Define and Set Up Metric Space
Similar to defining the hyperparameter space, you need to define the metric space to run a SigOpt Experiment.
The entire metric space is defined as a list of objects, where each object represents a metric.
Each metric is given a
name, an objective and a strategy. Metrics with a strategy of constraint are given a threshold. Metrics with a strategy of optimize can optionally be assigned a threshold.The metric name is a string used to keep track of the different metrics in the experiment.
The objective is a flag that specifies if a metric is intended to be minimized or maximized by either setting
minimize or maximize.Python
YAML
dict(
name="name",
objective="minimize",
strategy="store"
)
metrics:
- name: name
objective: minimize
strategy: store
Python
YAML
dict(
name="name",
objective="maximize",
strategy="store"
)
metrics:
- name: name
objective: maximize
strategy: store
SigOpt allows three different types of metrics: stored, optimized and constrained. They are invoked by calling
store, optimize, and constraint respectively.Store metrics are for tracking purposes. SigOpt can handle up to 50 stored metrics per training run or experiment.Python
YAML
dict(
name="name",
objective="maximize",
strategy="store"
)
metrics:
- name: name
objective: maximize
strategy: store
Optimize metrics are for finding a minimum or a maximum value. SigOpt handles up to two optimized metrics - for more information visit multimetric optimization.Each optimized metric has the ability to specify a threshold which is a floating point number representing a threshold for success - for more information visit metric threshold.
Python
YAML
dict(
name="name",
objective="minimize",
strategy="optimize"
)
metrics:
- name: name
objective: minimize
strategy: optimize
Python
YAML
dict(
name="name",
objective="minimize",
threshold=100,
strategy="optimize"
)
metrics:
- name: name
objective: minimize
threshold: 100
strategy: optimize
Constraint metrics are for defining success thresholds without the need to find a minimum or maximum value. SigOpt handles up to four constraints - for more information visit metric constraints. The constraint is specified as a threshold which is a floating point number representing a threshold for success.Python
YAML
dict(
name="name",
objective="minimize",
threshold=100,
strategy="constraint"
)
metrics:
- name: name
objective: minimize
threshold: 100
strategy: constraint
Observations or Runs report the value of your metric evaluated on a given set of parameters. However, sometimes a set of parameters is not feasible for evaluation. We call this a metric failure since the model failed to obtain a metric. SigOpt has the ability to handle and track such Failures to learn the feasible regions of your domain.
Cases we recommend marking as Failures:
- Evaluating a metric is not possible because the SigOpt provided parameter assignments are infeasible/ not of interest
- Certain parameter configurations lead to out-of-memory error or run-time errors
If model training abruptly stops because a machine randomly fails, we recommend deleting that Run or Observation instead of marking it as failed.
Python
Java
from sigopt import Connection
conn = Connection(client_token="USER_TOKEN")
observation = conn.experiments(EXPERIMENT_ID).observations().create(
failed=True,
suggestion="SUGGESTION_ID"
)
import com.sigopt.SigOpt;
import com.sigopt.exception.SigoptException;
import com.sigopt.model.*;
import java.util.Arrays;
public class YourSigoptExperiment {
public static Experiment createExperiment() throws SigoptException {
Observation observation = new Experiment(EXPERIMENT_ID).observations().create()
.data(
new Observation.Builder()
.failed(true)
.suggestion("SUGGESTION_ID")
.build()
)
.call();
return experiment;
}
sigopt.log_failure()
If an infeasible region of the parameter space is known beforehand, it may be possible to predefine with Parameter Constraints. In situations in which feasibility is defined through thresholding on auxiliary non-optimized metric values, it may be more beneficial to use Metric Constraints.
Last modified 6mo ago