Struct smartnoise_runtime::proto::DpLinearRegression

pub struct DpLinearRegression {
    pub implementation: String,
    pub privacy_usage: Vec<PrivacyUsage, Global>,
}

DPLinearRegression Component

Returns differentially private estimates of the slope and intercept.

This struct represents an abstract computation. Arguments are provided via the graph. Additional options are set via the fields on this struct. The return is the result of the dp_linear_regression on the arguments.

Arguments

• data_x - Array - Predictor variable
• data_y - Array - Target variable
• k - Integer - Number of matchings. Memory usage is quadratic in K.
• lower_slope - Array - Estimated minimum possible value of the slope.
• upper_slope - Array - Estimated maximum possible value of the slope.
• lower_intercept - Array - Estimated minimum possible value of the intercept.
• upper_intercept - Array - Estimated maximum possible value of the intercept.

Returns

• Value - Array - Differentially private estimate of the slope and intercept of the line fit to the data.

Fields

implementation: String

Theil-Sen implementation to use. One of [theil-sen, theil-sen-k-match]

privacy_usage: Vec<PrivacyUsage, Global>

Object describing the type and amount of privacy to be used for the mechanism release.

Trait Implementations

impl Clone for DpLinearRegression

pub fn clone(&self) -> DpLinearRegression

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DpLinearRegression

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

Formats the value using the given formatter.

impl Default for DpLinearRegression

pub fn default() -> DpLinearRegression

Returns the “default value” for a type.

impl Expandable for DpLinearRegression

pub fn expand_component(
    &self,
    _privacy_definition: &Option<PrivacyDefinition>,
    component: &Component,
    public_arguments: &IndexMap<IndexKey, &Value, RandomState>,
    _properties: &IndexMap<IndexKey, ValueProperties, RandomState>,
    component_id: u32,
    maximum_id: u32
) -> Result<ComponentExpansion, Error>

Concrete implementation for an Expandable component that returns a patch that may be applied to a computation graph.

impl Message for DpLinearRegression

pub fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

pub fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

pub fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

pub fn clear(&mut self)

Clears the message, resetting all fields to their default.

pub fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message to a buffer.

pub fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message with a length-delimiter to a buffer.

pub fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

pub fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes a length-delimited instance of the message from the buffer.

pub fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes an instance of the message from a buffer, and merges it into self.

pub fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq<DpLinearRegression> for DpLinearRegression

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

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

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

This method tests for !=.

impl Report for DpLinearRegression

pub fn summarize(
    &self,
    node_id: u32,
    component: &Component,
    _public_arguments: IndexMap<IndexKey, &Value, RandomState>,
    _properties: IndexMap<IndexKey, ValueProperties, RandomState>,
    release: &Value,
    _variable_names: Option<&Vec<IndexKey, Global>>
) -> Result<Option<Vec<JSONRelease, Global>>, Error>

Summarize the relevant metadata around a computation in a readable, JSON-serializable format.

impl StructuralPartialEq for DpLinearRegression