Question

我正在尝试为随机森林和Logistic回归训练和运行多分类器。截至目前，在我的机器上（具有8GB RAM和i5内核），尽管数据大小几乎不超过34K记录，但仍需要花费一些时间来运行。有什么方法可以通过调整一些参数来加快当前的现有运行时间？

我仅在下面举例说明Logistic回归随机搜索。

X.shape
Out[9]: (34857, 18)

Y.shape
Out[10]: (34857,)

Y.unique()
Out[11]: array([7, 3, 8, 6, 1, 5, 9, 2, 4], dtype=int64)

params_logreg={'C':[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],
            'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
            'penalty':['l2'],
            'max_iter':[100,200,300,400,500],
            'multi_class':['multinomial']}
folds = 2
n_iter = 2
scoring= 'accuracy'
n_jobs= 1

model_logregression=LogisticRegression()
model_logregression = RandomizedSearchCV(model_logregression,X,Y,params_logreg,folds,n_iter,scoring,n_jobs)

[CV] solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9 
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV]  solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9, score=0.5663798049340218, total= 2.7min
[CV] solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9 
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.7min remaining:    0.0s

[CV]  solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9, score=0.5663625408848338, total= 4.2min
[CV] solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8 
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  7.0min remaining:    0.0s

[CV]  solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8, score=0.5663798049340218, total=  33.9s
[CV] solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8 
[CV]  solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8, score=0.5664773053308085, total=  26.6s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  8.0min finished```


It's taking about 8 mins to run for Logistic Regression. In contrast RandomForestClassifier takes only about 52 seconds.

Is there any way in which I can make this run faster by tweaking the parameters?

Answer 1

尝试标准化逻辑回归模型的数据。归一化的数据将有助于模型快速收敛。 Scikit-learn有几种方法可用于此目的，因此请检查其预处理部分以获取更多信息。

另外，您正在使用RandomizedSearchCV进行回归，因为创建和计算了多个模型并进行比较以获取最佳参数，这需要时间。

如何使多类别分类的运行时间更快？

1 个答案: