@jt-lab thanks for the contribution! Can I ask for an example of how this looked before and how it looks after the change? Thanks!

Bonus: If it comes with data so I can test on my end, even better!

@jt-lab thanks for the contribution! Can I ask for an example of how this looked before and how it looks after the change? Thanks!

Bonus: If it comes with data so I can test on my end, even better!

Here you go:
Code (note the two different orders of the levels in the conditional argument of plot_comparisons):

import pandas as pd
import bambi as bmb
from matplotlib.pylab import plt

df = pd.read_csv('simulated_data_order_prob.csv')

model = bmb.Model('p(correct, count) ~ factor1*factor2*factor3 + (factor3+factor2|individual)',
                  df, family='binomial')
trace = model.fit(tune=500, draws=1000, target_accept=0.7, init='adapt_diag')

f, ax = plt.subplots(2)
bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional={"factor1": ["A", "B", "C"], "factor3": [1, 2]},
    ax=ax[0]
) 
bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional={"factor1": ["C", "B", "A"], "factor3": [1, 2]},
    ax=ax[1]
)

Plot before the fix:
(e.g.: git+https://github.com/jt-lab/bambi.git@a072c84809fe62e6efe1d7818f3d5d28d81e00ed)

After the fix:
(e.g.: git+https://github.com/jt-lab/bambi.git)

Data set:
simulated_data_order_prob.csv
This is the same simulated data I also posted in question on the pymc discourse. I just added a further level for factor1 and matched the factor and level names with my original description of the problem.

@jt-lab thanks for finding the subtle bug and opening a PR (good attention to detail)👍🏼. The difference between

# user provided values only (no default values are computed for "factor1" and "factor3")
bmb.interpret.plot_comparisons(
        model=model, 
        idata=trace, 
        contrast={"factor2": ["X", "Y"]},
        conditional={"factor1": ["C", "A", "B"], "factor3": [1, 2]},
)

and

# computes default values for "factor1" and "factor3"
bmb.interpret.plot_comparisons(
        model=model, 
        idata=trace, 
        contrast={"factor2": ["X", "Y"]},
        conditional=["factor1", "factor3"],
)

is that in the latter code snippet, since no values are passed for factor1 and factor3, default values are computed for these covariates. In the Default contrast and conditional values section of the plot_comparisons docs, these details are outlined.

Thanks!

Also, you are right. This is because in def _plot_categoric(...), to determine the x-axis values we call get_unique_levels(plot_data[main]) which in turn uses np.unique to return the unique elements. By default np.unique also sorts this array. Then, once ax.setxticklabels(main_levels) is called, the bug is introduced.

@GStechschulte, thanks for the explantions! I'm not sure I understand the purpose of the dictionary conditional argument. When I list all levels of the factors, I get the same as with the list version defaults. When I list a subset of the levels, I get errors.

Unrelated: When reading through the plot_comparisons doc, I noticed this sentence "Bambi uses the ordering (keys if dict and elements if list)...". Im not sure I understand correctly, but dictionaries don't gurantee any order of the items or keys. So this might also be a source of unexpected behavior. So the user might need to pass an OrderedDict to be safe. But maybe i'm missing something!

Many thanks again!

I'm not sure I understand the purpose of the dictionary conditional argument. When I list all levels of the factors, I get the same as with the list version defaults

This is due to the data types of the data being used to fit the model.

When you pass the list ["factor1", "factor3"] for the conditional arg., factor1 is assigned as the "main" covariate and factor3 is assigned the "group" covariate. Then, since you don't specify any values to be associated with the factor covariates, a default value is computed for them. Default values for "main", "group", and "panel" are the following pseudocode:

if v == "main":
    
    if v == numeric:
        return np.linspace(v.min(), v.max(), 50)
    elif v == categorical:
        return np.unique(v)

elif v == "group":
    
    if v == numeric:
        return np.quantile(v, np.linspace(0, 1, 5))
    elif v == categorical:
        return np.unique(v)

elif v == "panel":
    
    if v == numeric:
        return np.quantile(v, np.linspace(0, 1, 5))
    elif v == categorical:
        return np.unique(v)

In your case, "main" and "group" are both categorical data types. Thus, the default values are the unique values.

When you pass a dict to the conditional arg., the keys of the dict are taken as the "main", "group", and "panel" covariates. Thus, {"factor1": ["A", "B", "C"], "factor3": [1, 2]} corresponds to {"main": "factor1", "group": "factor3"}. Since you passed values for the covariates, no default values are computed.

Then, since you ended up passing all the unique values for factor1, and factor3

df["factor1"].unique(), df["factor3"].unique()

(array(['A', 'B', 'C'], dtype=object), array([1, 2]))

as the dict values, the result of passing a list to conditional and the passing of the dict (stated above) results in the same data being generated. If you were to pass {"factor1": ["A", "B"], "factor3": [1]}, you would get a different result between the two.

Lastly, regarding

When I list a subset of the levels, I get errors.

This is due to a bug in the function def get_unique_levels(...) of utils.py. The code in the if block wasn't returning the unique levels, it was returning all levels. Thus, the reason for the shape error. Once the function returns unique levels

def get_unique_levels(x: np.ndarray) -> np.ndarray:
    return np.unique(x)

there are no more shape errors. As an aside, if factor3 should be a categorical data type, you should explicitly convert the data type (pd.Categorical(data["factor3"]), ordered=True) as such. Otherwise, Bambi and the interpret sub-package functions will treat it as numeric since it takes the values of [1, 2].

I hope this helps 👍🏼

Should I make a separate PR incorporating @jt-lab commits and my local changes to fix the bugs? Or should @jt-lab keep this PR and then commit my changes to fix the bugs? @tomicapretto

@GStechschulte if your fixes are related to what @jt-lab is doing here, I think @jt-lab could allow this PR to be edited by maintainers and then you could contribute here so everything is in place and we respect the original authorship. But if they are separate things, you could open a different PR.

But if you think that's too much, feel free to do something else.

@tomicapretto, how can I make it editable?

I think it's already enabled

I have pushed 3 commits to your branch. Now, the plots work with no errors. I also decided that your original commit to sort the dict values in ascending order is the most appropriate. In particular, the x-axis of graphs should be in ascending order $1, 2, 3,...,n$ or $A, B, C,...,n$.

Thanks a lot for raising the issue and for opening a PR 😄 It is much appreciated!

Below are a few of your examples:

f, ax = plt.subplots(2)
bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional={"factor1": ["A", "B", "C"], "factor3": [1, 2]},
    ax=ax[0]
) 
bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional={"factor1": ["C", "B", "A"], "factor3": [1, 2]},
    ax=ax[1]
)
plt.tight_layout();

bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional={"factor1": ["A", "C"], "factor3": [1, 2]},
    fig_kwargs=dict(figsize=(7, 3))
);

bmb.interpret.plot_comparisons(
    model=model,
    idata=trace,
    contrast={"factor2": ["X", "Y"]},
    conditional=["factor1", "factor3"],
    fig_kwargs=dict(figsize=(7, 3))
);

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@GStechschulte @jt-lab ok I struggled a little bit with git and the fact that we're working on @jt-lab's main branch (I used a different name locally). Nevertheless, it seems everything is working. I just made a small modification to the if-else structure, I think it's clearer now, and I also wrote a fix for the HSGP and the failing tests. We can merge once CI passes.

@tomicapretto Ahh great! And thanks for the HSGP fix 👍🏼

Mmmm. The test_plots.py passed locally before I pushed those three commits (Tomas's commits shouldn't cause them to fail). I will look into this evening.

Mmmm. The test_plots.py passed locally before I pushed those three commits (Tomas's commits shouldn't cause them to fail). I will look into this evening.

The problem happens when we test this

plot_slopes(model, idata, wrt={"Days": 2}, conditional={"Subject": 308}

and it's because it's calling sorted(308), which cannot be sorted because it's not iterable. I'm fixing it now :)

Codecov Report

Merging #731 (8d7e161) into main (e53f8da) will decrease coverage by 0.01%.
Report is 4 commits behind head on main.
The diff coverage is 83.33%.

@@            Coverage Diff             @@
##             main     #731      +/-   ##
==========================================
- Coverage   89.56%   89.55%   -0.01%     
==========================================
  Files          44       44              
  Lines        3525     3524       -1     
==========================================
- Hits         3157     3156       -1     
  Misses        368      368              

... and 1 file with indirect coverage changes

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