Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::StudySpec::MetricSpec::SafetyMetricConfig (v0.24.0)

Version 0.24.0

• 0.58.0 (latest)
• 0.57.0
• 0.56.0
• 0.55.0
• 0.54.0
• 0.53.0
• 0.52.0
• 0.51.0
• 0.50.0
• 0.49.0
• 0.48.0
• 0.47.0
• 0.46.0
• 0.45.0
• 0.44.0
• 0.43.0
• 0.42.0
• 0.41.0
• 0.40.0
• 0.39.0
• 0.38.0
• 0.37.0
• 0.36.0
• 0.35.0
• 0.34.0
• 0.33.0
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.0
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.1
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0
• 0.1.0

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::StudySpec::MetricSpec::SafetyMetricConfig.

Used in safe optimization to specify threshold levels and risk tolerance.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#desired_min_safe_trials_fraction

def desired_min_safe_trials_fraction() -> ::Float

Returns

• (::Float) — Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.

#desired_min_safe_trials_fraction=

def desired_min_safe_trials_fraction=(value) -> ::Float

Parameter

• value (::Float) — Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.

Returns

• (::Float) — Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.

#safety_threshold

def safety_threshold() -> ::Float

Returns

• (::Float) — Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

#safety_threshold=

def safety_threshold=(value) -> ::Float

Parameter

• value (::Float) — Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

Returns

• (::Float) — Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.