# 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 identically-distributed (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 and n_jobs instance variables of the predictor and feature_selector in model - if they are instances of either XGBoostRegressor or XGBoostClassifier - are set to 1. Internally, a seed of None will be mapped to 5543. Range: [0, $$\infty$$]

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 provided pipeline.

>>>>>>> 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,
)

# ----------------
# 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

 Deletes all pipelines associated with hyperparameter optimization, but the best pipeline. fit(container[, train, validation]) Launches the hyperparameter optimization. Reloads the hyperparameter optimization from the engine. Validate the parameters of the hyperparameter optimization.

Attributes

 best_pipeline The best pipeline that is part of the hyperparameter optimization. id Name of the hyperparameter optimization. name Returns the ID of the hyperparameter optimization. score The score to be optimized. type The algorithm used for the hyperparameter optimization.