| //! Logistic Regression module |
| //! |
| //! Contains implemention of logistic regression using |
| //! gradient descent optimization. |
| //! |
| //! The regressor will automatically add the intercept term |
| //! so you do not need to format the input matrices yourself. |
| //! |
| //! # Usage |
| //! |
| //! ``` |
| //! use rusty_machine::learning::logistic_reg::LogisticRegressor; |
| //! use rusty_machine::learning::SupModel; |
| //! use rusty_machine::linalg::Matrix; |
| //! use rusty_machine::linalg::Vector; |
| //! |
| //! let inputs = Matrix::new(4,1,vec![1.0,3.0,5.0,7.0]); |
| //! let targets = Vector::new(vec![0.,0.,1.,1.]); |
| //! |
| //! let mut log_mod = LogisticRegressor::default(); |
| //! |
| //! // Train the model |
| //! log_mod.train(&inputs, &targets).unwrap(); |
| //! |
| //! // Now we'll predict a new point |
| //! let new_point = Matrix::new(1,1,vec![10.]); |
| //! let output = log_mod.predict(&new_point).unwrap(); |
| //! |
| //! // Hopefully we classified our new point correctly! |
| //! assert!(output[0] > 0.5, "Our classifier isn't very good!"); |
| //! ``` |
| //! |
| //! We could have been more specific about the learning of the model |
| //! by using the `new` constructor instead. This allows us to provide |
| //! a `GradientDesc` object with custom parameters. |
| use std::vec::*; |
| use linalg::{Matrix, BaseMatrix}; |
| use linalg::Vector; |
| use learning::{LearningResult, SupModel}; |
| use learning::toolkit::activ_fn::{ActivationFunc, Sigmoid}; |
| use learning::toolkit::cost_fn::{CostFunc, CrossEntropyError}; |
| use learning::optim::grad_desc::GradientDesc; |
| use learning::optim::{OptimAlgorithm, Optimizable}; |
| use learning::error::Error; |
| |
| /// Logistic Regression Model. |
| /// |
| /// Contains option for optimized parameter. |
| #[derive(Debug)] |
| pub struct LogisticRegressor<A> |
| where A: OptimAlgorithm<BaseLogisticRegressor> |
| { |
| base: BaseLogisticRegressor, |
| alg: A, |
| } |
| |
| /// Constructs a default Logistic Regression model |
| /// using standard gradient descent. |
| impl Default for LogisticRegressor<GradientDesc> { |
| fn default() -> LogisticRegressor<GradientDesc> { |
| LogisticRegressor { |
| base: BaseLogisticRegressor::new(), |
| alg: GradientDesc::default(), |
| } |
| } |
| } |
| |
| impl<A: OptimAlgorithm<BaseLogisticRegressor>> LogisticRegressor<A> { |
| /// Constructs untrained logistic regression model. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::logistic_reg::LogisticRegressor; |
| /// use rusty_machine::learning::optim::grad_desc::GradientDesc; |
| /// |
| /// let gd = GradientDesc::default(); |
| /// let mut logistic_mod = LogisticRegressor::new(gd); |
| /// ``` |
| pub fn new(alg: A) -> LogisticRegressor<A> { |
| LogisticRegressor { |
| base: BaseLogisticRegressor::new(), |
| alg: alg, |
| } |
| } |
| |
| /// Get the parameters from the model. |
| /// |
| /// Returns an option that is None if the model has not been trained. |
| pub fn parameters(&self) -> Option<&Vector<f64>> { |
| self.base.parameters() |
| } |
| } |
| |
| impl<A> SupModel<Matrix<f64>, Vector<f64>> for LogisticRegressor<A> |
| where A: OptimAlgorithm<BaseLogisticRegressor> |
| { |
| /// Train the logistic regression model. |
| /// |
| /// Takes training data and output values as input. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::logistic_reg::LogisticRegressor; |
| /// use rusty_machine::linalg::Matrix; |
| /// use rusty_machine::linalg::Vector; |
| /// use rusty_machine::learning::SupModel; |
| /// |
| /// let mut logistic_mod = LogisticRegressor::default(); |
| /// let inputs = Matrix::new(3,2, vec![1.0, 2.0, 1.0, 3.0, 1.0, 4.0]); |
| /// let targets = Vector::new(vec![5.0, 6.0, 7.0]); |
| /// |
| /// logistic_mod.train(&inputs, &targets).unwrap(); |
| /// ``` |
| fn train(&mut self, inputs: &Matrix<f64>, targets: &Vector<f64>) -> LearningResult<()> { |
| let ones = Matrix::<f64>::ones(inputs.rows(), 1); |
| let full_inputs = ones.hcat(inputs); |
| |
| let initial_params = vec![0.5; full_inputs.cols()]; |
| |
| let optimal_w = self.alg.optimize(&self.base, &initial_params[..], &full_inputs, targets); |
| self.base.set_parameters(Vector::new(optimal_w)); |
| Ok(()) |
| } |
| |
| /// Predict output value from input data. |
| /// |
| /// Model must be trained before prediction can be made. |
| fn predict(&self, inputs: &Matrix<f64>) -> LearningResult<Vector<f64>> { |
| if let Some(v) = self.base.parameters() { |
| let ones = Matrix::<f64>::ones(inputs.rows(), 1); |
| let full_inputs = ones.hcat(inputs); |
| Ok((full_inputs * v).apply(&Sigmoid::func)) |
| } else { |
| Err(Error::new_untrained()) |
| } |
| } |
| } |
| |
| /// The Base Logistic Regression model. |
| /// |
| /// This struct cannot be instantianated and is used internally only. |
| #[derive(Debug)] |
| pub struct BaseLogisticRegressor { |
| parameters: Option<Vector<f64>>, |
| } |
| |
| impl BaseLogisticRegressor { |
| /// Construct a new BaseLogisticRegressor |
| /// with parameters set to None. |
| fn new() -> BaseLogisticRegressor { |
| BaseLogisticRegressor { parameters: None } |
| } |
| } |
| |
| impl BaseLogisticRegressor { |
| /// Returns a reference to the parameters. |
| fn parameters(&self) -> Option<&Vector<f64>> { |
| self.parameters.as_ref() |
| } |
| |
| /// Set the parameters to `Some` vector. |
| fn set_parameters(&mut self, params: Vector<f64>) { |
| self.parameters = Some(params); |
| } |
| } |
| |
| /// Computing the gradient of the underlying Logistic |
| /// Regression model. |
| /// |
| /// The gradient is given by |
| /// |
| /// X<sup>T</sup>(h(Xb) - y) / m |
| /// |
| /// where `h` is the sigmoid function and `b` the underlying model parameters. |
| impl Optimizable for BaseLogisticRegressor { |
| type Inputs = Matrix<f64>; |
| type Targets = Vector<f64>; |
| |
| fn compute_grad(&self, |
| params: &[f64], |
| inputs: &Matrix<f64>, |
| targets: &Vector<f64>) |
| -> (f64, Vec<f64>) { |
| |
| let beta_vec = Vector::new(params.to_vec()); |
| let outputs = (inputs * beta_vec).apply(&Sigmoid::func); |
| |
| let cost = CrossEntropyError::cost(&outputs, targets); |
| let grad = (inputs.transpose() * (outputs - targets)) / (inputs.rows() as f64); |
| |
| (cost, grad.into_vec()) |
| } |
| } |