小编Gre*_*ick的帖子

我在Python中编写了一个混淆矩阵计算代码:

def conf_mat(prob_arr, input_arr):
        # confusion matrix
        conf_arr = [[0, 0], [0, 0]]

        for i in range(len(prob_arr)):
                if int(input_arr[i]) == 1:
                        if float(prob_arr[i]) < 0.5:
                                conf_arr[0][1] = conf_arr[0][1] + 1
                        else:
                                conf_arr[0][0] = conf_arr[0][0] + 1
                elif int(input_arr[i]) == 2:
                        if float(prob_arr[i]) >= 0.5:
                                conf_arr[1][0] = conf_arr[1][0] +1
                        else:
                                conf_arr[1][1] = conf_arr[1][1] +1

        accuracy = float(conf_arr[0][0] + conf_arr[1][1])/(len(input_arr))

prob_arr是我的分类代码返回的数组,示例数组是这样的:

 [1.0, 1.0, 1.0, 0.41592955657342651, 1.0, 0.0053405015805891975, 4.5321494433440449e-299, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.70943426182688163, 1.0, 1.0, 1.0, 1.0]

input_arr是数据集的原始类标签,它是这样的:

[2, 1, …

我有许多CSV文件,其中包含性别,年龄,诊断等列.

目前,它们的编码如下:

ID, gender, age, diagnosis
1,  male,   42,  asthma
1,  male,   42,  anxiety
2,  male,   19,  asthma
3,  female, 23,  diabetes
4,  female, 61,  diabetes
4,  female, 61,  copd

目标是将此数据转换为此目标格式:

旁注:如果可能的话,还可以将原始列名称添加到新列名称中,例如"age_42"或"gender_female".

ID, male, female, 42, 19, 23, 61, asthma, anxiety, diabetes, copd
1,  1,    0,      1,  0,  0,  0,  1,      1,       0,        0
2,  1,    0,      0,  1,  0,  0,  1,      0,       0,        0
3,  0,    1,      0,  0,  1,  0,  0,      0,       1,        0
4,  0, …

我正在运行 SARIMAX 模型，但在指定外生变量时遇到了问题。在第一个代码块（下面）中，我指定了一个外生变量 lesdata['LESpost'] 并且模型运行没有问题。但是，当我添加另一个外生变量时，我最终会收到一条错误消息（请参阅堆栈跟踪）。

ar = (1,0,1)      #  AR(1 3)
ma = (0)  #  No MA terms
mod1 = sm.tsa.statespace.SARIMAX(lesdata['emadm'], exog= (lesdata['LESpost'],lesdata['QOF']), trend='c', order=(ar,0,ma), mle_regression=True)

Traceback (most recent call last):

  File "<ipython-input-129-d1300aeaeffc>", line 4, in <module>
    mle_regression=True)

  File "C:\Users\danie\Anaconda2\lib\site-packages\statsmodels\tsa\statespace\sarimax.py", line 510, in __init__
    endog, exog=exog, k_states=k_states, k_posdef=k_posdef, **kwargs

  File "C:\Users\danie\Anaconda2\lib\site-packages\statsmodels\tsa\statespace\mlemodel.py", line 84, in __init__
    missing='none')

  File "C:\Users\danie\Anaconda2\lib\site-packages\statsmodels\tsa\base\tsa_model.py", line 43, in __init__
    super(TimeSeriesModel, self).__init__(endog, exog, missing=missing)

  File "C:\Users\danie\Anaconda2\lib\site-packages\statsmodels\base\model.py", line 212, in __init__
    super(LikelihoodModel, self).__init__(endog, exog, **kwargs)

  File "C:\Users\danie\Anaconda2\lib\site-packages\statsmodels\base\model.py", line 63, …

我有一个脚本,它获取查询ID的列表文件,并从uniprot中提取有机体和序列,代码运行良好,但它非常慢.我希望通过它处理大约400万个序列,但解析100个序列大约需要5分钟:

real    5m32.452s
user    0m0.651s
sys 0m0.135s

代码使用python的检索模块.我在网上看到我可以使用.session()属性,但是当我尝试这个时,我收到以下错误:

Traceback (most recent call last):
File "retrieve.py", line 14, in <module>
result = session.get(baseURL, payload)
TypeError: get() takes exactly 2 arguments (3 given)

代码列在这里:

import requests

baseURL = 'http://www.uniprot.org/uniprot/'

sample = open('sample.txt','r')
out = open('out','w')

for line in sample:
    query = line.strip()
    payload = {
        'query': query, 
        'format':'tab',
        'columns': 'id, entry_name, organism, sequence'
    }
    result = requests.get(baseURL, payload)
    if result.ok:
        out.write(query + '\t' + result.text[41:] + '\n')

输入格式示例:

EDP09046
ONI31767
ENSFALT00000002630
EAS32469 …