Python基于sklearn库的分类算法简单应用示例

本文实例讲述了Python基于sklearn库的分类算法简单应用。分享给大家供大家参考,具体如下:

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:

# coding=gbk

'''

Created on 2016年6月4日

@author: bryan

'''

import time

from sklearn import metrics

import pickle as pickle

import pandas as pd

# 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 list(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):

data = pd.read_csv(data_file)

train = data[:int(len(data)*0.9)]

test = data[int(len(data)*0.9):]

train_y = train.label

train_x = train.drop('label', axis=1)

test_y = test.label

test_x = test.drop('label', axis=1)

return train_x, train_y, test_x, test_y

if __name__ == '__main__':

data_file = "H:\\Research\\data\\trainCG.csv"

thresh = 0.5

model_save_file = None

model_save = {}

test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM','SVMCV', '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)

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

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'))

测试结果如下:

reading training and testing data...

******************* NB ********************

training took 0.004986s!

precision: 78.08%, recall: 71.25%

accuracy: 74.17%

******************* KNN ********************

training took 0.017545s!

precision: 97.56%, recall: 100.00%

accuracy: 98.68%

******************* LR ********************

training took 0.061161s!

precision: 89.16%, recall: 92.50%

accuracy: 90.07%

******************* RF ********************

training took 0.040111s!

precision: 96.39%, recall: 100.00%

accuracy: 98.01%

******************* DT ********************

training took 0.004513s!

precision: 96.20%, recall: 95.00%

accuracy: 95.36%

******************* SVM ********************

training took 0.242145s!

precision: 97.53%, recall: 98.75%

accuracy: 98.01%

******************* SVMCV ********************

Fitting 3 folds for each of 14 candidates, totalling 42 fits

[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    6.8s finished

probability True

verbose False

coef0 0.0

degree 3

tol 0.001

shrinking True

cache_size 200

gamma 0.001

max_iter -1

C 1000

decision_function_shape None

random_state None

class_weight None

kernel rbf

training took 7.434668s!

precision: 98.75%, recall: 98.75%

accuracy: 98.68%

******************* GBDT ********************

training took 0.521916s!

precision: 97.56%, recall: 100.00%

accuracy: 98.68%

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希望本文所述对大家Python程序设计有所帮助。

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