This module provides functionality for doing dimensional stacking of uncertain factors in order to reveal patterns in the values for a single outcome of interests. It is inspired by the work reported here with one deviation.

Rather than using association rules to identify the uncertain factors to use, this code uses random forest based feature scoring instead. It is also possible to use the code provided here in combination with any other feature scoring or factor prioritization technique instead, or by simply selecting uncertain factors in some other manner.

ema_workbench.analysis.dimensional_stacking.create_pivot_plot(x, y, nr_levels=3, labels=True, categories=True, nbins=3, bin_labels=False)

convenience function for easily creating a pivot plot

  • x (DataFrame) –
  • y (1d ndarray) –
  • nr_levels (int, optional) – the number of levels in the pivot table. The number of uncertain factors included in the pivot table is two times the number of levels.
  • labels (bool, optional) – display names of uncertain factors
  • categories (bool, optional) – display category names for each uncertain factor
  • nbins (int, optional) – number of bins to use when discretizing continuous uncertain factors
  • bin_labels (bool, optional) – if True show bin interval / name, otherwise show only a number

Return type:


This function performs feature scoring using random forests, selects a number of high scoring factors based on the specified number of levels, creates a pivot table, and visualizes the table. This is a convenience function. For more control over the process, use the code in this function as a template.