Question

我有以下代码，一个基于BaseEstimator和ClassifierMixin在python中应用sklearn的非常简单的模型。它旨在报告城市的预测分数（y）（X）。在这里，作为一个简单的模型，我只希望它能够在城市被召唤时报告城市的平均得分作为其预测值。

class MeanClassifier(BaseEstimator, ClassifierMixin):
    def __inif__(self):
        self.cityid_ = []
        self.cntX = []

    def X3(self, X):
        self.cityid_, idx = np.unique(X, return_inverse = True)
        self.cntX = map(list(self.cityid_).index, X)
        return self.cntX

    def fit(self, X, y):
        self.meanclasses_, meanindicies = np.unique(y, return_inverse = True)
        self.cityid_, idx = np.unique(X, return_inverse = True)
        self.df = pd.DataFrame({"X":X, "y":y})
        self.mean_ = self.df.groupby(['X'].mean())

    def predict(self, X):
        return self.df['y']['X']

要使用该课程，我有B，其中城市是一个城市列表，在课堂上作为X和星星作为y。

B = MeanClassifier()
asncityid = city

B.fit(asncityid, stars)
pred = B.predict(asncityid[2]) #use the third city in the city list for prediction
print(pred)

当我运行此代码时，收到以下错误

  `File "ml2_cp.py", line 66, in <module>
   pred = B.predict(asncityid[2])
  File "ml2_cp.py", line 58, in predict
  return self.df['y']['X'] ## using sklearn requires all X inputs
  File "/opt/conda/lib/python2.7/site-packages/pandas/core/series.py", line 583, in __getitem__
  result = self.index.get_value(self, key)
  File "/opt/conda/lib/python2.7/site-packages/pandas/indexes/base.py", line 1980, in get_value
  tz=getattr(series.dtype, 'tz', None))
  File "pandas/index.pyx", line 103, in pandas.index.IndexEngine.get_value (pandas/index.c:3332)
  File "pandas/index.pyx", line 111, in pandas.index.IndexEngine.get_value (pandas/index.c:3035)
  File "pandas/index.pyx", line 161, in pandas.index.IndexEngine.get_loc (pandas/index.c:4084)
KeyError: 'X'`

我很困惑，但是，如何在def predict(self, X)中留下整个X列表我确信我的编写方式不正确，因为我也有y。请让我知道任何可能的解决方案，如果不清楚，我想进一步解释我的代码和问题。非常感谢你。

Answer 1

我想也许你想拥有

self.mean_ = self.df.groupby(['X']).mean()

而不是

self.mean_ = self.df.groupby(['X'].mean())

和

return self.mean_.ix[X].values

而不是

return self.df['y']['X']

在类中写出整个输入列表以进行模型预测

1 个答案: