feat: documents metric functions
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Volodymyr Orlov
+98
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@@ -1,83 +1,177 @@
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//! # Metric functions
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//!
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//! One way to build machine learning models is to use a constructive feedback loop through model evaluation.
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//! In a feedback loop you build your model first, then you get feedback from metrics, improve it and repeat until your model achieve desirable performance.
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//! Evaluation metrics helps to explain the performance of a model and compare models based on an objective criterion.
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//!
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//! Choosing the right metric is crucial while evaluating machine learning models. In SmartCore you will find metrics for these classes of ML models:
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//!
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//! * [Classification metrics](struct.ClassificationMetrics.html)
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//! * [Regression metrics](struct.RegressionMetrics.html)
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//!
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//! Example:
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//! ```
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//! use smartcore::linalg::naive::dense_matrix::*;
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//! use smartcore::linear::logistic_regression::LogisticRegression;
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//! use smartcore::metrics::*;
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//!
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//! let x = DenseMatrix::from_array(&[
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//! &[5.1, 3.5, 1.4, 0.2],
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//! &[4.9, 3.0, 1.4, 0.2],
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//! &[4.7, 3.2, 1.3, 0.2],
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//! &[4.6, 3.1, 1.5, 0.2],
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//! &[5.0, 3.6, 1.4, 0.2],
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//! &[5.4, 3.9, 1.7, 0.4],
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//! &[4.6, 3.4, 1.4, 0.3],
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//! &[5.0, 3.4, 1.5, 0.2],
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//! &[4.4, 2.9, 1.4, 0.2],
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//! &[4.9, 3.1, 1.5, 0.1],
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//! &[7.0, 3.2, 4.7, 1.4],
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//! &[6.4, 3.2, 4.5, 1.5],
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//! &[6.9, 3.1, 4.9, 1.5],
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//! &[5.5, 2.3, 4.0, 1.3],
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//! &[6.5, 2.8, 4.6, 1.5],
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//! &[5.7, 2.8, 4.5, 1.3],
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//! &[6.3, 3.3, 4.7, 1.6],
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//! &[4.9, 2.4, 3.3, 1.0],
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//! &[6.6, 2.9, 4.6, 1.3],
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//! &[5.2, 2.7, 3.9, 1.4],
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//! ]);
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//! let y: Vec<f64> = vec![
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//! 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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//! ];
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//!
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//! let lr = LogisticRegression::fit(&x, &y);
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//!
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//! let y_hat = lr.predict(&x);
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//!
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//! let acc = ClassificationMetrics::accuracy().get_score(&y, &y_hat);
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//! // or
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//! let acc = accuracy(&y, &y_hat);
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//! ```
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/// Accuracy score.
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pub mod accuracy;
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/// Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
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pub mod auc;
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/// F1 score, also known as balanced F-score or F-measure.
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pub mod f1;
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/// Mean absolute error regression loss.
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pub mod mean_absolute_error;
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/// Mean squared error regression loss.
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pub mod mean_squared_error;
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/// Computes the precision.
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pub mod precision;
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/// Coefficient of determination (R2).
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pub mod r2;
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/// Computes the recall.
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pub mod recall;
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use crate::linalg::BaseVector;
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use crate::math::num::RealNumber;
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/// Use these metrics to compare classification models.
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pub struct ClassificationMetrics {}
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/// Metrics for regression models.
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pub struct RegressionMetrics {}
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impl ClassificationMetrics {
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/// Accuracy score, see [accuracy](accuracy/index.html).
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pub fn accuracy() -> accuracy::Accuracy {
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accuracy::Accuracy {}
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}
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/// Recall, see [recall](recall/index.html).
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pub fn recall() -> recall::Recall {
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recall::Recall {}
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}
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/// Precision, see [precision](precision/index.html).
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pub fn precision() -> precision::Precision {
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precision::Precision {}
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}
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pub fn f1() -> f1::F1 {
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f1::F1 {}
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/// F1 score, also known as balanced F-score or F-measure, see [F1](f1/index.html).
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pub fn f1<T: RealNumber>(beta: T) -> f1::F1<T> {
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f1::F1 { beta: beta }
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}
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/// Area Under the Receiver Operating Characteristic Curve (ROC AUC), see [AUC](auc/index.html).
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pub fn roc_auc_score() -> auc::AUC {
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auc::AUC {}
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}
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}
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impl RegressionMetrics {
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/// Mean squared error, see [mean squared error](mean_squared_error/index.html).
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pub fn mean_squared_error() -> mean_squared_error::MeanSquareError {
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mean_squared_error::MeanSquareError {}
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}
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/// Mean absolute error, see [mean absolute error](mean_absolute_error/index.html).
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pub fn mean_absolute_error() -> mean_absolute_error::MeanAbsoluteError {
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mean_absolute_error::MeanAbsoluteError {}
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}
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/// Coefficient of determination (R2), see [R2](r2/index.html).
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pub fn r2() -> r2::R2 {
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r2::R2 {}
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}
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}
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/// Function that calculated accuracy score, see [accuracy](accuracy/index.html).
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/// * `y_true` - cround truth (correct) labels
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn accuracy<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::accuracy().get_score(y_true, y_pred)
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}
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/// Calculated recall score, see [recall](recall/index.html)
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn recall<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::recall().get_score(y_true, y_pred)
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}
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/// Calculated precision score, see [precision](precision/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn precision<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::precision().get_score(y_true, y_pred)
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}
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pub fn f1<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::f1().get_score(y_true, y_pred)
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/// Computes F1 score, see [F1](f1/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn f1<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V, beta: T) -> T {
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ClassificationMetrics::f1(beta).get_score(y_true, y_pred)
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}
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/// AUC score, see [AUC](auc/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred_probabilities` - probability estimates, as returned by a classifier.
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pub fn roc_auc_score<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred_probabilities: &V) -> T {
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ClassificationMetrics::roc_auc_score().get_score(y_true, y_pred_probabilities)
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}
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/// Computes mean squared error, see [mean squared error](mean_squared_error/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn mean_squared_error<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::mean_squared_error().get_score(y_true, y_pred)
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}
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/// Computes mean absolute error, see [mean absolute error](mean_absolute_error/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn mean_absolute_error<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::mean_absolute_error().get_score(y_true, y_pred)
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}
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/// Computes R2 score, see [R2](r2/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn r2<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::r2().get_score(y_true, y_pred)
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}
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