def test_dynamic_updates(self):
"""
TensorCoFi dynamic update
We will take a tensor cofi. Train the model, evaluate it. Then we remove all the user factors
and recompute them using the online_user_factors to check if the performance is almost the same...
"""
pyTF = PyTensorCoFi(n_factors=20, n_iterations=5, c_lambda=0.05, c_alpha=40)
evaluator = Evaluator()
tf = TensorCoFi(n_factors=2, n_iterations=100, c_lambda=0.05, c_alpha=40)
df = pd.read_csv(resource_filename(testfm.__name__, "data/movielenshead.dat"), sep="::", header=None,
names=["user", "item", "rating", "date", "title"])
training, testing = testfm.split.holdoutByRandom(df, 0.7)
users = {user: list(entries) for user, entries in training.groupby("user")["item"]}
tf.fit(training)
map1 = evaluator.evaluate_model(tf, testing) # map of the original model
#now we try to replace the original factors with on the fly computed factors
#lets iterate over the training data of items and the users
for u, items in users.items():
#user id in the tf
uid = tf.data_map[tf.get_user_column()][u] # user id
iids = [tf.data_map[tf.get_item_column()][i] for i in items] # item ids that user has seen
#original_factors = tf.factors["user"][uid]
new_factors = pyTF.online_user_factors(tf.factors[1], iids, p_param=40, lambda_param=0.05)
#replace original factors with the new factors
tf.factors[0][uid, :] = new_factors
#tf.update_user_factors(uid, new_factors)
#lets evaluate the new model with real-time updated factors
map2 = evaluator.evaluate_model(tf, testing)
#The difference should be smaller than 20%
assert abs(map1[0]-map2[0]) < 0.2*map1[0]
def test_user_model_update(self):
pyTF = PyTensorCoFi()
Y = np.array([[-1.0920831, -0.01566422], [-0.8727925, 0.22307773], [0.8753245, -0.80181429],
[-0.1338534, -0.51448172], [-0.2144651, -0.96081265]])
user_items = [1, 3, 4]
res = pyTF.online_user_factors(Y, user_items, p_param=10, lambda_param=0.01)
assert np.array([-1.18324547, -0.95040477]).all() == res.all(), "Results not equal"
def test_user_model_update(self):
pyTF = PyTensorCoFi()
Y = np.array([[-1.0920831, -0.01566422], [-0.8727925, 0.22307773],
[0.8753245, -0.80181429], [-0.1338534, -0.51448172],
[-0.2144651, -0.96081265]])
user_items = [1, 3, 4]
res = pyTF.online_user_factors(Y,
user_items,
p_param=10,
lambda_param=0.01)
assert np.array([-1.18324547,
-0.95040477]).all() == res.all(), "Results not equal"
def test_dynamic_updates(self):
"""
TensorCoFi dynamic update
We will take a tensor cofi. Train the model, evaluate it. Then we remove all the user factors
and recompute them using the online_user_factors to check if the performance is almost the same...
"""
pyTF = PyTensorCoFi(n_factors=20,
n_iterations=5,
c_lambda=0.05,
c_alpha=40)
evaluator = Evaluator()
tf = TensorCoFi(n_factors=2,
n_iterations=100,
c_lambda=0.05,
c_alpha=40)
df = pd.read_csv(resource_filename(testfm.__name__,
"data/movielenshead.dat"),
sep="::",
header=None,
names=["user", "item", "rating", "date", "title"])
training, testing = testfm.split.holdoutByRandom(df, 0.7)
users = {
user: list(entries)
for user, entries in training.groupby("user")["item"]
}
tf.fit(training)
map1 = evaluator.evaluate_model(tf,
testing) # map of the original model
#now we try to replace the original factors with on the fly computed factors
#lets iterate over the training data of items and the users
for u, items in users.items():
#user id in the tf
uid = tf.data_map[tf.get_user_column()][u] # user id
iids = [tf.data_map[tf.get_item_column()][i]
for i in items] # item ids that user has seen
#original_factors = tf.factors["user"][uid]
new_factors = pyTF.online_user_factors(tf.factors[1],
iids,
p_param=40,
lambda_param=0.05)
#replace original factors with the new factors
tf.factors[0][uid, :] = new_factors
#tf.update_user_factors(uid, new_factors)
#lets evaluate the new model with real-time updated factors
map2 = evaluator.evaluate_model(tf, testing)
#The difference should be smaller than 20%
assert abs(map1[0] - map2[0]) < 0.2 * map1[0]
def test_score_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
inp = [{"user": 10, "item": 100},
{"user": 10, "item": 110},
{"user": 12, "item": 120}]
inp = pd.DataFrame(inp)
tf.fit(inp)
uid = tf.data_map[tf.get_user_column()][10]
iid = tf.data_map[tf.get_item_column()][100]
tf.factors[0][uid, 0] = 0
tf.factors[0][uid, 1] = 1
tf.factors[1][iid, 0] = 1
tf.factors[1][iid, 1] = 5
self.assertEqual(0*1+1*5, tf.get_score(10, 100))
def test_tensor_score_against_testfm(self):
"""
[recommendation.models.TensorCoFi] Test tensorcofi scores with test.fm benchmark
"""
evaluator = Evaluator()
tc = TensorCoFi(n_users=len(self.df.user.unique()), n_items=len(self.df.item.unique()), n_factors=2)
ptc = PyTensorCoFi()
training, testing = testfm.split.holdoutByRandom(self.df, 0.9)
items = training.item.unique()
tc.fit(training)
ptc.fit(training)
tc_score = evaluator.evaluate_model(tc, testing, all_items=items)[0]
ptc_score = evaluator.evaluate_model(ptc, testing, all_items=items)[0]
assert abs(tc_score-ptc_score) < .15, \
"TensorCoFi score is not close enough to testfm benchmark (%.3f != %.3f)" % (tc_score, ptc_score)
def test_ids_returns_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
inp = [{"user": 10, "item": 100},
{"user": 10, "item": 110},
{"user": 12, "item": 120}]
inp = pd.DataFrame(inp)
tf.fit(inp)
# Test the id in map
uid = tf.data_map[tf.get_user_column()][10]
iid = tf.data_map[tf.get_item_column()][100]
self.assertEquals(uid, 0)
self.assertEquals(iid, 0)
# Test number of factors
self.assertEquals(len(tf.factors[0][uid, :]), tf.number_of_factors)
self.assertEquals(len(tf.factors[1][iid, :]), tf.number_of_factors)
def test_nogil_against_std_05(self):
"""
[EVALUATOR] Test the groups measure differences between python and c implementations for 5% training
"""
df = pd.read_csv(resource_filename(testfm.__name__, 'data/movielenshead.dat'),
sep="::", header=None, names=['user', 'item', 'rating', 'date', 'title'])
model = PyTensorCoFi()
ev = Evaluator(False)
ev_nogil = Evaluator()
results = {"implementation": [], "measure": []}
for i in range(SAMPLE_SIZE_FOR_TEST):
training, testing = testfm.split.holdoutByRandom(df, 0.05)
model.fit(training)
results["implementation"].append("Cython"), results["measure"].append(ev_nogil.evaluate_model(model, testing)[0])
results["implementation"].append("Python"), results["measure"].append(ev.evaluate_model(model, testing)[0])
#####################
# ANOVA over result #
#####################
assert_equality_in_groups(results, alpha=ALPHA, groups="implementation", test_var="measure")
def test_recommendation_with_testfm(self):
"""
[recommendation.api.GetRecommendation] Test recommendation with testfm
"""
data = np.array(zip(*map(lambda x: (x["user_id"]-1, x["item_id"]-1, 1.),
Inventory.objects.all().values("user_id", "item_id"))), dtype=np.float32)
users, items = zip(*Inventory.objects.all().values_list("user_id", "item_id"))
df = pd.DataFrame({"user": pd.Series(users), "item": pd.Series(items)}, dtype=np.float32)
evaluator = Evaluator(use_multi_threading=False)
tensor = TensorCoFi.get_model_from_cache()
tfm_tensor = PyTensorCoFi()
tfm_tensor.data_map = tensor.data_map
tfm_tensor.users_size = lambda: tensor.users_size()
tfm_tensor.items_size = lambda: tensor.items_size()
tfm_tensor.get_score = lambda user, item: \
np.dot(tfm_tensor.factors[0][tfm_tensor.data_map[tfm_tensor.get_user_column()][user]],
tfm_tensor.factors[1][tfm_tensor.data_map[tfm_tensor.get_item_column()][item]].transpose())
tfm_tensor.train(data.transpose())
items = df.item.unique()
t = evaluator.evaluate_model(tensor, df, all_items=items, non_relevant_count=100)
tfm = evaluator.evaluate_model(tfm_tensor, df, all_items=items, non_relevant_count=100)
assert abs(t[0] - tfm[0]) < 0.15, \
"Difference between testfm implementation and frappe is to high (%f, %f)" % (t[0], tfm[0])
def test_ids_returns_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
inp = [{
"user": 10,
"item": 100
}, {
"user": 10,
"item": 110
}, {
"user": 12,
"item": 120
}]
inp = pd.DataFrame(inp)
tf.fit(inp)
# Test the id in map
uid = tf.data_map[tf.get_user_column()][10]
iid = tf.data_map[tf.get_item_column()][100]
self.assertEquals(uid, 0)
self.assertEquals(iid, 0)
# Test number of factors
self.assertEquals(len(tf.factors[0][uid, :]), tf.number_of_factors)
self.assertEquals(len(tf.factors[1][iid, :]), tf.number_of_factors)
def test_score_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
inp = [{
"user": 10,
"item": 100
}, {
"user": 10,
"item": 110
}, {
"user": 12,
"item": 120
}]
inp = pd.DataFrame(inp)
tf.fit(inp)
uid = tf.data_map[tf.get_user_column()][10]
iid = tf.data_map[tf.get_item_column()][100]
tf.factors[0][uid, 0] = 0
tf.factors[0][uid, 1] = 1
tf.factors[1][iid, 0] = 1
tf.factors[1][iid, 1] = 5
self.assertEqual(0 * 1 + 1 * 5, tf.get_score(10, 100))
def test_fit_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
tf.fit(self.df)
#item and user are row vectors
self.assertEqual(len(self.df.user.unique()), tf.factors[0].shape[0])
self.assertEqual(len(self.df.item.unique()), tf.factors[1].shape[0])
def test_fit_for_python_version(self):
tf = PyTensorCoFi(n_factors=2)
tf.fit(self.df)
#item and user are row vectors
self.assertEqual(len(self.df.user.unique()), tf.factors[0].shape[0])
self.assertEqual(len(self.df.item.unique()), tf.factors[1].shape[0])
from testfm.evaluation.evaluator import Evaluator
from pkg_resources import resource_filename
from testfm.evaluation.parameter_tuning import ParameterTuning
if __name__ == "__main__":
eval = Evaluator(
) # Call this before loading the data to save memory (fork of process takes place)
# Prepare the data
df = pd.read_csv(resource_filename(testfm.__name__,
'data/movielenshead.dat'),
sep="::",
header=None,
names=['user', 'item', 'rating', 'date', 'title'])
print df.head()
training, testing = testfm.split.holdoutByRandom(df, 0.9)
print "Tuning the parameters."
tr, validation = testfm.split.holdoutByRandom(training, 0.7)
pt = ParameterTuning()
pt.set_max_iterations(100)
pt.set_z_value(90)
tf_params = pt.get_best_params(TensorCoFi, tr, validation)
print tf_params
tf = TensorCoFi()
tf.set_params(**tf_params)
tf.fit(training)
print tf.get_name().ljust(50),
print eval.evaluate_model(tf, testing, all_items=training.item.unique())
