[−][src]Struct gcp_client::google::cloud::bigquery::v2::model::training_run::TrainingOptions
Fields
max_iterations: i64
The maximum number of iterations in training. Used only for iterative training algorithms.
loss_type: i32
Type of loss function used during training run.
learn_rate: f64
Learning rate in training. Used only for iterative training algorithms.
l1_regularization: Option<f64>
L1 regularization coefficient.
l2_regularization: Option<f64>
L2 regularization coefficient.
min_relative_progress: Option<f64>
When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
warm_start: Option<bool>
Whether to train a model from the last checkpoint.
early_stop: Option<bool>
Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
input_label_columns: Vec<String>
Name of input label columns in training data.
data_split_method: i32
The data split type for training and evaluation, e.g. RANDOM.
data_split_eval_fraction: f64
The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
data_split_column: String
The column to split data with. This column won't be used as a feature.
- When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data.
- When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
learn_rate_strategy: i32
The strategy to determine learn rate for the current iteration.
initial_learn_rate: f64
Specifies the initial learning rate for the line search learn rate strategy.
label_class_weights: HashMap<String, f64>
Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
distance_type: i32
Distance type for clustering models.
num_clusters: i64
Number of clusters for clustering models.
model_uri: String
[Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
optimization_strategy: i32
Optimization strategy for training linear regression models.
kmeans_initialization_method: i32
The method used to initialize the centroids for kmeans algorithm.
kmeans_initialization_column: String
The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
Implementations
impl TrainingOptions
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pub fn loss_type(&self) -> LossType
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Returns the enum value of loss_type
, or the default if the field is set to an invalid enum value.
pub fn set_loss_type(&mut self, value: LossType)
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Sets loss_type
to the provided enum value.
pub fn data_split_method(&self) -> DataSplitMethod
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Returns the enum value of data_split_method
, or the default if the field is set to an invalid enum value.
pub fn set_data_split_method(&mut self, value: DataSplitMethod)
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Sets data_split_method
to the provided enum value.
pub fn learn_rate_strategy(&self) -> LearnRateStrategy
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Returns the enum value of learn_rate_strategy
, or the default if the field is set to an invalid enum value.
pub fn set_learn_rate_strategy(&mut self, value: LearnRateStrategy)
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Sets learn_rate_strategy
to the provided enum value.
pub fn distance_type(&self) -> DistanceType
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Returns the enum value of distance_type
, or the default if the field is set to an invalid enum value.
pub fn set_distance_type(&mut self, value: DistanceType)
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Sets distance_type
to the provided enum value.
pub fn optimization_strategy(&self) -> OptimizationStrategy
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Returns the enum value of optimization_strategy
, or the default if the field is set to an invalid enum value.
pub fn set_optimization_strategy(&mut self, value: OptimizationStrategy)
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Sets optimization_strategy
to the provided enum value.
pub fn kmeans_initialization_method(&self) -> KmeansInitializationMethod
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Returns the enum value of kmeans_initialization_method
, or the default if the field is set to an invalid enum value.
pub fn set_kmeans_initialization_method(
&mut self,
value: KmeansInitializationMethod
)
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&mut self,
value: KmeansInitializationMethod
)
Sets kmeans_initialization_method
to the provided enum value.
Trait Implementations
impl Clone for TrainingOptions
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fn clone(&self) -> TrainingOptions
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fn clone_from(&mut self, source: &Self)
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impl Debug for TrainingOptions
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impl Default for TrainingOptions
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fn default() -> TrainingOptions
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impl Message for TrainingOptions
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fn encode_raw<B>(&self, buf: &mut B) where
B: BufMut,
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B: BufMut,
fn merge_field<B>(
&mut self,
tag: u32,
wire_type: WireType,
buf: &mut B,
ctx: DecodeContext
) -> Result<(), DecodeError> where
B: Buf,
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&mut self,
tag: u32,
wire_type: WireType,
buf: &mut B,
ctx: DecodeContext
) -> Result<(), DecodeError> where
B: Buf,
fn encoded_len(&self) -> usize
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fn clear(&mut self)
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fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
B: BufMut,
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B: BufMut,
fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
B: BufMut,
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B: BufMut,
fn decode<B>(buf: B) -> Result<Self, DecodeError> where
B: Buf,
Self: Default,
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B: Buf,
Self: Default,
fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
B: Buf,
Self: Default,
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B: Buf,
Self: Default,
fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
B: Buf,
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B: Buf,
fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
B: Buf,
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B: Buf,
impl PartialEq<TrainingOptions> for TrainingOptions
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fn eq(&self, other: &TrainingOptions) -> bool
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fn ne(&self, other: &TrainingOptions) -> bool
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impl StructuralPartialEq for TrainingOptions
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Auto Trait Implementations
impl RefUnwindSafe for TrainingOptions
impl Send for TrainingOptions
impl Sync for TrainingOptions
impl Unpin for TrainingOptions
impl UnwindSafe for TrainingOptions
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T> Instrument for T
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fn instrument(self, span: Span) -> Instrumented<Self>
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fn in_current_span(self) -> Instrumented<Self>
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> IntoRequest<T> for T
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fn into_request(self) -> Request<T>
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impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,
fn vzip(self) -> V
impl<T> WithSubscriber for T
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fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
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S: Into<Dispatch>,