Xtrain,Xtest, ytrain,ytest = dataset.split_train_test(fill_nan=True)
weight_train,weight_test = dataset.get_weight_train_test()
eval_ = Evaluation()
all_result_df = pd.DataFrame(columns=['models','params','AMS','Accuracy','Precision','Recall']) # Notre tableau de résultat
optimal_parameters = pd.DataFrame(columns=['models','params','AMS']) # Notre tableau résultats optimaux
#Random forest
score_opt_ams = 0
for bootstrap in [True,False]:
for max_depth in range(3,10):
for n_estimators in [10, 30, 50, 100]:
rf = RandomForestClassifier(n_estimators=n_estimators,bootstrap=bootstrap,max_depth=max_depth)
rf.fit(Xtrain,ytrain)
ypred = rf.predict(Xtest)
score_ams = eval_.AMS(ytest,ypred,weights=weight_test)
score_accuracy = eval_.accuracy(ytest,ypred)
score_precision = eval_.precision(ytest,ypred)
score_recall = eval_.rappel(ytest,ypred)
l = {'models':'Random Forest','params':str(rf.get_params()),\
'AMS':score_ams,'Accuracy':score_accuracy,'Precision':score_precision,'Recall':score_recall}
all_result_df=all_result_df.append(l,ignore_index=True)
if score_ams > score_opt_ams:
optim_param = str(rf.get_params())
score_opt_ams = score_ams
l = {'models':'Random Forest','params':optim_param,'AMS':score_opt_ams}
optimal_parameters=optimal_parameters.append(l,ignore_index = True)
optimal_parameters.to_csv("optimal_parameters.csv",index=False)
all_result_df.to_csv("all_result.csv",index=False)
if __name__ == "__main__":
dataset = Dataset(drop_weight=True)
higgs_df = dataset.get_df()
Xtrain,Xtest, ytrain,ytest = dataset.split_train_test(fill_nan=True)
weight_train,weight_test = dataset.get_weight_train_test()
eval_ = Evaluation()
#Random forest
score_min_rf = 3.0
for bootstrap in [True,False]:
for max_depth in range(3,10):
for n_estimators in [10, 30, 50, 100, 200, 400, 600, 800, 1000]:
rf = RandomForestClassifier(n_estimators=n_estimators,bootstrap=bootstrap,max_depth=max_depth)
rf.fit(Xtrain,ytrain)
ypred = rf.predict(Xtest)
score = eval_.AMS(ytest,ypred,weights=weight_test)
if score >= score_min_rf:
print(score)
bootstrap_opt = bootstrap
max_depth_opt = max_depth
n_estimators_opt = n_estimators
score_min_rf = score
#Bagging
score_min_bagg = 3.0
for bootstrap in [True,False]:
for n_estimators in range(10,100,10):
bagging = BaggingClassifier(n_estimators=n_estimators,bootstrap=bootstrap)
bagging.fit(Xtrain,ytrain)
ypred = bagging.predict(Xtest)
score = eval_.AMS(ytest,ypred,weights=weight_test)
