pub struct Builder { /* private fields */ }Expand description
A builder for ClassifierEvaluationMetrics.
Implementations§
source§impl Builder
impl Builder
sourcepub fn accuracy(self, input: f64) -> Self
pub fn accuracy(self, input: f64) -> Self
The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.
sourcepub fn set_accuracy(self, input: Option<f64>) -> Self
pub fn set_accuracy(self, input: Option<f64>) -> Self
The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.
Examples found in repository?
src/json_deser.rs (lines 10061-10066)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn precision(self, input: f64) -> Self
pub fn precision(self, input: f64) -> Self
A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.
sourcepub fn set_precision(self, input: Option<f64>) -> Self
pub fn set_precision(self, input: Option<f64>) -> Self
A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.
Examples found in repository?
src/json_deser.rs (lines 10069-10074)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn recall(self, input: f64) -> Self
pub fn recall(self, input: f64) -> Self
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.
sourcepub fn set_recall(self, input: Option<f64>) -> Self
pub fn set_recall(self, input: Option<f64>) -> Self
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.
Examples found in repository?
src/json_deser.rs (lines 10077-10082)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn f1_score(self, input: f64) -> Self
pub fn f1_score(self, input: f64) -> Self
A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.
sourcepub fn set_f1_score(self, input: Option<f64>) -> Self
pub fn set_f1_score(self, input: Option<f64>) -> Self
A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.
Examples found in repository?
src/json_deser.rs (lines 10085-10090)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn micro_precision(self, input: f64) -> Self
pub fn micro_precision(self, input: f64) -> Self
A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.
sourcepub fn set_micro_precision(self, input: Option<f64>) -> Self
pub fn set_micro_precision(self, input: Option<f64>) -> Self
A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.
Examples found in repository?
src/json_deser.rs (lines 10093-10098)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn micro_recall(self, input: f64) -> Self
pub fn micro_recall(self, input: f64) -> Self
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.
sourcepub fn set_micro_recall(self, input: Option<f64>) -> Self
pub fn set_micro_recall(self, input: Option<f64>) -> Self
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.
Examples found in repository?
src/json_deser.rs (lines 10101-10106)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn micro_f1_score(self, input: f64) -> Self
pub fn micro_f1_score(self, input: f64) -> Self
A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
sourcepub fn set_micro_f1_score(self, input: Option<f64>) -> Self
pub fn set_micro_f1_score(self, input: Option<f64>) -> Self
A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
Examples found in repository?
src/json_deser.rs (lines 10109-10114)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn hamming_loss(self, input: f64) -> Self
pub fn hamming_loss(self, input: f64) -> Self
Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.
sourcepub fn set_hamming_loss(self, input: Option<f64>) -> Self
pub fn set_hamming_loss(self, input: Option<f64>) -> Self
Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.
Examples found in repository?
src/json_deser.rs (lines 10117-10122)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}sourcepub fn build(self) -> ClassifierEvaluationMetrics
pub fn build(self) -> ClassifierEvaluationMetrics
Consumes the builder and constructs a ClassifierEvaluationMetrics.
Examples found in repository?
src/json_deser.rs (line 10137)
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pub(crate) fn deser_structure_crate_model_classifier_evaluation_metrics<'a, I>(
tokens: &mut std::iter::Peekable<I>,
) -> Result<
Option<crate::model::ClassifierEvaluationMetrics>,
aws_smithy_json::deserialize::error::DeserializeError,
>
where
I: Iterator<
Item = Result<
aws_smithy_json::deserialize::Token<'a>,
aws_smithy_json::deserialize::error::DeserializeError,
>,
>,
{
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::ValueNull { .. }) => Ok(None),
Some(aws_smithy_json::deserialize::Token::StartObject { .. }) => {
#[allow(unused_mut)]
let mut builder = crate::model::classifier_evaluation_metrics::Builder::default();
loop {
match tokens.next().transpose()? {
Some(aws_smithy_json::deserialize::Token::EndObject { .. }) => break,
Some(aws_smithy_json::deserialize::Token::ObjectKey { key, .. }) => {
match key.to_unescaped()?.as_ref() {
"Accuracy" => {
builder = builder.set_accuracy(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Precision" => {
builder = builder.set_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"Recall" => {
builder = builder.set_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"F1Score" => {
builder = builder.set_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroPrecision" => {
builder = builder.set_micro_precision(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroRecall" => {
builder = builder.set_micro_recall(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"MicroF1Score" => {
builder = builder.set_micro_f1_score(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
"HammingLoss" => {
builder = builder.set_hamming_loss(
aws_smithy_json::deserialize::token::expect_number_or_null(
tokens.next(),
)?
.map(|v| v.to_f64_lossy()),
);
}
_ => aws_smithy_json::deserialize::token::skip_value(tokens)?,
}
}
other => {
return Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(format!(
"expected object key or end object, found: {:?}",
other
)),
)
}
}
}
Ok(Some(builder.build()))
}
_ => Err(
aws_smithy_json::deserialize::error::DeserializeError::custom(
"expected start object or null",
),
),
}
}