def get_resistance_recommender(user_id):
from recommender import algo
from load_data import data,item_detail,item_list
import random
# building model on training data
trainingSet = data.build_full_trainset()
algo.fit(trainingSet)
# generates predictions using trained model
def rating_pred(user_id,item_list,algo):
pred = {}
for i in item_list:
prediction = algo.predict(user_id, i)
pred[i] = prediction.est
return pred
def generate_weights(user_id,item_list,algo):
"""generates a list of items based on their weights"""
weighted_list = []
pred = rating_pred(user_id,item_list,algo)
for i in pred.keys():
if pred[i] <0: # if the predicted rating is negative
weighted_list += [i]
elif pred[i] <0.9: # if the predicted rating < 0.9
weighted_list += 2 * [i]
else: # if pred == 1
weighted_list += 3 * [i]
return weighted_list
def generate_recommendation(user_id,item_list,algo):
weighted_l = generate_weights(user_id,item_list,algo)
return random.choice(weighted_l)
# make sure activity type returned correctly
found = False
while not found:
activityId = str(generate_recommendation(user_id,item_list,algo))
if db.match_actvityType_by_id(activityId) =='Resistance exercise':
found = True
# formatting output data
return_str = db.match_acticityName_by_id(activityId)
return_dict = return_default.copy()
return_dict['result'] = return_str
return json.dumps(return_dict, ensure_ascii=False)
from recommender import algo
from load_data import data, item_list
import random
# building model on training data
trainingSet = data.build_full_trainset()
algo.fit(trainingSet)
def generate_weights(user_id, item_list, algo):
"""generates a list of items based on their weights"""
# generates predictions
def rating_pred(user_id, item_list, algo):
pred = {}
for i in item_list:
prediction = algo.predict(user_id, i)
pred[i] = prediction.est
return pred
# generates weights
weighted_list = []
pred = rating_pred(user_id, item_list, algo)
for i in pred.keys():
if pred[i] < 0: # if the predicted rating is negative
weighted_list += [i]
elif pred[i] < 0.9: # if the predicted rating < 0.9
weighted_list += 2 * [i]
else: # if pred == 1
weighted_list += 3 * [i]