Struct aws_sdk_evidently::operation::get_experiment_results::GetExperimentResultsInput

#[non_exhaustive]
pub struct GetExperimentResultsInput {
    pub project: Option<String>,
    pub experiment: Option<String>,
    pub start_time: Option<DateTime>,
    pub end_time: Option<DateTime>,
    pub metric_names: Option<Vec<String>>,
    pub treatment_names: Option<Vec<String>>,
    pub base_stat: Option<ExperimentBaseStat>,
    pub result_stats: Option<Vec<ExperimentResultRequestType>>,
    pub report_names: Option<Vec<ExperimentReportName>>,
    pub period: Option<i64>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

project: Option<String>

The name or ARN of the project that contains the experiment that you want to see the results of.

experiment: Option<String>

The name of the experiment to retrieve the results of.

start_time: Option<DateTime>

The date and time that the experiment started.

end_time: Option<DateTime>

The date and time that the experiment ended, if it is completed. This must be no longer than 30 days after the experiment start time.

metric_names: Option<Vec<String>>

The names of the experiment metrics that you want to see the results of.

treatment_names: Option<Vec<String>>

The names of the experiment treatments that you want to see the results for.

base_stat: Option<ExperimentBaseStat>

The statistic used to calculate experiment results. Currently the only valid value is mean, which uses the mean of the collected values as the statistic.

result_stats: Option<Vec<ExperimentResultRequestType>>

The statistics that you want to see in the returned results.

• PValue specifies to use p-values for the results. A p-value is used in hypothesis testing to measure how often you are willing to make a mistake in rejecting the null hypothesis. A general practice is to reject the null hypothesis and declare that the results are statistically significant when the p-value is less than 0.05.

• ConfidenceInterval specifies a confidence interval for the results. The confidence interval represents the range of values for the chosen metric that is likely to contain the true difference between the baseStat of a variation and the baseline. Evidently returns the 95% confidence interval.

• TreatmentEffect is the difference in the statistic specified by the baseStat parameter between each variation and the default variation.

• BaseStat returns the statistical values collected for the metric for each variation. The statistic uses the same statistic specified in the baseStat parameter. Therefore, if baseStat is mean, this returns the mean of the values collected for each variation.

report_names: Option<Vec<ExperimentReportName>>

The names of the report types that you want to see. Currently, BayesianInference is the only valid value.

period: Option<i64>

In seconds, the amount of time to aggregate results together.

Implementations

impl GetExperimentResultsInput

pub fn project(&self) -> Option<&str>

The name or ARN of the project that contains the experiment that you want to see the results of.

pub fn experiment(&self) -> Option<&str>

The name of the experiment to retrieve the results of.

pub fn start_time(&self) -> Option<&DateTime>

The date and time that the experiment started.

pub fn end_time(&self) -> Option<&DateTime>

The date and time that the experiment ended, if it is completed. This must be no longer than 30 days after the experiment start time.

pub fn metric_names(&self) -> &[String]

The names of the experiment metrics that you want to see the results of.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .metric_names.is_none().

pub fn treatment_names(&self) -> &[String]

The names of the experiment treatments that you want to see the results for.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .treatment_names.is_none().

pub fn base_stat(&self) -> Option<&ExperimentBaseStat>

The statistic used to calculate experiment results. Currently the only valid value is mean, which uses the mean of the collected values as the statistic.

pub fn result_stats(&self) -> &[ExperimentResultRequestType]

The statistics that you want to see in the returned results.

• PValue specifies to use p-values for the results. A p-value is used in hypothesis testing to measure how often you are willing to make a mistake in rejecting the null hypothesis. A general practice is to reject the null hypothesis and declare that the results are statistically significant when the p-value is less than 0.05.

• ConfidenceInterval specifies a confidence interval for the results. The confidence interval represents the range of values for the chosen metric that is likely to contain the true difference between the baseStat of a variation and the baseline. Evidently returns the 95% confidence interval.

• TreatmentEffect is the difference in the statistic specified by the baseStat parameter between each variation and the default variation.

• BaseStat returns the statistical values collected for the metric for each variation. The statistic uses the same statistic specified in the baseStat parameter. Therefore, if baseStat is mean, this returns the mean of the values collected for each variation.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .result_stats.is_none().

pub fn report_names(&self) -> &[ExperimentReportName]

The names of the report types that you want to see. Currently, BayesianInference is the only valid value.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .report_names.is_none().

pub fn period(&self) -> Option<i64>

In seconds, the amount of time to aggregate results together.

impl GetExperimentResultsInput

pub fn builder() -> GetExperimentResultsInputBuilder

Creates a new builder-style object to manufacture GetExperimentResultsInput.

Trait Implementations

impl Clone for GetExperimentResultsInput

fn clone(&self) -> GetExperimentResultsInput

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GetExperimentResultsInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for GetExperimentResultsInput

fn eq(&self, other: &GetExperimentResultsInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for GetExperimentResultsInput