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use indexmap::map::IndexMap;
use crate::{base, proto};
use crate::base::{Array, IndexKey, NodeProperties, Value};
use crate::components::{Expandable, Report};
use crate::errors::*;
use crate::utilities::{array::get_ith_column, prepend, privacy::spread_privacy_usage};
use crate::utilities::json::{AlgorithmInfo, JSONRelease, privacy_usage_to_json, value_to_json};
impl Expandable for proto::DpQuantile {
fn expand_component(
&self,
privacy_definition: &Option<proto::PrivacyDefinition>,
component: &proto::Component,
_public_arguments: &IndexMap<IndexKey, &Value>,
properties: &base::NodeProperties,
component_id: u32,
mut maximum_id: u32,
) -> Result<base::ComponentExpansion> {
let mut expansion = base::ComponentExpansion::default();
let argument_ids = component.arguments();
let data_id = *argument_ids.get::<IndexKey>(&"data".into())
.ok_or_else(|| Error::from("data is a required argument to DPQuantile"))?;
let mechanism = if self.mechanism.to_lowercase().as_str() == "automatic" {
if properties.contains_key::<IndexKey>(&"candidates".into()) {
"exponential"
} else {
let privacy_definition = privacy_definition.as_ref()
.ok_or_else(|| Error::from("privacy_definition must be known"))?;
if privacy_definition.protect_floating_point { "snapping" } else { "laplace" }
}.to_string()
} else {
self.mechanism.to_lowercase()
};
let mut quantile_args = indexmap![IndexKey::from("data") => data_id];
if mechanism.as_str() == "exponential" {
quantile_args.insert("candidates".into(), *argument_ids.get::<IndexKey>(&"candidates".into())
.ok_or_else(|| Error::from("candidates is a required argument to DPQuantile when the exponential mechanism is used."))?);
}
maximum_id += 1;
let id_quantile = maximum_id;
expansion.computation_graph.insert(id_quantile, proto::Component {
arguments: Some(proto::ArgumentNodeIds::new(quantile_args)),
variant: Some(proto::component::Variant::Quantile(proto::Quantile {
alpha: self.alpha,
interpolation: self.interpolation.clone(),
})),
omit: true,
submission: component.submission,
});
expansion.traversal.push(id_quantile);
let mut sanitize_args = IndexMap::new();
if self.mechanism.to_lowercase().as_str() == "exponential" {
sanitize_args.insert("utilities".into(), id_quantile);
sanitize_args.insert("candidates".into(), *argument_ids.get::<IndexKey>(&"candidates".into())
.ok_or_else(|| Error::from("candidates is a required argument to DPQuantile when the exponential mechanism is used."))?);
} else {
sanitize_args.insert("data".into(), id_quantile);
}
let variant = Some(match mechanism.as_str() {
"laplace" => proto::component::Variant::LaplaceMechanism(proto::LaplaceMechanism {
privacy_usage: self.privacy_usage.clone()
}),
"gaussian" => proto::component::Variant::GaussianMechanism(proto::GaussianMechanism {
privacy_usage: self.privacy_usage.clone(),
analytic: false
}),
"analyticgaussian" => proto::component::Variant::GaussianMechanism(proto::GaussianMechanism {
privacy_usage: self.privacy_usage.clone(),
analytic: true
}),
"exponential" => proto::component::Variant::ExponentialMechanism(proto::ExponentialMechanism {
privacy_usage: self.privacy_usage.clone()
}),
"snapping" => {
argument_ids.get::<IndexKey>(&"lower".into())
.map(|lower| sanitize_args.insert("lower".into(), *lower));
argument_ids.get::<IndexKey>(&"upper".into())
.map(|upper| sanitize_args.insert("upper".into(), *upper));
proto::component::Variant::SnappingMechanism(proto::SnappingMechanism {
privacy_usage: self.privacy_usage.clone()
})
},
_ => bail!("Unexpected invalid token {:?}", self.mechanism.as_str()),
});
expansion.computation_graph.insert(component_id, proto::Component {
arguments: Some(proto::ArgumentNodeIds::new(sanitize_args)),
variant,
omit: component.omit,
submission: component.submission,
});
Ok(expansion)
}
}
impl Report for proto::DpQuantile {
fn summarize(
&self,
node_id: u32,
component: &proto::Component,
_public_arguments: IndexMap<base::IndexKey, &Value>,
properties: NodeProperties,
release: &Value,
variable_names: Option<&Vec<base::IndexKey>>,
) -> Result<Option<Vec<JSONRelease>>> {
let data_property = properties.get::<base::IndexKey>(&"data".into())
.ok_or("data: missing")?.array()
.map_err(prepend("data:"))?.clone();
let mut releases = Vec::new();
let minimums = data_property.lower_float()?;
let maximums = data_property.upper_float()?;
let num_columns = data_property.num_columns()?;
let privacy_usages = spread_privacy_usage(&self.privacy_usage, num_columns as usize)?;
for column_number in 0..(num_columns as usize) {
let variable_name = variable_names
.and_then(|names| names.get(column_number)).cloned()
.unwrap_or_else(|| "[Unknown]".into());
releases.push(JSONRelease {
description: "DP release information".to_string(),
statistic: "DPQuantile".to_string(),
variables: serde_json::json!(variable_name.to_string()),
release_info: match release.ref_array()? {
Array::Float(v) => value_to_json(&get_ith_column(v, column_number)?.into())?,
Array::Int(v) => value_to_json(&get_ith_column(v, column_number)?.into())?,
_ => return Err("maximum must be numeric".into())
},
privacy_loss: privacy_usage_to_json(&privacy_usages[column_number].clone()),
accuracy: None,
submission: component.submission,
node_id,
postprocess: false,
algorithm_info: AlgorithmInfo {
name: "".to_string(),
cite: "".to_string(),
mechanism: self.mechanism.clone(),
argument: serde_json::json!({
"constraint": {
"lowerbound": minimums[column_number],
"upperbound": maximums[column_number]
}
}),
},
});
}
Ok(Some(releases))
}
}