dm.models.predictor package

Submodules

dm.models.predictor.THPredictorUtil module

Support for ventilation length prediction.

Creates training and testing sets for ventilation length prediction, including calculation of distance between a data point and cluster trendline (cluster centroid).

dm.models.predictor.THPredictorUtil.training_testing_data(data, splitting)

It creates training and testing data set.

Parameters
  • data – dictionary of data

  • splitting – the percentage part of data used for training purposes

Returns

training, testing data sets and minimal number of events that lasted certain time

dm.models.predictor.THPredictorUtil.training_testing_data_only_distance(training, testing, strategy, strategyFlag, one_line, test_points, cluster_boundaries, cluster_boundaries_all, training_file, testing_file)

It creates training and testing data sets including only attributes related to distance.

Parameters
  • training – list of training data

  • testing – list of testing data

  • strategy – strategy how to compute a slope of line(s)

  • strategyFlag – flag that denotes a used strategy for computation of a slope of line(s)

  • one_line – if only one line should be plotted

  • test_points – true if test points are plotted

  • cluster_boundaries – if cluster boundaries should be plotted

  • cluster_boundaries_all – if all cluster boundaries should be plotted

  • training_file – filename to which training data set is written

  • testing_file – filename to which training data set is written

Returns

training and testing data sets including only attributes related to distance

dm.models.predictor.THPredictorUtil.training_testing_data_with_distance(training, testing, strategy, strategyFlag, one_line, test_points, cluster_boundaries, cluster_boundaries_all, training_file, testing_file)

It creates training and testing data sets including attributes related to distance.

Parameters
  • training – list of training data

  • testing – list of testing data

  • strategy – strategy how to compute a slope of line(s)

  • strategyFlag – flag that denotes a used strategy for computation of a slope of line(s)

  • one_line – if only one line should be plotted

  • test_points – true if test points are plotted

  • cluster_boundaries – if cluster boundaries should be plotted

  • cluster_boundaries_all – if all cluster boundaries should be plotted

  • training_file – filename to which training data set is written

  • testing_file – filename to which training data set is written

Returns

training and testing data sets including attributes related to distance

dm.models.predictor.THPredictorUtil.training_testing_data_without_distance(training, testing, strategy, strategyFlag, one_line, test_points, cluster_boundaries, cluster_boundaries_all, training_file, testing_file)

It creates training and testing data sets without attributes related to distance.

Parameters
  • training – list of training data

  • testing – list of testing data

  • strategy – deprecated

  • strategyFlag – deprecated

  • one_line – deprecated

  • test_points – deprecated

  • cluster_boundaries – deprecated

  • cluster_boundaries_all – deprecated

  • training_file – filename to which training data set is written

  • testing_file – filename to which training data set is written

Returns

training and testing data sets without attributes related to distance

dm.models.predictor.generic_training_file module

Creates adapted models for predictor of optimal ventilation length.

dm.models.predictor.generic_training_file.create_bins(data, bins)

It creates required number of bins.

Parameters
  • data – dictionary of data

  • bins – number of bins

Returns

dictionary of data

dm.models.predictor.generic_training_file.training_data(json_f, cls, devs, model_type, interval_extension, mapper, func, lat, lon, weather)

It creates training data.

Parameters
  • json_f – dictionary of data

  • cls – list of clients

  • devs – list of devices

  • model_type – type of model - based on CO2 concentration or temperature and humidity

  • interval_extension – time shift that is subtracted or added to start or end of an interval respectively

  • mapper – dictionary containing mapping of attribute name in database to the name used in dataset

  • func – object that determines function used for attribute calculation

  • lat – latitude of a locality

  • lon – longitude of a locality

  • weather – object used to get information about weather

Returns

dictionary of training data

dm.models.predictor.generic_training_file.training_file_co2(events_file: str, no_event_time_shift: int, cls, devs, lat, lon, weather)

It creates training data for predictor based on CO2 concentration.

Parameters
  • events_file – file containing events

  • no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed

  • cls – list of clients

  • devs – list of devices

  • lat – latitude of a locality

  • lon – longitude of a locality

  • weather – object used to get information about weather

Returns

dictionary of training data for predictor based on CO2 concentration

dm.models.predictor.generic_training_file.training_file_t_h(events_file: str, no_event_time_shift: int, cls, devs, lat, lon, weather)

It creates training data for predictor based on temperature and humidity.

Parameters
  • events_file – file containing events

  • no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed

  • cls – list of clients

  • devs – list of devices

  • lat – latitude of a locality

  • lon – longitude of a locality

  • weather – object used to get information about weather

Returns

dictionary of training data for predictor based on temperature and humidity

dm.models.predictor.generic_training_file_from_local_db module

Creates training datasets from local database.

dm.models.predictor.generic_training_file_from_local_db.create_bins(data, bins)

It creates required number of bins.

Parameters
  • data – dictionary of data

  • bins – number of bins

Returns

dictionary of data

dm.models.predictor.generic_training_file_from_local_db.training_file_co2(events_file: str, no_event_time_shift: int)

It creates training data for predictor based on CO2 concentration.

Parameters
  • events_file – file containing events

  • no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed

Returns

dictionary of training data for predictor based on CO2 concentration.

dm.models.predictor.generic_training_file_from_local_db.training_file_t_h(events_file: str, no_event_time_shift: int)

It creates training data for predictor based on temperature and humidity.

Parameters
  • events_file – file containing events

  • no_event_time_shift – number of seconds subtracted from start of event to define event when a window was closed

Returns

dictionary of training data for predictor based on temperature and humidity.

Module contents