Python机器学习库scikit-learn

概述

scikit-learn 是机器学习领域非常热门的一个开源库，基于Python 语言写成。可以免费使用。 而且使用非常的简单，文档感人，非常值得去学习。

下面是一张scikit-learn的图谱：

我们可以看到，机器学习分为四大块，分别是 classification (分类)， clustering (聚类), regression (回归), dimensionality reduction (降维)。

安装scikit-learn

如果使用的是ubuntu则非常的简单，直接sudo apt-get install scikit-learn即可，这里可能会有要你安装别的依赖，也是同样的安装方法，如果是别的linux版本，可使用pip等工具进行安装。

测试：

# 不报错则表示安装成功>>> import sklearn>>> 

安装XGBDT

本质上还是GBDT，只是对GBDT进行了一些更改，叫X (Extreme) GBoosted，它把速度和效率做到了极致。在scikit-learn目前还没有这个分类器，因此要进行单独的安装。

这里对linux的安装进行说明，其余操作系统见：
http://xgboost.readthedocs.io/en/latest/build.html

# 拉取源码包git clone --recursive https://github.com/dmlc/xgboostcd xgboost# 编译make -j4# python包的安装# 首先安装工具sudo apt-get install python-setuptools#　进入目录，安装cd python-packagesudo python setup.py install#　测试不报错，成功>>> import xgboost>>> 

scikit-learn测试

测试的数据为，美国一个区域的糖尿病的情况，具有以下的信息：

Attribute Information:
1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skin fold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)
9. Class variable (0 or 1)

第9个是标签，即我们要预测的情况，0表示没有患病，1表示患病，数据集下载地址：
https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes

完整代码如下，可以进行一键测试多个算法：

#!usr/bin/env python  #-*- coding: utf-8 -*-  import time  from sklearn import metrics  import numpy as np  from numpy import *from sklearn import cross_validation# Multinomial Naive Bayes Classifier  def 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 Classifier  def knn_classifier(train_x, train_y):      from sklearn.neighbors import KNeighborsClassifier      model = KNeighborsClassifier(n_neighbors=10)      model.fit(train_x, train_y)      return model  # Logistic Regression Classifier  def 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 Classifier  def random_forest_classifier(train_x, train_y):      from sklearn.ensemble import RandomForestClassifier      model = RandomForestClassifier(n_estimators=100)      model.fit(train_x, train_y)      return model  # Decision Tree Classifier  def 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) Classifier  def gradient_boosting_classifier(train_x, train_y):      from sklearn.ensemble import GradientBoostingClassifier      model = GradientBoostingClassifier(n_estimators=40)      model.fit(train_x, train_y)      return model  # SVM Classifier  def 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 validation  def 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 model  # XGBoost Classfierdef extreme_gradient_boosting_classifier(train_x,train_y):    import xgboost    model = xgboost.XGBClassifier()    model.fit(train_x,train_y)    return model# read datasetdef read_data():      dataset = np.loadtxt('diabetes.txt',delimiter=',')    x = dataset[:,:8]    y = dataset[:,8]    seed = 7    test_size = 0.33    # split the dataset     train_x,test_x,train_y,test_y = cross_validation.train_test_split \        (x,y,test_size=test_size,random_state=seed)    return train_x, test_x, train_y, test_y  if __name__ == '__main__':            test_classifiers = ['NB','RF','SVM','KNN','LR','DT','GBDT','XGBDT']    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,                  'XGBDT':extreme_gradient_boosting_classifier    }      print 'reading training and testing data...'      train_x, test_x, train_y, test_y = read_data()      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 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)   

运行结果，可以看到在此问题上XGBDT效果稍微好一点，但是这是没有经过调参的，可以进行调参等预处理操作来改善效果。

yqtao@yqtao:~/machine-learn$ python sklean.py reading training and testing data...******************** Data Info *********************#training data: 514, #testing_data: 254, dimension: 8******************* NB ********************training took 0.001360s!precision: 48.35%, recall: 47.83%accuracy: 62.60%******************* RF ********************training took 0.198425s!precision: 71.26%, recall: 67.39%accuracy: 78.35%******************* SVMCV ********************Fitting 3 folds for each of 14 candidates, totalling 42 fits[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    1.5s finishedkernel rbfC 1verbose Falseprobability Truedegree 3shrinking Truemax_iter -1decision_function_shape Nonerandom_state Nonetol 0.001cache_size 200coef0 0.0gamma 0.0001class_weight Nonetraining took 1.536800s!precision: 69.84%, recall: 47.83%accuracy: 73.62%******************* KNN ********************training took 0.003870s!precision: 71.21%, recall: 51.09%accuracy: 74.80%******************* LR ********************training took 0.003629s!precision: 70.83%, recall: 55.43%accuracy: 75.59%******************* DT ********************training took 0.002498s!precision: 61.18%, recall: 56.52%accuracy: 71.26%******************* GBDT ********************training took 0.033451s!precision: 70.73%, recall: 63.04%accuracy: 77.17%******************* XGBDT ********************training took 0.232969s!precision: 70.45%, recall: 67.39%accuracy: 77.95%

参考资料：

http://scikit-learn.org/stable/
http://blog.csdn.net/zouxy09/article/details/48903179
http://blog.csdn.net/matrix_space/article/details/50541217