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Commit 2fe51128 authored by estellearrc's avatar estellearrc
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poop

parent d087fc1a
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RF/Python/RF_training.py
View file @ 2fe51128
......@@ -205,6 +205,8 @@ def train_RF(saveAlgo=False, path=""):
X_val, labelInd_val, cv=5)
scores_random_best_f1 = cross_val_score(model_best,
X_val, labelInd_val, cv=5, scoring="f1_micro")
predicted_probas = model_best.predict_proba(X_test)
print(predicted_probas[0, :])
# print("----------------Random Forest---------------------")
# print("Cross validation accuracy moyenne best:", scores_random_best.mean())
# print("Cross validation accuracy moyenne:", scores_random.mean())
......@@ -220,22 +222,24 @@ def train_RF(saveAlgo=False, path=""):
# print(prediction_random_best)
# print(model_best.apply(X_train))
# print(model_best.decision_path(X_train))
for e in model_best.estimators_:
# print(e.tree_)
# print(e.classes_)
# print(e.n_classes_)
# print(e.n_outputs_)
report = export_text(e)
# print(report)
# for e in model_best.estimators_:
# print(e.tree_)
# print(e.classes_)
# print(e.n_classes_)
# print(e.n_outputs_)
# report = export_text(e)
# print(report)
# help(sklearn.tree._tree.Tree)
file = path+'RF/RFTrained.h'
open(file, 'w').close()
with open(file, 'a') as f:
f.write("#ifndef RFTRAINED_H\n#define RFTRAINED_H\n#include <string>\n#include <numeric>\n#include <fstream>\n#include <map>\n#include \"../Helpers/globals.h\"\n#include <typeinfo>\n")
f.write("#ifndef RFTRAINED_H\n#define RFTRAINED_H\n#include <string>\n#include <numeric>\n#include <fstream>\n#include <map>\n#include \"../Helpers/globals.h\"\n#include <typeinfo>\n#include <vector>\n")
f.write("const int N_ESTIMATORS = "+str(model_best.n_estimators) +
";\nconst int MAX_DEPTH = "+str(model_best.max_depth)+";\n")
# f.write("int RFModel(int num_estimator, std::map < FTYPE, DataVector > &features){\n") #hard voting
# soft voting
f.write(
"int RFModel(int num_estimator, std::map < FTYPE, DataVector > &features){\n")
"void RFModel(int num_estimator, std::map < FTYPE, DataVector > &features, std::vector<double> &class_predicted_probas){\n")
f.write(
"DataVector featureVector = features[FTYPE::BINAVG];\nfeatureVector.insert(featureVector.end(), features[FTYPE::BINSTDEV].begin(), features[FTYPE::BINSTDEV].end());\n")
f.close()
......@@ -243,13 +247,14 @@ def train_RF(saveAlgo=False, path=""):
# Write in cpp header file RFTrainedcpp.h
with open(file, 'a') as f:
text = ""
text = str_tree(text, e, model_best.n_estimators, 0, 1)
text = str_tree(text, e, model_best.max_depth, 0, 1)
f.write("\nif(num_estimator == " +
str(count_estimator)+"){\n" + text + "}\n")
f.close()
count_estimator += 1
with open(file, 'a') as f:
f.write("return 0;}\n#endif")
# f.write("return 0;}\n#endif")
f.write("}\n#endif")
f.close()
print("Save algo in "+path + "/RF/RFTrained.h file!")
return model_best, history_best
......@@ -281,9 +286,13 @@ def str_tree(text, decision_tree, max_depth, node, depth):
value = None
if tree_.n_outputs == 1:
value = tree_.value[node][0]
# print(value)
# print(str(value)[1:-2].replace(".", ".,"))
else:
value = tree_.value[node].T[0]
# print(value)
class_name = np.argmax(value)
# print(class_name)
if (tree_.n_classes[0] != 1 and
tree_.n_outputs == 1):
......@@ -300,13 +309,19 @@ def str_tree(text, decision_tree, max_depth, node, depth):
"){\n" + str(left) + "}\n"
secondeLayerELSE = "else{\n"+str(right)+"}\n"
text += secondeLayerIF + secondeLayerELSE
print(text)
# print(text)
else: # leaf
text += "return " + str(int(class_name))+";\n"
# text += "return " + str(int(class_name))+";\n" # hard voting
# soft voting
text += "class_predicted_probas = {" + \
str(value)[1:-2].replace(".", ".,")+"};\n"
else:
subtree_depth = compute_depth(tree_, node)
if subtree_depth == 1:
text += "return " + str(int(class_name))+";\n"
# text += "return " + str(int(class_name))+";\n" # hard voting
# soft voting
text += "class_predicted_probas = {" + \
str(value)[1:-2].replace(".", ".,")+"};\n"
# print(text)
return text
......
RF/RFTrained.h
View file @ 2fe51128
This diff is collapsed.
RF/apply_RF.h
View file @ 2fe51128
......@@ -5,40 +5,84 @@
#include "../Helpers/evaluate_model.h"
#include <string>
#include <iostream>
#include <vector>
// #include <vector>
#include <cmath>
std::string majorityVoting(std::vector<int> &results)
std::string majorityVoting(std::vector<std::vector<double>> &results)
{
std::vector<float> probas(CLASS_N, 0.0);
for (int &r : results)
// soft voting (sum of probas) vs hard voting (majority vote)
std::vector<double> probas(CLASS_N, 0.0);
for (int j = 0; j < CLASS_N; j++)
{
probas[r] += 1.0 / N_ESTIMATORS;
for(int i = 0; i < N_ESTIMATORS; i++)
probas[j] += results[i][j] / CLASS_N;
}
float max = *max_element(probas.begin(), probas.end());
double max = *max_element(probas.begin(), probas.end());
// std::cout<<"max ="<<max<<std::endl;
std::string class_name = "";
std::cout<<"------------------------------"<<std::endl;
// std::cout<<"------------------------------"<<std::endl;
double vote = 0.0;
for (int k = 0; k < CLASS_N; k++)
{
std::cout<<probas[k]<<std::endl;
// std::cout<<probas[k]<<std::endl;
if (probas[k] == max)
class_name = genres[k];
{
// std::cout<<k<<std::endl;
class_name = genres[k];
}
}
return class_name;
}
// std::string majorityVoting(std::vector<int> &results)
// {
// // soft voting (sum of probas) vs hard voting (majority vote)
// std::vector<double> probas(CLASS_N, 0.0);
// for (auto &r : results)
// {
// probas[r] += 1.0 / N_ESTIMATORS;
// }
// double max = *max_element(probas.begin(), probas.end());
// // std::cout<<"max ="<<max<<std::endl;
// std::string class_name = "";
// std::cout<<"------------------------------"<<std::endl;
// double vote = 0.0;
// for (int k = 0; k < CLASS_N; k++)
// {
// vote += probas[k] / double(CLASS_N); //soft voting
// std::cout<<vote<<std::endl;
// // std::cout<<probas[k]<<std::endl;
// if (probas[k] == max) //hard voting
// class_name = genres[k];
// }
// vote = round(vote*100.0);
// // std::cout<<vote<<std::endl;
// int class_k = int(vote);
// class_name = genres[class_k];
// return class_name;
// }
std::string RFAlgo(std::map<FTYPE, DataVector> &features)
{
std::vector<int> results;
// std::vector<int> results;
std::vector<std::vector<double>> results;
std::vector<double> res(CLASS_N, 0.0);
for (int k = 0; k < N_ESTIMATORS; k++)
{
int res = RFModel(k, features);
// int res = RFModel(k, features, results);
RFModel(k, features, res);
double sum = std::accumulate(res.begin(), res.end(), 0.);
std::transform(res.begin(),res.end(), res.begin(), [sum](double c){ return c/sum; });
results.push_back(res);
// std::cout << res << " ";
// for(double &p : res)
// std::cout << p << " ";
// std::cout <<" "<<std::endl;
}
std::string result = majorityVoting(results);
// std::cout <<result<<std::endl;
return result;
}
......
RF/main.cpp
View file @ 2fe51128
......@@ -13,7 +13,7 @@ int main(int argc, char **argv)
std::cout << "Compute accuracy... \n";
real accuracy = compute_accuracy_CART("../../RF/file_test.csv", (vFunctionCallCART)RFAlgo, matrix);
std::cout << "Accuracy: " << accuracy << "\n";
std::cout << "Confusion matrix: \n";
print_2D_array(matrix);
// std::cout << "Confusion matrix: \n";
// print_2D_array(matrix);
return 0;
}
\ No newline at end of file
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