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  • 持久化机器学习模型(joblib方式)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.externals import joblib
    
    X_train = [[5],[6], [8], [10], [14], [18], [20], [20.1]]
    y_train = [[5],[7], [9], [13], [17.5], [18], [20], [25]]
    X_test = [[6], [8], [11], [16]]
    y_test = [[8], [12], [15], [18]]
    regressor = LinearRegression()
    regressor.fit(X_train, y_train)
    xx = np.linspace(0, 26, 100)
    #根据线性预测分析0-26的Y值
    yy = regressor.predict(xx.reshape(xx.shape[0], 1))
    #绘画X_Y关系直线
    plt.plot(xx, yy)
    plt.title('Pizza price regressed on diameter')
    plt.xlabel('Diameter in inches')
    plt.ylabel('Price in dollars')
    plt.axis([0, 25, 0, 25])
    plt.grid(True)
    plt.scatter(X_train, y_train)
    
    #持久化保存模型
    joblib.dump(value=regressor,filename="regressorModel20191023.gz",compress=True)
    print("model has saved!")
    #加载先前保存的模型
    model=joblib.load(filename="regressorModel20191023.gz")
    print("model has loaded!")
    print(type(model))
    #导入模型后再次预测分析0-26的Y值
    yy1= model.predict(xx.reshape(xx.shape[0], 1))
    #绘画X_Y关系直线
    plt.plot(xx, yy1)
    plt.show()
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  • 原文地址:https://www.cnblogs.com/starcrm/p/11726661.html
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