2.使用scikit-learn和Python进行超参数调整（Python代码，包括数据集）

# USAGE # python train_svr_random.py # import the necessary packages from pyimagesearch import config from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import RepeatedKFold from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.model_selection import train_test_split from scipy.stats import loguniform import pandas as pd # load the dataset, separate the features and labels, and perform a # training and testing split using 85% of the data for training and # 15% for evaluation print("[INFO] loading data...") dataset = pd.read_csv(config.CSV_PATH, names=config.COLS) dataX = dataset[dataset.columns[:-1]] dataY = dataset[dataset.columns[-1]] (trainX, testX, trainY, testY) = train_test_split(dataX, dataY, random_state=3, test_size=0.15) # standardize the feature values by computing the mean, subtracting # the mean from the data points, and then dividing by the standard # deviation scaler = StandardScaler() trainX = scaler.fit_transform(trainX) testX = scaler.transform(testX) # initialize model and define the space of the hyperparameters to # perform the grid-search over model = SVR() kernel = ["linear", "rbf", "sigmoid", "poly"] tolerance = loguniform(1e-6, 1e-3) C = [1, 1.5, 2, 2.5, 3] grid = dict(kernel=kernel, tol=tolerance, C=C) # initialize a cross-validation fold and perform a grid-search to # tune the hyperparameters print("[INFO] grid searching over the hyperparameters...") cvFold = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1) randomSearch = RandomizedSearchCV(estimator=model, n_jobs=-1, cv=cvFold, param_distributions=grid, scoring="neg_mean_squared_error") searchResults = randomSearch.fit(trainX, trainY) # extract the best model and evaluate it print("[INFO] evaluating...") bestModel = searchResults.best_estimator_ print("R2: {:.2f}".format(bestModel.score(testX, testY)))