sklearn 数据本地持久化

最新推荐文章于 2024-01-08 12:32:01 发布

AgtNN

最新推荐文章于 2024-01-08 12:32:01 发布

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分类专栏： ML python 文章标签： ML Python sklearn

本文链接：https://blog.youkuaiyun.com/cahesi/article/details/81449764

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本文通过使用支持向量机(SVM)算法，基于身高、体重和性别特征来预测个体是否偏胖。文中提供了完整的训练数据集及测试样本，并演示了如何训练模型、保存模型以及加载已训练好的模型进行预测。

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# encoding:utf-8

# 根据身高，体重,男女(1,2)预测此人胖否(0,1)

# 案例是我自己随意写的，当然符合实际情况

# 定义train_data,test_data二维数组[[height,weight,sex],···],train_target一维数组[胖否]

train_data = [[160, 60, 1], [155, 80, 1], [178, 53, 2], [158, 53, 2], [166, 45, 2], [170, 50, 2], [156, 56, 2], [166, 50, 1], [175, 55, 1], [188, 68, 1], [159, 41, 2], [166, 70, 1], [175, 85, 1], [188, 98, 1], [159, 61, 2], [160, 60, 1], [155, 80, 1], [178, 53, 2], [158, 53, 2], [166, 45, 2], [170, 50, 2], [156, 56, 2], [166, 50, 1], [175, 55, 1], [188, 68, 1], [159, 41, 2], [166, 70, 1], [175, 85, 1], [188, 98, 1], [159, 61, 2], [160, 60, 1], [155, 80, 1], [178, 53, 2], [158, 53, 2], [166, 45, 2], [170, 50, 2], [156, 56, 2], [166, 50, 1], [175, 55, 1], [188, 68, 1], [159, 41, 2], [166, 70, 1], [175, 85, 1], [188, 98, 1], [159, 61, 2]]

train_target = [1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1]

test_data = [[166, 45, 2], [172, 52, 1], [156, 60, 1], [150, 70, 2]]

test_target = [0, 0, 1, 1]

print test_target

print 'SVM :'

from sklearn import svm

# 持久化

from sklearn.externals import joblib

clf = svm.SVC()

clf.fit(train_data, train_target)

joblib.dump(clf, "train_model.m")

result = clf.predict(test_data)

# print type(result) # <type 'numpy.ndarray'>转成list 用 result.tolist()

print result # [0 1 1 1]

# a = joblib.load("train_model.m")

#

# print a.predit(test_data)

# joblib.

# print 'LogisticRegression :'

# from sklearn.linear_model import LogisticRegression

#

# clf = LogisticRegression().fit(train_data, train_target)

# result = clf.predict_proba(test_data)

# print result

# # [[ 0.95138903 0.04861097]

# # [ 0.85670921 0.14329079]

# # [ 0.18763392 0.81236608]

# # [ 0.01270012 0.98729988]]

# print 'LinearSVC :'

# from sklearn.svm import LinearSVC

#

# clf = LinearSVC()

# clf.fit(train_data, train_target)

# result = clf.predict(test_data)

# print result # [0 0 0 1]

#

# print 'GaussianNB :'

# from sklearn.naive_bayes import GaussianNB

# gnb = GaussianNB().fit(train_data,train_target)

# result = gnb.predict(test_data)

# print result #[0 0 1 1]

'''

这个下面是如何使用已经训练好的测试数据

'''

#encoding:utf-8

from sklearn.externals import joblib

from sklearn import svm

clf = svm.SVC()

clf = joblib.load("train_model.m")

test_data = [[166, 45, 2], [172, 52, 1], [156, 60, 1], [150, 70, 2], [172, 52, 1], [156, 60, 1], [150, 70, 2], [172, 52, 1], [156, 60, 1], [150, 70, 2]]

print clf.predict(test_data)

joblib.dump(clf, "train_model2.m")