from BinaryProbabilisticMatrixFactorization import BPMF
if __name__ == "__main__":
# file_path = "data/ml-100k/u.data"
file_path = "/home/lxu/Documents/StudentLearningProcess/skill_builder_data_corrected_withskills_finished.csv"
pmf = BPMF()
pmf.set_params({
"num_feat": 6,
"epsilon": 2,
"_lambda": 0.1,
"momentum": 0.8,
"maxepoch": 100,
"num_batches": 300,
"batch_size": 1000
})
ratings, order = load_rating_data(file_path)
print(len(np.unique(ratings[:, 0])), len(np.unique(ratings[:, 1])),
pmf.num_feat)
train, test, order_train, order_test = train_test_split(
ratings, order, test_size=0.2) # spilt_rating_dat(ratings)
pmf.two_step_fit(train, test, order_train, order_test,
len(np.unique(ratings[:, 0])),
len(np.unique(ratings[:, 1])))
# Check performance by plotting train and test errors
plt.plot(range(pmf.maxepoch),
pmf.logloss_train,
marker='o',
label='Training Data')
from ProbabilisticMatrixFactorization import PMF
import glob
from os.path import exists, basename
import sys
if __name__ == "__main__":
for file_path in sorted(glob.glob("./monthly-data-bkp/monthly-data/"+sys.argv[1]+"*")):
print(file_path)
if( not exists("./monthly-pmf/"+basename(file_path)+"_u")):
print("Processing ...:"+basename(file_path));
pmf = PMF()
pmf.set_params({"num_feat": 30, "epsilon": 1, "_lambda": 0.1, "momentum": 0.8, "maxepoch": 30, "num_batches": 100,
"batch_size": 1000})
ratings = load_rating_data(file_path)
print(len(np.unique(ratings[:, 0])), len(np.unique(ratings[:, 1])), pmf.num_feat)
train, test = train_test_split(ratings, test_size=0.0) # spilt_rating_dat(ratings)
pmf.fit(train, test, basename(file_path))
# Check performance by plotting train and test errors
#plt.plot(range(pmf.maxepoch), pmf.rmse_train, marker='o', label='Training Data')
#plt.plot(range(pmf.maxepoch), pmf.rmse_test, marker='v', label='Test Data')
#plt.title('The MovieLens Dataset Learning Curve')
#plt.xlabel('Number of Epochs')
#plt.ylabel('RMSE')
#plt.legend()
#plt.grid()
#plt.show()
#print("precision_acc,recall_acc:" + str(pmf.topK(test)))
import matplotlib.pyplot as plt
import numpy as np
from LoadData import load_rating_data, spilt_rating_dat
from sklearn.model_selection import train_test_split
from ProbabilisticMatrixFactorization import PMF
if __name__ == "__main__":
file_path = "data/ml-100k/u.data"
pmf = PMF()
pmf.set_params({"num_feat": 10, "epsilon": 1, "_lambda": 0.1, "momentum": 0.8, "maxepoch": 10, "num_batches": 100,
"batch_size": 1000})
ratings = load_rating_data(file_path)
print(len(np.unique(ratings[:, 0])), len(np.unique(ratings[:, 1])), pmf.num_feat)
train, test = train_test_split(ratings, test_size=0.2) # spilt_rating_dat(ratings)
pmf.fit(train, test)
# Check performance by plotting train and test errors
plt.plot(range(pmf.maxepoch), pmf.rmse_train, marker='o', label='Training Data')
plt.plot(range(pmf.maxepoch), pmf.rmse_test, marker='v', label='Test Data')
plt.title('The MovieLens Dataset Learning Curve')
plt.xlabel('Number of Epochs')
plt.ylabel('RMSE')
plt.legend()
plt.grid()
plt.show()
print("precision_acc,recall_acc:" + str(pmf.topK(test)))