dict:
count += 1
#if count == 500:
#print pred_index_list
# break
# if rtable_len <= 10:
# print('Debugging %s' %('{percent:.2%}'.format(percent=count * 1.0 / rtable_len)))
# else:
# if count in progress_dict:
# print('Debugging %s' %('{percent:.2%}'.format(percent=progress_dict[count])))
if mg._verbose:
count_ += 1
if count_ % per_count == 0:
print str(
mg._percent * count_ / per_count) + ' percentage done !!!'
elif mg._progbar:
bar.update()
rkgram_set = rtable_kgram_dict[rkey]
if len(rkgram_set) == 0:
continue
cand_set = {}
if rkey in candidate_index_key_set:
cand_set = indexed_candidate_set.ix[rkey].index.values
ltable_index_set = get_potential_match_set(rkgram_set, inverted_index)
for lkey in ltable_index_set:
if lkey in cand_set:
continue
jac_sim = jaccard_kgram_sim(ltable_kgram_dict[lkey], rkgram_set)
if len(pred_index_list) == output_size:
hq.heappushpop(pred_index_list, (jac_sim, lkey, rkey))
else:
hq.heappush(pred_index_list, (jac_sim, lkey, rkey))
ret_data_frame = generate_prediction_table(ltable_filtered,
rtable_filtered,
pred_index_list)
# """This print is for debugging"""
#print ret_data_frame
ret_mtable = MTable(ret_data_frame)
ret_mtable.set_property('foreign_key_ltable', ltable_key)
ret_mtable.set_property('foreign_key_rtable', rtable_key)
ret_mtable.set_property('ltable', ltable)
ret_mtable.set_property('rtable', rtable)
return ret_mtable