LPU
LPU is a collection of utility classes/functions for language processing.
日本語版のドキュメントは README.ja.md にあります。
Requirements
- Python 3.10 or later
The core features depend only on the standard library. The features listed
under Optional features additionally require the
smt extra.
Installation
Installing from PyPI release
$ pip install lpu
To also use the word alignment models and the Double-Array Trie:
$ pip install 'lpu[smt]'
Installing from GitHub master
$ pip install 'lpu[smt] @ git+https://github.com/akivajp/lpu.git'
Modules
lpu.common.config
Inheritable and serializable configuration classes, useful for calling functions with many arguments.
from lpu.common.config import Config
conf = Config({'threshold': 0.5, 'nested': {'depth': 3}})
conf.update(threshold=0.8)
print(conf.data.threshold) # 0.8
print(conf['nested.depth']) # 3
print(conf.to_json()) # {"threshold": 0.8, "nested": {"depth": 3}}
A Config keeps two layers: values passed positionally form the inherited
base layer, while values assigned afterwards go to the main layer.
to_dict() returns the main layer only; pass upstream=True (the default
of to_json()) to include the inherited values.
lpu.common.environ
Handling environment variables with stacks, useful for changing and reverting global settings such as debugging modes.
import os
from lpu.common import environ
with environ.push(LPU_DEBUG='1') as layer:
layer.set('MY_SETTING', 'temporary')
...
# both variables are restored here
Only variables assigned through set() (or given to push()) are tracked
and restored.
lpu.common.files
Utility functions for file handling, including transparent access to gzip files.
from lpu.common import files
# opens plain or gzip files alike, detected by extension and magic number
with files.open('corpus.txt.gz', 'rt') as f:
for line in f:
...
files.getContentSize('corpus.txt.gz') # uncompressed size
files.wait_file('lockfile', timeout=60)
lpu.common.logging
Enhanced logging objects (built on the standard logging library) with
colorizing features and control through environment variables.
from lpu.common import logging
logger = logging.getColorLogger(__name__)
with logging.using_config(__name__, debug=True):
some_variable = 42
logger.debug_print(some_variable) # some_variable => int(42)
debug_print parses the caller's source line, so the printed value is
labelled with the expression it came from. The level follows the
LPU_DEBUG / DEBUG and LPU_QUIET / QUIET environment variables.
lpu.common.progress
Utility classes and functions for progress reporting (progress bars) that work with file-type objects and iterators.
from lpu.common import files, progress
# progress over a gzip file is measured by the position in the
# compressed stream, so the percentage is known without decompressing
# the whole file first
for line in progress.view(files.open('corpus.txt.gz', 'rb'), 'loading'):
...
for item in progress.view(iter(range(1000)), 'processing'):
...
lpu.common.text
Conversion between bytes and str: to_str (tolerates invalid byte
sequences), to_unicode (strict) and to_bytes.
lpu.common.numbers
toNumber converts a value to int when it is close enough to an integer,
plus intToBytes / intFromBytes.
lpu.common.colors
put_color wraps text in ANSI escape sequences.
lpu.common.validation
check_argument_type raises a TypeError with a readable message listing
the expected types.
lpu.common.vocab
StringEnumerator maps strings to sequential IDs and back. The
phrase-level helpers (phrase2id, id2phrase, phraseMap) are backed by
the Double-Array Trie and therefore need the smt extra; the enumerator
itself does not.
lpu.data_structs.trees
Tree expressions and operations: TreeNode, parseSExpression,
calcEditDistance (sequence edit distance) and calcTreeEditDistance.
from lpu.data_structs import trees
tree = trees.TreeNode.fromS('(S (NP the cat) (VP sat))')
trees.calcEditDistance('kitten', 'sitting') # 3
trees.calcTreeEditDistance('(S (NP a))', '(S (NP b))') # 1
lpu.metrics
Evaluation metrics for machine translation.
from lpu.metrics import bleu, ribes
ref = 'the cat sat on the mat'.split()
hyp = 'the cat sat on a mat'.split()
bleu.eval_bleu(ref, hyp, 4)
ribes.eval_ribes(ref, hyp)
bleu is a simple sentence-level implementation with no tokenization of
its own; use sacrebleu for
publishable, comparable scores. ribes implements
NKT * P**alpha * BP**beta following Isozaki et al. (2010).
Optional features
These require the smt extra (pip install 'lpu[smt]'), which pulls in
numpy and pycedar, and a compiled build of the package.
lpu.data_structs.trie
Dictionaries and ID maps implemented on a Double-Array Trie: IDMap,
TwoWayIDMap and Dict.
lpu.smt.align.ibm_models
Classes to train and estimate word alignment based on the IBM models (model 1 and model 2).
lpu.smt.trans_models
Records, tables and operations over Moses / Travatar style phrase and rule tables, including phrase table triangulation for pivot translation.
These modules come from work on statistical machine translation and are
kept for reference; for current word alignment work, tools such as
fast_align, awesome-align or SimAlign are the usual choice.
Commands
LPU also installs directly executable commands.
lpu-abspath
$ lpu-abspath [-h] filepath [filepath ...]
Get absolute paths of given files or directories.
lpu-clean-parallel
$ lpu-clean-parallel [-h] [--min min_length] [--max max_length] \
[--ratio ratio] [--target-directory directory_path] [--escape] \
[--normalize] filepath [filepath ...] output_tag
Filter a parallel corpus by length and length ratio, keeping the lines of every side aligned.
lpu-dialog
$ lpu-dialog [-h] [--exist filepath] [--continue] [--yes] [--no]
Show a message on condition, wait and receive the user's response.
lpu-exec-parallel
$ lpu-exec-parallel [-h] [--input filepath] [--output filepath] \
[--splitsize num_lines] [--chunks num_files] [--threads num_threads] \
[--tmpdir directory_path] [--verbose] [--interval seconds] command
Execute a command in multiple processes by splitting the target file.
lpu-guess-langcode
$ lpu-guess-langcode [-h] filepath [filepath ...]
Guess the language codes from the given file names.
lpu-progress
$ lpu-progress [-h] [--lines] [--refresh seconds] [--header string] \
[filepath [filepath ...]]
Show the progress of pipe I/O.
lpu-random-split
$ lpu-random-split [-h] --input filepath [filepath ...] \
[--prefixes prefix [prefix ...]] [--suffixes suffix [suffix ...]] \
--tags tag [tag ...] --split-sizes size [size ...] [--ignore-empty] \
[--quiet] [--debug] [--random-seed seed] [--ids [suffix]]
Split a parallel corpus at random into several parts, keeping the line
correspondence between the sides. One of --prefixes or --suffixes is
required.
lpu-wait-files
$ lpu-wait-files [-h] [--quiet] [--debug] [--delay seconds] \
[--interval seconds] [--timeout seconds] filepath [filepath ...]
Wait until the files are found.
Word alignment commands
$ lpu-word-align-train [-h] [--save-scores filepath] \
[--decode-align filepath] [--iteration-limit num_iterations] \
[--threshold min_probability] [--nbest integer] [--character] \
[--debug] [--quiet] src_path trg_path save_trans_path [save_align_path]
$ lpu-word-align-score [-h] [--save-scores filepath] \
[--decode-align filepath] [--character] [--debug] [--quiet] \
src_path trg_path trans_path [align_path]
Train the IBM word alignment models on a parallel corpus, and score or
decode alignments with a trained model. Requires the smt extra.
Phrase / rule table commands
$ lpu-smt-convert-extract [-h] ...
$ lpu-smt-filter [-h] ...
$ lpu-smt-make-glue-rules [-h] ...
$ lpu-smt-normalize [-h] ...
$ lpu-smt-triangulate [-h] ...
Operations over Moses / Travatar style phrase and rule tables.
lpu-smt-triangulate builds a pivot phrase table from two tables sharing
a pivot language. Requires the smt extra. Pass --help to each command
for its options.
Development
$ uv sync
$ uv run pytest
The test suite runs against an editable build, so the C extensions are
exercised. Tests that need the extensions, numpy or pycedar skip
themselves when those are unavailable.
To install without building any C extension:
$ LPU_NO_EXTENSIONS=1 pip install .
License
MIT. See LICENSE.
1 maintainer
Metadata
1 maintainer
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