def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.PersonalPopularityModel()
updater = rs.PersonalPopularityModelUpdater()
updater.set_model(model)
simple_learner = rs.SimpleLearner()
simple_learner.add_simple_updater(updater)
simple_learner.set_model(model)
learner = rs.LearnerPeriodicDelayedWrapper(
**self.parameter_defaults(period=86400, delay=86400))
learner.set_wrapped_learner(simple_learner)
model = model
learner = learner
return {'config': config, 'model': model, 'learner': learner}
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.PopularityModel()
updater = rs.PopularityTimeFrameModelUpdater(**self.parameter_defaults(
tau=86400
))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
return {
'config': config,
'model': model,
'learner': learner
}
def _config(self, top_k, seed):
model_parameters = self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
user_attributes=None,
item_attributes=None,
)
if (model_parameters['user_attributes'] is not None):
container = rs.FileSparseAttributeContainer()
container.load_from_file(model_parameters['user_attributes'])
model_parameters['user_attributes'] = container
if (model_parameters['item_attributes'] is not None):
container = rs.FileSparseAttributeContainer()
container.load_from_file(model_parameters['item_attributes'])
model_parameters['item_attributes'] = container
model = rs.FmModel(**model_parameters)
updater = rs.FmModelUpdater(
**self.parameter_defaults(learning_rate=0.05, ))
updater.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0.0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(updater)
return (model, [negative_sample_generator], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.TransitionProbabilityModel()
updater = rs.TransitionProbabilityModelUpdater(
**self.parameter_defaults(filter_freq_updates=False,
mode_="normal",
label_transition_mode_=False,
label_file_name_=""))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
filters = [model]
return {'config': config, 'model': model, 'learner': learner}
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.NearestNeighborModel(
**self.parameter_defaults(gamma=0.8,
norm="num",
direction="forward",
gamma_threshold=0,
num_of_neighbors=10))
updater = rs.NearestNeighborModelUpdater(**self.parameter_defaults(
compute_similarity_period=86400, period_mode="time-based"))
updater.set_model(model)
learner = rs.SimpleLearner()
learner.add_simple_updater(updater)
learner.set_model(model)
model = model
learner = learner
filters = [model]
return {'config': config, 'model': model, 'learner': learner}
def _config(self, top_k, seed):
model = rs.PopularityModel()
updater = rs.PopularityTimeFrameModelUpdater(**self.parameter_defaults(
tau=86400
))
updater.set_model(model)
return (model, updater, [], [])
def _config(self, top_k, seed):
model = rs.TransitionProbabilityModel()
updater = rs.TransitionProbabilityModelUpdater(
**self.parameter_defaults(filter_freq_updates=False,
mode_="normal",
label_transition_mode_=False,
label_file_name_=""))
updater.set_model(model)
return (model, updater, [model], [])
def _config(self, top_k, seed):
model = ag.PopularityModel()
updater = ag.PopularityModelUpdater()
updater.set_model(model)
label_filter = ag.LabelFilter(**self.parameter_defaults(
label_file_name=""))
adapter = ag.WhitelistFilter2ModelAdapter()
adapter.set_model(model)
adapter.set_whitelist_filter(label_filter)
return (adapter, [updater, label_filter], []
) #note: do not forget to add filter as an updater
def _fit(self, recommender_data, users, items, matrix):
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
learner = rs.OfflineIteratingLearner(
**self.parameter_defaults(seed=67439852, ))
learner.set_model(model)
learner.add_simple_updater(updater)
learner.set_recommender_data(recommender_data)
return (model, learner)
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner = rs.ImplicitGradientLearner()
learner.add_gradient_updater(updater)
learner.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0.0,
initialize_all=False,
seed=0,
))
learner.set_negative_sample_generator(negative_sample_generator)
pointWise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(pointWise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
fmfilter = rs.FactorModelFilter()
fmfilter.set_model(model)
prediction_creator = rs.PredictionCreatorGlobal(
**self.parameter_defaults(
top_k=10000,
# initial_threshold=1000,
lookback=0))
prediction_creator.set_model(model)
prediction_creator.set_filter(fmfilter)
online_predictor = rs.OnlinePredictor(**self.parameter_defaults(
min_time=0, time_frame=86400, file_name=""))
online_predictor.set_prediction_creator(prediction_creator)
config['loggers'].append(online_predictor)
return {'config': config, 'model': model, 'learner': learner}
def _fit(self, recommender_data, users, items, matrix):
model = rs.PopularityModel()
updater = rs.PopularityModelUpdater()
updater.set_model(model)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
seed=0,
number_of_iterations=0,
shuffle=False,
)
learner.add_updater(updater)
return (model, learner)
def _config(self, top_k, seed):
model = rs.NearestNeighborModel(**self.parameter_defaults(
gamma=0.8,
norm="num",
direction="forward",
gamma_threshold=0,
num_of_neighbors=10
))
updater = rs.NearestNeighborModelUpdater(**self.parameter_defaults(
compute_similarity_period=86400,
period_mode="time-based"
))
updater.set_model(model)
return (model, updater, [])
def recommend(self, users=None, k=100, exclude_known=True):
"""Give toplist recommendations for users.
Parameters
----------
users : list
List of users to give recommendation for.
k : int
Size of toplists
exclude_known : bool
Whether to exclude (user,item) pairs in the train dataset from the toplists.
Returns
-------
pandas.DataFrame
DataFrame of recommendations, with columns **user**, **item** and **rank**.
"""
rs.collect()
dummy_model_filter = rs.DummyModelFilter()
dummy_model_filter.set_items(self.items)
dummy_model_filter.set_users(self.users)
pred_creator = rs.PredictionCreatorPersonalized(
top_k=k, lookback=1 if exclude_known else 0)
pred_creator.set_filter(dummy_model_filter)
pred_creator.set_train_matrix(self.matrix)
pred_creator.set_model(self.model)
ranking_computer = rs.OfflineRankingComputer(top_k=k)
ranking_computer.set_items(self.items)
if users is None:
ranking_computer.set_users(self.users)
else:
ranking_computer.set_users(
rs.VectorInt(pd.Series(users).unique().tolist()))
ranking_computer.set_toplist_creator(pred_creator)
created_objects = rs.get_and_clean()
# rs.initialize_all(created_objects)
for i in created_objects:
rs.run_self_test(i)
preds = ranking_computer.compute()
preds_df = pd.DataFrame({
'user': preds.users,
'item': preds.items,
'rank': preds.ranks
}).sort_values(['user', 'rank'])[['user', 'item', 'rank']]
return preds_df
def readFactorModel(file, dimensions):
"""Utility for reading binary models --saved by online experiments-- into
pandas DataFrames.
"""
r = ag.FactorModelReader()
uif = r.read(file, dimensions)
users = []
user_factors = []
for f in uif.user_factors:
users.append(f.entity)
user_factors.append(f.factors)
items = []
item_factors = []
for f in uif.item_factors:
items.append(f.entity)
item_factors.append(f.factors)
user_df = pd.DataFrame.from_records(user_factors,
columns=range(1, dimensions + 1))
user_df['user'] = users
user_df.set_index('user', inplace=True)
item_df = pd.DataFrame.from_records(item_factors,
columns=range(1, dimensions + 1))
item_df['item'] = items
item_df.set_index('item', inplace=True)
return (user_df, item_df)
def _fit(self, recommender_data, users, items, matrix):
model = rs.EigenFactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
seed=67439852,
))
learner = rs.OfflineEigenFactorModelALSLearner(
**self.parameter_defaults(
number_of_iterations=3,
regularization_lambda=0.0001,
alpha=40,
implicit=1,
clear_before_fit=1,
))
learner.set_model(model)
return (model, learner)
def test_init(self):
df = pd.DataFrame({
'time': [30, 20, 10],
'item': [3, 2, 1],
'duration': [10, 10, 10]
})
a = AvailabilityFilter(df) #todo: needs experiment environment
r = rs.RecDat()
r.time = 21
def fit(self, X, y=None, columns={}):
"""Fit the model to a dataset.
Parameters
----------
X : pandas.DataFrame
The input data, must contain the columns **user** and **item**. May contain the **score** column as well.
y : pandas.Series or list
The target values. If not set (and X doesn't contain the score column), it is assumed to be constant 1 (implicit recommendation).
columns : dict
Optionally the mapping of the input DataFrame's columns' names to the expected ones.
"""
rs.collect()
data = X
if y is None:
if 'score' not in X:
data['score'] = np.ones(len(X))
else:
if 'score' in X:
raise ValueError("y and score column both provided")
else:
data['score'] = y
recommender_data = DataframeData(data, columns=columns)
matrix = recommender_data.get_full_matrix()
users = rs.VectorInt([])
items = rs.VectorInt([])
recommender_data.get_users_into(users)
recommender_data.get_items_into(items)
(model, learner) = self._fit(recommender_data, users, items, matrix)
created_objects = rs.get_and_clean()
rs.initialize_all(created_objects)
for i in created_objects:
rs.run_self_test(i)
self.check_unused_parameters()
learner.fit(recommender_data)
self.objects = created_objects
self.model = model
self.items = items
self.users = users
self.matrix = matrix
self.recommender_data = recommender_data
def test_init(self):
df = pd.DataFrame({
'time': [30, 20, 10],
'item': [3, 2, 1],
'duration': [10, 10, 10]
})
a = prs.AvailabilityFilter(df)
r = rs.RecDat()
r.time = 21
a.run(r)
def _config(self, top_k, seed):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner = rs.ImplicitGradientLearner()
learner.add_gradient_updater(updater)
learner.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0.0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return (model, learner, [])
def _fit(self, recommender_data, users, items, matrix):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
seed=254938879,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(negative_rate=0))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner = rs.OfflineIteratingImplicitLearner(**self.parameter_defaults(
seed=254938879,
number_of_iterations=9,
))
learner.set_gradient_computer(gradient_computer)
learner.set_negative_sample_generator(negative_sample_generator)
learner.set_model(model)
learner.set_recommender_data(recommender_data)
learner.add_gradient_updater(updater)
return (model, learner)
def _config(self, top_k, seed):
model = rs.AsymmetricFactorModel(
**self.parameter_defaults(begin_min=-0.01,
begin_max=0.01,
dimension=10,
use_sigmoid=False,
norm_type="exponential",
gamma=0.8,
initialize_all=False))
gradient_updater = rs.AsymmetricFactorModelGradientUpdater(
**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.AsymmetricFactorModelUpdater()
simple_updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(gradient_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=20,
initialize_all=False,
seed=928357823,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [negative_sample_generator, simple_updater], [])
def _fit(self, recommender_data, users, items, matrix):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
seed=self.parameter_default('factor_seed', 67439852),
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0, initialize_all=False, max_item=-1))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
negative_sample_generator.add_updater(gradient_computer)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
**self.parameter_defaults(
seed=self.parameter_default('learner_seed', 254938879),
number_of_iterations=9,
shuffle=True,
))
learner.add_iterate_updater(negative_sample_generator)
return (model, learner)
def _fit(self, recommender_data, users, items, matrix):
model = rs.NearestNeighborModel(
gamma=1,
norm="off",
direction="both",
gamma_threshold=0,
num_of_neighbors=self.parameter_default("num_of_neighbors", 10),
)
updater = rs.NearestNeighborModelUpdater(
period_mode="off",
)
updater.set_model(model)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
seed=254938879,
number_of_iterations=0,
shuffle=False,
)
learner.add_updater(updater)
return (model, learner)
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
seed=0,
out_file=None,
filters=[],
loggers=[],
)
model = rs.SvdppModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
use_sigmoid=False,
norm_type="exponential",
gamma=0.8,
user_vector_weight=0.5,
history_weight=0.5
))
gradient_updater = rs.SvdppModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
learner = rs.ImplicitGradientLearner()
learner.add_gradient_updater(gradient_updater)
learner.add_simple_updater(simple_updater)
learner.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=20,
initialize_all=False,
seed=928357823,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return {
'config': config,
'model': model,
'learner': learner
}
def _config(self, top_k, seed):
#config = self.parameter_defaults(
# top_k=100,
# evaluation_start_time=0,
# seed=0,
# out_file=None,
# filters=[],
# loggers=[],
#)
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=100,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [negative_sample_generator], [])
def _config(self, top_k, seed):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner_parameters = self.parameter_defaults(
number_of_iterations=3,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name="",
timeframe_length=0,
)
if (learner_parameters['timeframe_length'] == 0):
learner_parameters.pop('timeframe_length', None)
learner = rs.OfflineImplicitGradientLearner(**learner_parameters)
else:
learner = rs.PeriodicTimeframeImplicitGradientLearner(
**learner_parameters)
learner.set_model(model)
learner.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return (model, learner, [], [])
def predict(self, X):
"""Predict the target values on X.
Parameters
----------
X : pandas.DataFrame
The input data, must contain the columns **user** and **item**.
Returns
-------
list
List of predictions
"""
predictor = rs.MassPredictor()
predictor.set_model(self.model)
return predictor.predict(X['user'].tolist(), X['item'].tolist())
def _config(self, top_k, seed):
model = rs.CombinedModel(**self.parameter_defaults(
los_file_name="my_log_file",
log_frequency=100000,
use_user_weights=False,
))
pop_model = rs.PopularityModel()
model.add_model(pop_model)
pop_updater = rs.PopularityModelUpdater()
pop_updater.set_model(pop_model)
factor_model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
model.add_model(factor_model)
factor_updater = rs.FactorModelGradientUpdater(
**self.parameter_defaults(learning_rate=0.05,
regularization_rate=0.0))
factor_updater.set_model(factor_model)
objective = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(objective)
gradient_computer.set_model(factor_model)
gradient_computer.add_gradient_updater(factor_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=10,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [pop_updater, negative_sample_generator], [], [])
def config(self, elems):
config = self.parameter_defaults(
top_k=100,
min_time=0,
loggers=[],
)
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
learner = rs.OfflineImplicitGradientLearner(
**self.parameter_defaults(number_of_iterations=3,
start_time=-1,
period_length=86400,
write_model=False,
read_model=False,
clear_model=False,
learn=True,
base_out_file_name="",
base_in_file_name=""))
learner.set_model(model)
# learner.set_recommender_data_iterator(elems['recommender_data_iterator'])
learner.add_gradient_updater(updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=0,
initialize_all=False,
))
# negative_sample_generator.set_train_matrix(elems['train_matrix'])
# negative_sample_generator.set_items(elems['items'])
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return {'config': config, 'model': model, 'learner': learner}
def _fit(self, recommender_data, users, items, matrix):
model = rs.SvdppModel(**self.parameter_defaults(
begin_min=self.parameter_default("begin_min", -0.01),
begin_max=self.parameter_default("begin_max", 0.01),
dimension=self.parameter_default("dimension", 10),
use_sigmoid=False,
norm_type="constant",
gamma=1,
user_vector_weight=0.5,
history_weight=0.5))
gradient_updater = rs.SvdppModelGradientUpdater(
**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(gradient_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(
negative_rate=9, initialize_all=False, max_item=-1))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
negative_sample_generator.add_updater(gradient_computer)
learner = rs.OfflineIteratingOnlineLearnerWrapper(
**self.parameter_defaults(
seed=254938879,
number_of_iterations=20,
shuffle=True,
))
learner.add_early_updater(simple_updater)
learner.add_iterate_updater(negative_sample_generator)
return (model, learner)
