Added doctest for parameter_learning

LICENSE-APACHE

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LICENSE-MIT

@@ -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
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-TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
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-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.

README.md

@@ -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.

reCTBN/Cargo.toml

@@ -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" }

reCTBN/src/parameter_learning.rs

@@ -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,

reCTBN/src/params.rs

@@ -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 => Err(ParamsError::ParametersNotInitialized(String::from(
+            Option::None => {
+                warn!("Cim not initialized for node {}", self.get_label());
+                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 => Err(ParamsError::ParametersNotInitialized(String::from(
+            Option::None => {
+                warn!("Cim not initialized for node {}", self.get_label());
+                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",
             )));

reCTBN/src/process.rs

@@ -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
     ///

reCTBN/src/process/ctbn.rs

@@ -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()

reCTBN/src/process/ctmp.rs

@@ -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(
+            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")
     }