mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
66016ac289
* running UD eval * printing timing of tokenizer: tokens per second * timing of default English model * structured output and parameterization to compare different runs * additional flag to allow evaluation without parsing info * printing verbose log of errors for manual inspection * printing over- and undersegmented cases (and combo's) * add under and oversegmented numbers to Score and structured output * print high-freq over/under segmented words and word shapes * printing examples as part of the structured output * print the results to file * batch run of different models and treebanks per language * cleaning up code * commandline script to process all languages in spaCy & UD * heuristic to remove blinded corpora and option to run one single best per language * pathlib instead of os for file paths
288 lines
13 KiB
Python
288 lines
13 KiB
Python
import spacy
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import time
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import re
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import plac
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import operator
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import datetime
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from pathlib import Path
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import xml.etree.ElementTree as ET
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from spacy.cli.ud import conll17_ud_eval
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from spacy.cli.ud.ud_train import write_conllu
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from spacy.lang.lex_attrs import word_shape
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from spacy.util import get_lang_class
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# All languages in spaCy - in UD format (note that Norwegian is 'no' instead of 'nb')
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ALL_LANGUAGES = "ar, ca, da, de, el, en, es, fa, fi, fr, ga, he, hi, hr, hu, id, " \
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"it, ja, no, nl, pl, pt, ro, ru, sv, tr, ur, vi, zh"
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# Non-parsing tasks that will be evaluated (works for default models)
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EVAL_NO_PARSE = ['Tokens', 'Words', 'Lemmas', 'Sentences', 'Feats']
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# Tasks that will be evaluated if check_parse=True (does not work for default models)
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EVAL_PARSE = ['Tokens', 'Words', 'Lemmas', 'Sentences', 'Feats', 'UPOS', 'XPOS', 'AllTags', 'UAS', 'LAS']
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# Minimum frequency an error should have to be printed
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PRINT_FREQ = 20
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# Maximum number of errors printed per category
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PRINT_TOTAL = 10
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space_re = re.compile("\s+")
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def load_model(modelname, add_sentencizer=False):
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""" Load a specific spaCy model """
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loading_start = time.time()
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nlp = spacy.load(modelname)
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if add_sentencizer:
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nlp.add_pipe(nlp.create_pipe('sentencizer'))
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loading_end = time.time()
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loading_time = loading_end - loading_start
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if add_sentencizer:
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return nlp, loading_time, modelname + '_sentencizer'
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return nlp, loading_time, modelname
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def load_default_model_sentencizer(lang):
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""" Load a generic spaCy model and add the sentencizer for sentence tokenization"""
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loading_start = time.time()
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lang_class = get_lang_class(lang)
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nlp = lang_class()
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nlp.add_pipe(nlp.create_pipe('sentencizer'))
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loading_end = time.time()
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loading_time = loading_end - loading_start
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return nlp, loading_time, lang + "_default_" + 'sentencizer'
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def split_text(text):
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return [space_re.sub(" ", par.strip()) for par in text.split("\n\n")]
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def get_freq_tuples(my_list, print_total_threshold):
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""" Turn a list of errors into frequency-sorted tuples thresholded by a certain total number """
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d = {}
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for token in my_list:
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d.setdefault(token, 0)
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d[token] += 1
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return sorted(d.items(), key=operator.itemgetter(1), reverse=True)[:print_total_threshold]
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def _contains_blinded_text(stats_xml):
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""" Heuristic to determine whether the treebank has blinded texts or not """
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tree = ET.parse(stats_xml)
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root = tree.getroot()
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total_tokens = int(root.find('size/total/tokens').text)
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unique_lemmas = int(root.find('lemmas').get('unique'))
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# assume the corpus is largely blinded when there are less than 1% unique tokens
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return (unique_lemmas / total_tokens) < 0.01
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def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
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"""" Fetch the txt files for all treebanks for a given set of languages """
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all_treebanks = dict()
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treebank_size = dict()
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for l in languages:
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all_treebanks[l] = []
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treebank_size[l] = 0
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for treebank_dir in ud_dir.iterdir():
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if treebank_dir.is_dir():
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for txt_path in treebank_dir.iterdir():
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if txt_path.name.endswith('-ud-' + corpus + '.txt'):
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file_lang = txt_path.name.split('_')[0]
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if file_lang in languages:
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gold_path = treebank_dir / txt_path.name.replace('.txt', '.conllu')
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stats_xml = treebank_dir / "stats.xml"
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# ignore treebanks where the texts are not publicly available
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if not _contains_blinded_text(stats_xml):
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if not best_per_language:
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all_treebanks[file_lang].append(txt_path)
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# check the tokens in the gold annotation to keep only the biggest treebank per language
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else:
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with gold_path.open(mode='r', encoding='utf-8') as gold_file:
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gold_ud = conll17_ud_eval.load_conllu(gold_file)
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gold_tokens = len(gold_ud.tokens)
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if treebank_size[file_lang] < gold_tokens:
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all_treebanks[file_lang] = [txt_path]
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treebank_size[file_lang] = gold_tokens
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return all_treebanks
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def run_single_eval(nlp, loading_time, print_name, text_path, gold_ud, tmp_output_path, out_file, print_header,
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check_parse, print_freq_tasks):
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"""" Run an evaluation of a model nlp on a certain specified treebank """
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with text_path.open(mode='r', encoding='utf-8') as f:
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flat_text = f.read()
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# STEP 1: tokenize text
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tokenization_start = time.time()
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texts = split_text(flat_text)
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docs = list(nlp.pipe(texts))
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tokenization_end = time.time()
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tokenization_time = tokenization_end - tokenization_start
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# STEP 2: record stats and timings
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tokens_per_s = int(len(gold_ud.tokens) / tokenization_time)
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print_header_1 = ['date', 'text_path', 'gold_tokens', 'model', 'loading_time', 'tokenization_time', 'tokens_per_s']
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print_string_1 = [str(datetime.date.today()), text_path.name, len(gold_ud.tokens),
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print_name, "%.2f" % loading_time, "%.2f" % tokenization_time, tokens_per_s]
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# STEP 3: evaluate predicted tokens and features
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with tmp_output_path.open(mode="w", encoding="utf8") as tmp_out_file:
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write_conllu(docs, tmp_out_file)
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with tmp_output_path.open(mode="r", encoding="utf8") as sys_file:
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sys_ud = conll17_ud_eval.load_conllu(sys_file, check_parse=check_parse)
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tmp_output_path.unlink()
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scores = conll17_ud_eval.evaluate(gold_ud, sys_ud, check_parse=check_parse)
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# STEP 4: format the scoring results
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eval_headers = EVAL_PARSE
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if not check_parse:
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eval_headers = EVAL_NO_PARSE
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for score_name in eval_headers:
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score = scores[score_name]
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print_string_1.extend(["%.2f" % score.precision,
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"%.2f" % score.recall,
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"%.2f" % score.f1])
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print_string_1.append("-" if score.aligned_accuracy is None else "%.2f" % score.aligned_accuracy)
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print_string_1.append("-" if score.undersegmented is None else "%.4f" % score.under_perc)
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print_string_1.append("-" if score.oversegmented is None else "%.4f" % score.over_perc)
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print_header_1.extend([score_name + '_p', score_name + '_r', score_name + '_F', score_name + '_acc',
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score_name + '_under', score_name + '_over'])
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if score_name in print_freq_tasks:
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print_header_1.extend([score_name + '_word_under_ex', score_name + '_shape_under_ex',
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score_name + '_word_over_ex', score_name + '_shape_over_ex'])
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d_under_words = get_freq_tuples(score.undersegmented, PRINT_TOTAL)
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d_under_shapes = get_freq_tuples([word_shape(x) for x in score.undersegmented], PRINT_TOTAL)
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d_over_words = get_freq_tuples(score.oversegmented, PRINT_TOTAL)
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d_over_shapes = get_freq_tuples([word_shape(x) for x in score.oversegmented], PRINT_TOTAL)
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# saving to CSV with ; seperator so blinding ; in the example output
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print_string_1.append(
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str({k: v for k, v in d_under_words if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
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print_string_1.append(
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str({k: v for k, v in d_under_shapes if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
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print_string_1.append(
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str({k: v for k, v in d_over_words if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
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print_string_1.append(
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str({k: v for k, v in d_over_shapes if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
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# STEP 5: print the formatted results to CSV
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if print_header:
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out_file.write(';'.join(map(str, print_header_1)) + '\n')
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out_file.write(';'.join(map(str, print_string_1)) + '\n')
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def run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks):
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"""" Run an evaluation for each language with its specified models and treebanks """
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print_header = True
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for tb_lang, treebank_list in treebanks.items():
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print()
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print("Language", tb_lang)
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for text_path in treebank_list:
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print(" Evaluating on", text_path)
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gold_path = text_path.parent / (text_path.stem + '.conllu')
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print(" Gold data from ", gold_path)
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# nested try blocks to ensure the code can continue with the next iteration after a failure
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try:
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with gold_path.open(mode='r', encoding='utf-8') as gold_file:
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gold_ud = conll17_ud_eval.load_conllu(gold_file)
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for nlp, nlp_loading_time, nlp_name in models[tb_lang]:
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try:
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print(" Benchmarking", nlp_name)
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tmp_output_path = text_path.parent / str('tmp_' + nlp_name + '.conllu')
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run_single_eval(nlp, nlp_loading_time, nlp_name, text_path, gold_ud, tmp_output_path, out_file,
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print_header, check_parse, print_freq_tasks)
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print_header = False
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except Exception as e:
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print(" Ran into trouble: ", str(e))
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except Exception as e:
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print(" Ran into trouble: ", str(e))
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@plac.annotations(
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out_path=("Path to output CSV file", "positional", None, Path),
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ud_dir=("Path to Universal Dependencies corpus", "positional", None, Path),
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check_parse=("Set flag to evaluate parsing performance", "flag", "p", bool),
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langs=("Enumeration of languages to evaluate (default: all)", "option", "l", str),
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exclude_trained_models=("Set flag to exclude trained models", "flag", "t", bool),
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exclude_multi=("Set flag to exclude the multi-language model as default baseline", "flag", "m", bool),
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hide_freq=("Set flag to avoid printing out more detailed high-freq tokenization errors", "flag", "f", bool),
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corpus=("Whether to run on train, dev or test", "option", "c", str),
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best_per_language=("Set flag to only keep the largest treebank for each language", "flag", "b", bool)
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)
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def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_trained_models=False, exclude_multi=False,
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hide_freq=False, corpus='train', best_per_language=False):
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""""
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Assemble all treebanks and models to run evaluations with.
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When setting check_parse to True, the default models will not be evaluated as they don't have parsing functionality
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"""
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languages = [lang.strip() for lang in langs.split(",")]
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print_freq_tasks = []
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if not hide_freq:
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print_freq_tasks = ['Tokens']
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# fetching all relevant treebank from the directory
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treebanks = fetch_all_treebanks(ud_dir, languages, corpus, best_per_language)
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print()
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print("Loading all relevant models for", languages)
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models = dict()
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# multi-lang model
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multi = None
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if not exclude_multi and not check_parse:
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multi = load_model('xx_ent_wiki_sm', add_sentencizer=True)
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# initialize all models with the multi-lang model
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for lang in languages:
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models[lang] = [multi] if multi else []
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# add default models if we don't want to evaluate parsing info
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if not check_parse:
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# Norwegian is 'nb' in spaCy but 'no' in the UD corpora
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if lang == 'no':
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models['no'].append(load_default_model_sentencizer('nb'))
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else:
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models[lang].append(load_default_model_sentencizer(lang))
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# language-specific trained models
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if not exclude_trained_models:
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if 'de' in models:
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models['de'].append(load_model('de_core_news_sm'))
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if 'es' in models:
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models['es'].append(load_model('es_core_news_sm'))
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models['es'].append(load_model('es_core_news_md'))
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if 'pt' in models:
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models['pt'].append(load_model('pt_core_news_sm'))
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if 'it' in models:
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models['it'].append(load_model('it_core_news_sm'))
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if 'nl' in models:
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models['nl'].append(load_model('nl_core_news_sm'))
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if 'en' in models:
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models['en'].append(load_model('en_core_web_sm'))
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models['en'].append(load_model('en_core_web_md'))
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models['en'].append(load_model('en_core_web_lg'))
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if 'fr' in models:
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models['fr'].append(load_model('fr_core_news_sm'))
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models['fr'].append(load_model('fr_core_news_md'))
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with out_path.open(mode='w', encoding='utf-8') as out_file:
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run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks)
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if __name__ == "__main__":
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plac.call(main)
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