alpenglow.cpp package¶

The classes in this module are usually not used directly, but instead through the alpenglow.Getter class. For more info, read

Note that there are some C++ classes that have no python interface. These are not documented here.

Filters¶

The function of the filter interface is limiting the available set of items. Current filters are whitelist-type filters, implementing alpenglow.cpp.WhitelistFilter.

To use a filter in an experiment, wrap the model into the filter using alpenglow.cpp.WhitelistFilter2ModelAdapter.

Example:

class LabelExperiment(prs.OnlineExperiment):
    '''Sample experiment illustrating the usage of LabelFilter. The
    experiment contains a PopularityModel and a LabelFilter.'''
    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)

class alpenglow.cpp.WhitelistFilter¶

Bases: sip.wrapper

Filter interface for classes that implement white list type filtering.

active(RecDat* rec_dat)¶

Returns whether the item is active for the user.

Parameters: rec_dat (RecDat*) – The sample containing the user and the item.
Returns: Whether the item is available for the user.
Return type: bool

get_whitelist(int user)¶

Returns the set of active items for the user.

Parameters: user (int) – The whitelist will be computed for the given user.
Returns: The list of allowed items for the given user. The second element of the pair is an upper bound for the score of the item for the given user (or the score itself).
Return type: std::vector<int,double>

class alpenglow.cpp.WhitelistFilter2ModelAdapter¶

Bases: alpenglow.cpp.Model

Adapter class to filter the output of a model.

By chaining the adapter in front of a model, we can filter the output of the model, allowing only items on the whitelist filter to the toplist.

Note that as the currently implemented whitelists contain only a few elements, the adapter interface algorithm is optimized for short whitelists. We ignore the RSI of the model.

prediction(RecDat*)¶

Returns filtered prediction value. The score of the items that are not on the whitelist is set to 0. Overrides method inherited from alpenglow.cpp.Model.prediction(), see also documentation there.

Parameters: rec_dat (RecDat*) – The sample we query the prediction for.
Returns: The prediction score, modified based on the filter. If the item is not on the whitelist, the returned score is 0, otherwise the score returned by the model.
Return type: double

self_test()¶

Tests whether the model and the whitelist filter is set.

Returns: Whether all necessary objects are set.
Return type: bool

set_model(Model* model)¶

Sets model whose output is filtered.

Parameters: model (Model*) – The model whose output is filtered.

set_whitelist_filter(WhitelistFilter* whitelist_filter)¶

Sets whitelist filter.

Parameters: whitelist_filter (WhitelistFilter*) – The whitelist filter we use for filtering the output of the model.

class alpenglow.cpp.LabelFilterParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.LabelFilter. See documentation there.

label_file_name¶

class alpenglow.cpp.LabelFilter¶

Bases: alpenglow.cpp.WhitelistFilter, alpenglow.cpp.Updater

White list filter class that allows items having the same label (e.g. artist) as the item that was previously interacted by the user. Requires updates (i.e., add the object to the online experiment as an updater).

Sample usage:

import alpenglow as ag

model = ag.PopularityModel()
updater = ag.PopularityModelUpdater()
updater.set_model(model)
label_filter = ag.LabelFilter(
    label_file_name = "/path/to/file/"
)
adapter = ag.WhitelistFilter2ModelAdapter()
adapter.set_model(model)
adapter.set_whitelist_filter(label_filter)

active(RecDat*)¶: Implements alpenglow.cpp.WhitelistFilter.active().

get_whitelist(int user)¶: Implements alpenglow.cpp.WhitelistFilter.get_whitelist().

self_test()¶: Returns true.

update(RecDat* rec_dat)¶: Implements alpenglow.cpp.Updater.update().

class alpenglow.cpp.AvailabilityFilter¶

Bases: alpenglow.cpp.WhitelistFilter, alpenglow.cpp.NeedsExperimentEnvironment

This filter filters the set of available items based on (time,itemId,duration) triplets. These have to be preloaded before using this filter.

Sample code

f = rs.AvailabilityFilter()
f.add_availability(20,1,10) #item 1 is available in the time interval (20,30)

active(RecDat* rec_dat)¶

Returns whether the item is active for the user.

Parameters: rec_dat (RecDat*) – The sample containing the user and the item.
Returns: Whether the item is available for the user.
Return type: bool

add_availability()¶

get_whitelist(int user)¶

Returns the set of active items for the user.

Parameters: user (int) – The whitelist will be computed for the given user.
Returns: The list of allowed items for the given user. The second element of the pair is an upper bound for the score of the item for the given user (or the score itself).
Return type: std::vector<int,double>

self_test()¶

Offline evaluators¶

Use offline evaluators in traditional, fixed train/test split style learning. Check the code of alpenglow.offline.OfflineModel.OfflineModel descendants for usage examples.

class alpenglow.cpp.PrecisionRecallEvaluatorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.PrecisionRecallEvaluator. See documentation there.

cutoff¶

test_file_name¶

test_file_type¶

time¶

class alpenglow.cpp.PrecisionRecallEvaluator¶

Bases: alpenglow.cpp.OfflineEvaluator

evaluate()¶

self_test()¶

set_model()¶

set_train_data()¶

class alpenglow.cpp.OfflineRankingComputerParameters¶

Bases: sip.wrapper

top_k¶

class alpenglow.cpp.OfflinePredictions¶

Bases: sip.wrapper

items¶

ranks¶

scores¶

users¶

class alpenglow.cpp.OfflineRankingComputer¶

Bases: sip.wrapper

compute()¶

set_items()¶

set_toplist_creator()¶

set_users()¶

class alpenglow.cpp.OfflineEvaluator¶

Bases: sip.wrapper

evaluate()¶

self_test()¶

Recommender data¶

This module contains the classes that are responsible for reading in the dataset and serving it to other classes of the experiment.

Interface alpenglow.cpp.RecommenderData is the anchestor for classes that read in the dataset. The two most frequently used implementations are alpenglow.cpp.DataframeData and alpenglow.cpp.LegacyRecommenderData.

Interface alpenglow.cpp.RecommenderDataIterator is the anchestor for classes that serve the data to the classes in the online experiment. See The anatomy of an online experiment for general information. The most frequently used implementations are alpenglow.cpp.ShuffleIterator and alpenglow.cpp.SimpleIterator.

class alpenglow.cpp.RandomOnlineIteratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.RandomOnlineIterator. See documentation there.

seed¶

class alpenglow.cpp.RandomOnlineIterator¶

Bases: alpenglow.cpp.RecommenderDataIterator

This RecommenderDataIterator shuffles the samples keeping the timestamps ordered and serves them in a fixed random order. Note that the samples are modified, the nth sample of the random order gets the timestamp of the nth sample of the original data.

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get()

get_actual()¶: See alpenglow.cpp.RecommenderDataIterator.get_actual()

get_following_timestamp()¶: See alpenglow.cpp.RecommenderDataIterator.get_following_timestamp()

get_future(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get_future()

next()¶: See alpenglow.cpp.RecommenderDataIterator.next()

self_test()¶: Tests if the class is set up correctly.

class alpenglow.cpp.ShuffleIteratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.ShuffleIterator. See documentation there.

seed¶

class alpenglow.cpp.ShuffleIterator¶

Bases: alpenglow.cpp.RecommenderDataIterator

autocalled_initialize()¶: See alpenglow.cpp.Initializable.autocalled_initialize()

get(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get()

get_actual()¶: See alpenglow.cpp.RecommenderDataIterator.get_actual()

get_following_timestamp()¶: See alpenglow.cpp.RecommenderDataIterator.get_following_timestamp()

get_future(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get_future()

next()¶: See alpenglow.cpp.RecommenderDataIterator.next()

self_test()¶: Tests if the class is set up correctly.

class alpenglow.cpp.DataframeData¶

Bases: alpenglow.cpp.RecommenderData

add_recdats()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get()¶

size()¶

class alpenglow.cpp.SimpleIterator¶

Bases: alpenglow.cpp.RecommenderDataIterator

This RecommenderDataIterator serves the samples in the original order.

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get()

get_actual()¶: See alpenglow.cpp.RecommenderDataIterator.get_actual()

get_following_timestamp()¶: See alpenglow.cpp.RecommenderDataIterator.get_following_timestamp()

get_future(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get_future()

next()¶: See alpenglow.cpp.RecommenderDataIterator.next()

class alpenglow.cpp.RecommenderDataIterator¶

Bases: alpenglow.cpp.Initializable

Iterator-like interface that serves the dataset as a time series in the online experiment. The class also provides random access to the time series. Implementations assume that the data is already sorted by time.

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get(int index)¶

This method provides random access to the past samples of the time series. It reaches an error if index is larger than the index of the current sample, i.e. if one tries to access data from the future through this function.

Use get_counter() to get the index of the newest available sample. Use get_future() to get data from the future.

Parameters: index (int) – The index of sample to return.
Returns: A pointer to the sample.
Return type: RecDat*

get_actual()¶

Returns: A pointer to the actual sample.
Return type: RecDat*

get_counter()¶

Returns: Index of the actual sample.
Return type: int

get_following_timestamp()¶

Returns: The timestamp of the next sample in the future, i.e., when will the next event happen.
Return type: double

get_future(int index)¶

This method provides random access to the complete time series, including future.

Use get() to safely get data from the past.

Parameters: index (int) – The index of sample to return.
Returns: A pointer to the sample.
Return type: RecDat*

has_next()¶

Returns: Whether the iterator has’t reached the end of the time series.
Return type: bool

next()¶

Advances the iterator and returns a pointer to the following sample.

Returns: A pointer to the following sample.
Return type: RecDat*

restart()¶: Restarts the iterator.

self_test()¶: Tests if the class is set up correctly.

set_recommender_data(RecommenderData* data)¶

Sets the dataset that we iterate on.

Parameters: data (RecommenderData*) – The dataset.

size()¶

Returns: The number of the samples.
Return type: int

class alpenglow.cpp.FactorRepr¶

Bases: sip.wrapper

entity¶

factors¶

class alpenglow.cpp.UserItemFactors¶

Bases: sip.wrapper

item_factors¶

user_factors¶

class alpenglow.cpp.FactorModelReader¶

Bases: sip.wrapper

read()¶

class alpenglow.cpp.EigenFactorModelReader¶

Bases: sip.wrapper

read()¶

class alpenglow.cpp.RandomIteratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.RandomIterator. See documentation there.

seed¶

shuffle_mode¶

class alpenglow.cpp.RandomIterator¶

Bases: alpenglow.cpp.RecommenderDataIterator

RecommenderDataIterator class that completely shuffles data. Note that the timestamps won’t stay in increasing order, that may cause faults in some time-dependent models.

Sample offline usage: alpenglow.cpp.OfflineIteratingOnlineLearnerWrapper

autocalled_initialize()¶: See alpenglow.cpp.Initializable.autocalled_initialize().

get(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get()

get_actual()¶: See alpenglow.cpp.RecommenderDataIterator.get_actual()

get_following_timestamp()¶: See alpenglow.cpp.RecommenderDataIterator.get_following_timestamp()

get_future(int index)¶: See alpenglow.cpp.RecommenderDataIterator.get_future()

next()¶: See alpenglow.cpp.RecommenderDataIterator.next()

restart()¶: Restarts the iterator. In auto_shuffle mode it also reshuffles the dataset.

self_test()¶: Tests if the class is set up correctly.

shuffle()¶: Reshuffles the dataset.

class alpenglow.cpp.InlineAttributeReader¶

Bases: sip.wrapper

read_attribute()¶

self_test()¶

class alpenglow.cpp.RecDat¶

Bases: sip.wrapper

Struct representing a training instance.

category¶: model specific, mostly deprecated

eval¶: whether this record is to be used for evaluation

id¶: index of row in DataFrame; 0 based index if missing

item¶: item id

score¶: value of score column; 1 if column missing

time¶: value of time column; 0 based index if missing

user¶: user id

class alpenglow.cpp.RecPred¶

Bases: sip.wrapper

prediction¶

score¶

class alpenglow.cpp.RecommenderData¶

Bases: alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

get()¶

get_all_items()¶

get_all_users()¶

get_full_matrix()¶

get_items_into()¶

get_max_item_id()¶

get_max_user_id()¶

get_rec_data()¶

get_users_into()¶

set_rec_data()¶

size()¶

class alpenglow.cpp.LegacyRecommenderDataParameters¶

Bases: sip.wrapper

experiment_termination_time¶

file_name¶

type¶

class alpenglow.cpp.LegacyRecommenderData¶

Bases: alpenglow.cpp.RecommenderData

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

read_from_file()¶

set_attribute_container()¶

Utils¶

This module contains miscellaneous helper classes.

class alpenglow.cpp.PeriodComputerParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.PeriodComputer. See documentation there.

period_length¶

period_mode¶

start_time¶

class alpenglow.cpp.PeriodComputer¶

Bases: alpenglow.cpp.Updater, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

Helper class to compute periods in the time series, e.g., update a model weekly or log statistics after every 10000th sample.

The class has two modes:

period_mode==”time”: the time is based on the timestamp queried from recommender_data_iterator.
period_mode==”samplenum”: the time is based on the number of calls to update().

The class is notified about the progress of time by calls to the update() function.

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

end_of_period()¶

True if the current sample is the last in the current period.

If period_mode==time, the function returns true if the timestamp of the next sample falls into the next timeframe. If period_mode==samplenum, the function returns true if the next call to update() will increase the number of the timeframe.

Returns: True if the current sample is the last in the current period.
Return type: bool

get_period_num()¶

Returns the number of the current period, i.e. timestamp/period_length+1.

If period_mode==time, timestamp is the time field of the current recdat. If period_mode==samplenum, timestamp is the number of calls to the update() function.

Returns: The index of the current period.
Return type: int

self_test()¶: Returns true.

set_parameters()¶

set_recommender_data_iterator()¶

update(RecDat*)¶

Notifies the class that time has changed.

If period_mode==samplenum, the time is increased by 1. If period_mode==time, the timestamp of the next sample is queried from recommender_data_iterator. The parameter value is not used at all.

Parameters: rec_dat (RecDat*) – The current sample. Not used by the implementation.

class alpenglow.cpp.SpMatrix¶

Bases: sip.wrapper

Implements a sparse matrix. Obtaining a row is O(1), obtaining a value in a row is logarithmic in the length of the row.

clear()¶: Deletes all elements and rows from the matrix.

erase(int row_id, int col_id)¶

Removes the value from (row_id,col_id). If no value exists at that position, nothing happens.

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.

get(int row_id, int col_id)¶

Returns the value of (row_id,col_id). If no value exists at that position, returns 0.

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.

Returns

The value of (row_id,col_id) or 0 if no value.

Return type

double

has_value(int row_id, int col_id)¶

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.

Returns

Whether the matrix has value at (row_id,col_id).

Return type

bool

increase(int row_id, int col_id, double value)¶

Increases (row_id,col_id) with value or inserts value if (row_id,col_id) doesn’t exist (i.e. not existing value is equivalent to 0 value). The matrix is expanded and the row is created as necessary.

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.
value (double) – The new value.

insert(int row_id, int col_id, double value)¶

Inserts value to (row_id,col_id). If the value already exists, the insertion fails silently (the container doesn’t change. The matrix is expanded and the row is created as necessary.

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.
value (double) – The value to be inserted.

read_from_file(std::string file_name)¶

Reads matrix from file file_name. Format of the lines of the file is “row_id col_id value” . In case of repeating row_id col_id pairs, the first value will be used. Writes the size of the matrix to stderr. If the file can’t be opened, fails silently not changing the container.

Parameters: file_name (std::string) – The name of the file to read from.

resize(int row_id)¶

Expands the matrix to contain at least row_id rows. The matrix won’t be shrunken. Creates an empty row object at index row_id.

Parameters: row_id (int) – The index of the row.

row_size(int row_id)¶

Returns the number of the elements of the sparse row row_id. Returns 0 if the row doesn’t exist.

Parameters: row_id (int) – The index of the row.
Returns: The size of the row or 0 if the row doesn’t exist.
Return type: int

size()¶

Returns
Return type: The largest row index.

update(int row_id, int col_id, double value)¶

Updates (row_id,col_id) to value or inserts value if (row_id,col_id) doesn’t exist. The matrix is expanded and the row is created as necessary.

Parameters

row_id (int) – The index of the row.
col_id (int) – The index of the column.
value (double) – The new value.

write_into_file(std::string file_name)¶

Writes matrix into file file_name. Format of the lines of the file is “row_id col_id value” , space separated. If the file can’t be opened, fails silently.

Parameters: file_name (std::string) – The name of the file to write into.

class alpenglow.cpp.Random¶

Bases: sip.wrapper

This class implements a pseudorandom generator.

The next state is computed as state = state*multiplier % mod where multiplier = 48271 and mod = 2147483647.

The initial state value can be set through the parameter of the constructor, or using the set() function.

Most of the functions are available without the max parameter providing a double value between [0,1) with a similar distribution as the discrete functions.

get(int max)¶

Get a uniform pseudorandom value between 0 and max-1.

Largest possible value is max-1.

Parameters: max (int) – The upper bound of the random value.
Returns: The pseudorandom value.
Return type: int

get_arctg(double y, int max)¶

Get a pseudorandom value between 0 and max-1 with decaying distribution.

Probability of smaller values is larger. The largest possible value is max-1.

Parameters

y (double) – The parameter of the distribution.
max (int) – The upper bound of the random value.

Returns

The pseudorandom value.

Return type

double

get_boolean(double prob)¶

Get a pseudorandom true-or-false value.

Parameters: prob (double) – The probability of the true value.
Returns: The pseudorandom value.
Return type: bool

get_discrete(std::vector<double>& distribution)¶

Get a pseudorandom value following a given discrete distribution.

The sum of the values in the given vector should be 1. If the sum is more or less, the probability of the largest value(s) will differ from the specified probability. The values should be non-negative.

Parameters: distribution (std::vector<double>&) – The probability of output value i is distribution[i].
Returns: The pseudorandom value.
Return type: int

get_geometric(double prob, int max)¶

Get a pseudorandom value between 0 and max-1 with geometric distribution.

Probability of smaller values is larger. The largest possible value is max-1. The probability of value i is proportional to (1-prob)*prob^i`.

Parameters

prob (double) – The parameter of the distribution.
max (int) – The upper bound of the random value.

Returns

The pseudorandom value.

Return type

int

get_linear(int max)¶

Get a pseudorandom value between 0 and max-1 with linear distribution.

Probability of smaller values is smaller. The largest possible value is max-1.

Parameters: max (int) – The upper bound of the random value.
Returns: The pseudorandom value.
Return type: int

self_test()¶

set(int seed)¶: Set the state of the random generator.

class alpenglow.cpp.RankComputerParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.RankComputer. See documentation there.

exclude_known¶

random_seed¶

top_k¶

class alpenglow.cpp.RankComputer¶

Bases: alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get_rank()¶

self_test()¶

set_items()¶

set_model()¶

set_parameters()¶

set_top_pop_container()¶

set_train_matrix()¶

class alpenglow.cpp.Bias¶

Bases: sip.wrapper

clear()¶

get()¶

init()¶

update()¶

class alpenglow.cpp.SparseAttributeContainerParameters¶: Bases: sip.wrapper

class alpenglow.cpp.SparseAttributeContainer¶

Bases: sip.wrapper

get_max_attribute_index()¶

class alpenglow.cpp.FileSparseAttributeContainer¶

Bases: alpenglow.cpp.SparseAttributeContainer

load_from_file()¶

class alpenglow.cpp.Recency¶

Bases: sip.wrapper

get()¶

update()¶

class alpenglow.cpp.PowerLawRecencyParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.PowerLawRecency. See documentation there.

delta_t¶

exponent¶

class alpenglow.cpp.PowerLawRecency¶

Bases: alpenglow.cpp.Recency

get()¶

update()¶

class alpenglow.cpp.PopContainer¶

Bases: sip.wrapper

Container for storing the popularity of items.

clear()¶: Clears the container. After this operations, the popularity of all the items is 0.

get(int item)¶

Returns the popularity value of the item.

Parameters: item (int) – The item.
Returns: The popularity value of the item.
Return type: int

increase(int item)¶

Increases the popularity of the item.

Parameters: item (int) – The item.

reduce(int item)¶

Reduces the popularity of the item.

Parameters: item (int) – The item.

class alpenglow.cpp.TopPopContainer¶

Bases: sip.wrapper

Helper class for storing the items sorted by popularity.

Sample code

x = rs.TopPopContainer()
x.increase(1)
x.increase(1)
x.increase(3)
x.increase(4)
x.increase(1)

print("The most popular item is")
print(x.get_item(0)) #returns 1
print("The second most popular item is")
print(x.get_item(1)) #returns 3 or 4

create(int item)¶: Adds an item to the container. The item will have 0 popularity, but will be counted in size().

get_item(int index)¶

Returns the index’th item from the popularity toplist.

Parameters: index (int) – Index of the item in the popularity toplist. The index of the most popular item is 0.
Returns: The appropriate item from the toplist.
Return type: int

increase(int item)¶: Increases the popularity of the item.

size()¶

Returns the length of the complete toplist, i.e. the number of items having at least a popularity of 1 or that were added through create().

Returns: The length of the complete toplist.
Return type: int

class alpenglow.cpp.ToplistCreatorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.ToplistCreator. See documentation there.

exclude_known¶

top_k¶

class alpenglow.cpp.ToplistCreator¶

Bases: alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run()¶

self_test()¶

set_items()¶

set_model()¶

set_train_matrix()¶

class alpenglow.cpp.ToplistCreatorGlobalParameters¶

Bases: alpenglow.cpp.ToplistCreatorParameters

Constructor parameter struct for alpenglow.cpp.ToplistCreatorGlobalParameters : public ToplistCreator. See documentation there.

initial_threshold¶

class alpenglow.cpp.ToplistCreatorGlobal¶

Bases: alpenglow.cpp.ToplistCreator

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run()¶

self_test()¶

set_filter()¶

class alpenglow.cpp.ToplistCreatorPersonalizedParameters¶

Bases: alpenglow.cpp.ToplistCreatorParameters

Constructor parameter struct for alpenglow.cpp.ToplistCreatorPersonalizedParameters : public ToplistCreator. See documentation there.

class alpenglow.cpp.ToplistCreatorPersonalized¶

Bases: alpenglow.cpp.ToplistCreator

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run()¶

self_test()¶

Gradient computers¶

This module contains the gradient computer classes that implement gradient computation necessary in gradient methods. See alpenglow.experiments.FactorExperiment for an example.

class alpenglow.cpp.GradientComputer¶

Bases: alpenglow.cpp.Updater

add_gradient_updater()¶

self_test()¶: Returns true.

set_model()¶

class alpenglow.cpp.GradientComputerPointWise¶

Bases: alpenglow.cpp.GradientComputer

self_test()¶: Returns true.

set_objective()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

Objectives¶

This module contains the implementation of objective functions that are necessary for gradient computation in gradient learning methods. See alpenglow.experiments.FactorExperiment for a usage example.

class alpenglow.cpp.ObjectiveMSE¶

Bases: alpenglow.cpp.ObjectivePointWise

get_gradient()¶

class alpenglow.cpp.ObjectivePointWise¶

Bases: sip.wrapper

get_gradient()¶

self_test()¶

class alpenglow.cpp.ObjectivePairWise¶

Bases: sip.wrapper

self_test()¶

class alpenglow.cpp.ObjectiveListWise¶

Bases: sip.wrapper

get_gradient()¶

self_test()¶

General interfaces¶

This module contains the general interfaces that are implemented by classes belonging to different modules.

class alpenglow.cpp.Initializable¶

Bases: sip.wrapper

This interface signals that the implementing class has to be initialized by the experiment runner. The experiment runner calls the initialize() method, which in return calls the class-specific implementation of autocalled_initialize() and sets the is_initialized() flag if the initialization was successful. The autocalled_initialize() method can check whether the neccessary dependencies have been initialized or not before initializing the instance; and should return the success value accordingly.

If the initialization was not successful, the experiment runner keeps trying to initialize the not-yet initialized objects, thus resolving dependency chains.

Initializing and inheritance. Assume that class Parent implements Initializable, and the descendant Child needs further initialization. In that case Child has to override autocalled_initialize(), and call Parent::autocalled_initialize() in the overriding function first, continuing only if the parent returned true. If the init of the parent was succesful, but the children failed, then the children has to store the success of the parent and omit calling the initialization of the parent later.

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

initialize()¶

Returns: Whether the initialization was successful.
Return type: bool

is_initialized()¶

Returns: Whether the component has already been initialized.
Return type: bool

class alpenglow.cpp.Updater¶

Bases: sip.wrapper

Interface for updating alpenglow.cpp.Model instances or other objects of the simulation. Objects may implement this interface themselves or have one or more associated Updater types.

Examples:

alpenglow.cpp.TransitionProbabilityModel and alpenglow.cpp.TransitionProbabilityModelUpdater
alpenglow.cpp.PopularityModel has two updating algorithms:

alpenglow.cpp.PopularityModelUpdater

alpenglow.cpp.PopularityTimeframeModelUpdater

alpenglow.cpp.PeriodComputer implements the Updater interface

In the online experiment, updaters are organized into a chain. See The anatomy of an online experiment for details.

self_test()¶: Returns true.

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.NeedsExperimentEnvironment¶

Bases: sip.wrapper

set_experiment_environment()¶

Negative sample generators¶

All the samples in an implicit dataset are positive samples. To make gradient methods work, we need to provide negative samples too. This module contains classes that implement different negative sample generation algorithms. These classes implement alpenglow.cpp.NegativeSampleGenerator. The most frequently used implementation is alpenglow.cpp.UniformNegativeSampleGenerator.

class alpenglow.cpp.UniformNegativeSampleGeneratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.UniformNegativeSampleGenerator. See documentation there.

filter_repeats¶

negative_rate¶

seed¶

class alpenglow.cpp.UniformNegativeSampleGenerator¶

Bases: alpenglow.cpp.NegativeSampleGenerator, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

generate()¶

self_test()¶: Returns true.

set_items()¶

set_train_matrix()¶

class alpenglow.cpp.PooledPositiveSampleGeneratorParameters¶

Bases: sip.wrapper

pool_size¶

positive_rate¶

seed¶

class alpenglow.cpp.PooledPositiveSampleGenerator¶

Bases: alpenglow.cpp.NegativeSampleGenerator

Generates positive samples from a pool.

For details, see:

Frigó, E., Pálovics, R., Kelen, D., Kocsis, L., & Benczúr, A. (2017). Online ranking prediction in non-stationary environments. Section 3.5.

generate()¶

get_implicit_train_data()¶

self_test()¶: Returns true.

class alpenglow.cpp.NegativeSampleGenerator¶

Bases: alpenglow.cpp.Updater

add_updater()¶

self_test()¶: Returns true.

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

Offline learners¶

Use offline learners in traditional, fixed train/test split style learning. Check the code of alpenglow.offline.OfflineModel.OfflineModel descendants for usage examples.

class alpenglow.cpp.OfflineIteratingOnlineLearnerWrapperParameters¶

Bases: sip.wrapper

number_of_iterations¶

seed¶

shuffle¶

class alpenglow.cpp.OfflineIteratingOnlineLearnerWrapper¶

Bases: alpenglow.cpp.OfflineLearner

add_early_updater()¶

add_iterate_updater()¶

add_updater()¶

fit()¶

self_test()¶

class alpenglow.cpp.OfflineEigenFactorModelALSLearnerParameters¶

Bases: sip.wrapper

alpha¶

clear_before_fit¶

implicit¶

number_of_iterations¶

regularization_lambda¶

class alpenglow.cpp.OfflineEigenFactorModelALSLearner¶

Bases: alpenglow.cpp.OfflineLearner

fit()¶

iterate()¶

self_test()¶

set_copy_from_model()¶

set_copy_to_model()¶

set_model()¶

class alpenglow.cpp.OfflineLearner¶

Bases: sip.wrapper

fit()¶

self_test()¶

class alpenglow.cpp.OfflineExternalModelLearnerParameters¶

Bases: sip.wrapper

in_name_base¶

mode¶

out_name_base¶

class alpenglow.cpp.OfflineExternalModelLearner¶

Bases: alpenglow.cpp.OfflineLearner

fit()¶

set_model()¶

Loggers¶

Loggers implement evaluators, statistics etc. in the online experiment. These classes implement interface alpenglow.cpp.Logger. See The anatomy of an online experiment for a general view.

class alpenglow.cpp.OnlinePredictions¶

Bases: sip.wrapper

ids¶

items¶

ranks¶

scores¶

times¶

users¶

class alpenglow.cpp.PredictionLogger¶

Bases: alpenglow.cpp.Logger

get_predictions()¶

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_prediction_creator()¶

class alpenglow.cpp.RankingLog¶

Bases: sip.wrapper

id¶

item¶

prediction¶

rank¶

score¶

time¶

user¶

class alpenglow.cpp.RankingLogs¶

Bases: sip.wrapper

logs¶

top_k¶

class alpenglow.cpp.MemoryRankingLoggerParameters¶

Bases: sip.wrapper

evaluation_start_time¶

memory_log¶

out_file¶

random_seed¶

top_k¶

class alpenglow.cpp.MemoryRankingLogger¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

get_ranking_logs()¶

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_items()¶

set_model()¶

set_ranking_logs()¶

set_top_pop_container()¶

set_train_matrix()¶

class alpenglow.cpp.ProceedingLogger¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_data_iterator()¶

class alpenglow.cpp.TransitionModelLoggerParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.TransitionModelLogger. See documentation there.

period_length¶

timeline_logfile_name¶

top_k¶

toplist_length_logfile_basename¶

class alpenglow.cpp.TransitionModelLogger¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_model()¶

set_pop_container()¶

set_train_matrix()¶

class alpenglow.cpp.Logger¶

Bases: sip.wrapper

Interface for evaluating the model and logging results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnineExperiment calls loggers for each sample and at the end of the experiment. See alpenglow.cpp.OnineExperiment for details.

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

class alpenglow.cpp.OnlinePredictorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.OnlinePredictor. See documentation there.

evaluation_start_time¶

file_name¶

time_frame¶

class alpenglow.cpp.OnlinePredictor¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_prediction_creator()¶

class alpenglow.cpp.MemoryUsageLogger¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_data_iterator()¶

class alpenglow.cpp.InterruptLogger¶

Bases: alpenglow.cpp.Logger

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.ConditionalMetaLogger¶

Bases: alpenglow.cpp.Logger

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

set_logger()¶

should_run()¶

class alpenglow.cpp.ListConditionalMetaLoggerParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.ListConditionalMetaLogger. See documentation there.

should_run_vector¶

class alpenglow.cpp.ListConditionalMetaLogger¶

Bases: alpenglow.cpp.ConditionalMetaLogger

should_run()¶

class alpenglow.cpp.InputLoggerParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.InputLogger. See documentation there.

output_file¶

class alpenglow.cpp.InputLogger¶

Bases: alpenglow.cpp.Logger, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

run(RecDat* rec_dat)¶

Evaluates the model and logs results, statistics, simulation data, debug info etc.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this method. It is not allowed to modify the model or other simulation objects through this function.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

self_test()¶: Returns true.

Online experiment¶

The central classes of the online experiments.

class alpenglow.cpp.OnlineExperimentParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.OnlineExperiment. See documentation there.

evaluation_start_time¶

exclude_known¶

experiment_termination_time¶

initialize_all¶

max_item¶

max_user¶

random_seed¶

top_k¶

class alpenglow.cpp.OnlineExperiment¶

Bases: sip.wrapper

The central class of the online experiment.

It queries samples from the dataset, then one-by-one for each sample

calls loggers that are set using add_logger(),
updates the environment and common statistics, see alpenglow.cpp.ExperimentEnvironment,
calls the updaters that are set using add_updater().

At the end of the experiment, it calls end loggers that are set using add_end_logger().

See alpenglow.OnlineExperiment.OnlineExperiment for details.

add_end_logger(Logger* logger)¶

Adds a logger instance, that will be called once at the end of the experiment.

Parameters: logger (Logger*) – Pointer to the logger to be added.

add_logger(Logger* logger)¶

Adds a logger instance.

Parameters: logger (Logger*) – Pointer to the logger to be added.

add_updater(Updater* updater)¶

Adds an updater.

Parameters: updater (Updater*) – Pointer to the updater to be added.

inject_experiment_environment_into(NeedsExperimentEnvironment* object)¶

Sets the experiment environment into another object that requires it.

In the online experiment, this method is automatically called with all the objects that implement alpenglow.cpp.NeedsExperimentEnvironment, injecting the dependency where it is necessary. See the code of alpenglow.OnlineExperiment.OnlineExperiment for details.

run()¶: Runs the experiment.

self_test()¶

Tests if the dataset is set.

Furthermore, the test produces a warning message if no loggers are set because in that case the the experiment will produce no output.

Returns: Whether the tests were successful.
Return type: bool

set_recommender_data_iterator(RecommenderDataIterator* recommender_data_iterator)¶

Sets the dataset of the experiment.

Parameters: recommender_data_iterator (RecommenderDataIterator*) – Pointer to the dataset.

class alpenglow.cpp.ExperimentEnvironment¶

Bases: sip.wrapper

Class that stores, updates and serves common simulation data and parameters, e.g. length of the top list, dataset and popularity of the items.

In the online experiment, the central class alpenglow.cpp.OnlineExperiment updates this class and the common statistic containers. This class is updated after calling loggers and before calling updaters (for each sample). See details there. Other objects are not allowed to modify this class or the statistic containers, even if they have non-const access (exception: the common Random).

get_evaluation_start_time()¶

Returns: The beginning timestamp of evaluation. Elements in the time series before that timestamp will not be evaluated. Note that not all evaluator classes consider this value.
Return type: int

get_exclude_known()¶

Returns: Whether each user-item pair should be evaluated only at the first occurrence, i.e., known user-item pairs should not be evaluated at repeated occurrences.
Return type: bool

get_experiment_termination_time()¶

Returns: The last timestamp of evaluation. Elements in the time series after that timestamp will not be evaluated. Note that not all evaluator classes consider this value.
Return type: int

get_initialize_all()¶

Returns: Whether all the users and items exist from the beginning of the experiment, or they appear only when they are mentioned first in a sample. If set, recode the dataset so that the users and items are numbered starting 0 or 1 continuously. Skipped ids are treated as existing too.
Return type: bool

get_items()¶

Returns: A pointer to the list of known items. In the online experiment, the list is updated by this class for each sample after the call of the loggers and before the call to the updaters. If initialize_all==True, the list is filled with items at the beginning of the experiment.
Return type: std::vector<int>*

get_max_item_id()¶

Returns: The maximal item id int the whole experiment.
Return type: int

get_max_user_id()¶

Returns: The maximal user id int the whole experiment.
Return type: int

get_popularity_container()¶

Returns: A pointer to a container containing the popularity statistics of known items.
Return type: PopContainer*

get_popularity_sorted_container()¶

Returns: A pointer to a container containing the popularity statistics of known items. The items can be acquired in popularity order. The container contains all known items.
Return type: TopPopContainer*

get_recommender_data_iterator()¶

Returns: A pointer to the data iterator containing the time series of the experiment.
Return type: RecommenderDataIterator*

get_time()¶

get_top_k()¶

Returns: The top list length in the current experiment. Note that not all classes consider this value.
Return type: int

get_train_matrix()¶

Returns: A pointer to the current training data in a sparse matrix form.
Return type: SpMatrix*

get_users()¶

Returns: A pointer to the list of known users. In the online experiment, the list is updated by this class for each sample after the call of the loggers and before the call to the updaters. If initialize_all==True, the list is filled with users at the beginning of the experiment.
Return type: std::vector<int>*

is_first_occurrence_of_item()¶

Returns: Whether the current item is mentioned for the first time in the current sample. If initialize_all==False, equals to is_new_item().
Return type: bool

is_first_occurrence_of_user()¶

Returns: Whether the current user is mentioned for the first time in the current sample. If initialize_all==False, equals to is_new_user().
Return type: bool

is_item_existent()¶

Returns: Whether the item exists. If initialize_all==True, returns constant true value for items<=max_item_id, because all items exsist from the begininning of the experiment. Note that new items come into existence after te call to loggers, before the call to updaters.
Return type: bool

is_item_new_for_user()¶

Returns: Whether the current item is new for the current user, i.e., this is the first occurrence of this user-item pair in the time series. Note that the value is updated only when the loggers had been called already.
Return type: bool

is_new_item()¶

Returns: Whether the current item is new, i.e. come to existence with the current sample. If initialize_all==True, returns constant false value, because all items exsist from the begininning of the experiment. Note that new items come into existence after te call to loggers, before the call to updaters.
Return type: bool

is_new_user()¶

Returns: Whether the current user is new, i.e. come to existence with the current sample. If initialize_all==True, returns constant false value, because all users exsist from the begininning of the experiment. Note that new users come into existence after te call to loggers, before the call to updaters.
Return type: bool

is_user_existing()¶

Returns: Whether the user exists. If initialize_all==True, returns constant true value for users<=max_user_id, because all users exsist from the begininning of the experiment. Note that new users come into existence after te call to loggers, before the call to updaters.
Return type: bool

self_test()¶

set_parameters()¶: Sets the parameters of the experiment. Called by alpenglow.cpp.OnlineExperiment.

set_recommender_data_iterator()¶: Sets the dataset (the time series) of the experiment. Called by alpenglow.cpp.OnlineExperiment.

update(RecDat* rec_dat)¶

Updates the container.

In the online experiment, alpenglow.cpp.OnlineExperiment calls this function after the call to the loggers and before the call to the updaters. Other classes should not call this function. :param rec_dat: The newest available sample of the experiment. :type rec_dat: RecDat*

Models¶

The prediction models in the experiments. The model interface is alpenglow.cpp.Model. See Rank computation optimization about different evaluation methods.

Factor models¶

This module contains the matrix factorization based models.

class alpenglow.cpp.EigenFactorModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.EigenFactorModel. See documentation there.

begin_max¶

begin_min¶

dimension¶

lemp_bucket_size¶

seed¶

class alpenglow.cpp.EigenFactorModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.Initializable

add()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

prediction()¶

resize()¶

self_test()¶

class alpenglow.cpp.FactorModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.FmModelUpdaterParameters¶

Bases: sip.wrapper

learning_rate¶

class alpenglow.cpp.FmModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.FmModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.FmModel. See documentation there.

begin_max¶

begin_min¶

dimension¶

item_attributes¶

seed¶

user_attributes¶

class alpenglow.cpp.FmModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.Initializable

add()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

prediction()¶

self_test()¶

class alpenglow.cpp.AsymmetricFactorModelGradientUpdaterParameters¶

Bases: sip.wrapper

cumulative_item_updates¶

learning_rate¶

class alpenglow.cpp.AsymmetricFactorModelGradientUpdater¶

Bases: alpenglow.cpp.ModelGradientUpdater

self_test()¶

set_model()¶

update()¶

class alpenglow.cpp.SvdppModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.FactorModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.FactorModel. See documentation there.

begin_max¶

begin_min¶

dimension¶

initialize_all¶

lemp_bucket_size¶

max_item¶

max_user¶

seed¶

use_item_bias¶

use_sigmoid¶

use_user_bias¶

class alpenglow.cpp.FactorModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.SimilarityModel, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

add()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

prediction()¶

self_test()¶

set_item_recency()¶

set_user_recency()¶

similarity()¶

class alpenglow.cpp.AsymmetricFactorModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.SvdppModelGradientUpdaterParameters¶

Bases: sip.wrapper

cumulative_item_updates¶

learning_rate¶

class alpenglow.cpp.SvdppModelGradientUpdater¶

Bases: alpenglow.cpp.ModelGradientUpdater

self_test()¶

set_model()¶

update()¶

class alpenglow.cpp.FactorModelGlobalRankingScoreIterator¶

Bases: alpenglow.cpp.GlobalRankingScoreIterator, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

get_global_items()¶

get_global_users()¶

run()¶

self_test()¶

set_experiment_environment()¶

set_items()¶

set_model()¶

set_users()¶

class alpenglow.cpp.SvdppModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.SvdppModel. See documentation there.

begin_max¶

begin_min¶

dimension¶

gamma¶

history_weight¶

initialize_all¶

max_item¶

max_user¶

norm_type¶

seed¶

use_sigmoid¶

user_vector_weight¶

class alpenglow.cpp.SvdppModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

add()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

prediction()¶

self_test()¶

class alpenglow.cpp.FactorModelGradientUpdaterParameters¶

Bases: sip.wrapper

learning_rate¶

learning_rate_bias¶

regularization_rate¶

regularization_rate_bias¶

turn_off_item_bias_updates¶

turn_off_item_factor_updates¶

turn_off_user_bias_updates¶

turn_off_user_factor_updates¶

class alpenglow.cpp.FactorModelGradientUpdater¶

Bases: alpenglow.cpp.ModelGradientUpdater

self_test()¶

set_model()¶

update()¶

class alpenglow.cpp.AsymmetricFactorModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.AsymmetricFactorModel. See documentation there.

begin_max¶

begin_min¶

dimension¶

gamma¶

initialize_all¶

max_item¶

norm_type¶

seed¶

use_sigmoid¶

class alpenglow.cpp.AsymmetricFactorModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

add()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

clear()¶

prediction()¶

self_test()¶

Baseline models¶

This submodule contains the simple baseline models like nearest neighbor or most popular.

class alpenglow.cpp.TransitionProbabilityModel¶

Bases: alpenglow.cpp.Model

clear()¶

prediction()¶

self_test()¶

class alpenglow.cpp.NearestNeighborModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.NearestNeighborModel. See documentation there.

direction¶

gamma¶

gamma_threshold¶

norm¶

num_of_neighbors¶

class alpenglow.cpp.NearestNeighborModel¶

Bases: alpenglow.cpp.Model

Item similarity based model.

See source of alpenglow.experiments.NearestNeighborExperiment for a usage example.

prediction(RecDat* rec_dat)¶: Implements alpenglow.cpp.Model.prediction().

self_test()¶

Tests whether the model is assembled appropriately.

Returns: Whether the model is assembled appropriately.
Return type: bool

class alpenglow.cpp.PopularityTimeFrameModelUpdaterParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.PopularityTimeFrameModelUpdater. See documentation there.

tau¶

class alpenglow.cpp.PopularityTimeFrameModelUpdater¶

Bases: alpenglow.cpp.Updater

Time-aware updater for PopularityModel, which only considers the last tau time interval when calculating popularities. Note that the time window ends at the timestamp of the last updating sample. the timestamp of the sample in the prediction call is not considered.

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.PersonalPopularityModel¶

Bases: alpenglow.cpp.Model

prediction()¶

class alpenglow.cpp.PersonalPopularityModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.PopularityModel¶

Bases: alpenglow.cpp.Model

prediction()¶

class alpenglow.cpp.NearestNeighborModelUpdaterParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.NearestNeighborModelUpdater. See documentation there.

compute_similarity_period¶

period_mode¶

class alpenglow.cpp.NearestNeighborModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.TransitionProbabilityModelUpdaterParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.TransitionProbabilityModelUpdater. See documentation there.

filter_freq_updates¶

label_file_name¶

label_transition_mode¶

mode¶

class alpenglow.cpp.TransitionProbabilityModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.PopularityModelUpdater¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_model()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

Model combination¶

This module contains the models that combine other models. The most frequently used class is alpenglow.cpp.CombinedModel. See Model combination for a usage example.

class alpenglow.cpp.ToplistCombinationModelParameters¶

Bases: sip.wrapper

seed¶

top_k¶

class alpenglow.cpp.ToplistCombinationModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

add()¶

add_model()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

inject_wms_into()¶

prediction()¶

self_test()¶

class alpenglow.cpp.WeightedModelStructure¶

Bases: sip.wrapper

distribution_¶

is_initialized()¶

models_¶

class alpenglow.cpp.WMSUpdater¶

Bases: sip.wrapper

set_wms()¶

class alpenglow.cpp.RandomChoosingCombinedModelParameters¶

Bases: sip.wrapper

seed¶

class alpenglow.cpp.RandomChoosingCombinedModel¶

Bases: alpenglow.cpp.Model, alpenglow.cpp.Initializable

add()¶

add_model()¶

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

inject_wms_into()¶

prediction()¶

self_test()¶

class alpenglow.cpp.CombinedModelParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.CombinedModel. See documentation there.

log_file_name¶

log_frequency¶

use_user_weights¶

class alpenglow.cpp.CombinedModel¶

Bases: alpenglow.cpp.Model

add()¶

add_model()¶

prediction()¶

class alpenglow.cpp.RandomChoosingCombinedModelExpertUpdaterParameters¶

Bases: sip.wrapper

eta¶

loss_type¶

top_k¶

class alpenglow.cpp.RandomChoosingCombinedModelExpertUpdater¶

Bases: alpenglow.cpp.Updater, alpenglow.cpp.WMSUpdater, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

self_test()¶: Returns true.

set_experiment_environment()¶

set_wms()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.Evaluator¶

Bases: sip.wrapper

get_loss()¶

get_score()¶

self_test()¶

class alpenglow.cpp.CombinedDoubleLayerModelGradientUpdaterParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.CombinedDoubleLayerModelGradientUpdater. See documentation there.

always_learn¶

global_learning_rate¶

global_regularization_rate¶

learning_rate¶

regularization_rate¶

start_combination_learning_time¶

class alpenglow.cpp.CombinedDoubleLayerModelGradientUpdater¶

Bases: alpenglow.cpp.ModelGradientUpdater

self_test()¶

set_model()¶

update()¶

class alpenglow.cpp.TopListRecommender¶

Bases: sip.wrapper

get_top_list()¶

class alpenglow.cpp.MassPredictor¶

Bases: sip.wrapper

predict()¶

set_model()¶

class alpenglow.cpp.Model¶

Bases: sip.wrapper

add()¶

clear()¶

prediction()¶

read()¶

self_test()¶

write()¶

class alpenglow.cpp.ExternalModelParameters¶

Bases: sip.wrapper

mode¶

class alpenglow.cpp.ExternalModel¶

Bases: alpenglow.cpp.Model

add()¶

clear()¶

prediction()¶

read_predictions()¶

self_test()¶

class alpenglow.cpp.PythonModel¶: Bases: alpenglow.cpp.Model

class alpenglow.cpp.PythonToplistModel¶: Bases: alpenglow.cpp.PythonModel, alpenglow.cpp.TopListRecommender

class alpenglow.cpp.PythonRankingIteratorModel¶

Bases: alpenglow.cpp.PythonModel, alpenglow.cpp.RankingScoreIteratorProvider

iterator_get_next_()¶

iterator_has_next_()¶

class alpenglow.cpp.SimilarityModel¶

Bases: sip.wrapper

self_test()¶

similarity()¶

class alpenglow.cpp.ModelGradientUpdater¶

Bases: sip.wrapper

self_test()¶

update()¶

class alpenglow.cpp.ModelMultiUpdater¶

Bases: sip.wrapper

self_test()¶

update()¶

class alpenglow.cpp.RankingScoreIteratorProvider¶: Bases: sip.wrapper

class alpenglow.cpp.GlobalRankingScoreIterator¶

Bases: sip.wrapper

run()¶

self_test()¶

Data generators¶

The classes in this module are responsible for generating data subsets from the past. This is necessary for embedding offline models into the online framework, that needs to be updated in a batch. See alpenglow.experiments.BatchFactorExperiment for a usage example.

class alpenglow.cpp.DataGenerator¶

Bases: sip.wrapper

generate_recommender_data()¶

self_test()¶

class alpenglow.cpp.SamplingDataGeneratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.SamplingDataGenerator. See documentation there.

distribution¶

geometric_param¶

number_of_samples¶

seed¶

y¶

class alpenglow.cpp.SamplingDataGenerator¶

Bases: alpenglow.cpp.DataGenerator, alpenglow.cpp.Initializable, alpenglow.cpp.NeedsExperimentEnvironment

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

generate_recommender_data()¶

self_test()¶

set_recommender_data_iterator()¶

class alpenglow.cpp.CompletePastDataGenerator¶

Bases: alpenglow.cpp.DataGenerator, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

generate_recommender_data()¶

self_test()¶

set_recommender_data_iterator()¶

class alpenglow.cpp.TimeframeDataGeneratorParameters¶

Bases: sip.wrapper

Constructor parameter struct for alpenglow.cpp.TimeframeDataGenerator. See documentation there.

timeframe_length¶

class alpenglow.cpp.TimeframeDataGenerator¶

Bases: alpenglow.cpp.DataGenerator, alpenglow.cpp.NeedsExperimentEnvironment, alpenglow.cpp.Initializable

autocalled_initialize()¶

Has to be implemented by the component.

Returns: Whether the initialization was successful.
Return type: bool

generate_recommender_data()¶

self_test()¶

set_recommender_data_iterator()¶

Online learners¶

This module contains classes that modifiy the learning process, e.g. delay the samples or feed them in a batch into offline learning methods.

class alpenglow.cpp.LearnerPeriodicDelayedWrapperParameters¶

Bases: sip.wrapper

delay¶

period¶

class alpenglow.cpp.LearnerPeriodicDelayedWrapper¶

Bases: alpenglow.cpp.Updater

self_test()¶: Returns true.

set_wrapped_learner()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.

class alpenglow.cpp.PeriodicOfflineLearnerWrapperParameters¶

Bases: sip.wrapper

base_in_file_name¶

base_out_file_name¶

clear_model¶

learn¶

read_model¶

write_model¶

class alpenglow.cpp.PeriodicOfflineLearnerWrapper¶

Bases: alpenglow.cpp.Updater

add_offline_learner()¶

self_test()¶: Returns true.

set_data_generator()¶

set_model()¶

set_period_computer()¶

update(RecDat* rec_dat)¶

Updates the associated model or other object of the simulation.

Parameters: rec_dat (RecDat*) – The newest available sample of the experiment.