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
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：
Python sklearn库 分类算法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%