Python的准备工作
Python 一个备受欢迎的点是社区支持很多，有非常多优秀的库或者模块。但是某些库之间有时候也存在依赖，所以要安装这些库也是挺繁琐的过程。但总有人忍受不了这种 繁琐，都会开发出不少自动化的工具来节省各位客官的时间。其中，Anaconda是一个非常好的安装工具。
１. Anaconda安装
这是一个非常齐全的python发行版本，最新的版本提供了多达195个流行的python包，包含了我们常用的numpy、scipy等等科学计算的包。有了它，妈妈再也不用担心我焦头烂额地安装一个又一个依赖包了。Anaconda在手，轻松我有！下载地址如下：现在的版本有python2.7版本和python3.5版本，下载好对应版本、对应系统的anaconda，它实际上是一个sh脚本文件，大约280M左右。我下载的是linux版的python 2.7版本。
下载成功后，在终端执行（2.7版本）：
# bash Anaconda2-2.4.1-Linux-x86_64.sh
在安装的过程中，会问你安装路径，直接回车默认就可以了
2. 将python添加到环境变量中
如果在安装Anaconda的过程中没有将安装路径添加到系统环境变量中，需要在安装后手工添加：
1、在终端输入$sudo gedit /etc/profile，打开profile文件。
2、在文件末尾添加一行：export PATH=/home/grant/anaconda2/bin:$PATH，其中，将“/home/grant/anaconda2/bin”替换为你实际的安装路径。保存。
3. 使环境变量生效
方法1：
让/etc/profile文件修改后立即生效 ,可以使用如下命令:# . /etc/profile注意: . 和 /etc/profile 有空格方法2：让/etc/profile文件修改后立即生效 ,可以使用如下命令:# source /etc/profile附：Linux中source命令的用法source命令：source命令也称为“点命令”，也就是一个点符号（.）。source命令通常用于重新执行刚修改的初始化文件，使之立即生效，而不必注销并重新登录。用法： source filename 或 . filename
4. scikit-learn 安装
在终端执行命令：conda install scikit-learn
一直 “Enter" 或 ”yes" 即可完成安装。真的很方便。 5. scikit-learn 测试
#!usr/bin/env python#-*- coding: utf-8 -*-import sysimport osimport timefrom sklearn import metricsimport numpy as npimport cPickle as picklereload(sys)sys.setdefaultencoding('utf8')# Multinomial Naive Bayes Classifierdef naive_bayes_classifier(train_x, train_y): from sklearn.naive_bayes import MultinomialNB model = MultinomialNB(alpha=0.01) model.fit(train_x, train_y) return model# KNN Classifierdef knn_classifier(train_x, train_y): from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier() model.fit(train_x, train_y) return model# Logistic Regression Classifierdef logistic_regression_classifier(train_x, train_y): from sklearn.linear_model import LogisticRegression model = LogisticRegression(penalty='l2') model.fit(train_x, train_y) return model# Random Forest Classifierdef random_forest_classifier(train_x, train_y): from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=8) model.fit(train_x, train_y) return model# Decision Tree Classifierdef decision_tree_classifier(train_x, train_y): from sklearn import tree model = tree.DecisionTreeClassifier() model.fit(train_x, train_y) return model# GBDT(Gradient Boosting Decision Tree) Classifierdef gradient_boosting_classifier(train_x, train_y): from sklearn.ensemble import GradientBoostingClassifier model = GradientBoostingClassifier(n_estimators=200) model.fit(train_x, train_y) return model# SVM Classifierdef svm_classifier(train_x, train_y): from sklearn.svm import SVC model = SVC(kernel='rbf', probability=True) model.fit(train_x, train_y) return model# SVM Classifier using cross validationdef svm_cross_validation(train_x, train_y): from sklearn.grid_search import GridSearchCV from sklearn.svm import SVC model = SVC(kernel='rbf', probability=True) param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]} grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1) grid_search.fit(train_x, train_y) best_parameters = grid_search.best_estimator_.get_params() for para, val in best_parameters.items(): print para, val model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True) model.fit(train_x, train_y) return modeldef read_data(data_file): import gzip f = gzip.open(data_file, "rb") train, val, test = pickle.load(f) f.close() train_x = train[0] train_y = train[1] test_x = test[0] test_y = test[1] return train_x, train_y, test_x, test_y if __name__ == '__main__': data_file = "mnist.pkl.gz" thresh = 0.5 model_save_file = None model_save = {} test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM', 'GBDT'] classifiers = {'NB':naive_bayes_classifier, 'KNN':knn_classifier, 'LR':logistic_regression_classifier, 'RF':random_forest_classifier, 'DT':decision_tree_classifier, 'SVM':svm_classifier, 'SVMCV':svm_cross_validation, 'GBDT':gradient_boosting_classifier } print 'reading training and testing data...' train_x, train_y, test_x, test_y = read_data(data_file) num_train, num_feat = train_x.shape num_test, num_feat = test_x.shape is_binary_class = (len(np.unique(train_y)) == 2) print '******************** Data Info *********************' print '#training data: %d, #testing_data: %d, dimension: %d' % (num_train, num_test, num_feat) for classifier in test_classifiers: print '******************* %s ********************' % classifier start_time = time.time() model = classifiers[classifier](train_x, train_y) print 'training took %fs!' % (time.time() - start_time) predict = model.predict(test_x) if model_save_file != None: model_save[classifier] = model if is_binary_class: precision = metrics.precision_score(test_y, predict) recall = metrics.recall_score(test_y, predict) print 'precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall) accuracy = metrics.accuracy_score(test_y, predict) print 'accuracy: %.2f%%' % (100 * accuracy) if model_save_file != None: pickle.dump(model_save, open(model_save_file, 'wb'))
测试的分类器包括：
classifiers = {'NB':naive_bayes_classifier, 'KNN':knn_classifier, 'LR':logistic_regression_classifier, 'RF':random_forest_classifier, 'DT':decision_tree_classifier, 'SVM':svm_classifier, 'SVMCV':svm_cross_validation, 'GBDT':gradient_boosting_classifier }使用数据集为：
本次使用手写体库进行实验：。共5万训练样本和1万测试样本。
最终结果如下：
感谢分享：http://blog.csdn.net/zouxy09/article/details/48903179
http://www.cnblogs.com/hdulzt/p/7156095.html