Code Snippetss

动态创建类的函数

def set_class_func(CLASS, FUNC):
    try:
        funclist = [i for i in dir(FUNC) if callable(getattr(FUNC,i)) and not i.startswith('_') ]
        for func in funclist:
            setattr(CLASS,str(func),classmethod(func))    
    except Exception as e:
        import sys
        print("Error When Create Class Method From {} TO {}".format(CLASS,FUNC))
        sys.exit(1)

文件夹路径

import os
print(os.path.dirname(os.path.abspath("__file__")))

print without new line

print('Loading Data...',end='')
print('Done)

AttributeError: 'LinearSVC' object has no attribute 'predict_proba', https://stackoverflow.com/questions/35076586/linearsvc-vs-svckernel-linear-conflicting-arguments

LinearSVC(probability=True) # 提示没有该属性
#Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in  the choice of penalties and loss functions and should scale better to large numbers of samples.
# http://scikit-learn.org/stable/modules/svm.html
SVC( kernel="linear", probability=True)

添加自定义函数的cross_validation支持, https://stackoverflow.com/questions/20330445/how-to-write-a-custom-estimator-in-sklearn-and-use-cross-validation-on-it

    class A:
        def __init__(self,l):
            self.l = l

        def get_params(self, deep = False):
            return {'l':self.l}

生成requirements.txt

pipreqs .

LinearSVC 没有 predict_proba https://stackoverflow.com/questions/26478000/converting-linearsvcs-decision-function-to-probabilities-scikit-learn-python

 from sklearn.calibration import CalibratedClassifierCV
 svm = LinearSVC()
 clf = CalibratedClassifierCV(svm) 
 clf.fit(X_train, y_train)
 y_proba = clf.predict_proba(X_test)

BaggingClassifier is Roughly Equivalent to RandomForestClassifier

from sklearn.tree import DecisionTreeClassifier
BaggingClassifier((DecisionTreeClassifier, splitter="random",max_leaf_nodes=16),
                n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1)