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Begin AdaBoost integration

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This commit is contained in:
Ricardo Montañana Gómez
2025-06-18 11:27:11 +02:00
parent 023d5613b4
commit 415a7ae608
10 changed files with 1001 additions and 56 deletions
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20
src/experimental_clfs/AdaBoost.h
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@@ -21,9 +21,9 @@ namespace bayesnet {
std::vector<std::string> graph(const std::string& title = "") const override;
// AdaBoost specific methods
void setNEstimators(int n_estimators) { this->n_estimators = n_estimators; }
void setNEstimators(int n_estimators) { this->n_estimators = n_estimators; checkValues(); }
int getNEstimators() const { return n_estimators; }
void setBaseMaxDepth(int depth) { this->base_max_depth = depth; }
void setBaseMaxDepth(int depth) { this->base_max_depth = depth; checkValues(); }
int getBaseMaxDepth() const { return base_max_depth; }
// Get the weight of each base estimator
@@ -35,6 +35,11 @@ namespace bayesnet {
// Override setHyperparameters from BaseClassifier
void setHyperparameters(const nlohmann::json& hyperparameters) override;
torch::Tensor predict(torch::Tensor& X) override;
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
protected:
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
@@ -45,6 +50,8 @@ namespace bayesnet {
std::vector<double> alphas; // Weight of each base estimator
std::vector<double> training_errors; // Training error at each iteration
torch::Tensor sample_weights; // Current sample weights
int n_classes; // Number of classes in the target variable
int n; // Number of features
// Train a single base estimator
std::unique_ptr<Classifier> trainBaseEstimator(const torch::Tensor& weights);
@@ -57,6 +64,15 @@ namespace bayesnet {
// Normalize weights to sum to 1
void normalizeWeights();
// Check if hyperparameters values are valid
void checkValues() const;
// Make predictions for a single sample
int predictSample(const torch::Tensor& x) const;
// Make probabilistic predictions for a single sample
torch::Tensor predictProbaSample(const torch::Tensor& x) const;
};
}