LatinHypercubeSearch¶

class
getml.hyperopt.
LatinHypercubeSearch
(param_space, pipeline, score='rmse', n_iter=100, seed=5483, **kwargs)[source]¶ Latin hypercube sampling of the hyperparameters.
Uses a multidimensional, uniform cumulative distribution function to drawn the random numbers from. For drawing n_iter samples, the distribution will be divided in n_iter`*`n_iter hypercubes of equal size (n_iter per dimension). n_iter of them will be selected in such a way only one per dimension is used and an independent and identicallydistributed (iid) random number is drawn within the boundaries of the hypercube.
A latin hypercube search can be seen as a compromise between a grid search, which iterates through the entire hyperparameter space, and a random search, which draws completely random samples from the hyperparameter space.
 Args:
 param_space (dict):
Dictionary containing numerical arrays of length two holding the lower and upper bounds of all parameters which will be altered in pipeline during the hyperparameter optimization.
If we have two feature learners and one predictor, the hyperparameter space might look like this:
param_space = { "feature_learners": [ { "num_features": [10, 50], }, { "max_depth": [1, 10], "min_num_samples": [100, 500], "num_features": [10, 50], "reg_lambda": [0.0, 0.1], "shrinkage": [0.01, 0.4] }], "predictors": [ { "reg_lambda": [0.0, 10.0] } ] }
If we only want to optimize the predictor, then we can leave out the feature learners.
 pipeline (
Pipeline
): Base pipeline used to derive all models fitted and scored during the hyperparameter optimization. Be careful in constructing it since only those parameters present in param_space will be overwritten. It defines the data schema and any hyperparameters that are not optimized.
 score (str, optional):
The score to optimize. Must be from
metrics
. n_iter (int, optional):
Number of iterations in the hyperparameter optimization and thus the number of parameter combinations to draw and evaluate. Range: [1, \(\infty\)]
 seed (int, optional):
Seed used for the random number generator that underlies the sampling procedure to make the calculation reproducible. Due to nature of the underlying algorithm this is only the case if the fit is done without multithreading. To reflect this, a seed of None represents an unreproducible and is only allowed to be set to an actual integer if both
num_threads
andn_jobs
instance variables of thepredictor
andfeature_selector
in model  if they are instances of eitherXGBoostRegressor
orXGBoostClassifier
 are set to 1. Internally, a seed of None will be mapped to 5543. Range: [0, \(\infty\)]
 <<<<<<< HEAD
 Raises:
KeyError: If an unsupported instance variable is encountered.
TypeError: If any instance variable is of wrong type.
ValueError: If any instance variable does not match its possible choices (string) or is out of the expected bounds (numerical).
ValueError: If no
predictor
is present in the providedpipeline
.
 >>>>>>> develop
 Example:
from getml import data from getml import datasets from getml import engine from getml import feature_learning from getml.feature_learning import aggregations from getml.feature_learning import loss_functions from getml import hyperopt from getml import pipeline from getml import predictors #  engine.set_project("examples") #  population_table, peripheral_table = datasets.make_numerical() #  # Construct placeholders population_placeholder = data.Placeholder("POPULATION") peripheral_placeholder = data.Placeholder("PERIPHERAL") population_placeholder.join(peripheral_placeholder, "join_key", "time_stamp") #  # Base model  any parameters not included # in param_space will be taken from this. fe1 = feature_learning.Multirel( aggregation=[ aggregations.Count, aggregations.Sum ], loss_function=loss_functions.SquareLoss, num_features=10, share_aggregations=1.0, max_length=1, num_threads=0 ) #  # Base model  any parameters not included # in param_space will be taken from this. fe2 = feature_learning.Relboost( loss_function=loss_functions.SquareLoss, num_features=10 ) #  # Base model  any parameters not included # in param_space will be taken from this. predictor = predictors.LinearRegression() #  pipe = pipeline.Pipeline( population=population_placeholder, peripheral=[peripheral_placeholder], feature_learners=[fe1, fe2], predictors=[predictor] ) #  # Build a hyperparameter space. # We have two feature learners and one # predictor, so this is how we must # construct our hyperparameter space. # If we only wanted to optimize the predictor, # we could just leave out the feature_learners. param_space = { "feature_learners": [ { "num_features": [10, 50], }, { "max_depth": [1, 10], "min_num_samples": [100, 500], "num_features": [10, 50], "reg_lambda": [0.0, 0.1], "shrinkage": [0.01, 0.4] }], "predictors": [ { "reg_lambda": [0.0, 10.0] } ] } #  # Wrap a LatinHypercubeSearch around the reference model latin_search = hyperopt.LatinHypercubeSearch( pipeline=pipe, param_space=param_space, n_iter=30, score=pipeline.metrics.rsquared ) latin_search.fit( population_table_training=population_table, population_table_validation=population_table, peripheral_tables=[peripheral_table] )
Methods
clean_up
()Deletes all pipelines associated with hyperparameter optimization, but the best pipeline.
fit
(container[, train, validation])Launches the hyperparameter optimization.
refresh
()Reloads the hyperparameter optimization from the engine.
validate
()Validate the parameters of the hyperparameter optimization.
Attributes
The best pipeline that is part of the hyperparameter optimization.
Name of the hyperparameter optimization.
Returns the ID of the hyperparameter optimization.
The score to be optimized.
The algorithm used for the hyperparameter optimization.