mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
faaa832518
* Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit0b7e52c797
. * Revert "Switch to qsort" This reverts commita98d71a942
. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commited1060cf59
. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
321 lines
12 KiB
Python
321 lines
12 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import uuid
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from .templates import TPL_DEP_SVG, TPL_DEP_WORDS, TPL_DEP_ARCS, TPL_ENTS
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from .templates import TPL_ENT, TPL_ENT_RTL, TPL_FIGURE, TPL_TITLE, TPL_PAGE
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from ..util import minify_html, escape_html, registry
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from ..errors import Errors
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DEFAULT_LANG = "en"
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DEFAULT_DIR = "ltr"
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class DependencyRenderer(object):
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"""Render dependency parses as SVGs."""
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style = "dep"
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def __init__(self, options={}):
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"""Initialise dependency renderer.
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options (dict): Visualiser-specific options (compact, word_spacing,
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arrow_spacing, arrow_width, arrow_stroke, distance, offset_x,
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color, bg, font)
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"""
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self.compact = options.get("compact", False)
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self.word_spacing = options.get("word_spacing", 45)
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self.arrow_spacing = options.get("arrow_spacing", 12 if self.compact else 20)
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self.arrow_width = options.get("arrow_width", 6 if self.compact else 10)
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self.arrow_stroke = options.get("arrow_stroke", 2)
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self.distance = options.get("distance", 150 if self.compact else 175)
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self.offset_x = options.get("offset_x", 50)
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self.color = options.get("color", "#000000")
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self.bg = options.get("bg", "#ffffff")
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self.font = options.get("font", "Arial")
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self.direction = DEFAULT_DIR
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self.lang = DEFAULT_LANG
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def render(self, parsed, page=False, minify=False):
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"""Render complete markup.
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parsed (list): Dependency parses to render.
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page (bool): Render parses wrapped as full HTML page.
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minify (bool): Minify HTML markup.
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RETURNS (unicode): Rendered SVG or HTML markup.
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"""
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# Create a random ID prefix to make sure parses don't receive the
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# same ID, even if they're identical
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id_prefix = uuid.uuid4().hex
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rendered = []
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for i, p in enumerate(parsed):
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if i == 0:
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settings = p.get("settings", {})
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self.direction = settings.get("direction", DEFAULT_DIR)
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self.lang = settings.get("lang", DEFAULT_LANG)
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render_id = "{}-{}".format(id_prefix, i)
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svg = self.render_svg(render_id, p["words"], p["arcs"])
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rendered.append(svg)
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if page:
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content = "".join([TPL_FIGURE.format(content=svg) for svg in rendered])
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markup = TPL_PAGE.format(
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content=content, lang=self.lang, dir=self.direction
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)
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else:
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markup = "".join(rendered)
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if minify:
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return minify_html(markup)
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return markup
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def render_svg(self, render_id, words, arcs):
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"""Render SVG.
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render_id (int): Unique ID, typically index of document.
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words (list): Individual words and their tags.
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arcs (list): Individual arcs and their start, end, direction and label.
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RETURNS (unicode): Rendered SVG markup.
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"""
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self.levels = self.get_levels(arcs)
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self.highest_level = len(self.levels)
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self.offset_y = self.distance / 2 * self.highest_level + self.arrow_stroke
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self.width = self.offset_x + len(words) * self.distance
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self.height = self.offset_y + 3 * self.word_spacing
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self.id = render_id
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words = [self.render_word(w["text"], w["tag"], i) for i, w in enumerate(words)]
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arcs = [
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self.render_arrow(a["label"], a["start"], a["end"], a["dir"], i)
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for i, a in enumerate(arcs)
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]
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content = "".join(words) + "".join(arcs)
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return TPL_DEP_SVG.format(
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id=self.id,
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width=self.width,
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height=self.height,
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color=self.color,
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bg=self.bg,
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font=self.font,
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content=content,
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dir=self.direction,
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lang=self.lang,
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)
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def render_word(self, text, tag, i):
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"""Render individual word.
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text (unicode): Word text.
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tag (unicode): Part-of-speech tag.
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i (int): Unique ID, typically word index.
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RETURNS (unicode): Rendered SVG markup.
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"""
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y = self.offset_y + self.word_spacing
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x = self.offset_x + i * self.distance
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if self.direction == "rtl":
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x = self.width - x
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html_text = escape_html(text)
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return TPL_DEP_WORDS.format(text=html_text, tag=tag, x=x, y=y)
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def render_arrow(self, label, start, end, direction, i):
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"""Render individual arrow.
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label (unicode): Dependency label.
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start (int): Index of start word.
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end (int): Index of end word.
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direction (unicode): Arrow direction, 'left' or 'right'.
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i (int): Unique ID, typically arrow index.
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RETURNS (unicode): Rendered SVG markup.
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"""
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if start < 0 or end < 0:
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error_args = dict(start=start, end=end, label=label, dir=direction)
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raise ValueError(Errors.E157.format(**error_args))
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level = self.levels.index(end - start) + 1
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x_start = self.offset_x + start * self.distance + self.arrow_spacing
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if self.direction == "rtl":
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x_start = self.width - x_start
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y = self.offset_y
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x_end = (
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self.offset_x
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+ (end - start) * self.distance
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+ start * self.distance
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- self.arrow_spacing * (self.highest_level - level) / 4
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)
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if self.direction == "rtl":
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x_end = self.width - x_end
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y_curve = self.offset_y - level * self.distance / 2
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if self.compact:
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y_curve = self.offset_y - level * self.distance / 6
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if y_curve == 0 and len(self.levels) > 5:
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y_curve = -self.distance
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arrowhead = self.get_arrowhead(direction, x_start, y, x_end)
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arc = self.get_arc(x_start, y, y_curve, x_end)
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label_side = "right" if self.direction == "rtl" else "left"
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return TPL_DEP_ARCS.format(
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id=self.id,
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i=i,
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stroke=self.arrow_stroke,
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head=arrowhead,
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label=label,
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label_side=label_side,
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arc=arc,
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)
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def get_arc(self, x_start, y, y_curve, x_end):
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"""Render individual arc.
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x_start (int): X-coordinate of arrow start point.
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y (int): Y-coordinate of arrow start and end point.
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y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
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x_end (int): X-coordinate of arrow end point.
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RETURNS (unicode): Definition of the arc path ('d' attribute).
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"""
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template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
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if self.compact:
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template = "M{x},{y} {x},{c} {e},{c} {e},{y}"
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return template.format(x=x_start, y=y, c=y_curve, e=x_end)
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def get_arrowhead(self, direction, x, y, end):
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"""Render individual arrow head.
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direction (unicode): Arrow direction, 'left' or 'right'.
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x (int): X-coordinate of arrow start point.
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y (int): Y-coordinate of arrow start and end point.
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end (int): X-coordinate of arrow end point.
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RETURNS (unicode): Definition of the arrow head path ('d' attribute).
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"""
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if direction == "left":
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pos1, pos2, pos3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
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else:
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pos1, pos2, pos3 = (
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end,
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end + self.arrow_width - 2,
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end - self.arrow_width + 2,
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)
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arrowhead = (
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pos1,
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y + 2,
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pos2,
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y - self.arrow_width,
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pos3,
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y - self.arrow_width,
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)
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return "M{},{} L{},{} {},{}".format(*arrowhead)
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def get_levels(self, arcs):
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"""Calculate available arc height "levels".
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Used to calculate arrow heights dynamically and without wasting space.
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args (list): Individual arcs and their start, end, direction and label.
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RETURNS (list): Arc levels sorted from lowest to highest.
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"""
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levels = set(map(lambda arc: arc["end"] - arc["start"], arcs))
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return sorted(list(levels))
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class EntityRenderer(object):
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"""Render named entities as HTML."""
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style = "ent"
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def __init__(self, options={}):
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"""Initialise dependency renderer.
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options (dict): Visualiser-specific options (colors, ents)
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"""
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colors = {
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"ORG": "#7aecec",
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"PRODUCT": "#bfeeb7",
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"GPE": "#feca74",
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"LOC": "#ff9561",
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"PERSON": "#aa9cfc",
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"NORP": "#c887fb",
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"FACILITY": "#9cc9cc",
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"EVENT": "#ffeb80",
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"LAW": "#ff8197",
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"LANGUAGE": "#ff8197",
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"WORK_OF_ART": "#f0d0ff",
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"DATE": "#bfe1d9",
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"TIME": "#bfe1d9",
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"MONEY": "#e4e7d2",
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"QUANTITY": "#e4e7d2",
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"ORDINAL": "#e4e7d2",
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"CARDINAL": "#e4e7d2",
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"PERCENT": "#e4e7d2",
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}
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user_colors = registry.displacy_colors.get_all()
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for user_color in user_colors.values():
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colors.update(user_color)
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colors.update(options.get("colors", {}))
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self.default_color = "#ddd"
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self.colors = colors
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self.ents = options.get("ents", None)
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self.direction = DEFAULT_DIR
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self.lang = DEFAULT_LANG
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template = options.get("template")
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if template:
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self.ent_template = template
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else:
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if self.direction == "rtl":
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self.ent_template = TPL_ENT_RTL
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else:
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self.ent_template = TPL_ENT
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def render(self, parsed, page=False, minify=False):
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"""Render complete markup.
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parsed (list): Dependency parses to render.
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page (bool): Render parses wrapped as full HTML page.
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minify (bool): Minify HTML markup.
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RETURNS (unicode): Rendered HTML markup.
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"""
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rendered = []
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for i, p in enumerate(parsed):
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if i == 0:
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settings = p.get("settings", {})
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self.direction = settings.get("direction", DEFAULT_DIR)
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self.lang = settings.get("lang", DEFAULT_LANG)
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rendered.append(self.render_ents(p["text"], p["ents"], p.get("title")))
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if page:
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docs = "".join([TPL_FIGURE.format(content=doc) for doc in rendered])
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markup = TPL_PAGE.format(content=docs, lang=self.lang, dir=self.direction)
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else:
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markup = "".join(rendered)
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if minify:
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return minify_html(markup)
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return markup
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def render_ents(self, text, spans, title):
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"""Render entities in text.
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text (unicode): Original text.
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spans (list): Individual entity spans and their start, end and label.
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title (unicode or None): Document title set in Doc.user_data['title'].
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"""
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markup = ""
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offset = 0
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for span in spans:
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label = span["label"]
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start = span["start"]
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end = span["end"]
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additional_params = span.get("params", {})
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entity = escape_html(text[start:end])
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fragments = text[offset:start].split("\n")
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for i, fragment in enumerate(fragments):
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markup += escape_html(fragment)
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if len(fragments) > 1 and i != len(fragments) - 1:
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markup += "</br>"
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if self.ents is None or label.upper() in self.ents:
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color = self.colors.get(label.upper(), self.default_color)
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ent_settings = {"label": label, "text": entity, "bg": color}
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ent_settings.update(additional_params)
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markup += self.ent_template.format(**ent_settings)
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else:
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markup += entity
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offset = end
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markup += escape_html(text[offset:])
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markup = TPL_ENTS.format(content=markup, dir=self.direction)
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if title:
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markup = TPL_TITLE.format(title=title) + markup
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return markup
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