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// This file is part of Tetcore. // Copyright (C) 2019-2021 Parity Technologies (UK) Ltd. SPDX-License-Identifier: Apache-2.0 // Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except // in compliance with the License. You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software distributed under the License // is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express // or implied. See the License for the specific language governing permissions and limitations under // the License. //! A set of election algorithms to be used with a tetcore runtime, typically within the staking //! sub-system. Notable implementation include: //! //! - [`seq_phragmen`]: Implements the Phragmén Sequential Method. An un-ranked, relatively fast //! election method that ensures PJR, but does not provide a constant factor approximation of the //! maximin problem. //! - [`phragmms()`]: Implements a hybrid approach inspired by Phragmén which is executed faster but //! it can achieve a constant factor approximation of the maximin problem, similar to that of the //! MMS algorithm. //! - [`balance`]: Implements the star balancing algorithm. This iterative process can push a //! solution toward being more `balances`, which in turn can increase its score. //! //! ### Terminology //! //! This crate uses context-independent words, not to be confused with staking. This is because the //! election algorithms of this crate, while designed for staking, can be used in other contexts as //! well. //! //! `Voter`: The entity casting some votes to a number of `Targets`. This is the same as `Nominator` //! in the context of staking. `Target`: The entities eligible to be voted upon. This is the same as //! `Validator` in the context of staking. `Edge`: A mapping from a `Voter` to a `Target`. //! //! The goal of an election algorithm is to provide an `ElectionResult`. A data composed of: //! - `winners`: A flat list of identifiers belonging to those who have won the election, usually //! ordered in some meaningful way. They are zipped with their total backing stake. //! - `assignment`: A mapping from each voter to their winner-only targets, zipped with a ration //! denoting the amount of support given to that particular target. //! //! ```rust //! # use tp_npos_elections::*; //! # use tp_runtime::Perbill; //! // the winners. //! let winners = vec![(1, 100), (2, 50)]; //! let assignments = vec![ //! // A voter, giving equal backing to both 1 and 2. //! Assignment { //! who: 10, //! distribution: vec![(1, Perbill::from_percent(50)), (2, Perbill::from_percent(50))], //! }, //! // A voter, Only backing 1. //! Assignment { who: 20, distribution: vec![(1, Perbill::from_percent(100))] }, //! ]; //! //! // the combination of the two makes the election result. //! let election_result = ElectionResult { winners, assignments }; //! ``` //! //! The `Assignment` field of the election result is voter-major, i.e. it is from the perspective of //! the voter. The struct that represents the opposite is called a `Support`. This struct is usually //! accessed in a map-like manner, i.e. keyed by voters, therefor it is stored as a mapping called //! `SupportMap`. //! //! Moreover, the support is built from absolute backing values, not ratios like the example above. //! A struct similar to `Assignment` that has stake value instead of ratios is called an //! `StakedAssignment`. //! //! //! More information can be found at: <https://arxiv.org/abs/2004.12990> #![cfg_attr(not(feature = "std"), no_std)] use arithmetic::{ traits::{Bounded, UniqueSaturatedInto, Zero}, Normalizable, PerThing, Rational128, ThresholdOrd, }; use tetcore_std::{ cell::RefCell, cmp::Ordering, collections::btree_map::BTreeMap, convert::{TryFrom, TryInto}, fmt::Debug, ops::Mul, prelude::*, rc::Rc, }; use tet_core::RuntimeDebug; use codec::{Decode, Encode}; #[cfg(feature = "std")] use serde::{Deserialize, Serialize}; #[cfg(test)] mod mock; #[cfg(test)] mod tests; mod phragmen; mod balancing; mod phragmms; mod node; mod reduce; mod helpers; pub use reduce::reduce; pub use helpers::*; pub use phragmen::*; pub use phragmms::*; pub use balancing::*; // re-export the compact macro, with the dependencies of the macro. #[doc(hidden)] pub use codec; #[doc(hidden)] pub use arithmetic; /// Simple Extension trait to easily convert `None` from index closures to `Err`. /// /// This is only generated and re-exported for the compact solution code to use. #[doc(hidden)] pub trait __OrInvalidIndex<T> { fn or_invalid_index(self) -> Result<T, Error>; } impl<T> __OrInvalidIndex<T> for Option<T> { fn or_invalid_index(self) -> Result<T, Error> { self.ok_or(Error::CompactInvalidIndex) } } /// A common interface for all compact solutions. /// /// See [`tp-npos-elections-compact`] for more info. pub trait CompactSolution: Sized { /// The maximum number of votes that are allowed. const LIMIT: usize; /// The voter type. Needs to be an index (convert to usize). type Voter: UniqueSaturatedInto<usize> + TryInto<usize> + TryFrom<usize> + Debug + Copy + Clone; /// The target type. Needs to be an index (convert to usize). type Target: UniqueSaturatedInto<usize> + TryInto<usize> + TryFrom<usize> + Debug + Copy + Clone; /// The weight/accuracy type of each vote. type Accuracy: PerThing128; /// Build self from a `assignments: Vec<Assignment<A, Self::Accuracy>>`. fn from_assignment<FV, FT, A>( assignments: Vec<Assignment<A, Self::Accuracy>>, voter_index: FV, target_index: FT, ) -> Result<Self, Error> where A: IdentifierT, for<'r> FV: Fn(&'r A) -> Option<Self::Voter>, for<'r> FT: Fn(&'r A) -> Option<Self::Target>; /// Convert self into a `Vec<Assignment<A, Self::Accuracy>>` fn into_assignment<A: IdentifierT>( self, voter_at: impl Fn(Self::Voter) -> Option<A>, target_at: impl Fn(Self::Target) -> Option<A>, ) -> Result<Vec<Assignment<A, Self::Accuracy>>, Error>; /// Get the length of all the voters that this type is encoding. /// /// This is basically the same as the number of assignments, or number of active voters. fn voter_count(&self) -> usize; /// Get the total count of edges. /// /// This is effectively in the range of {[`Self::voter_count`], [`Self::voter_count`] * /// [`Self::LIMIT`]}. fn edge_count(&self) -> usize; /// Get the number of unique targets in the whole struct. /// /// Once presented with a list of winners, this set and the set of winners must be /// equal. fn unique_targets(&self) -> Vec<Self::Target>; /// Get the average edge count. fn average_edge_count(&self) -> usize { self.edge_count() .checked_div(self.voter_count()) .unwrap_or(0) } /// Remove a certain voter. /// /// This will only search until the first instance of `to_remove`, and return true. If /// no instance is found (no-op), then it returns false. /// /// In other words, if this return true, exactly **one** element must have been removed from /// `self.len()`. fn remove_voter(&mut self, to_remove: Self::Voter) -> bool; /// Compute the score of this compact solution type. fn score<A, FS>( self, winners: &[A], stake_of: FS, voter_at: impl Fn(Self::Voter) -> Option<A>, target_at: impl Fn(Self::Target) -> Option<A>, ) -> Result<ElectionScore, Error> where for<'r> FS: Fn(&'r A) -> VoteWeight, A: IdentifierT, { let ratio = self.into_assignment(voter_at, target_at)?; let staked = helpers::assignment_ratio_to_staked_normalized(ratio, stake_of)?; let supports = to_supports(winners, &staked)?; Ok(supports.evaluate()) } } // re-export the compact solution type. pub use tp_npos_elections_compact::generate_solution_type; /// an aggregator trait for a generic type of a voter/target identifier. This usually maps to /// tetcore's account id. pub trait IdentifierT: Clone + Eq + Default + Ord + Debug + codec::Codec {} impl<T: Clone + Eq + Default + Ord + Debug + codec::Codec> IdentifierT for T {} /// Aggregator trait for a PerThing that can be multiplied by u128 (ExtendedBalance). pub trait PerThing128: PerThing + Mul<ExtendedBalance, Output = ExtendedBalance> {} impl<T: PerThing + Mul<ExtendedBalance, Output = ExtendedBalance>> PerThing128 for T {} /// The errors that might occur in the this crate and compact. #[derive(Eq, PartialEq, RuntimeDebug)] pub enum Error { /// While going from compact to staked, the stake of all the edges has gone above the total and /// the last stake cannot be assigned. CompactStakeOverflow, /// The compact type has a voter who's number of targets is out of bound. CompactTargetOverflow, /// One of the index functions returned none. CompactInvalidIndex, /// An error occurred in some arithmetic operation. ArithmeticError(&'static str), /// The data provided to create support map was invalid. InvalidSupportEdge, } /// A type which is used in the API of this crate as a numeric weight of a vote, most often the /// stake of the voter. It is always converted to [`ExtendedBalance`] for computation. pub type VoteWeight = u64; /// A type in which performing operations on vote weights are safe. pub type ExtendedBalance = u128; /// The score of an assignment. This can be computed from the support map via /// [`EvaluateSupport::evaluate`]. pub type ElectionScore = [ExtendedBalance; 3]; /// A winner, with their respective approval stake. pub type WithApprovalOf<A> = (A, ExtendedBalance); /// A pointer to a candidate struct with interior mutability. pub type CandidatePtr<A> = Rc<RefCell<Candidate<A>>>; /// A candidate entity for the election. #[derive(RuntimeDebug, Clone, Default)] pub struct Candidate<AccountId> { /// Identifier. who: AccountId, /// Score of the candidate. /// /// Used differently in seq-phragmen and max-score. score: Rational128, /// Approval stake of the candidate. Merely the sum of all the voter's stake who approve this /// candidate. approval_stake: ExtendedBalance, /// The final stake of this candidate. Will be equal to a subset of approval stake. backed_stake: ExtendedBalance, /// True if this candidate is already elected in the current election. elected: bool, /// The round index at which this candidate was elected. round: usize, } /// A vote being casted by a [`Voter`] to a [`Candidate`] is an `Edge`. #[derive(Clone, Default)] pub struct Edge<AccountId> { /// Identifier of the target. /// /// This is equivalent of `self.candidate.borrow().who`, yet it helps to avoid double borrow /// errors of the candidate pointer. who: AccountId, /// Load of this edge. load: Rational128, /// Pointer to the candidate. candidate: CandidatePtr<AccountId>, /// The weight (i.e. stake given to `who`) of this edge. weight: ExtendedBalance, } #[cfg(feature = "std")] impl<A: IdentifierT> tetcore_std::fmt::Debug for Edge<A> { fn fmt(&self, f: &mut tetcore_std::fmt::Formatter<'_>) -> tetcore_std::fmt::Result { write!(f, "Edge({:?}, weight = {:?})", self.who, self.weight) } } /// A voter entity. #[derive(Clone, Default)] pub struct Voter<AccountId> { /// Identifier. who: AccountId, /// List of candidates approved by this voter. edges: Vec<Edge<AccountId>>, /// The stake of this voter. budget: ExtendedBalance, /// Load of the voter. load: Rational128, } #[cfg(feature = "std")] impl<A: IdentifierT> std::fmt::Debug for Voter<A> { fn fmt(&self, f: &mut tetcore_std::fmt::Formatter<'_>) -> tetcore_std::fmt::Result { write!(f, "Voter({:?}, budget = {}, edges = {:?})", self.who, self.budget, self.edges) } } impl<AccountId: IdentifierT> Voter<AccountId> { /// Returns none if this voter does not have any non-zero distributions. /// /// Note that this might create _un-normalized_ assignments, due to accuracy loss of `P`. Call /// site might compensate by calling `normalize()` on the returned `Assignment` as a /// post-precessing. pub fn into_assignment<P: PerThing>(self) -> Option<Assignment<AccountId, P>> { let who = self.who; let budget = self.budget; let distribution = self .edges .into_iter() .filter_map(|e| { let per_thing = P::from_rational_approximation(e.weight, budget); // trim zero edges. if per_thing.is_zero() { None } else { Some((e.who, per_thing)) } }).collect::<Vec<_>>(); if distribution.len() > 0 { Some(Assignment { who, distribution }) } else { None } } /// Try and normalize the votes of self. /// /// If the normalization is successful then `Ok(())` is returned. /// /// Note that this will not distinguish between elected and unelected edges. Thus, it should /// only be called on a voter who has already been reduced to only elected edges. /// /// ### Errors /// /// This will return only if the internal `normalize` fails. This can happen if the sum of the /// weights exceeds `ExtendedBalance::max_value()`. pub fn try_normalize(&mut self) -> Result<(), &'static str> { let edge_weights = self.edges.iter().map(|e| e.weight).collect::<Vec<_>>(); edge_weights.normalize(self.budget).map(|normalized| { // here we count on the fact that normalize does not change the order. for (edge, corrected) in self.edges.iter_mut().zip(normalized.into_iter()) { let mut candidate = edge.candidate.borrow_mut(); // first, subtract the incorrect weight candidate.backed_stake = candidate.backed_stake.saturating_sub(edge.weight); edge.weight = corrected; // Then add the correct one again. candidate.backed_stake = candidate.backed_stake.saturating_add(edge.weight); } }) } /// Same as [`Self::try_normalize`] but the normalization is only limited between elected edges. pub fn try_normalize_elected(&mut self) -> Result<(), &'static str> { let elected_edge_weights = self .edges .iter() .filter_map(|e| if e.candidate.borrow().elected { Some(e.weight) } else { None }) .collect::<Vec<_>>(); elected_edge_weights.normalize(self.budget).map(|normalized| { // here we count on the fact that normalize does not change the order, and that vector // iteration is deterministic. for (edge, corrected) in self .edges .iter_mut() .filter(|e| e.candidate.borrow().elected) .zip(normalized.into_iter()) { let mut candidate = edge.candidate.borrow_mut(); // first, subtract the incorrect weight candidate.backed_stake = candidate.backed_stake.saturating_sub(edge.weight); edge.weight = corrected; // Then add the correct one again. candidate.backed_stake = candidate.backed_stake.saturating_add(edge.weight); } }) } } /// Final result of the election. #[derive(RuntimeDebug)] pub struct ElectionResult<AccountId, P: PerThing> { /// Just winners zipped with their approval stake. Note that the approval stake is merely the /// sub of their received stake and could be used for very basic sorting and approval voting. pub winners: Vec<WithApprovalOf<AccountId>>, /// Individual assignments. for each tuple, the first elements is a voter and the second is the /// list of candidates that it supports. pub assignments: Vec<Assignment<AccountId, P>>, } /// A voter's stake assignment among a set of targets, represented as ratios. #[derive(RuntimeDebug, Clone, Default)] #[cfg_attr(feature = "std", derive(PartialEq, Eq, Encode, Decode))] pub struct Assignment<AccountId, P: PerThing> { /// Voter's identifier. pub who: AccountId, /// The distribution of the voter's stake. pub distribution: Vec<(AccountId, P)>, } impl<AccountId: IdentifierT, P: PerThing128> Assignment<AccountId, P> { /// Convert from a ratio assignment into one with absolute values aka. [`StakedAssignment`]. /// /// It needs `stake` which is the total budget of the voter. /// /// Note that this might create _un-normalized_ assignments, due to accuracy loss of `P`. Call /// site might compensate by calling `try_normalize()` on the returned `StakedAssignment` as a /// post-precessing. /// /// If an edge ratio is [`Bounded::min_value()`], it is dropped. This edge can never mean /// anything useful. pub fn into_staked(self, stake: ExtendedBalance) -> StakedAssignment<AccountId> { let distribution = self .distribution .into_iter() .filter_map(|(target, p)| { // if this ratio is zero, then skip it. if p.is_zero() { None } else { // NOTE: this mul impl will always round to the nearest number, so we might both // overflow and underflow. let distribution_stake = p * stake; Some((target, distribution_stake)) } }) .collect::<Vec<(AccountId, ExtendedBalance)>>(); StakedAssignment { who: self.who, distribution, } } /// Try and normalize this assignment. /// /// If `Ok(())` is returned, then the assignment MUST have been successfully normalized to 100%. /// /// ### Errors /// /// This will return only if the internal `normalize` fails. This can happen if sum of /// `self.distribution.map(|p| p.deconstruct())` fails to fit inside `UpperOf<P>`. A user of /// this crate may statically assert that this can never happen and safely `expect` this to /// return `Ok`. pub fn try_normalize(&mut self) -> Result<(), &'static str> { self.distribution .iter() .map(|(_, p)| *p) .collect::<Vec<_>>() .normalize(P::one()) .map(|normalized_ratios| self.distribution .iter_mut() .zip(normalized_ratios) .for_each(|((_, old), corrected)| { *old = corrected; }) ) } } /// A voter's stake assignment among a set of targets, represented as absolute values in the scale /// of [`ExtendedBalance`]. #[derive(RuntimeDebug, Clone, Default)] #[cfg_attr(feature = "std", derive(PartialEq, Eq, Encode, Decode))] pub struct StakedAssignment<AccountId> { /// Voter's identifier pub who: AccountId, /// The distribution of the voter's stake. pub distribution: Vec<(AccountId, ExtendedBalance)>, } impl<AccountId> StakedAssignment<AccountId> { /// Converts self into the normal [`Assignment`] type. /// /// NOTE: This will always round down, and thus the results might be less than a full 100% `P`. /// Use a normalization post-processing to fix this. The data type returned here will /// potentially get used to create a compact type; a compact type requires sum of ratios to be /// less than 100% upon un-compacting. /// /// If an edge stake is so small that it cannot be represented in `T`, it is ignored. This edge /// can never be re-created and does not mean anything useful anymore. pub fn into_assignment<P: PerThing>(self) -> Assignment<AccountId, P> where AccountId: IdentifierT, { let stake = self.total(); let distribution = self.distribution .into_iter() .filter_map(|(target, w)| { let per_thing = P::from_rational_approximation(w, stake); if per_thing == Bounded::min_value() { None } else { Some((target, per_thing)) } }) .collect::<Vec<(AccountId, P)>>(); Assignment { who: self.who, distribution, } } /// Try and normalize this assignment. /// /// If `Ok(())` is returned, then the assignment MUST have been successfully normalized to /// `stake`. /// /// NOTE: current implementation of `.normalize` is almost safe to `expect()` upon. The only /// error case is when the input cannot fit in `T`, or the sum of input cannot fit in `T`. /// Sadly, both of these are dependent upon the implementation of `VoteLimit`, i.e. the limit of /// edges per voter which is enforced from upstream. Hence, at this crate, we prefer returning a /// result and a use the name prefix `try_`. pub fn try_normalize(&mut self, stake: ExtendedBalance) -> Result<(), &'static str> { self.distribution .iter() .map(|(_, ref weight)| *weight) .collect::<Vec<_>>() .normalize(stake) .map(|normalized_weights| self.distribution .iter_mut() .zip(normalized_weights.into_iter()) .for_each(|((_, weight), corrected)| { *weight = corrected; }) ) } /// Get the total stake of this assignment (aka voter budget). pub fn total(&self) -> ExtendedBalance { self.distribution.iter().fold(Zero::zero(), |a, b| a.saturating_add(b.1)) } } /// A structure to demonstrate the election result from the perspective of the candidate, i.e. how /// much support each candidate is receiving. /// /// This complements the [`ElectionResult`] and is needed to run the balancing post-processing. /// /// This, at the current version, resembles the `Exposure` defined in the Staking pallet, yet they /// do not necessarily have to be the same. #[derive(Default, RuntimeDebug, Encode, Decode, Clone, Eq, PartialEq)] #[cfg_attr(feature = "std", derive(Serialize, Deserialize))] pub struct Support<AccountId> { /// Total support. pub total: ExtendedBalance, /// Support from voters. pub voters: Vec<(AccountId, ExtendedBalance)>, } /// A target-major representation of the the election outcome. /// /// Essentially a flat variant of [`SupportMap`]. /// /// The main advantage of this is that it is encodable. pub type Supports<A> = Vec<(A, Support<A>)>; /// Linkage from a winner to their [`Support`]. /// /// This is more helpful than a normal [`Supports`] as it allows faster error checking. pub type SupportMap<A> = BTreeMap<A, Support<A>>; /// Helper trait to convert from a support map to a flat support vector. pub trait FlattenSupportMap<A> { /// Flatten the support. fn flatten(self) -> Supports<A>; } impl<A> FlattenSupportMap<A> for SupportMap<A> { fn flatten(self) -> Supports<A> { self.into_iter().collect::<Vec<_>>() } } /// Build the support map from the winners and assignments. /// /// The list of winners is basically a redundancy for error checking only; It ensures that all the /// targets pointed to by the [`Assignment`] are present in the `winners`. pub fn to_support_map<A: IdentifierT>( winners: &[A], assignments: &[StakedAssignment<A>], ) -> Result<SupportMap<A>, Error> { // Initialize the support of each candidate. let mut supports = <SupportMap<A>>::new(); winners.iter().for_each(|e| { supports.insert(e.clone(), Default::default()); }); // build support struct. for StakedAssignment { who, distribution } in assignments.iter() { for (c, weight_extended) in distribution.iter() { if let Some(support) = supports.get_mut(c) { support.total = support.total.saturating_add(*weight_extended); support.voters.push((who.clone(), *weight_extended)); } else { return Err(Error::InvalidSupportEdge) } } } Ok(supports) } /// Same as [`to_support_map`] except it calls `FlattenSupportMap` on top of the result to return a /// flat vector. /// /// Similar to [`to_support_map`], `winners` is used for error checking. pub fn to_supports<A: IdentifierT>( winners: &[A], assignments: &[StakedAssignment<A>], ) -> Result<Supports<A>, Error> { to_support_map(winners, assignments).map(FlattenSupportMap::flatten) } /// Extension trait for evaluating a support map or vector. pub trait EvaluateSupport<K> { /// Evaluate a support map. The returned tuple contains: /// /// - Minimum support. This value must be **maximized**. /// - Sum of all supports. This value must be **maximized**. /// - Sum of all supports squared. This value must be **minimized**. fn evaluate(self) -> ElectionScore; } /// A common wrapper trait for both (&A, &B) and &(A, B). /// /// This allows us to implemented something for both `Vec<_>` and `BTreeMap<_>`, such as /// [`EvaluateSupport`]. pub trait TupleRef<K, V> { fn extract(&self) -> (&K, &V); } impl<K, V> TupleRef<K, V> for &(K, V) { fn extract(&self) -> (&K, &V) { (&self.0, &self.1) } } impl<K, V> TupleRef<K, V> for (K, V) { fn extract(&self) -> (&K, &V) { (&self.0, &self.1) } } impl<K, V> TupleRef<K, V> for (&K, &V) { fn extract(&self) -> (&K, &V) { (self.0, self.1) } } impl<A, C, I> EvaluateSupport<A> for C where C: IntoIterator<Item = I>, I: TupleRef<A, Support<A>>, A: IdentifierT, { fn evaluate(self) -> ElectionScore { let mut min_support = ExtendedBalance::max_value(); let mut sum: ExtendedBalance = Zero::zero(); // NOTE: The third element might saturate but fine for now since this will run on-chain and // need to be fast. let mut sum_squared: ExtendedBalance = Zero::zero(); for item in self { let (_, support) = item.extract(); sum = sum.saturating_add(support.total); let squared = support.total.saturating_mul(support.total); sum_squared = sum_squared.saturating_add(squared); if support.total < min_support { min_support = support.total; } } [min_support, sum, sum_squared] } } /// Compares two sets of election scores based on desirability and returns true if `this` is better /// than `that`. /// /// Evaluation is done in a lexicographic manner, and if each element of `this` is `that * epsilon` /// greater or less than `that`. /// /// Note that the third component should be minimized. pub fn is_score_better<P: PerThing>(this: ElectionScore, that: ElectionScore, epsilon: P) -> bool { match this .iter() .zip(that.iter()) .map(|(thi, tha)| ( thi.ge(&tha), thi.tcmp(&tha, epsilon.mul_ceil(*tha)), )) .collect::<Vec<(bool, Ordering)>>() .as_slice() { // epsilon better in the score[0], accept. [(_, Ordering::Greater), _, _] => true, // less than epsilon better in score[0], but more than epsilon better in the second. [(true, Ordering::Equal), (_, Ordering::Greater), _] => true, // less than epsilon better in score[0, 1], but more than epsilon better in the third [(true, Ordering::Equal), (true, Ordering::Equal), (_, Ordering::Less)] => true, // anything else is not a good score. _ => false, } } /// Converts raw inputs to types used in this crate. /// /// This will perform some cleanup that are most often important: /// - It drops any votes that are pointing to non-candidates. /// - It drops duplicate targets within a voter. pub(crate) fn setup_inputs<AccountId: IdentifierT>( initial_candidates: Vec<AccountId>, initial_voters: Vec<(AccountId, VoteWeight, Vec<AccountId>)>, ) -> (Vec<CandidatePtr<AccountId>>, Vec<Voter<AccountId>>) { // used to cache and access candidates index. let mut c_idx_cache = BTreeMap::<AccountId, usize>::new(); let candidates = initial_candidates .into_iter() .enumerate() .map(|(idx, who)| { c_idx_cache.insert(who.clone(), idx); Rc::new(RefCell::new(Candidate { who, ..Default::default() })) }) .collect::<Vec<CandidatePtr<AccountId>>>(); let voters = initial_voters.into_iter().filter_map(|(who, voter_stake, votes)| { let mut edges: Vec<Edge<AccountId>> = Vec::with_capacity(votes.len()); for v in votes { if edges.iter().any(|e| e.who == v) { // duplicate edge. continue; } if let Some(idx) = c_idx_cache.get(&v) { // This candidate is valid + already cached. let mut candidate = candidates[*idx].borrow_mut(); candidate.approval_stake = candidate.approval_stake.saturating_add(voter_stake.into()); edges.push( Edge { who: v.clone(), candidate: Rc::clone(&candidates[*idx]), ..Default::default() } ); } // else {} would be wrong votes. We don't really care about it. } if edges.is_empty() { None } else { Some(Voter { who, edges: edges, budget: voter_stake.into(), load: Rational128::zero(), }) } }).collect::<Vec<_>>(); (candidates, voters,) }