我是Python的新手,我想知道是否有更好的解决方案来动态显示对象下的所有属性(关键字、woe_code、时间戳)。
原始代码:
trends = models.Trends.query.all()
for t in trends:
print t.keyword, t.woe_id, t.woe_code, t.timestamp #I know this is wrong, hardcoding the attributes.
新代码:
trends = models.Trends.query.all()
for t in trends:
for k, v in var
我读过这篇关于开发python的信用记分卡的文章,其中指出,在绑定连续变量时,需要确保:
1. Each bin should have at least 5% of the observations
2. Each bin should be non-zero for both good and bad loans
3. The WOE should be distinct for each category. Similar groups should be aggregated or binned together. It is because the bins with similar
my_trends = api.GetTrendsWoeid(my_woe_id)
trends = json.loads(my_trends)
但是我得到了错误:提升
TypeError(f'the JSON object must be str, bytes or bytearray, '
TypeError: the JSON object must be str, bytes or bytearray, not list
。我使用的是python 3.7。还有python-twitter。我做错了什么?
我不知道我的代码有什么问题
import pandas as pd
import numpy as np
woe = [1.1147295474833758,0.364043491078754,-0.05525053172192353,-0.3950007109750665,-0.6784658191115104,-0.9522135140050229,-1.1441658353033486]
iv = [0.29078213954085946,0.29078213954085946,0.29078213954085946,0.29078213954085946,0.2907821395408
#include <iostream>
int main() {
int woe; // weight on earth
int wom = woe * 2; // weight on mars
std::cout << "Enter your weight on earth: \n";
std::cin >> woe;
std::cout << "Your weight on mars would be " << wom << " Pounds\
我有一个问题,要用它们的权重证据来替换名义变量的范畴值。我已经成功地计算了体重的证据,但我未能用它们各自的权重证据来替换它们的分类值。
这是我的密码:
for i in range (26962):
for j in range (202):
if INDUSTRY_CD_3_preprocessing[i] == woe_INDUSTRY_CD_3[0]['Value'][j]:
INDUSTRY_CD_3_preprocessing[i] = woe_INDUSTRY_CD_3[0]['WoE'][j]
IND
我希望这个图在x轴上从左到右显示一条递增的线。因此,x轴需要按如下顺序排列: J H G C A B E F D I K L 然后,这条线将从左到右递增。 df1 = pd.DataFrame({'Col1': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L'],
'Col2': [0,
我有分类变量: Y = 0 or 1 X = X1; X2 Z = Z1; Z2; Z3; Z4 V = V1; V2; V3; V4 如何才能得到所有变量的问题,以及如何应用逻辑回归? 首先,我不知道如何获得将用于logistic回归模型的woe向量。例如,我使用woe() woe(Data=MyData,"X",FALSE,"Y",10,Bad=1,Good=0) 我得到了 BIN BAD GOOD TOTAL BAD% GOOD% TOTAL% WOE IV BAD_SPLIT X1 0 33117 3867 36984 0.798
我正在使用OptBinning包来存储一些数字数据。我跟着做这件事。在中,我读到“.查看BinningProcess的最佳方法是作为OptimalBinning的包装器”,这意味着它们都应该提供相同的输出。但是,我看到它们为某些特性提供了不同的输出,而对于其他特性则提供了相同的输出。为什么是这种情况?下面是一个例子,说明这两种方法如何导致相同的输出“平均半径”,而不是“最坏的半径”使用乳腺癌数据在雪橇。
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from skl
我已经打开了一个在eclipse环境中包含build.xml和ivy.xml的项目。在我应用Run As..>Ant Built by right click build.xml之后,我遇到了这个错误:
BUILD FAILED
Target "retrieve" does not exist in the project "aaa". It is used from target "compile".
我的build.xml文件:
<project name="WOE" default="build&