def __init__(self,
step=1,
aggregation_type=None,
window=1,
shift=0,
local_vars_configuration=None): # noqa: E501
"""InputWindowConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._step = None
self._aggregation_type = None
self._window = None
self._shift = None
self.discriminator = None
if step is not None:
self.step = step
if aggregation_type is not None:
self.aggregation_type = aggregation_type
if window is not None:
self.window = window
if shift is not None:
self.shift = shift
def __init__(self, optimization_algorithm=OptimizationAlgorithm.VIDNEROVANERUDA, crossover_distribution_index=20, crossover_probability=0.9, mutation_distribution_index=20, mutation_probability=None, proc_timeout_seconds=10800, max_num_of_generations=50, population_size=50, hyper_volume=ConvergencyCriterion(), local_vars_configuration=None): # noqa: E501
"""AnnOptimizationEngineConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._optimization_algorithm = None
self._crossover_distribution_index = None
self._crossover_probability = None
self._mutation_distribution_index = None
self._mutation_probability = None
self._proc_timeout_seconds = None
self._max_num_of_generations = None
self._population_size = None
self._hyper_volume = None
self.discriminator = None
if optimization_algorithm is not None:
self.optimization_algorithm = optimization_algorithm
if crossover_distribution_index is not None:
self.crossover_distribution_index = crossover_distribution_index
if crossover_probability is not None:
self.crossover_probability = crossover_probability
if mutation_distribution_index is not None:
self.mutation_distribution_index = mutation_distribution_index
if mutation_probability is not None:
self.mutation_probability = mutation_probability
self.proc_timeout_seconds = proc_timeout_seconds
self.max_num_of_generations = max_num_of_generations
if population_size is not None:
self.population_size = population_size
self.hyper_volume = hyper_volume
def __init__(self, input_window_range_configs=None, output_window_configs=None, output_sample_step=1, dropout=None, batch_size=512, dataset_id=None, validation_set_id=None, inputs=None, output_ranges=None, problem_type=ProblemType.REGRESSION, binary_optimization_metric=BinaryMetric.ROC_AUC, regression_optimization_metric=RegressionMetric.MAE, hidden_layer_count_range=None, neurons_per_layer=None, training_algorithms=["Adadelta","Adagrad","Adam","Adamax","Nadam","RMSprop","SGD"], activation_functions=["Elu","HardSigmoid","Linear","ReLu","Selu","Sigmoid","SoftMax","SoftPlus","SoftSign","TanH"], max_epoch=3000, cross_validation=False, validation_split=0.2, random_seed=300, engine_config=AnnOptimizationEngineConfig(), local_vars_configuration=None): # noqa: E501
"""AnnSeriesOptimizationConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._input_window_range_configs = None
self._output_window_configs = None
self._output_sample_step = None
self._dropout = None
self._batch_size = None
self._dataset_id = None
self._validation_set_id = None
self._inputs = None
self._output_ranges = None
self._problem_type = None
self._binary_optimization_metric = None
self._regression_optimization_metric = None
self._hidden_layer_count_range = None
self._neurons_per_layer = None
self._training_algorithms = None
self._activation_functions = None
self._max_epoch = None
self._cross_validation = None
self._validation_split = None
self._random_seed = None
self._engine_config = None
self.discriminator = None
self.input_window_range_configs = input_window_range_configs
self.output_window_configs = output_window_configs
if output_sample_step is not None:
self.output_sample_step = output_sample_step
self.dropout = dropout
if batch_size is not None:
self.batch_size = batch_size
self.dataset_id = dataset_id
self.validation_set_id = validation_set_id
self.inputs = inputs
self.output_ranges = output_ranges
if problem_type is not None:
self.problem_type = problem_type
if binary_optimization_metric is not None:
self.binary_optimization_metric = binary_optimization_metric
if regression_optimization_metric is not None:
self.regression_optimization_metric = regression_optimization_metric
self.hidden_layer_count_range = hidden_layer_count_range
self.neurons_per_layer = neurons_per_layer
self.training_algorithms = training_algorithms
self.activation_functions = activation_functions
self.max_epoch = max_epoch
if cross_validation is not None:
self.cross_validation = cross_validation
self.validation_split = validation_split
self.random_seed = random_seed
self.engine_config = engine_config
def __init__(self, dataset_id=None, validation_set_id=None, inputs=None, output_ranges=None, validation_split=0.2, random_seed=300, problem_type=ProblemType.REGRESSION, binary_optimization_metric=BinaryMetric.ROC_AUC, regression_optimization_metric=RegressionMetric.MAE, n_estimators=None, max_depth=None, min_child_weight=None, gamma=None, subsample=None, colsample_bytree=None, reg_alpha=None, learning_rate=None, engine_config=OptimizationEngineConfig(), local_vars_configuration=None): # noqa: E501
"""XGBoostOptimizationConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._dataset_id = None
self._validation_set_id = None
self._inputs = None
self._output_ranges = None
self._validation_split = None
self._random_seed = None
self._problem_type = None
self._binary_optimization_metric = None
self._regression_optimization_metric = None
self._n_estimators = None
self._max_depth = None
self._min_child_weight = None
self._gamma = None
self._subsample = None
self._colsample_bytree = None
self._reg_alpha = None
self._learning_rate = None
self._engine_config = None
self.discriminator = None
self.dataset_id = dataset_id
self.validation_set_id = validation_set_id
self.inputs = inputs
self.output_ranges = output_ranges
if validation_split is not None:
self.validation_split = validation_split
self.random_seed = random_seed
if problem_type is not None:
self.problem_type = problem_type
if binary_optimization_metric is not None:
self.binary_optimization_metric = binary_optimization_metric
if regression_optimization_metric is not None:
self.regression_optimization_metric = regression_optimization_metric
if n_estimators is not None:
self.n_estimators = n_estimators
if max_depth is not None:
self.max_depth = max_depth
if min_child_weight is not None:
self.min_child_weight = min_child_weight
if gamma is not None:
self.gamma = gamma
if subsample is not None:
self.subsample = subsample
if colsample_bytree is not None:
self.colsample_bytree = colsample_bytree
if reg_alpha is not None:
self.reg_alpha = reg_alpha
if learning_rate is not None:
self.learning_rate = learning_rate
self.engine_config = engine_config
def __init__(self,
epoch=None,
guid=None,
state=None,
generation=None,
total_generations=None,
validation_set_error=None,
training_set_error=None,
best_model=None,
start_date_time=None,
estimated_date_time=None,
generation_seconds=None,
metric_name=None,
local_vars_configuration=None): # noqa: E501
"""AnnOptimizationStatus - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._epoch = None
self._guid = None
self._state = None
self._generation = None
self._total_generations = None
self._validation_set_error = None
self._training_set_error = None
self._best_model = None
self._start_date_time = None
self._estimated_date_time = None
self._generation_seconds = None
self._metric_name = None
self.discriminator = None
if epoch is not None:
self.epoch = epoch
if guid is not None:
self.guid = guid
if state is not None:
self.state = state
if generation is not None:
self.generation = generation
if total_generations is not None:
self.total_generations = total_generations
if validation_set_error is not None:
self.validation_set_error = validation_set_error
if training_set_error is not None:
self.training_set_error = training_set_error
self.best_model = best_model
if start_date_time is not None:
self.start_date_time = start_date_time
if estimated_date_time is not None:
self.estimated_date_time = estimated_date_time
if generation_seconds is not None:
self.generation_seconds = generation_seconds
self.metric_name = metric_name
def __init__(self,
dataset_id=None,
validation_set_id=None,
inputs=None,
output_ranges=None,
problem_type=ProblemType.REGRESSION,
binary_optimization_metric=BinaryMetric.ROC_AUC,
regression_optimization_metric=RegressionMetric.MAE,
validation_split=0.2,
random_seed=300,
engine_config=OptimizationEngineConfig(),
number_of_estimators=None,
max_depth=None,
max_features=None,
local_vars_configuration=None): # noqa: E501
"""RandomForestOptimizationConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._dataset_id = None
self._validation_set_id = None
self._inputs = None
self._output_ranges = None
self._problem_type = None
self._binary_optimization_metric = None
self._regression_optimization_metric = None
self._validation_split = None
self._random_seed = None
self._engine_config = None
self._number_of_estimators = None
self._max_depth = None
self._max_features = None
self.discriminator = None
self.dataset_id = dataset_id
self.validation_set_id = validation_set_id
self.inputs = inputs
self.output_ranges = output_ranges
if problem_type is not None:
self.problem_type = problem_type
if binary_optimization_metric is not None:
self.binary_optimization_metric = binary_optimization_metric
if regression_optimization_metric is not None:
self.regression_optimization_metric = regression_optimization_metric
self.validation_split = validation_split
self.random_seed = random_seed
self.engine_config = engine_config
if number_of_estimators is not None:
self.number_of_estimators = number_of_estimators
if max_depth is not None:
self.max_depth = max_depth
if max_features is not None:
self.max_features = max_features
def __init__(self,
version='5.1.0',
local_vars_configuration=None): # noqa: E501
"""ServiceInfo - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._version = None
self.discriminator = None
self.version = version
def __init__(self,
input_window_configs=None,
output_window_configs=None,
output_sample_step=1,
batch_size=512,
dataset_id=None,
input_ranges=None,
output_layer=None,
hidden_layer_configs=None,
training_algorithm=None,
max_epoch=3000,
cross_validation=False,
validation_split=0.2,
random_seed=300,
local_vars_configuration=None): # noqa: E501
"""AnnSeriesTrainingConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._input_window_configs = None
self._output_window_configs = None
self._output_sample_step = None
self._batch_size = None
self._dataset_id = None
self._input_ranges = None
self._output_layer = None
self._hidden_layer_configs = None
self._training_algorithm = None
self._max_epoch = None
self._cross_validation = None
self._validation_split = None
self._random_seed = None
self.discriminator = None
self.input_window_configs = input_window_configs
self.output_window_configs = output_window_configs
if output_sample_step is not None:
self.output_sample_step = output_sample_step
if batch_size is not None:
self.batch_size = batch_size
self.dataset_id = dataset_id
self.input_ranges = input_ranges
self.output_layer = output_layer
self.hidden_layer_configs = hidden_layer_configs
if training_algorithm is not None:
self.training_algorithm = training_algorithm
self.max_epoch = max_epoch
if cross_validation is not None:
self.cross_validation = cross_validation
self.validation_split = validation_split
self.random_seed = random_seed
def __init__(self, activation_function=None, outputs=None, local_vars_configuration=None): # noqa: E501
"""AnnLayerConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._activation_function = None
self._outputs = None
self.discriminator = None
if activation_function is not None:
self.activation_function = activation_function
self.outputs = outputs
def __init__(self, range=None, encoding=False, local_vars_configuration=None): # noqa: E501
"""OutputConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._range = None
self._encoding = None
self.discriminator = None
self.range = range
if encoding is not None:
self.encoding = encoding
def __init__(self, configuration=None, header_name=None, header_value=None,
cookie=None, pool_threads=1):
if configuration is None:
configuration = Configuration()
self.configuration = configuration
self.pool_threads = pool_threads
self.rest_client = rest.RESTClientObject(configuration)
self.default_headers = {}
if header_name is not None:
self.default_headers[header_name] = header_value
self.cookie = cookie
# Set default User-Agent.
self.user_agent = 'OpenAPI-Generator/1.0.0/python'
self.client_side_validation = configuration.client_side_validation
def __init__(self, data_set=None, network_id=None, input_ranges=None, output_ranges=None, local_vars_configuration=None): # noqa: E501
"""PredictionArrayConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._data_set = None
self._network_id = None
self._input_ranges = None
self._output_ranges = None
self.discriminator = None
self.data_set = data_set
self.network_id = network_id
self.input_ranges = input_ranges
self.output_ranges = output_ranges
def __init__(self,
window=1,
shift=0,
local_vars_configuration=None): # noqa: E501
"""OutputWindowConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._window = None
self._shift = None
self.discriminator = None
if window is not None:
self.window = window
if shift is not None:
self.shift = shift
def __init__(self,
number_of_latest_generations=10,
percentage_of_tolerance=5,
local_vars_configuration=None): # noqa: E501
"""ConvergencyCriterion - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._number_of_latest_generations = None
self._percentage_of_tolerance = None
self.discriminator = None
if number_of_latest_generations is not None:
self.number_of_latest_generations = number_of_latest_generations
if percentage_of_tolerance is not None:
self.percentage_of_tolerance = percentage_of_tolerance
def __init__(self, id=None, epoch_count=None, errors_on_validation_set=None, errors_on_training_set=None, local_vars_configuration=None): # noqa: E501
"""TrainedNetwork - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._id = None
self._epoch_count = None
self._errors_on_validation_set = None
self._errors_on_training_set = None
self.discriminator = None
self.id = id
if epoch_count is not None:
self.epoch_count = epoch_count
self.errors_on_validation_set = errors_on_validation_set
self.errors_on_training_set = errors_on_training_set
def __init__(self,
min=None,
max=None,
local_vars_configuration=None): # noqa: E501
"""Range - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._min = None
self._max = None
self.discriminator = None
if min is not None:
self.min = min
if max is not None:
self.max = max
def __init__(self, hidden_layers=None, training_algorithm=None, output_layer_activation_function=None, feature_selection=None, local_vars_configuration=None): # noqa: E501
"""RnnModel - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._hidden_layers = None
self._training_algorithm = None
self._output_layer_activation_function = None
self._feature_selection = None
self.discriminator = None
self.hidden_layers = hidden_layers
if training_algorithm is not None:
self.training_algorithm = training_algorithm
if output_layer_activation_function is not None:
self.output_layer_activation_function = output_layer_activation_function
self.feature_selection = feature_selection
def __init__(self,
n_estimators=None,
max_depth=None,
min_child_weight=None,
gamma=None,
subsample=None,
colsample_bytree=None,
reg_alpha=None,
learning_rate=None,
feature_selection=None,
local_vars_configuration=None): # noqa: E501
"""XGBoostModel - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._n_estimators = None
self._max_depth = None
self._min_child_weight = None
self._gamma = None
self._subsample = None
self._colsample_bytree = None
self._reg_alpha = None
self._learning_rate = None
self._feature_selection = None
self.discriminator = None
if n_estimators is not None:
self.n_estimators = n_estimators
if max_depth is not None:
self.max_depth = max_depth
if min_child_weight is not None:
self.min_child_weight = min_child_weight
if gamma is not None:
self.gamma = gamma
if subsample is not None:
self.subsample = subsample
if colsample_bytree is not None:
self.colsample_bytree = colsample_bytree
if reg_alpha is not None:
self.reg_alpha = reg_alpha
if learning_rate is not None:
self.learning_rate = learning_rate
self.feature_selection = feature_selection
def __init__(self, number_of_estimators=None, max_depth=None, max_features=None, feature_selection=None, local_vars_configuration=None): # noqa: E501
"""RandomForestModel - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._number_of_estimators = None
self._max_depth = None
self._max_features = None
self._feature_selection = None
self.discriminator = None
if number_of_estimators is not None:
self.number_of_estimators = number_of_estimators
if max_depth is not None:
self.max_depth = max_depth
if max_features is not None:
self.max_features = max_features
self.feature_selection = feature_selection
def __init__(self,
window=None,
shift=None,
step=None,
aggregation_types=["None", "Avg", "Sum"],
local_vars_configuration=None): # noqa: E501
"""InputWindowRangeConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._window = None
self._shift = None
self._step = None
self._aggregation_types = None
self.discriminator = None
self.window = window
self.shift = shift
self.step = step
self.aggregation_types = aggregation_types
def __init__(self,
neuron_count=None,
activation_function=None,
dropout=None,
local_vars_configuration=None): # noqa: E501
"""AnnHiddenLayerConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._neuron_count = None
self._activation_function = None
self._dropout = None
self.discriminator = None
if neuron_count is not None:
self.neuron_count = neuron_count
if activation_function is not None:
self.activation_function = activation_function
if dropout is not None:
self.dropout = dropout
def __init__(self,
type=None,
title=None,
status=None,
detail=None,
instance=None,
local_vars_configuration=None): # noqa: E501
"""ProblemDetails - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._type = None
self._title = None
self._status = None
self._detail = None
self._instance = None
self.discriminator = None
self.type = type
self.title = title
self.status = status
self.detail = detail
self.instance = instance
def __init__(self, local_vars_configuration=None): # noqa: E501
"""RandomForestModelType - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self.discriminator = None
def __init__(self, local_vars_configuration=None): # noqa: E501
"""RegressionMetric - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self.discriminator = None
def __init__(self, local_vars_configuration=None): # noqa: E501
"""OptimizationState - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self.discriminator = None
def __init__(self,
dropout=None,
batch_size=512,
recurrent_dropout=None,
recurrent_output_count=1,
dataset_id=None,
validation_set_id=None,
custom_metric_id=None,
custom_metric=None,
custom_metric_minimization=True,
binary_classification_threshold=None,
custom_metric_parameters=None,
inputs=None,
outputs=None,
hidden_layer_count_range=None,
neurons_per_layer=None,
training_algorithms=[
"Adadelta", "Adagrad", "Adam", "Adamax", "Nadam",
"RMSprop", "SGD"
],
activation_functions=[
"Elu", "HardSigmoid", "Linear", "ReLu", "Selu", "Sigmoid",
"SoftMax", "SoftPlus", "SoftSign", "TanH"
],
recurrent_activation_functions=[
"Elu", "HardSigmoid", "Linear", "ReLu", "Selu", "Sigmoid",
"SoftMax", "SoftPlus", "SoftSign", "TanH"
],
max_epoch=3000,
validation_split=0.2,
random_seed=300,
recurrent_input_count_range=None,
engine_config=OptimizationEngineConfig(),
local_vars_configuration=None): # noqa: E501
"""RnnOptimizationConfig - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._dropout = None
self._batch_size = None
self._recurrent_dropout = None
self._recurrent_output_count = None
self._dataset_id = None
self._validation_set_id = None
self._custom_metric_id = None
self._custom_metric = None
self._custom_metric_minimization = None
self._binary_classification_threshold = None
self._custom_metric_parameters = None
self._inputs = None
self._outputs = None
self._hidden_layer_count_range = None
self._neurons_per_layer = None
self._training_algorithms = None
self._activation_functions = None
self._recurrent_activation_functions = None
self._max_epoch = None
self._validation_split = None
self._random_seed = None
self._recurrent_input_count_range = None
self._engine_config = None
self.discriminator = None
self.dropout = dropout
if batch_size is not None:
self.batch_size = batch_size
self.recurrent_dropout = recurrent_dropout
if recurrent_output_count is not None:
self.recurrent_output_count = recurrent_output_count
self.dataset_id = dataset_id
self.validation_set_id = validation_set_id
self.custom_metric_id = custom_metric_id
self.custom_metric = custom_metric
if custom_metric_minimization is not None:
self.custom_metric_minimization = custom_metric_minimization
self.binary_classification_threshold = binary_classification_threshold
self.custom_metric_parameters = custom_metric_parameters
self.inputs = inputs
self.outputs = outputs
self.hidden_layer_count_range = hidden_layer_count_range
self.neurons_per_layer = neurons_per_layer
self.training_algorithms = training_algorithms
self.activation_functions = activation_functions
self.recurrent_activation_functions = recurrent_activation_functions
self.max_epoch = max_epoch
self.validation_split = validation_split
self.random_seed = random_seed
self.recurrent_input_count_range = recurrent_input_count_range
self.engine_config = engine_config
def __init__(self, local_vars_configuration=None): # noqa: E501
"""NeuralNetworkTrainingAlgorithm - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self.discriminator = None