title: Getting feature_importances_ after getting optimal TPOT pipeline?
body: <p>I've read through a few pages but need someone to help explain how to make this work for.</p>
  
  <p>I'm using <code>TPOTRegressor()</code> to get an optimal pipeline, but from there I would love to be able to plot the <code>.feature_importances_</code> of the pipeline it returns:</p>
  
  <pre><code>best_model = TPOTRegressor(cv=folds, generations=2, population_size=10, verbosity=2, random_state=seed) #memory='./PipelineCache',       memory='auto',
  best_model.fit(X_train, Y_train)
  feature_importance = best_model.fitted_pipeline_.steps[-1][1].feature_importances_
  </code></pre>
  
  <p>I saw this kind of set up from a now closed issue on Github, but currently I get the error:</p>
  
  <pre><code>Best pipeline: LassoLarsCV(input_matrix, normalize=True)
  
  Traceback (most recent call last):
    File "main2.py", line 313, in &lt;module&gt;
      feature_importance = best_model.fitted_pipeline_.steps[-1][1].feature_importances_
  AttributeError: 'LassoLarsCV' object has no attribute 'feature_importances_'
  </code></pre>
  
  <p>So, how would I get these feature importances from the optimal pipeline, regardless of which one it lands on? Or is this even possible? Or does someone have a better way of going about trying to plot feature importances from a TPOT run?</p>
  
  <p>Thanks!</p>
  
  <p><strong>UPDATE</strong></p>
  
  <p>For clarification, what is meant by Feature Importance is the determination of how important each feature (X's) of your dataset is in determining the predicted (Y) label, using a barchart to plot each feature's level of importance in coming up with its predictions. TPOT doesn't do this directly (I don't think), so I was thinking I'd grab the pipeline it came up with, re-run it on the training data, and then somehow use a <code>.feature_imprtances_</code> to then be able to graph the feature importances, as these are all sklearn regressor's I'm using?</p>