Question

我正在Lalonde数据集上进行逻辑回归以估计倾向得分。我使用了logit中的statsmodels.statsmodels.formula.api函数，并用C()包裹了协变量，使它们成为分类变量。将age和educ视为连续变量会导致成功收敛，但是将它们分类将引发错误

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.617306
         Iterations: 35
---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
<ipython-input-29-bae905b632a4> in <module>
----> 1 psmodel = fsms.logit('treatment ~ 1 + C(age) + C(educ) + C(black) + C(hisp) + C(married) + C(nodegr)', tdf).fit()
      2 tdf['ps'] = psmodel.predict()
      3 tdf.head()

~/venv/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py in fit(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)
   1832         bnryfit = super(Logit, self).fit(start_params=start_params,
   1833                 method=method, maxiter=maxiter, full_output=full_output,
-> 1834                 disp=disp, callback=callback, **kwargs)
   1835 
   1836         discretefit = LogitResults(self, bnryfit)

~/venv/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py in fit(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)
    218         mlefit = super(DiscreteModel, self).fit(start_params=start_params,
    219                 method=method, maxiter=maxiter, full_output=full_output,
--> 220                 disp=disp, callback=callback, **kwargs)
    221 
    222         return mlefit # up to subclasses to wrap results

~/venv/lib/python3.7/site-packages/statsmodels/base/model.py in fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)
    471             Hinv = cov_params_func(self, xopt, retvals)
    472         elif method == 'newton' and full_output:
--> 473             Hinv = np.linalg.inv(-retvals['Hessian']) / nobs
    474         elif not skip_hessian:
    475             H = -1 * self.hessian(xopt)

~/venv/lib/python3.7/site-packages/numpy/linalg/linalg.py in inv(a)
    549     signature = 'D->D' if isComplexType(t) else 'd->d'
    550     extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
--> 551     ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)
    552     return wrap(ainv.astype(result_t, copy=False))
    553 

~/venv/lib/python3.7/site-packages/numpy/linalg/linalg.py in _raise_linalgerror_singular(err, flag)
     95 
     96 def _raise_linalgerror_singular(err, flag):
---> 97     raise LinAlgError("Singular matrix")
     98 
     99 def _raise_linalgerror_nonposdef(err, flag):

LinAlgError: Singular matrix

要进行复制，请加载Lalonde dataset（您可以从R data(lalonde)写入csv）并运行以下代码

import numpy as np
import pandas as pd
from statsmodels.formula import api as fsms

filename = 'lalonde.csv'
df = pd.read_csv(filename)
tdf = df.drop(['re74', 're75', 'u74', 'u75'], axis=1)
formula = 'treat ~ 1 + C(age) + C(educ) + C(black) + C(hisp) + C(married) + C(nodegr)'
psmodel = fsms.logit(formula, tdf).fit()

不知道为什么在训练过程中无法收敛/变成奇异的黑森州。

有趣的是，我在网上找到了一些有关因果推断和lalonde数据集的示例，这些示例并未将变量归类，这对我来说毫无意义。一个示例是Microsoft DoWhy，它直接使用了来自sklearn的LogisticRegression。看起来它并没有对变量进行分类。

还有其他一些类似的示例，它们涉及在Lalonde数据集上运行逻辑回归而不将变量归类。这些是数据中的数字，但不应将其视为连续的值。至少我认为，如果每个值都不属于一个类别，则应将它们放入垃圾箱。但这是一个不同的问题，在CrossValidated上更合适。有人可以帮助我理解为什么会出现此错误，以及消除该错误的正确方法是什么？

LinAlgError：来自Statsmodels逻辑回归的奇异矩阵

0 个答案: