1 sklearn模型的儲存和載入API

• from sklearn.externals import joblib
  • 儲存：joblib.dump(estimator, 'test.pkl')
  • 載入：estimator = joblib.load('test.pkl')

2 線性迴歸的模型儲存載入案例

from sklearn.datasets import load_boston
from sklearn.model_selection import  train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import  LinearRegression,SGDRegressor,RidgeCV,Ridge
from sklearn.metrics import mean_squared_error
#from sklearn.externals import joblib
import joblib


def dump_load() :
    """
    模型儲存和載入
    :return:
    """
    # 1.獲取資料
    boston = load_boston()
    # 2.資料處理
    # 2.1 分割資料
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target,random_state=22,test_size=0.2)
    # 3.特徵工程-資料標準化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    # # 4.機器學習-線性迴歸(梯度下降)
    # # 4.1 模型訓練
    # estimator = Ridge(alpha=1.0)
    # estimator.fit(x_train, y_train)
    #
    # # 4.2 模型儲存
    # joblib.dump(estimator,"./data/test1.pkl")

     # # 4.3 模型載入
    estimator=joblib.load("./data/test1.pkl")
    # 5.模型評估
    y_predict = estimator.predict(x_test)
    print("預測值為:\n", y_predict)
    print("模型中的係數為:\n", estimator.coef_)
    print("模型中的偏置為:\n", estimator.intercept_)
    # 評價指標 均方誤差
    error = mean_squared_error(y_test, y_predict)
    print("均方誤差：\n", error)

    return None
#呼叫函式
if __name__=='__main__':
  dump_load()
 

5.4 結果

直接呼叫模型和原本模型中的結果是一樣的

注:保證結果一致，需要引數一樣，同時執行時需要運行當前的程式碼