Commit 8379effb authored by Stéphane NGNEPIEPAYE WEMBE's avatar Stéphane NGNEPIEPAYE WEMBE
Browse files

[EVALUATION OF OUR CODE] : Now we have the confusion matrix of each approch...

[EVALUATION OF OUR CODE] : Now we have the confusion matrix of each approch and we can compare with SKLearn confusion matrix
parent cdb487fa
This diff is collapsed.
...@@ -4,6 +4,37 @@ ...@@ -4,6 +4,37 @@
#include <cmath> #include <cmath>
#include <fstream> #include <fstream>
int M = 10;
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
void get_confusion_matrix_in_csv_file(std::ifstream &file, std::vector<std::vector<int>> &confusion_matrix_sklearn)
{
CSVRow line;
for (int i = 0; i < M; ++i) {
file >> line;
for (int j = 0; j < M; ++j) {
confusion_matrix_sklearn[i][j] = (std::stoi(line[j]));
}
}
}
void print_confusion_matrix(std::vector<std::vector<int>> &confusion_matrix)
{
std::cout<<"["<<std::endl;
for (int i = 0; i < M; ++i) {
std::cout<<"[ ";
for (int j = 0; j < M; ++j) {
std::cout<<confusion_matrix[i][j]<<" ";
}
std::cout<<"]"<<std::endl;
}
std::cout<<"]"<<std::endl;
}
double relu(double x) double relu(double x)
{ {
...@@ -11,7 +42,6 @@ double relu(double x) ...@@ -11,7 +42,6 @@ double relu(double x)
else return 0; else return 0;
} }
double softmax(double* tab, int size, int indice) double softmax(double* tab, int size, int indice)
{ {
double S = 0; double S = 0;
...@@ -21,8 +51,7 @@ double softmax(double* tab, int size, int indice) ...@@ -21,8 +51,7 @@ double softmax(double* tab, int size, int indice)
return exp(tab[indice])/S; return exp(tab[indice])/S;
} }
int ann_predict(std::vector<real> feature){
double ann_predict(std::vector<real> feature){
double layer1_node[N0]; double layer1_node[N0];
double layer2_node[N1]; double layer2_node[N1];
double output_layer_node[N2]; double output_layer_node[N2];
...@@ -49,7 +78,7 @@ double ann_predict(std::vector<real> feature){ ...@@ -49,7 +78,7 @@ double ann_predict(std::vector<real> feature){
} }
layer2_node[i]+= B1[i]; layer2_node[i]+= B1[i];
layer2_node[i] = relu(layer2_node[i]); layer2_node[i] = relu(layer2_node[i]);
// std::cout << layer1_node[i] << std::endl; // std::cout << layer2_node[i] << std::endl;
} }
// output layer // output layer
for(int i = 0; i<N2; ++i) for(int i = 0; i<N2; ++i)
...@@ -66,8 +95,8 @@ double ann_predict(std::vector<real> feature){ ...@@ -66,8 +95,8 @@ double ann_predict(std::vector<real> feature){
double predict = 0; double predict = 0;
double best_predict = predict; double best_predict = predict;
int best_index = 0; int best_index = 0;
predict = softmax(output_layer_node,N2, 0);
for (int i = 0; i < N2; ++i) { for (int i = 1; i < N2; ++i) {
predict = softmax(output_layer_node,N2, i); predict = softmax(output_layer_node,N2, i);
// std::cout << predict<< std::endl; // std::cout << predict<< std::endl;
if (predict>best_predict) if (predict>best_predict)
...@@ -80,12 +109,6 @@ double ann_predict(std::vector<real> feature){ ...@@ -80,12 +109,6 @@ double ann_predict(std::vector<real> feature){
return best_index; return best_index;
} }
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value) double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value)
{ {
double valid = 0; double valid = 0;
...@@ -93,43 +116,59 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu ...@@ -93,43 +116,59 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu
else else
{ {
for (int i = 0; i < truth_value.size(); ++i) { for (int i = 0; i < truth_value.size(); ++i) {
// std::cout << predicted_value[i] << " " << truth_value[i] << std::endl;
if (predicted_value[i] == truth_value[i]) valid++; if (predicted_value[i] == truth_value[i]) valid++;
} }
// std::cout << truth_value.size() << " " << predicted_value.size() << std::endl;
// std::cout << "valid number is " << valid << std::endl;
return valid/double (truth_value.size()); return valid/double (truth_value.size());
} }
} }
void build_confusion_matrix(std::vector<int>& truth_value, std::vector<int>& predicted_value, std::vector<std::vector<int>> &confusion_matrix)
{
for (int i = 0; i < truth_value.size(); ++i) {
confusion_matrix[predicted_value[i]][truth_value[i]]++;
}
}
int main(){ int main(){
std::ifstream file("../../DATASETS/path_test_files_ANN.csv"); std::ifstream file("../../DATASETS/path_test_files_ANN.csv");
CSVRow files_name_row; CSVRow files_name_row;
CSVRow style_name_row; CSVRow style_name_row;
CSVRow accuracy_SKLearn; CSVRow accuracy_SKLearn;
std::vector<std::vector<int>> confusion_matrix_sklearn(M, std::vector<int>(M, 0));
std::vector<std::vector<int>> our_confusion_matrix(M, std::vector<int>(M, 0));
file >> files_name_row; file >> files_name_row;
file >> style_name_row; file >> style_name_row;
file >> accuracy_SKLearn; file >> accuracy_SKLearn;
get_confusion_matrix_in_csv_file(file,confusion_matrix_sklearn);
file.close(); file.close();
std::vector<int> style_test_set(style_name_row.size()); std::vector<int> style_test_set(style_name_row.size());
std::vector<int> style_predict; std::vector<int> style_predict;
transform_to_index(style_name_row,style_test_set); transform_to_index(style_name_row,style_test_set);
// std::cout << "size of test set: " << files_name_row.size() << std::endl;
// std::cout << "size of test set : " << style_name_row.size() << std::endl;
for (int i = 0; i < files_name_row.size(); ++i) { for (int i = 0; i < files_name_row.size(); ++i) {
if (!files_name_row[i].empty()) { if (!files_name_row[i].empty()) {
// std::cout <<style_test_set[i]<< std::endl; // std::cout <<style_test_set[i]<< std::endl;
std::filesystem::path fileName = files_name_row[i]; std::filesystem::path fileName = files_name_row[i];
auto bins = compute_features_for(fileName); auto bins = compute_features_for(fileName);
auto featuring = bins.begin()->second; std::vector<real> featuring;
auto featuring_avg = bins[FTYPE::BINAVG];
auto featuring_std = bins[FTYPE::BINSTDEV];
featuring.insert(featuring.cbegin(), featuring_avg.begin(), featuring_avg.end());
featuring.insert(featuring.cend(), featuring_std.begin(), featuring_std.end());
style_predict.push_back(ann_predict(featuring)); style_predict.push_back(ann_predict(featuring));
// std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl; // std::cout << "featuring size " << featuring.size() << std::endl;
} else style_predict.push_back(-1); } else style_predict.push_back(-1);
} }
build_confusion_matrix(style_test_set,style_predict,our_confusion_matrix);
std::cout << "The confusion matrix given by SKlearn is : " << std::endl;
print_confusion_matrix(confusion_matrix_sklearn);
std::cout << "The confusion matrix given by our code is : " << std::endl;
print_confusion_matrix(our_confusion_matrix);
std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl; std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl;
std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl; std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl;
return 0; return 0;
......
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...@@ -60,12 +60,24 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes)) ...@@ -60,12 +60,24 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes))
accuracy = (Y_pred == Y_test).mean() accuracy = (Y_pred == Y_test).mean()
print('Test Accuracy : %.3f'%accuracy) print('Test Accuracy : %.3f'%accuracy)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
M = confusion_matrix(Y_test, Y_pred, labels=classes)
with open('../../DATASETS/path_test_files_CART.csv', 'w') as f: with open('../../DATASETS/path_test_files_CART.csv', 'w') as f:
# using csv.writer method from CSV package # using csv.writer method from CSV package
write = csv.writer(f) write = csv.writer(f)
write.writerow(files_test) write.writerow(files_test)
write.writerow(Y_test) write.writerow(Y_test)
write.writerow([accuracy]) write.writerow([accuracy])
for i in range(M.shape[0]):
write.writerow(M[i])
with open('../../DATASETS/path_train_files_CART.csv', 'w') as f: with open('../../DATASETS/path_train_files_CART.csv', 'w') as f:
# using csv.writer method from CSV package # using csv.writer method from CSV package
...@@ -73,16 +85,6 @@ with open('../../DATASETS/path_train_files_CART.csv', 'w') as f: ...@@ -73,16 +85,6 @@ with open('../../DATASETS/path_train_files_CART.csv', 'w') as f:
write.writerow(files_train) write.writerow(files_train)
write.writerow(Y_train) write.writerow(Y_train)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
......
This diff is collapsed.
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
#include <iostream> #include <iostream>
#include <fstream> #include <fstream>
//using namespace std; int M = 10;
std::istream& operator>>(std::istream& str, CSVRow& data) std::istream& operator>>(std::istream& str, CSVRow& data)
{ {
...@@ -11,6 +11,30 @@ std::istream& operator>>(std::istream& str, CSVRow& data) ...@@ -11,6 +11,30 @@ std::istream& operator>>(std::istream& str, CSVRow& data)
return str; return str;
} }
void get_confusion_matrix_in_csv_file(std::ifstream &file, std::vector<std::vector<int>> &confusion_matrix_sklearn)
{
CSVRow line;
for (int i = 0; i < M; ++i) {
file >> line;
for (int j = 0; j < M; ++j) {
confusion_matrix_sklearn[i][j] = (std::stoi(line[j]));
}
}
}
void print_confusion_matrix(std::vector<std::vector<int>> &confusion_matrix)
{
std::cout<<"["<<std::endl;
for (int i = 0; i < M; ++i) {
std::cout<<"[ ";
for (int j = 0; j < M; ++j) {
std::cout<<confusion_matrix[i][j]<<" ";
}
std::cout<<"]"<<std::endl;
}
std::cout<<"]"<<std::endl;
}
double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value) double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value)
{ {
double valid = 0; double valid = 0;
...@@ -18,46 +42,58 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu ...@@ -18,46 +42,58 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu
else else
{ {
for (int i = 0; i < truth_value.size(); ++i) { for (int i = 0; i < truth_value.size(); ++i) {
// std::cout << predicted_value[i] << " " << truth_value[i] << std::endl;
if (predicted_value[i] == truth_value[i]) valid++; if (predicted_value[i] == truth_value[i]) valid++;
} }
// std::cout << truth_value.size() << " " << predicted_value.size() << std::endl;
// std::cout << "valid number is " << valid << std::endl;
return valid/double (truth_value.size()); return valid/double (truth_value.size());
} }
} }
void build_confusion_matrix(std::vector<int>& truth_value, std::vector<int>& predicted_value, std::vector<std::vector<int>> &confusion_matrix)
{
for (int i = 0; i < truth_value.size(); ++i) {
confusion_matrix[predicted_value[i]][truth_value[i]]++;
}
}
int main(){ int main(){
// for crop recommendation
// std::vector<double> feature{90,42,43,20.87974371,82.00274423,6.502985292000001,202.9355362};
// cout << "cart_predict(feature) : " << cart_predict(feature) << endl;
std::ifstream file("../../DATASETS/path_test_files_CART.csv"); std::ifstream file("../../DATASETS/path_test_files_CART.csv");
CSVRow files_name_row; CSVRow files_name_row;
CSVRow style_name_row; CSVRow style_name_row;
CSVRow accuracy_SKLearn; CSVRow accuracy_SKLearn;
std::vector<std::vector<int>> confusion_matrix_sklearn(M, std::vector<int>(M, 0));
std::vector<std::vector<int>> our_confusion_matrix(M, std::vector<int>(M, 0));
file >> files_name_row; file >> files_name_row;
file >> style_name_row; file >> style_name_row;
file >> accuracy_SKLearn; file >> accuracy_SKLearn;
get_confusion_matrix_in_csv_file(file,confusion_matrix_sklearn);
file.close(); file.close();
std::vector<int> style_test_set(style_name_row.size()); std::vector<int> style_test_set(style_name_row.size());
std::vector<int> style_predict; std::vector<int> style_predict;
transform_to_index(style_name_row,style_test_set); transform_to_index(style_name_row,style_test_set);
// std::cout << "size of test set: " << files_name_row.size() << std::endl;
// std::cout << "size of test set : " << style_name_row.size() << std::endl;
for (int i = 0; i < files_name_row.size(); ++i) { for (int i = 0; i < files_name_row.size(); ++i) {
if (!files_name_row[i].empty()) { if (!files_name_row[i].empty()) {
// std::cout <<style_test_set[i]<< std::endl; // std::cout <<style_test_set[i]<< std::endl;
std::filesystem::path fileName = files_name_row[i]; std::filesystem::path fileName = files_name_row[i];
auto bins = compute_features_for(fileName); auto bins = compute_features_for(fileName);
auto featuring = bins.begin()->second; std::vector<real> featuring;
auto featuring_avg = bins[FTYPE::BINAVG];
auto featuring_std = bins[FTYPE::BINSTDEV];
featuring.insert(featuring.cbegin(), featuring_avg.begin(), featuring_avg.end());
featuring.insert(featuring.cend(), featuring_std.begin(), featuring_std.end());
style_predict.push_back(cart_predict(featuring)); style_predict.push_back(cart_predict(featuring));
// std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl; // std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl;
} else style_predict.push_back(-1); } else style_predict.push_back(-1);
} }
build_confusion_matrix(style_test_set,style_predict,our_confusion_matrix);
std::cout << "The confusion matrix given by SKlearn is : " << std::endl;
print_confusion_matrix(confusion_matrix_sklearn);
std::cout << "The confusion matrix given by our code is : " << std::endl;
print_confusion_matrix(our_confusion_matrix);
std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl; std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl;
std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl; std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl;
return 0; return 0;
......
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0.5575757575757576 0.5575757575757576
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0,20,1,0,0,0,0,1,0,0
0,0,19,4,0,3,4,3,0,6
2,1,0,23,2,0,1,2,4,3
2,0,1,2,15,1,0,1,8,0
1,9,1,0,0,28,0,0,0,1
0,0,0,0,0,0,29,0,0,2
0,3,3,2,1,1,1,14,3,0
1,0,5,2,5,0,0,0,12,4
2,1,8,6,3,1,4,1,3,8
...@@ -46,12 +46,32 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes)) ...@@ -46,12 +46,32 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes))
accuracy = (Y_pred == Y_test).mean() accuracy = (Y_pred == Y_test).mean()
print('Test Accuracy : %.3f' % accuracy) print('Test Accuracy : %.3f' % accuracy)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
M = confusion_matrix(Y_test, Y_pred, labels=classes)
# with open('../../DATASETS/test_features_SVM.csv', 'w') as f:
# # using csv.writer method from CSV package
# write = csv.writer(f)
# for i in range(X_test.shape[0]):
# write.writerow(X_test[i])
with open('../../DATASETS/path_test_files_SVM.csv', 'w') as f: with open('../../DATASETS/path_test_files_SVM.csv', 'w') as f:
# using csv.writer method from CSV package # using csv.writer method from CSV package
write = csv.writer(f) write = csv.writer(f)
write.writerow(files_test) write.writerow(files_test)
write.writerow(Y_test) write.writerow(Y_test)
write.writerow([accuracy]) write.writerow([accuracy])
for i in range(M.shape[0]):
write.writerow(M[i])
with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f: with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f:
# using csv.writer method from CSV package # using csv.writer method from CSV package
...@@ -59,20 +79,12 @@ with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f: ...@@ -59,20 +79,12 @@ with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f:
write.writerow(files_train) write.writerow(files_train)
write.writerow(Y_train) write.writerow(Y_train)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
# linearsvc coefs # linearsvc coefs
H = clf.named_steps['linearsvc'].coef_ H = clf.named_steps['linearsvc'].coef_
b = clf.named_steps['linearsvc'].intercept_ b = clf.named_steps['linearsvc'].intercept_
mean = clf.named_steps['standardscaler'].mean_ mean = clf.named_steps['standardscaler'].mean_
var = clf.named_steps['standardscaler'].var_ var = clf.named_steps['standardscaler'].var_
print("les classes : ",clf.named_steps['linearsvc'].classes_)
print("mean : ", mean, "var : ", var) print("mean : ", mean, "var : ", var)
print("coef.shape : ", H.shape, "coefs : ", H, "b.shape : ", b.shape, "b : ", b) print("coef.shape : ", H.shape, "coefs : ", H, "b.shape : ", b.shape, "b : ", b)
...@@ -121,7 +133,7 @@ def gen_h_code(): ...@@ -121,7 +133,7 @@ def gen_h_code():