rmontanana/BayesNet
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Add Convergence hyperparameter

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This commit is contained in:
Ricardo Montañana Gómez 2023-09-07 11:27:35 +02:00
parent d908f389f5
commit 506369e46b
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 36 additions and 13 deletions
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6
.vscode/launch.json vendored
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@ -25,9 +25,9 @@
"program": "${workspaceFolder}/build/src/Platform/main",
"args": [
"-m",
"AODE",
"BoostAODE",
"-p",
"/home/rmontanana/Code/discretizbench/datasets",
"/Users/rmontanana/Code/discretizbench/datasets",
"--stratified",
"-d",
"mfeat-morphological",
@ -35,7 +35,7 @@
// "--hyperparameters",
// "{\"repeatSparent\": true, \"maxModels\": 12}"
],
"cwd": "/home/rmontanana/Code/discretizbench",
"cwd": "/Users/rmontanana/Code/discretizbench",
},
{
"type": "lldb",

40
src/BayesNet/BoostAODE.cc
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@ -3,6 +3,7 @@
#include "BayesMetrics.h"
#include "Colors.h"
#include "Folding.h"
#include <limits.h>
namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble() {}
@ -13,7 +14,7 @@ namespace bayesnet {
void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending" };
const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence" };
checkHyperparameters(validKeys, hyperparameters);
if (hyperparameters.contains("repeatSparent")) {
repeatSparent = hyperparameters["repeatSparent"];
@ -24,27 +25,30 @@ namespace bayesnet {
if (hyperparameters.contains("ascending")) {
ascending = hyperparameters["ascending"];
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
}
}
void BoostAODE::validationInit()
{
auto y_ = dataset.index({ -1, "..." });
auto fold = platform::StratifiedKFold(5, y_, 271);
// save input dataset
dataset_ = torch::clone(dataset);
// save input dataset
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({ "...", train_t });
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
y_train = dataset.index({ -1, train_t });
X_test = dataset.index({ "...", test_t });
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
y_test = dataset.index({ -1, test_t });
// Build dataset with train data
dataset = X_train;
buildDataset(y_train);
m = X_train.size(1);
auto n_classes = states.at(className).size();
metrics = Metrics(dataset, features, className, n_classes);
// Build dataset with train data
buildDataset(y_train);
}
void BoostAODE::trainModel(const torch::Tensor& weights)
{
@ -56,9 +60,18 @@ namespace bayesnet {
Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
bool exitCondition = false;
unordered_set<int> featuresUsed;
// Variables to control the accuracy finish condition
double priorAccuracy = 0.0;
double delta = 1.0;
double threshold = 1e-4;
int tolerance = convergence ? 5 : INT_MAX; // number of times the accuracy can be lower than the threshold
int count = 0; // number of times the accuracy is lower than the threshold
fitted = true; // to enable predict
// Step 0: Set the finish condition
// if not repeatSparent a finish condition is run out of features
// n_models == maxModels
// epsiolon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
while (!exitCondition) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
@ -81,7 +94,6 @@ namespace bayesnet {
}
featuresUsed.insert(feature);
model = std::make_unique<SPODE>(feature);
n_models++;
model->fit(dataset, features, className, states, weights_);
auto ypred = model->predict(X_train);
// Step 3.1: Compute the classifier amout of say
@ -102,12 +114,22 @@ namespace bayesnet {
// Step 3.4: Store classifier and its accuracy to weigh its future vote
models.push_back(std::move(model));
significanceModels.push_back(alpha_t);
exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5;
n_models++;
auto y_val_predict = predict(X_test);
double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
if (priorAccuracy == 0) {
priorAccuracy = accuracy;
} else {
delta = accuracy - priorAccuracy;
}
if (delta < threshold) {
count++;
}
exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
}
if (featuresUsed.size() != features.size()) {
status = WARNING;
}
weights.copy_(weights_);
}
vector<string> BoostAODE::graph(const string& title) const
{

1
src/BayesNet/BoostAODE.h
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@ -19,6 +19,7 @@ namespace bayesnet {
bool repeatSparent = false;
int maxModels = 0;
bool ascending = false; //Process KBest features ascending or descending order
bool convergence = false; //if true, stop when the model does not improve
};
}
#endif

2
src/BayesNet/Ensemble.cc
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@ -24,7 +24,7 @@ namespace bayesnet {
// i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
vector<double> votes(numClasses, 0.0);
for (int j = 0; j < n_models; ++j) {
votes[y_pred_[i][j]] += significanceModels[j];
votes[y_pred_[i][j]] += significanceModels.at(j);
}
// argsort in descending order
auto indices = argsort(votes);