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evalClassification.py
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evalClassification.py
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# =============================================================================
# AUSTRALIAN NATIONAL UNIVERSITY OPEN SOURCE LICENSE (ANUOS LICENSE)
# VERSION 1.3
#
# The contents of this file are subject to the ANUOS License Version 1.3
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at:
#
# https://sourceforge.net/projects/febrl/
#
# Software distributed under the License is distributed on an "AS IS"
# basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. See
# the License for the specific language governing rights and limitations
# under the License.
#
# The Original Software is: "evalClassification.py"
#
# The Initial Developer of the Original Software is:
# Dr Peter Christen (Research School of Computer Science, The Australian
# National University)
#
# Copyright (C) 2002 - 2011 the Australian National University and
# others. All Rights Reserved.
#
# Contributors:
#
# Alternatively, the contents of this file may be used under the terms
# of the GNU General Public License Version 2 or later (the "GPL"), in
# which case the provisions of the GPL are applicable instead of those
# above. The GPL is available at the following URL: http://www.gnu.org/
# If you wish to allow use of your version of this file only under the
# terms of the GPL, and not to allow others to use your version of this
# file under the terms of the ANUOS License, indicate your decision by
# deleting the provisions above and replace them with the notice and
# other provisions required by the GPL. If you do not delete the
# provisions above, a recipient may use your version of this file under
# the terms of any one of the ANUOS License or the GPL.
# =============================================================================
#
# Freely extensible biomedical record linkage (Febrl) - Version 0.4.2
#
# See: http://datamining.anu.edu.au/linkage.html
#
# =============================================================================
"""Module to evaluate the various classification techniques implemented in the
Febrl module 'classification.py'.
Uses Febrl-0.4.
Peter Christen, 2008/01/30
Defines a series of data sets, indices and comparisons, then runs them and
evaluates their linkage quality using various classifiers.
TODO:
- maybe also add sampling for training to the various classifiers
"""
# =============================================================================
# Imports go here
import logging
import os
import time
#import Gnuplot
import classification
import comparison
import dataset
import encode
import indexing
import measurements
import mymath
import output
# =============================================================================
# Various settings
do_plotting = False
do_complexity = True
n = 10 # Number of cross validation folds
do_opt_thres = True # Flags, set to True to do certain classifiers
do_svm = True
do_kmeans = True
do_ffirst = True
do_tailor = True
do_two_step_thres = True
do_two_step_near = True
do_timing = Truee
# File name for writing results to (incl. date and time)
#
result_file_name = './results/evalClassification-' + \
time.strftime('%Y%m%d-%H%M')+'.res'
progress_precentage = 10
num_random_select_iterations = 10
# Various possible deduplication indexing (blocking) techniques
#
index_dedup_block_method = ('block',)
#index_dedup_block_method = ('sort', 3)
#index_dedup_block_method = ('qgram', 2, False, 0.8)
weight_vect_dir = './weight-vectors/' # Where weight vector files are stored
# =============================================================================
def get_measures(result_list):
"""Function which calculates quality measures from raw classification
counts.
Returns accuracy, precision, recall, and f-measure values.
"""
tp, fn, fp, tn = result_list
tp = float(tp)
tn = float(tn)
fp = float(fp)
fn = float(fn)
if ((tp != 0) or (fp != 0) or (tn != 0) or (fn != 0)):
acc = (tp + tn) / (tp + fp + tn + fn)
else:
acc = 0.0
if ((tp != 0) or (fp != 0)):
prec = tp / (tp + fp)
else:
prec = 0.0
if ((tp != 0) or (fn != 0)):
reca = tp / (tp + fn)
else:
reca = 0.0
if ((prec != 0.0) or (reca != 0.0)):
fmeas = 2*(prec*reca) / (prec+reca)
else:
fmeas = 0.0
return acc, prec, reca, fmeas
# =============================================================================
# Define a project logger
my_logger = logging.getLogger() # New logger at root level
my_logger.setLevel(logging.WARNING)
#my_logger.setLevel(logging.INFO)
# =============================================================================
# Open the results file
#
res_file = open(result_file_name, 'w')
# =============================================================================
# Generate a list with information for each data set:
# 1) A tuple with the two data set objects (will be the same for deduplication)
# 2) The data set index object
# 3) A list with tuples on a field comparison selection, each having:
# a) A comment string
# b) A list with the fields used in one experiment for the function
# classification.extract_collapse()
# 4) The function to be used to check for true matches and non-matches
#
experiment_list = []
# =============================================================================
# Define original input data sets, indices and field comparisons for them
# -----------------------------------------------------------------------------
# The publicly available Census data set with synthetic personal names and
# addresses (taken from SecondString data repository).
#
# - The 'entity_id' attribute (2nd attribute) contains entity numbers.
# - No record identifer is available.
#
census_ds_A = dataset.DataSetCSV(description='Census data set A',
access_mode='read',
delimiter='\t',
rec_ident='rec_id',
header_line=False,
field_list=[('relation',0),
('entity_id',1),
('surname',2),
('given_name',3),
('middle_inital',4),
('zipcode',5),
('suburb',6)],
file_name = './data/secondstring/censusTextSegmentedA.tab')
census_ds_B = dataset.DataSetCSV(description='Census data set B',
access_mode='read',
delimiter='\t',
rec_ident='rec_id',
header_line=False,
field_list=[('relation',0),
('entity_id',1),
('surname',2),
('given_name',3),
('middle_inital',4),
('zipcode',5),
('suburb',6)],
file_name = './data/secondstring/censusTextSegmentedB.tab')
census_index_list = [[['surname', 'surname', False, False, None,
[encode.dmetaphone,3]]],
[['given_name','given_name', False, False, None,
[encode.dmetaphone,3]]],
[['surname', 'surname', False, False, 1, []],
['given_name','given_name', False, False, 1, []]],
[['zipcode', 'zipcode', False, False, None, []]],
[['suburb', 'suburb', False, False, None,
[encode.dmetaphone,3]]]]
# Exact comparison of 'relation' and 'entity_id': If 'relation' is different
# and 'entity_id' is the same it is a match, otherwise not
#
census_entity_id_exact = comparison.FieldComparatorExactString(desc = \
'entity_id_exact')
census_surname_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'surname_winkler')
census_given_name_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'given_name_winkler')
census_suburb_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'suburb_winkler')
census_surname_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'surname_qgram')
census_given_name_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'given_name_qgram')
census_suburb_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'suburb_qgram')
census_surname_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'surname_bagdist')
census_given_name_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'given_name_bagdist')
census_suburb_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'suburb_bagdist')
census_surname_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'surname_lcs')
census_given_name_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'given_name_lcs')
census_suburb_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'suburb_lcs')
census_middle_inital_exact = comparison.FieldComparatorExactString(desc = \
'middle_inital_exact')
census_zipcode_exact = comparison.FieldComparatorExactString(desc = \
'zipcode_exact')
census_zipcode_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=2,
desc = 'zipcode_keydiff')
census_fc_list = [(census_entity_id_exact, 'entity_id', 'entity_id'),
(census_surname_winkler, 'surname', 'surname'),
(census_given_name_winkler, 'given_name', 'given_name'),
(census_suburb_winkler, 'suburb', 'suburb'),
(census_surname_qgram, 'surname', 'surname'),
(census_given_name_qgram, 'given_name', 'given_name'),
(census_suburb_qgram, 'suburb', 'suburb'),
(census_surname_bagdist, 'surname', 'surname'),
(census_given_name_bagdist, 'given_name', 'given_name'),
(census_suburb_bagdist, 'suburb', 'suburb'),
(census_surname_lcs, 'surname', 'surname'),
(census_given_name_lcs, 'given_name', 'given_name'),
(census_suburb_lcs, 'suburb', 'suburb'),
(census_middle_inital_exact,'middle_inital','middle_inital'),
(census_zipcode_exact, 'zipcode', 'zipcode'),
(census_zipcode_keydiff, 'zipcode', 'zipcode')]
census_rec_comp = comparison.RecordComparator(census_ds_A, census_ds_B,
census_fc_list,
'Census record comparator')
# List with sub-sets of the field comparisons for experiments
#
census_sel_list = [('Winkler', [ (1,), (2,), (3,),(13,),(15,)]),
('Q-Gram', [ (4,), (5,), (6,),(13,),(15,)]),
('Bag-Distance',[ (7,), (8,), (9,),(13,),(15,)]),
('LCS', [(10,),(11,),(12,),(13,),(15,)])]
cens_bigmatch_index = indexing.BigMatchIndex(descrip = 'Census BigMatch index',
dataset1 = census_ds_A,
dataset2 = census_ds_B,
weight_vec_file = weight_vect_dir + \
'census-bigmatch-index-weight-vectors.csv',
rec_comparator = census_rec_comp,
progress=progress_precentage,
block_method = index_dedup_block_method,
index_def = census_index_list)
cens_full_index = indexing.FullIndex(description = 'Census Full index',
dataset1 = census_ds_A,
dataset2 = census_ds_B,
weight_vec_file = weight_vect_dir + \
'census-full-index-weight-vectors.csv',
rec_comparator = census_rec_comp,
progress=progress_precentage,
index_def = []) # Not needed for full ind.
# Function to be used to check for true matches and non-matches
#
def census_check_funct(rec_id1, rec_id2, weight_vec):
return (weight_vec[0] == 1.0)
# Function to be used to extract the record identifier from a raw record
#
def census_get_id_funct(rec):
return rec[1]
experiment_list.append(((census_ds_A,census_ds_B), cens_bigmatch_index,
census_sel_list, census_check_funct,
census_get_id_funct))
experiment_list.append(((census_ds_A,census_ds_B), cens_full_index,
census_sel_list, census_check_funct,
census_get_id_funct))
# -----------------------------------------------------------------------------
# The publicly available Restaurant data set (Fodors/ Zagats) restaurant names
# and addresses (taken from SecondString data repository).
#
# - The 'class' attribute contains entity numbers.
# - No record identifer is available.
#
rest_ds = dataset.DataSetCSV(description='Restaurant data set',
access_mode='read',
rec_ident='rec_id',
header_line=True,
file_name = './data/secondstring/restaurant.csv')
rest_index_list = [[['phone', 'phone', False, False, None,
[encode.get_substring,0,4]]],
[['type', 'type', False, False, None, [encode.soundex,4]]],
[['city', 'city', False, False, None,
[encode.dmetaphone,4]]]]
# Exact comparison of 'class:' If the same it is a match, otherwise not
#
rest_class_exact = comparison.FieldComparatorExactString(desc = 'class_exact')
rest_name_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'name_winkler')
rest_addr_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'addr_winkler')
rest_city_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'city_winkler')
rest_name_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'name_qgram')
rest_addr_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'addr_qgram')
rest_city_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'city_qgram')
rest_name_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'name_bagdist')
rest_addr_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'addr_bagdist')
rest_city_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'city_bagdist')
rest_name_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'name_lcs')
rest_addr_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'addr_lcs')
rest_city_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'city_lcs')
rest_fc_list = [(rest_class_exact, 'class', 'class'),
(rest_name_winkler, 'name', 'name'),
(rest_addr_winkler, 'addr', 'addr'),
(rest_city_winkler, 'city', 'city'),
(rest_name_qgram, 'name', 'name'),
(rest_addr_qgram, 'addr', 'addr'),
(rest_city_qgram, 'city', 'city'),
(rest_name_bagdist, 'name', 'name'),
(rest_addr_bagdist, 'addr', 'addr'),
(rest_city_bagdist, 'city', 'city'),
(rest_name_lcs, 'name', 'name'),
(rest_addr_lcs, 'addr', 'addr'),
(rest_city_lcs, 'city', 'city')]
rest_rec_comp = comparison.RecordComparator(rest_ds, rest_ds, rest_fc_list,
'Restaurant record comparator')
# List with sub-sets of the field comparisons for experiments
#
rest_sel_list = [('Winkler', [ (1,), (2,), (3,)]),
('Q-Gram', [ (4,), (5,), (6,)]),
('Bag-Distance',[ (7,), (8,), (9,)]),
('LCS', [(10,),(11,),(12,)])]
rest_dedup_index = indexing.DedupIndex(description = 'Restaurant Dedup index',
dataset1 = rest_ds,
dataset2 = rest_ds,
weight_vec_file = weight_vect_dir + \
'restaurant-dedup-index-weight-vectors.csv',
rec_comparator = rest_rec_comp,
progress=progress_precentage,
block_method = index_dedup_block_method,
index_def = rest_index_list)
rest_full_index = indexing.FullIndex(description = 'Restaurant Full index',
dataset1 = rest_ds,
dataset2 = rest_ds,
weight_vec_file = weight_vect_dir + \
'restaurant-full-index-weight-vectors.csv',
rec_comparator = rest_rec_comp,
progress=progress_precentage,
index_def = []) # Not needed for full ind.
# Function to be used to check for true matches and non-matches
#
def rest_check_funct(rec_id1, rec_id2, weight_vec):
return (weight_vec[0] == 1.0)
# Function to be used to extract the record identifier from a raw record
#
def rest_get_id_funct(rec):
return rec[-1]
experiment_list.append(((rest_ds, rest_ds), rest_dedup_index, rest_sel_list,
rest_check_funct, rest_get_id_funct))
experiment_list.append(((rest_ds, rest_ds), rest_full_index, rest_sel_list,
rest_check_funct, rest_get_id_funct))
# -----------------------------------------------------------------------------
# The publicly available Cora containing bibliographic citations.
cora_ds = dataset.DataSetCSV(description='Cora data set',
access_mode='read',
rec_ident='rec_id',
delimiter='\t',
header_line=False,
field_list=[('unknown',0),
('paper_id',1),
('author_list',2),
('pub_details',3),
('title',4),
('affiliation',5),
('conf_journal',6),
('location',7),
('publisher',8),
('year',9),
('pages',10),
('editors',11),
('appear',12),
('month',13)],
file_name = './data/secondstring/cora.tab')
cora_index_list = [[['author_list','author_list', False, False, None,
[encode.dmetaphone,3]]],
[['title', 'title', False, False, None,
[encode.dmetaphone,3]]],
[['conf_journal','conf_journal', False, False, None,
[encode.dmetaphone,3]]],
[['year', 'year', False, False, None,
[encode.dmetaphone,3]]]]
# Exact comparison of 'paper_id': If the same it is a match, otherwise not
#
cora_paper_id_exact = comparison.FieldComparatorExactString(desc = \
'paper_id_exact')
cora_author_list_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'author_list_winkler')
cora_title_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'title_winkler')
cora_conf_journal_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'conf_journal_winkler')
cora_author_list_qgram = comparison.FieldComparatorQGram(thres=0,
common_divisor='average',
desc = 'author_list_qgram')
cora_title_qgram = comparison.FieldComparatorQGram(thres=0,
common_divisor='average',
desc = 'title_qgram')
cora_conf_journal_qgram = comparison.FieldComparatorQGram(thres=0,
common_divisor='average',
desc = 'conf_journal_qgram')
cora_author_list_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'author_list_bagdist')
cora_title_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'title_bagdist')
cora_conf_journal_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'conf_journal_bagdist')
cora_author_list_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'author_list_qgram')
cora_title_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'title_qgram')
cora_conf_journal_lcs = comparison.FieldComparatorLCS(thres=0,
min_common_len=2,
common_divisor='average',
desc = 'conf_journal_qgram')
cora_year_exact = comparison.FieldComparatorExactString(desc = 'year_exact')
cora_year_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=1,
desc = 'year_keydiff')
cora_pages_exact = comparison.FieldComparatorExactString(desc = 'pages_exact')
cora_pages_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=2,
desc = 'pages_keydiff')
cora_fc_list = [(cora_paper_id_exact, 'paper_id', 'paper_id'),
(cora_author_list_winkler, 'author_list', 'author_list'),
(cora_title_winkler, 'title', 'title'),
(cora_conf_journal_winkler, 'conf_journal','conf_journal'),
(cora_author_list_qgram, 'author_list', 'author_list'),
(cora_title_qgram, 'title', 'title'),
(cora_conf_journal_qgram, 'conf_journal','conf_journal'),
(cora_author_list_bagdist, 'author_list', 'author_list'),
(cora_title_bagdist, 'title', 'title'),
(cora_conf_journal_bagdist, 'conf_journal','conf_journal'),
(cora_author_list_lcs, 'author_list', 'author_list'),
(cora_title_lcs, 'title', 'title'),
(cora_conf_journal_lcs, 'conf_journal','conf_journal'),
(cora_year_exact, 'year', 'year'),
(cora_year_keydiff, 'year', 'year'),
(cora_pages_exact, 'pages', 'pages'),
(cora_pages_keydiff, 'pages', 'pages')]
cora_rec_comp = comparison.RecordComparator(cora_ds, cora_ds, cora_fc_list,
'Cora record comparator')
# List with sub-sets of the field comparisons for experiments
#
cora_sel_list = [('Winkler', [ (1,), (2,), (3,),(14,),(16,)]),
('Q-Gram', [ (4,), (5,), (6,),(14,),(16,)]),
('Bag-Distance',[ (7,), (8,), (9,),(14,),(16,)]),
('LCS', [(10,),(11,),(12,),(14,),(16,)])]
cora_dedup_index = indexing.DedupIndex(description = 'Cora Dedup index',
dataset1 = cora_ds,
dataset2 = cora_ds,
weight_vec_file = weight_vect_dir + \
'cora-dedup-index-weight-vectors.csv',
rec_comparator = cora_rec_comp,
progress=progress_precentage,
block_method = index_dedup_block_method,
index_def = cora_index_list)
# Function to be used to check for true matches and non-matches
#
def cora_check_funct(rec_id1, rec_id2, weight_vec):
return (weight_vec[0] == 1.0)
# Function to be used to extract the record identifier from a raw record
#
def cora_get_id_funct(rec):
return rec[1]
experiment_list.append(((cora_ds, cora_ds), cora_dedup_index, cora_sel_list,
cora_check_funct, cora_get_id_funct))
# -----------------------------------------------------------------------------
# Synthetic data sets of different sizes generated with Febrl data generator
# Index and field comparisons are the same for all synthetic data sets
#
synth_index_list = [[['surname', 'surname', False, False, None,
[encode.soundex]],
['postcode', 'postcode', False, False, None,
[encode.get_substring,0,2]]],
[['given_name', 'given_name', False, False, None,
[encode.soundex]],
['postcode', 'postcode', False, False, None,
[encode.get_substring,1,3]]],
[['suburb', 'suburb', False, False, None,
[encode.soundex]],
['postcode', 'postcode', False, False, None,
[encode.get_substring,2,4]]],
[['suburb', 'suburb', False, False, None,
[encode.soundex]],
['street_number', 'street_number', False, False, None,
[]]],
[['postcode', 'postcode', False, False, None, []],
['age', 'age', False, False, None, []]],
[['address_1', 'address_1', False, False, 3, []],
['age', 'age', False, False, None, []]],
[['surname', 'surname', False, False, 3, []],
['state', 'state', False, False, None, []]],
[['given_name', 'given_name', False, False, 3, []],
['state', 'state', False, False, None, []]],
[['surname', 'surname', False, False, None,
[encode.get_substring,0,2]],
['given_name', 'given_name', False, False, None,
[encode.get_substring,0,2]]]]
synth_surname_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'surname_winkler')
synth_given_name_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'given_name_winkler')
synth_address_1_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'address_1_winkler')
synth_suburb_winkler = comparison.FieldComparatorWinkler(thres=0,
desc = 'suburb_winkler')
synth_surname_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'surname_qgram')
synth_given_name_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'given_name_qgram')
synth_address_1_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'address_1_qgram')
synth_suburb_qgram = comparison.FieldComparatorQGram(thres=0, q=2,
common_divisor='average',
desc = 'suburb_qgram')
synth_surname_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'surname_bagdist')
synth_given_name_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'given_name_bagdist')
synth_address_1_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'address_1_bagdist')
synth_suburb_bagdist = comparison.FieldComparatorBagDist(thres=0,
desc = 'suburb_bagdist')
synth_surname_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'surname_lcs')
synth_given_name_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'given_name_lcs')
synth_address_1_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'address_1_lcs')
synth_suburb_lcs = comparison.FieldComparatorLCS(thres=0, min_common_len=2,
common_divisor='average',
desc = 'suburb_lcs')
synth_street_num_exact = comparison.FieldComparatorExactString(desc = \
'street_num_exact')
synth_street_num_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=1,
desc = 'street_num_keydiff')
synth_postcode_exact = comparison.FieldComparatorExactString(desc = \
'postcode_exact')
synth_postcode_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=1,
desc = 'postcode_keydiff')
synth_state_exact = comparison.FieldComparatorExactString(desc = \
'state_exact')
synth_state_keydiff = comparison.FieldComparatorKeyDiff(max_key_diff=1,
desc = 'state_keydiff')
synth_fc_list = [(synth_surname_winkler, 'surname', 'surname'),
(synth_given_name_winkler, 'given_name', 'given_name'),
(synth_address_1_winkler, 'address_1', 'address_1'),
(synth_suburb_winkler, 'suburb', 'suburb'),
(synth_surname_qgram, 'surname', 'surname'),
(synth_given_name_qgram, 'given_name', 'given_name'),
(synth_address_1_qgram, 'address_1', 'address_1'),
(synth_suburb_qgram, 'suburb', 'suburb'),
(synth_surname_bagdist, 'surname', 'surname'),
(synth_given_name_bagdist, 'given_name', 'given_name'),
(synth_address_1_bagdist, 'address_1', 'address_1'),
(synth_suburb_bagdist, 'suburb', 'suburb'),
(synth_surname_lcs, 'surname', 'surname'),
(synth_given_name_lcs, 'given_name', 'given_name'),
(synth_address_1_lcs, 'address_1', 'address_1'),
(synth_suburb_lcs, 'suburb', 'suburb'),
(synth_street_num_exact, 'street_number', 'street_number'),
(synth_street_num_keydiff, 'street_number', 'street_number'),
(synth_postcode_exact, 'postcode', 'postcode'),
(synth_postcode_keydiff, 'postcode', 'postcode'),
(synth_state_exact, 'state', 'state'),
(synth_state_keydiff, 'state', 'state')]
# List with sub-sets of the field comparisons for experiments
#
synth_sel_list = [('Winkler', [ (0,), (1,), (2,), (3,),(17,),(19,),(21,)]),
('Q-Gram', [ (4,), (5,), (6,), (7,),(17,),(19,),(21,)]),
('Bag-Distance',[ (8,), (9,),(10,),(11,),(17,),(19,),(21,)]),
('LCS', [(12,),(13,),(14,),(15,),(17,),(19,),(21,)])]
# Function to be used to check for true matches and non-matches
#
def synth_check_funct(rec_id1, rec_id2, weight_vec):
return (rec_id1[:-1] == rec_id2[:-1])
# Function to be used to extract the record identifier from a raw record
#
def synth_get_id_funct(rec):
return rec[0][:-1]
# Loop over different data set sizes - - - - - - - - - - - - - - - - - - - - -
#
for size in ['B_1000', 'C_1000', 'B_2500', 'C_2500',
'B_5000', 'C_5000', 'B_10000', 'C_10000',
'B_25000', 'C_25000', 'B_50000', 'C_50000']:
ds_file_name = 'dataset_%s.csv.gz' % (size)
synth_ds = dataset.DataSetCSV(description='Febrl synthetic data set %s' % \
(size),
access_mode='read',
rec_ident='rec_id',
header_line=True,
file_name = './data/dedup-dsgen/%s' % \
(ds_file_name))
synth_rec_comp = comparison.RecordComparator(synth_ds, synth_ds,
synth_fc_list,
'Febrl synthetic data record' \
+ ' comparator')
synth_dedup_index = indexing.DedupIndex(description = 'Febrl synthetic ' + \
' Dedup index',
dataset1 = synth_ds,
dataset2 = synth_ds,
weight_vec_file = weight_vect_dir + \
'synth-%s-dedup-index-weight-vectors.csv' % (size),
rec_comparator = synth_rec_comp,
progress=progress_precentage,
block_method = index_dedup_block_method,
index_def = synth_index_list)
experiment_list.append(((synth_ds, synth_ds), synth_dedup_index,
synth_sel_list, synth_check_funct,
synth_get_id_funct))
# =============================================================================
# Run all the experiments
#
res_dict = {} # With data set names as keys and a list with results each
########## Select experiments #######################
#
# Census: 0,1, Restaurant: 2,3, Cora: 4,
# DS-Gen B: 5,7,...15, DS-Gen C: 6,8,...,16
#
# Started 1 Feb:
#experiment_list =[experiment_list[0],experiment_list[2],experiment_list[4],
# experiment_list[6],experiment_list[8],experiment_list[10],
# experiment_list[12],experiment_list[14],experiment_list[16]]
# Started 15 Feb:
#experiment_list =[experiment_list[6],experiment_list[8],experiment_list[10],
# experiment_list[12],experiment_list[14],experiment_list[16]]
# Started 19 Feb:
#experiment_list =[experiment_list[12],experiment_list[14],experiment_list[16]]
# Started 21 Feb:
#experiment_list =[experiment_list[12]]
# Started 21 Feb (later)
#experiment_list =[experiment_list[4]]
# Started 22 Feb
#experiment_list =[experiment_list[4]]
# Started 25 Feb:
#experiment_list =[experiment_list[12]]
# Started 26 Feb:
#experiment_list =[experiment_list[12]]
# Started 27 Feb:
experiment_list =[experiment_list[4]]
#####################################################
for experiment in experiment_list:
res_list = [] # List with all results for this data set, a list for each
# experiment
data_set_a = experiment[0][0]
data_set_b = experiment[0][1]
data_set_index = experiment[1]
field_comp_sel = experiment[2]
match_check_funct = experiment[3]
get_id_funct = experiment[4]
res_list_key = (data_set_a.description, data_set_b.description)
res_file.write(os.linesep + '='*84 + os.linesep + os.linesep)
if (data_set_a == data_set_b):
res_file.write('Run deduplication experiments for data set:' + os.linesep)
res_file.write('===========================================' + os.linesep)
res_file.write(' %s' % (data_set_a.description) + os.linesep)
res_file.write(os.linesep)
res_file.write(' Number of records in data set: %d' % \
(data_set_a.num_records) + os.linesep)
else: # Linkage of two data sets
res_file.write('Run linkage experiments for data sets:' + os.linesep)
res_file.write('======================================' + os.linesep)
res_file.write(' A: %s' % (data_set_a.description) + os.linesep)
res_file.write(' B: %s' % (data_set_b.description) + os.linesep)
res_file.write(os.linesep)
res_file.write(' Number of records in data sets: %d / %d ' % \
(data_set_a.num_records, data_set_b.num_records) + \
os.linesep)
res_file.write(os.linesep)
res_file.write(' Index: %s' % (data_set_index.description) + os.linesep)
res_file.write(os.linesep)
res_file.flush()
# Check if weight vector file is available for this experiment - - - - - - -
#
weight_vec_file = data_set_index.weight_vec_file
if (weight_vec_file != None):
# Check if the weight vector file or it's GZipped version is available
#
file_avail = (os.access(weight_vec_file, os.R_OK) or \
os.access(weight_vec_file+'.gz', os.R_OK) or \
os.access(weight_vec_file+'.GZ', os.R_OK)) # Can read file
else:
file_avail = False
if (file_avail == True): # File is available, so load it - - - - - - - - - -
res_file.write(' Load weight vector dictionary from file:' + os.linesep)
res_file.write(' %s' % (weight_vec_file) + os.linesep)
res_file.flush()
[field_names_list, w_vec_dict] = \
output.LoadWeightVectorFile(weight_vec_file)
res_file.write(' Field names from weight vector file:' + os.linesep)
res_file.write(' %s' % (str(field_names_list)) + os.linesep)
res_file.flush()
else: # Have to calculate weight vectors - - - - - - - - - - - - - - - - - -
res_file.write(' Build and compact index, then run comparison step' + \
os.linesep)
data_set_index.build()
data_set_index.compact()
my_logger.setLevel(logging.INFO)
w_vec_run_data = data_set_index.run()
my_logger.setLevel(logging.WARNING)
if (w_vec_run_data == None): # Has been written to file - - - - - - - - -
[field_names_list, w_vec_dict] = \
output.LoadWeightVectorFile(weight_vec_file)
else:
[field_names_list, w_vec_dict] = w_vec_run_data
num_w_vec = len(w_vec_dict)
res_file.write(os.linesep)
res_file.write(' Returned dictionary with %d weight vectors' \
% (num_w_vec) + os.linesep)
res_file.flush()
# Get the true matches and true non-matches in the weight vector dictionary
#
true_m_set, true_nm_set = \
classification.get_true_matches_nonmatches(w_vec_dict,
match_check_funct)
num_true_m = len(true_m_set)
num_true_nm = len(true_nm_set)
assert (num_true_m + num_true_nm) == num_w_vec
res_file.write(' Number of true matches and non-matches: %d / %d' % \
(num_true_m, num_true_nm) + os.linesep)
res_file.write(os.linesep)
res_file.flush()
# Check that true quality is 100% for all measures - - - - - - - - - - - - -
#
acc, prec, reca, fmeas = measurements.quality_measures(w_vec_dict,
true_m_set,
true_nm_set,
match_check_funct)
assert acc == 1.0, 'Accuracy of true matches and non-matches not 100%'
assert prec == 1.0, 'Precision of true matches and non-matches not 100%'
assert prec == 1.0, 'Recall of true matches and non-matches not 100%'
assert fmeas == 1.0, 'F-measure of true matches and non-matches not 100%'
# Get quality and complexity measures for weight vectors - - - - - - - - -
#
if (do_complexity == True):
rr = measurements.reduction_ratio(w_vec_dict, data_set_a, data_set_b)
pc = measurements.pairs_completeness(w_vec_dict, data_set_a, data_set_b,
get_id_funct, match_check_funct)
pq = measurements.pairs_quality(w_vec_dict, match_check_funct)
res_file.write(' Reduction ratio: %7.2f%%' % (100.0*rr) + os.linesep)
res_file.write(' Pairs completeness: %7.2f%%' % (100.0*pc) + os.linesep)
res_file.write(' Pairs quality: %7.2f%%' % (100.0*pq) + os.linesep)
res_file.write(os.linesep)
res_file.write('-'*84 + os.linesep + os.linesep)
# ===========================================================================
# End of initialisation, now perform various classifications
#############
field_comp_sel = [field_comp_sel[0]] #### ONLY DO WINKLER #################
#############
# Loop over the field comparison selections ---------------------------------
#
for (sel_name, sel_list) in field_comp_sel:
exp_res_list = [sel_name] # All results for this experiment
num_weights = len(sel_list)
res_file.write(' Field comparison selection: %s' % (sel_name) + \
os.linesep)
res_file.write(' ----------------------------'+'-'*len(sel_name) + \
os.linesep + os.linesep)
res_file.write(' Number of fields comparisons: %d' % (num_weights) + \
os.linesep)
field_names = []
for tup in sel_list:
field_names.append(field_names_list[tup[0]])
res_file.write(' Selected fields:' + os.linesep)
res_file.write(' %s' % (str(field_names)) + os.linesep + os.linesep)
sel_w_vec_dict = classification.extract_collapse_weight_vectors(sel_list,
w_vec_dict)
if (do_plotting == True): # Plot true match and non-match sets
plot_weight_vectors(sel_w_vec_dict, true_m_set, true_m_set,
true_nm_set, true_nm_set, 'True match status')
# Write header of result tables to file - - - - - - - - - - - - - - - - - -
#
res_file.write(' '+'='*80 + os.linesep)
res_file.write(' Classifier experiment | Acc | ' + \
'Prec | Reca | F-Meas| Time (s)' + os.linesep)
res_file.write(' '+'='*80 + os.linesep)
# Now conduct the various classification experiments ----------------------
# -------------------------------------------------------------------------
# Classify using a one-dimensional classifier that knows true match status
#
res_file.write(' ' + \
'Optimal threshold classifier (1-dimensional)'.center(79) \
+ os.linesep)
res_file.write(' ' + '-'*80 + os.linesep)
one_dim_sel_list = [tuple(range(len(sel_list)))]
one_dim_w_vec_dict = \
classification.extract_collapse_weight_vectors(one_dim_sel_list,
sel_w_vec_dict)
assert len(one_dim_w_vec_dict) == num_w_vec
# Three variations: minimise pos-neg, pos only, neg only - - - - - - - - -
#
opt_thres_param_list = [('Minimise false pos-neg ', 'pos-neg'),
('Minimise false pos only', 'pos'),
('Minimise false neg only', 'neg')]
if (do_opt_thres == False):
opt_thres_param_list = [] # Don't perform these experiments
opt_thres_res_list = []
for (class_name, min_method_name) in opt_thres_param_list:
opt_thres_classifier = classification.OptimalThreshold(bin_width = 0.01,
min_method = min_method_name)
start_time = time.time()