def recommend_similar_place_user_viewed(user_id):
try:
sql = "SELECT count(*) as times, place_id FROM place_user_log where user_id=%(user_id)s and place_id!='' group by place_id;"
params = {"user_id" : int(user_id)}
ds = read_data_from_db(sql, params)
sql2 = "SELECT event_type FROM place_user_log where user_id=%(user_id)s and event_type!='VIEW_DETAIL';"
ds2 = read_data_from_db(sql2, params)
chosen_cats_as_string = ' '.join(set(map(lambda x: x.split(': ')[1], ds2['event_type'])))
if (len(ds) == 0 and (not chosen_cats_as_string)):
return "not found", 404
else:
df_cat_per_item = get_cat_per_item()
if chosen_cats_as_string:
get_liked_cats_at_the_first_time(chosen_cats_as_string, df_cat_per_item, ds)
user_data_with_cat_of_items = df_cat_per_item.reset_index().merge(ds, on='place_id')
recommendations, simi_list = similar_to_user_profile(user_data_with_cat_of_items, df_cat_per_item)
place_ids = df_cat_per_item.loc[recommendations, 'place_id'].tolist()
recommended_items = df_cat_per_item.loc[recommendations]
x = recommended_items.reset_index().join(simi_list)
simi_items = tuple(int(x) for x in place_ids)
similar_places = get_list_db_objects_from_ids(simi_items)
return Response(similar_places.to_json(orient="records"), status=200, mimetype='application/json')
except Exception as e:
print(str(e))
return "", 500
def recommend_place(user_id):
try:
find_user_rating = 'SELECT * FROM rating_place where user_id=%(user_id)s;'
params = {"user_id" : int(user_id)}
user_rating = read_data_from_db(find_user_rating, params)
sql = 'SELECT user_id, place_id, rating FROM rating_place'
ds = read_data_from_db(sql, None)
if len(ds) > 0 and len(user_rating) >0:
reader = Reader()
data = Dataset.load_from_df(ds[['user_id', 'place_id', 'rating']], reader=reader)
alg = SVD()
alg.fit(data.build_full_trainset())
iids = ds['place_id'].unique()
rated_iids = ds.loc[ds['user_id'] == user_id, 'place_id']
iids_to_pred = np.setdiff1d(iids, rated_iids)
testset = [[user_id, iid, 4.] for iid in iids_to_pred]
predictions = alg.test(testset)
evaluate_surprise_alg(predictions)
predictions.sort(key=lambda x: x.est, reverse=True)
list_of_ids = []
for i in range(50 if len(predictions) >= 50 else len(predictions)):
list_of_ids.append(int(predictions[i].iid))
similar_places = get_list_db_objects_from_ids(tuple(list_of_ids))
return Response(similar_places.to_json(orient="records"), status=200, mimetype='application/json')
return "not found", 404
except Exception as e:
print(str(e))
return "", 500
def get_cat_per_item():
cuisine_sql = 'SELECT id, cuisine_id, res_id FROM cuisine_restaurant where stt=1;'
ds = read_data_from_db(cuisine_sql, None)
df_cuisine_per_item = ds.groupby('res_id')['cuisine_id'].agg(
_concatenate_cuisine_of_item)
feature_sql = 'SELECT id, feature_id, res_id FROM feature_restaurant where stt=1;'
ds = read_data_from_db(feature_sql, None)
df_feature_per_item = ds.groupby('res_id')['feature_id'].agg(
_concatenate_feature_of_item)
meal_sql = 'SELECT id, meal_id, res_id FROM meal_restaurant where stt=1;'
ds = read_data_from_db(meal_sql, None)
df_meal_per_item = ds.groupby('res_id')['meal_id'].agg(
_concatenate_meal_of_item)
type_sql = 'SELECT id, type_id, res_id FROM foodtype_restaurant where stt=1;'
ds = read_data_from_db(type_sql, None)
df_type_per_item = ds.groupby('res_id')['type_id'].agg(
_concatenate_type_of_item)
df_data = pd.merge(df_cuisine_per_item,
df_feature_per_item,
how='left',
on='res_id')
df_data = pd.merge(df_data, df_meal_per_item, how='left', on='res_id')
df_data = pd.merge(df_data, df_type_per_item, how='left', on='res_id')
serie_data = df_data[df_data.columns[0:]].apply(
lambda x: ' '.join(x.dropna().astype(str)), axis=1)
df_cat_per_item = pd.DataFrame({
'res_id': serie_data.index,
'item_cats': serie_data.values
})
return df_cat_per_item
def get_cat_per_item ():
sql = 'SELECT id, place_id, activity_id FROM activity_place where stt=1;'
ds = read_data_from_db(sql, None)
df_cat_per_item = ds.groupby('place_id')['activity_id'].agg(_concatenate_cats_of_item)
df_cat_per_item.name = 'item_cats'
df_cat_per_item = df_cat_per_item.reset_index()
df_cat_per_item[~df_cat_per_item.item_cats.isnull()].reset_index(drop=True)
return df_cat_per_item
def get_list_db_objects_from_ids(tuple_of_item):
simi_items_as_string = ','.join(map(str, tuple_of_item))
get_simi_items_query = "SELECT * FROM tour where id in %(simi_items)s ORDER BY FIND_IN_SET(id, %(ordered_list)s);"
params = {
"simi_items": tuple_of_item,
"ordered_list": simi_items_as_string
}
ds = read_data_from_db(get_simi_items_query, params)
return ds
