spark.mllib源代码阅读-优化算法1-Gradient
Spark中定义的损失函数及梯度,在看源代码之前,先回想一下机器学习中定义了哪些损失函数,毕竟梯度求解是为优化求解损失函数服务的。
监督学习问题是在如果空间F中选取模型f作为决策函数。对于给定的输入X,由f(X)给出对应的输出Y,这个输出的预測值f(X)与真实值Y可能一致也可能不一致,用一个损失函数(lossfunction)或代价函数(cost function)来度量预測错误的程度。损失函数是f(X)和Y的非负实值函数,记作L(Y, f(X)).
统计学习中经常使用的损失函数有下面几种:
(1) 0-1损失函数(0-1 loss function):
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" 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(2) 平方损失函数(quadraticloss function)
aaarticlea/png;base64,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" alt="" />
(3) 绝对损失函数(absolute lossfunction)
aaarticlea/png;base64,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" alt="" />
(4) 对数损失函数(logarithmicloss function) 或对数似然损失函数(log-likelihood loss function)
aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAASYAAAAmCAYAAABpszZuAAANQ0lEQVR4Ae1ceWxNzxefVltUSWurUmqLfS8tVVpKrLEEsSZiiZJKaCgpFSQi0YhdYg0tf9i1qH1fa18SFCH2rYqi1Dq/fE6+5+b2ee3zVvfnzSSv996ZMzOf+cyZc88stx5SSilUUAwoBhQDBmLA00BYFBTFgGJAMUAMKMOkFEExoBgwHAPKMBmuSxQgxYBiQBkmpQOKAcWA4RhQhslwXaIAKQYUA8owKR1QDCgGDMeAMkyG6xIFSDGgGFCGSemAYkAxYDgGlGEyXJcoQIoBxcBfMUy/fv0i5tPS0kRcXJyhe8HRWL99+yYGDBjgsDb//PlTDBkyRKxfv57KZLxcgaV0llNX6xlgrh2lx67WDetb7MIc+CTF1vDz50/Kunz5clm5cmXp5+eHz1tk+fLlZZUqVeSUKVMoneXw8OvXL4q7c+eOjIiIkE+fPqXnPXv2yKpVq8oyZcrIgIAAWa5cOdm9e3dK+/HjB11TUlJkUFCQLF68ONU1cuRIirf0h+vfsWOHbN68uWzUqJGsWbOmbNCggQwNDZUtW7ak+7CwMLl7926tOGdg/fLlC9WpVSKlZHzHjx+X1atXp/aXLl2aeIyJiZFfv36V79+/J9w+Pj7EcYUKFWRsbCwV8+TJE8p37NgxembcXIeldJYz4vXBgweyb9++pCvQMUcH5t5ddcPRfDqqPOGIgjBwcnJyZO3atWWJEiXkvXv3aCAh3jTwoOnYsaOEoeHw+fNn+fLlS9mnTx8aeHFxcTIvL0+yUcIVeWE8+vXrJy9cuCBzc3M5+x9dP378KJ8/f04DHAb0xIkT9IyBe/PmTTlq1Cjp6ekpN2zYQOU5A2t+fr5s06aNWbwwWu/evZPLli0jDqKioiTkv3//TvLgq2TJknLNmjXy4cOH8tOnT5pR27ZtGxlzcMK4kYkHXmHpZoEYKBKcnDlzRuPDWdDcUTecxaUjyrXbMPEgePHiBQ3qyMjIQnHxIGHPgI0NMnA5GRkZpITNmjXTyuGBOX/+fNm7d28t3pYbDOZSpUqRETXND8MIgwUD6yysGGjh4eGmVRd4hkGuWLGihHcEzxLh9u3bhAvcmQbGWrduXTlt2jTTZK0thaX/lsEgEdwueE3FihWT8+bNcyoyd9QNpxJqR+F2Gyb2aNLT02lQz5gxg4wMGxM9Npbt378/eSdI4zjcwzjhjR8YGEhlXbp0STNY+/btk40bNyYvAXKstPryi7pn+aNHj1LZPA1E/fzbv38/DQBMpxiXo7HC6LRo0aJQqFxvfHw84Zw7d6588+aNDA4Ollu3bqV8375903hBBOdZtGiRxBTP1FO1lF4omL+cwLjZg4SX7IzgzrrhDD4dUabDDNOkSZNoIB0+fJhwcWebgsTUBN7Apk2bfpPjPDAaHh4eMikpiWTgNdSqVUveunWLntm7Mi27qGdW8jlz5hDOtWvX0oDGIMZAR4BRhceUkJBAz87Aircy1rkKC8zBtWvXyAOF9waDvGLFCspizuAzH3fv3iX8R44cKVC8pfQCwgZ6YC4wdcfaI7xNBG4PDDHWCbEWiZfJrFmzbELvzrphE2EuyGS3YWKM8AKwKP327VuO0q6sYIiAW451HHNGhhVk165dNMBQJtZcMJDT0tKoPJbRCv/DG1bmLl26SG9vb5mdnV0gJ69jtGvXjrwyJDoDqyWPCfUCK34YcDCUkydPJqzmjJK+EVinw8YBG1Z9Gu4tpZvKG+EZ/e3v7y979uxJcFiXxowZQ97huXPnKP78+fPEVWpqqgabZeElY/rcrVs3iaWG6dOnS7xIOSjdYCaMc3WIYXr16hVNgdq2bUst444210xMl2DAsCaFYE4W0znsyvn6+kqsiyQnJ5OspYFprj593IcPH6hcDPZBgwbJoUOHyoEDB0osxGMKhAV3LIJycAZWSx4T6uZ28kYABiECDzTGp78yj02aNJEwvqbBUrqpvFGes7KyyOAsWbJEg7Rq1SqKO336NMVx27Brx+t3HIddVi8vL7l+/XqSxYYH+h+elj64s27oeTDKvZc9JxNwjsPT01NcvnxZ4LxM+/btqTjEFytWTLs/f/68aNOmDT2/ePFCFC9eXPj7+9Ozh4cHXfkP8pYpU0b06tVLrFu3TkRERIiEhAQq38vLNriM88aNG+Lt27eiY8eOIiYmRiAe9QMP4oKDgxkGXf8G1h8/fgi0E21+/fq1CAoKEqmpqWLWrFl0j384asoZwHJ8iRIlxLt37wq040/S9Rk+f/4s4uPjRU5ODvVvYf/kFDjAoZ+fn3aOSl+OI+5PnTpFxUAPED59+iQSExNFkyZNRNu2bUkvoGv5+fkCuD9+/EhywJadnU1nxqBLw4cPp/imTZtSf3fu3JmelW4QDYb7Y9tI/68ZrLAnTpygmKioKLqywuJ68eJFMX36dHH06FFKgwLBmPn4+PxXivnL9+/fKWHmzJnaoDMvaTmWcZ49e5aER44cKYYOHfpbRlZSTnA1Vhh3GKXk5GTi68KFCyI2NlasXbtWbNmyRUyYMIEMARt9xokruEYArzAopsFSul7e19dXrFy5Uh9l1/3GjRvFggULqG1oo7kAnYBRjoyMFEuXLtVEDh06JICnUaNGFHf69Glq3+jRo+kZfQY+Hj16JD58+CDCwsK0vGvWrCGDNXbsWOINdRw7dkx8/fpVdOrUieSUbmh0GerGLsOEjkbHHj58mJQDbyMExEEBMUhwIrl///5ao6FESIPhgadiGjCAoKAnT54UlStXJi8BcTywTOX/5JnzQimBDUoODHpDhLbgpw+uwAo8wIGft7e3SElJIc4yMzOJUxhQGCZ4j+PHjyd4yMNtYrwcB+4wkE2DpXRTeUc+Dxs2TOBnbQBm6EG7du1IV+DtwotFv7ABQj+CNxgscKiv58iRIyTbokULzfODDqCf2YNnHt1ZN6ztF1fI22WY0KmYNly/fl2EhoaKSpUqEWYoDn4YXBkZGWLu3LlaWypUqEBvLLzdcM8DBgJ8//TpU/H48WNSMhg3vQHRCrLiBooI7wceU5UqVUTjxo1JOYGxqOAKrOCQ+Tpw4IBYuHAhGXpMZ8FHhw4daDp77tw5cenSJREeHm4WMg+wvLw8rR/0gpbS9bJGmcpBr16+fEkeJF5+O3bsEJiCwRiVK1eOIGPqigAdw4tx0KBBWlOgY9WqVRMBAQGabh08eJDkOL/SDY0uQ93YbJh4Pr9161bycBo2bChevXpFSoM3G6ZuSUlJom/fvqQY3Or69euTt/T+/XsyTByPKwYi3vj79++n6FatWlF5ehlz95s3b6Y1kWnTpmleBctBieG6Hz9+XOTm5pILj2dMmfCmLSo4A6tpfffv3yeDc+XKFRqAEydOJF6AG8bk5s2bomzZssQN2gdOa9asKUJCQgoUBVkY32fPnmnTFL2ApXS9rKOncvqyrbmHPiGgvatXrybPOzo6mtoOzxJLB1hTwnQXL6/09PQCfQqvCN4me8KLFi0SWVlZAjwiKN0gGoz5x9ZV+O3bt9OOGQ7+VapUSdaoUUPWq1dP1q9fX4aEhNCOGr6Z4++3uB6cRcGOG75NQtBv/+/cuVO2atWKvrvDpy34ng1nWLDNXVTA9i/sGjCYhr179xJObKPjmALqxk4fdmcQitrpcgZW0125lStXSuxm4jOVrl270g87UQjYWZowYQLtGuLoQOfOnWnHbcuWLQWw8w4UPgUCDwcPHqR0/mMpneWMdkW/45hAnTp1ZGJiogbv6tWrsmHDhrJ9+/bE2+DBg+kQKgS4rejX169fy+joaNp5HTduHH3OhPNx2G1FULqhUWq4G7yJXRbYCGAAYmseQW+YbAUCBd68eXORJ6qtLdtZWE0Nk7W4zMkzh/iYumzZstpBRJa1lM5y/+oVJ8ZhtKdOnUovJ3Nn7axp+7+kG9a025WyBVd7nezUYaqGgF0STNfwzG62rVWjjJIlS9JaTGBgoK3F/JbPGVh/q8RBEbx+hF2oESNGCF534eItpbPcv3TFehzWnbDmhiWB2rVrC0z5ccyA15xsbe+/pBu2cuD0fK60gqgLrjZ+OM2N6aC9gd9e+Bcgixcvtre4AvkdjRWF/8nJ7wIgLDxw+w8dOkSnvuEN8HQGWS2lWyj+/zYZn6hgWov/woBPi3DSG1N5/lyHebG1gf+Cbtjadlfkc+lUDg3iQZOZmSlbt25t9b8uMUfKlStXaC2Kv3kzJ2NLnDOwYiqn/88JtuAyzYOPfLEOw/9LynTQWUo3Le9feJ49ezatdfbo0YPWlnAinI0S96s97eQyHKnHf0M37OHAmXldbpjQGB44+DCV15qc2Uh7ynY0VqyHFfWvYazFivUjfFqzcOFCysp4uRxL6SynrtYzwFw7So9drRvWt9h1OTxQldPni6oCxYBiQDFgBQMuXfy2ApcSVQwoBtyYAWWY3LjzVdMVA0ZlQBkmo/aMwqUYcGMGlGFy485XTVcMGJUBZZiM2jMKl2LAjRlQhsmNO181XTFgVAaUYTJqzyhcigE3ZkAZJjfufNV0xYBRGfgf6zq3IP+EKpgAAAAASUVORK5CYII=" alt="" />
(5)间隔损失函数(hinge loss)
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9w2U9ofU++/9o07ajScJuF06ZQ7I4wWhEaTIsmtCdTXh+zjjRpUTCFrviRfo5NqZLbQ9qcExTK46ZoRxOXBNHSkHk0Y87PdPHecvLmx4RO9ENaVlHdDdNYftvi9I9o35hGZNHAn366mU3G5hUREUEqlaqoYZSRU2krHsdm4t7lv+DQMwABYhbqu8cRmZC/eWMgaxuA4TYd0UQmNlgJI5bBpf8UTA47ic+XBSGq23p0q/OydxADrVrDy9QZpuMmoLeLsbc1rpuMI0tX4JiFH9aO8CwlzDRuUxlpAqtPwY0zOrTw94c7p8fDuGRk9m8OJikOye/4wt9fBD17FvGpWnjXfIT4Mxze8/dHG06P+5dvQzm4PRwb+mNp6FNkBB/EychwkMwCzYYFYbtPcyzceQUZyXG4nd4KOHcGD2v1waD6UqQmZUHvbo3M26eR2TQA/k0JOu1pXEr0Q4CnI2Qm9eAz4n0sCTyC/4TfR99R/JJm9IhEcP9kKzY6u2DGhhOICCdAXhdjQpZjQR+HgoqMSAYza2uYSe1gayvK3ZvoM5EYG4lDN3PAimugZ0AAxPz30eORSBAp4GrzAfxaOxas0gWdVXJCquiIaaGzcPX7cNw9fAT3LXwxZ/cQtNDFYsDIkagDd7RQlBT7lNe2ACbpELYeyUT9kRPRr4Fpof2Zj49PxbQqg5ivvVh/OoQC2thSwxFhFPWQF5dDiSfDKS4lV3Tm5fn0gdsw2nFDVcjTvBjYXxQVNoKavNOOpv98t+QV8UWDf0xKrzlBQV5OhOaf0pH7FVRbn0jB7eXk4rmAIrPUJaweFey7ipuxmid0Izqc4lJzBZdtW3y9+3RsogdZuo2h7QnaSrOvSr4kXUEkY4JpUJ/uNPKbTbRh2zqa3NKafJYl0JMrURQ8wJO8Qo7Scz5+Mn5YDaVdPEjb1s+iYT59aODSk5UGirGY/99pjnQxQdTfw5ZcA9fRUYNjK79GnDaHrod/TP06+FPI8VLC/PJ3Wa011enH6KvWNtQtJL502yoYZQodWx9Ibu+0pinbbpPuJRMsqFaBxJtBPMPA9XR0UV/6cNCnNNBFSiK3gTS5kwu5dllAh1XF/NxAl0V/rhlPvXp9S1HGQXcFQHjbm7DRi6lvjy405Ost9EtEHKVpy3fHns1Op4igIRQao660W/nVibVWeYI+by4nNO5fum2xWnr8ZwTt3DqHhnbtQX4Lj5fwS42Ka/MGES9PCS6Rtk3qSV06eZGXzwTafqcY0lVc339wSw1FfONLHwaG0eVnxf/g520HR5d9g7ZOLIdt6bIobtUY8vWdRxGv6SeGlfYL9IrtMIVWAgL/TARKut/xz0RD0FpAoIoQEIhXRUALYgQEjBEQiGeMhpAWEKgiBATiVRHQghgBAWMEBOIZoyGkBQSqCAGBeFUEtCBGQMAYAYF4xmgIaQGBKkJAIF4VAS2IERAwRkAgnjEaQlpAoIoQ+D8Frqz7SXZDCwAAAABJRU5ErkJggg==" alt="" />
在不考虑过拟合的情况下。损失函数越小,模型就越好。
Spark中定义梯度和损失函数求解的类包含一个Gradient基类及其三个实现类:
Gradient
梯度计算的抽象类,定义了计算梯度值和损失函数值的compute函数:
def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = {
val gradient = Vectors.zeros(weights.size)
val loss = compute(data, label, weights, gradient)
(gradient, loss)
}
后面的梯度计算类都继承子Gradient类并实现compute函数。
LeastSquaresGradient
实现了最小二乘法进行线性回归的梯度计算方法。
其对compute函数进行的覆写
override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = {
val diff = dot(data, weights) - label
val loss = diff * diff / 2.0
val gradient = data.copy
scal(diff, gradient)//常数乘以向量 更新后的gradient即为梯度 gradient=(y - lable)* x
(gradient, loss)
}
使用场景:
1、 參数预计的方法是最小化误差的平方和,其他预计方法不适合用此梯度算子。
2、 Spark实现的是线性回归的梯度计算。非线性回归的梯度计算不适合使用此算子。
HingeGradient
实现了最大化分类间距的hinge loss进行參数预计的梯度下降方法,对compute函数进行的覆写:
class HingeGradient extends Gradient {
override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = {
val dotProduct = dot(data, weights)
// Our loss function with {0, 1} labels is max(0, 1 - (2y - 1) (f_w(x)))
// Therefore the gradient is -(2y - 1)*x
val labelScaled = 2 * label - 1.0
if (1.0 > labelScaled * dotProduct) {
val gradient = data.copy
scal(-labelScaled, gradient)
(gradient, 1.0 - labelScaled * dotProduct)
} else {
(Vectors.sparse(weights.size, Array.empty, Array.empty), 0.0)
}
}
使用场景:
适用于利用最大化分类间隔思想来构建分类器,典型的使用如SVM。
LogisticGradient
使用对数似然损失函数对Logistic分类/回归进行參数预计的梯度下降方法。实现的代码比較长,在此就不贴了,在内部分了2分类和多分类两种情况进行计算。
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