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use std::collections::{BTreeMap, HashMap};
use crate::approx::{
approximate_asv, approximate_asv_adaptive, approximate_asv_adaptive_batched,
approximate_asv_batched,
};
use crate::cache::value_cached;
use crate::dag_dp::dag_exact_asv;
use crate::dag_dp_sparse::{ExactDagConfig, dag_exact_asv_sparse};
use crate::error::CausasvError;
use crate::graph::{Dag, NodeId};
use crate::sampler::{AdaptiveSamplingConfig, SamplingConfig};
use crate::topo::enumerate_topos;
use crate::tree::tree_exact_asv;
/// Result of an ASV computation.
#[derive(Debug)]
pub struct AsvResult {
/// Per-node ASV values in ascending NodeId order.
pub values: BTreeMap<NodeId, f64>,
/// Number of topological orderings (exact) or samples used (approximate).
pub n_samples: usize,
/// RNG seed used; None for exact computation or unseeded approximate.
pub seed: Option<u64>,
/// True if exact (brute-force or tree-exact), false if approximate.
pub is_exact: bool,
/// Effective Sample Size for approximate methods: ESS = (Σw)² / Σw².
/// ESS ≈ n_samples → uniform IS weights (reliable). ESS ≪ n_samples → high variance.
/// None for exact methods.
pub effective_sample_size: Option<f64>,
/// True if adaptive sampling converged before max_samples. None for non-adaptive methods.
pub converged: Option<bool>,
/// Per-node IS standard error estimates. None for exact and non-adaptive approx.
pub stderr: Option<BTreeMap<NodeId, f64>>,
/// Number of order ideals visited by exact_dag_sparse. None for other methods.
pub n_order_ideals: Option<usize>,
/// Fraction of 2^n states visited: n_order_ideals / 2^n. None for other methods.
pub state_ratio: Option<f64>,
/// Estimated memory used by sparse DP in MB. None for other methods.
pub memory_mb: Option<f64>,
}
/// Entry point for ASV computation over a causal DAG.
pub struct AsvExplainer {
dag: Dag,
}
impl AsvExplainer {
pub fn new(dag: Dag) -> Self {
Self { dag }
}
/// Brute-force exact ASV: enumerates all topological orderings.
/// Correct for any DAG; only practical for n ≤ ~8.
pub fn exact<F>(&self, value_fn: F) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.dag.validate()?;
let n = self.dag.node_count();
if n > 64 {
return Err(CausasvError::InvalidConfig(format!(
"bitmask coalitions require n ≤ 64, got {n}"
)));
}
let orderings = enumerate_topos(&self.dag)?;
let n_orderings = orderings.len();
let mut phi = vec![0.0f64; n];
let mut cache = HashMap::<u64, f64>::new();
for ordering in &orderings {
let mut prefix_mask: u64 = 0;
for &node in ordering {
let without = prefix_mask;
let with_node = prefix_mask | (1u64 << node.0);
phi[node.0 as usize] += value_cached(&mut cache, &value_fn, with_node)?
- value_cached(&mut cache, &value_fn, without)?;
prefix_mask = with_node;
}
}
let scale = 1.0 / n_orderings as f64;
let values = (0..n).map(|i| (NodeId(i as u32), phi[i] * scale)).collect();
Ok(AsvResult {
values,
n_samples: n_orderings,
seed: None,
is_exact: true,
effective_sample_size: None,
converged: None,
stderr: None,
n_order_ideals: None,
state_ratio: None,
memory_mb: None,
})
}
/// Approximate ASV via IS-weighted topological order sampling.
/// Works for any DAG size; use a large n_samples for accuracy.
pub fn approximate<F>(
&self,
value_fn: F,
config: SamplingConfig,
) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError> + Send + Sync,
{
self.dag.validate()?;
approximate_asv(&self.dag, value_fn, config)
}
/// Exact ASV for rooted directed trees. Returns Err(NotRootedTree) if the graph is not one.
/// Validates tree structure before computing; otherwise identical to `exact`.
pub fn exact_tree<F>(&self, value_fn: F) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.dag.validate()?;
tree_exact_asv(&self.dag, value_fn)
}
/// Exact ASV for any DAG via order-ideal DP. Practical for n ≤ 20.
///
/// Computes `dp[mask]` = number of linear extensions with prefix `mask`,
/// then accumulates weighted marginal contributions. O(2^n × n) time.
pub fn exact_dag<F>(&self, value_fn: F) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.dag.validate()?;
dag_exact_asv(&self.dag, value_fn)
}
/// Exact ASV for general DAGs using sparse order-ideal DP.
///
/// BFS-enumerates only valid order ideals — sets S where all parents of every
/// i ∈ S are also in S. For sparse DAGs the number of valid order ideals can be
/// far less than 2^n, enabling exact computation for n > 20.
///
/// Uses the default `ExactDagConfig` (max_nodes=28, memory_limit_bytes=2GiB).
/// Returns `Err` if n > max_nodes or if the memory limit is exceeded mid-BFS.
pub fn exact_dag_sparse<F>(&self, value_fn: F) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.exact_dag_sparse_with_config(value_fn, &ExactDagConfig::default())
}
/// Like `exact_dag_sparse` but with a custom `ExactDagConfig`.
pub fn exact_dag_sparse_with_config<F>(
&self,
value_fn: F,
config: &ExactDagConfig,
) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.dag.validate()?;
let (result, _, _, _) = dag_exact_asv_sparse(&self.dag, value_fn, config)?;
Ok(result)
}
/// Automatic method selection based on graph size and structure.
///
/// Dispatch rules:
/// - n ≤ 8: `exact` — brute-force, lowest overhead for small n
/// - n > 8, rooted directed tree: `exact_tree` — order-ideal DP
/// - 8 < n ≤ 20: `exact_dag` — dense order-ideal DP (O(2^n × n))
/// - 20 < n ≤ 28: `exact_dag_sparse` — sparse BFS over order ideals
/// - n > 28: `approximate` — IS-weighted sampling
///
/// `config` is used only when the approximate path is taken.
/// For 20 < n ≤ 28, `exact_dag_sparse` may return `Err` if the memory
/// limit is exceeded — call `approximate` explicitly in that case.
pub fn auto<F>(&self, value_fn: F, config: SamplingConfig) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError> + Send + Sync,
{
self.dag.validate()?;
let n = self.dag.node_count();
if n <= 8 {
self.exact(value_fn)
} else if crate::tree::find_rooted_tree_root(&self.dag).is_ok() {
self.exact_tree(value_fn)
} else if n <= 20 {
self.exact_dag(value_fn)
} else if n <= 28 {
self.exact_dag_sparse(value_fn)
} else {
self.approximate(value_fn, config)
}
}
/// Adaptive approximate ASV: runs sampling in batches and stops when estimates
/// converge (relative change < `config.rel_tol` and ESS ratio ≥ `config.ess_ratio_min`),
/// or when `config.max_samples` is reached.
///
/// Always single-threaded for deterministic convergence behavior.
/// Returns per-node standard error estimates alongside ASV values.
pub fn approximate_adaptive<F>(
&self,
value_fn: F,
config: AdaptiveSamplingConfig,
) -> Result<AsvResult, CausasvError>
where
F: Fn(&[NodeId]) -> Result<f64, CausasvError>,
{
self.dag.validate()?;
approximate_asv_adaptive(&self.dag, value_fn, config)
}
/// Batched approximate ASV: like `approximate`, but evaluates coalitions in batches
/// via `value_fn_batch(coalitions) -> values` to reduce per-call overhead (e.g. Python GIL).
///
/// Set `config.batch_size` to control how many samples to collect per batch call.
/// With `batch_size=1`, results are identical to `approximate` for the same seed.
pub fn approximate_batched<F>(
&self,
value_fn_batch: F,
config: SamplingConfig,
) -> Result<AsvResult, CausasvError>
where
F: Fn(&[Vec<NodeId>]) -> Result<Vec<f64>, CausasvError>,
{
self.dag.validate()?;
approximate_asv_batched(&self.dag, value_fn_batch, config)
}
/// Adaptive batched approximate ASV: like `approximate_adaptive`, but evaluates
/// coalitions in batches via `value_fn_batch`.
///
/// Each sampling batch (of `config.batch_size` samples) becomes one `value_fn_batch` call.
pub fn approximate_adaptive_batched<F>(
&self,
value_fn_batch: F,
config: AdaptiveSamplingConfig,
) -> Result<AsvResult, CausasvError>
where
F: Fn(&[Vec<NodeId>]) -> Result<Vec<f64>, CausasvError>,
{
self.dag.validate()?;
approximate_asv_adaptive_batched(&self.dag, value_fn_batch, config)
}
}