Python机器学习库scikit-learn实践

2017/01/18 10:53
阅读数 116

一、概述

算法分类

线性 逻辑回归、朴素贝叶斯、最大熵等
 

训练和预测的效率比较高,但最终效果对特征的依赖程度较高,需要数据在特征层面上是线性可分的。

非线性 随机森林、决策树、神经网络、核机器等
  可以建模复杂的分类面,从而能更好的拟合数据

二、Scikit-learn的python实践

2.1、Python的准备工作

安装一个python的库有以下三种方法:

1)Anaconda:下载地址如下:http://www.continuum.io/downloads

2)Pip:在pip官网https://pypi.python.org/pypi/pip

3)源码包:如果上述两种方法都没有找到你的库,那你直接把库的源码下载回来,解压,然后在目录中会有个setup.py文件。执行#python setup.py install 即可把这个库安装到python的默认库目录中。

2.2、Scikit-learn的测试

  scikit-learn已经包含在Anaconda中。也可以在官方下载源码包进行安装。本文代码里封装了如下机器学习算法,我们修改数据加载函数,即可一键测试:

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
    }

train_test.py

#!usr/bin/env python
#-*- coding: utf-8 -*-
 
import sys
import os
import time
from sklearn import metrics
import numpy as np
import cPickle as pickle
 
reload(sys)
sys.setdefaultencoding('utf8')
 
# 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()
    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=8)
    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=200)
    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
 
def 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'))

四、测试结果

 本次使用mnist手写体库进行实验:

http://deeplearning.net/data/mnist/mnist.pkl.gz。共5万训练样本和1万测试样本。

       代码运行结果如下:

reading training and testing data...
******************** Data Info *********************
#training data: 50000, #testing_data: 10000, dimension: 784
******************* NB ********************
training took 0.287000s!
accuracy: 83.69%
******************* KNN ********************
training took 31.991000s!
accuracy: 96.64%
******************* LR ********************
training took 101.282000s!
accuracy: 91.99%
******************* RF ********************
training took 5.442000s!
accuracy: 93.78%
******************* DT ********************
training took 28.326000s!
accuracy: 87.23%
******************* SVM ********************
training took 3152.369000s!
accuracy: 94.35%
******************* GBDT ********************
training took 7623.761000s!
accuracy: 96.18%

在这个数据集中,由于数据分布的团簇性较好(如果对这个数据库了解的话,看它的t-SNE映射图就可以看出来。由于任务简单,其在deep learning界已被认为是toy dataset),因此KNN的效果不赖。GBDT是个非常不错的算法,在kaggle等大数据比赛中,状元探花榜眼之列经常能见其身影。三个臭皮匠赛过诸葛亮,还是被验证有道理的,特别是三个臭皮匠还能力互补的时候!

       还有一个在实际中非常有效的方法,就是融合这些分类器,再进行决策。例如简单的投票,效果都非常不错。建议在实践中,大家都可以尝试下。

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