Changes¶
0.10.1 (2020-08-29)¶
New and Changed:¶
Expanded text statistics and refactored into a sub-package (PR #307)
Refactored
text_stats
module into a sub-package with the same name and top-level API, but restructured under the hood for better consistencyImproved performance, API, and documentation on the main
TextStats
class, and improved documentation on many of the individual stats functionsAdded new readability tests for texts in Arabic (Automated Arabic Readability Index), Spanish (µ-legibility and perspecuity index), and Turkish (a lang-specific formulation of Flesch Reading Ease)
Breaking change: Removed
TextStats.basic_counts
andTextStats.readability_stats
attributes, since typically only one or a couple needed for a given use case; also, some of the readability tests are language-specific, which meant bad results could get mixed in with good ones
Improved and standardized some code quality and performance (PR #305, #306)
Standardized error messages via top-level
errors.py
moduleReplaced
str.format()
with f-strings (almost) everywhere, for performance and readabilityFixed a whole mess of linting errors, significantly improving code quality and consistency
Improved package configuration, and maintenance (PRs #298, #305, #306)
Added automated GitHub workflows for building and testing the package, linting and formatting, publishing new releases to PyPi, and building documentation (and ripped out Travis CI)
Added a makefile with common commands for dev work, plus instructions
Adopted the new
pyproject.toml
package configuration standard; updated and streamlinedsetup.py
andsetup.cfg
accordingly; and removedrequirements.txt
Moved all source code into a
/src
directory, for technical reasonsAdded
mypy
-specific config file to reduce output noisiness when type-checking
Improved and moved package documentation (PR #309)
Moved the docs site back to ReadTheDocs (https://textacy.readthedocs.io)! Pardon the years-long detour into GitHub Pages…
Enabled markdown-based documentation using
recommonmark
instead ofm2r
, and migrated all “narrative” docs from.rst
to equivalent.md
filesAdded auto-generated summary tables to many sections of the API Reference, to help users get an overview of functionality and better find what they’re looking for; also added auto-generated section heading references
Tidied up and further standardized docstrings throughout the code
Kept up with the Python ecosystem
Trained a v1.1 language identifier model using
scikit-learn==0.23.0
, and bumped the upper bound on that dependency’s version accordinglyUpdated and parametrized many tests using modern
pytest
functionality (PR #306)Got
textacy
versions 0.9.1 and 0.10.0 up onconda-forge
(Issue #294)Added spectral seriation as a term-ordering technique when making a “Termite” visualization by taking advantage of
pandas.DataFrame
functionality, and otherwise tidied up the default for nice-looking plots (PR #295)
Fixed:¶
Corrected an incorrect and misleading reference in the quickstart docs (Issue #300, PR #302)
Fixed a bug in the
delete_words()
augmentation transform (Issue #308)
Contributors:¶
Special thanks to @tbsexton, @marius-mather, and @rmax for their contributions! 💐
0.10.0 (2020-03-01)¶
New:¶
Added a logo to textacy’s documentation and social preview :page_with_curl:
Added type hints throughout the code base, for more expressive type indicators in docstrings and for static type checkers used by developers to code more effectively (PR #289)
Added a preprocessing function to normalize sequences of repeating characters (Issue #275)
Changed:¶
Improved core
Corpus
functionality using recent additions to spacy (PR #285)Re-implemented
Corpus.save()
andCorpus.load()
using spacy’s newDocBin
class, which resolved a few bugs/issues (Issue #254)Added
n_process
arg toCorpus.add()
to set the number of parallel processes used when adding many items to a corpus, following spacy’s updates tonlp.pipe()
(Issue #277)Bumped minimum spaCy version from 2.0.12 => 2.2.0, accordingly
Added handling for zero-width whitespaces into
normalize_whitespace()
function (Issue #278)Improved a couple rough spots in package administration:
Moved package setup information into a declarative configuration file, in an attempt to keep up with evolving best practices for Python packaging
Simplified the configuration and interoperability of sphinx + github pages for generating package documentation
Fixed:¶
Fixed typo in ConceptNet docstring (Issue #280)
Trained and distributed a
LangIdentifier
model usingscikit-learn==0.22
, to prevent ambiguous errors when trying to load a file that didn’t exist (Issues #291, #292)
0.9.1 (2019-09-03)¶
Changed:¶
Tweaked
TopicModel
class to work with newer versions ofscikit-learn
, and updated version requirements accordingly from>=0.18.0,<0.21.0
to>=0.19
Fixed:¶
Fixed residual bugs in the script for training language identification pipelines, then trained and released one using
scikit-learn==0.19
to prevent errors for users on that version
0.9.0 (2019-09-03)¶
Note: textacy
is now PY3-only! 🎉 Specifically, support for PY2.7 has been dropped, and the minimum PY3 version has been bumped to 3.6 (PR #261). See below for related changes.
New:¶
Added
augmentation
subpackage for basic text data augmentation (PR #268, #269)implemented several transformer functions for substituting, inserting, swapping, and deleting elements of text at both the word- and character-level
implemented an
Augmenter
class for combining multiple transforms and applying them to spaCyDoc
s in a randomized but configurable mannerNote: This API is provisional, and subject to change in future releases.
Added
resources
subpackage for standardized access to linguistic resources (PR #265)DepecheMood++: high-coverage emotion lexicons for understanding the emotions evoked by a text. Updated from a previous version, and now features better English data and Italian data with expanded, consistent functionality.
removed
lexicon_methods.py
module with previous implementation
ConceptNet: multilingual knowledge base for representing relationships between words, similar to WordNet. Currently supports getting word antonyms, hyponyms, meronyms, and synonyms in dozens of languages.
Added
UDHR
dataset, a collection of translations of the Universal Declaration of Human Rights (PR #271)
Changed:¶
Updated and extended functionality previously blocked by PY2 compatibility while reducing code bloat / complexity
made many args keyword-only, to prevent user error
args accepting strings for directory / file paths now also accept
pathlib.Path
objects, withpathlib
adopted widely under the hoodincreased minimum versions and/or uncapped maximum versions of several dependencies, including
jellyfish
,networkx
, andnumpy
Added a Portuguese-specific formulation of Flesch Reading Ease score to
text_stats
(PR #263)Reorganized and grouped together some like functionality
moved core functionality for loading spaCy langs and making spaCy docs into
spacier.core
, out ofcache.py
anddoc.py
moved some general-purpose functionality from
dataset.utils
toio.utils
andutils.py
moved function for loading “hyphenator” out of
cache.py
and intotext_stats.py
, where it’s used
Re-trained and released language identification pipelines using a better mix of training data, for slightly improved performance; also added the script used to train the pipeline
Changed API Reference docs to show items in source code rather than alphabetical order, which should make the ordering more human-friendly
Updated repo README and PyPi metadata to be more consistent and representative of current functionality
Removed previously deprecated
textacy.io.split_record_fields()
function
Fixed:¶
Fixed a regex for cleaning up crufty terms to prevent catastrophic backtracking in certain edge cases (true story: this bug was encountered in production code, and ruined my day)
Fixed bad handling of edge cases in sCAKE keyterm extraction (Issue #270)
Changed order in which URL regexes are applied in
preprocessing.replace_urls()
to properly handle certain edge case URLs (Issue #267)
Contributors:¶
Thanks much to @hugoabonizio for the contribution. 🤝
0.8.0 (2019-07-14)¶
New and Changed:¶
Refactored and expanded text preprocessing functionality (PR #253)
Moved code from a top-level
preprocess
module into apreprocessing
sub-package, and reorganized it in the processAdded new functions:
replace_hashtags()
to replace hashtags like#FollowFriday
or#spacyIRL2019
with_TAG_
replace_user_handles()
to replace user handles like@bjdewilde
or@spacy_io
with_USER_
replace_emojis()
to replace emoji symbols like 😉 or 🚀 with_EMOJI_
normalize_hyphenated_words()
to join hyphenated words back together, likeantici- pation
=>anticipation
normalize_quotation_marks()
to replace “fancy” quotation marks with simple ascii equivalents, like“the god particle”
=>"the god particle"
Changed a couple functions for clarity and consistency:
replace_currency_symbols()
now replaces all dedicated ascii and unicode currency symbols with_CUR_
, rather than just a subset thereof, and no longer provides for replacement with the corresponding currency code (like€
=>EUR
)remove_punct()
now has afast (bool)
kwarg rather thanmethod (str)
Removed
normalize_contractions()
,preprocess_text()
, andfix_bad_unicode()
functions, since they were bad/awkward and more trouble than they were worth
Refactored and expanded keyterm extraction functionality (PR #257)
Moved code from a top-level
keyterms
module into ake
sub-package, and cleaned it up / standardized arg names / better shared functionality in the processAdded new unsupervised keyterm extraction algorithms: YAKE (
ke.yake()
), sCAKE (ke.scake()
), and PositionRank (ke.textrank()
, with non-default parameter values)Added new methods for selecting candidate keyterms: longest matching subsequence candidates (
ke.utils.get_longest_subsequence_candidates()
) and pattern-matching candidates (ke.utils.get_pattern_matching_candidates()
)Improved speed of SGRank implementation, and generally optimized much of the code
Improved document similarity functionality (PR #256)
Added a character ngram-based similarity measure (
similarity.character_ngrams()
), for something that’s useful in different contexts than the other measuresRemoved Jaro-Winkler string similarity measure (
similarity.jaro_winkler()
), since it didn’t add much beyond other measuresImproved speed of Token Sort Ratio implementation
Replaced
python-levenshtein
dependency withjellyfish
, for its active development, better documentation, and actually-compliant license
Added customizability to certain functionality
Added options to
Doc._.to_bag_of_words()
andCorpus.word_counts()
for filtering out stop words, punctuation, and/or numbers (PR #249)Allowed for objects that look like
sklearn
-style topic modeling classes to be passed intotm.TopicModel()
(PR #248)Added options to customize rc params used by
matplotlib
when drawing a “termite” plot inviz.draw_termite_plot()
(PR #248)
Removed deprecated functions with direct replacements:
io.utils.get_filenames()
andspacier.components.merge_entities()
Contributors:¶
Huge thanks to @kjoshi and @zf109 for the PRs! 🙌
0.7.1 (2019-06-25)¶
New:¶
Added a default, built-in language identification classifier that’s moderately fast, moderately accurate, and covers a relatively large number of languages [PR #247]
Implemented a Google CLD3-inspired model in
scikit-learn
and trained it on ~1.5M texts in ~130 different languages spanning a wide variety of subject matter and stylistic formality; overall, speed and performance compare favorably to other open-source options (langid
,langdetect
,cld2-cffi
, andcld3
)Dropped
cld2-cffi
dependency [Issue #246]
Added
extract.matches()
function to extract spans from a document matching one or more pattern of per-token (attribute, value) pairs, with optional quantity qualifiers; this is a convenient interface to spaCy’s rule-basedMatcher
and a more powerful replacement for textacy’s existing (now deprecated)extract.pos_regex_matches()
Added
preprocess.normalize_unicode()
function to transform unicode characters into their canonical forms; this is a less-intensive consolation prize for the previously-removedfix_unicode()
function
Changed:¶
Enabled loading blank spaCy
Language
pipelines (tokenization only – no model-based tagging, parsing, etc.) viaload_spacy_lang(name, allow_blank=True)
for use cases that don’t rely on annotations; disabled by default to avoid unwelcome surprisesChanged inclusion/exclusion and de-duplication of entities and ngrams in
to_terms_list()
[Issues #169, #179]entities = True
=> include entities, and drop exact duplicate ngramsentities = False
=> don’t include entities, and also drop exact duplicate ngramsentities = None
=> use ngrams as-is without checking against entities
Moved
to_collection()
function from thedatasets.utils
module to the top-levelutils
module, for use throughout the code baseAdded
quoting
option toio.read_csv()
andio.write_csv()
, for problematic casesDeprecated the
spacier.components.merge_entities()
pipeline component, an implementation of which has since been added into spaCy itselfUpdated documentation for developer convenience and reader clarity
Split API reference docs into related chunks, rather than having them all together in one long page, and tidied up headers
Fixed errors / inconsistencies in various docstrings (a never-ending struggle…)
Ported package readme and changelog from
.rst
to.md
format
Fixed:¶
The
NotImplementedError
previously added topreprocess.fix_unicode()
is now raised rather than returned [Issue #243]
0.7.0 (2019-05-13)¶
New and Changed:¶
Removed textacy.Doc, and split its functionality into two parts
New: Added
textacy.make_spacy_doc()
as a convenient and flexible entry point for making spaCyDoc
s from text or (text, metadata) pairs, with optional spaCy language pipeline specification. It’s similar totextacy.Doc.__init__
, with the exception that text and metadata are passed in together as a 2-tuple.New: Added a variety of custom doc property and method extensions to the global
spacy.tokens.Doc
class, accessible via itsDoc._
“underscore” property. These are similar to the properties/methods ontextacy.Doc
, they just require an interstitial underscore. For example,textacy.Doc.to_bag_of_words()
=>spacy.tokens.Doc._.to_bag_of_words()
.New: Added functions for setting, getting, and removing these extensions. Note that they are set automatically when textacy is imported.
Simplified and improved performance of textacy.Corpus
Documents are now added through a simpler API, either in
Corpus.__init__
orCorpus.add()
; they may be one or a stream of texts, (text, metadata) pairs, or existing spaCyDoc
s. When adding many documents, the spaCy language processing pipeline is used in a faster and more efficient way.Saving / loading corpus data to disk is now more efficient and robust.
Note:
Corpus
is now a collection of spaCyDoc
s rather thantextacy.Doc
s.
Simplified, standardized, and added Dataset functionality
New: Added an
IMDB
dataset, built on the classic 2011 dataset commonly used to train sentiment analysis models.New: Added a base
Wikimedia
dataset, from which a reworkedWikipedia
dataset and a separateWikinews
dataset inherit. The underlying data source has changed, from XML db dumps of raw wiki markup to JSON db dumps of (relatively) clean text and metadata; now, the code is simpler, faster, and totally language-agnostic.Dataset.records()
now streams (text, metadata) pairs rather than a dict containing both text and metadata, so users don’t need to know field names and split them into separate streams before creatingDoc
orCorpus
objects from the data.Filtering and limiting the number of texts/records produced is now clearer and more consistent between
.texts()
and.records()
methods on a givenDataset
— and more performant!Downloading datasets now always shows progress bars and saves to the same file names. When appropriate, downloaded archive files’ contents are automatically extracted for easy inspection.
Common functionality (such as validating filter values) is now standardized and consolidated in the
datasets.utils
module.
Quality of life improvements
Reduced load time for
import textacy
from ~2-3 seconds to ~1 second, by lazy-loading expensive variables, deferring a couple heavy imports, and dropping a couple dependencies. Specifically:ftfy
was dropped, and aNotImplementedError
is now raised in textacy’s wrapper function,textacy.preprocess.fix_bad_unicode()
. Users with bad unicode should now directly callftfy.fix_text()
.ijson
was dropped, and the behavior oftextacy.read_json()
is now simpler and consistent with other functions for line-delimited data.mwparserfromhell
was dropped, since the reworkedWikipedia
dataset no longer requires complicated and slow parsing of wiki markup.
Renamed certain functions and variables for clarity, and for consistency with existing conventions:
textacy.load_spacy()
=>textacy.load_spacy_lang()
textacy.extract.named_entities()
=>textacy.extract.entities()
textacy.data_dir
=>textacy.DEFAULT_DATA_DIR
filename
=>filepath
anddirname
=>dirpath
when specifying full paths to files/dirs on disk, andtextacy.io.utils.get_filenames()
=>textacy.io.utils.get_filepaths()
accordinglycompiled regular expressions now consistently start with
RE_
SpacyDoc
=>Doc
,SpacySpan
=>Span
,SpacyToken
=>Token
,SpacyLang
=>Language
as variables and in docs
Removed deprecated functionality
top-level
spacy_utils.py
andspacy_pipelines.py
are gone; use equivalent functionality in thespacier
subpackage insteadmath_utils.py
is gone; it was long neglected, and never actually used
Replaced
textacy.compat.bytes_to_unicode()
andtextacy.compat.unicode_to_bytes()
withtextacy.compat.to_unicode()
andtextacy.compat.to_bytes()
, which are safer and accept either binary or text strings as input.Moved and renamed language detection functionality,
textacy.text_utils.detect_language()
=>textacy.lang_utils.detect_lang()
. The idea is to add more/better lang-related functionality here in the future.Updated and cleaned up documentation throughout the code base.
Added and refactored many tests, for both new and old functionality, significantly increasing test coverage while significantly reducing run-time. Also, added a proper coverage report to CI builds. This should help prevent future errors and inspire better test-writing.
Bumped the minimum required spaCy version:
v2.0.0
=>v2.0.12
, for access to their full set of custom extension functionality.
Fixed:¶
The progress bar during an HTTP download now always closes, preventing weird nesting issues if another bar is subsequently displayed.
Filtering datasets by multiple values performed either a logical AND or OR over the values, which was confusing; now, a logical OR is always performed.
The existence of files/directories on disk is now checked properly via
os.path.isfile()
oros.path.isdir()
, rather thanos.path.exists()
.Fixed a variety of formatting errors raised by sphinx when generating HTML docs.
0.6.3 (2019-03-23)¶
New:¶
Added a proper contributing guide and code of conduct, as well as separate GitHub issue templates for different user situations. This should help folks contribute to the project more effectively, and make maintaining it a bit easier, too. [Issue #212]
Gave the documentation a new look, using a template popularized by
requests
. Added documentation on dealing with multi-lingual datasets. [Issue #233]Made some minor adjustments to package dependencies, the way they’re specified, and the Travis CI setup, making for a faster and better development experience.
Confirmed and enabled compatibility with v2.1+ of
spacy
. :dizzy:
Changed:¶
Improved the
Wikipedia
dataset class in a variety of ways: it can now read Wikinews db dumps; access records in namespaces other than the usual “0” (such as category pages in namespace “14”); parse and extract category pages in several languages, including in the case of bad wiki markup; and filter out section headings from the accompanying text via aninclude_headings
kwarg. [PR #219, #220, #223, #224, #231]Removed the
transliterate_unicode()
preprocessing function that transliterated non-ascii text into a reasonable ascii approximation, for technical and philosophical reasons. Also removed its GPL-licensedunidecode
dependency, for legal-ish reasons. [Issue #203]Added convention-abiding
exclude
argument to the function that writesspacy
docs to disk, to limit which pipeline annotations are serialized. Replaced the existing but non-standardinclude_tensor
arg.Deprecated the
n_threads
argument inCorpus.add_texts()
, which had not been working inspacy.pipe
for some time and, as of v2.1, is defunct.Made many tests model- and python-version agnostic and thus less likely to break when
spacy
releases new and improved models.Auto-formatted the entire code base using
black
; the results aren’t always more readable, but they are pleasingly consistent.
Fixed:¶
Fixed bad behavior of
key_terms_from_semantic_network()
, where an error would be raised if no suitable key terms could be found; now, an empty list is returned instead. [Issue #211]Fixed variable name typo so
GroupVectorizer.fit()
actually works. [Issue #215]Fixed a minor typo in the quick-start docs. [PR #217]
Check for and filter out any named entities that are entirely whitespace, seemingly caused by an issue in
spacy
.Fixed an undefined variable error when merging spans. [Issue #225]
Fixed a unicode/bytes issue in experimental function for deserializing
spacy
docs in “binary” format. [Issue #228, PR #229]
Contributors:¶
Many thanks to @abevieiramota, @ckot, @Jude188, and @digest0r for their help!
0.6.2 (2018-07-19)¶
Changed:¶
Add a
spacier.util
module, and add / reorganize relevant functionalitymove (most)
spacy_util
functions here, and add a deprecation warning to thespacy_util
modulerename
normalized_str()
=>get_normalized_text()
, for consistency and clarityadd a function to split long texts up into chunks but combine them into a single
Doc
. This is a workaround for a current limitation of spaCy’s neural models, whose RAM usage scales with the length of input text.
Add experimental support for reading and writing spaCy docs in binary format, where multiple docs are contained in a single file. This functionality was supported by spaCy v1, but is not in spaCy v2; I’ve implemented a workaround that should work well in most situations, but YMMV.
Package documentation is now “officially” hosted on GitHub pages. The docs are automatically built on and deployed from Travis via
doctr
, so they stay up-to-date with the master branch on GitHub. Maybe someday I’ll get ReadTheDocs to successfully buildtextacy
once again…Minor improvements/updates to documentation
Fixed:¶
Add missing return statement in deprecated
text_stats.flesch_readability_ease()
function (Issue #191)Catch an empty graph error in bestcoverage-style keyterm ranking (Issue #196)
Fix mishandling when specifying a single named entity type to in/exclude in
extract.named_entities
(Issue #202)Make
networkx
usage in keyterms module compatible with v1.11+ (Issue #199)
0.6.1 (2018-04-11)¶
New:¶
Add a new spacier sub-package for spaCy-oriented functionality (#168, #187)
Thus far, this includes a
components
module with two custom spaCy pipeline components: one to compute text stats on parsed documents, and another to merge named entities into single tokens in an efficient manner. More to come!Similar functionality in the top-level
spacy_pipelines
module has been deprecated; it will be removed in v0.7.0.
Changed:¶
Update the readme, usage, and API reference docs to be clearer and (I hope) more useful. (#186)
Removing punctuation from a text via the
preprocessing
module now replaces punctuation marks with a single space rather than an empty string. This gives better behavior in many situations; for example, “won’t” => “won t” rather than “wont”, the latter of which is a valid word with a different meaning.Categories are now correctly extracted from non-English language Wikipedia datasets, starting with French and German and extendable to others. (#175)
Log progress when adding documents to a corpus. At the debug level, every doc’s addition is logged; at the info level, only one message per batch of documents is logged. (#183)
Fixed:¶
Fix two breaking typos in
extract.direct_quotations()
. (issue #177)Prevent crashes when adding non-parsed documents to a
Corpus
. (#180)Fix bugs in
keyterms.most_discriminating_terms()
that usedvsm
functionality as it was before the changes in v0.6.0. (#189)Fix a breaking typo in
vsm.matrix_utils.apply_idf_weighting()
, and rename the problematic kwarg for consistency with related functions. (#190)
Contributors:¶
Big thanks to @sammous, @dixiekong (nice name!), and @SandyRogers for the pull requests, and many more for pointing out various bugs and the rougher edges / unsupported use cases of this package.
0.6.0 (2018-02-25)¶
Changed:¶
Rename, refactor, and extend I/O functionality (PR #151)
Related read/write functions were moved from
read.py
andwrite.py
into format-specific modules, and similar functions were consolidated into one with the addition of an arg. For example,write.write_json()
andwrite.write_json_lines()
=>json.write_json(lines=True|False)
.Useful functionality was added to a few readers/writers. For example,
write_json()
now automatically handles python dates/datetimes, writing them to disk as ISO-formatted strings rather than raising a TypeError (“datetime is not JSON serializable”, ugh). CSVs can now be written to / read from disk when each row is a dict rather than a list. Reading/writing HTTP streams now allows for basic authentication.Several things were renamed to improve clarity and consistency from a user’s perspective, most notably the subpackage name:
fileio
=>io
. Others:read_file()
andwrite_file()
=>read_text()
andwrite_text()
;split_record_fields()
=>split_records()
, although I kept an alias to the old function for folks;auto_make_dirs
boolean kwarg =>make_dirs
.io.open_sesame()
now handles zip files (provided they contain only 1 file) as it already does for gzip, bz2, and lzma files. On a related note, Python 2 users can now open lzma (.xz
) files if they’ve installedbackports.lzma
.
Improve, refactor, and extend vector space model functionality (PRs #156 and #167)
BM25 term weighting and document-length normalization were implemented, and and users can now flexibly add and customize individual components of an overall weighting scheme (local scaling + global scaling + doc-wise normalization). For API sanity, several additions and changes to the
Vectorizer
init params were required — sorry bout it!Given all the new weighting possibilities, a
Vectorizer.weighting
attribute was added for curious users, to give a mathematical representation of how values in a doc-term matrix are being calculated. Here’s a simple and a not-so-simple case:>>> Vectorizer(apply_idf=True, idf_type='smooth').weighting 'tf * log((n_docs + 1) / (df + 1)) + 1' >>> Vectorizer(tf_type='bm25', apply_idf=True, idf_type='smooth', apply_dl=True).weighting '(tf * (k + 1)) / (tf + k * (1 - b + b * (length / avg(lengths))) * log((n_docs - df + 0.5) / (df + 0.5))'
Terms are now sorted alphabetically after fitting, so you’ll have a consistent and interpretable ordering in your vocabulary and doc-term-matrix.
A
GroupVectorizer
class was added, as a child ofVectorizer
and an extension of typical document-term matrix vectorization, in which each row vector corresponds to the weighted terms co-occurring in a single document. This allows for customized grouping, such as by a shared author or publication year, that may span multiple documents, without forcing users to merge /concatenate those documents themselves.Lastly, the
vsm.py
module was refactored into avsm
subpackage with two modules. Imports should stay the same, but the code structure is now more amenable to future additions.
Miscellaneous additions and improvements
Flesch Reading Ease in the
textstats
module is now multi-lingual! Language- specific formulations for German, Spanish, French, Italian, Dutch, and Russian were added, in addition to (the default) English. (PR #158, prompted by Issue #155)Runtime performance, as well as docs and error messages, of functions for generating semantic networks from lists of terms or sentences were improved. (PR #163)
Labels on named entities from which determiners have been dropped are now preserved. There’s still a minor gotcha, but it’s explained in the docs.
The size of
textacy
’s data cache can now be set via an environment variable,TEXTACY_MAX_CACHE_SIZE
, in case the default 2GB cache doesn’t meet your needs.Docstrings were improved in many ways, large and small, throughout the code. May they guide you even more effectively than before!
The package version is now set from a single source. This isn’t for you so much as me, but it does prevent confusing version mismatches b/w code, pypi, and docs.
All tests have been converted from
unittest
topytest
style. They run faster, they’re more informative in failure, and they’re easier to extend.
Fixed:¶
Fixed an issue where existing metadata associated with a spacy Doc was being overwritten with an empty dict when using it to initialize a textacy Doc. Users can still overwrite existing metadata, but only if they pass in new data.
Added a missing import to the README’s usage example. (#149)
The intersphinx mapping to
numpy
got fixed (and items forscipy
andmatplotlib
were added, too). Taking advantage of that, a bunch of broken object links scattered throughout the docs got fixed.Fixed broken formatting of old entries in the changelog, for your reading pleasure.
0.5.0 (2017-12-04)¶
Changed:¶
Bumped version requirement for spaCy from < 2.0 to >= 2.0 — textacy no longer works with spaCy 1.x! It’s worth the upgrade, though. v2.0’s new features and API enabled (or required) a few changes on textacy’s end
textacy.load_spacy()
takes the same inputs as the newspacy.load()
, i.e. a packagename
string and an optional list of pipes todisable
textacy’s
Doc
metadata and language string are now stored inuser_data
directly on the spaCyDoc
object; although the API from a user’s perspective is unchanged, this made the next change possibleDoc
andCorpus
classes are now de/serialized via pickle into a single file — no more side-car JSON files for metadata! Accordingly, the.save()
and.load()
methods on both classes have a simpler API: they take a single string specifying the file on disk where data is stored.
Cleaned up docs, imports, and tests throughout the entire code base.
docstrings and https://textacy.readthedocs.io ‘s API reference are easier to read, with better cross-referencing and far fewer broken web links
namespaces are less cluttered, and textacy’s source code is easier to follow
import textacy
takes less than half the time from beforethe full test suite also runs about twice as fast, and most tests are now more robust to changes in the performance of spaCy’s models
consistent adherence to conventions eases users’ cognitive load :)
The module responsible for caching loaded data in memory was cleaned up and improved, as well as renamed: from
data.py
tocache.py
, which is more descriptive of its purpose. Otherwise, you shouldn’t notice much of a difference besides things working correctly.All loaded data (e.g. spacy language pipelines) is now cached together in a single LRU cache whose max size is set to 2GB, and the size of each element in the cache is now accurately computed. (tl;dr:
sys.getsizeof
does not work on non-built-in objects like, say, aspacy.tokens.Doc
.)Loading and downloading of the DepecheMood resource is now less hacky and weird, and much closer to how users already deal with textacy’s various
Dataset
s, In fact, it can be downloaded in exactly the same way as the datasets via textacy’s new CLI:$ python -m textacy download depechemood
. P.S. A brief guide for using the CLI got added to the README.
Several function/method arguments marked for deprecation have been removed. If you’ve been ignoring the warnings that print out when you use
lemmatize=True
instead ofnormalize='lemma'
(etc.), now is the time to update your calls!Of particular note: The
readability_stats()
function has been removed; useTextStats(doc).readability_stats
instead.
Fixed:¶
In certain situations, the text of a spaCy span was being returned without whitespace between tokens; that has been avoided in textacy, and the source bug in spaCy got fixed (by yours truly! https://github.com/explosion/spaCy/pull/1621).
When adding already-parsed
Doc
s to aCorpus
, includingmetadata
now correctly overwrites any existing metadata on those docs.Fixed a couple related issues involving the assignment of a 2-letter language string to the
.lang
attribute ofDoc
andCorpus
objects.textacy’s CLI wasn’t correctly handling certain dataset kwargs in all cases; now, all kwargs get to their intended destinations.
0.4.2 (2017-11-28)¶
New:¶
Added a CLI for downloading
textacy
-related data, inspired by thespaCy
equivalent. It’s temporarily undocumented, but to see available commands and options, just pass the usual flag:$ python -m textacy --help
. Expect more functionality (and docs!) to be added soonish. (#144)Note: The existing
Dataset.download()
methods work as before, and in fact, they are being called under the hood from the command line.
Changed:¶
Made usage of
networkx
v2.0-compatible, and therefore dropped the <2.0 version requirement on that dependency. Upgrade as you please! (#131)Improved the regex for identifying phone numbers so that it’s easier to view and interpret its matches. (#128)
Fixed:¶
Fixed caching of counts on
textacy.Doc
instance-specific, rather than shared by all instances of the class. Oops.Fixed currency symbols regex, so as not to replace all instances of the letter “z” when a custom string is passed into
replace_currency_symbols()
. (#137)Fixed README usage example, which skipped downloading of dataset data. Btw, see above for another way! (#124)
Fixed typo in the API reference, which included the SupremeCourt dataset twice and omitted the RedditComments dataset. (#129)
Fixed typo in
RedditComments.download()
that prevented it from downloading any data. (#143)
Contributors:¶
Many thanks to @asifm, @harryhoch, and @mdlynch37 for submitting PRs!
0.4.1 (2017-07-27)¶
Changed:¶
Added key classes to the top-level
textacy
imports, for convenience:textacy.text_stats.TextStats
=>textacy.TextStats
textacy.vsm.Vectorizer
=>textacy.Vectorizer
textacy.tm.TopicModel
=>textacy.TopicModel
Added tests for
textacy.Doc
and updated the README’s usage example
Fixed:¶
Added explicit encoding when opening Wikipedia database files in text mode to resolve an issue when doing so without encoding on Windows (PR #118)
Fixed
keyterms.most_discriminating_terms
to use thevsm.Vectorizer
class rather than thevsm.doc_term_matrix
function that it replaced (PR #120)Fixed mishandling of a couple optional args in
Doc.to_terms_list
Contributors:¶
Thanks to @minketeer and @Gregory-Howard for the fixes!
0.4.0 (2017-06-21)¶
New and Changed:¶
Refactored and expanded built-in
corpora
, now calleddatasets
(PR #112)The various classes in the old
corpora
subpackage had a similar but frustratingly not-identical API. Also, some fetched the corresponding dataset automatically, while others required users to do it themselves. Ugh.These classes have been ported over to a new
datasets
subpackage; they now have a consistent API, consistent features, and consistent documentation. They also have some new functionality, including pain-free downloading of the data and saving it to disk in a stream (so as not to use all your RAM).Also, there’s a new dataset: A collection of 2.7k Creative Commons texts from the Oxford Text Archive, which rounds out the included datasets with English-language, 16th-20th century literary works. (h/t @JonathanReeve)
A
Vectorizer
class to convert tokenized texts into variously weighted document-term matrices (Issue #69, PR #113)This class uses the familiar
scikit-learn
API (which is also consistent with thetextacy.tm.TopicModel
class) to convert one or more documents in the form of “term lists” into weighted vectors. An initial set of documents is used to build up the matrix vocabulary (via.fit()
), which can then be applied to new documents (via.transform()
).It’s similar in concept and usage to sklearn’s
CountVectorizer
orTfidfVectorizer
, but doesn’t convolve the tokenization task as they do. This means users have more flexibility in deciding which terms to vectorize. This class outright replaces thetextacy.vsm.doc_term_matrix()
function.
Customizable automatic language detection for
Doc
sAlthough
cld2-cffi
is fast and accurate, its installation is problematic for some users. Since other language detection libraries are available (e.g.langdetect
andlangid
), it makes sense to let users choose, as needed or desired.First,
cld2-cffi
is now an optional dependency, i.e. is not installed by default. To install it, dopip install textacy[lang]
or (for it and all other optional deps) dopip install textacy[all]
. (PR #86)Second, the
lang
param used to instantiateDoc
objects may now be a callable that accepts a unicode string and returns a standard 2-letter language code. This could be a function that useslangdetect
under the hood, or a function that always returns “de” – it’s up to users. Note that the default value is nowtextacy.text_utils.detect_language()
, which usescld2-cffi
, so the default behavior is unchanged.
Customizable punctuation removal in the
preprocessing
module (Issue #91)Users can now specify which punctuation marks they wish to remove, rather than always removing all marks.
In the case that all marks are removed, however, performance is now 5-10x faster by using Python’s built-in
str.translate()
method instead of a regular expression.
textacy
, installable viaconda
(PR #100)The package has been added to Conda-Forge (here), and installation instructions have been added to the docs. Hurray!
textacy
, now with helpful badgesBuilds are now automatically tested via Travis CI, and there’s a badge in the docs showing whether the build passed or not. The days of my ignoring broken tests in
master
are (probably) over…There are also badges showing the latest releases on GitHub, pypi, and conda-forge (see above).
Fixed:¶
Fixed the check for overlap between named entities and unigrams in the
Doc.to_terms_list()
method (PR #111)Corpus.add_texts()
uses CPU_COUNT - 1 threads by default, rather than always assuming that 4 cores are available (Issue #89)Added a missing coding declaration to a test file, without which tests failed for Python 2 (PR #99)
readability_stats()
now catches an exception raised on empty documents and logs a message, rather than barfing with an unhelpfulZeroDivisionError
. (Issue #88)Added a check for empty terms list in
terms_to_semantic_network
(Issue #105)Added and standardized module-specific loggers throughout the code base; not a bug per sé, but certainly some much-needed housecleaning
Added a note to the docs about expectations for bytes vs. unicode text (PR #103)
Contributors:¶
Thanks to @henridwyer, @rolando, @pavlin99th, and @kyocum for their contributions! :raised_hands:
0.3.4 (2017-04-17)¶
New and Changed:¶
Improved and expanded calculation of basic counts and readability statistics in
text_stats
module.Added a
TextStats()
class for more convenient, granular access to individual values. See usage docs for more info. When calculating, say, just one readability statistic, performance with this class should be slightly better; if calculating all statistics, performance is worse owing to unavoidable, added overhead in Python for variable lookups. The legacy functiontext_stats.readability_stats()
still exists and behaves as before, but a deprecation warning is displayed.Added functions for calculating Wiener Sachtextformel (PR #77), LIX, and GULPease readability statistics.
Added number of long words and number of monosyllabic words to basic counts.
Clarified the need for having spacy models installed for most use cases of textacy, in addition to just the spacy package.
README updated with comments on this, including links to more extensive spacy documentation. (Issues #66 and #68)
Added a function,
compat.get_config()
that includes information about which (if any) spacy models are installed.Recent changes to spacy, including a warning message, will also make model problems more apparent.
Added an
ngrams
parameter tokeyterms.sgrank()
, allowing for more flexibility in specifying valid keyterm candidates for the algorithm. (PR #75)Dropped dependency on
fuzzywuzzy
package, replacing usage offuzz.token_sort_ratio()
with a textacy equivalent in order to avoid license incompatibilities. As a bonus, the new code seems to perform faster! (Issue #62)Note: Outputs are now floats in [0.0, 1.0], consistent with other similarity functions, whereas before outputs were ints in [0, 100]. This has implications for
match_threshold
values passed tosimilarity.jaccard()
; a warning is displayed and the conversion is performed automatically, for now.
A MANIFEST.in file was added to include docs, tests, and distribution files in the source distribution. This is just good practice. (PR #65)
Fixed:¶
Known acronym-definition pairs are now properly handled in
extract.acronyms_and_definitions()
(Issue #61)WikiReader no longer crashes on null page element content while parsing (PR #64)
Fixed a rare but perfectly legal edge case exception in
keyterms.sgrank()
, and added a window width sanity check. (Issue #72)Fixed assignment of 2-letter language codes to
Doc
andCorpus
objects when the lang parameter is specified as a full spacy model name.Replaced several leftover print statements with proper logging functions.
Contributors:¶
Big thanks to @oroszgy, @rolando, @covuworie, and @RolandColored for the pull requests!
0.3.3 (2017-02-10)¶
New and Changed:¶
Added a consistent
normalize
param to functions and methods that require token/span text normalization. Typically, it takes one of the following values: ‘lemma’ to lemmatize tokens, ‘lower’ to lowercase tokens, False-y to not normalize tokens, or a function that converts a spacy token or span into a string, in whatever way the user prefers (e.g.spacy_utils.normalized_str()
).Functions modified to use this param:
Doc.to_bag_of_terms()
,Doc.to_bag_of_words()
,Doc.to_terms_list()
,Doc.to_semantic_network()
,Corpus.word_freqs()
,Corpus.word_doc_freqs()
,keyterms.sgrank()
,keyterms.textrank()
,keyterms.singlerank()
,keyterms.key_terms_from_semantic_network()
,network.terms_to_semantic_network()
,network.sents_to_semantic_network()
Tweaked
keyterms.sgrank()
for higher quality results and improved internal performance.When getting both n-grams and named entities with
Doc.to_terms_list()
, filtering out numeric spans for only one is automatically extended to the other. This prevents unexpected behavior, such as passingfilter_nums=True
but getting numeric named entities back in the terms list.
Fixed:¶
keyterms.sgrank()
no longer crashes if a term is missing fromidfs
mapping. (@jeremybmerrill, issue #53)Proper nouns are no longer excluded from consideration as keyterms in
keyterms.sgrank()
andkeyterms.textrank()
. (@jeremybmerrill, issue #53)Empty strings are now excluded from consideration as keyterms — a bug inherited from spaCy. (@mlehl88, issue #58)
0.3.2 (2016-11-15)¶
New and Changed:¶
Preliminary inclusion of custom spaCy pipelines
updated
load_spacy()
to include explicit path and create_pipeline kwargs, and removed the already-deprecatedload_spacy_pipeline()
function to avoid confusion around spaCy languages and pipelinesadded
spacy_pipelines
module to hold implementations of custom spaCy pipelines, including a basic one that merges entities into single tokensnote: necessarily bumped minimum spaCy version to 1.1.0+
see the announcement here: https://explosion.ai/blog/spacy-deep-learning-keras
To reduce code bloat, made the
matplotlib
dependency optional and dropped thegensim
dependencyto install
matplotlib
at the same time as textacy, do$ pip install textacy[viz]
bonus:
backports.csv
is now only installed for Py2 usersthanks to @mbatchkarov for the request
Improved performance of
textacy.corpora.WikiReader().texts()
; results should stream faster and have cleaner plaintext content than when they were produced bygensim
. This should also fix a bug reported in Issue #51 by @baiskAdded a
Corpus.vectors
property that returns a matrix of shape (# documents, vector dim) containing the average word2vec-style vector representation of constituent tokens for allDoc
s
0.3.1 (2016-10-19)¶
Changed:¶
Updated spaCy dependency to the latest v1.0.1; set a floor on other dependencies’ versions to make sure everyone’s running reasonably up-to-date code
Fixed:¶
Fixed incorrect kwarg in
sgrank
‘s call toextract.ngrams()
(@patcollis34, issue #44)Fixed import for
cachetool
‘shashkey
, which changed in the v2.0 (@gramonov, issue #45)
0.3.0 (2016-08-23)¶
New and Changed:¶
Refactored and streamlined
TextDoc
; changed name toDoc
simplified init params:
lang
can now be a language code string or an equivalentspacy.Language
object, andcontent
is either a string orspacy.Doc
; param values and their interactions are better checked for errors and inconsistenciesrenamed and improved methods transforming the Doc; for example,
.as_bag_of_terms()
is now.to_bag_of_terms()
, and terms can be returned as integer ids (default) or as strings with absolute, relative, or binary frequencies as weightsadded performant
.to_bag_of_words()
method, at the cost of less customizability of what gets included in the bag (no stopwords or punctuation); words can be returned as integer ids (default) or as strings with absolute, relative, or binary frequencies as weightsremoved methods wrapping
extract
functions, in favor of simply calling that function on the Doc (see below for updates toextract
functions to make this more convenient); for example,TextDoc.words()
is nowextract.words(Doc)
removed
.term_counts()
method, which was redundant withDoc.to_bag_of_terms()
renamed
.term_count()
=>.count()
, and checking + caching results is now smarter and faster
Refactored and streamlined
TextCorpus
; changed name toCorpus
added init params: can now initialize a
Corpus
with a stream of texts, spacy or textacy Docs, and optional metadatas, analogous toDoc
; accordingly, removed.from_texts()
class methodrefactored, streamlined, bug-fixed, and made consistent the process of adding, getting, and removing documents from
Corpus
getting/removing by index is now equivalent to the built-in
list
API:Corpus[:5]
gets the first 5Doc
s, anddel Corpus[:5]
removes the first 5, automatically keeping track of corpus statistics for total # docs, sents, and tokensgetting/removing by boolean function is now done via the
.get()
and.remove()
methods, the latter of which now also correctly tracks corpus statsadding documents is split across the
.add_text()
,.add_texts()
, and.add_doc()
methods for performance and clarity reasons
added
.word_freqs()
and.word_doc_freqs()
methods for getting a mapping of word (int id or string) to global weight (absolute, relative, binary, or inverse frequency); akin to a vectorized representation (see:textacy.vsm
) but in non-vectorized form, which can be usefulremoved
.as_doc_term_matrix()
method, which was just wrapping another function; so, instead ofcorpus.as_doc_term_matrix((doc.as_terms_list() for doc in corpus))
, dotextacy.vsm.doc_term_matrix((doc.to_terms_list(as_strings=True) for doc in corpus))
Updated several
extract
functionsalmost all now accept either a
textacy.Doc
orspacy.Doc
as inputrenamed and improved parameters for filtering for or against certain POS or NE types; for example,
good_pos_tags
is nowinclude_pos
, and will accept either a single POS tag as a string or a set of POS tags to filter for; same goes forexclude_pos
, and analogouslyinclude_types
, andexclude_types
Updated corpora classes for consistency and added flexibility
enforced a consistent API:
.texts()
for a stream of plain text documents and.records()
for a stream of dicts containing both text and metadataadded filtering options for
RedditReader
, e.g. by date or subreddit, consistent with other corpora (similar tweaks toWikiReader
may come later, but it’s slightly more complicated…)added a nicer
repr
forRedditReader
andWikiReader
corpora, consistent with other corpora
Moved
vsm.py
andnetwork.py
into the top-level oftextacy
and thus removed therepresentations
subpackagerenamed
vsm.build_doc_term_matrix()
=>vsm.doc_term_matrix()
, because the “build” part of it is obvious
Renamed
distance.py
=>similarity.py
; all returned values are now similarity metrics in the interval [0, 1], where higher values indicate higher similarityRenamed
regexes_etc.py
=>constants.py
, without additional changesRenamed
fileio.utils.split_content_and_metadata()
=>fileio.utils.split_record_fields()
, without further changes (except for tweaks to the docstring)Added functions to read and write delimited file formats:
fileio.read_csv()
andfileio.write_csv()
, where the delimiter can be any valid one-char string; gzip/bzip/lzma compression is handled automatically when availableAdded better and more consistent docstrings and usage examples throughout the code base
0.2.8 (2016-08-03)¶
New:¶
Added two new corpora!
the CapitolWords corpus: a collection of 11k speeches (~7M tokens) given by the main protagonists of the 2016 U.S. Presidential election that had previously served in the U.S. Congress — including Hillary Clinton, Bernie Sanders, Barack Obama, Ted Cruz, and John Kasich — from January 1996 through June 2016
the SupremeCourt corpus: a collection of 8.4k court cases (~71M tokens) decided by the U.S. Supreme Court from 1946 through 2016, with metadata on subject matter categories, ideology, and voting patterns
DEPRECATED: the Bernie and Hillary corpus, which is a small subset of CapitolWords that can be easily recreated by filtering CapitolWords by
speaker_name={'Bernie Sanders', 'Hillary Clinton'}
Changed:¶
Refactored and improved
fileio
subpackagemoved shared (read/write) functions into separate
fileio.utils
modulealmost all read/write functions now use
fileio.utils.open_sesame()
, enabling seamless fileio for uncompressed or gzip, bz2, and lzma compressed files; relative/user-home-based paths; and missing intermediate directories. NOTE: certain file mode / compression pairs simply don’t work (this is Python’s fault), so users may run into exceptions; in Python 3, you’ll almost always want to use text mode (‘wt’ or ‘rt’), but in Python 2, users can’t read or write compressed files in text mode, only binary mode (‘wb’ or ‘rb’)added options for writing json files (matching stdlib’s
json.dump()
) that can help save spacefileio.utils.get_filenames()
now matches for/against a regex pattern rather than just a contained substring; using the old params will now raise a deprecation warningBREAKING:
fileio.utils.split_content_and_metadata()
now hasitemwise=False
by default, rather thanitemwise=True
, which means that splitting multi-document streams of content and metadata into parallel iterators is now the default actionadded
compression
param toTextCorpus.save()
and.load()
to optionally write metadata json file in compressed formmoved
fileio.write_conll()
functionality toexport.doc_to_conll()
, which converts a spaCy doc into a ConLL-U formatted string; writing that string to disk would require a separate call tofileio.write_file()
Cleaned up deprecated/bad Py2/3
compat
imports, and added better functionality for Py2/3 stringsnow
compat.unicode_type
used for text data,compat.bytes_type
for binary data, andcompat.string_types
for when either will doalso added
compat.unicode_to_bytes()
andcompat.bytes_to_unicode()
functions, for converting between string types
Fixed:¶
Fixed document(s) removal from
TextCorpus
objects, including correct decrementing of.n_docs
,.n_sents
, and.n_tokens
attributes (@michelleful #29)Fixed OSError being incorrectly raised in
fileio.open_sesame()
on missing fileslang
parameter inTextDoc
andTextCorpus
can now be unicode or bytes, which was bug-like
0.2.5 (2016-07-14)¶
Fixed:¶
Added (missing)
pyemd
andpython-levenshtein
dependencies to requirements and setup filesFixed bug in
data.load_depechemood()
arising from the Py2csv
module’s inability to take unicode as input (thanks to @robclewley, issue #25)
0.2.4 (2016-07-14)¶
New and Changed:¶
New features for
TextDoc
andTextCorpus
classesadded
.save()
methods and.load()
classmethods, which allows for fast serialization of parsed documents/corpora and associated metadata to/from disk — with an important caveat: ifspacy.Vocab
object used to serialize and deserialize is not the same, there will be problems, making this format useful as short-term but not long-term storageTextCorpus
may now be instantiated with an already-loaded spaCy pipeline, which may or may not have all models loaded; it can still be instantiated using a language code string (‘en’, ‘de’) to load a spaCy pipeline that includes all models by defaultTextDoc
methods wrappingextract
andkeyterms
functions now have full documentation rather than forwarding users to the wrapped functions themselves; more irritating on the dev side, but much less irritating on the user side :)
Added a
distance.py
module containing several document, set, and string distance metricsword movers: document distance as distance between individual words represented by word2vec vectors, normalized
“word2vec”: token, span, or document distance as cosine distance between (average) word2vec representations, normalized
jaccard: string or set(string) distance as intersection / overlap, normalized, with optional fuzzy-matching across set members
hamming: distance between two strings as number of substititions, optionally normalized
levenshtein: distance between two strings as number of substitions, deletions, and insertions, optionally normalized (and removed a redundant function from the still-orphaned
math_utils.py
module)jaro-winkler: distance between two strings with variable prefix weighting, normalized
Added
most_discriminating_terms()
function tokeyterms
module to take a collection of documents split into two exclusive groups and compute the most discriminating terms for group1-and-not-group2 as well as group2-and-not-group1
Fixed:¶
fixed variable name error in docs usage example (thanks to @licyeus, PR #23)
0.2.3 (2016-06-20)¶
New and Changed:¶
Added
corpora.RedditReader()
class for streaming Reddit comments from disk, with.texts()
method for a stream of plaintext comments and.comments()
method for a stream of structured comments as dicts, with basic filtering by text length and limiting the number of comments returnedRefactored functions for streaming Wikipedia articles from disk into a
corpora.WikiReader()
class, with.texts()
method for a stream of plaintext articles and.pages()
method for a stream of structured pages as dicts, with basic filtering by text length and limiting the number of pages returnedUpdated README and docs with a more comprehensive — and correct — usage example; also added tests to ensure it doesn’t get stale
Updated requirements to latest version of spaCy, as well as added matplotlib for
viz
Fixed:¶
textacy.preprocess.preprocess_text()
is now, once again, imported at the top level, so easily reachable viatextacy.preprocess_text()
(@bretdabaker #14)viz
subpackage now included in the docs’ API referencemissing dependencies added into
setup.py
so pip install handles everything for folks
0.2.2 (2016-05-05)¶
New and Changed:¶
Added a
viz
subpackage, with two types of plots (so far):viz.draw_termite_plot()
, typically used to evaluate and interpret topic models; conveniently accessible from thetm.TopicModel
classviz.draw_semantic_network()
for visualizing networks such as those output byrepresentations.network
Added a “Bernie & Hillary” corpus with 3000 congressional speeches made by Bernie Sanders and Hillary Clinton since 1996
corpora.fetch_bernie_and_hillary()
function automatically downloads to and loads from disk this corpus
Modified
data.load_depechemood
function, now downloads data from GitHub source if not found on diskRemoved
resources/
directory from GitHub, hence all the downloadin’Updated to spaCy v0.100.7
German is now supported! although some functionality is English-only
added
textacy.load_spacy()
function for loading spaCy packages, taking advantage of the newspacy.load()
API; added a DeprecationWarning fortextacy.data.load_spacy_pipeline()
proper nouns’ and pronouns’
.pos_
attributes are now correctly assigned ‘PROPN’ and ‘PRON’; hence, modifiedregexes_etc.POS_REGEX_PATTERNS['en']
to include ‘PROPN’modified
spacy_utils.preserve_case()
to check for language-agnostic ‘PROPN’ POS rather than English-specific ‘NNP’ and ‘NNPS’ tags
Added
text_utils.clean_terms()
function for cleaning up a sequence of single- or multi-word strings by stripping leading/trailing junk chars, handling dangling parens and odd hyphenation, etc.
Fixed:¶
textstats.readability_stats()
now correctly gets the number of words in a doc from its generator function (@gryBox #8)removed NLTK dependency, which wasn’t actually required
text_utils.detect_language()
now warns vialogging
rather than aprint()
statementfileio.write_conll()
documentation now correctly indicates that the filename param is not optional
0.2.0 (2016-04-11)¶
New and Changed:¶
Added
representations
subpackage; includes modules for network and vector space model (VSM) document and corpus representationsDocument-term matrix creation now takes documents represented as a list of terms (rather than as spaCy Docs); splits the tokenization step from vectorization for added flexibility
Some of this functionality was refactored from existing parts of the package
Added
tm
(topic modeling) subpackage, with a mainTopicModel
class for training, applying, persisting, and interpreting NMF, LDA, and LSA topic models through a single interfaceVarious improvements to
TextDoc
andTextCorpus
classesTextDoc
can now be initialized from a spaCy DocRemoved caching from
TextDoc
, because it was a pain and weird and probably not all that usefulextract
-based methods are now generators, like the functions they wrapAdded
.as_semantic_network()
and.as_terms_list()
methods toTextDoc
TextCorpus.from_texts()
now takes advantage of multithreading via spaCy, if available, and document metadata can be passed in as a paired iterable of dicts
Added read/write functions for sparse scipy matrices
Added
fileio.read.split_content_and_metadata()
convenience function for splitting (text) content from associated metadata when reading data from disk into aTextDoc
orTextCorpus
Renamed
fileio.read.get_filenames_in_dir()
tofileio.read.get_filenames()
and added functionality for matching/ignoring files by their names, file extensions, and ignoring invisible filesRewrote
export.docs_to_gensim()
, now significantly fasterImports in
__init__.py
files for main and subpackages now explicit
Fixed:¶
textstats.readability_stats()
no longer filters out stop words (@henningko #7)Wikipedia article processing now recursively removes nested markup
extract.ngrams()
now filters out ngrams with any space-only tokensfunctions with
include_nps
kwarg changed toinclude_ncs
, to match the renaming of the associated function fromextract.noun_phrases()
toextract.noun_chunks()
0.1.4 (2016-02-26)¶
New:¶
Added
corpora
subpackage withwikipedia.py
module; functions for streaming pages from a Wikipedia db dump as plain text or structured dataAdded
fileio
subpackage with functions for reading/writing content from/to disk in common formatsJSON formats, both standard and streaming-friendly
text, optionally compressed
spacy documents to/from binary
0.1.3 (2016-02-22)¶
New:¶
Added
export.py
module for exporting textacy/spacy objects into “third-party” formats; so far, just gensim and conll-uAdded
compat.py
module for Py2/3 compatibility hacksAdded
TextDoc.merge()
andspacy_utils.merge_spans()
for merging spans into single tokens within aspacy.Doc
, uses Spacy’s recent implementation
Changed:¶
Renamed
extract.noun_phrases()
toextract.noun_chunks()
to match Spacy’s APIChanged extract functions to generators, rather than returning lists
Fixed:¶
Whitespace tokens now always filtered out of
extract.words()
listsSome Py2/3 str/unicode issues fixed
Broken tests in
test_extract.py
no longer broken