def test_ranking(self):
data = pd.read_csv(
"python/test_alpenglow/test_data_4",
sep=' ',
header=None,
names=['time', 'user', 'item', 'id', 'score', 'eval'])
exp = FactorModel(
factor_seed=254938879,
negative_rate=9,
number_of_iterations=20,
)
exp.fit(data)
preds = exp.recommend(exclude_known=False, k=20)
if (compiler == "gcc" and stdlib == "libstdc++"):
print(preds['item'].tolist())
assert preds['item'].tolist() == \
[94, 166, 30, 225, 98, 299, 300, 442, 372, 337, 196, 455, 338, 462, 256, 250, 429, 36, 496, 38, 338, 256, 372, 455, 98, 94, 282, 166, 177, 383, 462, 30, 128, 102, 337, 168, 225, 479, 69, 120, 94, 98, 300, 166, 30, 299, 225, 196, 442, 455, 427, 429, 293, 250, 38, 247, 255, 372, 496, 337, 94, 30, 166, 225, 299, 442, 300, 98, 372, 337, 196, 496, 250, 215, 462, 40, 36, 338, 429, 62, 94, 30, 166, 225, 300, 299, 442, 98, 196, 372, 337, 250, 455, 462, 204, 429, 496, 247, 256, 38, 94, 300, 204, 86, 165, 128, 166, 247, 30, 225, 196, 383, 256, 462, 102, 442, 497, 429, 337, 299, 94, 300, 30, 166, 225, 204, 196, 462, 299, 337, 442, 165, 86, 372, 250, 455, 247, 256, 102, 98, 30, 94, 166, 225, 299, 442, 300, 98, 196, 372, 337, 215, 250, 462, 496, 36, 40, 429, 338, 455, 94, 30, 166, 225, 300, 299, 442, 98, 196, 337, 250, 462, 372, 204, 215, 496, 40, 429, 455, 256, 94, 166, 256, 30, 372, 225, 462, 338, 383, 455, 337, 98, 102, 442, 36, 282, 128, 247, 177, 168, 94, 30, 166, 225, 299, 300, 442, 372, 98, 196, 462, 455, 337, 250, 215, 36, 496, 338, 429, 256, 30, 94, 225, 166, 300, 299, 442, 196, 462, 337, 250, 372, 204, 215, 98, 496, 429, 455, 247, 36, 30, 94, 225, 166, 299, 300, 442, 196, 372, 337, 98, 462, 250, 215, 496, 36, 455, 40, 429, 338, 383, 338, 444, 97, 168, 165, 4, 255, 128, 483, 98, 277, 436, 330, 372, 6, 156, 122, 40, 464, 94, 300, 30, 166, 225, 299, 196, 442, 98, 204, 250, 337, 462, 372, 455, 429, 247, 86, 165, 496, 30, 94, 166, 225, 299, 442, 300, 98, 372, 337, 196, 462, 215, 250, 496, 36, 338, 455, 40, 256, 94, 166, 256, 455, 247, 128, 98, 30, 300, 102, 225, 462, 383, 204, 282, 372, 177, 196, 86, 375, 94, 30, 166, 300, 225, 98, 299, 196, 442, 337, 204, 455, 250, 372, 462, 256, 429, 247, 38, 427, 94, 30, 166, 300, 225, 299, 196, 442, 98, 455, 204, 337, 462, 250, 372, 429, 256, 247, 215, 38, 94, 30, 166, 225, 299, 300, 98, 442, 196, 337, 372, 455, 462, 250, 215, 496, 204, 429, 338, 256, 300, 94, 98, 429, 427, 250, 204, 299, 196, 166, 371, 255, 165, 442, 496, 38, 86, 450, 30, 266, 94, 30, 166, 225, 300, 299, 442, 196, 250, 337, 462, 204, 98, 372, 215, 496, 429, 40, 165, 455, 30, 225, 300, 462, 166, 94, 299, 442, 215, 204, 196, 372, 165, 86, 383, 337, 250, 452, 81, 102, 98, 338, 94, 120, 166, 97, 40, 256, 30, 225, 299, 156, 337, 442, 255, 414, 444, 325, 496, 62, 94, 30, 166, 225, 300, 462, 372, 442, 196, 299, 455, 247, 337, 98, 256, 383, 204, 128, 102, 86, 94, 166, 30, 225, 372, 299, 455, 462, 247, 383, 442, 36, 98, 300, 196, 256, 177, 215, 128, 102, 94, 30, 166, 225, 300, 299, 442, 462, 372, 196, 337, 98, 455, 256, 204, 247, 250, 383, 128, 86, 94, 30, 166, 225, 299, 372, 442, 98, 300, 462, 455, 337, 196, 36, 338, 215, 256, 247, 250, 177, 30, 299, 94, 166, 225, 442, 300, 98, 372, 215, 196, 337, 496, 250, 462, 36, 40, 429, 338, 255, 94, 30, 166, 300, 225, 299, 196, 442, 98, 337, 250, 204, 462, 372, 455, 429, 247, 496, 256, 86, 94, 30, 166, 225, 299, 300, 442, 196, 98, 250, 337, 496, 372, 429, 462, 215, 204, 40, 36, 455, 94, 166, 30, 225, 300, 98, 455, 256, 196, 204, 462, 337, 372, 247, 442, 299, 128, 102, 86, 165, 30, 94, 166, 225, 299, 442, 300, 98, 337, 372, 196, 250, 496, 215, 40, 462, 338, 36, 429, 255, 94, 30, 166, 225, 299, 300, 98, 442, 196, 250, 337, 372, 496, 429, 455, 462, 255, 427, 215, 204, 94, 300, 30, 166, 225, 196, 204, 455, 299, 98, 442, 337, 462, 250, 372, 86, 165, 256, 247, 38, 30, 94, 166, 225, 299, 442, 337, 372, 300, 98, 338, 462, 215, 196, 496, 36, 40, 250, 120, 256, 94, 30, 166, 225, 98, 299, 372, 442, 337, 338, 256, 462, 300, 455, 36, 196, 120, 215, 40, 444, 455, 427, 300, 204, 247, 128, 98, 94, 38, 86, 375, 196, 165, 177, 468, 371, 450, 293, 282, 266, 299, 30, 94, 300, 225, 442, 166, 196, 98, 250, 496, 372, 455, 215, 429, 293, 255, 36, 292, 25, 94, 166, 30, 225, 300, 98, 299, 442, 196, 455, 337, 372, 250, 429, 462, 204, 247, 38, 256, 427, 94, 30, 166, 225, 299, 442, 300, 337, 98, 372, 462, 196, 338, 250, 215, 40, 496, 256, 36, 455, 94, 30, 166, 225, 299, 300, 442, 98, 372, 196, 337, 462, 455, 250, 256, 338, 215, 36, 496, 429, 94, 30, 166, 225, 299, 300, 442, 98, 337, 372, 196, 462, 250, 215, 496, 455, 36, 338, 40, 429, 299, 30, 225, 442, 166, 94, 215, 496, 98, 372, 300, 40, 337, 250, 36, 338, 444, 196, 120, 255, 299, 30, 338, 442, 166, 225, 120, 94, 40, 215, 36, 496, 98, 444, 337, 372, 97, 255, 414, 462, 94, 30, 166, 225, 299, 300, 442, 98, 372, 196, 337, 462, 250, 455, 215, 496, 36, 338, 256, 429, 300, 204, 94, 30, 196, 225, 166, 165, 337, 455, 86, 462, 102, 256, 250, 375, 177, 372, 38, 197, 30, 94, 166, 225, 300, 299, 442, 462, 337, 196, 204, 372, 98, 250, 215, 455, 256, 496, 40, 429, 94, 30, 166, 225, 300, 462, 372, 299, 442, 337, 196, 98, 256, 455, 204, 250, 215, 36, 247, 338, 30, 94, 225, 166, 299, 442, 300, 372, 337, 462, 215, 196, 98, 250, 496, 36, 40, 338, 429, 455, 94, 166, 30, 225, 300, 98, 299, 442, 196, 372, 337, 455, 462, 250, 247, 429, 256, 128, 496, 204, 30, 94, 166, 225, 299, 442, 98, 372, 338, 337, 300, 215, 36, 496, 462, 196, 40, 120, 250, 444, 94, 30, 166, 225, 299, 300, 442, 372, 98, 337, 196, 462, 250, 455, 215, 36, 338, 496, 256, 40, 256, 102, 455, 94, 383, 128, 462, 247, 282, 204, 166, 177, 375, 86, 372, 30, 337, 165, 225, 98, 30, 94, 300, 225, 166, 204, 462, 165, 86, 196, 299, 337, 383, 442, 102, 256, 372, 452, 247, 215, 30, 166, 225, 338, 94, 299, 442, 40, 120, 337, 98, 372, 496, 215, 36, 97, 444, 256, 462, 62, 94, 30, 166, 225, 300, 299, 442, 196, 98, 372, 337, 250, 462, 455, 204, 429, 247, 496, 256, 215, 94, 30, 166, 225, 299, 300, 442, 98, 372, 337, 196, 462, 250, 455, 215, 496, 36, 338, 256, 204, 30, 94, 299, 166, 225, 442, 300, 98, 196, 337, 250, 496, 372, 215, 40, 429, 462, 36, 338, 255, 94, 166, 30, 98, 225, 299, 300, 442, 372, 337, 196, 455, 338, 250, 256, 462, 429, 496, 36, 38, 94, 30, 166, 225, 299, 300, 442, 98, 196, 372, 337, 250, 496, 462, 215, 429, 455, 36, 338, 40, 94, 30, 166, 225, 299, 98, 300, 442, 372, 337, 196, 455, 462, 250, 338, 256, 429, 36, 496, 215, 94, 30, 166, 225, 299, 300, 442, 98, 372, 196, 337, 462, 455, 250, 215, 36, 256, 338, 496, 429, 94, 30, 166, 225, 300, 299, 196, 442, 337, 462, 204, 455, 372, 250, 98, 215, 256, 429, 86, 165, 299, 30, 98, 94, 225, 166, 442, 496, 338, 255, 444, 372, 250, 300, 215, 196, 120, 292, 36, 455]
assert NdcgScore(data, preds,
top_k=20) == pytest.approx(0.6862576799209609,
abs=5 * 1e-3)
preds2 = exp.recommend(users=[1, 2], exclude_known=False)
assert preds2['user'].unique().tolist() == [1, 2]
preds = exp.recommend(exclude_known=True)
joined_preds = preds.join(data.set_index(['user', 'item']),
on=['user', 'item'],
how='inner',
rsuffix="_right")
assert len(joined_preds) == 0
def test_rmse(self):
data = pd.read_csv(
"python/test_alpenglow/test_data_4",
sep=' ',
header=None,
names=['time', 'user', 'item', 'id', 'score', 'eval'])
model = FactorModel(
negative_rate=9,
number_of_iterations=20,
)
model.fit(data)
def predict(model, user, item):
rd = rs.RecDat()
rd.user = user
rd.item = item
return model.prediction(rd)
errors = [(1 - predict(model.model, u, i))**2
for (u, i) in data[['user', 'item']].values]
rmse = np.sqrt(pd.Series(errors)).mean()
self.assertAlmostEqual(0.31307693145764515, rmse)
def test_rmse(self):
data = pd.read_csv(
"python/test_alpenglow/test_data_4",
sep=' ',
header=None,
names=['time', 'user', 'item', 'id', 'score', 'eval'])
model = FactorModel(
factor_seed=254938879,
negative_rate=9,
number_of_iterations=20,
)
model.fit(data)
def predict(model, user, item):
rd = rs.RecDat()
rd.user = user
rd.item = item
return model.prediction(rd)
errors = [(1 - predict(model.model, u, i))**2
for (u, i) in data[['user', 'item']].values]
rmse = np.sqrt(pd.Series(errors)).mean()
assert rmse == pytest.approx(0.31249014160992433, abs=1e-2)
def test_ranking(self):
data = pd.read_csv(
"python/test_alpenglow/test_data_4",
sep=' ',
header=None,
names=['time', 'user', 'item', 'id', 'score', 'eval'])
exp = FactorModel(
negative_rate=9,
number_of_iterations=20,
)
exp.fit(data)
preds = exp.recommend()
assert preds['item'].tolist() == \
[94, 30, 166, 225, 300, 299, 442, 196, 455, 337, 372, 462, 250, 256, 427, 204, 38, 247, 429, 62, 36, 338, 496, 215, 177, 293, 255, 128, 40, 156, 165, 86, 375, 102, 483, 371, 444, 211, 383, 450, 266, 292, 108, 25, 120, 468, 251, 97, 497, 282, 414, 4, 168, 69, 491, 298, 197, 277, 452, 181, 479, 81, 325, 16, 421, 263, 330, 54, 161, 434, 295, 271, 99, 464, 351, 400, 395, 5, 458, 348, 245, 22, 216, 118, 105, 236, 221, 377, 440, 191, 425, 17, 436, 205, 127, 278, 265, 77, 80, 284, 197, 300, 16, 427, 86, 116, 165, 440, 29, 6, 491, 234, 204, 215, 403, 318, 356, 436, 139, 181, 99, 452, 296, 425, 58, 295, 468, 95, 108, 266, 281, 483, 271, 78, 351, 299, 112, 429, 419, 250, 12, 434, 478, 284, 192, 93, 127, 362, 254, 488, 205, 105, 330, 178, 142, 15, 445, 206, 302, 409, 497, 260, 390, 273, 481, 111, 450, 152, 72, 63, 294, 146, 19, 45, 232, 237, 65, 48, 276, 426, 7, 252, 380, 185, 268, 136, 228, 406, 144, 446, 381, 122, 459, 270, 35, 27, 349, 441, 49, 87, 166, 225, 30, 38, 196, 299, 177, 293, 442, 250, 247, 256, 483, 292, 337, 36, 25, 156, 444, 211, 450, 215, 108, 266, 414, 491, 204, 330, 375, 181, 351, 282, 251, 348, 62, 102, 86, 128, 271, 4, 165, 97, 40, 69, 96, 298, 146, 174, 16, 277, 99, 197, 120, 421, 168, 325, 454, 245, 425, 284, 161, 221, 236, 216, 383, 205, 54, 127, 22, 464, 233, 400, 81, 80, 118, 112, 78, 278, 191, 403, 479, 5, 436, 478, 318, 377, 356, 17, 234, 458, 6, 419, 265, 58, 95, 77, 289, 122, 139, 296, 30, 225, 166, 94, 299, 300, 372, 337, 98, 462, 196, 215, 36, 496, 250, 40, 338, 62, 455, 256, 429, 444, 204, 120, 255, 38, 247, 293, 211, 383, 292, 434, 165, 427, 128, 156, 25, 483, 263, 97, 177, 491, 81, 251, 86, 102, 181, 452, 414, 497, 400, 168, 375, 99, 4, 271, 395, 295, 450, 371, 277, 325, 108, 479, 266, 188, 351, 197, 421, 298, 464, 246, 195, 69, 162, 440, 494, 498, 53, 206, 330, 418, 410, 447, 425, 23, 281, 205, 127, 54, 192, 161, 5, 93, 348, 314, 12, 78, 105, 112, 94, 225, 30, 166, 300, 442, 299, 98, 337, 250, 62, 372, 496, 40, 215, 462, 204, 293, 255, 429, 427, 38, 36, 455, 247, 165, 128, 338, 25, 444, 156, 292, 86, 256, 266, 450, 177, 108, 371, 375, 251, 483, 97, 497, 211, 120, 383, 434, 491, 102, 468, 263, 81, 181, 400, 395, 99, 4, 452, 414, 271, 421, 325, 464, 277, 22, 245, 216, 298, 295, 351, 168, 118, 127, 191, 425, 197, 195, 479, 221, 236, 16, 330, 54, 206, 69, 278, 188, 282, 80, 161, 436, 478, 146, 440, 5, 162, 246, 377, 78, 204, 225, 166, 30, 196, 337, 247, 98, 62, 375, 250, 462, 102, 299, 497, 256, 40, 372, 429, 468, 156, 427, 266, 108, 450, 496, 395, 38, 455, 452, 251, 371, 177, 483, 282, 263, 255, 168, 215, 298, 245, 118, 16, 197, 325, 338, 216, 81, 22, 97, 69, 464, 54, 188, 191, 236, 161, 479, 278, 36, 80, 221, 25, 458, 211, 400, 5, 195, 277, 246, 162, 53, 292, 498, 410, 418, 414, 23, 494, 447, 116, 29, 284, 377, 434, 17, 77, 4, 440, 403, 289, 265, 120, 318, 488, 58, 6, 296, 397, 234, 94, 225, 30, 166, 442, 299, 98, 337, 204, 372, 250, 62, 247, 128, 40, 496, 455, 256, 215, 86, 429, 36, 383, 427, 38, 102, 338, 293, 375, 255, 156, 497, 177, 251, 483, 452, 371, 211, 266, 450, 108, 292, 263, 395, 25, 468, 444, 81, 120, 97, 400, 434, 168, 414, 282, 69, 298, 197, 421, 188, 277, 325, 479, 4, 54, 464, 16, 491, 161, 195, 246, 245, 162, 118, 5, 22, 498, 53, 216, 418, 181, 410, 236, 494, 458, 271, 221, 23, 447, 191, 278, 99, 80, 295, 436, 330, 377, 440, 116, 17, 225, 166, 94, 442, 300, 98, 337, 372, 215, 196, 462, 250, 36, 40, 496, 62, 338, 455, 429, 204, 256, 255, 444, 120, 38, 427, 247, 292, 165, 293, 483, 383, 434, 211, 97, 128, 414, 25, 156, 251, 452, 86, 81, 497, 177, 263, 491, 181, 102, 400, 298, 371, 450, 395, 325, 99, 271, 375, 266, 168, 295, 108, 4, 421, 188, 464, 330, 351, 197, 498, 246, 205, 162, 348, 479, 494, 53, 440, 195, 410, 418, 277, 281, 447, 23, 206, 425, 69, 192, 314, 127, 93, 468, 12, 105, 78, 245, 22, 112, 353, 166, 442, 300, 215, 337, 196, 250, 462, 372, 496, 36, 429, 165, 247, 455, 256, 338, 383, 128, 452, 86, 255, 497, 251, 434, 483, 292, 395, 427, 414, 38, 263, 444, 102, 120, 188, 293, 400, 97, 298, 25, 211, 246, 410, 162, 53, 494, 418, 371, 447, 23, 156, 181, 195, 450, 325, 491, 421, 375, 266, 108, 271, 464, 348, 99, 278, 295, 236, 330, 351, 221, 468, 168, 197, 205, 80, 245, 22, 118, 105, 216, 206, 281, 191, 101, 440, 488, 69, 479, 78, 93, 314, 192, 112, 96, 174, 12, 454, 353, 127, 166, 30, 94, 225, 462, 455, 337, 442, 98, 102, 177, 300, 36, 299, 247, 168, 211, 69, 282, 196, 204, 128, 215, 375, 479, 197, 86, 120, 429, 161, 156, 38, 5, 165, 54, 452, 277, 458, 444, 427, 483, 4, 263, 250, 16, 62, 17, 77, 81, 377, 265, 289, 496, 330, 497, 397, 440, 40, 132, 29, 414, 116, 293, 113, 205, 251, 371, 295, 357, 400, 6, 403, 454, 348, 234, 468, 233, 281, 318, 436, 356, 12, 271, 488, 58, 192, 188, 93, 95, 491, 434, 296, 314, 255, 298, 139, 146, 195, 362, 353, 166, 225, 94, 442, 98, 462, 337, 256, 36, 196, 215, 338, 250, 204, 429, 496, 247, 40, 427, 62, 38, 177, 383, 211, 102, 444, 255, 120, 483, 128, 156, 165, 293, 414, 86, 292, 375, 168, 452, 371, 69, 282, 251, 298, 97, 81, 181, 197, 497, 25, 4, 479, 263, 491, 434, 277, 450, 348, 330, 295, 266, 400, 351, 271, 108, 161, 54, 5, 395, 105, 188, 99, 325, 205, 468, 458, 440, 421, 16, 498, 162, 246, 96, 418, 494, 53, 78, 357, 174, 410, 454, 112, 464, 281, 23, 17, 447, 377, 139, 265, 30, 225, 94, 299, 337, 372, 215, 98, 62, 40, 250, 496, 204, 36, 165, 247, 128, 383, 429, 338, 86, 256, 263, 455, 434, 293, 444, 255, 497, 452, 81, 292, 102, 251, 120, 38, 211, 188, 25, 427, 156, 483, 375, 246, 498, 177, 162, 181, 53, 410, 97, 494, 418, 23, 447, 414, 195, 491, 168, 108, 266, 99, 271, 450, 371, 298, 277, 421, 479, 325, 4, 197, 351, 464, 468, 69, 278, 236, 54, 221, 206, 161, 295, 5, 80, 22, 78, 118, 458, 105, 245, 112, 216, 348, 191, 488, 440, 127, 425, 281, 166, 94, 300, 40, 98, 250, 496, 215, 372, 36, 462, 204, 338, 255, 429, 292, 444, 120, 293, 434, 165, 38, 97, 25, 455, 427, 128, 247, 156, 395, 256, 414, 497, 181, 483, 251, 86, 263, 450, 383, 211, 491, 81, 452, 266, 298, 108, 400, 99, 325, 188, 464, 246, 421, 498, 162, 375, 494, 271, 53, 351, 410, 447, 418, 371, 23, 4, 278, 221, 236, 102, 195, 78, 112, 80, 295, 177, 127, 22, 206, 168, 425, 245, 216, 419, 118, 281, 191, 440, 468, 192, 277, 93, 314, 105, 348, 12, 205, 353, 478, 414, 53, 348, 181, 447, 246, 292, 494, 78, 410, 174, 139, 351, 96, 278, 101, 236, 162, 23, 498, 369, 188, 357, 105, 395, 221, 80, 419, 418, 298, 112, 94, 30, 225, 166, 299, 98, 442, 196, 337, 250, 204, 372, 62, 462, 455, 496, 247, 40, 165, 429, 427, 215, 128, 256, 38, 36, 86, 293, 255, 338, 156, 177, 102, 375, 383, 483, 450, 371, 266, 497, 108, 292, 25, 251, 444, 211, 468, 97, 452, 120, 414, 81, 395, 298, 263, 434, 421, 491, 400, 325, 282, 168, 4, 197, 464, 277, 69, 181, 245, 22, 99, 216, 118, 16, 271, 479, 191, 236, 221, 295, 188, 54, 278, 330, 161, 80, 351, 195, 5, 436, 127, 162, 458, 246, 498, 440, 425, 348, 53, 205, 225, 166, 94, 299, 442, 300, 372, 98, 337, 462, 196, 215, 36, 455, 250, 338, 496, 256, 40, 62, 204, 429, 247, 444, 255, 38, 427, 383, 120, 211, 165, 128, 293, 483, 177, 102, 156, 292, 86, 434, 414, 452, 251, 97, 81, 25, 497, 263, 375, 168, 491, 371, 400, 181, 4, 450, 298, 69, 197, 271, 479, 295, 395, 325, 266, 99, 277, 108, 330, 282, 421, 188, 351, 464, 205, 440, 348, 54, 161, 468, 246, 498, 5, 195, 162, 281, 494, 53, 418, 410, 458, 447, 314, 192, 206, 23, 425, 105, 93, 12, 94, 165, 86, 102, 455, 225, 30, 462, 375, 196, 337, 177, 372, 427, 468, 69, 429, 156, 497, 371, 452, 38, 16, 197, 62, 266, 442, 168, 161, 450, 483, 54, 108, 250, 5, 458, 479, 251, 298, 299, 395, 293, 277, 211, 29, 81, 188, 116, 236, 284, 377, 263, 421, 221, 80, 17, 338, 77, 118, 245, 278, 289, 265, 436, 216, 191, 36, 357, 414, 22, 418, 498, 53, 4, 410, 403, 397, 162, 6, 23, 246, 318, 494, 325, 234, 447, 330, 356, 348, 454, 113, 464, 58, 139, 195, 101, 132, 233, 296, 488, 94, 225, 30, 166, 300, 299, 442, 337, 250, 62, 372, 455, 165, 247, 462, 427, 128, 429, 40, 496, 86, 38, 256, 293, 215, 255, 36, 156, 375, 102, 450, 371, 266, 177, 338, 108, 483, 383, 497, 468, 251, 25, 292, 97, 452, 211, 298, 395, 444, 414, 81, 282, 421, 120, 325, 263, 464, 197, 400, 245, 22, 434, 216, 16, 69, 118, 168, 4, 491, 236, 221, 277, 191, 278, 80, 188, 181, 479, 54, 99, 161, 271, 330, 295, 436, 5, 351, 195, 284, 498, 162, 458, 246, 348, 127, 53, 410, 418, 447, 494, 166, 442, 196, 337, 215, 40, 372, 496, 429, 462, 427, 256, 247, 165, 255, 38, 86, 483, 338, 128, 414, 292, 293, 371, 251, 497, 156, 97, 383, 266, 102, 25, 108, 177, 395, 81, 444, 120, 375, 468, 211, 434, 325, 421, 181, 188, 400, 464, 348, 278, 221, 236, 498, 330, 491, 263, 80, 245, 351, 162, 246, 22, 197, 53, 118, 410, 216, 494, 418, 295, 447, 99, 282, 271, 205, 105, 191, 23, 112, 78, 195, 454, 16, 69, 96, 168, 4, 101, 440, 419, 281, 174, 233, 127, 314, 369, 192, 425, 488, 436, 300, 442, 372, 250, 256, 196, 429, 462, 36, 38, 204, 338, 215, 496, 40, 255, 62, 156, 483, 414, 247, 292, 293, 177, 371, 450, 97, 120, 444, 165, 266, 128, 375, 211, 86, 25, 108, 102, 181, 251, 468, 325, 348, 383, 351, 497, 168, 282, 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496, 338, 215, 293, 40, 255, 128, 165, 177, 156, 86, 375, 102, 483, 371, 444, 450, 383, 266, 292, 211, 108, 25, 120, 97, 251, 468, 497, 414, 282, 4, 168, 298, 491, 69, 452, 197, 277, 181, 81, 325, 421, 263, 434, 479, 395, 16, 464, 99, 271, 400, 295, 330, 54, 161, 351, 245, 5, 22, 216, 348, 236, 118, 458, 221, 191, 105, 278, 80, 440, 425, 436, 127, 377, 17, 205, 77, 265, 284, 30, 225, 94, 300, 442, 98, 196, 337, 250, 372, 496, 215, 462, 40, 62, 204, 36, 429, 455, 427, 38, 338, 256, 293, 165, 292, 128, 483, 444, 86, 25, 156, 251, 371, 177, 414, 97, 211, 120, 383, 497, 450, 266, 108, 102, 434, 81, 375, 491, 452, 181, 298, 395, 400, 263, 468, 325, 271, 421, 99, 295, 464, 351, 330, 4, 348, 188, 168, 245, 22, 197, 216, 236, 221, 278, 118, 105, 498, 277, 246, 162, 191, 80, 195, 494, 205, 69, 53, 127, 282, 410, 447, 418, 425, 479, 440, 23, 206, 96, 78, 30, 225, 300, 299, 442, 337, 372, 196, 462, 455, 256, 250, 204, 247, 36, 62, 215, 429, 427, 338, 496, 38, 40, 128, 165, 177, 293, 255, 86, 102, 383, 156, 375, 483, 211, 444, 292, 371, 120, 414, 497, 251, 450, 25, 266, 108, 168, 97, 282, 452, 468, 298, 69, 81, 4, 263, 197, 395, 181, 277, 479, 491, 434, 400, 325, 54, 161, 421, 351, 348, 330, 16, 295, 5, 271, 188, 464, 458, 99, 105, 236, 221, 278, 80, 162, 246, 498, 440, 245, 494, 53, 418, 410, 377, 205, 17, 447, 118, 22, 96, 30, 225, 94, 299, 442, 300, 98, 372, 337, 462, 196, 455, 215, 36, 250, 256, 338, 496, 40, 62, 204, 429, 247, 38, 427, 444, 383, 255, 128, 177, 211, 165, 293, 120, 102, 156, 483, 86, 292, 375, 25, 251, 168, 434, 452, 414, 263, 97, 497, 81, 371, 491, 4, 181, 400, 69, 450, 479, 277, 197, 266, 108, 282, 271, 295, 395, 99, 298, 325, 330, 54, 161, 351, 5, 421, 468, 188, 440, 464, 458, 348, 205, 246, 162, 498, 195, 16, 494, 53, 105, 17, 281, 418, 425, 377, 410, 77, 265, 447, 23, 94, 98, 442, 337, 372, 462, 256, 250, 215, 36, 62, 247, 429, 40, 496, 165, 427, 338, 128, 86, 38, 383, 102, 483, 255, 177, 156, 375, 452, 293, 414, 298, 497, 371, 211, 450, 251, 120, 292, 266, 97, 444, 282, 108, 81, 468, 395, 168, 25, 69, 263, 325, 434, 421, 188, 479, 400, 348, 4, 464, 330, 181, 16, 498, 236, 277, 221, 278, 162, 246, 418, 53, 205, 410, 161, 491, 494, 295, 80, 54, 447, 245, 23, 351, 5, 440, 118, 105, 99, 22, 271, 216, 454, 191, 458, 281, 195, 101, 78, 112, 429, 211, 271, 462, 371, 295, 414, 483, 206, 464, 325, 375, 105, 22, 421, 263, 96, 221, 400, 216, 132, 146, 174, 251, 236, 245, 80, 281, 168, 191, 397, 468, 298, 278, 192, 440, 118, 265, 377, 17, 256, 357, 113, 348, 77, 247, 353, 128, 395, 330, 403, 93, 362, 436, 314, 12, 356, 479, 234, 318, 289, 95, 139, 296, 6, 284, 481, 87, 268, 122, 323, 409, 408, 63, 142, 499, 19, 446, 237, 441, 473, 48, 45, 0, 232, 136, 390, 228, 150, 65, 111, 276, 260, 58, 331, 405, 90, 344, 178, 461]
preds2 = exp.recommend(users=[1, 2], exclude_known=False)
assert preds2['user'].unique().tolist() == [1, 2]
preds = exp.recommend(exclude_known=True)
joined_preds = preds.join(data.set_index(['user', 'item']),
on=['user', 'item'],
how='inner')
assert len(joined_preds) == 0
import pandas as pd
from alpenglow.offline.models import FactorModel
import alpenglow.Getter as rs
data = pd.read_csv("../../python/test_alpenglow/test_data_4",
sep=' ',
header=None,
names=['time', 'user', 'item', 'id', 'score', 'eval'])
model = FactorModel(
factor_seed=254938879,
dimension=10,
negative_rate=9,
number_of_iterations=20,
)
model.fit(data)
model.model.write("output_file") #writes model to output_file
rd = rs.RecDat()
rd.user = 3
rd.item = 5
print("prediction for user=3, item=5:", model.model.prediction(rd))
#model2 must have the same dimension
model2 = FactorModel(
factor_seed=1234,
dimension=10,
negative_rate=0,
number_of_iterations=0,
)
#to create the inner model but avoid training, we need to run fit()
#on an empty dataset
#for dimension, learning_rate, regularization_rate in product(
# [10, 20, 50, 200],
# [0.001, 0.01, 0.05, 0.1],
# [0, 0.001, 0.01, 0.1, 1]):
dimension = 50
learning_rate = 0.01
regularization_rate = 0
print("Fitting model.. dim={}, learning_rate={}, regularization={}".format(
dimension, learning_rate, regularization_rate))
train = data[:n].copy()
validate = data[n:].copy()
factor_model = FactorModel(
dimension=dimension,
learning_rate=learning_rate,
regularization_rate=regularization_rate,
seed=2,
number_of_iterations=15,
)
factor_model.fit(train)
# Evaluate model
user_counts = train["user"].value_counts()
item_counts = train["item"].value_counts()
min_ratings = 10
def filter(i):
item_id = validate["item"][i]
user_id = validate["user"][i]
return (item_counts.get(item_id, 0) >= min_ratings and
user_counts.get(user_id, 0) >= min_ratings)
print("Evaluating model.. min_ratings={}".format(min_ratings))
import os
import sys
import pandas as pd
import numpy as np
import alpenglow.offline
from alpenglow.offline.models import FactorModel
threads = 10
for i in range(1, 14):
print("running batch %d" % i)
batch = pd.read_csv("batches/batch_%d_train.dat" % i)
test_users = pd.read_csv("batches/batch_%d_test.dat" % i)
model = FactorModel(
negative_rate=5,
number_of_iterations=3,
learning_rate=0.05,
)
model.fit(batch)
real_test_users = test_users.user[test_users.user.isin(
batch.user.unique())].values
# ranklists = get_ranklists(model, real_test_users, batch.item.unique(), None)
ranklists = model.recommend(real_test_users)
ranklists[['user', 'item',
'rank']].to_csv('batches/batch_%d_predictions.dat' % i,
sep=' ',
header=False,
index=False)