rmontanana/BayesNet
rmontanana/BayesNet
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Add hyperparameters and processing order to Boost

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-20 20:31:23 +02:00
parent 4964aab722
commit 6b317accf1
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
5 changed files with 54 additions and 35 deletions
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4
.vscode/launch.json vendored
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@ -31,7 +31,9 @@
"--discretize",
"--stratified",
"-d",
"iris"
"glass",
"--hyperparameters",
"{\"repeatSparent\": true, \"maxModels\": 12}"
],
"cwd": "/Users/rmontanana/Code/discretizbench",
},

28
src/BayesNet/BayesMetrics.cc
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@ -21,25 +21,39 @@ namespace bayesnet {
}
samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
}
vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, unsigned k)
vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
{
// Return the K Best features
auto n = samples.size(0) - 1;
if (k == 0) {
k = n;
}
// compute scores
scoresKBest.reserve(n);
scoresKBest.clear();
featuresKBest.clear();
auto label = samples.index({ -1, "..." });
for (int i = 0; i < n; ++i) {
scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights));
featuresKBest.push_back(i);
}
// sort & reduce scores and features
sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
{ return scoresKBest[i] > scoresKBest[j]; });
sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
featuresKBest.resize(k);
scoresKBest.resize(k);
if (ascending) {
sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
{ return scoresKBest[i] < scoresKBest[j]; });
sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>());
if (k < n) {
for (int i = 0; i < n - k; ++i) {
featuresKBest.erase(featuresKBest.begin());
scoresKBest.erase(scoresKBest.begin());
}
}
} else {
sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
{ return scoresKBest[i] > scoresKBest[j]; });
sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
featuresKBest.resize(k);
scoresKBest.resize(k);
}
return featuresKBest;
}
vector<double> Metrics::getScoresKBest() const

2
src/BayesNet/BayesMetrics.h
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@ -21,7 +21,7 @@ namespace bayesnet {
Metrics() = default;
Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates);
Metrics(const vector<vector<int>>& vsamples, const vector<int>& labels, const vector<string>& features, const string& className, const int classNumStates);
vector<int> SelectKBestWeighted(const torch::Tensor& weights, unsigned k = 0);
vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending=false, unsigned k = 0);
vector<double> getScoresKBest() const;
double mutualInformation(const Tensor& firstFeature, const Tensor& secondFeature, const Tensor& weights);
vector<float> conditionalEdgeWeights(vector<float>& weights); // To use in Python

51
src/BayesNet/BoostAODE.cc
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@ -1,8 +1,9 @@
#include "BoostAODE.h"
#include <set>
#include "BayesMetrics.h"
namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble(), repeatSparent(false) {}
BoostAODE::BoostAODE() : Ensemble() {}
void BoostAODE::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
@ -12,40 +13,41 @@ namespace bayesnet {
if (hyperparameters.contains("repeatSparent")) {
repeatSparent = hyperparameters["repeatSparent"];
}
if (hyperparameters.contains("maxModels")) {
maxModels = hyperparameters["maxModels"];
}
if (hyperparameters.contains("ascending")) {
ascending = hyperparameters["ascending"];
}
}
void BoostAODE::trainModel(const torch::Tensor& weights)
{
models.clear();
n_models = 0;
int max_models = .1 * n > 10 ? .1 * n : n;
if (maxModels == 0)
maxModels = .1 * n > 10 ? .1 * n : n;
Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
auto X_ = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
auto y_ = dataset.index({ -1, "..." });
bool exitCondition = false;
vector<int> featuresUsed;
unordered_set<int> featuresUsed;
// Step 0: Set the finish condition
// if not repeatSparent a finish condition is run out of features
// n_models == max_models
// n_models == maxModels
int numClasses = states[className].size();
while (!exitCondition) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, n); // Get all the features sorted
auto feature = featureSelection[0];
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
unique_ptr<Classifier> model;
if (!repeatSparent) {
if (n_models == 0) {
models.resize(n); // Resize for n==nfeatures SPODEs
significanceModels.resize(n);
}
auto feature = featureSelection[0];
if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
bool found = false;
for (int i = 0; i < featureSelection.size(); ++i) {
if (find(featuresUsed.begin(), featuresUsed.end(), i) != featuresUsed.end()) {
for (auto feat : featureSelection) {
if (find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
continue;
}
found = true;
feature = i;
featuresUsed.push_back(feature);
n_models++;
feature = feat;
break;
}
if (!found) {
@ -53,7 +55,9 @@ namespace bayesnet {
continue;
}
}
featuresUsed.insert(feature);
model = std::make_unique<SPODE>(feature);
n_models++;
model->fit(dataset, features, className, states, weights_);
auto ypred = model->predict(X_);
// Step 3.1: Compute the classifier amout of say
@ -68,15 +72,12 @@ namespace bayesnet {
double totalWeights = torch::sum(weights_).item<double>();
weights_ = weights_ / totalWeights;
// Step 3.4: Store classifier and its accuracy to weigh its future vote
if (!repeatSparent) {
models[feature] = std::move(model);
significanceModels[feature] = significance;
} else {
models.push_back(std::move(model));
significanceModels.push_back(significance);
n_models++;
}
exitCondition = n_models == max_models;
models.push_back(std::move(model));
significanceModels.push_back(significance);
exitCondition = n_models == maxModels;
}
if (featuresUsed.size() != features.size()) {
cout << "Warning: BoostAODE did not use all the features" << endl;
}
weights.copy_(weights_);
}

4
src/BayesNet/BoostAODE.h
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@ -13,7 +13,9 @@ namespace bayesnet {
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights) override;
private:
bool repeatSparent;
bool repeatSparent=false;
int maxModels=0;
bool ascending=false; //Process KBest features ascending or descending order
};
}
#endif