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ConvexStopConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration for ConvexStopPolicy.
Attributes |
|
---|---|
Name | Description |
max_num_steps |
int
Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds. |
min_num_steps |
int
Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds. |
autoregressive_order |
int
The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points. |
learning_rate_parameter_name |
str
The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial. |
use_seconds |
bool
This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds. |
Methods
ConvexStopConfig
ConvexStopConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration for ConvexStopPolicy.