From 972f514e6bc88e0dd651dfb966c5ec9aacb6a62e Mon Sep 17 00:00:00 2001
From: Patrick Jentsch <p.jentsch@uni-bielefeld.de>
Date: Fri, 16 Jun 2023 17:35:54 +0200
Subject: [PATCH] Implementation of visdata v2
---
.../cqi_over_socketio/cqi_corpora_corpus.py | 179 ++++++++++++------
1 file changed, 124 insertions(+), 55 deletions(-)
diff --git a/app/corpora/cqi_over_socketio/cqi_corpora_corpus.py b/app/corpora/cqi_over_socketio/cqi_corpora_corpus.py
index 79b1a800..fff6a0b3 100644
--- a/app/corpora/cqi_over_socketio/cqi_corpora_corpus.py
+++ b/app/corpora/cqi_over_socketio/cqi_corpora_corpus.py
@@ -49,62 +49,131 @@ def cqi_corpora_corpus_update_db(cqi_client: cqi.CQiClient, corpus_name: str):
@socketio_login_required
@cqi_over_socketio
def cqi_corpora_corpus_get_visualization_data(cqi_client: cqi.CQiClient, corpus_name: str):
- cqi_corpus = cqi_client.corpora.get(corpus_name)
+ corpus = cqi_client.corpora.get(corpus_name)
+ text = corpus.structural_attributes.get('text')
+ s = corpus.structural_attributes.get('s')
+ ent = corpus.structural_attributes.get('ent')
+ word = corpus.positional_attributes.get('word')
+ lemma = corpus.positional_attributes.get('lemma')
+ pos = corpus.positional_attributes.get('pos')
+ simple_pos = corpus.positional_attributes.get('simple_pos')
payload = {}
- payload['num_tokens'] = cqi_corpus.size
- cqi_word_attr = cqi_corpus.positional_attributes.get('word')
- payload['num_unique_words'] = cqi_word_attr.lexicon_size
- payload['word_freqs'] = dict(zip(cqi_word_attr.values_by_ids(list(range(0, cqi_word_attr.lexicon_size))), cqi_word_attr.freqs_by_ids(list(range(0, cqi_word_attr.lexicon_size)))))
- # payload['word_freqs'].sort(key=lambda a: a[1], reverse=True)
- # payload['word_freqs'] = {k: v for k, v in payload['word_freqs']}
- cqi_lemma_attr = cqi_corpus.positional_attributes.get('lemma')
- payload['num_unique_lemmas'] = cqi_lemma_attr.lexicon_size
- payload['lemma_freqs'] = dict(zip(cqi_lemma_attr.values_by_ids(list(range(0, cqi_lemma_attr.lexicon_size))), cqi_lemma_attr.freqs_by_ids(list(range(0, cqi_lemma_attr.lexicon_size)))))
- # payload['lemma_freqs'].sort(key=lambda a: a[1], reverse=True)
- # payload['lemma_freqs'] = {k: v for k, v in payload['lemma_freqs']}
- cqi_s_attr = cqi_corpus.structural_attributes.get('s')
- payload['num_sentences'] = cqi_s_attr.size
- # assuming all tokens are in a sentence
- payload['average_sentence_length'] = payload['num_tokens'] / payload['num_sentences'] if payload['num_sentences'] != 0 else 0
- # payload['average_sentence_length'] = 0
- # for s_id in range(0, cqi_s_attr.size):
- # s_lbound, s_rbound = cqi_s_attr.cpos_by_id(s_id)
- # payload['average_sentence_length'] += s_rbound - s_lbound + 1
- # payload['average_sentence_length'] /= payload['num_sentences']
- cqi_ent_type_attr = cqi_corpus.structural_attributes.get('ent_type')
- payload['num_ent_types'] = cqi_ent_type_attr.size
- payload['ent_type_freqs'] = dict(Counter(cqi_ent_type_attr.values_by_ids(list(range(0, cqi_ent_type_attr.size)))))
- payload['num_unique_ent_types'] = len(payload['ent_type_freqs'])
- payload['texts'] = []
- cqi_text_attr = cqi_corpus.structural_attributes.get('text')
- for text_id in range(0, cqi_text_attr.size):
- text_lbound, text_rbound = cqi_text_attr.cpos_by_id(text_id)
- text_cpos_list = list(range(text_lbound, text_rbound + 1))
- text_payload = {}
- text_payload['num_tokens'] = text_rbound - text_lbound + 1
- text_word_ids = cqi_word_attr.ids_by_cpos(text_cpos_list)
- print(text_word_ids)
- text_payload['num_unique_words'] = len(set(text_word_ids))
- text_payload['word_freqs'] = dict(Counter(cqi_word_attr.values_by_ids(text_word_ids)))
- text_lemma_ids = cqi_lemma_attr.ids_by_cpos(text_cpos_list)
- text_payload['num_unique_lemmas'] = len(set(text_lemma_ids))
- text_payload['lemma_freqs'] = dict(Counter(cqi_word_attr.values_by_ids(text_lemma_ids)))
- text_s_attr_ids = list(filter(lambda x: x != -1, cqi_s_attr.ids_by_cpos(text_cpos_list)))
- text_payload['num_sentences'] = len(set(text_s_attr_ids))
- # assuming all tokens are in a sentence
- text_payload['average_sentence_length'] = text_payload['num_tokens'] / text_payload['num_sentences'] if text_payload['num_sentences'] != 0 else 0
- # text_payload['average_sentence_length'] = 0
- # for text_s_id in range(0, cqi_s_attr.size):
- # text_s_lbound, text_s_rbound = cqi_s_attr.cpos_by_id(text_s_id)
- # text_payload['average_sentence_length'] += text_s_rbound - text_s_lbound + 1
- # text_payload['average_sentence_length'] /= text_payload['num_sentences']
- text_ent_type_ids = list(filter(lambda x: x != -1, cqi_ent_type_attr.ids_by_cpos(text_cpos_list)))
- text_payload['num_ent_types'] = len(set(text_ent_type_ids))
- text_payload['ent_type_freqs'] = dict(Counter(cqi_ent_type_attr.values_by_ids(text_ent_type_ids)))
- text_payload['num_unique_ent_types'] = len(text_payload['ent_type_freqs'])
- for text_sub_attr in cqi_corpus.structural_attributes.list(filters={'part_of': cqi_text_attr}):
- text_payload[text_sub_attr.name[(len(cqi_text_attr.name) + 1):]] = text_sub_attr.values_by_ids([text_id])[0]
- payload['texts'].append(text_payload)
+ payload['corpus'] = {'lexicon': {}, 'values': []}
+ payload['corpus']['lexicon'][0] = {
+ 'bounds': [0, corpus.size - 1],
+ 'counts': {
+ 'text': text.size,
+ 's': s.size,
+ 'ent': ent.size,
+ 'token': corpus.size
+ },
+ 'freqs': {
+ 'word': dict(
+ zip(
+ range(0, word.lexicon_size),
+ word.freqs_by_ids(list(range(0, word.lexicon_size)))
+ )
+ ),
+ 'lemma': dict(
+ zip(
+ range(0, lemma.lexicon_size),
+ lemma.freqs_by_ids(list(range(0, lemma.lexicon_size)))
+ )
+ ),
+ 'pos': dict(
+ zip(
+ range(0, pos.lexicon_size),
+ pos.freqs_by_ids(list(range(0, pos.lexicon_size)))
+ )
+ ),
+ 'simple_pos': dict(
+ zip(
+ range(0, simple_pos.lexicon_size),
+ simple_pos.freqs_by_ids(list(range(0, simple_pos.lexicon_size)))
+ )
+ )
+ }
+ }
+ payload['text'] = {'lexicon': {}, 'values': None}
+ for text_id in range(0, text.size):
+ text_lbound, text_rbound = text.cpos_by_id(text_id)
+ text_cpos_range = range(text_lbound, text_rbound + 1)
+ text_s_ids = s.ids_by_cpos(list(text_cpos_range))
+ text_ent_ids = ent.ids_by_cpos(list(text_cpos_range))
+ payload['text']['lexicon'][text_id] = {
+ 'bounds': [text_lbound, text_rbound],
+ 'counts': {
+ 's': len([x for x in text_s_ids if x != -1]),
+ 'ent': len([x for x in text_ent_ids if x != -1]),
+ 'token': text_rbound - text_lbound + 1
+ },
+ 'freqs': {
+ 'word': dict(
+ Counter(word.ids_by_cpos(list(text_cpos_range)))
+ ),
+ 'lemma': dict(
+ Counter(lemma.ids_by_cpos(list(text_cpos_range)))
+ ),
+ 'pos': dict(
+ Counter(pos.ids_by_cpos(list(text_cpos_range)))
+ ),
+ 'simple_pos': dict(
+ Counter(simple_pos.ids_by_cpos(list(text_cpos_range)))
+ )
+ }
+ }
+ payload['text']['values'] = [
+ sub_attr.name[(len(text.name) + 1):]
+ for sub_attr in corpus.structural_attributes.list(filters={'part_of': text})
+ ]
+ payload['s'] = {'lexicon': {}, 'values': None}
+ for s_id in range(0, s.size):
+ payload['s']['lexicon'][s_id] = {
+ # 'bounds': s.cpos_by_id(s_id)
+ }
+ payload['s']['values'] = [
+ sub_attr.name[(len(s.name) + 1):]
+ for sub_attr in corpus.structural_attributes.list(filters={'part_of': s})
+ ]
+ payload['ent'] = {'lexicon': {}, 'values': None}
+ for ent_id in range(0, ent.size):
+ payload['ent']['lexicon'][ent_id] = {
+ # 'bounds': ent.cpos_by_id(ent_id)
+ }
+ payload['ent']['values'] = [
+ sub_attr.name[(len(ent.name) + 1):]
+ for sub_attr in corpus.structural_attributes.list(filters={'part_of': ent})
+ ]
+ payload['lookups'] = {
+ 'corpus': {},
+ 'text': {},
+ 's': {},
+ 'ent': {},
+ 'word': dict(
+ zip(
+ range(0, word.lexicon_size),
+ word.values_by_ids(list(range(0, word.lexicon_size)))
+ )
+ ),
+ 'lemma': dict(
+ zip(
+ range(0, lemma.lexicon_size),
+ lemma.values_by_ids(list(range(0, lemma.lexicon_size)))
+ )
+ ),
+ 'pos': dict(
+ zip(
+ range(0, pos.lexicon_size),
+ pos.values_by_ids(list(range(0, pos.lexicon_size)))
+ )
+ ),
+ 'simple_pos': dict(
+ zip(
+ range(0, simple_pos.lexicon_size),
+ simple_pos.values_by_ids(list(range(0, simple_pos.lexicon_size)))
+ )
+ )
+ }
# print(payload)
return {'code': 200, 'msg': 'OK', 'payload': payload}
--
GitLab