rmontanana/BayesNet
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Exception if hyperparameters not valid

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-24 12:09:35 +02:00
parent 86ffdfd6f3
commit 1c6af619b5
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
16 changed files with 47 additions and 13 deletions
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19
.clang-uml Normal file
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@ -0,0 +1,19 @@
compilation_database_dir: build
output_directory: puml
diagrams:
myproject_class:
type: class
glob:
- src/bayesnet/*.cc
- src/platform/*.cc
using_namespace: bayesnet
include:
namespaces:
- bayesnet
- platform
exclude:
namespaces:
- myproject::detail
plantuml:
after:
- 'note left of {{ alias("MyProjectMain") }}: Main class of myproject library.'

1
src/BayesNet/AODE.h
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@ -10,7 +10,6 @@ namespace bayesnet {
AODE();
virtual ~AODE() {};
vector<string> graph(const string& title = "AODE") const override;
void setHyperparameters(nlohmann::json& hyperparameters) override {};
};
}
#endif

2
src/BayesNet/AODELd.cc
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@ -6,7 +6,7 @@ namespace bayesnet {
AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
checkInput(X_, y_);
features = features_;
className = className_;
Xf = X_;

3
src/BayesNet/AODELd.h
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@ -14,9 +14,8 @@ namespace bayesnet {
AODELd();
AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_) override;
virtual ~AODELd() = default;
vector<string> graph(const string& name = "AODE") const override;
vector<string> graph(const string& name = "AODELd") const override;
static inline string version() { return "0.0.1"; };
void setHyperparameters(nlohmann::json& hyperparameters) override {};
};
}
#endif // !AODELD_H

9
src/BayesNet/Classifier.cc
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@ -71,6 +71,9 @@ namespace bayesnet {
}
void Classifier::checkFitParameters()
{
if (torch::is_floating_point(dataset)) {
throw invalid_argument("dataset (X, y) must be of type Integer");
}
if (n != features.size()) {
throw invalid_argument("X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
}
@ -160,4 +163,10 @@ namespace bayesnet {
}
}
}
void Classifier::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid, default is no hyperparameters
const vector<string> validKeys = { };
checkHyperparameters(validKeys, hyperparameters);
}
}

1
src/BayesNet/Classifier.h
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@ -43,6 +43,7 @@ namespace bayesnet {
vector<string> show() const override;
vector<string> topological_order() override;
void dump_cpt() const override;
void setHyperparameters(nlohmann::json& hyperparameters) override;
};
}
#endif

2
src/BayesNet/KDBLd.cc
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@ -5,7 +5,7 @@ namespace bayesnet {
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
checkInput(X_, y_);
features = features_;
className = className_;
Xf = X_;

1
src/BayesNet/KDBLd.h
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@ -13,7 +13,6 @@ namespace bayesnet {
KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "KDB") const override;
Tensor predict(Tensor& X) override;
void setHyperparameters(nlohmann::json& hyperparameters) override {};
static inline string version() { return "0.0.1"; };
};
}

9
src/BayesNet/Proposal.cc
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@ -9,6 +9,15 @@ namespace bayesnet {
delete value;
}
}
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
{
if (!torch::is_floating_point(X)) {
throw std::invalid_argument("X must be a floating point tensor");
}
if (torch::is_floating_point(y)) {
throw std::invalid_argument("y must be an integer tensor");
}
}
map<string, vector<int>> Proposal::localDiscretizationProposal(const map<string, vector<int>>& oldStates, Network& model)
{
// order of local discretization is important. no good 0, 1, 2...

1
src/BayesNet/Proposal.h
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@ -13,6 +13,7 @@ namespace bayesnet {
Proposal(torch::Tensor& pDataset, vector<string>& features_, string& className_);
virtual ~Proposal();
protected:
void checkInput(const torch::Tensor& X, const torch::Tensor& y);
torch::Tensor prepareX(torch::Tensor& X);
map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
map<string, vector<int>> fit_local_discretization(const torch::Tensor& y);

1
src/BayesNet/SPODE.h
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@ -12,7 +12,6 @@ namespace bayesnet {
explicit SPODE(int root);
virtual ~SPODE() {};
vector<string> graph(const string& name = "SPODE") const override;
void setHyperparameters(nlohmann::json& hyperparameters) override {};
};
}
#endif

6
src/BayesNet/SPODELd.cc
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@ -5,7 +5,7 @@ namespace bayesnet {
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
checkInput(X_, y_);
features = features_;
className = className_;
Xf = X_;
@ -20,9 +20,11 @@ namespace bayesnet {
}
SPODELd& SPODELd::fit(torch::Tensor& dataset, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{
if (!torch::is_floating_point(dataset)) {
throw std::runtime_error("Dataset must be a floating point tensor");
}
Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
y = dataset.index({ -1, "..." }).clone();
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
className = className_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y

1
src/BayesNet/SPODELd.h
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@ -13,7 +13,6 @@ namespace bayesnet {
SPODELd& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "SPODE") const override;
Tensor predict(Tensor& X) override;
void setHyperparameters(nlohmann::json& hyperparameters) override {};
static inline string version() { return "0.0.1"; };
};
}

1
src/BayesNet/TAN.h
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@ -11,7 +11,6 @@ namespace bayesnet {
TAN();
virtual ~TAN() {};
vector<string> graph(const string& name = "TAN") const override;
void setHyperparameters(nlohmann::json& hyperparameters) override {};
};
}
#endif

2
src/BayesNet/TANLd.cc
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@ -5,7 +5,7 @@ namespace bayesnet {
TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
checkInput(X_, y_);
features = features_;
className = className_;
Xf = X_;

1
src/BayesNet/TANLd.h
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@ -14,7 +14,6 @@ namespace bayesnet {
vector<string> graph(const string& name = "TAN") const override;
Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; };
void setHyperparameters(nlohmann::json& hyperparameters) override {};
};
}
#endif // !TANLD_H