sic is a module for string normalization. Given a string, it separates
sequences of alphabetical, numeric, and punctuation characters, as well
as performs more complex transformation (i.e. separates or replaces specific
words or individual symbols). It comes with set of default normalization rules
to transliterate and tokenize greek letters and replace accented characters
with their base form. It also allows using custom normalization configurations.
Basic usage:
import sic
builder = sic.Builder()
machine = builder.build_normalizer()
x = machine.normalize('abc123xyzalphabetagammag')
print(x)The output will be:
abc 123 xyz alpha beta gamma gsicis designed to work in Python 3 environment.siconly needs Python Standard Library (no other packages).
To get wheel for Windows (Python >= 3.6) or source code package for Linux:
pip install sicTo get source code package regardless the OS:
pip install sic --no-binary sicWheels and .tar.gz can also be downloaded from the project's repository.
Wheels contain binaries compiled from cythonized code. Source code package is pure Python. Cythonized version performs better on short strings, while non-cythonized version performs better on long strings, so one may be preferred over another depending on usage scenario. The benchmark is below.
| STRING LENGTH | REPEATS | VERSION | MEAN TIME (s) |
|---|---|---|---|
| 71 | 10000 | .tar.gz | 1.8 |
| 71 | 10000 | wheel | 0.5 |
| 710000 | 1 | .tar.gz | 2.7 |
| 710000 | 1 | wheel | 15.9 |
sic implements tokenization, i.e. it splits a given string into tokens and
processes those tokens according to the rules specified in a configuration
file. Basic tokenization includes separating groups of alphabetical, numerical,
and punctuation characters within a string, thus turning them into separate
words (for future reference, we'll call such words tokens). For instance,
abc-123 will be transformed into abc - 123, having tokens abc, -, and
123.
What happens next to initially tokenized string must be defined using XML in
configuration file(s). Entry point to default tokenizer applied to a string is
sic/tokenizer.standard.xml.
Below is the template and description for tokenizer config.
<!-- tokenizer.config.xml -->
<!--
This is the description of config file for tokenizer.
General structure:
<tokenizer>
+-<import>
+-...
+-<import>
+-<setting>
+-...
+-<setting>
+-<split>
+-...
+-<split>
+-<token>
+-...
+-<token>
+-<character>
+-...
+-<character>
-->
<?xml version="1.0" encoding="UTF-8"?>
<!-- There must be single root element, and it must be <tokenizer>: -->
<tokenizer name="$name">
<!-- $name: string label for this tokenizer -->
<!--
Direct children of <tokenizer> are <import>, <setting>, <split>,
<token>, and/or <character> elements (there can be zero to many
declarations of any of those)
-->
<!-- <import> elements point at other tokenizer configs to merge with -->
<import file="$file" />
<!-- $file: path to file with another tokenizer config -->
<!-- <setting> elements define high-level tokenizer settings -->
<setting name="$name" value="$value" />
<!--
Names and value requirements for /tokenizer/setting elements:
$name="cs": $value="0"|"1" (if "1", this tokenizer will be case-sensitive)
$name="bypass": $value="0"|"1" (if "1", this tokenizer will do nothing,
regardless the rest content of this file)
-->
<!--
<split> elements define substrings that should be separated from text as
tokens
-->
<split where="$where" value="$value" />
<!--
$where="l"|"r"|"m" ("l" for left, "r" for right, "m" for middle)
$value: string that will be handled as token when it's either in the
beginning of word ($where="l"), at the end of word ($where="r"), or in
the middle ($where="m")
-->
<!--
<token> elements define tokens that should be replaced with other tokens
(or with nothing => removed)
-->
<token to="$to" from="$from" />
<!--
$to: string that should replace the token specified in $from
$from: string that is the token to be replaced by another string specified
in $to
-->
<!--
<character> elements define single characters that should be replaced with
other single characters
-->
<character to="$to" from="$from" />
<!--
$to: character that should replace the another character specified in $from
$from: character that should be replaced by another character specified in
$to
-->
</tokenizer>Below are descriptions and examples of tokenizer config elements.
| ELEMENT | ATTRIBUTES | DESCRIPTION | EXAMPLE |
|---|---|---|---|
<import> |
file="path/to/another/config.xml" | Import tokenization rules from another tokenizer config. | |
<setting> |
name="bypass" value="?" | If present and value="1", all tokenization rules are ignored, as if there was no tokenization at all (left for debug purposes). | |
<setting> |
name="cs" value="?" | If value="1", string is processed case-sensitively; if value="0" - case-insensitively; if not present, tokenizer is case-insensitive. | |
<split> |
where="l" value="?" | Separates token specified in value from left part of a bigger token. |
where="l" value="kappa": nf kappab --> nf kappa b |
<split> |
where="m" value="?" | Separates token specified in value when it is found in the middle of a bigger token. |
where="m" value="kappa": nfkappab --> nf kappa b |
<split> |
where="r" value="?" | Separates token specified in value from right part of a bigger token. |
where="r" value="gamma": ifngamma --> ifn gamma |
<token> |
to="?" from="?" | Replaces token specified in from with another token specified in to. |
to="protein" from="gene": nf kappa b gene --> nf kappa b protein |
<character> |
to="?" from="?" | Replaces character specified in from with another character specified in to. |
to="e" from="ë": citroën --> citroen |
Attribute where of <split> element may have any combination of l, m, or
r literals if the specified substring is required to be separated in different
places of a bigger string. So, instead of three different elements
<split where="l" value="word" />
<split where="m" value="word" />
<split where="r" value="word" />using the following single one
<split where="lmr" value="word" />will achieve the same result.
Transformation is applied in the following order:
- Replacing characters
- Splitting tokens
- Replacing tokens
When splitting tokens, longer ones shadow shorter ones. Token replacement instructions may contradict each other locally, but in entire set they must converge so that each token has only one replacement option (otherwise ValueError exception will be thrown).
import sicFor detailed description of all function and methods, see comments in the source code.
This class is designed to instanly create tokenization rules directly in Python. It is neither convenient nor recommended for complex normalization tasks, but can be handy for small ones where using external XML config might seem an overkill.
# instantiate Model
model = sic.Model()
# model is case-sensitive
model.case_sensitive = True
# model will do nothing
model.bypass = TrueMethod sic.Model.add_rule adds single tokenization instruction to the
Model instance:
# equivalent to XML <split where="lmr" value="beta" />
model.add_rule(sic.SplitToken('beta', 'lmr'))
# equivalent to XML <token to="good" from="bad" />
model.add_rule(sic.ReplaceToken('bad', 'good'))
# equivalent to XML <character to="z" from="a" />
model.add_rule(sic.ReplaceCharacter('a', 'z'))NB: in case new
sic.ReplaceTokenorsic.ReplaceCharinstruction contradicts something that is already in the model, the newer instruction overrides older instruction:model.add_rule(sic.ReplaceToken('bad', 'good')) model.add_rule(sic.ReplaceToken('bad', 'better'))"bad" --> "good" will not be used; "bad" --> "better" will be used instead
Method sic.Model.remove_rule removes single tokenization instruction from
Model instance if it is there:
model.remove_rule(sic.ReplaceToken('bad', 'good'))
# tokenization rule that fits definition above will be removed from modelFunction sic.Builder.build_normalizer() reads tokenization config,
instantiates sic.Normalizer class that would perform tokenization according
to rules specified in a given config, and returns this sic.Normalizer class
instance.
| ARGUMENT | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| endpoint | str, Model | None | Path to tokenizer configuration file. |
# create Builder object
builder = sic.Builder()
# create Normalizer object with default set of rules
machine = builder.build_normalizer()
# create Normalizer object with custom set of rules
machine = builder.build_normalizer('/path/to/config.xml')
# create Normalizer object using ad hoc model
model = sic.Model()
model.add_rule(sic.SplitToken('beta', 'lmr'))
machine = builder.build_normalizer(model)Method sic.Normalizer.save() saves data structure from instance of
sic.Normalizer class to a specified file (pickle).
| ARGUMENT | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| filename | str | n/a | Path and name of file to write. |
Function sic.Normalizer.load() reads specified file (pickle) and places
data structure in sic.Normalizer instance.
| ARGUMENT | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| filename | str | n/a | Path and name of file to read. |
Function sic.Normalizer.normalize() performs string normalization
according to the rules ingested at the time of class initialization, and
returns normalized string.
| ARGUMENT | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| source_string | str | n/a | String to normalize. |
| word_separator | str | ' ' | Word delimiter (single character). |
| normalizer_option | int | 0 | Mode of post-processing. |
| control_character | str | '\x00' | Character masking word delimiter (single character) |
word_separator: Specified character will be considered a boundary between
tokens. The default value is ' ' (space) which seems reasonable choice for
natural language. However any character can be specified, which might be more
useful in certain context.
normalizer_option: The value can be either one of 0, 1, 2, or 3 and
controls the way tokenized string is post-processed:
| VALUE | MODE |
|---|---|
| 0 | No post-processing. |
| 1 | Rearrange tokens in alphabetical order. |
| 2 | Rearrange tokens in alphabetical order and remove duplicates. |
| 3 | Remove all added word separators. |
control_character: Implementation detail - character that used as word
delimiter inserted in a parsed string at the run time. If parsed string
initially included this character somewhere, normalization will return error.
The value is set to \x00 by default.
Property sic.Normalizer.result retains the result of last call for
sic.Normalizer.normalize function as dict object with the following keys:
| KEY | VALUE TYPE | DESCRIPTION |
|---|---|---|
| 'original' | str | Original string value that was processed. |
| 'normalized' | str | Returned normalized string value. |
| 'map' | list(int) | Map between original and normalized strings. |
| 'r_map' | list(list(int)) | Reverse map between original and normalized strings. |
sic.Normalizer.result['map']: Not only sic.Normalizer.normalize() generates
normalized string out of originally provided, it also tries to map character
indexes in normalized string back on those in the original one. This map is
represented as list of integers where item index is character position in
normalized string and item value is character position in original string. This
is only valid when normalizer_option argument for sic.Normalizer.normalize()
call has been set to 0.
sic.Normalizer.result['r_map']: Reverse map between character locations in
original string and its normalized reflection (item index is character position
in original string; item value is list [x, y] where x and y are
respectively lowest and highest indexes of mapped character in normalized
string).
sic.build_normalizer() implicitly creates single instance of sic.Normalizer
class accessible globally from sic namespace. Arguments are same as for
sic.Builder.build_normalizer() function.
sic.save() saves data structure stored in global instance of sic.Normalizer
class to a specified file (pickle). Arguments are same as for
sic.Normalizer.save() method.
sic.load() reads specified file (pickle) and places data structure in global
instance of sic.Normalizer class stored in that file. Arguments are same as
for sic.Normalizer.load() function.
sic.normalize(*args, **kwargs) either uses global class sic.Normalizer or
instantly creates new local sic.Normalizer class, and uses it to perform
requested string normalization.
| ARGUMENT | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| source_string | str | n/a | String to normalize. |
| word_separator | str | ' ' | Word delimiter (single character). |
| normalizer_option | int | 0 | Mode of post-processing. |
| control_character | str | '\x00' | Character masking word delimiter (single character) |
| tokenizer_config | str | None | Path to tokenizer configuration file. |
If tokenizer_config argument is not provided, the function will use global
instance of sic.Normalizer class (will create it if it is not initialized).
sic.reset() resets global sic.Normalizer instance to None, forcing
subsequently called sic.normalize() to create new global instance again if it
needs it.
sic.result attribute retains the value of sic.Normalizer.result property
that belonged to most recently used sic.Normalizer instance accessed from
sic.normalize() function (either global or local).
Python 3.6 does not support PEP-562
(module attributes). So in Python 3.6, use function sic.result() rather than
attribute sic.result:
sic.result() # will work in Python >= 3.6
sic.result # will work in Python >= 3.7import sic
# create Builder object
builder = sic.Builder()
# create Normalizer object with default set of rules
machine = builder.build_normalizer()
# using default word_separator and normalizer_option
x = machine.normalize('alpha-2-macroglobulin-p')
print(x) # 'alpha - 2 - macroglobulin - p'
print(machine.result)
"""
{
'original': 'alpha-2-macroglobulin-p',
'normalized': 'alpha - 2 - macroglobulin - p',
'map': [
0, 1, 2, 3, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 21, 22, 22
],
'r_map: [
[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 13], [14, 14], [15, 15], [16, 16], [17, 17], [18, 18], [19, 19], [20, 20], [21, 21], [22, 22], [23, 23], [24, 24], [25, 26], [27, 28]
]
}
"""x = machine.normalize('alpha-2-macroglobulin-p', word_separator='|')
print(x) # 'alpha|-|2|-|macroglobulin|-|p'# using normalizer_option=1
x = machine.normalize('alpha-2-macroglobulin-p', normalizer_option=1)
print(x) # '- - - 2 alpha macroglobulin p'# using normalizer_option=2
x = machine.normalize('alpha-2-macroglobulin-p', normalizer_option=2)
print(x) # '- 2 alpha macroglobulin p'# using normalizer_option=3
# assuming normalization config includes the following:
# <setting name="cs" value="0" />
# <split value="mis" where="l" />
# <token to="spelling" from="speling" />
x = machine.normalize('Misspeling')
print(x) # 'Misspelling'# normalize() with default instance
x = sic.normalize('alpha-2-macroglobulin-p', word_separator='|')
print(x) # 'alpha|-|2|-|macroglobulin|-|p'
# custom configuration for implicitly instantiated normalizer
sic.build_normalizer('/path/to/config.xml')
x = sic.normalize('some string')
print(x) # will be normalized according to config at /path/to/config.xml
# custom config and normalization in one line
x = sic.normalize('some string', tokenizer_config='/path/to/another/config.xml')
print(x) # will be normalized according to config at /path/to/another/config.xmlmachine.save('/path/to/file') # will write /path/to/file
another_machine = sic.Normalizer()
another_machine.load('/path/to/file') # will read /path/to/file# (assuming `machine` is sic.Normalizer instance armed with tokenization ruleset)
new_ruleset = [sic.ReplaceToken('from', 'to'), sic.SplitToken('token', 'r')]
new_ruleset_string = ''.join([rule.decode() for rule in new_ruleset])
machine.make_tokenizer(new_ruleset_string, update=True) # rules from `new_ruleset` will be added to the normalizer