v0.3.1: responsive classifier components

Version 0.3.1:

This version is mostly about pre-calculating and optimizing the classifier statistics
components. Those components should now be much more responsive with large datasets.

New Features

• new methods roc_auc_curve(pos_label) and pr_auc_curve(pos_label)
• new method get_classification_df(...) to get dataframe with number of labels
  above and below a given cutoff.
  • this now gets used by plot_classification(..)
• new method confusion_matrix(cutoff, binary, pos_label)
• added parameters sort_features to FeatureInputComponent:
  • defaults to 'shap': order features by mean absolute shap
  • if set to 'alphabet' features are sorted alphabetically
• added parameter fill_row_first to FeatureInputComponent:
  • defaults to True: fill first row first, then next row, etc
  • if False: fill first column first, then second column, etc

Bug Fixes

• categorical mappings now updateable with pandas<=1.2 and python==3.6
• title now overridable for RegressionRandomIndexComponent
• added assert check on summary_type for ShapSummaryComponent

Improvements

• pre-Calculating lift_curve_df only once and then storing for each pos_label
  • plus: storing only 100 evenly spaced rows of lift_curve_df
  • dashboard should be more responsive for large datasets
• pre-calculating roc_auc_curve and pr_auc_curve
  • dashboard should be more responsive for large datasets
• pre-calculating confusion matrices
  • dashboard should be more responsive for large datasets
• pre-calculating classification_dfs
  • dashboard should be more responsive for large datasets
• confusion matrix: added axis title, moved predicted labels to bottom of graph
• precision plot component: when only adjusting cutoff, simply updating the cutoff
  line, without recalculating the plot.