How to pass a parameter to only one part of a pipeline object in scikit learn?
<p>I need to pass a parameter, <code>sample_weight</code>, to my <code>RandomForestClassifier</code> like so:</p>

<pre><code>X = np.array([[2.0, 2.0, 1.0, 0.0, 1.0, 3.0, 3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
        1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 5.0, 3.0,
        2.0, '0'],
       [15.0, 2.0, 5.0, 5.0, 0.466666666667, 4.0, 3.0, 2.0, 0.0, 0.0, 0.0,
        0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0,
        7.0, 14.0, 2.0, '0'],
       [3.0, 4.0, 3.0, 1.0, 1.33333333333, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0,
        0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
        9.0, 8.0, 2.0, '0'],
       [3.0, 2.0, 3.0, 0.0, 0.666666666667, 2.0, 2.0, 1.0, 0.0, 0.0, 0.0,
        0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
        5.0, 3.0, 1.0, '0']], dtype=object)

y = np.array([ 0.,  0.,  1.,  0.])

m = sklearn.ensemble.RandomForestClassifier(
        random_state=0, 
        oob_score=True, 
        n_estimators=100,
        min_samples_leaf=5, 
        max_depth=10)

m.fit(X, y, sample_weight=np.array([3,4,2,3]))
</code></pre>

<p>The above code works perfectly fine.  Then, I try to do this in a pipeline object like so, using pipeline object instead of only random forest:</p>

<pre><code>m = sklearn.pipeline.Pipeline([
    ('feature_selection', sklearn.feature_selection.SelectKBest(
        score_func=sklearn.feature_selection.f_regression,
        k=25)),
    ('model', sklearn.ensemble.RandomForestClassifier(
        random_state=0, 
        oob_score=True, 
        n_estimators=500,
        min_samples_leaf=5, 
        max_depth=10))])

m.fit(X, y, sample_weight=np.array([3,4,2,3]))
</code></pre>

<p>Now this breaks in the <code>fit</code> method with "<code>ValueError: need more than 1 value to unpack</code>".  </p>

<pre><code>ValueError                                Traceback (most recent call last)
&lt;ipython-input-212-c4299f5b3008&gt; in &lt;module&gt;()
     25         max_depth=10))])
     26 
---&gt; 27 m.fit(X, y, sample_weights=np.array([3,4,2,3]))

/usr/local/lib/python2.7/dist-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
    128         data, then fit the transformed data using the final estimator.
    129         """
--&gt; 130         Xt, fit_params = self._pre_transform(X, y, **fit_params)
    131         self.steps[-1][-1].fit(Xt, y, **fit_params)
    132         return self

/usr/local/lib/python2.7/dist-packages/sklearn/pipeline.pyc in _pre_transform(self, X, y, **fit_params)
    113         fit_params_steps = dict((step, {}) for step, _ in self.steps)
    114         for pname, pval in six.iteritems(fit_params):
--&gt; 115             step, param = pname.split('__', 1)
    116             fit_params_steps[step][param] = pval
    117         Xt = X

ValueError: need more than 1 value to unpack
</code></pre>

<p>I am using <em><code>sklearn</code> version <code>0.14</code></em>.<br>
I think that the problem is that the <code>F selection</code> step in the pipeline does not take in an argument for sample_weights.  how do I pass this parameter to only one step in the pipeline with I run "<code>fit</code>"?  Thanks.</p>