/
standardisation.py
2387 lines (1896 loc) · 97.4 KB
/
standardisation.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: "standardisation.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 standardisation.py - Classes for cleaning and standardisations.
This module provides classes for record cleaning and standardisations,
either based on rules or a machine learning approach (Hidden Markov models)
TODO
- PC 29/11/2002 Check for missing values in date fields (data standardiser)
And how to deal with them?
"""
# =============================================================================
# Import necessary modules (Python standard modules first, then Febrl modules)
import logging
import os
import string
import time
import auxiliary
import mymath
import phonenum
# =============================================================================
class RecordStandardiser:
"""Main class for the standardisation process. Implements a method to clean
and standardise all records in a data set.
A record standardiser will include one or more component standardisers.
Currently four different types of components can be standardised: names,
addresses, date and phone numbers. A list of component standardisers needs
to be given when a record standardiser is initialised.
A record standardiser needs the following instance variables to be set
when it is initialised:
input_dataset Reference to the input data set. Has to be initalised
in read access mode.
output_dataset Reference to the output data set. Has to be initalised
in write access mode.
comp_stand_list List with one or more component standardisers.
log_funct This can be a Python function or method which will log
(print or save to a file) a progress report message.
It is assumed that this function or method has one
input argument of type string (the message to be
printed). Default is None, in which case the normal
logging module will be used.
progress_report Can be set to a percentage number between 1 and 50 in
which case a progress report is logged during the
record pair comparison stage (in the run() method)
every selected percentage number. Default values is
10. If set to None no progress report will be logged.
pass_field_list A list of tuples (input_field, output_field) that will
simply be passed from the input data set to the output
data set without being cleaned or standardised. Each
input_field must be in the field list of the input
data set and correspondingly each output_filed in the
list of field of the output data set. Default value for
this argument is an empty list.
The standardise() method can be used to standardise all records from the
input data set and write them into the output data set.
"""
# ---------------------------------------------------------------------------
def __init__(self, **kwargs):
"""Constructor.
"""
self.description = ''
self.inp_dataset = None
self.out_dataset = None
self.comp_stand_list = [] # A list of one or more component standardiser
# tuples (each made of the component
# standardiser itself, a list with the field
# indices from the input data set, and a list
# of the field indices from the output data
# set. Only the standardisers themselves will
# be provided as input, the field index lists
# will be generated from them during
# initialisation.
self.progress_report = 10
self.log_funct = None
self.pass_field_list = []
# The indices of the pass fields into the input and output data sets
#
self.pass_field_index_list = []
# Process all keyword arguments
#
for (keyword, value) in kwargs.items():
if (keyword.startswith('desc')):
auxiliary.check_is_string('description', value)
self.description = value
elif (keyword.startswith('input_d')):
auxiliary.check_is_list('Input dataset field list', value.field_list)
self.in_dataset = value
elif (keyword.startswith('output_d')):
auxiliary.check_is_list('Output dataset field list', value.field_list)
self.out_dataset = value
elif (keyword.startswith('comp_stand')):
auxiliary.check_is_list('comp_stand_list', value)
self.comp_stand_list = value
elif (keyword.startswith('progr')):
if (value == None):
self.progress_report = value
else:
auxiliary.check_is_integer('progress_report', value)
if ((value < 1) or (value > 50)):
logging.exception('Illegal value for progress report, must be ' + \
'False or between 1 and 50: "%s"' % \
(str(value)))
raise Exception
self.progress_report = value
elif (keyword.startswith('log_f')):
auxiliary.check_is_function_or_method('log_funct', value)
self.log_funct = value
elif (keyword.startswith('pass_fi')):
auxiliary.check_is_list('pass_field_list', value)
self.pass_field_list = value
else:
logging.exception('Illegal constructor argument keyword: '+keyword)
raise Exception
# Check if the needed attributes are set - - - - - - - - - - - - - - - - -
#
auxiliary.check_is_not_none('Input dataset', self.in_dataset)
auxiliary.check_is_not_none('Output dataset', self.out_dataset)
auxiliary.check_is_not_none('Component standardiser list',
self.comp_stand_list)
if (self.in_dataset.access_mode != 'read'):
logging.exception('Input data set "%s" must be in "read" access mode' % \
(in_dataset.description))
raise Exception
if (self.out_dataset.access_mode != 'write'):
logging.exception('Output data set "%s" must be in "write" access ' % \
(out_dataset.description) + 'mode')
raise Exception
# Get a list of the fields from the input and output data sets
#
in_dataset_fields = []
for f_data in self.in_dataset.field_list:
in_dataset_fields.append(f_data[0]) # Append field name only
out_dataset_fields = []
for f_data in self.out_dataset.field_list:
out_dataset_fields.append(f_data[0])
# Check if there is no conflict in the output fields definition - - - - - -
#
output_fields_set = set()
for cs in self.comp_stand_list:
for field in cs.out_fields:
if (field != None): # Only check fields that are not set to None
if (field in output_fields_set):
logging.exception('Output fields definition conflict with ' + \
'field "%s"' % (str(field)))
raise Exception
output_fields_set.add(field)
# Check that fields in pass field list are in input and output data sets -
#
pass_out_field_set = set() # Check for duplicate output field names
i = 0
for field_tuple in self.pass_field_list:
auxiliary.check_is_tuple('pass_field_list[%d]' % (i), field_tuple)
if (len(field_tuple) != 2):
logging.exception('Field tuple in "pass_field_list" does not ' + \
'contain two fields: %s' % (str(field_tuple)))
raise Exception
in_field, out_field = field_tuple
if (in_field not in in_dataset_fields):
logging.exception('Input field in "pass_field_list" is not in the ' + \
'input data set: %s' % (str(field_tuple)))
raise Exception
if (out_field not in out_dataset_fields):
logging.exception('Output field in "pass_field_list" is not in the' + \
' output data set: %s' % (str(field_tuple)))
raise Exception
if (out_field in pass_out_field_set):
logging.exception('Output field in "pass_field_list" appears twice' + \
' field tuples: %s' % (str(self.pass_field_list)))
raise Exception
pass_out_field_set.add(out_field)
# Check for conflicts with component standardiser output fields
#
if (out_field in output_fields_set):
logging.exception('Output field in "pass_field_list" is also an ' + \
'output field of a component standardiser: %s' % \
(str(self.pass_field_list)))
raise Exception
in_pass_field_ind = in_dataset_fields.index(in_field)
out_pass_field_ind = out_dataset_fields.index(out_field)
self.pass_field_index_list.append((in_pass_field_ind,out_pass_field_ind))
i += 1
# Check all component standardisers and generate their lists of field - - -
# indices to be used.
#
for i in range(len(self.comp_stand_list)):
cs = self.comp_stand_list[i]
in_field_indices = []
in_not_none = 0
for in_f in cs.in_fields:
if (in_f != None):
in_not_none += 1
if in_f not in in_dataset_fields:
logging.exception('Component standardiser "%s" contains a ' % \
(cs.description) + 'field that is not in ' + \
'the input data set: %s' % (in_f))
raise Exception
in_field_indices.append(in_dataset_fields.index(in_f))
else:
in_field_indices.append(None)
if (in_not_none == 0):
logging.exception('No input field, or only None input fields ' + \
'defined for component standardiser "%s"' % \
(cs.description))
raise Exception
# Now the same for output fields
#
out_field_indices = []
out_not_none = 0
for out_f in cs.out_fields:
if (out_f != None):
out_not_none += 1
if out_f not in out_dataset_fields:
logging.exception('Component standardiser "%s" contains a ' % \
(cs.description) + 'field that is not in the' + \
' output data set: %s' % (out_f))
raise Exception
out_field_indices.append(out_dataset_fields.index(out_f))
else:
out_field_indices.append(None)
if (out_not_none == 0):
logging.exception('No output field, or only None output fields' + \
'defined for component standardiser "%s"' % \
(cs.description))
raise Exception
self.comp_stand_list[i] = (cs, in_field_indices, out_field_indices)
# A log message - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
logging.info('Initialised record standardiser: %s' % (self.description))
logging.info(' Input data set: %s' % (self.in_dataset.description))
logging.info(' Output data set: %s' % (self.out_dataset.description))
if (self.progress_report == None):
logging.info(' No progress reported.')
else:
logging.info(' Progress report every: %d%%' % (self.progress_report))
logging.info(' Pass field list: %s' % (str(self.pass_field_list)))
logging.info(' Component standardisers:')
for cs in self.comp_stand_list:
logging.info(' Name: %s' % (str(cs[0].description)))
logging.info(' Input fields: %s' % (str(cs[0].in_fields)))
logging.info(' Input field indices: %s' % (str(cs[1])))
logging.info(' Output fields: %s' % (str(cs[0].out_fields)))
logging.info(' Output field indices: %s' % (str(cs[2])))
# ---------------------------------------------------------------------------
def standardise(self):
"""Standardise all records in the input data set and write them into the
output data set.
"""
num_output_fields = len(self.out_dataset.field_list)
# Calculate a counter for the progress report
#
if (self.progress_report != None):
progress_report_cnt = int(self.in_dataset.num_records / \
(100.0 / self.progress_report))
progress_report_cnt = max(1, progress_report_cnt) # Make it positive
else: # So no progress report is being logged
progress_report_cnt = in_dataset.num_records + 1
start_time = time.time()
rec_read = 0 # Number of records read from data set
# Loop over all records from input data set - - - - - - - - - - - - - - - -
#
for (rec_ident, in_rec) in self.in_dataset.readall():
out_rec = ['']*num_output_fields # Create output record with empty fields
# First copy pass fields from input to output record - - - - - - - - - -
#
for (in_field_ind, out_field_ind) in self.pass_field_index_list:
out_rec[out_field_ind] = in_rec[in_field_ind]
# Process one component standardiser after the other - - - - - - - - - -
#
for cs_details in self.comp_stand_list:
cs = cs_details[0]
in_field_index_list = cs_details[1]
out_field_index_list = cs_details[2]
num_input_fields = len(in_field_index_list)
# First get the input record field values
#
in_rec_val_list = []
for in_index in in_field_index_list:
if (in_index != None):
in_rec_val_list.append(in_rec[in_index])
# Check for word spilling if more than one field - - - - - - - - - - -
#
if ((cs.check_word_spill == True) and (num_input_fields > 1)):
in_str = '' # The input string for the component standardiser
for in_val in in_rec_val_list:
spill_flag = cs.check_field_spill(in_str, in_val)
if (spill_flag == True):
in_str = in_str + in_val
else: # Use given field separator
in_str = in_str + cs.field_sep + in_val
else:
in_str = cs.field_sep.join(in_rec_val_list)
# Clean the input string - - - - - - - - - - - - - - - - - - - - - - -
#
clean_in_str = cs.clean_component(in_str)
out_field_list = cs.standardise(in_str, clean_in_str)
assert len(out_field_list) == len(out_field_index_list), \
(clean_in_str, out_field_list)
i = 0
for out_index in out_field_index_list:
if (out_index != None):
out_rec[out_index] = out_field_list[i]
i += 1
# Write the standardised record into the output data set
#
self.out_dataset.write({rec_ident:out_rec})
rec_read += 1
if ((rec_read % progress_report_cnt) == 0): # Log progress - - - - - - -
used_time = time.time() - start_time
perc_done = 100.0 * rec_read / self.in_dataset.num_records
rec_time = used_time / rec_read # Time per record read and indexed
togo_time = (self.in_dataset.num_records - rec_read) * rec_time
used_sec_str = auxiliary.time_string(used_time)
rec_sec_str = auxiliary.time_string(rec_time)
togo_sec_str = auxiliary.time_string(togo_time)
log_str = 'Read and standardised %d of %d records (%d%%) in %s (%s' % \
(rec_read, self.in_dataset.num_records, round(perc_done),
used_sec_str, rec_sec_str) + \
' per record), estimated %s until finished.' % (togo_sec_str)
logging.info(log_str)
if (self.log_funct != None):
self.log_funct(log_str)
memory_usage_str = auxiliary.get_memory_usage()
if (memory_usage_str != None):
logging.info(' '+memory_usage_str)
# Finalise all component standardisers() - - - - - - - - - - - - - - - - -
#
for cs_details in self.comp_stand_list:
cs_details[0].finalise()
used_sec_str = auxiliary.time_string(time.time()-start_time)
rec_time_str = auxiliary.time_string((time.time()-start_time) / \
self.in_dataset.num_records)
logging.info('Read and standardised %d records in %s (%s per record)' % \
(self.in_dataset.num_records, used_sec_str, rec_time_str))
logging.info('')
# =============================================================================
class ComponentStandardiser:
"""Base class for component standardisers.
The following arguments must be given to the constructor of the base class
and all derived classes:
input_fields A list containing field names (that must be in the input
data set)
output_fields A list containing field names (that must be in the output
data set)
The following optional arguments can be used with all component
standardisers:
description A string describing the component standardiser.
field_separator A character that is used to join together field values
in a component before cleaning and standardisation is
done. Default values is the empty string ''.
check_word_spill A Flag, if set to true and a component is made of
several fields and the field_separator is set to a
whitespace character (' ') then the field spill method
will be called. Default is False.
tag_table Reference to a tag lookup table.
corr_list Reference to a correction list.
Note that for date and phone number standardisers the field separator
will automatically be set to the empty string '' and word spilling will
be set to False.
"""
# ---------------------------------------------------------------------------
def __init__(self, base_kwargs):
"""Constructor
"""
# General attributes for all component standardisers.
#
self.description = ''
self.in_fields = None # A string with a list of field names form the
# input data set, that will be standardised. The
# values in the fields will be concatenated into
# one string with the field separator string
# in between the values.
self.out_fields = None # A list of field names form the output data set
# into which the standardised component values
# will be stored.
self.corr_list = [] # A correction list for the cleaning step.
self.tag_table = {} # A lookup table containing tags and values with
# their corrections
self.field_sep = '' # Field separator character
self.check_word_spill = False # Flag for checking word spilling
self.alphanum = string.letters+string.digits # For word spill method
# Process base keyword arguments (all data set specific keywords were
# processed in the derived class constructor)
#
for (keyword, value) in base_kwargs.items():
if (keyword.startswith('desc')):
auxiliary.check_is_string('description', value)
self.description = value
elif (keyword.startswith('input_f')):
auxiliary.check_is_list('input_fields', value)
self.in_fields = value
elif (keyword.startswith('output_f')):
auxiliary.check_is_list('output_fields', value)
self.out_fields = value
elif (keyword.startswith('field_s')):
auxiliary.check_is_string('field_separator', value)
self.field_sep = value
elif (keyword.startswith('check_wo')):
auxiliary.check_is_flag('check_word_spill', value)
self.check_word_spill = value
elif (keyword.startswith('corr_l')):
auxiliary.check_is_list('corr_list', value)
self.corr_list = value
elif (keyword.startswith('tag_t')):
auxiliary.check_is_dictionary('tag_table', value)
self.tag_table = value
else:
logging.exception('Illegal constructor argument keyword: %s' % \
(str(keyword)))
raise Exception
# Check if fields are defined and not empty - - - - - - - - - - - - - - - -
#
auxiliary.check_is_list('input_fields', self.in_fields)
auxiliary.check_is_list('output_fields', self.out_fields)
if (len(self.in_fields) == 0) or (len(self.out_fields) == 0):
logging.exception('At least one of the field lists is empty.')
raise Exception
if (self.field_sep == ''): # No word spill checking with empty separator
self.check_word_spill = False
# ---------------------------------------------------------------------------
def clean_component(self, in_str):
"""Clean the given input string using a correction list.
This method cleans the input string by checking if any of the 'original'
strings given in the correction list is in the string. If so it will
replace such an original string with the corresponding replacement
string. It also strips off all leading and trailing spaces and makes all
letters lowercase. A cleaned string is returned.
"""
if (in_str.strip() == ''): # Check if the string only contains whitespaces
return ''
tmp_str = in_str.replace('\t',' ') # Replace tabs with whitespaces
# Make all lowercase and add leading and trailing whitespaces - - - - - -
# (this is to make sure replacement strings do match at beginning and end)
#
tmp_str = ' '+tmp_str.lower()+' '
for (org, repl) in self.corr_list: # Check for strings in correction list
if (org in tmp_str):
tmp_str = tmp_str.replace(org, repl)
# Make sure commas are separated from words so they become list elements -
#
tmp_str = tmp_str.replace(',', ' , ')
while (' ' in tmp_str): # Make sure there are no repeated spaces
tmp_str = tmp_str.replace(' ', ' ')
out_str = tmp_str.strip()
return out_str
# --------------------------------------------------------------------------
def check_field_spill(self, str1, str2):
"""Method to check if a known word is spilling from one string into
another.
Uses the keys the tag look-up table to check for known words.
Return 'True' if a word spills over, otherwise 'False'
"""
org_str1 = str1 # Keep copies of the original input string
org_str2 = str2
if ((str1 == '') or (str2 == '')): # One string is empty
return False
# Check if the characters at the 'boundary' are either letters or digits
#
if (str1[-1] in self.alphanum) and (str2[0] in self.alphanum):
min_ind = -len(str1)
ind =-1 # Start with last character
while (str1[ind] in self.alphanum) and (ind > min_ind):
ind -= 1
test_str1 = str1[ind:] # Only keep last word in string 1
max_ind = len(str2)
ind =0 # Start with first character
while (str1[ind] in self.alphanum) and (ind < max_ind):
ind += 1
test_str2 = str2[:ind] # Only keep first word in string 2
# Concatenate into one word and make a tuple so it conforms to tag table
# keys
#
check_tuple = (test_str1.lower() + test_str2.lower(),)
if (check_tuple in self.tag_table): # Found a word spilling
logging.info(' Found word spilling: "%s","%s" -> "%s"' % \
(org_str1, org_str2, check_word))
return True
else:
return False
# ---------------------------------------------------------------------------
def standardise(self, in_str, clean_in_str):
"""Standardise the given input string.
Will return a list with the standardised fields corresponding to the
list of output fields defined in the component standardiser.
See implementations in derived classes for details.
"""
logging.exception('Override abstract method in derived class')
raise Exception
# ---------------------------------------------------------------------------
def __tag_component__(self, in_str):
"""Tag an input component string using the tag look-up table and make it a
list.
This method cleans the input string and extracts words, numbers and
separators into a list. Each element of this list is assigned one or
more tags. A 'greedy tagger' is applied, which cheques sequences of list
elements in the given lookup table (longer sequences first) and replaces
them with the string and tag from the lookup table if found.
Returns two lists:
token_list Contains the tokens (words, numbers, etc.) from the input
string separated at whitespace characters.
tag_list One or more tags for each of the tokens in the token list.
"""
# First, split input string into elements at spaces - - - - - - - - - - - -
#
org_list = in_str.split() # The original list from the input string
token_list = [] # The initially empty list of tokens
tag_list = [] # The initially empty list of tags
max_key_len = self.tag_table.max_key_length
while (org_list != []): # As long as not all elements have been processed
tmp_list = org_list[:max_key_len]
# Extract longest sub-list
tmp_val = [] # Start with empty value
tmp_key = tuple(tmp_list)
while (tmp_key != ()): # As long as key not empty and not found in lookup
if (self.tag_table.has_key(tmp_key)):
tmp_val = self.tag_table[tmp_key]
break
tmp_key = tmp_key[:-1] # Remove last element in key
if (tmp_val != []): # A value has been found in the dictionary - - - - -
tmp_len = len(tmp_key) # Length of found sequence
if (tmp_val[0] != ''): # It's not an empty value
token_list.append(tmp_val[0]) # Append corrected word (or sequence)
tag_list.append(tmp_val[1]) # Append tag or tags
else: # No value has been found in the lookup dictionary, try other tags
tmp_val = org_list[0] # Value is first element in the original list
tmp_len = 1
if (len(tmp_val) == 1) and (tmp_val.isalpha()): # A 1-letter word - -
token_list.append(tmp_val)
tag_list.append('II')
elif (tmp_val.isdigit()): # Element is a number - - - - - - - - - - -
token_list.append(tmp_val)
if (len(tmp_val) == 4):
tag_list.append('N4')
else:
tag_list.append('NU')
elif (not tmp_val.isalpha()) and tmp_val.isalnum(): # Alpha-numeric -
token_list.append(tmp_val)
tag_list.append('AN')
elif (tmp_val == '-'): # Element is a hyphen - - - - - - - - - - - - -
# Only append if previous element is not a hyphen, vertical bar, or
# brackets
#
if ((token_list == []) or \
(tag_list[-1] not in ['HY','VB','OB','CB'])):
token_list.append(tmp_val)
tag_list.append('HY')
elif (tmp_val == ','): # Element is a comma - - - - - - - - - - - - -
pass # Don't append
elif (tmp_val == '|'): # Element is a vertical bar - - - - - - - - - -
# Remove a pair of vertical bars
#
if ((token_list != []) and (tag_list[-1] == 'VB')):
tag_list = tag_list[:-1]
token_list = token_list[:-1]
# Replace previous hyphen a vertical bar
#
elif ((token_list != []) and (tag_list[-1] == 'HY')):
token_list[-1] = tmp_val
tag_list[-1] = 'VB'
else: # Simple append vertical bar
token_list.append(tmp_val)
tag_list.append('VB')
elif (tmp_val == '('): # Element is an opening bracket - - - - - - - -
# Replace previous hyphen with an opening bracket
#
if ((token_list != []) and (tag_list[-1] == 'HY')):
token_list[-1] = tmp_val
tag_list[-1] = 'OB'
# Only append if previous element is not an opening bracket
#
elif ((token_list == []) or (tag_list[-1] != 'OB')):
token_list.append(tmp_val)
tag_list.append('OB')
elif (tmp_val == ')'): # Element is an closing bracket - - - - - - - -
# Remove a pair of opening / closing brackets
#
if ((token_list != []) and (tag_list[-1] == 'OB')):
tag_list = tag_list[:-1]
token_list = token_list[:-1]
# Replace previous hyphen with a closing bracket
#
elif ((token_list != []) and (tag_list[-1] == 'HY')):
token_list[-1] = tmp_val
tag_list[-1] = 'CB'
# Only append if previous element is not a closing bracket
#
elif ((token_list == []) or (tag_list[-1] != 'CB')):
token_list.append(tmp_val)
tag_list.append('CB')
else: # An unknown element - - - - - - - - - - - - - - - - - - - - - -
token_list.append(tmp_val)
tag_list.append('UN')
# Finally remove the processed elements from the original element list
#
org_list = org_list[tmp_len:] # Remove processed elements
# Remove certain elements from start and end - - - - - - - - - - - - - - -
#
while ((len(tag_list) > 1) and (tag_list[0] in ['CO','HY','SP'])):
tag_list = tag_list[1:]
token_list = token_list[1:]
while ((len(tag_list) > 1) and (tag_list[-1] in ['CO','HY','SP'])):
tag_list = tag_list[:-1]
token_list = token_list[:-1]
# Remove vertical bars or brackets from start and end - - - - - - - - - - -
#
while ((len(tag_list) > 2) and \
((tag_list[0] == 'VB') and (tag_list[-1] == 'VB')) or \
((tag_list[0] == 'OB') and (tag_list[-1] == 'CB'))):
tag_list = tag_list[1:-1]
token_list = token_list[1:-1]
return [token_list, tag_list]
# ---------------------------------------------------------------------------
def __write_hmm_train_record__(self, in_str, tok_list, tag_perm_list,
state_list, hmm_prob):
"""Write the given record, the list of its tag sequence permutations, and
possibly its HMM state sequence into the training file.
If the sequence frequency file is given also insert the sequences with
their probabilities and input strings into the sequence frequency
dictionary.
"""
self.hmm_train_fp.write('# Input string: %s' % (in_str)+os.linesep)
self.hmm_train_fp.write('# Token list: %s' % (tok_list)+os.linesep)
list_len = len(tok_list)
if (state_list == None): # Create an empty state sequenct list
state_list = [' ']*list_len
for tag_list in tag_perm_list:
assert len(tag_list) == len(tok_list)
tag_state_list = []
for i in range(list_len):
tag_state_list.append(tag_list[i]+':'+state_list[i])
tag_state_str = ', '.join(tag_state_list)
self.hmm_train_fp.write('%s' % (tag_state_str)+os.linesep)
# Add sequence into sequence probability dictionary if defined - - - - -
#
if (self.hmm_seq_prob_file != None):
seq_list = self.hmm_seq_prob_dict.get(tag_state_str, [])
seq_list.append((in_str, hmm_prob))
self.hmm_seq_prob_dict[tag_state_str] = seq_list
self.hmm_train_fp.write(os.linesep)
# ---------------------------------------------------------------------------
def __write_hmm_seq_prob_file__(self, hmm_name_str):
"""Write the dictionary with sequence frequencies into file.
Sort according to frequency (count of occurrence) of a sequence, with
high frequencies first (as these are the ones one wants to get correct).
"""
try:
self.hmm_seq_prob_fp = open(self.hmm_seq_prob_file, 'w')
except:
logging.exception('Cannot write to HMM sequence probability file: ' + \
'"%s"' % (self.hmm_seq_prob_file))
self.hmm_seq_prob_fp.write('#'+'#'*70 + os.linesep) # Write a header
self.hmm_seq_prob_fp.write('# HMM sequence probabilities for: "%s"' % \
(hmm_name_str)+ os.linesep)
self.hmm_seq_prob_fp.write('#' + os.linesep)
self.hmm_seq_prob_fp.write('# Created '+time.ctime(time.time()) + \
os.linesep)
self.hmm_seq_prob_fp.write('#' + os.linesep)
sorted_seq_key_list = [] # Make a list for easy sorting
num_rec = 0 # Count total number of records
for (tag_state_str, seq_list) in self.hmm_seq_prob_dict.iteritems():
hmm_prob = self.hmm_seq_prob_dict[tag_state_str][0][1]
sorted_seq_key_list.append((len(seq_list), hmm_prob, tag_state_str))
num_rec += len(seq_list)
sorted_seq_key_list.sort(reverse=True)
cum_freq = 0 # Cumulative frequency seen so far
for (freq, hmm_prob, tag_state_str) in sorted_seq_key_list:
self.hmm_seq_prob_fp.write('# Pattern: %s' % (tag_state_str) + \
os.linesep)
cum_freq += freq
freq_perc = 100.0*freq / float(num_rec)
cum_freq_perc = 100.0*cum_freq / float(num_rec)
self.hmm_seq_prob_fp.write('# Frequency: %d (%.2f%%, cumulative:' % \
(freq, freq_perc) + ' %.2f%%)' % \
(cum_freq_perc) + os.linesep)
examples = self.hmm_seq_prob_dict[tag_state_str]
self.hmm_seq_prob_fp.write('# Normalised HMM Viterbi probability: ' + \
'%.20f' % (hmm_prob) + os.linesep)
self.hmm_seq_prob_fp.write('# Example input strings:' + os.linesep)
for (example_str, prob) in examples[:10]: # Only 10 first examples
self.hmm_seq_prob_fp.write('# %s' % (example_str) + os.linesep)
self.hmm_seq_prob_fp.write('%s' % (tag_state_str) + os.linesep)
self.hmm_seq_prob_fp.write(os.linesep)
self.hmm_seq_prob_fp.write('# End.' + os.linesep)
self.hmm_seq_prob_fp.close()
# ---------------------------------------------------------------------------
def finalise(self):
"""Method to do any final work, such as writing information to files.
"""
return # Nothing to do for general case.
# ---------------------------------------------------------------------------
def log(self, instance_var_list = None):
"""Write a log message with the basic component standardiser instance
variables plus the instance variable provided in the given input list
(assumed to contain pairs of names (strings) and values).
"""
logging.info('')
logging.info('Component standardiser: "%s"' % (self.description))
logging.info(' Input fields: %s' % (str(self.in_fields)))
logging.info(' Output fields: %s' % (str(self.out_fields)))
logging.info(' Field separator: "%s"' % (self.field_sep))
logging.info(' Check word spilling: %s' % (str(self.check_word_spill)))
if (self.corr_list != []):
logging.info(' Length of correction list: %d' % (len(self.corr_list)))
if (self.tag_table != {}):
logging.info(' Length of tag lookup table: %d' % (len(self.tag_table)))
if (instance_var_list != None):
logging.info(' Standardiser specific variables:')
max_name_len = 0
for (name, value) in instance_var_list:
max_name_len = max(max_name_len, len(name))
for (name, value) in instance_var_list:
pad_spaces = (max_name_len-len(name))*' '
logging.info(' %s %s' % (name+':'+pad_spaces, str(value)))
# =============================================================================
class DateStandardiser(ComponentStandardiser):
"""Class for standardising dates into three field: day, month, year.
The 'output_fields' must be a list of three fields, the first field is for
day, the second for month and the third for year. Fields can be set to
'None' if no output is to be written, as long as at least one field is
set. For example, if one is only interested in the year, then the output
fields can be set to: output_fields = [None, None, "year_field"].
Note that field spill checking is not possible for this standardiser.
The additional arguments (besides the base class arguments) that need to
be set when this standardiser is initialised are:
parse_formats A list containing one or several strings with a date
parsing format. The parsing formats must each contain
three of the following format strings with a space in
between (e.g. '%d %m %Y'):
%b Abbreviated month name (Jan, Feb, Mar, etc.)
%B Full month name (January, February, etc.)
%d Day of the month as a decimal number [01,31]
%m Month as a decimal number [01,12]
%y Year without century as a decimal number [00,99]
%Y Year with century as a decimal number
The parsing routine tries one format after the other and
as soon as a format is successful returns the
corresponding parsed date. Date formats more commonly
used should therefore be at the top of the parse format
list.
Besides the spaces between the three parsing directives,
format strings must not contain any other characters,
such as '\/:-' etc. as they will be removed from the
input values before date parsing is attempted.
pivot_year Value of pivot year (between 00 and 99) that controls
expansion of two-digit year values into four-digit year
values. Two-digits years smaller than the pivot year will
be expanded into 20XX, years larger and equal than the
pivot year will be expanded into 19xx
For example: pivot_year = 03: 68 -> 1968
03 -> 1903
02 -> 2002