grid search 超参数寻优

Posted 2020-10-05

http://scikit-learn.org/stable/modules/grid_search.html

1. 超参数寻优方法 gridsearchCV 和  RandomizedSearchCV

2. 参数寻优的技巧进阶

   2.1. Specifying an objective metric

        By default, parameter search uses the score function of the estimator to evaluate a parameter setting. These are thesklearn.metrics.accuracy_score for classification and sklearn.metrics.r2_score for regression.

  2.2 Specifying multiple metrics for evaluation

      Multimetric scoring can either be specified as a list of strings of predefined scores names or a dict mapping the scorer name to the scorer function and/or the predefined scorer name(s).

       http://scikit-learn.org/stable/modules/model_evaluation.html#multimetric-scoring

  2.3 Composite estimators and parameter spaces  。pipeline 方法

        http://scikit-learn.org/stable/modules/pipeline.html#pipeline

      

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [(‘reduce_dim‘, PCA()), (‘clf‘, SVC())]
>>> pipe = Pipeline(estimators)
>>> pipe  # check pipe
         Pipeline(memory=None,
         steps=[(‘reduce_dim‘, PCA(copy=True,...)),
                (‘clf‘, SVC(C=1.0,...))])

>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.naive_bayes import MultinomialNB
>>> from sklearn.preprocessing import Binarizer
>>> make_pipeline(Binarizer(), MultinomialNB()) 
Pipeline(memory=None,
         steps=[(‘binarizer‘, Binarizer(copy=True, threshold=0.0)),
                (‘multinomialnb‘, MultinomialNB(alpha=1.0,
                                                class_prior=None,
                                                fit_prior=True))])

>>> pipe.set_params(clf__C=10)  # 给clf 设定参数

>>> from sklearn.model_selection import GridSearchCV
>>> param_grid = dict(reduce_dim__n_components=[2, 5, 10],
...                   clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)