Interpret Advanced Usage The interpret module is inspired by the R package marginaleffects and ports the core functionality of {marginaleffects} to Bambi. To close the gap of non-supported functionality in Bambi, interpret now provides a set of helper functions to aid the user in more advanced and complex analysis not covered within the comparisons, predictions, and slopes functions. These helper functions are data_grid and select_draws. The data_grid can be used to create a pairwise grid of data points for the user to pass to model.predict. Subsequently, select_draws is used to select the draws from the posterior (or posterior predictive) group of the InferenceData object returned by the predict method that correspond to the data points that “produced” that draw. ...

#| code-fold: true import matplotlib.pyplot as plt import torch import numpy as np from botorch.acquisition import qLogExpectedImprovement from botorch.fit import fit_gpytorch_model from botorch.models import SingleTaskGP from botorch.optim import optimize_acqf from gpytorch.mlls import ExactMarginalLogLikelihood from torch.distributions import Normal plt.style.use("https://raw.githubusercontent.com/GStechschulte/filterjax/main/docs/styles.mplstyle") Outcome constraints In optimization, it is often the goal that we need to optimize an objective function while satisfying some constraints. For example, we may want to minimize the scrap rate by finding the optimal process parameters of an manufacturing machine. However, we know the scrap rate cannot be below 0. In another setting, we may want to maximize the throughput of a machine, but we know that the throughput cannot exceed the maximum belt speed of the machine. Thus, we need to find regions in the search space that both yield high objective values and satisfy these constraints. In this blog, we will focus on inequality outcome constraints. That is, the domain of the objective function is ...

Survival Models Survival models, also known as time-to-event models, are specialized statistical methods designed to analyze the time until the occurrence of an event of interest. In this notebook, a review of survival analysis (using non-parametric and parametric methods) and censored data is provided, followed by a survival model implementation in Bambi. This blog post is a copy of the survival models documentation I wrote for Bambi. The original post can be found here. ...

Predict New Groups In Bambi, it is possible to perform predictions on new, unseen, groups of data that were not in the observed data used to fit the model with the argument sample_new_groups in the model.predict() method. This is useful in the context of hierarchical modeling, where groups are assumed to be a sample from a larger group. This blog post is a copy of the zero inflated models documentation I wrote for Bambi. The original post can be found here. ...

#| code-fold: true import arviz as az import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np import pandas as pd import warnings import bambi as bmb warnings.filterwarnings("ignore", category=FutureWarning) WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions. Ordinal Regression This blog post is a copy of the ordinal models documentation I wrote for Bambi. The original post can be found here. In some scenarios, the response variable is discrete, like a count, and ordered. Common examples of such data come from questionnaires where the respondent is asked to rate a product, service, or experience on a scale. This scale is often referred to as a Likert scale. For example, a five-level Likert scale could be: ...

#| code-fold: true import arviz as az import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np import pandas as pd import scipy.stats as stats import seaborn as sns import warnings import bambi as bmb warnings.simplefilter(action='ignore', category=FutureWarning) WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions. Zero inflated models This blog post is a copy of the zero inflated models documentation I wrote for Bambi. The original post can be found here. ...

My project “Better tools to interpret complex Bambi regression models” was completed under the organization of NumFOCUS, and mentors Tomás Capretto and Osvaldo Martin. Before I describe the project, objectives, and work completed, I would like to thank my mentors Tomás and Osvaldo for their precious time and support throughout the summer. They were always available and timely in communicating over Slack and GitHub, and provided valuable feedback during code reviews. Additionally, I would like to thank NumFOCUS and the Google Summer of Code (GSoC) program for providing the opportunity to work on such an open source project over the summer. It has been an invaluable experience, and I look forward to contributing to open source projects in the future. ...

It is currently the beginning of week ten of Google Summer of Code 2023. According to the original deliverables table outlined in my proposal, the goal was to have opened a draft PR for the basic functionality of the plot_slopes. Subsequently, week 11 was reserved to further develop the plot_slopes function, and to write tests and a notebook for the documentation, respectively. However, at the beginning of week ten, I have a PR open with the majority of the functionality that marginaleffects has for slopes. In addition, I also exposed the slopes function, added tests, and have a PR open for the documentation. ...

It is currently the end of week five of Google Summer of Code 2023. According to the original deliverables table outlined in my proposal, the goal was to have opened a draft PR for the core functionality of the plot_comparisons. Subsequently, week six and seven were to be spent further developing the plot_comparisons function, and writing tests and a demo notebook for the documentation, respectively. However, at the end of week five, I have a PR open with the majority of the functionality that marginaleffects has. In addition, I also exposed the comparisons function, added tests (which can and will be improved), and have started on documentation. ...

A variant of the Metropolis-Hastings (MH) algorithm that uses clever proposals and is therefore more efficient (you can get a good approximate of the posterior with far fewer samples) is Gibbs sampling. A problem with MH is the need to choose the proposal distribution, and the fact that the acceptance rate may be low. The improvement arises from adaptive proposals in which the distribution of proposed parameter values adjusts itself intelligently, depending upon the parameter values at the moment. This dependence upon the parameters at that moment is an exploitation of conditional independence properties of a graphical model to automatically create a good proposal, with acceptance probability equal to one. ...