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# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# look for it an load it in.
# __Note:__ if you want to train from scratch, delete the training_file
if os.path.exists(training_file):
print 'reading labeled examples from ', training_file
deduper.train(data_sample, training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(training_file)
# ## Blocking
print 'blocking...'
# Initialize our blocker. We'll learn our blocking rules if we haven't
# loaded them from a saved settings file.
blocker = deduper.blockingFunction()
# Save our weights and predicates to disk. If the settings file
# exists, we will skip all the training and learning next time we run
# this file.
deduper.writeSettings(settings_file)
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# look for it an load it in.
# __Note:__ if you want to train from scratch, delete the training_file
if os.path.exists(training_file):
print 'reading labeled examples from ', training_file
deduper.train(data_sample, training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(training_file)
# ## Blocking
print 'blocking...'
# Initialize our blocker. We'll learn our blocking rules if we haven't
# loaded them from a saved settings file.
blocker = deduper.blockingFunction(constrained_matching)
# Save our weights and predicates to disk. If the settings file
# exists, we will skip all the training and learning next time we run
# this file.
deduper.writeSettings(settings_file)
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(training_file):
print 'reading labeled examples from ', training_file
deduper.train(data_sample, training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(training_file)
# Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize the blocker
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(ppc,
dupes,
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,
# 1: [[{field:val dict of record 1}, {field_val dict of record 2}], ...(more match pairs)]
# }
if os.path.exists(r_training_file):
print 'reading labeled examples from ', r_training_file
deduper.train(data_sample, r_training_file)
# ## Active learning
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
print 'starting active labeling...'
deduper.train(data_sample, dedupe.training.consoleLabel)
# When finished, save our training away to disk
deduper.writeTraining(r_training_file)
# ## Blocking
deduper.blocker_types.update({'Custom': (dedupe.predicates.wholeSetPredicate,
dedupe.predicates.commonSetElementPredicate),
'LatLong' : (dedupe.predicates.latLongGridPredicate,)
}
)
time_start = time.time()
print 'blocking...'
# Initialize our blocker, which determines our field weights and blocking
# predicates based on our training data
#blocker = deduper.blockingFunction(r_ppc, r_uncovered_dupes)
blocker, ppc_final, ucd_final = patent_util.blockingSettingsWrapper(r_ppc,