def test_add_dedup(self): indexer1 = Full() indexer2 = Block(left_on='var_arange', right_on='var_arange') expected = indexer1.index(self.a).union(indexer2.index(self.a)) indexer = recordlinkage.Index() indexer.add( [Full(), Block(left_on='var_arange', right_on='var_arange')]) result = indexer.index(self.a) ptm.assert_index_equal(result, expected)
def _link_index(self, df_a, df_b): indexer = recordlinkage.Index() for blocking_keys in self.block_on: indexer.add(Block(blocking_keys)) return indexer.index(df_a, df_b)
def _dedup_index(self, df_a): indexer = rl.Index() for blocking_keys in self.block_on: indexer.add(Block(blocking_keys)) return indexer.index(df_a)
def processing(df, sourceid): if sourceid == 1: postal_indexer = Block('PostCodeKey') postal_pairs = postal_indexer.index(df) for i in [20, 40, 60, 80, 100]: if (len(postal_pairs) / i) < 1000000: intervalparts = i break else: intervalparts = 100 # Get Interval Parts inter = intervals(intervalparts, len(postal_pairs)) comp_postal = recordlinkage.Compare(n_jobs=20) comp_postal.string('BusinessNameKey', 'BusinessNameKey', method='jarowinkler', label='BusinesNameCompare') comp_postal.string('TradestyleKey', 'BusinessNameKey', method='jarowinkler', label='BNTSCompare') comp_postal.string('AddressKey', 'AddressKey', method='jarowinkler', label='AddressCompare') cv_full = comp_postal.compute(postal_pairs[0:inter[1]], df) cv_full = cv_full[ ((cv_full.BusinesNameCompare.between(0.95, 1, inclusive=True)) | (cv_full.BNTSCompare.between(0.95, 1, inclusive=True))) & (cv_full.AddressCompare.between(0.95, 1, inclusive=True))] for i in range(1, len(inter) - 1): cv = comp_postal.compute(postal_pairs[inter[i] + 1:inter[i + 1]], df) cv = cv[((cv.BusinesNameCompare.between(0.95, 1, inclusive=True)) | (cv.BNTSCompare.between(0.95, 1, inclusive=True))) & (cv.AddressCompare.between(0.95, 1, inclusive=True))] frames = [cv_full, cv] cv_full = pd.concat(frames) del cv # print(df.columns) # print(cv_full.columns) return df, cv_full
def test_dedup_single_blocking_key_vs_block(self): indexers = [ NeighbourhoodBlock('var_block10', max_nulls=1), NeighbourhoodBlock( left_on='var_block10', right_on='var_block10', max_nulls=1), Block('var_block10'), ] self.assert_index_comparisons(eq, indexers, self.a) self.assert_index_comparisons(gt, indexers[-2:], self.incomplete_a)
def get_test_algorithms(): """Return list of algorithms""" return [ Full(), Block(on='var_arange'), SortedNeighbourhood(on='var_arange'), Random(10, random_state=100, replace=True), Random(10, random_state=100, replace=False) ]
def test_dedup_multiple_blocking_keys_vs_Block(self): indexers = [ NeighbourhoodBlock(['var_single', 'var_block10'], max_nulls=1), NeighbourhoodBlock(left_on=['var_single', 'var_block10'], right_on=['var_single', 'var_block10'], max_nulls=1), Block(['var_single', 'var_block10']), ] self.assert_index_comparisons(eq, indexers, self.a) self.assert_index_comparisons(gt, indexers[-2:], self.incomplete_a)
def test_annotation_link(tmp_path): path = tmp_path / "febrl_annotation_link.json" # get febrl4 file df_a, df_b, matches = load_febrl4(return_links=True) # get record pairs indexer = Block("given_name", "given_name") pairs = indexer.index(df_a, df_b) # create annotation file # write an annotation file for the Febrl4 dataset. rl.write_annotation_file(path, pairs[0:10], df_a, df_b) # read the result result = rl.read_annotation_file(path) assert result.links is None assert result.distinct is None
def test_link_single_blocking_key_vs_Block(self): indexers = [ NeighbourhoodBlock('var_arange', max_nulls=1), NeighbourhoodBlock(left_on='var_arange', right_on='var_arange', max_nulls=1), Block('var_arange'), ] self.assert_index_comparisons(eq, indexers, self.a, self.b) self.assert_index_comparisons(gt, indexers[-2:], self.incomplete_a, self.incomplete_b)
def ProcessData(patientDataList, fetchedHospitalData): # Read from the directory filelist = pd.read_csv( '/home/bizzzzzzzzzzzzu/Music/MedicalPortal/MedicPortal DataProcessing/FetchedData/' + fetchedHospitalData) # Indexation step indexer = p.Index() indexer.add(Block(left_on='fatherName', right_on='fatherName')) candidate_links = indexer.index(patientDataList, filelist) # print((candidate_links)) # Comparison step compare_cl = p.Compare() # compare_cl.exact('_id','_id',label='_id') compare_cl.exact('name', 'name', label='name') compare_cl.exact('fatherName', 'fatherName', label='fatherName') compare_cl.exact('grandFatherName', 'grandFatherName', label='grandFatherName') compare_cl.exact('gender', 'gender', label='gender') compare_cl.exact('dateOfBirth', 'dateOfBirth', label='dateOfBirth') compare_cl.exact('dayOfBirth', 'dayOfBirth', label='dayOfBirth') compare_cl.exact('monthOfBirth', 'monthOfBirth', label='monthOfBirth') compare_cl.exact('yearOfBirth', 'yearOfBirth', label='yearOfBirth') compare_cl.exact('age', 'age', label='age') # compare_cl.exact('address','address',label='address') # compare_cl.exact('phoneNumber','phoneNumber',label='phoneNumber') features = compare_cl.compute(candidate_links, patientDataList, filelist) if features.empty: return None else: # Classification step ''' Use the KMeans Classifier This classifier is equivalent to the Unsupervised record linkage approach ''' # # classifier = p.LogisticRegressionClassifier(coefficients=coefficients,intercept=intercept) classifier = p.LogisticRegressionClassifier() classifier.fit(golden_pairs, golden_matches_index) links = classifier.predict(features) return links
def test_depr_on_argument(self): index_cl_new = Block('var_arange') pairs_new = index_cl_new.index(self.a) index_cl_old = Block(on='var_arange') pairs_old = index_cl_old.index(self.a) ptm.assert_index_equal(pairs_new, pairs_old)
def test_blocking_algorithm_link(self): """BLOCKING: test blocking algorithm for linking""" # situation 1: eye index index_cl1 = Block(on='var_arange') pairs1 = index_cl1.index((self.a, self.b)) assert len(pairs1) == len(self.a) assert pairs1.is_unique # situation 2: 10 blocks index_cl2 = Block(on='var_block10') pairs2 = index_cl2.index((self.a, self.b)) assert len(pairs2) == len(self.a) * 10 assert pairs2.is_unique # situation 3: full index index_cl3 = Block(on='var_single') pairs3 = index_cl3.index((self.a, self.b)) assert len(pairs3) == len(self.a) * len(self.b) assert pairs3.is_unique
def block(self, *args, **kwargs): """Add a block index. Shortcut of :class:`recordlinkage.index.Block`:: from recordlinkage.index import Block indexer = recordlinkage.Index() indexer.add(Block()) """ indexer = Block(*args, **kwargs) self.add(indexer) return self
def test_depr_on_argument(self): index_cl_new = Block('var_arange') pairs_new = index_cl_new.index(self.a) with pytest.deprecated_call(): index_cl_old = Block(on='var_arange') pairs_old = index_cl_old.index(self.a) pdt.assert_index_equal(pairs_new, pairs_old)
def test_blocking_algorithm_dedup(self): """BLOCKING: test blocking algorithm for deduplication""" len_a = len(self.a) # situation 1: eye index index_cl1 = Block(on='var_arange') pairs1 = index_cl1.index(self.a) assert len(pairs1) == 0 assert pairs1.is_unique # situation 2: 10 blocks index_cl2 = Block(on='var_block10') pairs2 = index_cl2.index(self.a) assert len(pairs2) == (len_a * 10 - len_a) / 2 assert pairs2.is_unique # situation 3: full index index_cl3 = Block(on='var_single') pairs3 = index_cl3.index(self.a) assert len(pairs3) == (len_a * len_a - len_a) / 2 assert pairs3.is_unique
def test_multiple_blocking_keys(self): """BLOCKING: test multiple blocking keys""" # all the following cases return in the same index. # situation 1 index_cl1 = Block(['var_arange', 'var_block10']) pairs1 = index_cl1.index((self.a, self.b)) # situation 2 index_cl2 = Block(left_on=['var_arange', 'var_block10'], right_on=['var_arange', 'var_block10']) pairs2 = index_cl2.index((self.a, self.b)) # test ptm.assert_index_equal(pairs1, pairs2)
def build_indexer(dview: pd.DataFrame, exclude=['gender_pool']): # Identify which columns to index and how to do so blocking_columns = [ col for col in dview.columns if compare_in(col, blocked_identifiers) ] sngb_columns = [ col for col in dview.columns if compare_in(col, sneighbourhood_identifiers) ] # Build the indexer indexer = Index() # Add sorted neighbour conditions for col in sngb_columns: if not compare_in(col, exclude): indexer.add(SortedNeighbourhood(col)) # Add blocking conditions for col in blocking_columns: indexer.add(Block(col, col)) indexlogger.info(f'Constructed indexer: \n{indexer.algorithms}') return indexer
class TestIndexAlgorithmApi(TestData): """General unittest for the indexing API.""" @parameterized.expand(TEST_INDEXATION_OBJECTS) def test_repr(self, index_class): index_str = str(index_class) index_repr = repr(index_class) self.assertEqual(index_str, index_repr) start_str = '<{}'.format(index_class.__class__.__name__) self.assertTrue(index_str.startswith(start_str)) @parameterized.expand(TEST_INDEXATION_OBJECTS) def test_arguments(self, index_class): """Test the index method arguments""" # The following should work index_class.index(self.a) index_class.index(self.a, self.b) index_class.index((self.a)) index_class.index([self.a]) index_class.index((self.a, self.b)) index_class.index([self.a, self.b]) index_class.index(x=(self.a, self.b)) def test_iterative(self): """Test the iterative behaviour.""" # SINGLE STEP index_class = Full() pairs = index_class.index((self.a, self.b)) pairs = pd.DataFrame(index=pairs).sort_index() # MULTI STEP index_class = Full() pairs1 = index_class.index((self.a[0:50], self.b)) pairs2 = index_class.index((self.a[50:100], self.b)) pairs_split = pairs1.append(pairs2) pairs_split = pd.DataFrame(index=pairs_split).sort_index() ptm.assert_frame_equal(pairs, pairs_split) # note possible to sort MultiIndex, so made a frame out of it. @parameterized.expand(TEST_INDEXATION_OBJECTS) def test_empty_imput_dataframes(self, index_class): """Empty DataFrames""" # make an empty dataframe with the columns of self.a and self.b df_a = pd.DataFrame(columns=self.a.columns.tolist()) df_b = pd.DataFrame(columns=self.b.columns.tolist()) from recordlinkage.index import Random if not isinstance(index_class, Random): # make an index pairs = index_class.index((df_a, df_b)) # check if the MultiIndex has length 0 self.assertIsInstance(pairs, pd.MultiIndex) self.assertEqual(len(pairs), 0) else: with self.assertRaises(ValueError): index_class.index((df_a, df_b)) @parameterized.expand(TEST_INDEXATION_OBJECTS) def test_error_handling(self, index_class): """Test error handling on non-unique index.""" # make a non_unique index df_a = self.a.rename(index={self.a.index[1]: self.a.index[0]}, inplace=False) with self.assertRaises(ValueError): index_class.index(df_a) @parameterized.expand([ param(Full()), param(Block(on='var_arange')), param(SortedNeighbourhood(on='var_arange')), param(Random(10, random_state=100, replace=True)), param(Random(10, random_state=100, replace=False)) ]) def test_index_names_dedup(self, index_class): index_names = ['dedup', None, 'index', int(1)] expected = [ ['dedup_1', 'dedup_2'], [None, None], ['index_1', 'index_2'], ['1_1', '1_2'], ] for i, name in enumerate(index_names): index_A = pd.Index(self.a.index).rename(name) df_A = pd.DataFrame(self.a, index=index_A) pairs = index_class.index((df_A)) self.assertEqual(pairs.names, expected[i]) self.assertEqual(df_A.index.name, name) @parameterized.expand([ param(Full()), param(Block(on='var_arange')), param(SortedNeighbourhood(on='var_arange')), param(Random(10, random_state=100, replace=True)), param(Random(10, random_state=100, replace=False)) ]) def test_duplicated_index_names_dedup(self, index_class): # make an index for each dataframe with a new index name index_a = pd.Index(self.a.index, name='index') df_a = pd.DataFrame(self.a, index=index_a) # make the index pairs = index_class.index(df_a) self.assertEqual(pairs.names, ['index_1', 'index_2']) # check for inplace editing (not the intention) self.assertEqual(df_a.index.name, 'index') # make the index index_class.suffixes = ['_a', '_b'] pairs = index_class.index(df_a) self.assertEqual(pairs.names, ['index_a', 'index_b']) # check for inplace editing (not the intention) self.assertEqual(df_a.index.name, 'index') @parameterized.expand([ param(Full()), param(Block(on='var_arange')), param(SortedNeighbourhood(on='var_arange')), param(Random(10, random_state=100, replace=True)), param(Random(10, random_state=100, replace=False)) ]) def test_index_names_link(self, index_class): # tuples with the name of the first and second index index_names = [('index1', 'index2'), ('index1', None), (None, 'index2'), (None, None), (10, 'index2'), (10, 11)] for name_a, name_b in index_names: # make an index for each dataframe with a new index name index_a = pd.Index(self.a.index, name=name_a) df_a = pd.DataFrame(self.a, index=index_a) index_b = pd.Index(self.b.index, name=name_b) df_b = pd.DataFrame(self.b, index=index_b) pairs = index_class.index((df_a, df_b)) self.assertEqual(pairs.names, [name_a, name_b]) # check for inplace editing (not the intention) self.assertEqual(df_a.index.name, name_a) self.assertEqual(df_b.index.name, name_b) @parameterized.expand([ param(Full()), param(Block(on='var_arange')), param(SortedNeighbourhood(on='var_arange')), param(Random(10, random_state=100, replace=True)), param(Random(10, random_state=100, replace=False)) ]) def test_duplicated_index_names_link(self, index_class): # make an index for each dataframe with a new index name index_a = pd.Index(self.a.index, name='index') df_a = pd.DataFrame(self.a, index=index_a) index_b = pd.Index(self.b.index, name='index') df_b = pd.DataFrame(self.b, index=index_b) # make the index pairs = index_class.index((df_a, df_b)) self.assertEqual(pairs.names, ['index_1', 'index_2']) # check for inplace editing (not the intention) self.assertEqual(df_a.index.name, 'index') self.assertEqual(df_b.index.name, 'index') # make the index index_class.suffixes = ['_a', '_b'] pairs = index_class.index((df_a, df_b)) self.assertEqual(pairs.names, ['index_a', 'index_b']) # check for inplace editing (not the intention) self.assertEqual(df_a.index.name, 'index') self.assertEqual(df_b.index.name, 'index') @parameterized.expand(TEST_INDEXATION_OBJECTS) def test_pickle(self, index_class): """Test if it is possible to pickle the class.""" pickle_path = os.path.join(self.test_dir, 'pickle_compare_obj.pickle') # pickle before indexing pickle.dump(index_class, open(pickle_path, 'wb')) # compute the record pairs index_class.index(self.a, self.b) # pickle after indexing pickle.dump(index_class, open(pickle_path, 'wb'))
df = assign_postal_lat_lng(df) df.head(6) import recordlinkage as rl from recordlinkage.index import Full full_indexer = Full() pairs = full_indexer.index(df) print(f"Full index: {len(df)} records, {len(pairs)} pairs") from recordlinkage.index import Block postal_indexer = Block('postal') pairs = postal_indexer.index(df) print(f"Postal index: {len(pairs)} pairs") pairs.to_frame()[:10].values pd.DataFrame([[0.5, 0.8, 0.9, 1]], columns=['name', 'addr', 'postal', 'latlng'], index=pd.MultiIndex.from_arrays([[100], [200]])) comp = rl.Compare() comp.string('name', 'name', method='jarowinkler', label='name') comp.string('addr', 'addr', method='jarowinkler', label='addr')
from recordlinkage.compare import Exact, String from recordlinkage.datasets import load_febrl3 # set logging rl.logging.set_verbosity(rl.logging.INFO) # load dataset print('Loading data...') dfA, true_links = load_febrl3(return_links=True) print(len(dfA), 'records in dataset A') print(len(true_links), 'links in dataset A') # start indexing print('Build index...') indexer = rl.Index() indexer.add(Block('given_name')) indexer.add(Block('surname')) indexer.add(Block('soc_sec_id')) candidate_links = indexer.index(dfA) # start comparing print('Start comparing...') comparer = rl.Compare() comparer.add(Exact('given_name', 'given_name', label='given_name')) comparer.add(String('surname', 'surname', method='jarowinkler', threshold=0.85, label='surname')) comparer.add(Exact('date_of_birth', 'date_of_birth', label='date_of_birth')) comparer.add(Exact('suburb', 'suburb', label='suburb')) comparer.add(Exact('state', 'state', label='state')) comparer.add(String('address_1', 'address_1', threshold=0.85, label='address_1'))
import unittest import tempfile import shutil import pickle import numpy as np import pandas as pd import pandas.util.testing as ptm from parameterized import parameterized, param import recordlinkage from recordlinkage.index import Full, Block, SortedNeighbourhood, Random TEST_INDEXATION_OBJECTS = [ param(Full()), param(Block(on='var_arange')), param(SortedNeighbourhood(on='var_arange')), param(Random(10, random_state=100, replace=True)), param(Random(10, random_state=100, replace=False)) ] class TestData(unittest.TestCase): """Unittest object to setup test data.""" @classmethod def setUpClass(cls): n_a = 100 n_b = 150 cls.index_a = ['rec_a_%s' % i for i in range(0, n_a)]
def test_single_blocking_key(self): """BLOCKING: Test class arguments.""" # all the following cases return in the same index. # situation 1 index_cl1 = Block('var_arange') pairs1 = index_cl1.index((self.a, self.b)) # situation 2 index_cl2 = Block(on='var_arange') pairs2 = index_cl2.index((self.a, self.b)) # situation 3 index_cl3 = Block(left_on='var_arange', right_on='var_arange') pairs3 = index_cl3.index((self.a, self.b)) # situation 4 index_cl4 = Block(on=['var_arange']) pairs4 = index_cl4.index((self.a, self.b)) # situation 5 index_cl5 = Block(left_on=['var_arange'], right_on=['var_arange']) pairs5 = index_cl5.index((self.a, self.b)) # test ptm.assert_index_equal(pairs1, pairs2) ptm.assert_index_equal(pairs1, pairs3) ptm.assert_index_equal(pairs1, pairs4) ptm.assert_index_equal(pairs1, pairs5)