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| </pre><pre class="rust"><code><span class="doccomment">//! 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. |
| |
| </span><span class="kw">use </span>linalg::{Matrix, BaseMatrix}; |
| <span class="kw">use </span>linalg::Vector; |
| <span class="kw">use </span>learning::{LearningResult, SupModel}; |
| <span class="kw">use </span>learning::toolkit::activ_fn::{ActivationFunc, Sigmoid}; |
| <span class="kw">use </span>learning::toolkit::cost_fn::{CostFunc, CrossEntropyError}; |
| <span class="kw">use </span>learning::optim::grad_desc::GradientDesc; |
| <span class="kw">use </span>learning::optim::{OptimAlgorithm, Optimizable}; |
| <span class="kw">use </span>learning::error::Error; |
| |
| <span class="doccomment">/// Logistic Regression Model. |
| /// |
| /// Contains option for optimized parameter. |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>LogisticRegressor<A> |
| <span class="kw">where </span>A: OptimAlgorithm<BaseLogisticRegressor> |
| { |
| base: BaseLogisticRegressor, |
| alg: A, |
| } |
| |
| <span class="doccomment">/// Constructs a default Logistic Regression model |
| /// using standard gradient descent. |
| </span><span class="kw">impl </span>Default <span class="kw">for </span>LogisticRegressor<GradientDesc> { |
| <span class="kw">fn </span>default() -> LogisticRegressor<GradientDesc> { |
| LogisticRegressor { |
| base: BaseLogisticRegressor::new(), |
| alg: GradientDesc::default(), |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><A: OptimAlgorithm<BaseLogisticRegressor>> LogisticRegressor<A> { |
| <span class="doccomment">/// 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); |
| /// ``` |
| </span><span class="kw">pub fn </span>new(alg: A) -> LogisticRegressor<A> { |
| LogisticRegressor { |
| base: BaseLogisticRegressor::new(), |
| alg: alg, |
| } |
| } |
| |
| <span class="doccomment">/// Get the parameters from the model. |
| /// |
| /// Returns an option that is None if the model has not been trained. |
| </span><span class="kw">pub fn </span>parameters(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span>Vector<f64>> { |
| <span class="self">self</span>.base.parameters() |
| } |
| |
| <span class="doccomment">/// Set the parameters in the model. |
| </span><span class="kw">pub fn </span>set_parameters(<span class="kw-2">&mut </span><span class="self">self</span>, para: Vector<f64>) { |
| <span class="self">self</span>.base.set_parameters(para) |
| } |
| } |
| |
| <span class="kw">impl</span><A> SupModel<Matrix<f64>, Vector<f64>> <span class="kw">for </span>LogisticRegressor<A> |
| <span class="kw">where </span>A: OptimAlgorithm<BaseLogisticRegressor> |
| { |
| <span class="doccomment">/// 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(); |
| /// ``` |
| </span><span class="kw">fn </span>train(<span class="kw-2">&mut </span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>, targets: <span class="kw-2">&</span>Vector<f64>) -> LearningResult<()> { |
| <span class="kw">let </span>ones = Matrix::<f64>::ones(inputs.rows(), <span class="number">1</span>); |
| <span class="kw">let </span>full_inputs = ones.hcat(inputs); |
| |
| <span class="kw">let </span>initial_params = <span class="macro">vec!</span>[<span class="number">0.5</span>; full_inputs.cols()]; |
| |
| <span class="kw">let </span>optimal_w = <span class="self">self</span>.alg.optimize(<span class="kw-2">&</span><span class="self">self</span>.base, <span class="kw-2">&</span>initial_params[..], <span class="kw-2">&</span>full_inputs, targets); |
| <span class="self">self</span>.base.set_parameters(Vector::new(optimal_w)); |
| <span class="prelude-val">Ok</span>(()) |
| } |
| |
| <span class="doccomment">/// Predict output value from input data. |
| /// |
| /// Model must be trained before prediction can be made. |
| </span><span class="kw">fn </span>predict(<span class="kw-2">&</span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>) -> LearningResult<Vector<f64>> { |
| <span class="kw">if let </span><span class="prelude-val">Some</span>(v) = <span class="self">self</span>.base.parameters() { |
| <span class="kw">let </span>ones = Matrix::<f64>::ones(inputs.rows(), <span class="number">1</span>); |
| <span class="kw">let </span>full_inputs = ones.hcat(inputs); |
| <span class="prelude-val">Ok</span>((full_inputs * v).apply(<span class="kw-2">&</span>Sigmoid::func)) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } |
| } |
| |
| <span class="doccomment">/// The Base Logistic Regression model. |
| /// |
| /// This struct cannot be instantianated and is used internally only. |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>BaseLogisticRegressor { |
| parameters: <span class="prelude-ty">Option</span><Vector<f64>>, |
| } |
| |
| <span class="kw">impl </span>BaseLogisticRegressor { |
| <span class="doccomment">/// Construct a new BaseLogisticRegressor |
| /// with parameters set to None. |
| </span><span class="kw">fn </span>new() -> BaseLogisticRegressor { |
| BaseLogisticRegressor { parameters: <span class="prelude-val">None </span>} |
| } |
| } |
| |
| <span class="kw">impl </span>BaseLogisticRegressor { |
| <span class="doccomment">/// Returns a reference to the parameters. |
| </span><span class="kw">fn </span>parameters(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span>Vector<f64>> { |
| <span class="self">self</span>.parameters.as_ref() |
| } |
| |
| <span class="doccomment">/// Set the parameters to `Some` vector. |
| </span><span class="kw">fn </span>set_parameters(<span class="kw-2">&mut </span><span class="self">self</span>, params: Vector<f64>) { |
| <span class="self">self</span>.parameters = <span class="prelude-val">Some</span>(params); |
| } |
| } |
| |
| <span class="doccomment">/// 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. |
| </span><span class="kw">impl </span>Optimizable <span class="kw">for </span>BaseLogisticRegressor { |
| <span class="kw">type </span>Inputs = Matrix<f64>; |
| <span class="kw">type </span>Targets = Vector<f64>; |
| |
| <span class="kw">fn </span>compute_grad(<span class="kw-2">&</span><span class="self">self</span>, |
| params: <span class="kw-2">&</span>[f64], |
| inputs: <span class="kw-2">&</span>Matrix<f64>, |
| targets: <span class="kw-2">&</span>Vector<f64>) |
| -> (f64, Vec<f64>) { |
| |
| <span class="kw">let </span>beta_vec = Vector::new(params.to_vec()); |
| <span class="kw">let </span>outputs = (inputs * beta_vec).apply(<span class="kw-2">&</span>Sigmoid::func); |
| |
| <span class="kw">let </span>cost = CrossEntropyError::cost(<span class="kw-2">&</span>outputs, targets); |
| <span class="kw">let </span>grad = (inputs.transpose() * (outputs - targets)) / (inputs.rows() <span class="kw">as </span>f64); |
| |
| (cost, grad.into_vec()) |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../" data-current-crate="rusty_machine" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
