#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
dedupe provides the main user interface for the library the
Dedupe class
"""
import itertools
import logging
import pickle
import multiprocessing
import warnings
import os
import sqlite3
import tempfile
import numpy
import json
import rlr
import dedupe.core as core
import dedupe.serializer as serializer
import dedupe.blocking as blocking
import dedupe.clustering as clustering
import dedupe.datamodel as datamodel
import dedupe.labeler as labeler
import dedupe.predicates
from typing import (Mapping,
Optional,
List,
Tuple,
Set,
Dict,
Union,
Generator,
Iterable,
Sequence,
BinaryIO,
cast,
TextIO)
from typing_extensions import Literal
from dedupe._typing import (Data,
Clusters,
RecordPairs,
RecordID,
RecordDict,
Blocks,
TrainingExample,
LookupResults,
Links,
TrainingData,
Classifier,
JoinConstraint)
logger = logging.getLogger(__name__)
class Matching(object):
"""
Base Class for Record Matching Classes
"""
def __init__(self, num_cores: Optional[int], **kwargs) -> None:
if num_cores is None:
self.num_cores = multiprocessing.cpu_count()
else:
self.num_cores = num_cores
self._fingerprinter: Optional[blocking.Fingerprinter] = None
self.data_model: datamodel.DataModel
self.classifier: Classifier
self.predicates: List[dedupe.predicates.Predicate]
@property
def fingerprinter(self) -> blocking.Fingerprinter:
if self._fingerprinter is None:
raise ValueError('the record fingerprinter is not intialized, '
'please run the train method')
return self._fingerprinter
class IntegralMatching(Matching):
"""
This class is for linking class where we need to score all possible
pairs before deciding on any matches
"""
def score(self,
pairs: RecordPairs) -> numpy.ndarray:
"""
Scores pairs of records. Returns pairs of tuples of records id and
associated probabilites that the pair of records are match
Args:
pairs: Iterator of pairs of records
"""
try:
matches = core.scoreDuplicates(pairs,
self.data_model,
self.classifier,
self.num_cores)
except RuntimeError:
raise RuntimeError('''
You need to either turn off multiprocessing or protect
the calls to the Dedupe methods with a
`if __name__ == '__main__'` in your main module, see
https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods''')
return matches
class DedupeMatching(IntegralMatching):
"""
Class for Deduplication, extends Matching.
Use DedupeMatching when you have a dataset that can contain
multiple references to the same entity.
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
def partition(self,
data: Data,
threshold: float = 0.5) -> Clusters: # pragma: no cover
"""
Identifies records that all refer to the same entity, returns
tuples containing a sequence of record ids and corresponding
sequence of confidence score as a float between 0 and 1. The
record_ids within each set should refer to the same entity and the
confidence score is a measure of our confidence a particular entity
belongs in the cluster.
For details on the confidence score, see :func:`dedupe.Dedupe.cluster`.
This method should only used for small to moderately sized
datasets for larger data, you need may need to generate your
own pairs of records and feed them to :func:`~score`.
Args:
data: Dictionary of records, where the keys are record_ids
and the values are dictionaries with the keys being
field names
threshold: Number between 0 and 1 (Default is 0.5). We
will only consider put together records into
clusters if the `cophenetic similarity
<https://en.wikipedia.org/wiki/Cophenetic>`_ of
the cluster is greater than the threshold.
Lowering the number will increase recall,
raising it will increase precision
.. code:: python
> clusters = matcher.partition(data, threshold=0.5)
> print(duplicates)
[((1, 2, 3), (0.790, 0.860, 0.790)),
((4, 5), (0.720, 0.720)),
((10, 11), (0.899, 0.899))]
"""
pairs = self.pairs(data)
pair_scores = self.score(pairs)
clusters = self.cluster(pair_scores, threshold)
clusters = self._add_singletons(data, clusters)
clusters = list(clusters)
try:
mmap_file = pair_scores.filename
del pair_scores
os.remove(mmap_file)
except AttributeError:
pass
return clusters
def _add_singletons(self, data, clusters):
singletons = set(data.keys())
for record_ids, score in clusters:
singletons.difference_update(record_ids)
yield (record_ids, score)
for singleton in singletons:
yield (singleton, ), (1.0, )
def pairs(self, data):
'''
Yield pairs of records that share common fingerprints.
Each pair will occur at most once. If you override this
method, you need to take care to ensure that this remains
true, as downstream methods, particularly :func:`cluster`, assumes
that every pair of records is compared no more than once.
Args:
data: Dictionary of records, where the keys are record_ids
and the values are dictionaries with the keys being
field names
.. code:: python
> pairs = matcher.pairs(data)
> print(list(pairs))
[((1, {'name' : 'Pat', 'address' : '123 Main'}),
(2, {'name' : 'Pat', 'address' : '123 Main'})),
((1, {'name' : 'Pat', 'address' : '123 Main'}),
(3, {'name' : 'Sam', 'address' : '123 Main'}))
]
'''
self.fingerprinter.index_all(data)
id_type = core.sqlite_id_type(data)
# Blocking and pair generation are typically the first memory
# bottlenecks, so we'll use sqlite3 to avoid doing them in memory
with tempfile.TemporaryDirectory() as temp_dir:
con = sqlite3.connect(temp_dir + '/blocks.db')
con.execute('''CREATE TABLE blocking_map
(block_key text, record_id {id_type})
'''.format(id_type=id_type))
con.executemany("INSERT INTO blocking_map values (?, ?)",
self.fingerprinter(data.items()))
self.fingerprinter.reset_indices()
con.execute('''CREATE INDEX block_key_idx
ON blocking_map (block_key)''')
pairs = con.execute('''SELECT DISTINCT a.record_id, b.record_id
FROM blocking_map a
INNER JOIN blocking_map b
USING (block_key)
WHERE a.record_id < b.record_id''')
for a_record_id, b_record_id in pairs:
yield ((a_record_id, data[a_record_id]),
(b_record_id, data[b_record_id]))
pairs.close()
con.close()
def cluster(self,
scores: numpy.ndarray,
threshold: float = 0.5) -> Clusters:
r"""From the similarity scores of pairs of records, decide which groups
of records are all referring to the same entity.
Yields tuples containing a sequence of record ids and corresponding
sequence of confidence score as a float between 0 and 1. The
record_ids within each set should refer to the same entity and the
confidence score is a measure of our confidence a particular entity
belongs in the cluster.
Each confidence scores is a measure of how similar the record is
to the other records in the cluster. Let :math:`\phi(i,j)` be the pair-wise
similarity between records :math:`i` and :math:`j`. Let :math:`N` be the number of records in the cluster.
.. math::
\text{confidence score}_i = 1 - \sqrt {\frac{\sum_{j}^N (1 - \phi(i,j))^2}{N -1}}
This measure is similar to the average squared distance
between the focal record and the other records in the
cluster. These scores can be `combined to give a total score
for the cluster
<https://en.wikipedia.org/wiki/Variance#Discrete_random_variable>`_.
.. math::
\text{cluster score} = 1 - \sqrt { \frac{\sum_i^N(1 - \mathrm{score}_i)^2 \cdot (N - 1) } { 2 N^2}}
Args:
scores: a numpy `structured array <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ with a dtype of `[('pairs', id_type, 2),
('score', 'f4')]` where dtype is either a str
or int, and score is a number between 0 and
1. The 'pairs' column contains pairs of ids of
the records compared and the 'score' column
should contains the similarity score for that
pair of records.
For each pair, the smaller id should be first.
threshold: Number between 0 and 1. We will only consider
put together records into clusters if the
`cophenetic similarity
<https://en.wikipedia.org/wiki/Cophenetic>`_ of
the cluster is greater than the threshold.
Lowering the number will increase recall,
raising it will increase precision
Defaults to 0.5.
.. code:: python
> pairs = matcher.pairs(data)
> scores = matcher.scores(pairs)
> clusters = matcher.cluster(scores)
> print(list(clusters))
[((1, 2, 3), (0.790, 0.860, 0.790)),
((4, 5), (0.720, 0.720)),
((10, 11), (0.899, 0.899))]
"""
logger.debug("matching done, begin clustering")
yield from clustering.cluster(scores, threshold)
class RecordLinkMatching(IntegralMatching):
"""
Class for Record Linkage, extends Matching.
Use RecordLinkMatching when you have two datasets that you want to merge
"""
def pairs(self, data_1: Data, data_2: Data) -> RecordPairs:
"""
Yield pairs of records that share common fingerprints.
Each pair will occur at most once. If you override this
method, you need to take care to ensure that this remains
true, as downstream methods, particularly :func:`one_to_one`,
and :func:`many_to_one` assumes that every pair of records is
compared no more than once.
Args:
data_1: Dictionary of records from first dataset, where the
keys are record_ids and the values are dictionaries
with the keys being field names
data_2: Dictionary of records from second dataset, same
form as data_1
.. code:: python
> pairs = matcher.pairs(data_1, data_2)
> print(list(pairs))
[((1, {'name' : 'Pat', 'address' : '123 Main'}),
(2, {'name' : 'Pat', 'address' : '123 Main'})),
((1, {'name' : 'Pat', 'address' : '123 Main'}),
(3, {'name' : 'Sam', 'address' : '123 Main'}))
]
"""
self.fingerprinter.index_all(data_2)
id_type_a = core.sqlite_id_type(data_1)
id_type_b = core.sqlite_id_type(data_2)
# Blocking and pair generation are typically the first memory
# bottlenecks, so we'll use sqlite3 to avoid doing them in memory
with tempfile.TemporaryDirectory() as temp_dir:
con = sqlite3.connect(temp_dir + '/blocks.db')
con.executescript('''CREATE TABLE blocking_map_a
(block_key text, record_id {id_type_a});
CREATE TABLE blocking_map_b
(block_key text, record_id {id_type_b});
'''.format(id_type_a=id_type_a,
id_type_b=id_type_b))
con.executemany("INSERT INTO blocking_map_a values (?, ?)",
self.fingerprinter(data_1.items()))
con.executemany("INSERT INTO blocking_map_b values (?, ?)",
self.fingerprinter(data_2.items(), target=True))
self.fingerprinter.reset_indices()
con.executescript('''CREATE INDEX block_key_a_idx
ON blocking_map_a (block_key);
CREATE INDEX block_key_b_idx
ON blocking_map_b (block_key);''')
pairs = con.execute('''SELECT DISTINCT a.record_id, b.record_id
FROM blocking_map_a a
INNER JOIN blocking_map_b b
USING (block_key)''')
for a_record_id, b_record_id in pairs:
yield ((a_record_id, data_1[a_record_id]),
(b_record_id, data_2[b_record_id]))
pairs.close()
con.close()
def join(self,
data_1: Data,
data_2: Data,
threshold: float = 0.5,
constraint: JoinConstraint = "one-to-one") -> Links:
"""
Identifies pairs of records that refer to the same entity.
Returns pairs of record ids with a confidence score as a float
between 0 and 1. The record_ids within the pair should refer to the
same entity and the confidence score is the estimated probability that
the records refer to the same entity.
This method should only used for small to moderately sized
datasets for larger data, you need may need to generate your
own pairs of records and feed them to the :func:`~score`.
Args:
data_1: Dictionary of records from first dataset, where the
keys are record_ids and the values are dictionaries
with the keys being field names
data_2: Dictionary of records from second dataset, same form
as data_1
threshold: Number between 0 and 1 (default is .5). We
will consider records as potential
duplicates if the predicted probability of
being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
constraint: What type of constraint to put on a join.
'one-to-one'
Every record in data_1 can match at most
one record from data_2 and every record
from data_2 can match at most one record
from data_1. This is good for when both
data_1 and data_2 are from different
sources and you are interested in
matching across the sources. If,
individually, data_1 or data_2 have many
duplicates you will not get good
matches.
'many-to-one'
Every record in data_1 can match at most
one record from data_2, but more than
one record from data_1 can match to the
same record in data_2. This is good for
when data_2 is a lookup table and data_1
is messy, such as geocoding or matching
against golden records.
'many-to-many'
Every record in data_1 can match
multiple records in data_2 and vice
versa. This is like a SQL inner join.
.. code:: python
> links = matcher.join(data_1, data_2, threshold=0.5)
> print(list(links))
[((1, 2), 0.790),
((4, 5), 0.720),
((10, 11), 0.899)]
"""
assert constraint in {'one-to-one', 'many-to-one', 'many-to-many'}, (
'%s is an invalid constraint option. Valid options include '
'one-to-one, many-to-one, or many-to-many' % constraint)
pairs = self.pairs(data_1, data_2)
pair_scores = self.score(pairs)
if constraint == 'one-to-one':
links = self.one_to_one(pair_scores, threshold)
elif constraint == 'many-to-one':
links = self.many_to_one(pair_scores, threshold)
else:
links = pair_scores[pair_scores['score'] > threshold]
links = list(links)
try:
mmap_file = pair_scores.filename
del pair_scores
os.remove(mmap_file)
except AttributeError:
pass
return links
def one_to_one(self,
scores: numpy.ndarray,
threshold: float = 0.0) -> Links:
"""From the similarity scores of pairs of records, decide which
pairs refer to the same entity.
Every record in data_1 can match at most one record from
data_2 and every record from data_2 can match at most one
record from data_1. See
https://en.wikipedia.org/wiki/Injective_function.
This method is good for when both data_1 and data_2 are from
different sources and you are interested in matching across
the sources. If, individually, data_1 or data_2 have many duplicates
you will not get good matches.
Yields pairs of record ids with a confidence score as a float
between 0 and 1. The record_ids within the pair should refer to the
same entity and the confidence score is the estimated probability that
the records refer to the same entity.
Args:
scores: a numpy `structured array <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ with a dtype of `[('pairs', id_type, 2),
('score', 'f4')]` where dtype is either a str
or int, and score is a number between 0 and
1. The 'pairs' column contains pairs of ids of
the records compared and the 'score' column
should contains the similarity score for that
pair of records.
threshold: Number between 0 and 1 (default is 0.0). We
will consider records as potential
duplicates if the predicted probability of
being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
.. code:: python
> pairs = matcher.pairs(data)
> scores = matcher.scores(pairs, threshold=0.5)
> links = matcher.one_to_one(scores)
> print(list(links))
[((1, 2), 0.790),
((4, 5), 0.720),
((10, 11), 0.899)]
"""
if threshold:
scores = scores[scores['score'] > threshold]
logger.debug("matching done, begin clustering")
yield from clustering.greedyMatching(scores)
def many_to_one(self,
scores: numpy.ndarray,
threshold: float = 0.0) -> Links:
"""
From the similarity scores of pairs of records, decide which
pairs refer to the same entity.
Every record in data_1 can match at most one record from
data_2, but more than one record from data_1 can match to the same
record in data_2. See
https://en.wikipedia.org/wiki/Surjective_function
This method is good for when data_2 is a lookup table and data_1
is messy, such as geocoding or matching against golden records.
Yields pairs of record ids with a confidence score as a float
between 0 and 1. The record_ids within the pair should refer to the
same entity and the confidence score is the estimated probability that
the records refer to the same entity.
Args:
scores: a numpy `structured array <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ with a dtype of `[('pairs', id_type, 2),
('score', 'f4')]` where dtype is either a str
or int, and score is a number between 0 and
1. The 'pairs' column contains pairs of ids of
the records compared and the 'score' column
should contains the similarity score for that
pair of records.
threshold: Number between 0 and 1 (default is 0.0). We
will consider records as potential
duplicates if the predicted probability of
being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
.. code:: python
> pairs = matcher.pairs(data)
> scores = matcher.scores(pairs, threshold=0.5)
> links = matcher.many_to_one(scores)
> print(list(links))
[((1, 2), 0.790),
((4, 5), 0.720),
((7, 2), 0.623),
((10, 11), 0.899)]
"""
logger.debug("matching done, begin clustering")
yield from clustering.pair_gazette_matching(scores, threshold, 1)
class GazetteerMatching(Matching):
def __init__(self, num_cores: Optional[int], **kwargs) -> None:
super().__init__(num_cores, **kwargs)
self.temp_dir = tempfile.TemporaryDirectory()
self.db = self.temp_dir.name + '/blocks.db'
self.indexed_data: Dict[RecordID, RecordDict] = {}
def _close(self):
self.temp_dir.cleanup()
def __del__(self):
self._close()
def index(self, data: Data) -> None: # pragma: no cover
"""
Add records to the index of records to match against. If a record in
`canonical_data` has the same key as a previously indexed record, the
old record will be replaced.
Args:
data: a dictionary of records where the keys
are record_ids and the values are
dictionaries with the keys being
field_names
"""
self.fingerprinter.index_all(data)
id_type = core.sqlite_id_type(data)
con = sqlite3.connect(self.db)
con.execute('''CREATE TABLE IF NOT EXISTS indexed_records
(block_key text,
record_id {id_type},
UNIQUE(block_key, record_id))
'''.format(id_type=id_type))
con.executemany("REPLACE INTO indexed_records VALUES (?, ?)",
self.fingerprinter(data.items(), target=True))
con.execute('''CREATE INDEX IF NOT EXISTS
indexed_records_block_key_idx
ON indexed_records
(block_key)''')
con.commit()
con.close()
self.indexed_data.update(data)
def unindex(self, data: Data) -> None: # pragma: no cover
"""
Remove records from the index of records to match against.
Args:
data: a dictionary of records where the keys
are record_ids and the values are
dictionaries with the keys being
field_names
"""
for field in self.fingerprinter.index_fields:
self.fingerprinter.unindex({record[field]
for record
in data.values()},
field)
con = sqlite3.connect(self.db)
con.executemany('''DELETE FROM indexed_records
WHERE record_id = ?''',
((k, ) for k in data.keys()))
con.commit()
con.close()
for k in data:
del self.indexed_data[k]
def blocks(self, data: Data) -> Blocks:
"""
Yield groups of pairs of records that share fingerprints.
Each group contains one record from data_1 paired with the records
from the indexed records that data_1 shares a fingerprint with.
Each pair within and among blocks will occur at most once. If
you override this method, you need to take care to ensure that
this remains true, as downstream methods, particularly
:func:`many_to_n`, assumes that every pair of records is compared no
more than once.
Args:
data: Dictionary of records, where the keys are record_ids
and the values are dictionaries with the keys being
field names
.. code:: python
> pairs = matcher.pairs(data)
> print(list(pairs))
[[((1, {'name' : 'Pat', 'address' : '123 Main'}),
(8, {'name' : 'Pat', 'address' : '123 Main'})),
((1, {'name' : 'Pat', 'address' : '123 Main'}),
(9, {'name' : 'Sam', 'address' : '123 Main'}))
],
[((2, {'name' : 'Sam', 'address' : '2600 State'}),
(5, {'name' : 'Pam', 'address' : '2600 Stat'})),
((2, {'name' : 'Sam', 'address' : '123 State'}),
(7, {'name' : 'Sammy', 'address' : '123 Main'}))
]]
"""
id_type = core.sqlite_id_type(data)
con = sqlite3.connect(self.db, check_same_thread=False)
con.execute('BEGIN')
con.execute('''CREATE TEMPORARY TABLE blocking_map
(block_key text, record_id {id_type})
'''.format(id_type=id_type))
con.executemany("INSERT INTO blocking_map VALUES (?, ?)",
self.fingerprinter(data.items()))
pairs = con.execute('''SELECT DISTINCT a.record_id, b.record_id
FROM blocking_map a
INNER JOIN indexed_records b
USING (block_key)
ORDER BY a.record_id''')
pair_blocks = itertools.groupby(pairs,
lambda x: x[0])
for _, pair_block in pair_blocks:
yield [((a_record_id, data[a_record_id]),
(b_record_id, self.indexed_data[b_record_id]))
for a_record_id, b_record_id
in pair_block]
pairs.close()
con.execute("ROLLBACK")
con.close()
def score(self,
blocks: Blocks) -> Generator[numpy.ndarray, None, None]:
"""
Scores groups of pairs of records. Yields structured numpy arrays
representing pairs of records in the group and the associated
probability that the pair is a match.
Args:
blocks: Iterator of blocks of records
"""
matches = core.scoreGazette(blocks,
self.data_model,
self.classifier,
self.num_cores)
return matches
def many_to_n(self,
score_blocks: Iterable[numpy.ndarray],
threshold: float = 0.0,
n_matches: int = 1) -> Links:
"""
For each group of scored pairs, yield the highest scoring N pairs
Args:
score_blocks: Iterator of numpy `structured arrays <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`_,
each with a dtype of `[('pairs', id_type, 2),
('score', 'f4')]` where dtype is either a str
or int, and score is a number between 0 and
1. The 'pairs' column contains pairs of ids of
the records compared and the 'score' column
should contains the similarity score for that
pair of records.
threshold: Number between 0 and 1 (default is 0.0). We
will consider records as potential
duplicates if the predicted probability of
being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
n_matches: How many top scoring pairs to select per group
"""
yield from clustering.gazetteMatching(score_blocks,
threshold,
n_matches)
def search(self,
data: Data,
threshold: float = 0.0,
n_matches: int = 1,
generator: bool = False) -> LookupResults: # pragma: no cover
"""
Identifies pairs of records that could refer to the same entity,
returns tuples containing tuples of possible matches, with a
confidence score for each match. The record_ids within each
tuple should refer to potential matches from a messy data
record to canonical records. The confidence score is the
estimated probability that the records refer to the same
entity.
Args:
data: a dictionary of records from a messy
dataset, where the keys are record_ids and
the values are dictionaries with the keys
being field names.
threshold: a number between 0 and 1 (default is
0.5). We will consider records as
potential duplicates if the predicted
probability of being a duplicate is
above the threshold.
Lowering the number will increase
recall, raising it will increase
precision
n_matches: the maximum number of possible matches from
canonical_data to return for each record in
data. If set to `None` all possible
matches above the threshold will be
returned. Defaults to 1
generator: when `True`, match will generate a sequence of
possible matches, instead of a list. Defaults
to `False` This makes `match` a lazy method.
.. code:: python
> matches = gazetteer.search(data, threshold=0.5, n_matches=2)
> print(matches)
[(((1, 6), 0.72),
((1, 8), 0.6)),
(((2, 7), 0.72),),
(((3, 6), 0.72),
((3, 8), 0.65)),
(((4, 6), 0.96),
((4, 5), 0.63))]
"""
blocks = self.blocks(data)
pair_scores = self.score(blocks)
search_results = self.many_to_n(pair_scores, threshold, n_matches)
results = self._format_search_results(data, search_results)
if generator:
return results
else:
return list(results)
def _format_search_results(self,
search_d: Data,
results: Links) -> LookupResults:
seen: Set[RecordID] = set()
for result in results:
a: Optional[RecordID] = None
b: RecordID
score: float
prepared_result: List[Tuple[RecordID, float]] = []
for (a, b), score in result: # type: ignore
prepared_result.append((b, score))
assert a is not None
yield a, tuple(prepared_result)
seen.add(a)
for k in (search_d.keys() - seen):
yield k, ()
class StaticMatching(Matching):
"""
Class for initializing a dedupe object from a settings file.
"""
def __init__(self,
settings_file: BinaryIO,
num_cores: Optional[int] = None,
**kwargs) -> None: # pragma: no cover
"""
Args:
settings_file: A file object containing settings
info produced from the
:func:`~dedupe.api.ActiveMatching.write_settings` method.
num_cores: the number of cpus to use for parallel
processing, defaults to the number of cpus
available on the machine. If set to 0, then
multiprocessing will be disabled.
.. warning::
If using multiprocessing on Windows or Mac OS X, then
you must protect calls to the Dedupe methods with a
`if __name__ == '__main__'` in your main module, see
https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
"""
super().__init__(num_cores, **kwargs)
try:
self.data_model = pickle.load(settings_file)
self.classifier = pickle.load(settings_file)
self.predicates = pickle.load(settings_file)
except (KeyError, AttributeError):
raise SettingsFileLoadingException(
"This settings file is not compatible with "
"the current version of dedupe. This can happen "
"if you have recently upgraded dedupe.")
except: # noqa: E722
raise SettingsFileLoadingException(
"Something has gone wrong with loading the settings file. "
"Try deleting the file")
logger.info('Predicate set:')
for predicate in self.predicates:
logger.info(predicate)
self._fingerprinter = blocking.Fingerprinter(self.predicates)
class ActiveMatching(Matching):
"""
Class for training a matcher.
"""
classifier = rlr.RegularizedLogisticRegression()
def __init__(self,
variable_definition: Sequence[Mapping],
num_cores: Optional[int] = None,
**kwargs) -> None:
"""
Args:
variable_definition: A list of dictionaries describing
the variables will be used for
training a model. See :ref:`variable_definitions`
num_cores: the number of cpus to use for parallel
processing. If set to `None`, uses all cpus
available on the machine. If set to 0, then
multiprocessing will be disabled.
.. warning::
If using multiprocessing on Windows or Mac OS X, then
you must protect calls to the Dedupe methods with a
`if __name__ == '__main__'` in your main module, see
https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
"""
super().__init__(num_cores, **kwargs)
self.data_model = datamodel.DataModel(variable_definition)
self.training_pairs: TrainingData
self.training_pairs = {'distinct': [],
'match': []}
self.active_learner: Optional[Union[labeler.DedupeDisagreementLearner,
labeler.RecordLinkDisagreementLearner]]
self.active_learner = None
def cleanup_training(self) -> None: # pragma: no cover
'''
Clean up data we used for training. Free up memory.
'''
del self.training_pairs
del self.active_learner
def _read_training(self, training_file: TextIO) -> None:
'''
Read training from previously built training data file object
Args:
training_file: file object containing the training data
'''
logger.info('reading training from file')
training_pairs = json.load(training_file,
cls=serializer.dedupe_decoder)
try:
self.mark_pairs(training_pairs)
except AttributeError as e:
if "Attempting to fingerprint with an index predicate without indexing records" in str(e):
raise UserWarning('Training data has records not known '
'to the active learner. Read training '
'in before initializing the active '
'learner with the sample method, or '
'use the prepare_training method.')
else:
raise
def train(self,
recall: float = 1.00,
index_predicates: bool = True) -> None: # pragma: no cover
"""
Learn final pairwise classifier and fingerprinting rules. Requires that
adequate training data has been already been provided.
Args:
recall: The proportion of true dupe pairs in our
training data that that the learned fingerprinting
rules must cover. If we lower the recall, there will
be pairs of true dupes that we will never
directly compare.
recall should be a float between 0.0 and 1.0.
index_predicates: Should dedupe consider predicates
that rely upon indexing the
data. Index predicates can be slower
and take substantial memory.
"""
assert self.active_learner is not None, \
"Please initialize with the sample method"
examples, y = flatten_training(self.training_pairs)
self.classifier.fit(self.data_model.distances(examples), y)
self.predicates = self.active_learner.learn_predicates(
recall, index_predicates)
self._fingerprinter = blocking.Fingerprinter(self.predicates)
self.fingerprinter.reset_indices()
def write_training(self, file_obj: TextIO) -> None: # pragma: no cover
"""
Write a JSON file that contains labeled examples
:param file_obj: file object to write training data to
.. code:: python
with open('training.json', 'w') as f:
matcher.write_training(f)
"""
json.dump(self.training_pairs,
file_obj,
default=serializer._to_json,
ensure_ascii=True)
def write_settings(self,
file_obj: BinaryIO) -> None: # pragma: no cover
"""
Write a settings file containing the
data model and predicates to a file object
:param file_obj: file object to write settings data into
.. code:: python
with open('learned_settings', 'wb') as f:
matcher.write_settings(f)
"""
pickle.dump(self.data_model, file_obj)
pickle.dump(self.classifier, file_obj)
pickle.dump(self.predicates, file_obj)
def uncertain_pairs(self) -> List[TrainingExample]:
'''
Returns a list of pairs of records from the sample of record pairs
tuples that Dedupe is most curious to have labeled.
This method is mainly useful for building a user interface for training
a matching model.
.. code:: python
> pair = matcher.uncertain_pairs()
> print(pair)
[({'name' : 'Georgie Porgie'}, {'name' : 'Georgette Porgette'})]
'''
assert self.active_learner is not None, \
"Please initialize with the sample method"
return [self.active_learner.pop()]
def mark_pairs(self, labeled_pairs: TrainingData) -> None:
'''
Add users labeled pairs of records to training data and update the
matching model
This method is useful for building a user interface for training a
matching model or for adding training data from an existing source.
Args:
labeled_pairs: A dictionary with two keys, `match` and `distinct`
the values are lists that can contain pairs of
records
.. code:: python
labeled_examples = {'match' : [],
'distinct' : [({'name' : 'Georgie Porgie'},
{'name' : 'Georgette Porgette'})]
}
matcher.mark_pairs(labeled_examples)
'''
self._checkTrainingPairs(labeled_pairs)
self.training_pairs['match'].extend(labeled_pairs['match'])
self.training_pairs['distinct'].extend(labeled_pairs['distinct'])
if self.active_learner:
examples, y = flatten_training(labeled_pairs)
self.active_learner.mark(examples, y)
def _checkTrainingPairs(self, labeled_pairs: TrainingData) -> None:
try:
labeled_pairs.items()
labeled_pairs['match']
labeled_pairs['distinct']
except (AttributeError, KeyError):
raise ValueError('labeled_pairs must be a dictionary with keys '
'"distinct" and "match"')
if labeled_pairs['match']:
pair = labeled_pairs['match'][0]
self._checkRecordPair(pair)
if labeled_pairs['distinct']:
pair = labeled_pairs['distinct'][0]
self._checkRecordPair(pair)
if not labeled_pairs['distinct'] and not labeled_pairs['match']:
warnings.warn("Didn't return any labeled record pairs")
def _checkRecordPair(self, record_pair: TrainingExample) -> None:
try:
a, b = record_pair
except ValueError:
raise ValueError("The elements of data_sample must be pairs "
"of record_pairs")
try:
record_pair[0].keys() and record_pair[1].keys()
except AttributeError:
raise ValueError("A pair of record_pairs must be made up of two "
"dictionaries ")
self.data_model.check(record_pair[0])
self.data_model.check(record_pair[1])
[docs]class StaticDedupe(StaticMatching, DedupeMatching):
"""
Class for deduplication using saved settings. If you have already
trained a :class:`Dedupe` object and saved the settings, you can
load the saved settings with StaticDedupe.
"""
[docs]class Dedupe(ActiveMatching, DedupeMatching):
"""
Class for active learning deduplication. Use deduplication when you have
data that can contain multiple records that can all refer to the same
entity.
"""
canopies = True
ActiveLearner = labeler.DedupeDisagreementLearner
[docs] def prepare_training(self,
data: Data,
training_file: TextIO = None,
sample_size: int = 1500,
blocked_proportion: float = 0.9,
original_length: int = None) -> None:
'''
Initialize the active learner with your data and, optionally,
existing training data.
Sets up the learner.
Args:
data: Dictionary of records, where the keys are
record_ids and the values are dictionaries
with the keys being field names
training_file: file object containing training data
sample_size: Size of the sample to draw
blocked_proportion: The proportion of record pairs to be sampled from similar records, as opposed to randomly selected pairs. Defaults to 0.9.
original_length: If `data` is a subsample of all your data,
`original_length` should be the size of
your complete data. By default,
`original_length` defaults to the length of
`data`.
.. code:: python
matcher.prepare_training(data_d, 150000, .5)
# or
with open('training_file.json') as f:
matcher.prepare_training(data_d, training_file=f)
'''
if training_file:
self._read_training(training_file)
self._sample(data, sample_size, blocked_proportion, original_length)
def _sample(self,
data: Data,
sample_size: int = 15000,
blocked_proportion: float = 0.5,
original_length: int = None) -> None:
'''Draw a sample of record pairs from the dataset
(a mix of random pairs & pairs of similar records)
and initialize active learning with this sample
:param data: Dictionary of records, where the keys are
record_ids and the values are dictionaries
with the keys being field names
:param sample_size: Size of the sample to draw
:param blocked_proportion: Proportion of the sample that will be blocked
:param original_length: Length of original data, should be set
if `data` is a sample of full data
'''
self._checkData(data)
if not original_length:
original_length = len(data)
# We need the active learner to know about all our
# existing training data, so add them to data dictionary
examples, y = flatten_training(self.training_pairs)
self.active_learner = self.ActiveLearner(self.data_model,
data,
blocked_proportion,
sample_size,
original_length,
index_include=examples)
self.active_learner.mark(examples, y)
def _checkData(self, data: Data) -> None:
if len(data) == 0:
raise ValueError(
'Dictionary of records is empty.')
self.data_model.check(next(iter(data.values())))
class Link(ActiveMatching):
"""
Mixin Class for Active Learning Record Linkage
"""
canopies = False
ActiveLearner = labeler.RecordLinkDisagreementLearner
def prepare_training(self,
data_1: Data,
data_2: Data,
training_file: Optional[TextIO] = None,
sample_size: int = 15000,
blocked_proportion: float = 0.5,
original_length_1: Optional[int] = None,
original_length_2: Optional[int] = None) -> None:
'''
Initialize the active learner with your data and, optionally,
existing training data.
Args:
data_1: Dictionary of records from first dataset, where the
keys are record_ids and the values are dictionaries
with the keys being field names
data_2: Dictionary of records from second dataset, same
form as data_1
training_file: file object containing training data
sample_size: The size of the sample to draw. Defaults to 150,000
blocked_proportion: The proportion of record pairs to
be sampled from similar records,
as opposed to randomly selected
pairs. Defaults to 0.5.
original_length_1: If `data_1` is a subsample of your first dataset,
`original_length_1` should be the size of
the complete first dataset. By default,
`original_length_1` defaults to the length of
`data_1`
original_length_2: If `data_2` is a subsample of your first dataset,
`original_length_2` should be the size of
the complete first dataset. By default,
`original_length_2` defaults to the length of
`data_2`
.. code:: python
matcher.prepare_training(data_1, data_2, 150000)
with open('training_file.json') as f:
matcher.prepare_training(data_1, data_2, training_file=f)
'''
if training_file:
self._read_training(training_file)
self._sample(data_1,
data_2,
sample_size,
blocked_proportion,
original_length_1,
original_length_2)
def _sample(self,
data_1: Data,
data_2: Data,
sample_size: int = 15000,
blocked_proportion: float = 0.5,
original_length_1: int = None,
original_length_2: int = None) -> None:
'''
Draws a random sample of combinations of records from
the first and second datasets, and initializes active
learning with this sample
:param data_1: Dictionary of records from first dataset, where the
keys are record_ids and the values are dictionaries
with the keys being field names
:param data_2: Dictionary of records from second dataset, same
form as data_1
:param sample_size: Size of the sample to draw
'''
self._checkData(data_1, data_2)
# We need the active learner to know about all our
# existing training data, so add them to data dictionaries
examples, y = flatten_training(self.training_pairs)
self.active_learner = self.ActiveLearner(self.data_model,
data_1,
data_2,
blocked_proportion,
sample_size,
original_length_1,
original_length_2,
index_include=examples)
self.active_learner.mark(examples, y)
def _checkData(self, data_1: Data, data_2: Data) -> None:
if len(data_1) == 0:
raise ValueError(
'Dictionary of records from first dataset is empty.')
elif len(data_2) == 0:
raise ValueError(
'Dictionary of records from second dataset is empty.')
self.data_model.check(next(iter(data_1.values())))
self.data_model.check(next(iter(data_2.values())))
[docs]class RecordLink(Link, RecordLinkMatching):
"""
Class for active learning record linkage.
Use RecordLinkMatching when you have two datasets that you want to
join.
"""
[docs]class StaticRecordLink(StaticMatching, RecordLinkMatching):
"""
Class for record linkage using saved settings. If you have already
trained a RecordLink instance, you can load the saved settings with
StaticRecordLink.
"""
[docs]class Gazetteer(Link, GazetteerMatching):
"""
Class for active learning gazetteer matching.
Gazetteer matching is for matching a messy data set against a
'canonical dataset'. This class is useful for such tasks as matching messy
addresses against a clean list
"""
[docs]class StaticGazetteer(StaticMatching, GazetteerMatching):
"""
Class for gazetter matching using saved settings.
If you have already trained a :class:`Gazetteer` instance, you can
load the saved settings with StaticGazetteer.
"""
class EmptyTrainingException(Exception):
pass
class SettingsFileLoadingException(Exception):
pass
def flatten_training(training_pairs: TrainingData) -> Tuple[List[TrainingExample], numpy.ndarray]:
examples: List[TrainingExample] = []
y = []
for label in ('match', 'distinct'):
label = cast(Literal['match', 'distinct'], label)
pairs = training_pairs[label]
examples.extend(pairs)
encoded_y = 1 if label == 'match' else 0
y.extend([encoded_y] * len(pairs))
return examples, numpy.array(y)
