Define Hyperparameter Ranges
Hyperparameter tuning finds the best hyperparameter values for your model by searching over ranges of hyperparameters. You specify the hyperparameters and range of values over which to search by defining hyperparameter ranges for your tuning job. Choosing hyperparameters and ranges significantly affects the performance of your tuning job. For guidance on choosing hyperparameters and ranges, see Best Practices for Hyperparameter Tuning.
To define hyperparameter ranges by using the lowlevel API, you specify the names
of
hyperparameters and ranges of values in the ParameterRanges
field of the
HyperParameterTuningJobConfig
parameter that you pass to the
CreateHyperParameterTuningJob
operation. The
ParameterRanges
field has three subfields, one for each of the categorical,
integer, and continuous hyperparameter ranges. You can define up to 20 hyperparameters
to
search over. Each value of a categorical hyperparameter range counts as a hyperparameter
against the limit. Hyperparameter ranges have the following structure:
"ParameterRanges": { "CategoricalParameterRanges": [ { "Name": "tree_method", "Values": ["auto", "exact", "approx", "hist"] } ], "ContinuousParameterRanges": [ { "Name": "eta", "MaxValue" : "0.5", "MinValue": "0", "ScalingType": "Auto" } ], "IntegerParameterRanges": [ { "Name": "max_depth", "MaxValue": "10", "MinValue": "1", "ScalingType": "Auto" } ] }
Hyperparameter Scaling
For integer and continuous hyperparameter ranges, you can choose the scale you want
hyperparameter tuning to use to search the range of values by specifying a value for
the
ScalingType
field of the hyperparameter range. You can choose from the
following scaling types:
 Auto

SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
 Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Typically, you choose this if the range of all values from the lowest to the highest is relatively small (within one order of magnitude), because uniformly searching values from the range will give you a reasonable exploration of the entire range.
 Logarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
Choose logarithmic scaling when you are searching a range that spans several orders of magnitude. For example, if you are tuning a Tune a linear learner model model, and you specify a range of values between .0001 and 1.0 for the
learning_rate
hyperparameter, searching uniformly on a logarithmic scale gives you a better sample of the entire range than searching on a linear scale would, because searching on a linear scale would, on average, devote 90 percent of your training budget to only the values between .1 and 1.0, leaving only 10 percent of your training budget for the values between .0001 and .1.  ReverseLogarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. reverse logarithmic scaling is supported only for continuous hyperparameter ranges. It is not supported for integer hyperparameter ranges.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
Choose reverse logarithmic scaling when you are searching a range that is highly sensitive to small changes that are very close to 1.
For an example notebook that uses hyperparameter scaling, see https://github.com/awslabs/amazonsagemakerexamples/blob/master/hyperparameter_tuning/xgboost_random_log/hpo_xgboost_random_log.ipynb