def index_candidate_set(candidate_set, lrecord_id_to_index_map,
rrecord_id_to_index_map, verbose):
if len(candidate_set) == 0:
return {}
new_formatted_candidate_set = {}
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = \
cm.get_metadata_for_candset(candidate_set, logger, verbose)
# # validate metadata
# cm._validate_metadata_for_candset(candidate_set, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
# logger, verbose)
ltable_key_data = list(candidate_set[fk_ltable])
rtable_key_data = list(candidate_set[fk_rtable])
for i in range(len(ltable_key_data)):
if ltable_key_data[i] in lrecord_id_to_index_map and \
rtable_key_data[i] in rrecord_id_to_index_map:
# new_formatted_candidate_set.add((lrecord_id_to_index_map[ltable_key_data[i]],
# rrecord_id_to_index_map[rtable_key_data[i]]))
l_key_data = lrecord_id_to_index_map[ltable_key_data[i]]
r_key_data = rrecord_id_to_index_map[rtable_key_data[i]]
if l_key_data in new_formatted_candidate_set:
new_formatted_candidate_set[l_key_data].add(r_key_data)
else:
new_formatted_candidate_set[l_key_data] = {r_key_data}
return new_formatted_candidate_set
def _predict_candset(self, candset, verbose=False):
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
# # keep track of predictions
predictions = []
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # get the index of fk_ltable and fk_rtable from the cand. set
col_names = list(candset.columns)
lid_idx = col_names.index(fk_ltable)
rid_idx = col_names.index(fk_rtable)
# # iterate through the cand. set
for row in candset.itertuples(index=False):
l_row = l_df.ix[row[lid_idx]]
r_row = r_df.ix[row[rid_idx]]
res = self.apply_rules(l_row, r_row)
if res is True:
predictions.append(1)
else:
predictions.append(0)
return predictions
def add_output_attributes(candset, l_output_attrs=None, r_output_attrs=None, l_output_prefix='ltable_',
r_output_prefix='rtable_', validate=True, copy_props=True,
delete_from_catalog=True, verbose=False):
if not isinstance(candset, pd.DataFrame):
logger.error('Input object is not of type pandas data frame')
raise AssertionError('Input object is not of type pandas data frame')
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
if validate:
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
index_values = candset.index
df = _add_output_attributes(candset, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
l_output_attrs, r_output_attrs, l_output_prefix, r_output_prefix,
validate=False)
df.set_index(index_values, inplace=True)
if copy_props:
cm.init_properties(df)
cm.copy_properties(candset, df)
if delete_from_catalog:
cm.del_all_properties(candset)
return df
def _predict_candset(self, candset, verbose=False):
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
# # keep track of predictions
predictions = []
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # get the index of fk_ltable and fk_rtable from the cand. set
col_names = list(candset.columns)
lid_idx = col_names.index(fk_ltable)
rid_idx = col_names.index(fk_rtable)
# # iterate through the cand. set
for row in candset.itertuples(index=False):
l_row = l_df.ix[row[lid_idx]]
r_row = r_df.ix[row[rid_idx]]
res = self.apply_rules(l_row, r_row)
if res is True:
predictions.append(1)
else:
predictions.append(0)
return predictions
def test_get_metadata_for_candset_valid(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C = read_csv_metadata(path_c, ltable=A, rtable=B)
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(C, None, False)
self.assertEqual(key, '_id')
self.assertEqual(fk_ltable, 'ltable_ID')
self.assertEqual(fk_rtable, 'rtable_ID')
self.assertEqual(l_key, 'ID')
self.assertEqual(r_key, 'ID')
self.assertEqual(ltable.equals(A), True)
self.assertEqual(rtable.equals(B), True)
def train_test_split(labeled_data,
train_proportion=0.5,
random_state=None,
verbose=True):
if not isinstance(labeled_data, pd.DataFrame):
logger.error('Input table is not of type dataframe')
raise AssertionError('Input table is not of type dataframe')
log_info(
logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
labeled_data, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(labeled_data, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
num_rows = len(labeled_data)
assert train_proportion >= 0 and train_proportion <= 1, " Train proportion is expected to be between 0 and 1"
assert num_rows > 0, 'The input table is empty'
train_size = int(math.floor(num_rows * train_proportion))
test_size = int(num_rows - train_size)
# use sk learn to split the data
idx_values = pd.np.array(labeled_data.index.values)
idx_train, idx_test = cv.train_test_split(idx_values,
test_size=test_size,
train_size=train_size,
random_state=random_state)
# construct output tables.
lbl_train = labeled_data.ix[idx_train]
lbl_test = labeled_data.ix[idx_test]
# update catalog
cm.init_properties(lbl_train)
cm.copy_properties(labeled_data, lbl_train)
cm.init_properties(lbl_test)
cm.copy_properties(labeled_data, lbl_test)
# return output tables
result = OrderedDict()
result['train'] = lbl_train
result['test'] = lbl_test
return result
def execute(self, input_table, label_column, inplace=True, verbose=False):
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(input_table, logger,
verbose)
# # validate metadata
cm.validate_metadata_for_candset(input_table, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
assert ltable is not None, 'Left table is not set'
assert rtable is not None, 'Right table is not set'
assert label_column in input_table.columns, 'Label column not in the input table'
if inplace == False:
table = input_table.copy()
else:
table = input_table
# set the index and store it in l_tbl/r_tbl
l_tbl = ltable.set_index(l_key, drop=False)
r_tbl = rtable.set_index(r_key, drop=False)
# keep track of valid ids
y = []
column_names = list(input_table.columns)
lid_idx = column_names.index(l_key)
rid_idx = column_names.index(r_key)
id_idx = column_names.index(key)
label_idx = column_names.index(label_column)
test_idx = 0
idx = 0
for row in input_table.itertuples(index=False):
if row[label_idx] != self.value_to_set:
l_row = l_tbl.ix[row[lid_idx]]
r_row = r_tbl.ix[row[rid_idx]]
res = self.apply_rules(l_row, r_row)
if res == self.cond_status:
table.iat[idx, label_idx] = self.value_to_set
idx += 1
return table
def execute(self, input_table, label_column, inplace=True, verbose=False):
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
input_table, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(input_table, key, fk_ltable,
fk_rtable, ltable, rtable, l_key,
r_key, logger, verbose)
assert ltable is not None, 'Left table is not set'
assert rtable is not None, 'Right table is not set'
assert label_column in input_table.columns, 'Label column not in the input table'
if inplace == False:
table = input_table.copy()
else:
table = input_table
# set the index and store it in l_tbl/r_tbl
l_tbl = ltable.set_index(l_key, drop=False)
r_tbl = rtable.set_index(r_key, drop=False)
# keep track of valid ids
y = []
column_names = list(input_table.columns)
lid_idx = column_names.index(l_key)
rid_idx = column_names.index(r_key)
id_idx = column_names.index(key)
label_idx = column_names.index(label_column)
test_idx = 0
idx = 0
for row in input_table.itertuples(index=False):
if row[label_idx] != self.value_to_set:
l_row = l_tbl.ix[row[lid_idx]]
r_row = r_tbl.ix[row[rid_idx]]
res = self.apply_rules(l_row, r_row)
if res == self.cond_status:
table.iat[idx, label_idx] = self.value_to_set
idx += 1
return table
def add_output_attributes(candset,
l_output_attrs=None,
r_output_attrs=None,
l_output_prefix='ltable_',
r_output_prefix='rtable_',
validate=True,
copy_props=True,
delete_from_catalog=True,
verbose=False):
if not isinstance(candset, pd.DataFrame):
logger.error('Input object is not of type pandas data frame')
raise AssertionError('Input object is not of type pandas data frame')
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
candset, logger, verbose)
if validate:
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
index_values = candset.index
df = _add_output_attributes(candset,
fk_ltable,
fk_rtable,
ltable,
rtable,
l_key,
r_key,
l_output_attrs,
r_output_attrs,
l_output_prefix,
r_output_prefix,
validate=False)
df.set_index(index_values, inplace=True)
if copy_props:
cm.init_properties(df)
cm.copy_properties(candset, df)
if delete_from_catalog:
cm.del_all_properties(candset)
return df
def _validate_inputs(table, label_column_name, verbose):
"""
This function validates the inputs for the label_table function
"""
# Validate the input parameters
# # The input table table is expected to be of type pandas DataFrame
if not isinstance(table, pd.DataFrame):
logger.error('Input object is not of type data frame')
raise AssertionError('Input object is not of type data frame')
# # The label column name is expected to be of type string
if not isinstance(label_column_name, six.string_types):
logger.error('Input attr. is not of type string')
raise AssertionError('Input attr. is not of type string')
# # Check if the label column name is already present in the input table
if ch.check_attrs_present(table, label_column_name):
logger.error(
'The label column name (%s) is already present in the '
'input table', label_column_name)
raise AssertionError(
'The label column name (%s) is already present '
'in the input table', label_column_name)
# Now, validate the metadata for the input DataFrame as we have to copy
# these properties to the output DataFrame
# # First, display what metadata is required for this function
ch.log_info(
logger, 'Required metadata: cand.set key, fk ltable, '
'fk rtable, ltable, rtable, ltable key, rtable key', verbose)
# # Second, get the metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = \
cm.get_metadata_for_candset(table, logger, verbose)
# # Third, validate the metadata
cm.validate_metadata_for_candset(table, key, fk_ltable, fk_rtable, ltable,
rtable, l_key, r_key, logger, verbose)
# Return True if everything was successful
return True
def _validate_inputs(table, label_column_name, verbose):
"""
This function validates the inputs for the label_table function
"""
# Validate the input parameters
# # The input table table is expected to be of type pandas DataFrame
if not isinstance(table, pd.DataFrame):
logger.error('Input object is not of type data frame')
raise AssertionError('Input object is not of type data frame')
# # The label column name is expected to be of type string
if not isinstance(label_column_name, six.string_types):
logger.error('Input attr. is not of type string')
raise AssertionError('Input attr. is not of type string')
# # Check if the label column name is already present in the input table
if ch.check_attrs_present(table, label_column_name):
logger.error('The label column name (%s) is already present in the '
'input table', label_column_name)
raise AssertionError('The label column name (%s) is already present '
'in the input table', label_column_name)
# Now, validate the metadata for the input DataFrame as we have to copy
# these properties to the output DataFrame
# # First, display what metadata is required for this function
ch.log_info(logger, 'Required metadata: cand.set key, fk ltable, '
'fk rtable, ltable, rtable, ltable key, rtable key',
verbose)
# # Second, get the metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = \
cm.get_metadata_for_candset(table, logger, verbose)
# # Third, validate the metadata
cm.validate_metadata_for_candset(table, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key,
logger, verbose)
# Return True if everything was successful
return True
def block_candset(self, candset, l_overlap_attr, r_overlap_attr,
rem_stop_words=False, q_val=None, word_level=True, overlap_size=1,
verbose=True, show_progress=True):
# get and validate metadata
log_info(logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
# validate overlap attrs
self.validate_overlap_attrs(ltable, rtable, l_overlap_attr, r_overlap_attr)
# do blocking
# # initialize the progress bar
if show_progress:
bar = pyprind.ProgBar(len(candset))
# # set the index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # create lookup table for faster processing
l_dict = {}
for k, r in l_df.iterrows():
l_dict[k] = r
r_dict = {}
for k, r in r_df.iterrows():
r_dict[k] = r
# # list to keep track of valid ids
valid = []
l_id_pos = list(candset.columns).index(fk_ltable)
r_id_pos = list(candset.columns).index(fk_rtable)
# # iterate candset
for row in candset.itertuples(index=False):
# # update progress bar
if show_progress:
bar.update()
ltuple = l_dict[row[l_id_pos]]
rtuple = r_dict[row[r_id_pos]]
num_overlap = self.get_token_overlap_bt_two_tuples(ltuple, rtuple,
l_overlap_attr, r_overlap_attr,
q_val, rem_stop_words)
if num_overlap >= overlap_size:
valid.append(True)
else:
valid.append(False)
if len(candset) > 0:
candset = candset[valid]
else:
candset = pd.DataFrame(columns=candset.columns)
# update catalog
cm.set_candset_properties(candset, key, fk_ltable, fk_rtable, ltable, rtable)
# return candidate set
return candset
def extract_feature_vecs(candset, attrs_before=None, feature_table=None, attrs_after=None, verbose=True):
if not isinstance(candset, pd.DataFrame):
logger.error('Input cand.set is not of type dataframe')
raise AssertionError('Input cand.set is not of type dataframe')
# validate input parameters
if attrs_before != None:
if not check_attrs_present(candset, attrs_before):
logger.error('The attributes mentioned in attrs_before is not present ' \
'in the input table')
raise AssertionError('The attributes mentioned in attrs_before is not present ' \
'in the input table')
if attrs_after != None:
if not check_attrs_present(candset, attrs_after):
logger.error('The attributes mentioned in attrs_after is not present ' \
'in the input table')
raise AssertionError('The attributes mentioned in attrs_after is not present ' \
'in the input table')
if feature_table is None:
logger.error('Feature table cannot be null')
raise AssertionError('The feature table cannot be null')
log_info(logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
# extract features
id_list = [(r[fk_ltable], r[fk_rtable]) for i, r in candset.iterrows()]
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # apply feature functions
feat_vals = [apply_feat_fns(l_df.ix[x[0]], r_df.ix[x[1]], feature_table) for x in id_list]
# construct output table
table = pd.DataFrame(feat_vals)
# # rearrange the feature names in the given 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 = list_diff(attrs_before, [key, fk_ltable, fk_rtable])
attrs_before.reverse()
for a in attrs_before:
table.insert(0, a, candset[a])
# # insert keys
table.insert(0, fk_rtable, candset[fk_rtable])
table.insert(0, fk_ltable, candset[fk_ltable])
table.insert(0, key, candset[key])
# # insert attrs after
if attrs_after:
if not isinstance(attrs_after, list):
attrs_after = [attrs_after]
attrs_after = list_diff(attrs_after, [key, fk_ltable, fk_rtable])
attrs_after.reverse()
col_pos = len(table.columns)
for a in attrs_after:
table.insert(col_pos, a, candset[a])
col_pos += 1
# reset the index
table.reset_index(inplace=True, drop=True)
# # update the catalog
cm.init_properties(table)
cm.copy_properties(candset, table)
return table
def extract_feature_vecs(candset,
attrs_before=None,
feature_table=None,
attrs_after=None,
verbose=True):
if not isinstance(candset, pd.DataFrame):
logger.error('Input cand.set is not of type dataframe')
raise AssertionError('Input cand.set is not of type dataframe')
# validate input parameters
if attrs_before != None:
if not check_attrs_present(candset, attrs_before):
logger.error('The attributes mentioned in attrs_before is not present ' \
'in the input table')
raise AssertionError('The attributes mentioned in attrs_before is not present ' \
'in the input table')
if attrs_after != None:
if not check_attrs_present(candset, attrs_after):
logger.error('The attributes mentioned in attrs_after is not present ' \
'in the input table')
raise AssertionError('The attributes mentioned in attrs_after is not present ' \
'in the input table')
if feature_table is None:
logger.error('Feature table cannot be null')
raise AssertionError('The feature table cannot be null')
log_info(
logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
# extract features
id_list = [(r[fk_ltable], r[fk_rtable]) for i, r in candset.iterrows()]
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # apply feature functions
feat_vals = [
apply_feat_fns(l_df.ix[x[0]], r_df.ix[x[1]], feature_table)
for x in id_list
]
# construct output table
table = pd.DataFrame(feat_vals)
# # rearrange the feature names in the given 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 = list_diff(attrs_before, [key, fk_ltable, fk_rtable])
attrs_before.reverse()
for a in attrs_before:
table.insert(0, a, candset[a])
# # insert keys
table.insert(0, fk_rtable, candset[fk_rtable])
table.insert(0, fk_ltable, candset[fk_ltable])
table.insert(0, key, candset[key])
# # insert attrs after
if attrs_after:
if not isinstance(attrs_after, list):
attrs_after = [attrs_after]
attrs_after = list_diff(attrs_after, [key, fk_ltable, fk_rtable])
attrs_after.reverse()
col_pos = len(table.columns)
for a in attrs_after:
table.insert(col_pos, a, candset[a])
col_pos += 1
# reset the index
table.reset_index(inplace=True, drop=True)
# # update the catalog
cm.init_properties(table)
cm.copy_properties(candset, table)
return table
def block_candset(self, candset, verbose=True, show_progress=True):
# validate black box functionn
assert self.black_box_function != None, 'Black box function is not set'
# get and validate metadata
log_info(logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key,
logger, verbose)
# do blocking
# # initialize the progress bar
if show_progress:
bar = pyprind.ProgBar(len(candset))
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # create lookup table for faster processing
l_dict = {}
for k, r in l_df.iterrows():
l_dict[k] = r
r_dict = {}
for k, r in r_df.iterrows():
r_dict[k] = r
# # list to keep track of valid ids
valid = []
l_id_pos = list(candset.columns).index(fk_ltable)
r_id_pos = list(candset.columns).index(fk_rtable)
# # iterate candidate set
for row in candset.itertuples(index=False):
# # update progress bar
if show_progress:
bar.update()
ltuple = l_dict[row[l_id_pos]]
rtuple = r_dict[row[r_id_pos]]
res = self.black_box_function(ltuple, rtuple)
if res != True:
valid.append(True)
else:
valid.append(False)
# construct output table
if len(candset) > 0:
candset = candset[valid]
else:
candset = pd.DataFrame(columns=candset.columns)
# update catalog
cm.set_candset_properties(candset, key, fk_ltable, fk_rtable, ltable, rtable)
# return candidate set
return candset
def block_candset(self, candset, l_block_attr, r_block_attr, verbose=True,
show_progress=True):
self.validate_types_candset(candset, l_block_attr, r_block_attr,
verbose, show_progress)
# get and validate metadata
log_info(logger, 'Required metadata: cand.set key, fk ltable, ' +
'fk rtable, ltable, rtable, ltable key, rtable key',
verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key,
logger, verbose)
# validate input parameters
self.validate_block_attrs(ltable, rtable, l_block_attr, r_block_attr)
# do blocking
# # initialize progress bar
if show_progress:
prog_bar = pyprind.ProgBar(len(candset))
# # initialize list to keep track of valid ids
valid = []
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # get the indexes for the key attributes in the candset
col_names = list(candset.columns)
lkey_idx = col_names.index(fk_ltable)
rkey_idx = col_names.index(fk_rtable)
# # create a look up table for the blocking attribute values
l_dict = {}
r_dict = {}
# # iterate the rows in candset
for row in candset.itertuples(index=False):
# # update the progress bar
if show_progress:
prog_bar.update()
# # get the value of block attributes
row_lkey = row[lkey_idx]
if row_lkey not in l_dict:
l_dict[row_lkey] = l_df.ix[row_lkey, l_block_attr]
l_val = l_dict[row_lkey]
row_rkey = row[rkey_idx]
if row_rkey not in r_dict:
r_dict[row_rkey] = r_df.ix[row_rkey, r_block_attr]
r_val = r_dict[row_rkey]
if l_val == r_val:
valid.append(True)
else:
valid.append(False)
# construct output table
if len(candset) > 0:
out_table = candset[valid]
else:
out_table = pd.DataFrame(columns=candset.columns)
# update the catalog
cm.set_candset_properties(out_table, key, fk_ltable, fk_rtable,
ltable, rtable)
# return the output table
return out_table
def block_candset(self, candset, verbose=True, show_progress=True):
# validate rules
assert len(self.rules.keys()) > 0, 'There are no rules to apply'
# get and validate metadata
log_info(
logger, 'Required metadata: cand.set key, fk ltable, fk rtable, '
'ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
# do blocking
# # initialize the progress bar
if show_progress:
bar = pyprind.ProgBar(len(candset))
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # create lookup table for faster processing
l_dict = {}
for k, r in l_df.iterrows():
l_dict[k] = r
r_dict = {}
for k, r in r_df.iterrows():
r_dict[k] = r
# # list to keep track of valid ids
valid = []
l_id_pos = list(candset.columns).index(fk_ltable)
r_id_pos = list(candset.columns).index(fk_rtable)
# # iterate candidate set
for row in candset.itertuples(index=False):
# # update progress bar
if show_progress:
bar.update()
ltuple = l_dict[row[l_id_pos]]
rtuple = r_dict[row[r_id_pos]]
res = self.apply_rules(ltuple, rtuple)
if res != True:
valid.append(True)
else:
valid.append(False)
# construct output table
if len(candset) > 0:
candset = candset[valid]
else:
candset = pd.DataFrame(columns=candset.columns)
# update catalog
cm.set_candset_properties(candset, key, fk_ltable, fk_rtable, ltable,
rtable)
# return candidate set
return candset
def sample_table(table, sample_size, replace=False, verbose=False):
"""
Sample a pandas DataFrame (for labeling purposes).
This function samples a DataFrame, typically used for labeling
purposes. This function expects the input DataFrame containing the
metadata of a candidate set (such as key, fk_ltable, fk_rtable, ltable,
rtable). Specifically, this function creates a copy of the input
DataFrame, samples the data using uniform random sampling (uses 'random'
function from numpy to sample) and returns the sampled DataFrame.
Further, also copies the properties from the input DataFrame to the output
DataFrame.
Args:
table (DataFrame): Input DataFrame to be sampled. Specifically,
a DataFrame containing the metadata of a candidate set (such as
key, fk_ltable, fk_rtable, ltable, rtable) in the catalog.
sample_size (int): Number of samples to be picked up from the input
DataFrame.
replace (boolean): Flag to indicate whether sampling should be done
with replacement or not (default value is False).
verbose (boolean): Flag to indicate whether more detailed information
about the execution steps should be printed out (default value is
False).
Returns:
A new DataFrame with 'sample_size' number of rows. Further,
this function sets the output DataFrame's properties same as input
DataFrame.
Raises:
AssertionError: If the input table is not of type pandas DataFrame.
AssertionError: If the input DataFrame size is 0.
AssertionError: If the sample_size is greater than the input
DataFrame size.
Notes:
As mentioned in the above description, the output DataFrame is
updated (in the catalog) with the properties from the input
DataFrame. A subtle point to note here is, when the replace flag is
set to True, then the output DataFrame can contain duplicate keys.
In that case, this function will not set the key and it is up to
the user to fix it after the function returns.
"""
# Validate input parameters.
# # The input DataFrame is expected to be of type pandas DataFrame.
if not isinstance(table, pd.DataFrame):
logger.error('Input table is not of type pandas dataframe')
raise AssertionError('Input table is not of type pandas dataframe')
# # There should at least not-zero rows to sample from
if len(table) == 0:
logger.error('Size of the input table is 0')
raise AssertionError('Size of the input table is 0')
# # The sample size should be less than or equal to the number of rows in
# the input DataFrame
if len(table) < sample_size:
logger.error('Sample size is larger than the input table size')
raise AssertionError('Sample size is larger than the input table size')
# Now, validate the metadata for the input DataFrame as we have to copy
# these properties to the output DataFrame
# # First, display what metadata is required for this function
ch.log_info(
logger, 'Required metadata: cand.set key, fk ltable, '
'fk rtable, ltable, rtable, ltable key, rtable key', verbose)
# # Second, get the metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = \
cm.get_metadata_for_candset(table, logger, verbose)
# # Third, validate the metadata
cm.validate_metadata_for_candset(table, key, fk_ltable, fk_rtable, ltable,
rtable, l_key, r_key, logger, verbose)
# Get the sample set for the output table
sample_indices = pd.np.random.choice(len(table),
sample_size,
replace=replace)
# Sort the indices ordered by index value
sample_indices = sorted(sample_indices)
sampled_table = table.iloc[list(sample_indices)]
# Copy the properties
cm.init_properties(sampled_table)
# # If the replace is set to True, then we should check for the validity
# of key before setting it
if replace:
properties = cm.get_all_properties(table)
for property_name, property_value in six.iteritems(properties):
if property_name == 'key':
# Check for the validity of key before setting it
cm.set_key(sampled_table, property_value)
else:
# Copy the other properties as is
cm.set_property(sampled_table, property_name, property_value)
else:
cm.copy_properties(table, sampled_table)
# Return the sampled table
return sampled_table
def block_candset(self,
candset,
l_block_attr,
r_block_attr,
verbose=True,
show_progress=True):
self.validate_types_candset(candset, l_block_attr, r_block_attr,
verbose, show_progress)
# get and validate metadata
log_info(
logger, 'Required metadata: cand.set key, fk ltable, ' +
'fk rtable, ltable, rtable, ltable key, rtable key', verbose)
# # get metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = cm.get_metadata_for_candset(
candset, logger, verbose)
# # validate metadata
cm.validate_metadata_for_candset(candset, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key, logger,
verbose)
# validate input parameters
self.validate_block_attrs(ltable, rtable, l_block_attr, r_block_attr)
# do blocking
# # initialize progress bar
if show_progress:
prog_bar = pyprind.ProgBar(len(candset))
# # initialize list to keep track of valid ids
valid = []
# # set index for convenience
l_df = ltable.set_index(l_key, drop=False)
r_df = rtable.set_index(r_key, drop=False)
# # get the indexes for the key attributes in the candset
col_names = list(candset.columns)
lkey_idx = col_names.index(fk_ltable)
rkey_idx = col_names.index(fk_rtable)
# # create a look up table for the blocking attribute values
l_dict = {}
r_dict = {}
# # iterate the rows in candset
for row in candset.itertuples(index=False):
# # update the progress bar
if show_progress:
prog_bar.update()
# # get the value of block attributes
row_lkey = row[lkey_idx]
if row_lkey not in l_dict:
l_dict[row_lkey] = l_df.ix[row_lkey, l_block_attr]
l_val = l_dict[row_lkey]
row_rkey = row[rkey_idx]
if row_rkey not in r_dict:
r_dict[row_rkey] = r_df.ix[row_rkey, r_block_attr]
r_val = r_dict[row_rkey]
if l_val == r_val:
valid.append(True)
else:
valid.append(False)
# construct output table
if len(candset) > 0:
out_table = candset[valid]
else:
out_table = pd.DataFrame(columns=candset.columns)
# update the catalog
cm.set_candset_properties(out_table, key, fk_ltable, fk_rtable, ltable,
rtable)
# return the output table
return out_table
def sample_table(table, sample_size, replace=False, verbose=False):
"""
Sample a pandas DataFrame (for labeling purposes).
This function samples a DataFrame, typically used for labeling
purposes. This function expects the input DataFrame containing the
metadata of a candidate set (such as key, fk_ltable, fk_rtable, ltable,
rtable). Specifically, this function creates a copy of the input
DataFrame, samples the data using uniform random sampling (uses 'random'
function from numpy to sample) and returns the sampled DataFrame.
Further, also copies the properties from the input DataFrame to the output
DataFrame.
Args:
table (DataFrame): Input DataFrame to be sampled. Specifically,
a DataFrame containing the metadata of a candidate set (such as
key, fk_ltable, fk_rtable, ltable, rtable) in the catalog.
sample_size (int): Number of samples to be picked up from the input
DataFrame.
replace (boolean): Flag to indicate whether sampling should be done
with replacement or not (default value is False).
verbose (boolean): Flag to indicate whether more detailed information
about the execution steps should be printed out (default value is
False).
Returns:
A new DataFrame with 'sample_size' number of rows. Further,
this function sets the output DataFrame's properties same as input
DataFrame.
Raises:
AssertionError: If the input table is not of type pandas DataFrame.
AssertionError: If the input DataFrame size is 0.
AssertionError: If the sample_size is greater than the input
DataFrame size.
Notes:
As mentioned in the above description, the output DataFrame is
updated (in the catalog) with the properties from the input
DataFrame. A subtle point to note here is, when the replace flag is
set to True, then the output DataFrame can contain duplicate keys.
In that case, this function will not set the key and it is up to
the user to fix it after the function returns.
"""
# Validate input parameters.
# # The input DataFrame is expected to be of type pandas DataFrame.
if not isinstance(table, pd.DataFrame):
logger.error('Input table is not of type pandas dataframe')
raise AssertionError('Input table is not of type pandas dataframe')
# # There should at least not-zero rows to sample from
if len(table) == 0:
logger.error('Size of the input table is 0')
raise AssertionError('Size of the input table is 0')
# # The sample size should be less than or equal to the number of rows in
# the input DataFrame
if len(table) < sample_size:
logger.error('Sample size is larger than the input table size')
raise AssertionError('Sample size is larger than the input table size')
# Now, validate the metadata for the input DataFrame as we have to copy
# these properties to the output DataFrame
# # First, display what metadata is required for this function
ch.log_info(logger, 'Required metadata: cand.set key, fk ltable, '
'fk rtable, ltable, rtable, ltable key, rtable key',
verbose)
# # Second, get the metadata
key, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key = \
cm.get_metadata_for_candset(table, logger, verbose)
# # Third, validate the metadata
cm.validate_metadata_for_candset(table, key, fk_ltable, fk_rtable,
ltable, rtable, l_key, r_key,
logger, verbose)
# Get the sample set for the output table
sample_indices = pd.np.random.choice(len(table), sample_size,
replace=replace)
# Sort the indices ordered by index value
sample_indices = sorted(sample_indices)
sampled_table = table.iloc[list(sample_indices)]
# Copy the properties
cm.init_properties(sampled_table)
# # If the replace is set to True, then we should check for the validity
# of key before setting it
if replace:
properties = cm.get_all_properties(table)
for property_name, property_value in six.iteritems(properties):
if property_name == 'key':
# Check for the validity of key before setting it
cm.set_key(sampled_table, property_value)
else:
# Copy the other properties as is
cm.set_property(sampled_table, property_name, property_value)
else:
cm.copy_properties(table, sampled_table)
# Return the sampled table
return sampled_table
def test_get_metadata_for_candset_invalid_df(self):
cm.get_metadata_for_candset(None, None, False)
