title: SHAP function throws exception in plotting method
body: <p><a href="https://github.com/slundberg/shap/files/6729452/samples.zip" rel="nofollow noreferrer">samples.zip</a>
  The sample zipped folder contains:</p>
  <ol>
  <li>model.pkl</li>
  <li>x_test.csv</li>
  </ol>
  <p>To reproduce the problems, do the following steps:</p>
  <ol>
  <li>use <code>lin2 =joblib.load('model.pkl')</code> to load the linear regression model</li>
  <li>use <code>x_test_2 = pd.read_csv('x_test.csv').drop(['Unnamed: 0'],axis=1)</code> to load the <code>x_test_2</code></li>
  <li>run the code below to load the explainer</li>
  </ol>
  <pre><code>explainer_test = shap.Explainer(lin2.predict, x_test_2)
  shap_values_test = explainer_test(x_test_2)
  </code></pre>
  <ol start="4">
  <li>Then run <code>partial_dependence_plot</code> to see the error message:</li>
  </ol>
  <blockquote>
  <p>ValueError: x and y can be no greater than 2-D, but have shapes (2,) and (2, 1, 1)</p>
  </blockquote>
  <pre><code>sample_ind = 3
  shap.partial_dependence_plot(
      &quot;new_personal_projection_delta&quot;, 
      lin.predict, 
      x_test, model_expected_value=True,
      feature_expected_value=True, ice=False,
      shap_values=shap_values_test[sample_ind:sample_ind+1,:]
  )
  </code></pre>
  <ol start="5">
  <li>Run another function to plot waterfall to see error message:</li>
  </ol>
  <blockquote>
  <p>Exception: waterfall_plot requires a scalar base_values of the model output as the first parameter, but you have passed an array as the first parameter! Try shap.waterfall_plot(explainer.base_values[0], values[0], X[0]) or for multi-output models try shap.waterfall_plot(explainer.base_values[0], values[0][0], X[0]).</p>
  </blockquote>
  <p><code>shap.plots.waterfall(shap_values_test[sample_ind], max_display=14)</code></p>
  <h3>Questions:</h3>
  <ol>
  <li>Why I cannot run <code>partial_dependence_plot</code> &amp; <code>shap.plots.waterfall</code>?</li>
  <li>What changes I need to do with my input so I can run the methods above?</li>
  </ol>