def test_make_model():
'''
tag random data to generate model
'''
page = 50
no_rate_items = []
for i in range(1, page):
items, _ = get_items(None, None, i)
no_rate_items.extend(items)
size = len(no_rate_items)
like_ratio = 0.4
like_items = []
unlike_items = []
for item in no_rate_items:
if random.random() < like_ratio:
like_items.append(item)
else:
unlike_items.append(item)
print(f'like items: {len(like_items)}, unlike items: {len(unlike_items)}')
for item in like_items:
ItemRate.saveit(1, 1, item.fanhao)
for item in unlike_items:
ItemRate.saveit(1, 0, item.fanhao)
clf.train()
def load_data():
'''
load data from database and do processing
'''
rate_type = RATE_TYPE.USER_RATE.value
rate_value = None
page = None
items, _ = get_items(rate_type=rate_type, rate_value=rate_value, page=page)
return items
def index():
rate_type = RATE_TYPE.SYSTEM_RATE.value
rate_value = int(request.query.get('like', RATE_VALUE.LIKE.value))
page = int(request.query.get('page', 1))
items, page_info = get_items(
rate_type=rate_type, rate_value=rate_value, page=page)
today_update_count = db.get_today_update_count()
today_recommend_count = db.get_today_recommend_count()
msg = f'今日更新 {today_update_count} , 今日推荐 {today_recommend_count}'
return template('index', items=items, page_info=page_info, like=rate_value, path=request.path, msg=msg)
def tagit():
rate_value = request.query.get('like', None)
rate_value = None if rate_value == 'None' else rate_value
rate_type = None
if rate_value:
rate_value = int(rate_value)
rate_type = RATE_TYPE.USER_RATE
page = int(request.query.get('page', 1))
items, page_info = get_items(
rate_type=rate_type, rate_value=rate_value, page=page)
return template('tagit', items=items, page_info=page_info, like=rate_value, path=request.path)
def test_get_items():
rate_type = RATE_TYPE.SYSTEM_RATE
rate_value = RATE_VALUE.DISLIKE
page = None
items, page_info = get_items(rate_type=rate_type,
rate_value=rate_value,
page=page)
assert len(items) > 0
print(f'item count:{len(items)}')
print(
f'total_items: {page_info[0]}, total_page: {page_info[1]}, current_page: {page_info[2]}, page_size:{page_info[3]}'
)
def test_get_items2():
rate_type = None
rate_value = None
page = None
items, page_info = get_items(rate_type=rate_type,
rate_value=rate_value,
page=page)
assert len(items) > 0
print(f'item count:{len(items)}')
print(
f'total_items: {page_info[0]}, total_page: {page_info[1]}, current_page: {page_info[2]}, page_size:{page_info[3]}'
)
def prepare_predict_data():
# get not rated data
rate_type = None
rate_value = None
page = None
unrated_items, _ = get_items(rate_type=rate_type,
rate_value=rate_value,
page=page)
mlb = load_model(get_data_path(BINARIZER_PATH))
dicts = (as_dict(item) for item in unrated_items)
df = pd.DataFrame(dicts, columns=['id', 'tags'])
df.set_index('id', inplace=True)
X = mlb.transform(df.tags.values)
return df.index.values, X
