python函数中将变量名转换成字符串实例

考虑到在日常中,常常需要对模型指标输出,但涉及多个模型的时候,需要对其有标示输出,故需要将模型变量名转换成字符串。

看到的基本方法有两种:

一、方法层面:

方法1(函数内推荐):

def namestr(obj, namespace):

return [name for name in namespace if namespace[name] is obj]

print(namestr(lr_origin,globals()),'\n',

namestr(lr_origin,globals())[0])

输出:

‘lr_origin'

方法2:

import inspect, re

def varname(p):

for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:

m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)

if m:

return m.group(1)

varname(lr_origin)

输出:

'lr_origin'

二、示例

采用方法1

def small_feature_model(model,X_train=X_train,y_train=y_train,X_test=X_test, y_test=y_test):

pca = PCA(n_components=150,random_state=0,whiten=True)

pipeline = Pipeline([('scale',StandardScaler()),('pca',pca)])

processing = pipeline.fit(X_train)

X_train = processing.transform(X_train)

X_test = processing.transform(X_test)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

# print(namestr(model,globals()))

print('**small-%s的准确率**: %.3f' %(namestr(model,globals())[0],accuracy_score(y_pred=y_pred, y_true=y_test)))

small_feature_model(svm_origin)

输出

['svm_origin']

**small-svm_origin的准确率**: 0.789

for model in [svm_origin, svm_rbf, lr_origin]:

small_feature_model(model)

输出

**small-svm_origin的准确率**: 0.789

**small-svm_rbf的准确率**: 0.811

**small-lr_origin的准确率**: 0.835

采用方法2

def small_feature_model(model,X_train=X_train,y_train=y_train,X_test=X_test, y_test=y_test):

pca = PCA(n_components=150,random_state=0,whiten=True)

pipeline = Pipeline([('scale',StandardScaler()),('pca',pca)])

processing = pipeline.fit(X_train)

X_train = processing.transform(X_train)

X_test = processing.transform(X_test)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

# print(namestr(model,globals()))

print('**small-%s的准确率**: %.3f' %(varname(model),accuracy_score(y_pred=y_pred, y_true=y_test)))

small_feature_model(svm_origin)

输出

**small-model的准确率**: 0.789

for model in [svm_origin, svm_rbf, lr_origin]:

small_feature_model(model)

输出

**small-model的准确率**: 0.789

**small-model的准确率**: 0.811

**small-model的准确率**: 0.835

补充知识:一个python实现翻转字符串的函数

实现字符串翻转的函数(python)

string = 'abcdef'

def demo1(string):

if len(string) <= 1:

return string

return demo1(string[1:]) +string[0]

print(demo1(string))

中间用到了递归和切片不知道效率如何

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