def test_sni_with_blocking_link(self):
"""SNI: Test sni with blocking keys."""
# sni
index_class = SortedNeighbourhood(
'var_arange', window=3, block_on='var_arange')
pairs = index_class.index((self.a, self.b[0:len(self.a)]))
# the length of pairs is length(self.a)
assert len(pairs) == len(self.a)
def test_sni_with_blocking_dedup(self):
"""SNI: Test sni with blocking keys."""
# sni
index_class = SortedNeighbourhood(
'var_arange', window=3, block_on='var_arange')
pairs = index_class.index(self.a)
print(pairs.values)
# the length of pairs is 0
assert len(pairs) == 0
def test_sni_algorithm_dedup(self, window):
"""SNI: Test the window size (dedup)."""
# window = 7 # using paramereized tests instead
index_class = SortedNeighbourhood(on='var_arange', window=window)
pairs = index_class.index((self.a))
# the expected number of pairs
window_d = (window - 1) / 2
len_a = len(self.a)
n_pairs_expected = \
np.sum(np.arange(len_a - 1, len_a - (window_d + 1), -1))
# test
assert len(pairs) == n_pairs_expected
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_sni_algorithm_link(self, window):
"""SNI: Test the window size (link)."""
# window = 7 # using paramereized tests instead
index_class = SortedNeighbourhood(on='var_arange', window=window)
pairs = index_class.index((self.a, self.b[0:len(self.a)]))
# the expected number of pairs
window_d = (window - 1) / 2
len_a = len(self.a)
n_pairs_expected = \
len(self.a) + \
2 * np.sum(np.arange(len_a - 1, len_a - (window_d + 1), -1))
# test
print('expected number of pairs: %s' % n_pairs_expected)
print('number of pairs found: %s' % len(pairs))
assert len(pairs) == n_pairs_expected
def test_dedup_single_sorting_key_vs_SortedNeighbourhood(self, window):
indexers = [
NeighbourhoodBlock('var_arange', max_nulls=1, windows=window),
NeighbourhoodBlock(left_on='var_arange',
right_on='var_arange',
max_nulls=1,
windows=window),
SortedNeighbourhood('var_arange', window=window),
]
self.assert_index_comparisons(eq, indexers, self.a)
self.assert_index_comparisons(gt, indexers[-2:], self.incomplete_a)
def test_depr_on_argument(self):
index_cl_new = SortedNeighbourhood('var_arange')
pairs_new = index_cl_new.index(self.a)
index_cl_old = SortedNeighbourhood(on='var_arange')
pairs_old = index_cl_old.index(self.a)
ptm.assert_index_equal(pairs_new, pairs_old)
def test_dedup_with_blocking_vs_SortedNeighbourhood(self, window):
indexers = [
NeighbourhoodBlock(['var_arange', 'var_block10'],
max_nulls=1,
windows=[window, 1]),
NeighbourhoodBlock(left_on=['var_arange', 'var_block10'],
right_on=['var_arange', 'var_block10'],
max_nulls=1,
windows=[window, 1]),
SortedNeighbourhood('var_arange',
block_on='var_block10',
window=window),
]
self.assert_index_comparisons(eq, indexers, self.a)
self.assert_index_comparisons(gt, indexers[-2:], self.incomplete_a)
def sortedneighbourhood(self, *args, **kwargs):
"""Add a Sorted Neighbourhood Index.
Shortcut of :class:`recordlinkage.index.SortedNeighbourhood`::
from recordlinkage.index import SortedNeighbourhood
indexer = recordlinkage.Index()
indexer.add(SortedNeighbourhood())
"""
indexer = SortedNeighbourhood(*args, **kwargs)
self.add(indexer)
return self
def test_depr_on_argument(self):
index_cl_new = SortedNeighbourhood('var_arange')
pairs_new = index_cl_new.index(self.a)
with pytest.deprecated_call():
index_cl_old = SortedNeighbourhood(on='var_arange')
pairs_old = index_cl_old.index(self.a)
pdt.assert_index_equal(pairs_new, pairs_old)
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
def run_blocking(df_a, blocks, blocking="Standard"):
""" Run the blocking/indexing based on the blocking parameter """
logging.info("running blocking ....")
df_a = df_a.copy()
if (blocking == 'Standard'):
indexer = BlockUnion(block_on=blocks)
candidate_pairs = indexer.index(df_a)
if (blocking == "Full Indexing"):
indexer = rl.Index()
indexer.full()
candidate_pairs = indexer.index(df_a)
elif (blocking == 'SortedNeighbourhood'):
key = 'given_name'
indexer = SortedNeighbourhood(key, window=3)
candidate_pairs = indexer.index(df_a)
else:
indexer = BlockUnion(block_on=blocks)
candidate_pairs = indexer.index(df_a)
return candidate_pairs
def test_single_sorting_key(self):
"""SNI: Test class arguments."""
# all the following cases return in the same index.
# situation 1
index_cl1 = SortedNeighbourhood('var_arange')
pairs1 = index_cl1.index((self.a, self.b))
# situation 2
index_cl2 = SortedNeighbourhood(on='var_arange')
pairs2 = index_cl2.index((self.a, self.b))
# situation 3
index_cl3 = SortedNeighbourhood(left_on='var_arange',
right_on='var_arange')
pairs3 = index_cl3.index((self.a, self.b))
# situation 4
index_cl4 = SortedNeighbourhood(on=['var_arange'])
pairs4 = index_cl4.index((self.a, self.b))
# situation 5
index_cl5 = SortedNeighbourhood(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)
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'))
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)]
cls.index_b = ['rec_b_%s' % i for i in range(0, n_b)]
