* A collection of settings used for an AutoML job. *
* * @see AWS API * Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class AutoMLJobConfig implements Serializable, Cloneable, StructuredPojo { /** ** How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. *
*/ private AutoMLJobCompletionCriteria completionCriteria; /** ** The security configuration for traffic encryption or Amazon VPC settings. *
*/ private AutoMLSecurityConfig securityConfig; /** ** The configuration for splitting the input training dataset. *
** Type: AutoMLDataSplitConfig *
*/ private AutoMLDataSplitConfig dataSplitConfig; /** ** The configuration for generating a candidate for an AutoML job (optional). *
*/ private AutoMLCandidateGenerationConfig candidateGenerationConfig; /** *
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. *
* * @param completionCriteria * How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. */ public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { this.completionCriteria = completionCriteria; } /** ** How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. *
* * @return How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. */ public AutoMLJobCompletionCriteria getCompletionCriteria() { return this.completionCriteria; } /** ** How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. *
* * @param completionCriteria * How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { setCompletionCriteria(completionCriteria); return this; } /** ** The security configuration for traffic encryption or Amazon VPC settings. *
* * @param securityConfig * The security configuration for traffic encryption or Amazon VPC settings. */ public void setSecurityConfig(AutoMLSecurityConfig securityConfig) { this.securityConfig = securityConfig; } /** ** The security configuration for traffic encryption or Amazon VPC settings. *
* * @return The security configuration for traffic encryption or Amazon VPC settings. */ public AutoMLSecurityConfig getSecurityConfig() { return this.securityConfig; } /** ** The security configuration for traffic encryption or Amazon VPC settings. *
* * @param securityConfig * The security configuration for traffic encryption or Amazon VPC settings. * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLJobConfig withSecurityConfig(AutoMLSecurityConfig securityConfig) { setSecurityConfig(securityConfig); return this; } /** ** The configuration for splitting the input training dataset. *
** Type: AutoMLDataSplitConfig *
* * @param dataSplitConfig * The configuration for splitting the input training dataset. ** Type: AutoMLDataSplitConfig */ public void setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) { this.dataSplitConfig = dataSplitConfig; } /** *
* The configuration for splitting the input training dataset. *
** Type: AutoMLDataSplitConfig *
* * @return The configuration for splitting the input training dataset. ** Type: AutoMLDataSplitConfig */ public AutoMLDataSplitConfig getDataSplitConfig() { return this.dataSplitConfig; } /** *
* The configuration for splitting the input training dataset. *
** Type: AutoMLDataSplitConfig *
* * @param dataSplitConfig * The configuration for splitting the input training dataset. ** Type: AutoMLDataSplitConfig * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLJobConfig withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) { setDataSplitConfig(dataSplitConfig); return this; } /** *
* The configuration for generating a candidate for an AutoML job (optional). *
* * @param candidateGenerationConfig * The configuration for generating a candidate for an AutoML job (optional). */ public void setCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) { this.candidateGenerationConfig = candidateGenerationConfig; } /** ** The configuration for generating a candidate for an AutoML job (optional). *
* * @return The configuration for generating a candidate for an AutoML job (optional). */ public AutoMLCandidateGenerationConfig getCandidateGenerationConfig() { return this.candidateGenerationConfig; } /** ** The configuration for generating a candidate for an AutoML job (optional). *
* * @param candidateGenerationConfig * The configuration for generating a candidate for an AutoML job (optional). * @return Returns a reference to this object so that method calls can be chained together. */ public AutoMLJobConfig withCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) { setCandidateGenerationConfig(candidateGenerationConfig); return this; } /** *
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @see AutoMLMode
*/
public void setMode(String mode) {
this.mode = mode;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @see AutoMLMode
*/
public String getMode() {
return this.mode;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public AutoMLJobConfig withMode(String mode) {
setMode(mode);
return this;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public AutoMLJobConfig withMode(AutoMLMode mode) {
this.mode = mode.toString();
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getCompletionCriteria() != null)
sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(",");
if (getSecurityConfig() != null)
sb.append("SecurityConfig: ").append(getSecurityConfig()).append(",");
if (getDataSplitConfig() != null)
sb.append("DataSplitConfig: ").append(getDataSplitConfig()).append(",");
if (getCandidateGenerationConfig() != null)
sb.append("CandidateGenerationConfig: ").append(getCandidateGenerationConfig()).append(",");
if (getMode() != null)
sb.append("Mode: ").append(getMode());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AutoMLJobConfig == false)
return false;
AutoMLJobConfig other = (AutoMLJobConfig) obj;
if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null)
return false;
if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false)
return false;
if (other.getSecurityConfig() == null ^ this.getSecurityConfig() == null)
return false;
if (other.getSecurityConfig() != null && other.getSecurityConfig().equals(this.getSecurityConfig()) == false)
return false;
if (other.getDataSplitConfig() == null ^ this.getDataSplitConfig() == null)
return false;
if (other.getDataSplitConfig() != null && other.getDataSplitConfig().equals(this.getDataSplitConfig()) == false)
return false;
if (other.getCandidateGenerationConfig() == null ^ this.getCandidateGenerationConfig() == null)
return false;
if (other.getCandidateGenerationConfig() != null && other.getCandidateGenerationConfig().equals(this.getCandidateGenerationConfig()) == false)
return false;
if (other.getMode() == null ^ this.getMode() == null)
return false;
if (other.getMode() != null && other.getMode().equals(this.getMode()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode());
hashCode = prime * hashCode + ((getSecurityConfig() == null) ? 0 : getSecurityConfig().hashCode());
hashCode = prime * hashCode + ((getDataSplitConfig() == null) ? 0 : getDataSplitConfig().hashCode());
hashCode = prime * hashCode + ((getCandidateGenerationConfig() == null) ? 0 : getCandidateGenerationConfig().hashCode());
hashCode = prime * hashCode + ((getMode() == null) ? 0 : getMode().hashCode());
return hashCode;
}
@Override
public AutoMLJobConfig clone() {
try {
return (AutoMLJobConfig) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.AutoMLJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}