Metrics.hpp

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#pragma once
 
#include "stats/ConfusionMatrix.hpp"
#include "utils/Types.hpp"
 
#include <optional>
#include <variant>
 
 
namespace ppforest2::stats {
  struct ClassificationMetrics {
    using Maybe = std::optional<ClassificationMetrics>;
 
    ConfusionMatrix confusion_matrix;
 
    ClassificationMetrics() = default;
 
    explicit ClassificationMetrics(ConfusionMatrix cm)
        : confusion_matrix(std::move(cm)) {}
 
    ClassificationMetrics(types::GroupIdVector const& predictions, types::GroupIdVector const& actual)
        : confusion_matrix(predictions, actual) {}
 
    double error_rate() const { return static_cast<double>(confusion_matrix.error()); }
  };

  struct RegressionMetrics {
    using Maybe = std::optional<RegressionMetrics>;
 
    double mse       = 0.0; 
    double mae       = 0.0; 
    double r_squared = 0.0; 
 
    RegressionMetrics() = default;

    RegressionMetrics(types::OutcomeVector const& predictions, types::OutcomeVector const& actual);
  };
 
  // ---------------------------------------------------------------------------
  // Classification metrics
  // ---------------------------------------------------------------------------

  float accuracy(types::OutcomeVector const& predictions, types::GroupIdVector const& actual);

  double error_rate(types::OutcomeVector const& predictions, types::GroupIdVector const& actual);

  inline double error_rate(types::OutcomeVector const& predictions, types::OutcomeVector const& actual) {
    types::GroupIdVector const actual_int = actual.cast<types::GroupId>();
    return error_rate(predictions, actual_int);
  }
 
  // ---------------------------------------------------------------------------
  // Regression metrics
  // ---------------------------------------------------------------------------

  double mse(types::OutcomeVector const& predictions, types::OutcomeVector const& actual);

  double mae(types::OutcomeVector const& predictions, types::OutcomeVector const& actual);

  double r_squared(types::OutcomeVector const& predictions, types::OutcomeVector const& actual);

  using Metrics = std::variant<ClassificationMetrics, RegressionMetrics>;

  Metrics metrics_from_outcomes(types::OutcomeVector const& y_pred, types::OutcomeVector const& y, types::Mode mode);
}