import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.svm import SVC

#1. 데이터

x, y = load_iris(return_X_y=True)

x_train, x_test, y_train, y_test = train_test_split(
    x,y, train_size=0.8, shuffle=True, random_state=337
)

# scaler = MinMaxScaler()
# x_train = scaler.fit_transform(x_train)
# x_test = scaler.transform(x_test)

#2. 모델
# model = RandomForestClassifier()
# model = make_pipeline(MinMaxScaler(), RandomForestClassifier())#앞부분에 내가 사용할 스케일링, 이후에 모델 기재하면 됨
# model = make_pipeline(StandardScaler(), RandomForestClassifier())
# model = make_pipeline(StandardScaler(), SVC())
model = Pipeline([("std",StandardScaler()), ('svc',SVC())])   
#파이프라인은 리스트 형태로 입력해야함
#파이프라인은 튜플 형식, 이름 지정까지 함께 입력해야한다.

#3. 훈련
model.fit(x_train, y_train)

#4. 평가, 예측
result = model.score(x_test, y_test)
print('model.score :', result)
y_predict = model.predict(x_test)
print('ACC :', accuracy_score(y_test, y_predict))

m19_Pipeline2_01_classifier

m19_Pipeline2_02_regressor