# pyFTS.hyperparam package

## Module contents

## Submodules

## pyFTS.hyperparam.Util module

Common facilities for hyperparameter tunning

`pyFTS.hyperparam.Util.``create_hyperparam_tables`(conn)

  Create a sqlite3 table designed to store benchmark results.

  Parameters: | conn – a sqlite3 database connection

`pyFTS.hyperparam.Util.``insert_hyperparam`(data, conn)

  Insert benchmark data on database

  Parameters: | data – a tuple with the benchmark data with format:

  Dataset: Identify on which dataset the dataset was performed Tag: a user defined word that indentify a benchmark set Model: FTS model Transformation: The name of data transformation, if one was used mf: membership function Order: the order of the FTS method Partitioner: UoD partitioning scheme Partitions: Number of partitions alpha: alpha cut lags: lags Measure: accuracy measure Value: the measure value

  Parameters: | conn – a sqlite3 database connection
  Returns: |

`pyFTS.hyperparam.Util.``open_hyperparam_db`(name)

  Open a connection with a Sqlite database designed to store benchmark results.

  Parameters: | name – database filenem
  Returns: | a sqlite3 database connection

## pyFTS.hyperparam.GridSearch module

`pyFTS.hyperparam.GridSearch.``cluster_method`(individual, train, test)

`pyFTS.hyperparam.GridSearch.``dict_individual`(mf, partitioner, partitions, order, lags, alpha_cut)

`pyFTS.hyperparam.GridSearch.``execute`(hyperparams, datasetname, train, test, **kwargs)

`pyFTS.hyperparam.GridSearch.``process_jobs`(jobs, datasetname, conn)