def test_autoencoder_item_cf(data_model):
item_prediction, user_prediction = base_cf(data_model.data_matrix)
item_rmse = rmse(item_prediction, data_model.data_matrix)
# user_rmse = rmse(user_prediction, data_model.data_matrix)
auto = denoising_autoencoder_model(item_prediction,
training_epochs=1000,
model_path='./checkpoint_dir_song')
auto.DAE_model()
prediction, autoencoder_item_RMSE = auto.evaluate_model()
# item 推荐
item_training_dict = generate_top_recommendations(data_model, prediction,
50)
item_test_dict = get_test_sample_recommendations(data_model)
# 评估
item_ism_avg_recall_list, item_ism_avg_precision_list = calculate_precision_recall(
data_model, item_training_dict, item_test_dict, top=50)
redis = MyRedis()
redis.setList("base_autoencoder__item_cf", item_ism_avg_recall_list,
item_ism_avg_precision_list)
redis.setRmse("base_autoencoder__item_cf_Rmse", autoencoder_item_RMSE)
plot_precision_recall(item_ism_avg_recall_list,
item_ism_avg_precision_list,
"base_autoencoder__item_cf")
def test_base_cf(data_model):
item_prediction, user_prediction = base_cf(data_model.data_matrix)
item_rmse = rmse(item_prediction, data_model.data_matrix)
# user_rmse = rmse(user_prediction, data_model.data_matrix)
# item 推荐
item_training_dict = generate_top_recommendations(data_model,
item_prediction, 50)
item_test_dict = get_test_sample_recommendations(data_model)
# # user 推荐
# user_training_dict = generate_top_recommendations(data_model, user_prediction, 50)
# user_test_dict = get_test_sample_recommendations(data_model)
# 评估
item_ism_avg_recall_list, item_ism_avg_precision_list = calculate_precision_recall(
data_model, item_training_dict, item_test_dict, top=50)
# user_ism_avg_recall_list, user_ism_avg_precision_list = calculate_precision_recall(data_model, user_training_dict,
# user_test_dict,
# top=50)
redis = MyRedis()
redis.setList("base_cf_cos_item", item_ism_avg_recall_list,
item_ism_avg_precision_list)
redis.setRmse("base_cf_cos_item_Rmse", item_rmse)
# redis.setList("base_cf_cos_user", user_ism_avg_recall_list, user_ism_avg_precision_list)
plot_precision_recall(item_ism_avg_recall_list,
item_ism_avg_precision_list, "base_cf_cos_item")
def test_autoencoder_cf(data_model):
auto = denoising_autoencoder_model(data_model.data_matrix,
training_epochs=1000,
model_path='./checkpoint_dir_song')
auto.DAE_model()
prediction, autoencoder_cf_Rmse = auto.evaluate_model()
#推荐
training_dict = generate_top_recommendations(data_model, prediction, 50)
test_dict = get_test_sample_recommendations(data_model)
item_ism_avg_recall_list, item_ism_avg_precision_list = calculate_precision_recall(
data_model, training_dict, test_dict, top=50)
redis = MyRedis()
redis.setList("autoencoder_cf_model", item_ism_avg_recall_list,
item_ism_avg_precision_list)
redis.setRmse("autoencoder_cf_model_Rmse", autoencoder_cf_Rmse)
plot_precision_recall(item_ism_avg_recall_list,
item_ism_avg_precision_list, "autoencoder_cf_model")
def test_jaccard_cf(data_model):
# m = data_model(sample_number=4000, test_size=0.4)
jaccard = jaccard_model(data_model)
cooccurence_matrix = jaccard.construct_cooccurence_matrix()
# 推荐
training_dict = generate_top_recommendations(data_model,
cooccurence_matrix,
top=50)
test_dict = get_test_sample_recommendations(data_model)
# 评估
ism_avg_recall_list, ism_avg_precision_list = calculate_precision_recall(
data_model, training_dict, test_dict, top=50)
#保存到redis
redis = MyRedis()
redis.setList("jaccard_cf_model", ism_avg_recall_list,
ism_avg_precision_list)
plot_precision_recall(ism_avg_recall_list, ism_avg_precision_list,
"jaccard_cf_model")
def test_svd_cf(data_model):
K = 100
train_matrix = data_model.data_matrix
urm = csc_matrix(train_matrix, dtype=np.float)
U, S, Vt = computeSVD(urm, K)
prediction_matrix = computeEstimatedRatings(data_model, U, S, Vt)
svd_Rmse = rmse(train_matrix, prediction_matrix)
# 推荐
training_dict = generate_top_recommendations(data_model,
prediction_matrix,
top=50)
test_dict = get_test_sample_recommendations(data_model)
# 评估
ism_avg_recall_list, ism_avg_precision_list = calculate_precision_recall(
data_model, training_dict, test_dict, top=50)
redis = MyRedis()
redis.setList("svd_cf_model", ism_avg_recall_list, ism_avg_precision_list)
redis.setRmse("svd_cf_model_Rmse", svd_Rmse)
plot_precision_recall(ism_avg_recall_list, ism_avg_precision_list,
"svd_cf_model")
from evaluation.EvaluationModel import calculate_precision_recall, plot_precision_recall
from jaccard_cf.JaccardModel import jaccard_model
from recommender.RecommenderModel import generate_top_recommendations, get_test_sample_recommendations
m = data_model(sample_number=4000, test_size=0.4)
j = jaccard_model(m)
cooccurence_matrix = j.construct_cooccurence_matrix()
# 推荐
training_dict = generate_top_recommendations(m, cooccurence_matrix, top=50)
test_dict = get_test_sample_recommendations(m)
# 评估
ism_avg_precision_list, ism_avg_recall_list = calculate_precision_recall(
m, training_dict, test_dict, top=50)
plot_precision_recall(ism_avg_precision_list, ism_avg_recall_list,
"item_similarity_model")
# a = data_model(sample_number = 10000)
# a.generate_data_matrix()
# auto = denoising_autoencoder_model(a.data_matrix, training_epochs = 70, model_path='./checkpoint_dir_song')
# auto.DAE_model()
# prediction = auto.evaluate_model()
# print("prediction.shape")
# print(prediction.shape)
## 推荐
# training_dict = generate_top_recommendations(a, prediction,50)
# test_dict = get_test_sample_recommendations(a)
## 评估
# ism_avg_precision_list, ism_avg_recall_list = calculate_precision_recall(a, training_dict, test_dict,top=50)