rmontanana/BayesNet
rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests Packages Projects Releases 3 Wiki Activity

Refactor Hyperparameters management

Browse Source
This commit is contained in:
Ricardo Montañana Gómez 2023-11-19 22:36:27 +01:00
parent 89c4613591
commit 4f3a04058f
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
21 changed files with 1070 additions and 78 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
.vscode/launch.json vendored
View File

@ -21,7 +21,7 @@
{
"type": "lldb",
"request": "launch",
"name": "experiment",
"name": "experimentPy",
"program": "${workspaceFolder}/build_debug/src/Platform/b_main",
"args": [
"-m",
@ -35,6 +35,23 @@
],
"cwd": "/home/rmontanana/Code/discretizbench",
},
{
"type": "lldb",
"request": "launch",
"name": "experimentBayes",
"program": "${workspaceFolder}/build_debug/src/Platform/b_main",
"args": [
"-m",
"TAN",
"--stratified",
"--discretize",
"-d",
"iris",
"--hyperparameters",
"{\"repeatSparent\": true, \"maxModels\": 12}"
],
"cwd": "/home/rmontanana/Code/discretizbench",
},
{
"type": "lldb",
"request": "launch",

162
grid_stree.json Normal file
View File

@ -0,0 +1,162 @@
{
"balance-scale": {
"C": 10000.0,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000
},
"balloons": {
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000
},
"breast-cancer-wisc-diag": {
"C": 0.2,
"max_iter": 10000
},
"breast-cancer-wisc-prog": {
"C": 0.2,
"max_iter": 10000
},
"breast-cancer-wisc": {},
"breast-cancer": {},
"cardiotocography-10clases": {},
"cardiotocography-3clases": {},
"conn-bench-sonar-mines-rocks": {},
"cylinder-bands": {},
"dermatology": {
"C": 55,
"max_iter": 10000
},
"echocardiogram": {
"C": 7,
"gamma": 0.1,
"kernel": "poly",
"max_features": "auto",
"max_iter": 10000
},
"fertility": {
"C": 0.05,
"max_features": "auto",
"max_iter": 10000
},
"haberman-survival": {},
"heart-hungarian": {
"C": 0.05,
"max_iter": 10000
},
"hepatitis": {
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000
},
"ilpd-indian-liver": {},
"ionosphere": {
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000
},
"iris": {},
"led-display": {},
"libras": {
"C": 0.08,
"max_iter": 10000
},
"low-res-spect": {
"C": 0.05,
"max_iter": 10000
},
"lymphography": {
"C": 0.05,
"max_iter": 10000
},
"mammographic": {},
"molec-biol-promoter": {
"C": 0.05,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000
},
"musk-1": {
"C": 0.05,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000
},
"oocytes_merluccius_nucleus_4d": {
"C": 8.25,
"gamma": 0.1,
"kernel": "poly"
},
"oocytes_merluccius_states_2f": {},
"oocytes_trisopterus_nucleus_2f": {},
"oocytes_trisopterus_states_5b": {
"C": 0.11,
"max_iter": 10000
},
"parkinsons": {},
"pima": {},
"pittsburg-bridges-MATERIAL": {
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000
},
"pittsburg-bridges-REL-L": {},
"pittsburg-bridges-SPAN": {
"C": 0.05,
"max_iter": 10000
},
"pittsburg-bridges-T-OR-D": {},
"planning": {
"C": 7,
"gamma": 10.0,
"kernel": "rbf",
"max_iter": 10000
},
"post-operative": {
"C": 55,
"degree": 5,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000
},
"seeds": {
"C": 10000.0,
"max_iter": 10000
},
"statlog-australian-credit": {
"C": 0.05,
"max_features": "auto",
"max_iter": 10000
},
"statlog-german-credit": {},
"statlog-heart": {},
"statlog-image": {
"C": 7,
"max_iter": 10000
},
"statlog-vehicle": {},
"synthetic-control": {
"C": 0.55,
"max_iter": 10000
},
"tic-tac-toe": {
"C": 0.2,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000
},
"vertebral-column-2clases": {},
"wine": {
"C": 0.55,
"max_iter": 10000
},
"zoo": {
"C": 0.1,
"max_iter": 10000
}
}

6
src/BayesNet/BaseClassifier.h
View File

@ -6,8 +6,6 @@
namespace bayesnet {
enum status_t { NORMAL, WARNING, ERROR };
class BaseClassifier {
protected:
virtual void trainModel(const torch::Tensor& weights) = 0;
public:
// X is nxm std::vector, y is nx1 std::vector
virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
@ -30,6 +28,10 @@ namespace bayesnet {
std::vector<std::string> virtual topological_order() = 0;
void virtual dump_cpt()const = 0;
virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
protected:
virtual void trainModel(const torch::Tensor& weights) = 0;
std::vector<std::string> validHyperparameters;
};
}
#endif

9
src/BayesNet/BoostAODE.cc
View File

@ -10,7 +10,11 @@
#include "IWSS.h"
namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble() {}
BoostAODE::BoostAODE() : Ensemble()
{
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
}
void BoostAODE::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
@ -45,9 +49,6 @@ namespace bayesnet {
}
void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
checkHyperparameters(validKeys, hyperparameters);
if (hyperparameters.contains("repeatSparent")) {
repeatSparent = hyperparameters["repeatSparent"];
}

12
src/BayesNet/Classifier.cc
View File

@ -153,18 +153,8 @@ namespace bayesnet {
{
model.dump_cpt();
}
void Classifier::checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters)
{
for (const auto& item : hyperparameters.items()) {
if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid");
}
}
}
void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid, default is no hyperparameters
const std::vector<std::string> validKeys = { };
checkHyperparameters(validKeys, hyperparameters);
//For classifiers that don't have hyperparameters
}
}

3
src/BayesNet/Classifier.h
View File

@ -22,7 +22,6 @@ namespace bayesnet {
void checkFitParameters();
virtual void buildModel(const torch::Tensor& weights) = 0;
void trainModel(const torch::Tensor& weights) override;
void checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters);
void buildDataset(torch::Tensor& y);
public:
Classifier(Network model);
@ -44,7 +43,7 @@ namespace bayesnet {
std::vector<std::string> show() const override;
std::vector<std::string> topological_order() override;
void dump_cpt() const override;
void setHyperparameters(const nlohmann::json& hyperparameters) override;
void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
};
}
#endif

9
src/BayesNet/KDB.cc
View File

@ -1,12 +1,13 @@
#include "KDB.h"
namespace bayesnet {
KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
{
validHyperparameters = { "k", "theta" };
}
void KDB::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "k", "theta" };
checkHyperparameters(validKeys, hyperparameters);
if (hyperparameters.contains("k")) {
k = hyperparameters["k"];
}

6
src/Platform/Experiment.cc
View File

@ -170,9 +170,9 @@ namespace platform {
for (int nfold = 0; nfold < nfolds; nfold++) {
auto clf = Models::instance()->create(model);
setModelVersion(clf->getVersion());
if (hyperparameters.notEmpty(fileName)) {
clf->setHyperparameters(hyperparameters.get(fileName));
}
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, fileName);
clf->setHyperparameters(hyperparameters.get(fileName));
// Split train - test dataset
train_timer.start();
auto [train, test] = fold->getFold(nfold);

20
src/Platform/HyperParameters.cc
View File

@ -1,5 +1,6 @@
#include "HyperParameters.h"
#include <fstream>
#include <iostream>
namespace platform {
HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_)
@ -21,13 +22,24 @@ namespace platform {
// Check if hyperparameters are valid
for (const auto& dataset : datasets) {
if (!input_hyperparameters.contains(dataset)) {
throw std::runtime_error("Dataset " + dataset + " not found in hyperparameters file");
std::cerr << "*Warning: Dataset " << dataset << " not found in hyperparameters file" << " assuming default hyperparameters" << std::endl;
hyperparameters[dataset] = json({});
continue;
}
hyperparameters[dataset] = input_hyperparameters[dataset];
hyperparameters[dataset] = input_hyperparameters[dataset].get<json>();
}
}
json HyperParameters::get(const std::string& key)
void HyperParameters::check(const std::vector<std::string>& valid, const std::string& fileName)
{
return hyperparameters.at(key);
json result = hyperparameters.at(fileName);
for (const auto& item : result.items()) {
if (find(valid.begin(), valid.end(), item.key()) == valid.end()) {
throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid. Passed Hyperparameters are: " + result.dump(4));
}
}
}
json HyperParameters::get(const std::string& fileName)
{
return hyperparameters.at(fileName);
}
} /* namespace platform */

5
src/Platform/HyperParameters.h
View File

@ -13,8 +13,9 @@ namespace platform {
explicit HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_);
explicit HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file);
~HyperParameters() = default;
bool notEmpty(const std::string& key) const { return hyperparameters.at(key) != json(); }
json get(const std::string& key);
bool notEmpty(const std::string& key) const { return !hyperparameters.at(key).empty(); }
void check(const std::vector<std::string>& valid, const std::string& fileName);
json get(const std::string& fileName);
private:
std::map<std::string, json> hyperparameters;
};

11
src/PyClassifiers/ODTE.cc
View File

@ -1,15 +1,12 @@
#include "ODTE.h"
namespace pywrap {
ODTE::ODTE() : PyClassifier("odte", "Odte")
{
validHyperparameters = { "n_jobs", "n_estimators", "random_state" };
}
std::string ODTE::graph()
{
return callMethodString("graph");
}
void ODTE::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "n_jobs", "n_estimators", "random_state" };
checkHyperparameters(validKeys, hyperparameters);
this->hyperparameters = hyperparameters;
}
} /* namespace pywrap */

3
src/PyClassifiers/ODTE.h
View File

@ -6,10 +6,9 @@
namespace pywrap {
class ODTE : public PyClassifier {
public:
ODTE() : PyClassifier("odte", "Odte") {};
ODTE();
~ODTE() = default;
std::string graph();
void setHyperparameters(const nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* ODTE_H */

11
src/PyClassifiers/PyClassifier.cc
View File

@ -83,17 +83,6 @@ namespace pywrap {
}
void PyClassifier::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid, default is no hyperparameters
const std::vector<std::string> validKeys = { };
checkHyperparameters(validKeys, hyperparameters);
this->hyperparameters = hyperparameters;
}
void PyClassifier::checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters)
{
for (const auto& item : hyperparameters.items()) {
if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid");
}
}
}
} /* namespace pywrap */

1
src/PyClassifiers/PyClassifier.h
View File

@ -40,7 +40,6 @@ namespace pywrap {
void dump_cpt() const override {};
void setHyperparameters(const nlohmann::json& hyperparameters) override;
protected:
void checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters);
nlohmann::json hyperparameters;
void trainModel(const torch::Tensor& weights) override {};
private:

7
src/PyClassifiers/RandomForest.cc
View File

@ -1,11 +1,8 @@
#include "RandomForest.h"
namespace pywrap {
void RandomForest::setHyperparameters(const nlohmann::json& hyperparameters)
RandomForest::RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier", true)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "n_estimators", "n_jobs", "random_state" };
checkHyperparameters(validKeys, hyperparameters);
this->hyperparameters = hyperparameters;
validHyperparameters = { "n_estimators", "n_jobs", "random_state" };
}
} /* namespace pywrap */

3
src/PyClassifiers/RandomForest.h
View File

@ -5,9 +5,8 @@
namespace pywrap {
class RandomForest : public PyClassifier {
public:
RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier", true) {};
RandomForest();
~RandomForest() = default;
void setHyperparameters(const nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* RANDOMFOREST_H */

11
src/PyClassifiers/STree.cc
View File

@ -1,15 +1,12 @@
#include "STree.h"
namespace pywrap {
STree::STree() : PyClassifier("stree", "Stree")
{
validHyperparameters = { "C", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy", "gamma", "max_features", "degree" };
};
std::string STree::graph()
{
return callMethodString("graph");
}
void STree::setHyperparameters(const nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "C", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy" };
checkHyperparameters(validKeys, hyperparameters);
this->hyperparameters = hyperparameters;
}
} /* namespace pywrap */

3
src/PyClassifiers/STree.h
View File

@ -6,10 +6,9 @@
namespace pywrap {
class STree : public PyClassifier {
public:
STree() : PyClassifier("stree", "Stree") {};
STree();
~STree() = default;
std::string graph();
void setHyperparameters(const nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* STREE_H */

7
src/PyClassifiers/SVC.cc
View File

@ -1,11 +1,8 @@
#include "SVC.h"
namespace pywrap {
void SVC::setHyperparameters(const nlohmann::json& hyperparameters)
SVC::SVC() : PyClassifier("sklearn.svm", "SVC", true)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "C", "gamma", "kernel", "random_state" };
checkHyperparameters(validKeys, hyperparameters);
this->hyperparameters = hyperparameters;
validHyperparameters = { "C", "gamma", "kernel", "random_state" };
}
} /* namespace pywrap */

5
src/PyClassifiers/SVC.h
View File

@ -5,10 +5,9 @@
namespace pywrap {
class SVC : public PyClassifier {
public:
SVC() : PyClassifier("sklearn.svm", "SVC", true) {};
SVC();
~SVC() = default;
void setHyperparameters(const nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* STREE_H */
#endif /* SVC_H */

835
stree_results.json Normal file
View File

@ -0,0 +1,835 @@
[
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "balance-scale",
"accuracy": "0.97056",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 10000.0, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0135214",
"time_spent_std": "0.00111213",
"accuracy_std": "0.0150468",
"nodes": "7.0",
"leaves": "4.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "balloons",
"accuracy": "0.86",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.000804768",
"time_spent_std": "7.74797e-05",
"accuracy_std": "0.285015",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc-diag",
"accuracy": "0.972764",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
"time_spent": "0.00380772",
"time_spent_std": "0.000638676",
"accuracy_std": "0.0173132",
"nodes": "3.24",
"leaves": "2.12",
"depth": "2.12"
},
{
"date": "2021-04-11",
"time": "18:46:30",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc-prog",
"accuracy": "0.811128",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
"time_spent": "0.00767535",
"time_spent_std": "0.00148114",
"accuracy_std": "0.0584601",
"nodes": "5.84",
"leaves": "3.42",
"depth": "3.24"
},
{
"date": "2021-04-11",
"time": "18:46:31",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc",
"accuracy": "0.966661",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00652217",
"time_spent_std": "0.000726579",
"accuracy_std": "0.0139421",
"nodes": "8.88",
"leaves": "4.94",
"depth": "4.08"
},
{
"date": "2021-04-11",
"time": "18:46:32",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer",
"accuracy": "0.734211",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.023475",
"time_spent_std": "0.00584447",
"accuracy_std": "0.0479774",
"nodes": "21.72",
"leaves": "11.36",
"depth": "5.86"
},
{
"date": "2021-04-11",
"time": "18:49:08",
"type": "crossval",
"classifier": "stree",
"dataset": "cardiotocography-10clases",
"accuracy": "0.791487",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "3.10582",
"time_spent_std": "0.339218",
"accuracy_std": "0.0192082",
"nodes": "160.76",
"leaves": "80.88",
"depth": "22.86"
},
{
"date": "2021-04-11",
"time": "18:50:01",
"type": "crossval",
"classifier": "stree",
"dataset": "cardiotocography-3clases",
"accuracy": "0.900613",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "1.05228",
"time_spent_std": "0.138768",
"accuracy_std": "0.0154004",
"nodes": "47.68",
"leaves": "24.34",
"depth": "8.84"
},
{
"date": "2021-04-11",
"time": "18:50:01",
"type": "crossval",
"classifier": "stree",
"dataset": "conn-bench-sonar-mines-rocks",
"accuracy": "0.755528",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.011577",
"time_spent_std": "0.00341148",
"accuracy_std": "0.0678424",
"nodes": "6.08",
"leaves": "3.54",
"depth": "2.86"
},
{
"date": "2021-04-11",
"time": "18:50:17",
"type": "crossval",
"classifier": "stree",
"dataset": "cylinder-bands",
"accuracy": "0.715049",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.301143",
"time_spent_std": "0.109773",
"accuracy_std": "0.0367646",
"nodes": "26.2",
"leaves": "13.6",
"depth": "6.82"
},
{
"date": "2021-04-11",
"time": "18:50:19",
"type": "crossval",
"classifier": "stree",
"dataset": "dermatology",
"accuracy": "0.971833",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 55, \"max_iter\": 10000.0}",
"time_spent": "0.0377538",
"time_spent_std": "0.010726",
"accuracy_std": "0.0206883",
"nodes": "11.0",
"leaves": "6.0",
"depth": "6.0"
},
{
"date": "2021-04-11",
"time": "18:50:19",
"type": "crossval",
"classifier": "stree",
"dataset": "echocardiogram",
"accuracy": "0.814758",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00333449",
"time_spent_std": "0.000964686",
"accuracy_std": "0.0998078",
"nodes": "7.0",
"leaves": "4.0",
"depth": "3.54"
},
{
"date": "2021-04-11",
"time": "18:50:20",
"type": "crossval",
"classifier": "stree",
"dataset": "fertility",
"accuracy": "0.88",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00090271",
"time_spent_std": "8.96446e-05",
"accuracy_std": "0.0547723",
"nodes": "1.0",
"leaves": "1.0",
"depth": "1.0"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "haberman-survival",
"accuracy": "0.735637",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0171611",
"time_spent_std": "0.00334945",
"accuracy_std": "0.0434614",
"nodes": "23.4",
"leaves": "12.2",
"depth": "5.98"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "heart-hungarian",
"accuracy": "0.827522",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00493946",
"time_spent_std": "0.000738198",
"accuracy_std": "0.0505283",
"nodes": "10.16",
"leaves": "5.58",
"depth": "4.0"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "hepatitis",
"accuracy": "0.824516",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0021534",
"time_spent_std": "0.000133715",
"accuracy_std": "0.0738872",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:50:23",
"type": "crossval",
"classifier": "stree",
"dataset": "ilpd-indian-liver",
"accuracy": "0.723498",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0345243",
"time_spent_std": "0.015789",
"accuracy_std": "0.0384886",
"nodes": "16.04",
"leaves": "8.52",
"depth": "5.28"
},
{
"date": "2021-04-11",
"time": "18:50:24",
"type": "crossval",
"classifier": "stree",
"dataset": "ionosphere",
"accuracy": "0.953276",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.00881722",
"time_spent_std": "0.000843108",
"accuracy_std": "0.0238537",
"nodes": "3.16",
"leaves": "2.08",
"depth": "2.08"
},
{
"date": "2021-04-11",
"time": "18:50:24",
"type": "crossval",
"classifier": "stree",
"dataset": "iris",
"accuracy": "0.965333",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00357342",
"time_spent_std": "0.000400509",
"accuracy_std": "0.0319444",
"nodes": "5.0",
"leaves": "3.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:50:36",
"type": "crossval",
"classifier": "stree",
"dataset": "led-display",
"accuracy": "0.703",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.222106",
"time_spent_std": "0.0116922",
"accuracy_std": "0.0291204",
"nodes": "47.16",
"leaves": "24.08",
"depth": "17.76"
},
{
"date": "2021-04-11",
"time": "18:51:18",
"type": "crossval",
"classifier": "stree",
"dataset": "libras",
"accuracy": "0.788611",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.08, \"max_iter\": 10000.0}",
"time_spent": "0.841714",
"time_spent_std": "0.0830966",
"accuracy_std": "0.0516913",
"nodes": "82.28",
"leaves": "41.64",
"depth": "28.84"
},
{
"date": "2021-04-11",
"time": "18:51:41",
"type": "crossval",
"classifier": "stree",
"dataset": "low-res-spect",
"accuracy": "0.883782",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.446301",
"time_spent_std": "0.0411822",
"accuracy_std": "0.0324593",
"nodes": "27.4",
"leaves": "14.2",
"depth": "10.74"
},
{
"date": "2021-04-11",
"time": "18:51:41",
"type": "crossval",
"classifier": "stree",
"dataset": "lymphography",
"accuracy": "0.835034",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00539465",
"time_spent_std": "0.000754365",
"accuracy_std": "0.0590649",
"nodes": "9.04",
"leaves": "5.02",
"depth": "4.48"
},
{
"date": "2021-04-11",
"time": "18:51:43",
"type": "crossval",
"classifier": "stree",
"dataset": "mammographic",
"accuracy": "0.81915",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0227931",
"time_spent_std": "0.00328533",
"accuracy_std": "0.0222517",
"nodes": "7.4",
"leaves": "4.2",
"depth": "4.0"
},
{
"date": "2021-04-11",
"time": "18:51:43",
"type": "crossval",
"classifier": "stree",
"dataset": "molec-biol-promoter",
"accuracy": "0.767056",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.00130273",
"time_spent_std": "0.000105772",
"accuracy_std": "0.0910923",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:51:44",
"type": "crossval",
"classifier": "stree",
"dataset": "musk-1",
"accuracy": "0.916388",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0116367",
"time_spent_std": "0.000331845",
"accuracy_std": "0.0275208",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:51:55",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_merluccius_nucleus_4d",
"accuracy": "0.835125",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 8.25, \"gamma\": 0.1, \"kernel\": \"poly\"}",
"time_spent": "0.208895",
"time_spent_std": "0.0270573",
"accuracy_std": "0.0220961",
"nodes": "10.52",
"leaves": "5.76",
"depth": "4.42"
},
{
"date": "2021-04-11",
"time": "18:52:04",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_merluccius_states_2f",
"accuracy": "0.915365",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.182198",
"time_spent_std": "0.0294267",
"accuracy_std": "0.020396",
"nodes": "18.04",
"leaves": "9.52",
"depth": "5.3"
},
{
"date": "2021-04-11",
"time": "18:52:41",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_trisopterus_nucleus_2f",
"accuracy": "0.800986",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.717113",
"time_spent_std": "0.209608",
"accuracy_std": "0.0218449",
"nodes": "29.88",
"leaves": "15.44",
"depth": "7.38"
},
{
"date": "2021-04-11",
"time": "18:52:44",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_trisopterus_states_5b",
"accuracy": "0.922249",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.11, \"max_iter\": 10000.0}",
"time_spent": "0.0545047",
"time_spent_std": "0.00853014",
"accuracy_std": "0.0179203",
"nodes": "7.44",
"leaves": "4.22",
"depth": "3.6"
},
{
"date": "2021-04-11",
"time": "18:52:44",
"type": "crossval",
"classifier": "stree",
"dataset": "parkinsons",
"accuracy": "0.882051",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00795048",
"time_spent_std": "0.00176761",
"accuracy_std": "0.0478327",
"nodes": "8.48",
"leaves": "4.74",
"depth": "3.76"
},
{
"date": "2021-04-11",
"time": "18:52:48",
"type": "crossval",
"classifier": "stree",
"dataset": "pima",
"accuracy": "0.766651",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0750048",
"time_spent_std": "0.0213995",
"accuracy_std": "0.0297203",
"nodes": "17.4",
"leaves": "9.2",
"depth": "5.66"
},
{
"date": "2021-04-11",
"time": "18:52:48",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-MATERIAL",
"accuracy": "0.867749",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.00293318",
"time_spent_std": "0.000331469",
"accuracy_std": "0.0712226",
"nodes": "5.16",
"leaves": "3.08",
"depth": "3.02"
},
{
"date": "2021-04-11",
"time": "18:52:49",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-REL-L",
"accuracy": "0.632238",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0136311",
"time_spent_std": "0.00322964",
"accuracy_std": "0.101211",
"nodes": "16.32",
"leaves": "8.66",
"depth": "5.96"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-SPAN",
"accuracy": "0.659766",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00524256",
"time_spent_std": "0.00158822",
"accuracy_std": "0.1165",
"nodes": "9.84",
"leaves": "5.42",
"depth": "4.58"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-T-OR-D",
"accuracy": "0.861619",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00295627",
"time_spent_std": "0.000578594",
"accuracy_std": "0.0693747",
"nodes": "4.56",
"leaves": "2.78",
"depth": "2.68"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "planning",
"accuracy": "0.73527",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 10.0, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0030475",
"time_spent_std": "0.000172266",
"accuracy_std": "0.0669776",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:52:51",
"type": "crossval",
"classifier": "stree",
"dataset": "post-operative",
"accuracy": "0.711111",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 55, \"degree\": 5, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0018727",
"time_spent_std": "0.000481977",
"accuracy_std": "0.0753592",
"nodes": "2.64",
"leaves": "1.82",
"depth": "1.82"
},
{
"date": "2021-04-11",
"time": "18:52:52",
"type": "crossval",
"classifier": "stree",
"dataset": "seeds",
"accuracy": "0.952857",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 10000.0, \"max_iter\": 10000.0}",
"time_spent": "0.0203492",
"time_spent_std": "0.00518065",
"accuracy_std": "0.0279658",
"nodes": "9.88",
"leaves": "5.44",
"depth": "4.44"
},
{
"date": "2021-04-11",
"time": "18:52:52",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-australian-credit",
"accuracy": "0.678261",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00205337",
"time_spent_std": "0.00083162",
"accuracy_std": "0.0390498",
"nodes": "1.32",
"leaves": "1.16",
"depth": "1.16"
},
{
"date": "2021-04-11",
"time": "18:53:07",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-german-credit",
"accuracy": "0.7625",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.290754",
"time_spent_std": "0.0653152",
"accuracy_std": "0.0271892",
"nodes": "21.24",
"leaves": "11.12",
"depth": "6.18"
},
{
"date": "2021-04-11",
"time": "18:53:09",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-heart",
"accuracy": "0.822963",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0138923",
"time_spent_std": "0.00323664",
"accuracy_std": "0.044004",
"nodes": "14.56",
"leaves": "7.78",
"depth": "5.0"
},
{
"date": "2021-04-11",
"time": "18:56:43",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-image",
"accuracy": "0.955931",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"max_iter\": 10000.0}",
"time_spent": "4.27584",
"time_spent_std": "0.200362",
"accuracy_std": "0.00956073",
"nodes": "36.92",
"leaves": "18.96",
"depth": "10.8"
},
{
"date": "2021-04-11",
"time": "18:56:57",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-vehicle",
"accuracy": "0.793028",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.278833",
"time_spent_std": "0.0392173",
"accuracy_std": "0.030104",
"nodes": "23.88",
"leaves": "12.44",
"depth": "7.06"
},
{
"date": "2021-04-11",
"time": "18:57:07",
"type": "crossval",
"classifier": "stree",
"dataset": "synthetic-control",
"accuracy": "0.95",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.55, \"max_iter\": 10000.0}",
"time_spent": "0.205184",
"time_spent_std": "0.040793",
"accuracy_std": "0.0253859",
"nodes": "12.48",
"leaves": "6.74",
"depth": "6.5"
},
{
"date": "2021-04-11",
"time": "18:57:08",
"type": "crossval",
"classifier": "stree",
"dataset": "tic-tac-toe",
"accuracy": "0.984444",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0123015",
"time_spent_std": "0.000423728",
"accuracy_std": "0.00838747",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:57:09",
"type": "crossval",
"classifier": "stree",
"dataset": "vertebral-column-2clases",
"accuracy": "0.852903",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00576833",
"time_spent_std": "0.000910332",
"accuracy_std": "0.0408851",
"nodes": "6.04",
"leaves": "3.52",
"depth": "3.34"
},
{
"date": "2021-04-11",
"time": "18:57:09",
"type": "crossval",
"classifier": "stree",
"dataset": "wine",
"accuracy": "0.979159",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.55, \"max_iter\": 10000.0}",
"time_spent": "0.0019741",
"time_spent_std": "0.000137745",
"accuracy_std": "0.022427",
"nodes": "5.0",
"leaves": "3.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:57:10",
"type": "crossval",
"classifier": "stree",
"dataset": "zoo",
"accuracy": "0.957524",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.1, \"max_iter\": 10000.0}",
"time_spent": "0.00556221",
"time_spent_std": "0.000230106",
"accuracy_std": "0.0454615",
"nodes": "13.04",
"leaves": "7.02",
"depth": "7.02"
}
]