ray-project / tune-sklearn

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
https://docs.ray.io/en/master/tune/api_docs/sklearn.html
Apache License 2.0
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The code you provided will not run directly #222

Open yunhao2742 opened 3 years ago

yunhao2742 commented 3 years ago

"""Example using an sklearn Pipeline with TuneGridSearchCV. Example taken and modified from https://scikit-learn.org/stable/auto_examples/compose/ plot_compare_reduction.html """ from tune_sklearn import TuneSearchCV from tune_sklearn import TuneGridSearchCV from sklearn.datasets import load_digits from sklearn.pipeline import Pipeline from sklearn.svm import LinearSVC from sklearn.decomposition import PCA, NMF from sklearn.feature_selection import SelectKBest, chi2

pipe = Pipeline([

the reduce_dim stage is populated by the param_grid

("reduce_dim", "passthrough"),
("classify", LinearSVC(dual=False, max_iter=10000))

])

N_FEATURES_OPTIONS = [2, 4, 8] C_OPTIONS = [1, 10] param_grid = [ { "reduce_dim": [PCA(iterated_power=7), NMF()], "reduce_dimn_components": N_FEATURES_OPTIONS, "classify__C": C_OPTIONS }, { "reduce_dim": [SelectKBest(chi2)], "reduce_dimk": N_FEATURES_OPTIONS, "classify__C": C_OPTIONS }, ]

random = TuneSearchCV(pipe, param_grid, search_optimization="random") X, y = load_digits(return_X_y=True) random.fit(X, y) print(random.cvresults)

grid = TuneGridSearchCV(pipe, param_grid=param_grid) grid.fit(X, y) print(grid.cvresults) The code you provided will not run directly. The environment is already configured WinError 2] 系统找不到指定的文件。 File "C:\Users\dell\Downloads\测试3.py", line 38, in random.fit(X, y)