本示例演示了模型复杂性是如何影响预测精度和计算性能的。数据集是用于回归(或者分类)的波士顿住房(Boston Housing)数据集(或者20 Newsgroups)。对于每一类模型，我们通过选择相关的模型参数来改变模型的复杂度，并测量对计算性能(延迟)和预测能力(MSE或Hamming损失)的影响。

输出：

Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.25, loss='modified_huber',              penalty='elasticnet')Complexity: 4466 | Hamming Loss (Misclassification Ratio): 0.2491 | Pred. Time: 0.020835sBenchmarking SGDClassifier(alpha=0.001, l1_ratio=0.5, loss='modified_huber',              penalty='elasticnet')Complexity: 1663 | Hamming Loss (Misclassification Ratio): 0.2915 | Pred. Time: 0.015789sBenchmarking SGDClassifier(alpha=0.001, l1_ratio=0.75, loss='modified_huber',              penalty='elasticnet')Complexity: 880 | Hamming Loss (Misclassification Ratio): 0.3180 | Pred. Time: 0.013469sBenchmarking SGDClassifier(alpha=0.001, l1_ratio=0.9, loss='modified_huber',              penalty='elasticnet')Complexity: 639 | Hamming Loss (Misclassification Ratio): 0.3337 | Pred. Time: 0.011812sBenchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)Complexity: 69 | MSE: 31.8139 | Pred. Time: 0.000301sBenchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.25)Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000811sBenchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)Complexity: 244 | MSE: 22.3375 | Pred. Time: 0.000895sBenchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.75)Complexity: 351 | MSE: 21.3688 | Pred. Time: 0.001237sBenchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.9)Complexity: 404 | MSE: 21.1033 | Pred. Time: 0.001460sBenchmarking GradientBoostingRegressor(n_estimators=10)Complexity: 10 | MSE: 29.0148 | Pred. Time: 0.000120sBenchmarking GradientBoostingRegressor(n_estimators=50)Complexity: 50 | MSE: 8.6545 | Pred. Time: 0.000302sBenchmarking GradientBoostingRegressor()Complexity: 100 | MSE: 7.7179 | Pred. Time: 0.000264sBenchmarking GradientBoostingRegressor(n_estimators=200)Complexity: 200 | MSE: 6.7507 | Pred. Time: 0.000425sBenchmarking GradientBoostingRegressor(n_estimators=500)Complexity: 500 | MSE: 7.1471 | Pred. Time: 0.000922s

print(__doc__)# 作者: Eustache Diemert # 许可证: BSD 3 clauseimport timeimport numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.axes_grid1.parasite_axes import host_subplotfrom mpl_toolkits.axisartist.axislines import Axesfrom scipy.sparse.csr import csr_matrixfrom sklearn import datasetsfrom sklearn.utils import shufflefrom sklearn.metrics import mean_squared_errorfrom sklearn.svm import NuSVRfrom sklearn.ensemble import GradientBoostingRegressorfrom sklearn.linear_model import SGDClassifierfrom sklearn.metrics import hamming_loss# ############################################################################## 例程(Routines)# 初始化随机生成器np.random.seed(0)def generate_data(case, sparse=False):"""生成回归/分类数据。"""if case == 'regression':        X, y = datasets.load_boston(return_X_y=True)elif case == 'classification':        X, y = datasets.fetch_20newsgroups_vectorized(subset='all',                                                      return_X_y=True)    X, y = shuffle(X, y)    offset = int(X.shape[0] * 0.8)    X_train, y_train = X[:offset], y[:offset]    X_test, y_test = X[offset:], y[offset:]if sparse:        X_train = csr_matrix(X_train)        X_test = csr_matrix(X_test)else:        X_train = np.array(X_train)        X_test = np.array(X_test)    y_test = np.array(y_test)    y_train = np.array(y_train)    data = {'X_train': X_train, 'X_test': X_test, 'y_train': y_train,'y_test': y_test}return datadef benchmark_influence(conf):"""    changing_param：对MSE和延迟的基准影响    """    prediction_times = []    prediction_powers = []    complexities = []for param_value in conf['changing_param_values']:        conf['tuned_params'][conf['changing_param']] = param_value        estimator = conf['estimator'](**conf['tuned_params'])print("Benchmarking %s" % estimator)        estimator.fit(conf['data']['X_train'], conf['data']['y_train'])        conf['postfit_hook'](estimator)        complexity = conf['complexity_computer'](estimator)        complexities.append(complexity)        start_time = time.time()for _ in range(conf['n_samples']):            y_pred = estimator.predict(conf['data']['X_test'])        elapsed_time = (time.time() - start_time) / float(conf['n_samples'])        prediction_times.append(elapsed_time)        pred_score = conf['prediction_performance_computer'](            conf['data']['y_test'], y_pred)        prediction_powers.append(pred_score)print("Complexity: %d | %s: %.4f | Pred. Time: %fs\n" % (            complexity, conf['prediction_performance_label'], pred_score,            elapsed_time))return prediction_powers, prediction_times, complexitiesdef plot_influence(conf, mse_values, prediction_times, complexities):"""    绘制模型复杂性对准确度和延迟的影响。    """    plt.figure(figsize=(12, 6))    host = host_subplot(111, axes_class=Axes)    plt.subplots_adjust(right=0.75)    par1 = host.twinx()    host.set_xlabel('Model Complexity (%s)' % conf['complexity_label'])    y1_label = conf['prediction_performance_label']    y2_label = "Time (s)"    host.set_ylabel(y1_label)    par1.set_ylabel(y2_label)    p1, = host.plot(complexities, mse_values, 'b-', label="prediction error")    p2, = par1.plot(complexities, prediction_times, 'r-',                    label="latency")    host.legend(loc='upper right')    host.axis["left"].label.set_color(p1.get_color())    par1.axis["right"].label.set_color(p2.get_color())    plt.title('Influence of Model Complexity - %s' % conf['estimator'].__name__)    plt.show()def _count_nonzero_coefficients(estimator):    a = estimator.coef_.toarray()return np.count_nonzero(a)# ############################################################################## 主要代码regression_data = generate_data('regression')classification_data = generate_data('classification', sparse=True)configurations = [    {'estimator': SGDClassifier,'tuned_params': {'penalty': 'elasticnet', 'alpha': 0.001, 'loss':'modified_huber', 'fit_intercept': True, 'tol': 1e-3},'changing_param': 'l1_ratio','changing_param_values': [0.25, 0.5, 0.75, 0.9],'complexity_label': 'non_zero coefficients','complexity_computer': _count_nonzero_coefficients,'prediction_performance_computer': hamming_loss,'prediction_performance_label': 'Hamming Loss (Misclassification Ratio)','postfit_hook': lambda x: x.sparsify(),'data': classification_data,'n_samples': 30},    {'estimator': NuSVR,'tuned_params': {'C': 1e3, 'gamma': 2 ** -15},'changing_param': 'nu','changing_param_values': [0.1, 0.25, 0.5, 0.75, 0.9],'complexity_label': 'n_support_vectors','complexity_computer': lambda x: len(x.support_vectors_),'data': regression_data,'postfit_hook': lambda x: x,'prediction_performance_computer': mean_squared_error,'prediction_performance_label': 'MSE','n_samples': 30},    {'estimator': GradientBoostingRegressor,'tuned_params': {'loss': 'ls'},'changing_param': 'n_estimators','changing_param_values': [10, 50, 100, 200, 500],'complexity_label': 'n_trees','complexity_computer': lambda x: x.n_estimators,'data': regression_data,'postfit_hook': lambda x: x,'prediction_performance_computer': mean_squared_error,'prediction_performance_label': 'MSE','n_samples': 30},]for conf in configurations:    prediction_performances, prediction_times, complexities = \        benchmark_influence(conf)    plot_influence(conf, prediction_performances, prediction_times,                   complexities)

脚本的总运行时间： ( 0 分 20.261 秒)估计的内存使用量： 60 MB下载python源代码:plot_model_complexity_influence.py下载Jupyter notebook源代码:plot_model_complexity_influence.ipynb由Sphinx-Gallery生成的画廊☆☆☆为方便大家查阅，小编已将scikit-learn学习路线专栏文章统一整理到公众号底部菜单栏，同步更新中，关注公众号，点击左下方“系列文章”，如图：欢迎大家和我一起沿着scikit-learn文档这条路线，一起巩固机器学习算法基础。(添加微信：mthler，备注：sklearn学习，一起进【sklearn机器学习进步群】开启打怪升级的学习之旅。)‍