logistic_regression

This module implements logistic regression for scenario discovery.

The module draws its inspiration from Quinn et al (2018) 10.1029/2018WR022743 and Lamontagne et al (2019). The implementation here generalizes their work and embeds it in a more typical scenario discovery workflow with a posteriori selection of the appropriate number of dimensions to include. It is modeled as much as possible on the api used for PRIM and CART.

class ema_workbench.analysis.logistic_regression.Logit(x, y, threshold=0.95)

Implements an interactive version of logistic regression using BIC based forward selection

Parameters:

  • x (DataFrame)

  • y (numpy Array)

  • threshold (float)

coverage

coverage of currently selected model

Type:

float

density

density of currently selected model

Type:

float

res_dim

number of restricted dimensions of currently selected model

Type:

int

peeling_trajectory

stats for each model in peeling trajectory

Type:

DataFrame

models

list of models associated with each model on the peeling trajectory

Type:

list

inspect(i, step=0.1)

Inspect one of the models by showing the threshold tradeoff and summary2

Parameters:

  • i (int)

  • step (float between [0, 1])

plot_pairwise_scatter(i, threshold=0.95)

plot pairwise scatter plot of data points, with contours as background

Parameters:

  • i (int)

  • threshold (float)

Return type:

Figure instance

The lower triangle background is a binary contour based on the specified threshold. All axis not shown are set to a default value in the middle of their range

The upper triangle shows a contour map with the conditional probability, again setting all non shown dimensions to a default value in the middle of their range.

run(**kwargs)

run logistic regression using forward selection using a Bayesian Information Criterion for selecting whether and if so which dimension to add

Parameters:

  • details (kwargs are passed on to model.fit. For)

  • https (see)

show_threshold_tradeoff(i, cmap=<matplotlib.colors.ListedColormap object>, step=0.1)

Visualize the trade off between coverage and density for a given model i across the range of threshold values

Parameters:

  • i (int)

  • cmap (valid matplotlib colormap)

  • step (float, optional)

Return type:

a Figure instance

show_tradeoff(cmap=<matplotlib.colors.ListedColormap object>, annotated=False)

Visualize the trade off between coverage and density. Color is used to denote the number of restricted dimensions.

Parameters:

  • cmap (valid matplotlib colormap)

  • annotated (bool, optional. Shows point labels if True.)

Return type:

a Figure instance

update(model, selected)

helper function for adding a model to the collection of models and update the associated attributes

Parameters:

  • model (statsmodel fitted logit model)

  • selected (list of str)