def multi_classification():
digits = load_digits()
x = digits.data
y = digits.target
sample_parameter = {
'n_jobs': -1,
'min_samples_leaf': 2.0,
'n_estimators': 500,
'max_features': 0.55,
'criterion': 'mse',
'min_samples_split': 4.0,
'model': 'RFCLF',
'max_depth': 4.0
}
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)
clf_layer = mlc.layer.layer.ClassificationLayer()
print "single prediction"
y_train_predict,y_test_predict = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict
y_train_predict_proba,y_test_predict_proba = clf_layer.predict_proba(x_train,y_train,x_test,sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test,np.argmax(y_test_predict_proba,axis=1),'accuracy')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.ClassificationMultiBaggingLayer()
y_train_predict_proba,y_test_predict_proba = multi_bagging_clf.predict_proba(x_train,y_train,x_test,sample_parameter,times=5)
print evaluate_function(y_test,np.argmax(y_test_predict_proba,axis=1),'accuracy')
def bagging_regression():
digits = load_diabetes()
x = digits.data
y = digits.target
sample_parameter = {
'n_jobs': -1,
'min_samples_leaf': 2.0,
'n_estimators': 500,
'max_features': 0.55,
'criterion': 'mse',
'min_samples_split': 4.0,
'model': 'RFREG',
'max_depth': 4.0
}
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)
clf_layer = mlc.layer.layer.RegressionLayer()
print "single prediction"
#y_train_predict,y_test_predict = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict
y_train_predict_proba,y_test_predict_proba = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test,y_test_predict_proba,'mean_squared_error')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.RegressionBaggingLayer()
y_train_predict_proba,y_test_predict_proba = multi_bagging_clf.predict(x_train,y_train,x_test,sample_parameter,times=5)
print evaluate_function(y_test,y_test_predict_proba,'mean_squared_error')
def binary_classification():
digits = load_digits(n_class=2)
x = digits.data
y = digits.target
sample_parameter = {
"colsample_bytree": 0.9,
"min_child_weight": 10,
"num_round": 300,
"subsample": 0.7,
"eta": 0.2,
"max_depth": 4,
"gamma": 0.6000000000000001,
"model": "XGBREGLOGISTIC",
"objective": "binary:logistic"
}
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)
clf_layer = mlc.layer.layer.ClassificationBinaryLayer()
print "single prediction"
#y_train_predict,y_test_predict = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict
y_train_predict_proba,y_test_predict_proba = clf_layer.predict_proba(x_train,y_train,x_test,sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test,y_test_predict_proba,'auc')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.ClassificationBinaryBaggingLayer()
y_train_predict_proba,y_test_predict_proba = multi_bagging_clf.predict_proba(x_train,y_train,x_test,sample_parameter,times=5)
print evaluate_function(y_test,y_test_predict_proba,'auc')
def multi_classification():
digits = load_digits()
x = digits.data
y = digits.target
sample_parameter = {
'n_jobs': -1,
'min_samples_leaf': 2.0,
'n_estimators': 500,
'max_features': 0.55,
'criterion': 'mse',
'min_samples_split': 4.0,
'model': 'RFCLF',
'max_depth': 4.0
}
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
clf_layer = mlc.layer.layer.ClassificationLayer()
print "single prediction"
y_train_predict, y_test_predict = clf_layer.predict(
x_train, y_train, x_test, sample_parameter)
#print y_test_predict
y_train_predict_proba, y_test_predict_proba = clf_layer.predict_proba(
x_train, y_train, x_test, sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test, np.argmax(y_test_predict_proba, axis=1),
'accuracy')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.ClassificationMultiBaggingLayer()
y_train_predict_proba, y_test_predict_proba = multi_bagging_clf.predict_proba(
x_train, y_train, x_test, sample_parameter, times=5)
print evaluate_function(y_test, np.argmax(y_test_predict_proba, axis=1),
'accuracy')
def binary_classification():
digits = load_digits(n_class=2)
x = digits.data
y = digits.target
sample_parameter = {
"colsample_bytree": 0.9,
"min_child_weight": 10,
"num_round": 300,
"subsample": 0.7,
"eta": 0.2,
"max_depth": 4,
"gamma": 0.6000000000000001,
"model": "XGBREGLOGISTIC",
"objective": "binary:logistic"
}
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
clf_layer = mlc.layer.layer.ClassificationBinaryLayer()
print "single prediction"
#y_train_predict,y_test_predict = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict
y_train_predict_proba, y_test_predict_proba = clf_layer.predict_proba(
x_train, y_train, x_test, sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test, y_test_predict_proba, 'auc')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.ClassificationBinaryBaggingLayer()
y_train_predict_proba, y_test_predict_proba = multi_bagging_clf.predict_proba(
x_train, y_train, x_test, sample_parameter, times=5)
print evaluate_function(y_test, y_test_predict_proba, 'auc')
def bagging_regression():
digits = load_diabetes()
x = digits.data
y = digits.target
sample_parameter = {
'n_jobs': -1,
'min_samples_leaf': 2.0,
'n_estimators': 500,
'max_features': 0.55,
'criterion': 'mse',
'min_samples_split': 4.0,
'model': 'RFREG',
'max_depth': 4.0
}
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
clf_layer = mlc.layer.layer.RegressionLayer()
print "single prediction"
#y_train_predict,y_test_predict = clf_layer.predict(x_train,y_train,x_test,sample_parameter)
#print y_test_predict
y_train_predict_proba, y_test_predict_proba = clf_layer.predict(
x_train, y_train, x_test, sample_parameter)
#print y_test_predict_proba
print evaluate_function(y_test, y_test_predict_proba, 'mean_squared_error')
print "multi ensamble prediction"
multi_bagging_clf = mlc.layer.layer.RegressionBaggingLayer()
y_train_predict_proba, y_test_predict_proba = multi_bagging_clf.predict(
x_train, y_train, x_test, sample_parameter, times=5)
print evaluate_function(y_test, y_test_predict_proba, 'mean_squared_error')
