def create_trees_cumm(docs, weights_):
words, weights_trees, root_inds = [], [], []
for i, doc in enumerate(docs):
# print doc
doc_ = flatten_tree(doc)
words_, weights_tree_, root_ind_ = create_trees(doc_, weights_)
words.append(words_)
weights_trees.append(weights_tree_)
root_inds.append(root_ind_)
_helpScripts.print_perc(float32(i)/float32(len(docs)) * 100 + 1)
return words, weights_trees, root_inds
def create_trees_cumm(docs, weights_):
words, weights_trees, root_inds = [], [], []
for i, doc in enumerate(docs):
# print doc
doc_ = flatten_tree(doc)
words_, weights_tree_, root_ind_ = create_trees(doc_, weights_)
words.append(words_)
weights_trees.append(weights_tree_)
root_inds.append(root_ind_)
_helpScripts.print_perc(float32(i) / float32(len(docs)) * 100 + 1)
return words, weights_trees, root_inds
def generate_sents(art_dir, sent_count=None, pos_count=1, neg_count=3,
rng=np.random.RandomState(156), save_dir=None):
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
art_files = glob.glob(art_dir + '*.txt')
sents_true = []
sents_false = []
for ii, art_file in enumerate(art_files):
sents_true_, sents_false_ = generate_sent(art_file, pos_count=pos_count, neg_count=neg_count,
rng=np.random.RandomState(156), save_dir=save_dir)
sents_true.extend([sents_true_])
sents_false.extend([sents_false_])
_helpScripts.print_perc(float(ii) / float(len(art_files)) * 100 + 1,
suffix='performed {} from {}'.format(ii, len(art_files)))
return sents_true, sents_false
def select_themes(wh_scores, wh_themes, themes_quest=ARTICLES_QUEST):
"""
returns the themes according to the ranking
firstly wil be the best themes for each question returned and then according to the summ scores of the theme
"""
quest_n = len(wh_themes)
themes = np.zeros((quest_n, themes_quest), dtype=object)
for q in range(quest_n):
q_themes_best = set(wh_themes[q][:, 0])
themes[q, :len(q_themes_best)] = np.array(list(q_themes_best))
q_themes_rest = np.array(list(set(wh_themes[q].flatten()) - q_themes_best), dtype=object)
th_scores = np.array([np.sum(wh_scores[q][np.where(wh_themes[q] == q_themes_rest[i])]) for i in range(len(q_themes_rest))])
th_scores_sorted = np.argsort(th_scores)[::-1]
themes[q, len(q_themes_best):themes_quest] = q_themes_rest[th_scores_sorted[0:themes_quest-len(q_themes_best)]]
_helpScripts.print_perc(float32(q)/float32(quest_n) * 100 + 1)
return themes
def generate_sents(art_dir, sent_count=None, pos_count=1, neg_count=3, rng=np.random.RandomState(156), save_dir=None):
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
art_files = glob.glob(art_dir + "*.txt")
sents_true = []
sents_false = []
for ii, art_file in enumerate(art_files):
sents_true_, sents_false_ = generate_sent(
art_file, pos_count=pos_count, neg_count=neg_count, rng=np.random.RandomState(156), save_dir=save_dir
)
sents_true.extend([sents_true_])
sents_false.extend([sents_false_])
_helpScripts.print_perc(
float(ii) / float(len(art_files)) * 100 + 1, suffix="performed {} from {}".format(ii, len(art_files))
)
return sents_true, sents_false
def get_art_word_ind(sentences, vocab, sent_art, sent_l, pad_token_ind):
"""
generates word indexies for articles
"""
art_count = len(sentences)
article_inds = np.ones((art_count,
sent_art, sent_l), dtype='int16') * pad_token_ind
for art_ind in range(art_count):
article = sentences[art_ind]
try:
sent_ind = sents2ind(article, vocab)
except IndexError:
continue
article_inds[art_ind, :sent_ind.shape[0], :sent_ind.shape[1]] = sent_ind[:, :]
_helpScripts.print_perc(float32(art_ind)/float32(art_count) * 100 + 1,
suffix='{} articles prceeded from {}'.format(art_ind, art_count))
return article_inds
def get_art_word_ind(sentences, vocab, sent_art, sent_l, pad_token_ind):
"""
generates word indexies for articles
"""
art_count = len(sentences)
article_inds = np.ones(
(art_count, sent_art, sent_l), dtype='int16') * pad_token_ind
for art_ind in range(art_count):
article = sentences[art_ind]
try:
sent_ind = sents2ind(article, vocab)
except IndexError:
continue
article_inds[
art_ind, :sent_ind.shape[0], :sent_ind.shape[1]] = sent_ind[:, :]
_helpScripts.print_perc(
float32(art_ind) / float32(art_count) * 100 + 1,
suffix='{} articles prceeded from {}'.format(art_ind, art_count))
return article_inds
def select_themes(wh_scores, wh_themes, themes_quest=ARTICLES_QUEST):
"""
returns the themes according to the ranking
firstly wil be the best themes for each question returned and then according to the summ scores of the theme
"""
quest_n = len(wh_themes)
themes = np.zeros((quest_n, themes_quest), dtype=object)
for q in range(quest_n):
q_themes_best = set(wh_themes[q][:, 0])
themes[q, :len(q_themes_best)] = np.array(list(q_themes_best))
q_themes_rest = np.array(
list(set(wh_themes[q].flatten()) - q_themes_best), dtype=object)
th_scores = np.array([
np.sum(wh_scores[q][np.where(wh_themes[q] == q_themes_rest[i])])
for i in range(len(q_themes_rest))
])
th_scores_sorted = np.argsort(th_scores)[::-1]
themes[q, len(q_themes_best):themes_quest] = q_themes_rest[
th_scores_sorted[0:themes_quest - len(q_themes_best)]]
_helpScripts.print_perc(float32(q) / float32(quest_n) * 100 + 1)
return themes
