Question

刚开始使用ML并且需要一些帮助才能让sklearn与熊猫一起工作。

http://scikit-learn.org/stable/modules/feature_selection.html#feature-selection-as-part-of-a-pipeline

我正在读这篇文章并决定尝试使用我拥有的DataFrame。以下是我所做的，以及来自它的错误。如果我忽略了一些愚蠢的东西，我对所有这些都很陌生，所以请原谅我，但我觉得最好在这里问一下，而不是试图在没有真正理解的情况下找到答案。

谢谢你们！

In [518]: cols = ['A','B','C','D','E','F','G','H','I','J','K']

In [519]: x = df['Miss'].values

In [520]: y = df[list(cols)].values

In [532]: y.shape
Out[532]: (11345, 11)

In [533]: x.shape
Out[533]: (11345,)

clf = Pipeline([
  ('feature_selection', LinearSVC(penalty="l1", dual=False)),
  ('classification', RandomForestClassifier())])

In [536]: clf.fit(x,y)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/home/cschwalbach/as_research_repo/logs/<ipython-input-536-5c1831092d7a> in <module>()
----> 1 clf.fit(x,y)

/usr/lib64/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
    124         data, then fit the transformed data using the final estimator.
    125         """
--> 126         Xt, fit_params = self._pre_transform(X, y, **fit_params)
    127         self.steps[-1][-1].fit(Xt, y, **fit_params)
    128         return self

/usr/lib64/python2.7/site-packages/sklearn/pipeline.pyc in _pre_transform(self, X, y, **fit_params)
    114         for name, transform in self.steps[:-1]:
    115             if hasattr(transform, "fit_transform"):
--> 116                 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
    117             else:
    118                 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \

/usr/lib64/python2.7/site-packages/sklearn/base.pyc in fit_transform(self, X, y, **fit_params)
    362         else:
    363             # fit method of arity 2 (supervised transformation)

--> 364             return self.fit(X, y, **fit_params).transform(X)
    365
    366

/usr/lib64/python2.7/site-packages/sklearn/svm/base.pyc in fit(self, X, y)
    684             raise ValueError("X and y have incompatible shapes.\n"
    685                              "X has %s samples, but y has %s." %
--> 686                              (X.shape[0], y.shape[0]))
    687
    688         liblinear.set_verbosity_wrap(self.verbose)

ValueError: X and y have incompatible shapes.
X has 1 samples, but y has 124795.

Answer 1

大多数人使用X作为特征，使用y作为标签。不幸的是，你是一个相反的方式。所以你可能会对文档感到困惑。

请改用以下内容

In [519]: y = df['Miss'].values

In [520]: X = df[list(cols)].values

然后您可以按clf.fit(X, y)

调整模型

得到scikit学习与熊猫一起工作

1 个答案: