def tag(): if 'user' in session: user_id = session['user']['id'] tag_count, verify_count = get_tag_count(user_id) q = request.args.get('q') if (not session['is_admin']) and (q == 'verify' or q == '3-skips'): flash('Invalid credentials', 'error') return redirect(url_for('bp.login')) price_range_text_list = config.PRICE_RANGE_TEXT_LIST price_range_value_list = config.PRICE_RANGE_VALUE_LIST return render_template('tag_product.html', vendors=get_vendors(), available_cats=get_categories(), available_cats1=get_categories(), price_range_text_list=price_range_text_list, price_range_value_list=price_range_value_list, username=session['user']['name'], tag_count=tag_count, verify_count=verify_count, q=q, autoescape=False) else: return redirect(url_for('bp.login'))
def tag(): if 'user' in session: user_id = session['user']['id'] tag_count, verify_count = get_tag_count(user_id) q = request.args.get('q') if (not session['is_admin']) and (q == 'verify' or q == '3-skips'): flash('Invalid credentials', 'error') return redirect(url_for('bp.login')) price_range_text_list = config.PRICE_RANGE_TEXT_LIST price_range_value_list = config.PRICE_RANGE_VALUE_LIST return render_template('tag_product.html', vendors=get_vendors(), available_cats=get_categories(), available_cats1=get_categories(), price_range_text_list=price_range_text_list, price_range_value_list=price_range_value_list, username=session['user']['name'], tag_count=tag_count, verify_count=verify_count, q=q, autoescape=False) else: return redirect(url_for('bp.login'))
def get_knn_keywords(k=100, top_n=3, text=sampletext, counts=None): if not counts: counts = classify(k, text) category_lists = defaultdict(list) for k, v in Counter({ label: counts[label] for label in counts if label in utils.get_categories() }).most_common(top_n): lda_cat = category_models[k] dictionary_cat = category_dicts[k] bigram_cat = category_bigrams[k] for w in lda_cat.get_document_topics(dictionary_cat.doc2bow( bigram_cat[utils.tokenize(text)]), per_word_topics=True)[2]: category_lists[k].append(dictionary_cat[w[0]]) for w in lda_all.get_document_topics(dictionary_all.doc2bow( bigram_all[utils.tokenize(text)]), per_word_topics=True)[2]: category_lists['all'].append(dictionary_all[w[0]]) return category_lists
def task_definition(task_id, task_instance=None): task = utils.get_task_definition(task_id) org_name = utils.get_org(task["organization_id"], "name") org_slug = utils.get_org(task["organization_id"], "slug") task_name = task["name"] task_description = task["description"].replace("\n", "<br>") task_tags = task["tags"] mentors = task["assignments_profile_display_names"] days = task["time_to_complete_in_days"] task_categories = utils.get_categories(task_id) attachments = None if task_instance: if task_instance not in utils.tasks_attachments_cache: utils.tasks_attachments_cache[ task_instance] = utils.get_attachments(task_instance) else: utils.tasks_attachments_cache[task_instance] = [ "Too lazy to fix this." ] return render_template( "task.html", org_name=org_name, org_slug=org_slug, task_name=task_name, task_description=task_description, tags=task_tags, mentors=mentors, days=days, categories=task_categories, attachments=utils.tasks_attachments_cache[task_instance])
def enrich_category(X): items = X['item'].tolist() cat_dict = utils.get_categories(items) cat = [cat_dict[i] for i in items] X.loc[:, 'category'] = pd.Series(cat).values return X
def func(category1, category2): start_time = time.time() y_train1, y_train2 = get_categories(category1, category2) compare = Compare(dataloader_workers=0, verbose=2, distribution="bernoulli") compare.fit(y_train1, y_train2, 10000) elapsed_time = time.time() - start_time return dict( samples=pickle.dumps(compare.samples), elapsed_time=elapsed_time, )
def show_edit(request, videoid): requesthead = 'http://spark.bokecc.com/api/video?' q = {'videoid': videoid, 'userid': USERID, 'format': 'json'} categories = get_categories() my_json = get_json_result(requesthead, q) video = my_json['video'] images = video ['image-alternate'] return render_to_response('edit.html', {'video': video, 'images': images, 'categories': categories } )
def func(category1, category2, averaging, nrefits): start_time = time.time() np.random.seed(10 * category1 + category2) y_train1, y_train2 = get_categories(category1, category2) np.random.seed() htest = HTest(dataloader_workers=0, verbose=1, distribution="bernoulli", averaging=averaging) htest.fit(y_train1, y_train2, nrefits=nrefits) elapsed_time = time.time() - start_time return dict( pvalue=htest.pvalue, elapsed_time=elapsed_time, )
def add_subcat(): if 'user' in session and session['is_admin']: subcat_status = 'Not Added' if request.form: if len(request.form['subcat']) and request.form['category'] != '-1': subcat_status = add_new_subcat( request.form['category'], request.form['subcat'] ) else: subcat_status = 'Error' user_id = session['user']['id'] tag_count, verify_count = get_tag_count(user_id) return render_template("add_sub_cat.html", username=session['user']['name'], tag_count=tag_count, verify_count=verify_count, available_cats=get_categories(), subcat_status=subcat_status) else: flash('Invalid credentials', 'error') return redirect(url_for('bp.login'))
def enrich_category_count(X): items = X['item'].tolist() users = X['user'].tolist() cat_dict = utils.get_categories(items) nested = list(cat_dict.values()) cat = list(set([item for sublist in nested for item in sublist])) print(cat) print(len(cat)) user_cat_count = defaultdict(lambda: defaultdict(int)) for u, i in list(zip(users, items)): i_cat = cat_dict[i] for cat in i_cat: cat_dict[u][cat] += 1 for u, i in list(zip(users, items)): cat_vec = np.zeros(len(cat_dict)) i_cat = cat_dict[i]
def add_appliance(): form = NewApplianceForm() form.category.choices = utils.get_categories() if current_user.is_authenticated: if form.validate_on_submit(): try: appliance = Appliance( name = form.name.data, watts = form.watts.data, category = form.category.data ) db.session.add(appliance) db.session.commit() flash("New appliance added", "success") except IntegrityError: flash(f"{form.name.data} is already in the database", "danger") return redirect("/addappliance") return redirect("/") return render_template("new-app.html", form=form)
def split(root_path, train_prop, test_prop): val_prop = 1 - train_prop - test_prop categories = utils.get_categories(root_path) for cat in categories: npy_path_list = utils.take_cat_npys(root_path, cat) n = len(npy_path_list) train_number = int(n * train_prop) test_number = int(n * test_prop) val_number = int(n * val_prop) shuffle(npy_path_list) for i in range(train_number): save_to(npy_path_list[i], root_path, 'train', cat) for i in range(train_number, train_number + test_number): save_to(npy_path_list[i], root_path, 'test', cat) for i in range(train_number + test_number, n): save_to(npy_path_list[i], root_path, 'validation', cat)
def add_subcat(): if 'user' in session and session['is_admin']: subcat_status = 'Not Added' if request.form: if len(request.form['subcat'] ) and request.form['category'] != '-1': subcat_status = add_new_subcat(request.form['category'], request.form['subcat']) else: subcat_status = 'Error' user_id = session['user']['id'] tag_count, verify_count = get_tag_count(user_id) return render_template("add_sub_cat.html", username=session['user']['name'], tag_count=tag_count, verify_count=verify_count, available_cats=get_categories(), subcat_status=subcat_status) else: flash('Invalid credentials', 'error') return redirect(url_for('bp.login'))
import math import csv from collections import Counter from collections import defaultdict from gensim import corpora, models, similarities from gensim.utils import simple_preprocess, ClippedCorpus from gensim.parsing.preprocessing import STOPWORDS import nltk.stem as stem import utils import re import os categories = utils.get_categories() category_models = { category: models.LdaModel.load('./data/%s/category-titles-abstracts.lda' % category) for category in categories } category_dicts = { category: corpora.Dictionary.load( 'data/%s/category-titles-abstracts.dict' % category) for category in categories } category_bigrams = { category: models.Phrases.load('data/%s/bigram.bin' % category) for category in categories } lda_all = models.LdaModel.load('./data/corpus-titles-abstracts.lda') dictionary_all = corpora.Dictionary.load('data/corpus-titles-abstracts.dict')
def yolov2_coco(device=None, input_size=None, pretrained=True): model = _get_model('cfg/yolov2.cfg', 'weights/yolov2.weights', device, input_size, pretrained) cats = get_categories('classes/coco.names') return model, cats
def yolov2_tiny_voc(device=None, input_size=None, pretrained=True): model = _get_model('cfg/yolov2-tiny-voc.cfg', 'weights/yolov2-tiny-voc.weights', device, input_size, pretrained) cats = get_categories('classes/voc.names') return model, cats
# store the dictionary, for future reference dictionary.save('./data/%s/%s.dict' % (category, category_filename)) # memory-friendly bag-of-words class class BOW(object): def __iter__(self): for line, label in zip(open('./data/%s.csv' % corpus_filename), open('./data/corpus-labels.csv')): # assume there's one document per line, tokens separated by whitespace if category in label: yield dictionary.doc2bow(utils.tokenize(line)) else: pass # Now we can make a bag of words and do something with it by iterating over it arxiv_bow = BOW() corpora.MmCorpus.serialize('./data/%s/%s.mm' % (category, category_filename), arxiv_bow) # store to disk, for later use if __name__ == '__main__': #Set corpus name. This lets us select from "corpus-titles", "corpus-abstracts", and "corpus-titles-abstracts" corpus_filename = 'corpus-titles-abstracts' if args.category: generate_bow(corpus_filename, args.category, True, 0.05, 10) else: for category in utils.get_categories(): generate_bow(corpus_filename, category, True, 0.05, 10)
def enum_categories_and_remove(dir, tolerance): categories = utils.get_categories(dir) for cat in categories: remove_in_category(dir, cat, tolerance)
def show_search(request): categories = get_categories() return render_to_response('search.html', {'categories': categories})
def show_upload(request): categories = get_categories() return render_to_response('upload.html', {'categories': categories})