sklearn ------------机器学习的万能钥匙
数据加载首先，数据要被加载到内存中，才能对其操作。Scikit-Learn库在它的实现用使用了NumPy数组，所以我们将用NumPy来加载*.csv文件。让我们从UCI Machine Learning Repository下载其中一个数据集。
import numpy as np import urllib # url with dataset url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" # download the file raw_data = urllib.urlopen(url) # load the CSV file as a numpy matrix dataset = np.loadtxt(raw_data, delimiter=",") # separate the data from the target attributes X = dataset[:,0:7] y = dataset[:,
importnumpy asnp
import urllib
# url with dataset
url ="http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
# download the file
raw_data =urllib.urlopen(url)
# load the CSV file as a numpy matrix
dataset =np.loadtxt(raw_data,delimiter=",")
# separate the data from the target attributes
X =dataset[:,0:7]
y= dataset[:,8]
我们将在下面所有的例子里使用这个数据组，换言之，使用X特征物数组和y目标变量的值。
数据标准化
我们都知道大多数的梯度方法（几乎所有的机器学习算法都基于此）对于数据的缩放很敏感。因此，在运行算法之前，我们应该进行标准化，或所谓的规格化。标准化包括替换所有特征的名义值，让它们每一个的值在0和1之间。而对于规格化，它包括数据的预处理，使得每个特征的值有0和1的离差。Scikit-Learn库已经为其提供了相应的函数。
from sklearn import preprocessing # normalize the data attributes normalized_X = preprocessing.normalize(X) # standardize the data attributes standardized_X = preprocessing.scale(X)
fromsklearn importpreprocessing
# normalize the data attributes
normalized_X= preprocessing.normalize(X)
# standardize the data attributes
standardized_X= preprocessing.scale(X)
特征的选取
毫无疑问，解决一个问题最重要的是是恰当选取特征、甚至创造特征的能力。这叫做特征选取和特征工程。虽然特征工程是一个相当有创造性的过程，有时候更多的是靠直觉和专业的知识，但对于特征的选取，已经有很多的算法可供直接使用。如树算法就可以计算特征的信息量。
from sklearn import metrics from sklearn.ensemble import ExtraTreesClassifier model = ExtraTreesClassifier() model.fit(X, y) # display the relative importance of each attribute print(model.feature_importances_)
fromsklearn importmetrics
from sklearn.ensembleimport ExtraTreesClassifier
model= ExtraTreesClassifier()
model.fit(X,y)
# display the relative importance of each attribute
print(model.feature_importances_)
其他所有的方法都是基于对特征子集的高效搜索，从而找到最好的子集，意味着演化了的模型在这个子集上有最好的质量。递归特征消除算法（RFE）是这些搜索算法的其中之一，Scikit-Learn库同样也有提供。
from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression model = LogisticRegression() # create the RFE model and select 3 attributes rfe = RFE(model, 3) rfe = rfe.fit(X, y) # summarize the selection of the attributes print(rfe.support_) print(rfe.ranking_)
fromsklearn.feature_selectionimport RFE
from sklearn.linear_modelimport LogisticRegression
model= LogisticRegression()
# create the RFE model and select 3 attributes
rfe= RFE(model,3)
rfe =rfe.fit(X,y)
# summarize the selection of the attributes
print(rfe.support_)
print(rfe.ranking_)
算法的开发
正像我说的，Scikit-Learn库已经实现了所有基本机器学习的算法。让我来瞧一瞧它们中的一些。
逻辑回归
大多数情况下被用来解决分类问题（二元分类），但多类的分类（所谓的一对多方法）也适用。这个算法的优点是对于每一个输出的对象都有一个对应类别的概率。
from sklearn import metrics from sklearn.linear_model import LogisticRegression model = LogisticRegression() model.fit(X, y) print(model) # make predictions expected = y predicted = model.predict(X) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted))
fromsklearn importmetrics
from sklearn.linear_modelimport LogisticRegression
model= LogisticRegression()
model.fit(X,y)
print(model)
# make predictions
expected= y
predicted =model.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected,predicted))
print(metrics.confusion_matrix(expected,predicted))
朴素贝叶斯
它也是最有名的机器学习的算法之一，它的主要任务是恢复训练样本的数据分布密度。这个方法通常在多类的分类问题上表现的很好。
from sklearn import metrics from sklearn.naive_bayes import GaussianNB model = GaussianNB() model.fit(X, y) print(model) # make predictions expected = y predicted = model.predict(X) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted))
fromsklearn importmetrics
from sklearn.naive_bayesimport GaussianNB
model= GaussianNB()
model.fit(X,y)
print(model)
# make predictions
expected= y
predicted =model.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected,predicted))
print(metrics.confusion_matrix(expected,predicted))
k-最近邻
kNN（k-最近邻）方法通常用于一个更复杂分类算法的一部分。例如，我们可以用它的估计值做为一个对象的特征。有时候，一个简单的kNN算法在良好选择的特征上会有很出色的表现。当参数（主要是metrics）被设置得当，这个算法在回归问题中通常表现出最好的质量。
from sklearn import metrics from sklearn.neighbors import KNeighborsClassifier # fit a k-nearest neighbor model to the data model = KNeighborsClassifier() model.fit(X, y) print(model) # make predictions expected = y predicted = model.predict(X) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted))
fromsklearn importmetrics
from sklearn.neighborsimport KNeighborsClassifier
# fit a k-nearest neighbor model to the data
model =KNeighborsClassifier()
model.fit(X,y)
print(model)
# make predictions
expected =y
predicted= model.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected,predicted))
print(metrics.confusion_matrix(expected,predicted))
决策树
分类和回归树（CART）经常被用于这么一类问题，在这类问题中对象有可分类的特征且被用于回归和分类问题。决策树很适用于多类分类。
from sklearn import metrics from sklearn.tree import DecisionTreeClassifier # fit a CART model to the data model = DecisionTreeClassifier() model.fit(X, y) print(model) # make predictions expected = y predicted = model.predict(X) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted))
fromsklearn importmetrics
from sklearn.treeimport DecisionTreeClassifier
# fit a CART model to the data
model =DecisionTreeClassifier()
model.fit(X,y)
print(model)
# make predictions
expected =y
predicted= model.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected,predicted))
print(metrics.confusion_matrix(expected,predicted))
支持向量机
SVM（支持向量机）是最流行的机器学习算法之一，它主要用于分类问题。同样也用于逻辑回归，SVM在一对多方法的帮助下可以实现多类分类。
from sklearn import metrics from sklearn.svm import SVC # fit a SVM model to the data model = SVC() model.fit(X, y) print(model) # make predictions expected = y predicted = model.predict(X) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted))
fromsklearn importmetrics
from sklearn.svmimport SVC
# fit a SVM model to the data
model =SVC()
model.fit(X,y)
print(model)
# make predictions
expected =y
predicted= model.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected,predicted))
print(metrics.confusion_matrix(expected,predicted))
除了分类和回归问题，Scikit-Learn还有海量的更复杂的算法，包括了聚类， 以及建立混合算法的实现技术，如Bagging和Boosting。
如何优化算法的参数
在编写高效的算法的过程中最难的步骤之一就是正确参数的选择。一般来说如果有经验的话会容易些，但无论如何，我们都得寻找。幸运的是Scikit-Learn提供了很多函数来帮助解决这个问题。
作为一个例子，我们来看一下规则化参数的选择，在其中不少数值被相继搜索了：
import numpy as np from sklearn.linear_model import Ridge from sklearn.grid_search import GridSearchCV # prepare a range of alpha values to test alphas = np.array([1,0.1,0.01,0.001,0.0001,0]) # create and fit a ridge regression model, testing each alpha model = Ridge() grid = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas)) grid.fit(X, y) print(grid) # summarize the results of the grid search print(grid.best_score_) print(grid.best_estimator_.alpha)
importnumpy asnp
from sklearn.linear_modelimport Ridge
fromsklearn.grid_searchimport GridSearchCV
# prepare a range of alpha values to test
alphas= np.array([1,0.1,0.01,0.001,0.0001,0])
# create and fit a ridge regression model, testing each alpha
model= Ridge()
grid =GridSearchCV(estimator=model,param_grid=dict(alpha=alphas))
grid.fit(X,y)
print(grid)
# summarize the results of the grid search
print(grid.best_score_)
print(grid.best_estimator_.alpha)
有时候随机地从既定的范围内选取一个参数更为高效，估计在这个参数下算法的质量，然后选出最好的。
import numpy as np from scipy.stats import uniform as sp_rand from sklearn.linear_model import Ridge from sklearn.grid_search import RandomizedSearchCV # prepare a uniform distribution to sample for the alpha parameter param_grid = {'alpha': sp_rand()} # create and fit a ridge regression model, testing random alpha values model = Ridge() rsearch = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100) rsearch.fit(X, y) print(rsearch) # summarize the results of the random parameter search print(rsearch.best_score_) print(rsearch.best_estimator_.alpha)
importnumpy asnp
from scipy.statsimport uniformas sp_rand
fromsklearn.linear_modelimport Ridge
from sklearn.grid_searchimport RandomizedSearchCV
# prepare a uniform distribution to sample for the alpha parameter
param_grid ={'alpha':sp_rand()}
# create and fit a ridge regression model, testing random alpha values
model =Ridge()
rsearch= RandomizedSearchCV(estimator=model,param_distributions=param_grid,n_iter=100)
rsearch.fit(X,y)
print(rsearch)
# summarize the results of the random parameter search
print(rsearch.best_score_)
print(rsearch.best_estimator_.alpha)