- Compare the predicted rating with the actual rating
- Output the result
- Compute the Mean Absolute Error (MAE)
Inputs: method, filename
- method: ID of the collaborative filtering method to be used
- filename: name of the file containing the list of test cases
"""
if __name__ == '__main__':
filename = 'data/jester-data-1.csv'
items = {}
users = {}
matrix = []
matrix, users, items = parse(filename)
f = Filter(matrix, users, items)
if len(sys.argv) == 3:
method = sys.argv[1]
testFile = sys.argv[2]
testData = csv.reader(open(testFile, "r"))
results = f.execute(method, testData)
print_evaluation(f, method, results)
elif len(sys.argv) == 2 and sys.argv[1] == 'all':
method = "adj_weighted_sum"
testData1 = csv.reader(open("data/TestSet01.csv", "r"))
testData2 = csv.reader(open("data/TestSet02.csv", "r"))
testData3 = csv.reader(open("data/TestSet03.csv", "r"))
testData4 = csv.reader(open("data/TestSet04.csv", "r"))
results1 = f.execute(method, testData1)
return tests
if __name__ == '__main__':
if len(sys.argv) != 3:
print 'Expected input format: python EvaluateCFList.py <method> <testList>'
else:
filename = 'data/jester-data-1.csv'
items = {}
users = {}
matrix = []
size = int(sys.argv[2])
matrix, users, items = parse(filename)
testData = gen_tests(users, size)
f = Filter(matrix, users, items)
method = sys.argv[1]
print "Starting predictions"
if method == 'all':
w_results = f.execute('weighted_sum', testData)
a_w_results = f.execute('adj_weighted_sum', testData)
c_w_results = f.execute('cosine_weighted_sum', testData)
c_a_w_results = f.execute('cosine_adj_weighted_sum', testData)
print_evaluation(f, "Weighted Sum", w_results)
print_evaluation(f, "Adjusted Weighted Sum", a_w_results)
print_evaluation(f, "Cosine Weighted Sum", c_w_results)
print_evaluation(f, "Cosine Adjusted Weighted Sum", c_a_w_results)
else:
results = f.execute(method, testData)
print_evaluation(f, method, results)
- Compare the predicted rating with the actual rating
- Output the result
- Compute the Mean Absolute Error (MAE)
Inputs: method, filename
- method: ID of the collaborative filtering method to be used
- filename: name of the file containing the list of test cases
"""
if __name__ == '__main__':
filename = 'data/jester-data-1.csv'
items = {}
users = {}
matrix = []
matrix, users, items = parse(filename)
f = Filter(matrix, users, items)
if len(sys.argv) == 3:
method = sys.argv[1]
testFile = sys.argv[2]
testData = csv.reader(open(testFile, "r"))
results = f.execute(method, testData)
print_evaluation(f, method, results)
elif len(sys.argv) == 2 and sys.argv[1] == 'all':
method = "adj_weighted_sum"
testData1 = csv.reader(open("data/TestSet01.csv", "r"))
testData2 = csv.reader(open("data/TestSet02.csv", "r"))
testData3 = csv.reader(open("data/TestSet03.csv", "r"))
testData4 = csv.reader(open("data/TestSet04.csv", "r"))
results1 = f.execute(method, testData1)
if __name__ == '__main__':
if len(sys.argv) != 3:
print 'Expected input format: python EvaluateCFList.py <method> <testList>'
else:
filename = 'data/jester-data-1.csv'
items = {}
users = {}
matrix = []
size = int(sys.argv[2])
matrix, users, items = parse(filename)
testData = gen_tests(users, size)
f = Filter(matrix, users, items)
method = sys.argv[1]
print "Starting predictions"
if method == 'all':
w_results = f.execute('weighted_sum', testData)
a_w_results = f.execute('adj_weighted_sum', testData)
c_w_results = f.execute('cosine_weighted_sum', testData)
c_a_w_results = f.execute('cosine_adj_weighted_sum', testData)
print_evaluation(f, "Weighted Sum", w_results)
print_evaluation(f, "Adjusted Weighted Sum", a_w_results)
print_evaluation(f, "Cosine Weighted Sum", c_w_results)
print_evaluation(f, "Cosine Adjusted Weighted Sum", c_a_w_results)
else:
results = f.execute(method, testData)
print_evaluation(f, method, results)
