lerot.environment

class lerot.environment.CascadeUserModel(arg_str)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

Defines a cascade user model, simulating a user that inspects results starting from the top of a result list.

get_clicks(result_list, labels, **kwargs)

simulate clicks on list l

class lerot.environment.FederatedClickModel(arg_str)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

b(i, vert)

static getParamRescaled(rank, serp_len, param_vector)

static getVertClass(vert_type)

get_clicks(result_list, labels, **kwargs)

Simulate clicks on the result_list. - labels contain relevance labels indexed by the docid

get_examination_prob(result_list, **kwargs)

h(i, serp_len, vert)

p(i, serp_len)

class lerot.environment.PositionBasedUserModel(p)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

Defines a positions based user model.

get_clicks(result_list, labels, **kwargs)

simulate clicks on list l

get_examination_prob(result_list, **kwargs)

p(i)

class lerot.environment.RandomClickModel(p=0.5)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

Defines a positions based user model.

get_clicks(result_list, labels, **kwargs)

simulate clicks on list l

class lerot.environment.LivingLabsRealUser(key, doc_ids)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

KEY = ''

get_clicks(result_list, labels, **kwargs)

get_win(query, feedback_list, lerot_ranked_list)

Used for seznam site which interleaves ranked list with it’s own list Returns ‘ranked list winner’ with number of clicks of each ranker e.g. [0 2] where [lerot_list_score seznam_list_score]

runs = {}

upload_run(query, upload_list, runid)

Uploads a run to living-labs api.

class lerot.environment.RelevantUserModel(arg_str)

Bases: lerot.environment.AbstractUserModel.AbstractUserModel

Defines a user model that clicks on all relevant documents in a list with an optional limit

get_clicks(result_list, labels, **kwargs)