pyFTS.hyperparam package

Module contents

Submodules

pyFTS.hyperparam.Util module

Common facilities for hyperparameter optimization

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, dataset, **kwargs)

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

pyFTS.hyperparam.GridSearch.execute(hyperparams, datasetname, dataset, **kwargs)

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

pyFTS.hyperparam.Evolutionary module

Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS

pyFTS.hyperparam.Evolutionary.GeneticAlgorithm(dataset, **kwargs)

Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)

Parameters

• dataset – The time series to optimize the FTS

• ngen – An integer value with the maximum number of generations, default value: 30

• mgen – An integer value with the maximum number of generations without improvement to stop, default value 7

• npop – An integer value with the population size, default value: 20

• pcross – A float value between 0 and 1 with the probability of crossover, default: .5

• psel – A float value between 0 and 1 with the probability of selection, default: .5

• pmut – A float value between 0 and 1 with the probability of mutation, default: .3

• fts_method – The FTS method to optimize

• parameters – dict with model specific arguments for fts_method

• elitism – A boolean value indicating if the best individual must always survive to next population

• initial_operator – a function that receives npop and return a random population with size npop

• evalutation_operator – a function that receives a dataset and an individual and return its fitness

• selection_operator – a function that receives the whole population and return a selected individual

• crossover_operator – a function that receives the whole population and return a descendent individual

• mutation_operator – a function that receives one individual and return a changed individual

• window_size – An integer value with the the length of scrolling window for train/test on dataset

• train_rate – A float value between 0 and 1 with the train/test split ([0,1])

• increment_rate – A float value between 0 and 1 with the the increment of the scrolling window, relative to the window_size ([0,1])

• collect_statistics – A boolean value indicating to collect statistics for each generation

• distributed – A value indicating it the execution will be local and sequential (distributed=False), or parallel and distributed (distributed=’dispy’ or distributed=’spark’)

• cluster – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node

Returns

the best genotype

pyFTS.hyperparam.Evolutionary.crossover(population, **kwargs)

Crossover operation between two parents

Parameters

population – the original population

Returns

a genotype

pyFTS.hyperparam.Evolutionary.double_tournament(population, **kwargs)

Double tournament selection strategy.

Parameters

population –

Returns

pyFTS.hyperparam.Evolutionary.elitism(population, new_population, **kwargs)

Elitism operation, always select the best individual of the population and discard the worst

Parameters

• population –

• new_population –

Returns

pyFTS.hyperparam.Evolutionary.evaluate(dataset, individual, **kwargs)

Evaluate an individual using a sliding window cross validation over the dataset.

Parameters

• dataset – Evaluation dataset

• individual – genotype to be tested

• window_size – The length of scrolling window for train/test on dataset

• train_rate – The train/test split ([0,1])

• increment_rate – The increment of the scrolling window, relative to the window_size ([0,1])

• parameters – dict with model specific arguments for fit method.

Returns

a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value

pyFTS.hyperparam.Evolutionary.execute(datasetname, dataset, **kwargs)

Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods

Parameters

• datasetname –

• dataset – The time series to optimize the FTS

• file –

• experiments –

• distributed –

• ngen – An integer value with the maximum number of generations, default value: 30

• mgen – An integer value with the maximum number of generations without improvement to stop, default value 7

• npop – An integer value with the population size, default value: 20

• pcross – A float value between 0 and 1 with the probability of crossover, default: .5

• psel – A float value between 0 and 1 with the probability of selection, default: .5

• pmut – A float value between 0 and 1 with the probability of mutation, default: .3

• fts_method – The FTS method to optimize

• parameters – dict with model specific arguments for fts_method

• elitism – A boolean value indicating if the best individual must always survive to next population

• initial_operator – a function that receives npop and return a random population with size npop

• random_individual – create an random genotype

• evalutation_operator – a function that receives a dataset and an individual and return its fitness

• selection_operator – a function that receives the whole population and return a selected individual

• crossover_operator – a function that receives the whole population and return a descendent individual

• mutation_operator – a function that receives one individual and return a changed individual

• window_size – An integer value with the the length of scrolling window for train/test on dataset

• train_rate – A float value between 0 and 1 with the train/test split ([0,1])

• increment_rate – A float value between 0 and 1 with the the increment of the scrolling window, relative to the window_size ([0,1])

• collect_statistics – A boolean value indicating to collect statistics for each generation

• distributed – A value indicating it the execution will be local and sequential (distributed=False), or parallel and distributed (distributed=’dispy’ or distributed=’spark’)

• cluster – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node

Returns

the best genotype

pyFTS.hyperparam.Evolutionary.genotype(mf, npart, partitioner, order, alpha, lags, f1, f2)

Create the individual genotype

Parameters

• mf – membership function

• npart – number of partitions

• partitioner – partitioner method

• order – model order

• alpha – alpha-cut

• lags – array with lag indexes

• f1 – accuracy fitness value

• f2 – parsimony fitness value

Returns

the genotype, a dictionary with all hyperparameters

pyFTS.hyperparam.Evolutionary.initial_population(n, **kwargs)

Create a random population of size n

Parameters

n – the size of the population

Returns

a list with n random individuals

pyFTS.hyperparam.Evolutionary.lag_crossover2(best, worst)

Cross over two lag genes

Parameters

• best – best genotype

• worst – worst genotype

Returns

a tuple (order, lags)

pyFTS.hyperparam.Evolutionary.log_result(conn, datasetname, fts_method, result)

pyFTS.hyperparam.Evolutionary.mutation(individual, **kwargs)

Mutation operator

Parameters

• individual – an individual genotype

• pmut – individual probability o

Returns

pyFTS.hyperparam.Evolutionary.mutation_lags(lags, order)

Mutation operation for lags gene

Parameters

• lags –

• order –

Returns

pyFTS.hyperparam.Evolutionary.persist_statistics(datasetname, statistics)

pyFTS.hyperparam.Evolutionary.phenotype(individual, train, fts_method, parameters={}, **kwargs)

Instantiate the genotype, creating a fitted model with the genotype hyperparameters

Parameters

• individual – a genotype

• train – the training dataset

• fts_method – the FTS method

• parameters – dict with model specific arguments for fit method.

Returns

a fitted FTS model

pyFTS.hyperparam.Evolutionary.process_experiment(fts_method, result, datasetname, conn)

Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)

Parameters

• fts_method –

• result –

• datasetname –

• conn –

Returns

pyFTS.hyperparam.Evolutionary.random_genotype(**kwargs)

Create random genotype

Returns

the genotype, a dictionary with all hyperparameters

pyFTS.hyperparam.Evolutionary.tournament(population, objective, **kwargs)

Simple tournament selection strategy.

Parameters

• population – the population

• objective – the objective to be considered on tournament

Returns