text: Title: Extend PCA Visualizer with Component-Feature Strength
  Body: **Describe the solution you'd like**
  
  Provide an optional heatmap and color bar underneath the PCA visualizer (by shifting the lower axes) that shows the magnitude of each feature value to the component. This provides an explanation of which features are contributing the most to which component. 
  
  **Is your feature request related to a problem? Please describe.**
  
  Although we have the biplot mode to plot feature strengths, they can sometimes be visually overlapping or unintelligible, particularly if there is a large number of features. 
  
  **Examples**
  
  ![image from ios](https://user-images.githubusercontent.com/745966/45625627-52dff780-ba5b-11e8-81d5-7f2a82f6b1d6.jpg)
  
  Code to generate this:
  
  ```python
  fig, ax = plt.subplots(figsize=(8, 4))
  plt.imshow(pca.components_, interpolation = 'none', cmap = 'plasma')
  feature_names = list(cancer.feature_names)
  
  ax.set_xticks(np.arange(-.5, len(feature_names)));
  ax.set_yticks(np.arange(0.5, 2));
  ax.set_xticklabels(feature_names, rotation=90, ha='left', fontsize=12);
  ax.set_yticklabels(['First PC', 'Second PC'], va='bottom', fontsize=12);
  
  plt.colorbar(orientation='horizontal', ticks=[pca.components_.min(), 0,
                                                pca.components_.max()], pad=0.65);
  ```
  
  Though we will probably want to use the `pcolormesh` rather than `imshow` as in `Rank2D`, `ClassificationReport` and `ConfusionMatrix`. Additionally it might be a tad nicer if the color bar was above the feature plot so that the axes names were the last thing in the chart. 
  
  **Notes**
  
  This idea comes from page 55-56 of [Data Science Documentation](https://media.readthedocs.org/pdf/python-data-science/latest/python-data-science.pdf). I would be happy to include a citation to this in our documentation. (HTML version is [here](https://python-data-science.readthedocs.io/en/latest/unsupervised.html#principal-component-analysis)). @mapattacker any thoughts? 
  
  See also #476 for other updates to the PCA visualizer. 
  