sklearn学习——SVM例程总结(PCA+Pipline+cv+GridSearch)

Introduction

其实对于SVM调节超参数不需要这么复杂，因为gamma可能更重要一点，固定C=1，手动调节gamma即可。此外，sklearn的网格搜索极其的慢，下面的代码出来结果至少要半个多小时，如果有经验根本不需要。对于有经验的人来说或许看学习曲线就能知道调什么参数。但是为什么还要这么做呢？可能是为了装吧，或许更直观一点，不需要老中医式的随便开点良药，看看效果再换药了！

PCA：主成分分析

Pipline: 管道机制、官网

GridSearch：官网

Method

下面给出修改后的代码，里面都有注释，直接拿回去慢慢调：

数据是sklearn自带的，数据量不大，如果是比赛数据，根本没法跑，太慢了！！！

官网例程：比较三种降维方法：PCA+NMF(非负矩阵分解)+KBest

#!/usr/bin/python# -*- coding: utf-8 -*-"""=================================================================Selecting dimensionality reduction with Pipeline and GridSearchCV=================================================================This example constructs a pipeline that does dimensionalityreduction followed by prediction with a support vectorclassifier. It demonstrates the use of GridSearchCV andPipeline to optimize over different classes of estimators in asingle CV run -- unsupervised PCA and NMF dimensionalityreductions are compared to univariate feature selection duringthe grid search."""# Authors: Robert McGibbon, Joel Nothmanfrom __future__ import print_function, divisionimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import GridSearchCVfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import StratifiedShuffleSplit#分层洗牌分割交叉验证from sklearn.svm import LinearSVCfrom sklearn.decomposition import PCA, NMFfrom sklearn.feature_selection import SelectKBest, chi2digits = load_digits()print(__doc__)pipe = Pipeline([    ('reduce_dim', PCA()),    ('classify', LinearSVC())])N_FEATURES_OPTIONS = [2, 4, 8]C_OPTIONS = [1, 10, 100, 1000]param_grid = [    {        'reduce_dim': [PCA(iterated_power=7), NMF()],        'reduce_dim__n_components': N_FEATURES_OPTIONS,        'classify__C': C_OPTIONS    },    {        'reduce_dim': [SelectKBest(chi2)],        'reduce_dim__k': N_FEATURES_OPTIONS,        'classify__C': C_OPTIONS    },]reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']cv = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)grid = GridSearchCV(pipe, cv=3, n_jobs=2, param_grid=param_grid)grid.fit(digits.data, digits.target)mean_scores = np.array(grid.cv_results_['mean_test_score'])# scores are in the order of param_grid iteration, which is alphabeticalmean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS))# select score for best Cmean_scores = mean_scores.max(axis=0)bar_offsets = (np.arange(len(N_FEATURES_OPTIONS)) *               (len(reducer_labels) + 1) + .5)plt.figure()COLORS = 'bgrcmyk'for i, (label, reducer_scores) in enumerate(zip(reducer_labels, mean_scores)):    plt.bar(bar_offsets + i, reducer_scores, label=label, color=COLORS[i])plt.title("Comparing feature reduction techniques")plt.xlabel('Reduced number of features')plt.xticks(bar_offsets + len(reducer_labels) / 2, N_FEATURES_OPTIONS)plt.ylabel('Digit classification accuracy')plt.ylim((0, 1))plt.legend(loc='upper left')plt.show()

寻找最优超参数：

# -*- coding: utf-8 -*-"""Created on Wed Jul 26 22:06:34 2017@author: qiu"""from __future__ import print_function, divisionimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import GridSearchCVfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import StratifiedShuffleSplit#分层洗牌分割交叉验证from sklearn.svm import SVCfrom sklearn.decomposition import PCA, NMFfrom sklearn.feature_selection import SelectKBest, chi2digits = load_digits()#网格搜索可视化——热力图pipe = Pipeline(steps=[                           ('classify',  SVC())])C_range = np.logspace(-2, 1, 4)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份gamma_range = np.logspace(-9, -6, 4)param_grid = [    {        'classify__C': C_range,        'classify__gamma': gamma_range    },]cv = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)grid = GridSearchCV(pipe, param_grid=param_grid, cv=cv)#基于交叉验证的网格搜索。grid.fit(digits.data, digits.target)print("The best parameters are %s with a score of %0.2f"      % (grid.best_params_, grid.best_score_))#找到最佳超参数

未完待续。。。

网格搜索可视化——热力图，参考sklearn学习-SVM例程总结3(网格搜索+交叉验证——寻找最优超参数)

在获取最佳参数后画学习曲线，参考 

kaggle竞赛——Titanic：Machine Learning from Disaster