Predictive Explorer#

This function will automatically generate textboxes for a function, which makes it easier to explore how the prior predictive distribution changes when we change the priors. This function supports PreliZ, PyMC and Bambi Models.

import preliz as pz
import numpy as np
import pandas as pd

pz.style.library["preliz-doc"]["figure.dpi"] = 100
pz.style.library["preliz-doc"]["figure.figsize"] = (10, 4)
pz.style.use("preliz-doc")

Suppose you already have a model, but you are unsure about the implications of its parameters. You can write it using PreliZ distributions and put it inside a function with the arguments being the parameters you want to explore.

x = np.linspace(0, 1, 100)
def a_preliz_model(a_mu, a_sigma, c_sigma=1):
    a = pz.Normal(a_mu, a_sigma).rvs()
    c = pz.Gamma(mu=2, sigma=c_sigma).rvs()
    b = pz.Normal(np.exp(a)*x, c).rvs()
    return b

By calling predictive_explorer you will get textboxes with some default initial values and for you to explore.

pz.predictive_explorer(a_preliz_model)

After the parameter name, you will see a tuple indicating the valid range of the parameter. These values are inferred automatically by predictive_explorer.

Currently, we use a very simple heuristic to find the range, so take the suggestion with a pinch of salt predictive_explorer supports three types of plots: empirical cumulative distribution functions (CDFs), kernel density estimations (KDEs), and histograms. Additionally, you can also add custom Matplotlib code using the plot_func parameter.

# Custom function to plot a histogram
def custom_hist(predictions, ax):
    ax.hist(predictions.flatten(), bins='auto', alpha=0.7)

# plot_func set to custom_hist for preliz model
pz.predictive_explorer(a_preliz_model, samples=10, plot_func=custom_hist)

PyMC is an optional dependency of PreliZ, you only need it if you want to use the predictive_explorer function with PyMC models.

To install PyMC, you can run the following command:

conda install -c conda-forge pymc

# Add pymc
import pymc as pm

You can write the model using PyMC distributions and place it inside the function with the arguments being the parameters you want to explore.

x = np.linspace(0, 1, 100)
def a_pymc_model(a_mu, a_sigma, c_sigma=1):
    with pm.Model() as model:
        a = pm.Normal("a", a_mu, a_sigma)
        c = pm.Gamma("c", mu=2, sigma=c_sigma)
        b = pm.Normal("b", np.exp(a) * x, c, observed=[0] * 100)
    return model

The predictive_explorer function auto-detects that the model contains PyMC distributions, alternatively you can specify that the function should use the PyMC engine by providing the parameter engine=pymc.

pz.predictive_explorer(a_pymc_model)

After the parameter name, you will see a tuple indicating the valid range of the parameter. These values are inferred automatically by predictive_explorer.

Currently, we use a very simple heuristic to find the range, so take the suggestion with a pinch of salt predictive_explorer supports three types of plots: empirical cumulative distribution functions (CDFs), kernel density estimations (KDEs), and histograms. Additionally, you can also add custom Matplotlib code using the plot_func parameter

# Custom function to plot a histogram
def custom_hist(predictions, ax):
    ax.hist(predictions.flatten(), bins='auto', alpha=0.7)

# plot_func set to custom_hist for pymc model
pz.predictive_explorer(a_pymc_model, samples=10, plot_func=custom_hist)

Bambi is an optional dependency of PreliZ, you only need it if you want to use the predictive_explorer function with Bambi models.

To install Bambi, you can run the following command:

conda install -c conda-forge bambi

# Add bambi
import bambi as bmb

The predictive_explorer function allows you to write the model using Bambi distributions and inout it inside the function with the arguments being the parameters you want to explore.

data = pd.DataFrame(
{
    "y": np.random.normal(size=100),
    "x": np.random.normal(size=100),
}
)
def a_bambi_model(a_mu, a_sigma):
    prior = {"Intercept": bmb.Prior("Normal", mu=a_mu, sigma=a_sigma)}
    a_model = bmb.Model("y ~ x", data, priors=prior)
    return a_model

The predictive_explorer function automatically detects if the model contains Bambi distributions. Alternatively, you can specify that the function should use the Bambi engine by providing the parameter engine=bambi.

pz.predictive_explorer(a_bambi_model)

After the parameter name, you will see a tuple indicating the valid range of the parameter. These values are inferred automatically by predictive_explorer.

Currently, we use a very simple heuristic to find the range, so take the suggestion with a pinch of salt predictive_explorer supports three types of plots: empirical cumulative distribution functions (CDFs), kernel density estimations (KDEs), and histograms. Additionally, you can also add custom Matplotlib code using the plot_func parameter.

# Custom function to plot a histogram
def custom_hist(predictions, ax):
    ax.hist(predictions.flatten(), bins='auto', alpha=0.7)

pz.predictive_explorer(a_bambi_model, samples=10, plot_func=custom_hist)