class Hyperparams(hyperparams.Hyperparams): # search over these hyperparameters to tune performance q = hyperparams.UniformInt( default=3, lower=2, upper=10, description="degree of the polynomial to be fit", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) r = hyperparams.UniformInt( default=5, lower=2, upper=30, description="rank of the coefficient tensors to be fit", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) gamma = hyperparams.LogUniform( default=.01, lower=.0001, upper=10, description="l2 regularization to use on the tensor low-rank factors", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) alpha = hyperparams.LogUniform( default=.1, lower=.001, upper=1, description="variance of the random initialization of the factors", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) epochs = hyperparams.UniformInt( default=30, lower=1, upper=100, description="maximum iterations of LBFGS, or number of epochs of SFO", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) # control parameters determined once during pipeline building then fixed solver = hyperparams.Enumeration[str]( default="LBFGS", values=["SFO", "LBFGS"], description= "solver to use: LBFGS better for small enough datasets, SFO does minibached stochastic quasi-Newton to scale to large dataset", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ]) preprocess = hyperparams.Enumeration[str]( default="YES", values=["YES", "NO"], description= "whether to use a preprocessing that tends to work well for tensor machines", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ])
class Hyperparams(hyperparams.Hyperparams): # control parameters determined once during pipeline building then fixed coresetmultiplier = hyperparams.UniformInt( default=4, lower=2, upper=7, description= "coreset size, as a multiple of the number of input features", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) eps = hyperparams.LogUniform( default=1e-6, lower=1e-14, upper=1e-2, description="relative error stopping tolerance for IRLS solver", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ]) maxIters = hyperparams.UniformInt( default=100, lower=50, upper=500, description="maximum iterations of IRLS", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ])
class Hyperparams(hyperparams.Hyperparams): shapelet_length = hyperparams.LogUniform(lower = 0, upper = 1, default = 0.1, upper_inclusive = False, semantic_types = [ 'https://metadata.datadrivendiscovery.org/types/ControlParameter'], description = 'base shapelet length, expressed as fraction of length of time series') num_shapelet_lengths = hyperparams.UniformInt(lower = 1, upper = 100, default = 2, semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter'], description = 'number of different shapelet lengths') # default epoch size from https://tslearn.readthedocs.io/en/latest/auto_examples/plot_shapelets.html#sphx-glr-auto-examples-plot-shapelets-py epochs = hyperparams.UniformInt(lower = 1, upper = sys.maxsize, default = 200, semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter'], description = 'number of training epochs') pass
class Hyperparams(hyperparams.Hyperparams): # search over these hyperparameters to tune performance lparam = hyperparams.LogUniform(default=.01, lower=.0001, upper=1000, description="l2 regularization to use on the regression", semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']) degree = hyperparams.UniformInt(default=3, lower=2, upper=9, description="degree of the polynomial to fit", semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']) offset = hyperparams.LogUniform(default=.1, lower=.001, upper=2, description="value of constant feature to use in the regression", semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']) sf = hyperparams.LogUniform(default=.01, lower=.00001, upper=2, description="scale factor to use in the regression", semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']) # control parameters determined once during pipeline building then fixed eps = hyperparams.LogUniform(default=1e-3, lower=1e-14, upper=1e-2, description="relative error stopping tolerance for PCG solver", semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter']) maxIters = hyperparams.UniformInt(default=200, lower=50, upper=500, description="maximum iterations of PCG", semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'])
class Hyperparams(hyperparams.Hyperparams): # search over these hyperparameters to tune performance lparam = hyperparams.LogUniform( default=.01, lower=.0001, upper=1000, description="l2 regularization to use for the kernel regression", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) sigma = hyperparams.LogUniform( default=.01, lower=.0001, upper=1000, description="bandwidth (sigma) parameter for the kernel regression", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) # control parameters determined once during pipeline building then fixed eps = hyperparams.LogUniform( default=1e-4, lower=1e-14, upper=1e-2, description="relative error stopping tolerance for PCG solver", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ]) maxIters = hyperparams.UniformInt( default=200, lower=50, upper=500, description="maximum iterations of PCG", semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ])
class SSC_OMPHyperparams(hyperparams.Hyperparams): n_clusters = hyperparams.Bounded[int](lower=2, upper=None, default=2, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="number of subspaces/clusters to learn") sparsity_level = hyperparams.UniformInt(lower=3, upper=50, default=3, upper_inclusive = True, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="The algorithm terminates when it has selected this many regression coefficients.") thresh = hyperparams.LogUniform(lower=1e-10, upper=1, default=1e-6, upper_inclusive = True, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="Minimum regression residual for termination.")
class RNNHyperparams(hyperparams.Hyperparams): n_batch = hyperparams.Hyperparameter[int]( default=1, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_max_epoch = hyperparams.Hyperparameter[int]( default=1000, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_max_epoch_total = hyperparams.Hyperparameter[int]( default=100, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_neurons = hyperparams.Hyperparameter[int]( default=256, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_input_dim = hyperparams.Hyperparameter[int]( default=1, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_dense_dim = hyperparams.Hyperparameter[int]( default=128, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_output_dim = hyperparams.Hyperparameter[int]( default=3, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_patience = hyperparams.Hyperparameter[int]( default=100, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_lr_decay = hyperparams.Hyperparameter[int]( default=5, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) lr = hyperparams.LogUniform( default=1e-2, lower=1e-05, upper=1, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) lr_decay = hyperparams.Hyperparameter[float]( default=0.95, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) max_valid = hyperparams.Hyperparameter[int]( default=10, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) valid_loss_weight = hyperparams.Hyperparameter[float]( default=0.5, description='Maximum number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ])
class RNNHyperparams(hyperparams.Hyperparams): n_batch = hyperparams.Hyperparameter[int]( default=1, description='Maximum number of batch size', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_max_epoch = hyperparams.Hyperparameter[int]( default=1000, description='Maximum number of Epochs. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_max_epoch_total = hyperparams.Hyperparameter[int]( default=100, description='Maximum number of total Epoches. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_neurons = hyperparams.Hyperparameter[int]( default=256, description='Neurons in hidden layers', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_input_dim = hyperparams.Hyperparameter[int]( default=1, description='Number of input dimension', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_dense_dim = hyperparams.Hyperparameter[int]( default=128, description='Size of fully-connected layers', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_output_dim = hyperparams.Hyperparameter[int]( default=3, description='output dimension', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_patience = hyperparams.Hyperparameter[int]( default=100, description='Number of patience', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) n_lr_decay = hyperparams.Hyperparameter[int]( default=5, description='number of Learning rate decay', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) lr = hyperparams.LogUniform( default=1e-2, lower=1e-05, upper=1, description='Learning rate', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) lr_decay = hyperparams.Hyperparameter[float]( default=0.95, description='learning rate decay', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) max_valid = hyperparams.Hyperparameter[int]( default=10, description='Maximum valid number of iterations. Default is 300 ', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ]) valid_loss_weight = hyperparams.Hyperparameter[float]( default=0.5, description='Loss weight of validation set', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ])
class Hyperparams(hyperparams.Hyperparams): hidden_layer_sizes = hyperparams.Hyperparameter[List]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=List([30, 30]), ) activation = hyperparams.Enumeration[str]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], values=['identity', 'logistic', 'tanh', 'relu'], default='relu', description='Activation function for the hidden layer.') solver = hyperparams.Enumeration[str](semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], values=['lbfgs', 'sgd', 'adam'], default='adam', description='') learning_rate = hyperparams.Enumeration[str]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], values=['constant', 'invscaling', 'adaptive'], default='constant', description='') alpha = hyperparams.Hyperparameter[float](semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=0.0001, description='') beta_1 = hyperparams.Hyperparameter[float](semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=0.9, description='') beta_2 = hyperparams.Hyperparameter[float](semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=0.999, description='') learning_rate_init = hyperparams.Hyperparameter[float]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=0.001, ) tol = hyperparams.Hyperparameter[float]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=0.0001, ) max_iter = hyperparams.UniformInt( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter', 'https://metadata.datadrivendiscovery.org/types/ResourcesUseParameter', ], default=200, lower=1, upper=800, description='The maximum number of passes over the training data ') early_stopping = hyperparams.Hyperparameter[bool]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter', ], default=False, ) shuffle = hyperparams.Hyperparameter[bool]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter', ], default=True, description= 'Whether or not the training data should be shuffled after each iteration. ' ) warm_start = hyperparams.Hyperparameter[bool]( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter', ], default=False, description= 'When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. ' ) #hidden_layer_sizes=hyperparams.Hyperparameter[Tuple[int, int]]( # default=(100,1) #) epsilon = hyperparams.LogUniform( semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter', ], default=1e-8, lower=1e-08, upper=0.1, description= 'Value for numerical stability in adam. Only used when solver=’adam’') use_columns = hyperparams.Set( elements=hyperparams.Hyperparameter[int](-1), default=(), semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ], description= "A set of column indices to force primitive to operate on. If any specified column cannot be parsed, it is skipped.", ) exclude_columns = hyperparams.Set( elements=hyperparams.Hyperparameter[int](-1), default=(), semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ], description= "A set of column indices to not operate on. Applicable only if \"use_columns\" is not provided.", ) return_result = hyperparams.Enumeration( values=['append', 'replace', 'new'], default='replace', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ], description= "Should parsed columns be appended, should they replace original columns, or should only parsed columns be returned?", ) use_semantic_types = hyperparams.UniformBool( default=False, semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ], description= "Controls whether semantic_types metadata will be used for filtering columns in input dataframe." ) add_index_columns = hyperparams.UniformBool( default=True, semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/ControlParameter' ], description= "Also include primary index columns if input data has them. Applicable only if \"return_result\" is set to \"new\".", )
lower=5, upper=50, default=30, lower_inclusive=True, upper_inclusive=True, description='The maximum depth of the tree.', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ], ), learning_rate=hyperparams.LogUniform( lower=1e-4, upper=1e-1, default=0.05, lower_inclusive=True, upper_inclusive=True, description=r'Boosting learning rate (xgb\`s \"eta\")', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ], ), gamma=hyperparams.Constant[float]( default=0.0, description= 'Minimum loss reduction required to make a further partition on a leaf node of the tree', semantic_types=[ 'https://metadata.datadrivendiscovery.org/types/TuningParameter' ], ), min_child_weight=hyperparams.Constant[int]( default=1,