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LPU

test version python license

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 / trie extras.

Installation

Installing from PyPI release

$ pip install lpu

To also use the word alignment models:

$ pip install 'lpu[smt]'

To also use the Double-Array Trie:

$ pip install 'lpu[smt,trie]'

Installing from GitHub master

$ pip install 'lpu[smt,trie] @ 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)

# the safe_* helpers treat a missing source as a non-event (reported by the
# return value) while letting any other OSError through
files.safe_remove('workdir/*.tmp')        # number of files removed
files.safe_copy('model.bin', 'model.bak')
files.safe_link('model.bin', 'latest')    # hard link, symlink as fallback
files.safe_rename('model.tmp', 'model.bin')  # replaces the destination

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 trie extra; the enumerator itself does not.

IDMap is a richer vocabulary on top of the same idea: it reserves the special symbols <pad> / <s> / </s> / <unk>, counts how often each token was observed, can be truncated to the most frequent tokens, and can be saved to / loaded from a plain text file.

from lpu.common.vocab import IDMap, LabelMap

idmap = IDMap()
idmap.feed_corpus('train.tsv', 0)   # feeds column 0, line by line
idmap.truncate(32000)               # keeps the symbols + the top tokens
idmap.save('vocab.txt')

ids = idmap.encode('the cat', add_symbols=True)  # surrounded by bos / eos
idmap.decode(ids)                               # 'the cat'

LabelMap is an IDMap for label fields, where a field may carry several weighted labels (positive:3 negative:1). str2dist() turns such a field into a normalized probability distribution, and str2score() into the expected value of the numeric labels.

labels = LabelMap()
labels.feed_field('positive negative')
labels.str2dist('positive:3 negative:1')  # [0.75, 0.25]

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 (bleu, ribes) and for ranking (ranking).

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).

ranking evaluates a ranker from the ranks of the correct items, where a rank is 1-based and None means the item was not retrieved at all.

from lpu.metrics import ranking

ranks = [1, 2, None, 4]
ranking.calc_precision_at_k(ranks, 1)      # 0.25
ranking.calc_mean_reciprocal_rank(ranks)   # 0.4375

Optional features

These require the smt extra (pip install 'lpu[smt]'), which pulls in numpy, and a compiled build of the package. The Double-Array Trie additionally needs pycedar, installed with the trie extra (pip install 'lpu[smt,trie]'; pycedar publishes no Windows wheel, so the trie features are unavailable on Windows).

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.

Metadata

Release files for lpu 0.6.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for lpu 0.6.2
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Built distributions (wheels)

Table of built distributions (wheels) for lpu 0.6.2
File
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lpu-0.6.2-cp315-cp315-win32.whl CPython 3.15 CPython 3.15 Windows x86-32 Details
lpu-0.6.2-cp315-cp315-musllinux_1_2_x86_64.whl CPython 3.15 CPython 3.15 Linux musl 1.2+ x86-64 Details
lpu-0.6.2-cp315-cp315-musllinux_1_2_aarch64.whl CPython 3.15 CPython 3.15 Linux musl 1.2+ ARM64 Details
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