interpret predictions enhancements

[GStechschulte]

This PR adds new functionality to the predictions and plot_predictions functions in interpret and resolves #735. Users can now

• compute unit-level predictions
• compute predictions on user-provided values

Previously, users could only pass a string or list of covariates to compute conditional adjusted predictions. Now, predictions has "most of" the functionality that {marginaleffects} has. Additionally, the changes result in a more standard API when calling comparisons, predictions, and slopes. Each function has the arg. conditional in which the user can "condition" their estimates on. Furthermore, each function can now compute:

• unit-level (user passes None)
• user-provided (user passes a dictionary)
• grid of values (user passes a string or list of covariates)

Below, you will find a couple demos:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings

import bambi as bmb

data = bmb.load_data('mtcars')
data["cyl"] = data["cyl"].replace({4: "low", 6: "medium", 8: "high"})
data["gear"] = data["gear"].replace({3: "A", 4: "B", 5: "C"})
data["cyl"] = pd.Categorical(data["cyl"], categories=["low", "medium", "high"], ordered=True)

model = bmb.Model("mpg ~ 0 + hp * wt + cyl + gear", data)
idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)

# unit-level predictions
bmb.interpret.predictions(
    model,
    idata,
    conditional=None
)

cyl	gear	hp	wt	estimate	lower_3.0%	upper_97.0%
medium	B	110	2.620	22.233424	20.051966	24.476544
medium	B	110	2.875	21.320402	19.196886	23.344765
low	B	93	2.320	25.901435	24.255648	27.558330
medium	A	110	3.215	18.751708	16.259737	21.293185
high	A	175	3.440	16.908354	15.261489	18.666662

Unit level predictions and average over hp and wt to obtain marginal effects of gear and cyl:

bmb.interpret.plot_predictions(
    model,
    idata,
    conditional=None,
    average_by=["gear", "cyl"],
    fig_kwargs={"figsize": (7, 3)},
);

Compute a pairwise grid using user-provided values and compute predictions:

bmb.interpret.plot_predictions(
    model,
    idata,
    conditional={
        "hp": [100, 120],
        "cyl": ["low", "medium", "high"],
        "gear": "A",
    },
    subplot_kwargs={"main": "hp", "group": "gear", "panel": "cyl"},
    fig_kwargs={"figsize": (10, 4), "sharey": True},
    legend=True
);

To do:

• finish updating docs that use plot_predictions
• review the added test cases in plot_predictions

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[GStechschulte]

@tomicapretto although it is ~660 lines of code added 😵‍💫 a lot of it comes from docstring additions, more error handling, and tests. Most of the added functionality leveraged existing functions.

[tomicapretto]

@GStechschulte being 100% honest I can't understand all the changes as well as you do. I do see in many cases you made things more general and you are reusing code more, which is great. So once you finish the implementation, I trust your judgment to merge this.

Two more comments:

1. The test failure is because of some non-compatible type hint on Python 3.9. I _think it's fixed here but I'm not sure.
2. Do you think we can cut a 0.13.0 release after this? I just realized we don't have a release with the interpret submodule and I think it's quite mature at this point.

[GStechschulte]

being 100% honest I can't understand all the changes as well as you do

Mmm. That's not a good sign in my opinion. Is it the code diff that you are unsure about, or what has changed in the predictions functionality?

The test failure is because of some non-compatible type hint on Python 3.9. I _think it's fixed pymc-devs/pymc#6945 but I'm not sure.

It has been resolved.

Do you think we can cut a 0.13.0 release after this? I just realized we don't have a release with the interpret submodule and I think it's quite mature at this point.

Yup, I think we can go ahead with that 👍🏼

[tomicapretto]

Mmm. That's not a good sign in my opinion. Is it the code diff that you are unsure about, or what has changed in the predictions functionality?

Oh no, I don't mean this in a bad way at all. What I'm saying is that the submodule grew a lot, for good reasons, and I'm not as familiar with everything as you are. So I can only provide a high-level review without getting deep into details because it would take much more time.

[tomicapretto]

Looks great! Go ahead and merge if you don't plan to add anything.

[jt-lab]

@GStechschulte, many thanks for the work on the submodule, it really helps a lot! I can report that the preliminary average_by solutions already worked very well not only on the simulated data set but also on others. The predictions align well with the observed data.

[GStechschulte]

@jt-lab Thank you and this is great to hear! 😄 We / I really appreciate you taking the time to open the issues and to give feedback. Cheers!