部分贴合和热启动有什么区别? [英] What is the difference between partial fit and warm start?

问题描述

上下文:

我正在使用scikit库中的Passive Aggressor，并且困惑是使用热启动还是部分拟合.

I am using Passive Aggressor from scikit library and confused whether to use warm start or partial fit.

迄今为止的努力:

1. 引用了该线程讨论:

https://github.com/scikit-learn/scikit-learn/第/1585期

1. 彻底了解了 _fit 和 _partial_fit 的scikit代码.

1. Gone through the scikit code for _fit and _partial_fit.

我的观察:

1. _fit 依次调用 _partial_fit .

设置 warm_start 时， _fit 用 _partial_fit 调用 self.coef _

When warm_start is set, _fit calls _partial_fit with self.coef_

在没有 coef_init 参数和 self的情况下调用 _partial_fit 时.设置了coef _ ，它将继续使用 self.coef _

When _partial_fit is called without coef_init parameter and self.coef_ is set, it continues to use self.coef_

问题:

我觉得两者最终都提供了相同的功能.那么，它们之间的基本区别是什么?在哪些情况下使用了它们中的任何一个?

I feel both are ultimately providing the same functionalities.Then, what is the basic difference between them? In which contexts, either of them are used?

我缺少明显的东西吗?任何帮助表示赞赏！

Am I missing something evident? Any help is appreciated!

推荐答案

我不知道被动攻击者，但至少在使用

I don't know about the Passive Aggressor, but at least when using the SGDRegressor, partial_fit will only fit for 1 epoch, whereas fit will fit for multiple epochs (until the loss converges or max_iter is reached). Therefore, when fitting new data to your model, partial_fit will only correct the model one step towards the new data, but with fit and warm_start it will act as if you would combine your old data and your new data together and fit the model once until convergence.

示例:

from sklearn.linear_model import SGDRegressor
import numpy as np

np.random.seed(0)
X = np.linspace(-1, 1, num=50).reshape(-1, 1)
Y = (X * 1.5 + 2).reshape(50,)

modelFit = SGDRegressor(learning_rate="adaptive", eta0=0.01, random_state=0, verbose=1,
                     shuffle=True, max_iter=2000, tol=1e-3, warm_start=True)
modelPartialFit = SGDRegressor(learning_rate="adaptive", eta0=0.01, random_state=0, verbose=1,
                     shuffle=True, max_iter=2000, tol=1e-3, warm_start=False)
# first fit some data
modelFit.fit(X, Y)
modelPartialFit.fit(X, Y)
# for both: Convergence after 50 epochs, Norm: 1.46, NNZs: 1, Bias: 2.000027, T: 2500, Avg. loss: 0.000237
print(modelFit.coef_, modelPartialFit.coef_) # for both: [1.46303288]

# now fit new data (zeros)
newX = X
newY = 0 * Y

# fits only for 1 epoch, Norm: 1.23, NNZs: 1, Bias: 1.208630, T: 50, Avg. loss: 1.595492:
modelPartialFit.partial_fit(newX, newY)

# Convergence after 49 epochs, Norm: 0.04, NNZs: 1, Bias: 0.000077, T: 2450, Avg. loss: 0.000313:
modelFit.fit(newX, newY)

print(modelFit.coef_, modelPartialFit.coef_) # [0.04245779] vs. [1.22919864]
newX = np.reshape([2], (-1, 1))
print(modelFit.predict(newX), modelPartialFit.predict(newX)) # [0.08499296] vs. [3.66702685]

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