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Author SHA1 Message Date
AlessandroBregoli 87da2a7c9e Added doctest for parameter_learning 2 years ago
AlessandroBregoli 21ce0ffcb0 Added doc to: params, process, parameter_learning, ctmp, ctbn 2 years ago
Alessandro Bregoli d66173b961 Added doctest for ctmp 2 years ago
Alessandro Bregoli f176dd4fae Added logging to ctmp 2 years ago
AlessandroBregoli bfec2c7c60 Added log to params 2 years ago
  1. 176
      LICENSE-APACHE
  2. 23
      LICENSE-MIT
  3. 8
      README.md
  4. 1
      reCTBN/Cargo.toml
  5. 195
      reCTBN/src/parameter_learning.rs
  6. 73
      reCTBN/src/params.rs
  7. 14
      reCTBN/src/process.rs
  8. 12
      reCTBN/src/process/ctbn.rs
  9. 79
      reCTBN/src/process/ctmp.rs

@ -1,176 +0,0 @@
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@ -1,23 +0,0 @@
Permission is hereby granted, free of charge, to any
person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the
Software without restriction, including without
limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software
is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice
shall be included in all copies or substantial portions
of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.

@ -64,11 +64,3 @@ To generate the **documentation**:
```sh
cargo rustdoc --package reCTBN --open -- --default-theme=ayu
```
## License
This software is distributed under the terms of both the Apache License
(Version 2.0) and the MIT license.
See [LICENSE-APACHE](./LICENSE-APACHE) and [LICENSE-MIT](./LICENSE-MIT) for
details.

@ -15,6 +15,7 @@ statrs = "~0.16"
rand_chacha = "~0.3"
itertools = "~0.10"
rayon = "~1.6"
log = "~0.4"
[dev-dependencies]
approx = { package = "approx", version = "~0.5" }

@ -7,7 +7,17 @@ use ndarray::prelude::*;
use crate::params::*;
use crate::{process, tools::Dataset};
/// It defines the required methods for learn the `Parameters` from data.
pub trait ParameterLearning: Sync {
/// Fit the parameter of the `node` over a `dataset` given a `parent_set`
///
/// # Arguments
///
/// * `net`: a `NetworkProcess` instance
/// * `dataset`: a dataset compatible with `net` used for computing the sufficient statistics
/// * `node`: the node index for which we want to compute the sufficient statistics
/// * `parent_set`: an `Option` containing the parent set used for computing the parameters of
/// `node`. If `None`, the parent set defined in `net` will be used.
fn fit<T: process::NetworkProcess>(
&self,
net: &T,
@ -17,6 +27,19 @@ pub trait ParameterLearning: Sync {
) -> Params;
}
/// Compute the sufficient statistics of a parameters computed from a dataset
///
/// # Arguments
///
/// * `net`: a `NetworkProcess` instance
/// * `dataset`: a dataset compatible with `net` used for computing the sufficient statistics
/// * `node`: the node index for which we want to compute the sufficient statistics
/// * `parent_set`: the set of nodes (identified by indices) we want to use as parents of `node`
///
/// # Return
///
/// * A tuple containing the number of transitions (`Array3<usize>`) and the residence time
/// (`Array2<f64>`).
pub fn sufficient_statistics<T: process::NetworkProcess>(
net: &T,
dataset: &Dataset,
@ -70,6 +93,89 @@ pub fn sufficient_statistics<T: process::NetworkProcess>(
return (M, T);
}
/// Maximum Likelihood Estimation method for learning the parameters given a dataset.
///
/// # Example
/// ```rust
///
/// use std::collections::BTreeSet;
/// use reCTBN::process::NetworkProcess;
/// use reCTBN::params;
/// use reCTBN::process::ctbn::*;
/// use ndarray::arr3;
/// use reCTBN::tools::*;
/// use reCTBN::parameter_learning::*;
/// use approx::AbsDiffEq;
///
/// //Create the domain for a discrete node
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
///
/// //Create the parameters for a discrete node using the domain
/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
///
/// //Create the node using the parameters
/// let X1 = params::Params::DiscreteStatesContinousTime(param);
///
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
/// let X2 = params::Params::DiscreteStatesContinousTime(param);
///
/// //Initialize a ctbn
/// let mut net = CtbnNetwork::new();
///
/// //Add nodes
/// let X1 = net.add_node(X1).unwrap();
/// let X2 = net.add_node(X2).unwrap();
///
/// //Add an edge
/// net.add_edge(X1, X2);
///
/// //Add the CIMs for each node
/// match &mut net.get_node_mut(X1) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
/// }
/// }
/// match &mut net.get_node_mut(X2) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(
/// Ok(()),
/// param.set_cim(arr3(&[
/// [[-0.01, 0.01], [5.0, -5.0]],
/// [[-5.0, 5.0], [0.01, -0.01]]
/// ]))
/// );
/// }
/// }
///
/// //Generate a synthetic dataset from net
/// let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
///
/// //Initialize the `struct MLE`
/// let pl = MLE{};
///
/// // Fit the parameters for X2
/// let p = match pl.fit(&net, &data, X2, None) {
/// params::Params::DiscreteStatesContinousTime(p) => p,
/// };
///
/// // Check the shape of the CIM
/// assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
///
/// // Check if the learned parameters are close enough to the real ones.
/// assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
/// &arr3(&[
/// [[-0.01, 0.01], [5.0, -5.0]],
/// [[-5.0, 5.0], [0.01, -0.01]]
/// ]),
/// 0.1
/// ));
/// ```
pub struct MLE {}
impl ParameterLearning for MLE {
@ -114,6 +220,95 @@ impl ParameterLearning for MLE {
}
}
/// Bayesian Approach for learning the parameters given a dataset.
///
/// # Arguments
///
/// `alpha`: hyperparameter for the priori over the number of transitions.
/// `tau`: hyperparameter for the priori over the residence time.
///
/// # Example
///
/// ```rust
///
/// use std::collections::BTreeSet;
/// use reCTBN::process::NetworkProcess;
/// use reCTBN::params;
/// use reCTBN::process::ctbn::*;
/// use ndarray::arr3;
/// use reCTBN::tools::*;
/// use reCTBN::parameter_learning::*;
/// use approx::AbsDiffEq;
///
/// //Create the domain for a discrete node
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
///
/// //Create the parameters for a discrete node using the domain
/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
///
/// //Create the node using the parameters
/// let X1 = params::Params::DiscreteStatesContinousTime(param);
///
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
/// let X2 = params::Params::DiscreteStatesContinousTime(param);
///
/// //Initialize a ctbn
/// let mut net = CtbnNetwork::new();
///
/// //Add nodes
/// let X1 = net.add_node(X1).unwrap();
/// let X2 = net.add_node(X2).unwrap();
///
/// //Add an edge
/// net.add_edge(X1, X2);
///
/// //Add the CIMs for each node
/// match &mut net.get_node_mut(X1) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
/// }
/// }
/// match &mut net.get_node_mut(X2) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(
/// Ok(()),
/// param.set_cim(arr3(&[
/// [[-0.01, 0.01], [5.0, -5.0]],
/// [[-5.0, 5.0], [0.01, -0.01]]
/// ]))
/// );
/// }
/// }
///
/// //Generate a synthetic dataset from net
/// let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
///
/// //Initialize the `struct BayesianApproach`
/// let pl = BayesianApproach{alpha: 1, tau: 1.0};
///
/// // Fit the parameters for X2
/// let p = match pl.fit(&net, &data, X2, None) {
/// params::Params::DiscreteStatesContinousTime(p) => p,
/// };
///
/// // Check the shape of the CIM
/// assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
///
/// // Check if the learned parameters are close enough to the real ones.
/// assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
/// &arr3(&[
/// [[-0.01, 0.01], [5.0, -5.0]],
/// [[-5.0, 5.0], [0.01, -0.01]]
/// ]),
/// 0.1
/// ));
/// ```
pub struct BayesianApproach {
pub alpha: usize,
pub tau: f64,

@ -3,6 +3,7 @@
use std::collections::BTreeSet;
use enum_dispatch::enum_dispatch;
use log::{debug, error, trace, warn};
use ndarray::prelude::*;
use rand::Rng;
use rand_chacha::ChaCha8Rng;
@ -29,6 +30,7 @@ pub enum StateType {
/// methods required to describes a generic node.
#[enum_dispatch(Params)]
pub trait ParamsTrait {
///Reset the parameters
fn reset_params(&mut self);
/// Randomly generate a possible state of the node disregarding the state of the node and it's
@ -98,6 +100,7 @@ pub struct DiscreteStatesContinousTimeParams {
impl DiscreteStatesContinousTimeParams {
pub fn new(label: String, domain: BTreeSet<String>) -> DiscreteStatesContinousTimeParams {
debug!("Creation of node {}", label);
DiscreteStatesContinousTimeParams {
label,
domain,
@ -109,6 +112,7 @@ impl DiscreteStatesContinousTimeParams {
/// Getter function for CIM
pub fn get_cim(&self) -> &Option<Array3<f64>> {
debug!("Getting cim from node {}", self.label);
&self.cim
}
@ -119,10 +123,12 @@ impl DiscreteStatesContinousTimeParams {
/// * **Invalid CIM inserted** - it replaces the `self.cim` value with `None` and it returns
/// `ParamsError`.
pub fn set_cim(&mut self, cim: Array3<f64>) -> Result<(), ParamsError> {
debug!("Setting cim for node {}", self.label);
self.cim = Some(cim);
match self.validate_params() {
Ok(()) => Ok(()),
Err(e) => {
warn!("Validation cim faild for node {}", self.label);
self.cim = None;
Err(e)
}
@ -131,39 +137,54 @@ impl DiscreteStatesContinousTimeParams {
/// Unchecked version of the setter function for CIM.
pub fn set_cim_unchecked(&mut self, cim: Array3<f64>) {
debug!("Setting cim (unchecked) for node {}", self.label);
self.cim = Some(cim);
}
/// Getter function for transitions.
pub fn get_transitions(&self) -> &Option<Array3<usize>> {
debug!("Get transitions from node {}", self.label);
&self.transitions
}
/// Setter function for transitions.
pub fn set_transitions(&mut self, transitions: Array3<usize>) {
debug!("Set transitions for node {}", self.label);
self.transitions = Some(transitions);
}
/// Getter function for residence_time.
pub fn get_residence_time(&self) -> &Option<Array2<f64>> {
debug!("Get residence time from node {}", self.label);
&self.residence_time
}
/// Setter function for residence_time.
pub fn set_residence_time(&mut self, residence_time: Array2<f64>) {
debug!("Set residence time for node {}", self.label);
self.residence_time = Some(residence_time);
}
}
impl ParamsTrait for DiscreteStatesContinousTimeParams {
fn reset_params(&mut self) {
debug!(
"Setting cim, transitions and residence_time to None for node {}",
self.label
);
self.cim = Option::None;
self.transitions = Option::None;
self.residence_time = Option::None;
}
fn get_random_state_uniform(&self, rng: &mut ChaCha8Rng) -> StateType {
StateType::Discrete(rng.gen_range(0..(self.domain.len())))
let state = StateType::Discrete(rng.gen_range(0..(self.domain.len())));
trace!(
"Generate random state uniform. Node: {} - State: {:?}",
self.get_label(),
&state
);
return state;
}
fn get_random_residence_time(
@ -179,11 +200,20 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
Option::Some(cim) => {
let lambda = cim[[u, state, state]] * -1.0;
let x: f64 = rng.gen_range(0.0..=1.0);
Ok(-x.ln() / lambda)
let ret = -x.ln() / lambda;
trace!(
"Generate random residence time. Node: {} - Time: {}",
self.get_label(),
ret
);
Ok(ret)
}
Option::None => {
warn!("Cim not initialized for node {}", self.get_label());
Err(ParamsError::ParametersNotInitialized(String::from(
"CIM not initialized",
)))
}
Option::None => Err(ParamsError::ParametersNotInitialized(String::from(
"CIM not initialized",
))),
}
}
@ -220,11 +250,21 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
next_state.0 + 1
};
Ok(StateType::Discrete(next_state))
let next_state = StateType::Discrete(next_state);
trace!(
"Generate random state. Node: {} - State: {:?}",
self.get_label(),
next_state
);
Ok(next_state)
}
Option::None => {
warn!("Cim not initialized for node {}", self.get_label());
Err(ParamsError::ParametersNotInitialized(String::from(
"CIM not initialized",
)))
}
Option::None => Err(ParamsError::ParametersNotInitialized(String::from(
"CIM not initialized",
))),
}
}
@ -243,6 +283,7 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
// Check if the cim is initialized
if let None = self.cim {
warn!("Cim not initialized for node {}", self.get_label());
return Err(ParamsError::ParametersNotInitialized(String::from(
"CIM not initialized",
)));
@ -250,11 +291,13 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
let cim = self.cim.as_ref().unwrap();
// Check if the inner dimensions of the cim are equal to the cardinality of the variable
if cim.shape()[1] != domain_size || cim.shape()[2] != domain_size {
return Err(ParamsError::InvalidCIM(format!(
let message = format!(
"Incompatible shape {:?} with domain {:?}",
cim.shape(),
domain_size
)));
);
warn!("{}", message);
return Err(ParamsError::InvalidCIM(message));
}
// Check if the diagonal of each cim is non-positive
@ -262,6 +305,10 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
.axis_iter(Axis(0))
.any(|x| x.diag().iter().any(|x| x >= &0.0))
{
warn!(
"The diagonal of each cim for node {} must be non-positive",
self.get_label()
);
return Err(ParamsError::InvalidCIM(String::from(
"The diagonal of each cim must be non-positive",
)));
@ -273,6 +320,10 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
.iter()
.any(|x| f64::abs(x.clone()) > f64::EPSILON.sqrt())
{
warn!(
"The sum of each row of the cim for node {} must be 0",
self.get_label()
);
return Err(ParamsError::InvalidCIM(String::from(
"The sum of each row must be 0",
)));

@ -23,8 +23,20 @@ pub type NetworkProcessState = Vec<params::StateType>;
/// as a CTBN).
pub trait NetworkProcess: Sync {
fn initialize_adj_matrix(&mut self);
/// Add a **node** to the network
///
/// # Arguments
///
/// * `n` - instantiation of the `enum params::Params` describing a node
///
/// # Return
///
/// * A `Result` containing the `node_idx` automatically assigned if everything is fine,
/// or a `NetworkError` if something went wrong.
fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
/// Add an **directed edge** between a two nodes of the network.
/// Add a **directed edge** between a two nodes of the network.
///
/// # Arguments
///

@ -2,6 +2,7 @@
use std::collections::BTreeSet;
use log::info;
use ndarray::prelude::*;
use crate::params::{DiscreteStatesContinousTimeParams, Params, ParamsTrait, StateType};
@ -77,6 +78,7 @@ impl CtbnNetwork {
///
/// * The equivalent *CtmpProcess* computed from the current CtbnNetwork
pub fn amalgamation(&self) -> CtmpProcess {
info!("Network Amalgamation Started");
let variables_domain =
Array1::from_iter(self.nodes.iter().map(|x| x.get_reserved_space_as_parent()));
@ -138,21 +140,15 @@ impl CtbnNetwork {
return array_state;
}
/// Get the Adjacency Matrix.
pub fn get_adj_matrix(&self) -> Option<&Array2<u16>> {
self.adj_matrix.as_ref()
}
}
impl process::NetworkProcess for CtbnNetwork {
/// Initialize an Adjacency matrix.
fn initialize_adj_matrix(&mut self) {
self.adj_matrix = Some(Array2::<u16>::zeros(
(self.nodes.len(), self.nodes.len()).f(),
));
}
/// Add a new node.
fn add_node(&mut self, mut n: Params) -> Result<usize, process::NetworkError> {
n.reset_params();
self.adj_matrix = Option::None;
@ -160,7 +156,6 @@ impl process::NetworkProcess for CtbnNetwork {
Ok(self.nodes.len() - 1)
}
/// Connect two nodes with a new edge.
fn add_edge(&mut self, parent: usize, child: usize) {
if let None = self.adj_matrix {
self.initialize_adj_matrix();
@ -176,7 +171,6 @@ impl process::NetworkProcess for CtbnNetwork {
0..self.nodes.len()
}
/// Get the number of nodes of the network.
fn get_number_of_nodes(&self) -> usize {
self.nodes.len()
}
@ -221,7 +215,6 @@ impl process::NetworkProcess for CtbnNetwork {
.0
}
/// Get all the parents of the given node.
fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
self.adj_matrix
.as_ref()
@ -233,7 +226,6 @@ impl process::NetworkProcess for CtbnNetwork {
.collect()
}
/// Get all the children of the given node.
fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
self.adj_matrix
.as_ref()

@ -6,6 +6,75 @@ use crate::{
};
use super::{NetworkProcess, NetworkProcessState};
use log::warn;
/// This structure represents a Continuous Time Markov process
///
/// * Arguments
///
/// * `param` - An Option containing the parameters of the process
///
///```rust
/// use std::collections::BTreeSet;
/// use reCTBN::process::NetworkProcess;
/// use reCTBN::params;
/// use reCTBN::process::ctbn::*;
/// use ndarray::arr3;
///
/// //Create the domain for a discrete node
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
///
/// //Create the parameters for a discrete node using the domain
/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
///
/// //Create the node using the parameters
/// let X1 = params::Params::DiscreteStatesContinousTime(param);
///
/// let mut domain = BTreeSet::new();
/// domain.insert(String::from("A"));
/// domain.insert(String::from("B"));
/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
/// let X2 = params::Params::DiscreteStatesContinousTime(param);
///
/// //Initialize a ctbn
/// let mut net = CtbnNetwork::new();
///
/// //Add nodes
/// let X1 = net.add_node(X1).unwrap();
/// let X2 = net.add_node(X2).unwrap();
///
/// //Add an edge
/// net.add_edge(X1, X2);
/// match &mut net.get_node_mut(X1) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
/// }
/// }
///
/// match &mut net.get_node_mut(X2) {
/// params::Params::DiscreteStatesContinousTime(param) => {
/// assert_eq!(
/// Ok(()),
/// param.set_cim(arr3(&[
/// [[-0.01, 0.01], [5.0, -5.0]],
/// [[-5.0, 5.0], [0.01, -0.01]]
/// ]))
/// );
/// }
/// }
/// //Amalgamate the ctbn into a CtmpProcess
/// let ctmp = net.amalgamation();
///
/// //Extract the amalgamated params from the ctmp
///let params::Params::DiscreteStatesContinousTime(p_ctmp) = &ctmp.get_node(0);
///let p_ctmp = p_ctmp.get_cim().as_ref().unwrap();
///
/// //The shape of the params for an amalgamated ctmp can be computed as a Cartesian product of the
/// //domains variables of the ctbn
/// assert_eq!(p_ctmp.shape()[1], 4);
///```
pub struct CtmpProcess {
param: Option<Params>,
@ -28,13 +97,17 @@ impl NetworkProcess for CtmpProcess {
self.param = Some(n);
Ok(0)
}
Some(_) => Err(process::NetworkError::NodeInsertionError(
"CtmpProcess has only one node".to_string(),
)),
Some(_) => {
warn!("A CTMP do not support more than one node");
Err(process::NetworkError::NodeInsertionError(
"CtmpProcess has only one node".to_string(),
))
}
}
}
fn add_edge(&mut self, _parent: usize, _child: usize) {
warn!("A CTMP cannot have edges");
unimplemented!("CtmpProcess has only one node")
}

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