python - Pandas Statsmodels 使用 DF 预测器进行回归预测？

Question

使用 Pandas OLS，我可以按如下方式拟合和使用模型:

ols_test = pd.ols(y=merged2[:-1].Units, x=merged2[:-1].lastqu) #to exclude current year, then do forecast method
yrahead=(ols_test.beta['x'] * merged2.lastqu[-1:]) + ols_test.beta['intercept']

我需要切换到 statsmodels 以获得一些额外的功能(主要是残差图见(question here)

所以现在我有:

def fit_line2(x, y):
    X = sm.add_constant(x, prepend=True) #Add a column of ones to allow the calculation of the intercept
    model = sm.OLS(y, X,missing='drop').fit()
    """Return slope, intercept of best fit line."""
    X = sm.add_constant(x)
    return model

和:

model=fit_line2(merged2[:-1].lastqu,merged2[:-1].Units)
print fit.summary()

但是我无法获取

yrahead2=model.predict(merged2.lastqu[-1:]) 

或任何变体给我一个预测？请注意，pd.ols 使用相同的 merged2.lastqu[-1:] 来获取我想要“预测”的数据，无论我将什么放入 () 进行预测我都没有任何乐趣。看来statsmodels 想要 () 中的特定内容，而不是 pandas DF 单元格，我什至尝试只在其中输入一个数字，例如 2696，但仍然没有...我目前的错误是

----> 3 yrahead2=model.predict(merged2.lastqu[-1:])

/usr/lib/pymodules/python2.7/statsmodels/base/model.pyc in predict(self, exog, transform, *args, **kwargs)
   1004             exog = np.atleast_2d(exog) # needed in count model shape[1]
   1005 
-> 1006         return self.model.predict(self.params, exog, *args, **kwargs)
   1007 
   1008 

/usr/lib/pymodules/python2.7/statsmodels/regression/linear_model.pyc in predict(self, params, exog)
    253         if exog is None:
    254             exog = self.exog
--> 255         return np.dot(exog, params)
    256 
    257 class GLS(RegressionModel):

ValueError: objects are not aligned

> /usr/lib/pymodules/python2.7/statsmodels/regression/linear_model.py(255)predict()
    254             exog = self.exog
--> 255         return np.dot(exog, params)
    256 

Answer 1

我更喜欢统计模型的公式 api。至少为此，model.fit().predict 需要一个列与预测变量同名的 DataFrame。这是一个例子:

In [2]: df = pd.DataFrame({'X': np.arange(10), 'Y': np.arange(10) + np.random.randn(10)})

In [3]: mod = sm.OLS.from_formula("Y ~ X", df)

In [4]: res = mod.fit()

In [5]: exog = pd.DataFrame({"X": np.linspace(0, 10, 100)})

In [6]: res.predict(exog)
Out[6]: 
array([ 0.99817045,  1.07854804,  1.15892563,  1.23930322,  1.31968081,
        1.40005839,  1.48043598,  1.56081357,  1.64119116,  1.72156875,
        1.80194634,  1.88232393,  1.96270152,  2.04307911,  2.1234567 ,
        2.20383429,  2.28421188,  2.36458947,  2.44496706,  2.52534465,
        2.60572224,  2.68609983,  2.76647742,  2.84685501,  2.92723259,
        3.00761018,  3.08798777,  3.16836536,  3.24874295,  3.32912054,
        3.40949813,  3.48987572,  3.57025331,  3.6506309 ,  3.73100849,
        3.81138608,  3.89176367,  3.97214126,  4.05251885,  4.13289644,
        4.21327403,  4.29365162,  4.3740292 ,  4.45440679,  4.53478438,
        4.61516197,  4.69553956,  4.77591715,  4.85629474,  4.93667233,
        5.01704992,  5.09742751,  5.1778051 ,  5.25818269,  5.33856028,
        5.41893787,  5.49931546,  5.57969305,  5.66007064,  5.74044823,
        5.82082582,  5.9012034 ,  5.98158099,  6.06195858,  6.14233617,
        6.22271376,  6.30309135,  6.38346894,  6.46384653,  6.54422412,
        6.62460171,  6.7049793 ,  6.78535689,  6.86573448,  6.94611207,
        7.02648966,  7.10686725,  7.18724484,  7.26762243,  7.34800002,
        7.4283776 ,  7.50875519,  7.58913278,  7.66951037,  7.74988796,
        7.83026555,  7.91064314,  7.99102073,  8.07139832,  8.15177591,
        8.2321535 ,  8.31253109,  8.39290868,  8.47328627,  8.55366386,
        8.63404145,  8.71441904,  8.79479663,  8.87517421,  8.9555518 ])