def test_warp_kos_precision():
# Remove all negative examples
training = train.copy()
training.data[training.data < 1] = 0
training = training.tocsr()
training.eliminate_zeros()
model = LightFM(learning_rate=0.05, k=5,
loss='warp-kos')
model.fit_partial(training,
epochs=10)
train_precision = precision_at_k(model,
training,
10)
test_precision = precision_at_k(model,
test,
10)
full_train_auc = full_auc(model, training)
full_test_auc = full_auc(model, test)
assert train_precision > 0.44
assert test_precision > 0.06
assert full_train_auc > 0.9
assert full_test_auc > 0.87
def test_warp_precision_adadelta_multithreaded():
model = LightFM(learning_schedule='adadelta',
rho=0.95,
epsilon=0.000001,
loss='warp')
model.fit_partial(train,
epochs=10,
num_threads=4)
train_precision = precision_at_k(model,
train,
10)
test_precision = precision_at_k(model,
test,
10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.94
assert full_test_auc > 0.9
def test_logistic_precision():
model = LightFM()
model.fit_partial(train, epochs=10)
train_precision = precision_at_k(model, train, 10)
test_precision = precision_at_k(model, test, 10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.3
assert test_precision > 0.03
assert full_train_auc > 0.79
assert full_test_auc > 0.74
def test_warp_precision_max_sampled():
model = LightFM(learning_rate=0.05, max_sampled=1, loss='warp')
# This is equivalent to a no-op pass
# over the training data
model.max_sampled = 0
model.fit_partial(train, epochs=1)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
# The AUC should be no better than random
assert full_train_auc < 0.55
assert full_test_auc < 0.55
def test_bpr_precision_multithreaded():
model = LightFM(learning_rate=0.05, loss='bpr')
model.fit_partial(train, epochs=10, num_threads=4)
train_precision = precision_at_k(model, train, 10)
test_precision = precision_at_k(model, test, 10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.31
assert test_precision > 0.04
assert full_train_auc > 0.86
assert full_test_auc > 0.84
def test_warp_precision():
model = LightFM(learning_rate=0.05, loss='warp')
model.fit_partial(train, epochs=10)
train_precision = precision_at_k(model, train, 10)
test_precision = precision_at_k(model, test, 10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.94
assert full_test_auc > 0.9
def test_warp_precision_max_sampled():
model = LightFM(learning_rate=0.05,
max_sampled=1,
loss='warp')
# This is equivalent to a no-op pass
# over the training data
model.max_sampled = 0
model.fit_partial(train,
epochs=1)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
# The AUC should be no better than random
assert full_train_auc < 0.55
assert full_test_auc < 0.55
def test_logistic_precision():
model = LightFM()
model.fit_partial(train,
epochs=10)
train_precision = precision_at_k(model,
train,
10)
test_precision = precision_at_k(model,
test,
10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.3
assert test_precision > 0.03
assert full_train_auc > 0.79
assert full_test_auc > 0.74
def test_bpr_precision():
model = LightFM(learning_rate=0.05,
loss='bpr')
model.fit_partial(train,
epochs=10)
train_precision = precision_at_k(model,
train,
10)
test_precision = precision_at_k(model,
test,
10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.31
assert test_precision > 0.04
assert full_train_auc > 0.86
assert full_test_auc > 0.84
def test_warp_precision():
model = LightFM(learning_rate=0.05,
loss='warp')
model.fit_partial(train,
epochs=10)
train_precision = precision_at_k(model,
train,
10)
test_precision = precision_at_k(model,
test,
10)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.94
assert full_test_auc > 0.9
