Hyperparameter optimization¶
In the sections on Feature engineering and Predicting we learned how
to train both the feature learning algorithm and the machine learning algorithm
used for prediction in the getML engine. However, there are lots of parameters
involved. MultirelModel
,
RelboostModel
,
RelMTModel
,
MultirelTimeSeries
,
RelboostTimeSeries
,
RelMTTimeSeries
,
LinearRegression
,
LogisticRegression
,
XGBoostClassifier
, and
XGBoostRegressor
all have their own
settings. That is why you might want to use hyperparameter
optimization.
The most relevant parameters of these classes can be chosen to
constitute individual dimensions of a parameter space. For each
parameter, a lower and upper bound has to be provided and the
hyperparameter optimization will search the space within these
bounds. This will be done iteratively by drawing a specific parameter
combination, overwriting the corresponding parameters in a base pipeline,
and then fitting and scoring it. The algorithm used to draw from the
parameter space is represented by the different classes of
hyperopt
.
While RandomSearch
and
LatinHypercubeSearch
are purely statistical
approaches, GaussianHyperparameterSearch
uses prior knowledge obtained from evaluations of previous parameter
combinations to select the next one.
Tuning routines¶
The easiest way to conduct a hyperparameter optimization in getML are the
tuning routines tune_feature_learners()
and tune_predictors()
. They roughly work as follows:
They begin with a base pipeline, in which the default parameters for the feature learner or the predictor are used.
They then proceed by optimizing 2 or 3 parameters at a time using a
GaussianHyperparameterSearch
. If the best pipeline outperforms the base pipeline, the best pipeline becomes the new base pipeline.Taking the base pipeline from the previous steps, the tuning routine then optimizes the next 2 or 3 hyperparameters. If the best pipeline from that hyperparameter optimization outperforms the current base pipeline, that pipeline becomes the new base pipeline.
These steps are then repeated for more hyperparameters.
The following table lists the tuning recipes and hyperparameter subspaces for each step.
Tuning recipes and hyperparameter subspaces¶
Predictor 
Stage 
Hyperparameter 
Subspace 


1 (base parameters) 
reg_lambda 
[1E11, 100] 

learning_rate 
[0.5, 0.99] 

1 (base parameters) 
n_estimators 
[10, 1000] 

learning_rate 
[0.05, 0.3] 

2 (tree parameters) 
max_depth 
[1, 15] 

min_child_weights 
[1, 6] 

gamma 
[0, 5] 

3 (sampling parameters) 
colsample_bytree 
[0.75, 0.9] 

subsample 
[075, 0.9] 

4 (regularization parameters) 
reg_alpha 
[0, 5] 

reg_lambda 
[0, 10] 
Feature Leaner 
Stage 
Hyperparameter 
Subspace 

1 (base parameters) 
num_features 
[10, 50] 

shrinkage 
[0, 0.3] 

2 (tree parameters) 
max_length 
[0, 10] 

min_num_samples 
[1, 500] 

3 (regularization parameters) 
share_aggregations 
[0.1, 0.5] 

1 (base parameters) 
num_features 
[10, 50] 

shrinkage 
[0, 0.3] 

2 (tree parameters) 
max_length 
[0, 10] 

min_num_samples 
[1, 500] 

3 (regularization parameters) 
share_aggregations 
[0.1, 0.5] 

1 (base parameters) 
num_features 
[10, 50] 

shrinkage 
[0, 0.3] 

2 (tree parameters) 
max_depth 
[1, 8] 

min_num_samples 
[1, 500] 

3 (regularization parameters) 
reg_lambda 
[0, 0.0001] 
The advantage of the tuning routines is that they provide a convenient outofthebox for hyperparameter tuning. For most use cases, it is sufficient to tune the XGBoost predictor.
More advanced users can rely on the more lowlevel hyperparameter optimization routines.
Random search¶
A RandomSearch
draws random hyperparameter
sets from the hyperparameter space.
Latin hypercube search¶
A LatinHypercubeSearch
draws almost random
hyperparameter sets from the hyperparameter space, but ensures
that they are sufficiently different from each other.
Gaussian hyperparameter search¶
A GaussianHyperparameterSearch
search works like this:
It begins with a burnin phase, usually about 70% to 90% of all iterations. During that burnin phase, the hyperparameter space is sampled more or less at random, using either a random search or a latin hypercube search. You can control this phase using
ratio_iter
andsurrogate_burn_in_algorithm
.Once enough information has been collected, it fits a Gaussian process on the hyperparameters with the score we want to maximize or minimize as the predicted variable. Note that the Gaussian process has hyperparameters itself, which are also optimized. You can control this phase using
gaussian_kernel
,gaussian_optimization_algorithm
,gaussian_optimization_burn_in_algorithm
andgaussian_optimization_burn_ins
.It then uses the Gaussian process to predict the expected information (EI). The EI is a measure of how much additional information it might get from evaluating a particular point in the hyperparameter space. The expected information is to be maximized. The point in the hyperparameter space with the maximum expected information is the next point that is actually evaluated (meaning a new pipeline with these hyperparameters is trained). You can control this phase using
optimization_algorithm
,optimization_burn_ins
andoptimization_burn_in_algorithm
.
In a nutshell, the GaussianHyperparameterSearch behaves like human data scientists:
At first, it picks random hyperparameter combinations.
Once it has gained a better understanding of the hyperparameter space, it starts evaluating hyperparameter combinations that are particularly interesting.