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])

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)

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)

**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

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)

**small-model的准确率**: 0.789
**small-model的准确率**: 0.811
**small-model的准确率**: 0.835

string = 'abcdef'
def demo1(string):
 if len(string) <= 1:
  return string
 return demo1(string[1:]) +string[0]
print(demo1(string))