class TestMF(TestCase):
mf_engine = mf.MF()
test_data = mf.generate_test_data(100, 50, 1000)
mf_engine.fit(test_data)
def test_fit(self):
print("testing fit method")
TestMF.mf_engine.fit(TestMF.test_data)
def test_predict(self):
print("testing predict method")
n = 1000
pred_data = mf.generate_test_data(100, 50, n, indices_only=True)
predictions = TestMF.mf_engine.predict(pred_data)
self.assertTrue(len(predictions) == n)
for i in predictions.tolist():
self.assertTrue(type(i) is float)
self.assertFalse(math.isnan(i))
def test_cross_val(self):
print("testing cross val method")
val_data = mf.generate_test_data(100, 50, 1000)
TestMF.mf_engine.mf_cross_validation(val_data)
def test_factorq(self):
print("testing factor-q")
q = TestMF.mf_engine.q_factors()
self.assertEqual(q.shape,
(TestMF.mf_engine.model.n, TestMF.mf_engine.model.k))
def test_factorp(self):
print("testing factor-p")
p = TestMF.mf_engine.p_factors()
self.assertEqual(p.shape,
(TestMF.mf_engine.model.m, TestMF.mf_engine.model.k))
def recommend(A, test_set):
rr, cc, vv = scipy.sparse.find(A)
data = []
for i in range(rr.shape[0]):
data.append([rr[i], cc[i], vv[i]])
data = np.array(data)
row, col, record = test_set
ind = []
for i in range(row.shape[0]):
ind.append([row[i], col[i]])
ind = np.array(ind)
engine = mf.MF()
engine.fit(data, maxiter=100)
res = engine.predict(ind)
flag = np.ones(row.shape[0])
print(res)
return res, flag
class TestMF(TestCase):
mf_engine = mf.MF()
test_data = mf.generate_test_data(9000, 1000, 25000)
mf_engine.mf_fit(test_data)
def test_fit(self):
print("testing fit method")
TestMF.mf_engine.mf_fit(TestMF.test_data)
def test_predict(self):
print("testing predict method")
n = 1000
pred_data = mf.generate_test_data(9000, 1000, n, indices_only=True)
predictions = TestMF.mf_engine.mf_predict(pred_data)
self.assertTrue(len(predictions) == n)
for i in predictions.tolist():
self.assertTrue(type(i) is float)
self.assertFalse(math.isnan(i))
def test_cross_val(self):
print("testing cross val method")
val_data = mf.generate_test_data(9000, 1000, 10000)
TestMF.mf_engine.mf_cross_validation(val_data)
ind.append([row[i], col[i]])
ind = np.array(ind)
engine = mf.MF()
engine.fit(data, maxiter=100)
res = engine.predict(ind)
flag = np.ones(row.shape[0])
print(res)
return res, flag
if __name__ == '__main__':
A = np.random.randn(600, 10000)
A[A < 0] = 0
print(A)
all = []
for i in range(600):
for j in range(10000):
all.append([i, j, A[i][j]])
np.random.shuffle(all)
data = np.array(all[:90000])
print(data)
ind = []
for i in range(100):
ind.append([np.random.randint(0, 600), np.random.randint(0, 10000)])
ind = np.array(ind)
engine = mf.MF()
engine.fit(data)
res = engine.predict(ind)
print(res)