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`plot_predictions` breaks with HSGP
The attached dataset will help reproduce the issue, but basically:
formula = "p(Campus_Living, N) ~ 1 + (1 | Age_Group) + (1 | Major) + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True) + hsgp(Fall_Year, by=Major, m=10, c=2, scale=True, centered=True)"
m_bambi = bmb.Model(
formula=formula,
data=data,
family="binomial",
categorical=["Age_Group", "Major"],
)
m_bambi.build()
idata_bambi = m_bambi.fit()
## this line fails
bmb.interpret.plot_predictions(
m_bambi,
idata_bambi,
conditional={"Fall_Year": np.linspace(2012, 2025)},
);
yields (full traceback at end of post):
IndexError: index -1 is out of bounds for axis 1 with size 0
If you do formula = "p(Campus_Living, N) ~ 1 + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True)", the error becomesKeyError: 'Age_Group'.
Interestingly, if you change formula to "p(Campus_Living, N) ~ 1 + (1 | Age_Group) + (1 | Major) + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True)" or "p(Campus_Living, N) ~ 1 + (1 | Age_Group) + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True)", the plot will display (although I'm not sure it's right)!
It looks like some indexing is happening in by_values in bambi/model_components.py:262, which yields the error.
Thanks a lot for your help, and LMK if anything is unclear 🙏
Full error:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[225], line 1
----> 1 bmb.interpret.plot_predictions(
2 m_bambi,
3 idata_bambi,
4 conditional={"Fall_Year": np.linspace(2012, 2025)},
5 );
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/interpret/plotting.py:181, in plot_predictions(model, idata, conditional, average_by, target, sample_new_groups, pps, use_hdi, prob, transforms, legend, ax, fig_kwargs, subplot_kwargs)
174 if average_by is True:
175 raise ValueError(
176 "Plotting when 'average_by = True' is not possible as 'True' marginalizes "
177 "over all covariates resulting in a single prediction estimate. "
178 "Please pass a covariate(s) to 'average_by'."
179 )
--> 181 cap_data = predictions(
182 model=model,
183 idata=idata,
184 conditional=conditional,
185 average_by=average_by,
186 target=target,
187 pps=pps,
188 use_hdi=use_hdi,
189 prob=prob,
190 transforms=transforms,
191 sample_new_groups=sample_new_groups,
192 )
194 conditional_info = ConditionalInfo(model, conditional)
195 transforms = transforms if transforms is not None else {}
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/interpret/effects.py:532, in predictions(model, idata, conditional, average_by, target, pps, use_hdi, prob, transforms, sample_new_groups)
530 y_hat_mean = y_hat.mean(("chain", "draw"))
531 else:
--> 532 idata = model.predict(
533 idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False
534 )
535 y_hat = response_transform(idata["posterior"][response.name_target])
536 y_hat_mean = y_hat.mean(("chain", "draw"))
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/models.py:832, in Model.predict(self, idata, kind, data, inplace, include_group_specific, sample_new_groups)
829 else:
830 var_name = f"{response_aliased_name}_{name}"
--> 832 means_dict[var_name] = component.predict(
833 idata, data, include_group_specific, hsgp_dict, sample_new_groups
834 )
836 # Drop var/dim if already present. Needed for out-of-sample predictions.
837 if var_name in idata.posterior.data_vars:
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/model_components.py:185, in DistributionalComponent.predict(self, idata, data, include_group_specific, hsgp_dict, sample_new_groups)
182 linear_predictor_dims = linear_predictor_dims + (response_levels_dim,)
184 if self.design.common:
--> 185 linear_predictor += self.predict_common(
186 posterior, data, in_sample, to_stack_dims, design_matrix_dims, hsgp_dict
187 )
189 if self.design.group and include_group_specific:
190 linear_predictor += self.predict_group_specific(
191 posterior, data, in_sample, to_stack_dims, design_matrix_dims, sample_new_groups
192 )
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/model_components.py:262, in DistributionalComponent.predict_common(self, posterior, data, in_sample, to_stack_dims, design_matrix_dims, hsgp_dict)
256 # NOTE:
257 # The approach here differs from the one in the PyMC implementation.
258 # Here we have a single dot product with many zeros, while there we have many
259 # smaller dot products.
260 # It is subject to change here, but I don't want to mess up dims and coords.
261 if term.by_levels is not None:
--> 262 by_values = x_slice[:, -1].astype(int)
263 x_slice = x_slice[:, :-1]
264 x_slice_centered = (x_slice.data - term.mean[by_values]) / maximum_distance
IndexError: index -1 is out of bounds for axis 1 with size 0
Note that this error also appears when sampling posterior predictions -- m_bambi.predict(idata_bambi, kind="pps")
I've found where the bug is!
https://github.com/bambinos/bambi/blob/b5b9f093c623636ae3a8e2f0765b2f01ca26f4b0/bambi/model_components.py#L242-L246
In line 246 we're deleting columns of the design matrix based on the slice term_slice. This only happens for HSGP. The problem is that the slice for the second (and third, fourth, etc.) HSGP term does not reflect the shape of X at the time it's going to be used to slice X.
I think the fix is to delete the HSGP term data from the design matrix X only after having iterated over all the HSGP terms. I'll test it soon.
@AlexAndorra could you install Bambi from https://github.com/tomicapretto/bambi/tree/fix_hsgp_prediction and try it again?
In line 246 we're deleting columns of the design matrix based on the slice term_slice. This only happens for HSGP. The problem is that the slice for the second (and third, fourth, etc.) HSGP term does not reflect the shape of X at the time it's going to be used to slice X.
Makes sense indeed.
@AlexAndorra could you install Bambi from https://github.com/tomicapretto/bambi/tree/fix_hsgp_prediction and try it again?
Will do ASAP and of course keep you posted. Thanks for the lightning fast fix @tomicapretto 🙏
This works great for "p(Campus_Living, N) ~ 1 + (1 | Age_Group) + (1 | Major) + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True) + hsgp(Fall_Year, by=Major, m=10, c=2, scale=True, centered=True)" @tomicapretto 🍾
However, the error is still here when removing the group-specific effects: "p(Campus_Living, N) ~ 1 + hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True)",
The posterior predictions sample now, but the plot_predictions still fails: KeyError: 'Age_Group'.
This is triggered by:
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/model_components.py:233, in DistributionalComponent.predict_common(self, posterior, data, in_sample, to_stack_dims, design_matrix_dims, hsgp_dict)
231 X = self.design.common.design_matrix
232 else:
--> 233 X = self.design.common.evaluate_new_data(data).design_matrix
235 # Add offset columns to their own design matrix and remove then from common matrix
So it still looks like a problem when updating the design matrix. LMK if that's unclear 🙏
Full traceback:
KeyError Traceback (most recent call last)
Cell In[28], line 2
1 ## this line fails
----> 2 bmb.interpret.plot_predictions(
3 m_bambi,
4 idata_bambi,
5 conditional={"Fall_Year": np.linspace(2012, 2025)},
6 );
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/interpret/plotting.py:181, in plot_predictions(model, idata, conditional, average_by, target, sample_new_groups, pps, use_hdi, prob, transforms, legend, ax, fig_kwargs, subplot_kwargs)
174 if average_by is True:
175 raise ValueError(
176 "Plotting when 'average_by = True' is not possible as 'True' marginalizes "
177 "over all covariates resulting in a single prediction estimate. "
178 "Please pass a covariate(s) to 'average_by'."
179 )
--> 181 cap_data = predictions(
182 model=model,
183 idata=idata,
184 conditional=conditional,
185 average_by=average_by,
186 target=target,
187 pps=pps,
188 use_hdi=use_hdi,
189 prob=prob,
190 transforms=transforms,
191 sample_new_groups=sample_new_groups,
192 )
194 conditional_info = ConditionalInfo(model, conditional)
195 transforms = transforms if transforms is not None else {}
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/interpret/effects.py:532, in predictions(model, idata, conditional, average_by, target, pps, use_hdi, prob, transforms, sample_new_groups)
530 y_hat_mean = y_hat.mean(("chain", "draw"))
531 else:
--> 532 idata = model.predict(
533 idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False
534 )
535 y_hat = response_transform(idata["posterior"][response.name_target])
536 y_hat_mean = y_hat.mean(("chain", "draw"))
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/models.py:834, in Model.predict(self, idata, kind, data, inplace, include_group_specific, sample_new_groups)
831 else:
832 var_name = f"{response_aliased_name}_{name}"
--> 834 means_dict[var_name] = component.predict(
835 idata, data, include_group_specific, hsgp_dict, sample_new_groups
836 )
838 # Drop var/dim if already present. Needed for out-of-sample predictions.
839 if var_name in idata.posterior.data_vars:
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/model_components.py:185, in DistributionalComponent.predict(self, idata, data, include_group_specific, hsgp_dict, sample_new_groups)
182 linear_predictor_dims = linear_predictor_dims + (response_levels_dim,)
184 if self.design.common:
--> 185 linear_predictor += self.predict_common(
186 posterior, data, in_sample, to_stack_dims, design_matrix_dims, hsgp_dict
187 )
189 if self.design.group and include_group_specific:
190 linear_predictor += self.predict_group_specific(
191 posterior, data, in_sample, to_stack_dims, design_matrix_dims, sample_new_groups
192 )
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/model_components.py:233, in DistributionalComponent.predict_common(self, posterior, data, in_sample, to_stack_dims, design_matrix_dims, hsgp_dict)
231 X = self.design.common.design_matrix
232 else:
--> 233 X = self.design.common.evaluate_new_data(data).design_matrix
235 # Add offset columns to their own design matrix and remove then from common matrix
236 for term in self.offset_terms:
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/matrices.py:259, in CommonEffectsMatrix.evaluate_new_data(self, data)
256 new_instance.data = data
257 new_instance.env = self.env
258 new_instance.design_matrix = np.column_stack(
--> 259 [t.eval_new_data(data) for t in self.terms.values()]
260 )
261 new_instance.slices = self.slices
262 new_instance.evaluated = True
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/matrices.py:259, in <listcomp>(.0)
256 new_instance.data = data
257 new_instance.env = self.env
258 new_instance.design_matrix = np.column_stack(
--> 259 [t.eval_new_data(data) for t in self.terms.values()]
260 )
261 new_instance.slices = self.slices
262 new_instance.evaluated = True
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/terms.py:496, in Term.eval_new_data(self, data)
492 result = reduce(
493 get_interaction_matrix, [c.eval_new_data(data) for c in self.components]
494 )
495 else:
--> 496 result = self.components[0].eval_new_data(data)
497 return result
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/call.py:274, in Call.eval_new_data(self, data_mask)
255 """Evaluates the function call with new data.
256
257 This method evaluates the function call within a new data mask. If the transformation
(...)
271 categoric ones.
272 """
273 if self.kind in ["numeric", "categoric"]:
--> 274 x = self.call.eval(data_mask, self.env)
275 if self.kind == "numeric":
276 result = self.eval_new_data_numeric(x)
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/call_resolver.py:270, in LazyCall.eval(self, data_mask, env)
267 callee = self.stateful_transform
269 args = [arg.eval(data_mask, env) for arg in self.args]
--> 270 kwargs = {name: arg.eval(data_mask, env) for name, arg in self.kwargs.items()}
272 return callee(*args, **kwargs)
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/call_resolver.py:270, in <dictcomp>(.0)
267 callee = self.stateful_transform
269 args = [arg.eval(data_mask, env) for arg in self.args]
--> 270 kwargs = {name: arg.eval(data_mask, env) for name, arg in self.kwargs.items()}
272 return callee(*args, **kwargs)
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/call_resolver.py:141, in LazyVariable.eval(self, data_mask, env)
139 result = env.namespace[self.name]
140 except KeyError as e:
--> 141 raise e
142 return result
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/terms/call_resolver.py:139, in LazyVariable.eval(self, data_mask, env)
137 except KeyError:
138 try:
--> 139 result = env.namespace[self.name]
140 except KeyError as e:
141 raise e
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/formulae/environment.py:17, in VarLookupDict.__getitem__(self, key)
15 except KeyError:
16 pass
---> 17 raise KeyError(key)
KeyError: 'Age_Group'
Thanks a lot @AlexAndorra. formulae is raising the correct error.
The error lies in interpret and is happening because when creating the data for plot_predictions, we first return all covariates specified in the model. However, we have a bug when an arg. is passed to by of the HSGP term. Only Fall_Year is being returned. Both Fall_Year and Age_Group should be returned. This isn't a problem with your first model formula because Age_Group is a group-specific term.
As a workaround until I fix the bug I suggest the following.
bmb.interpret.plot_predictions(
m_bambi_2,
idata_bambi_2,
conditional={
"Fall_Year": np.linspace(2012, 2025),
"Age_Group": data.Age_Group.unique()
},
subplot_kwargs={
"main": "Fall_Year",
"group": "Age_Group",
"panel": "Age_Group"
},
fig_kwargs={"figsize": (18, 4), "sharey": True},
legend=False
);
We are able to pass Age_Group to conditional because we know a priori that it should exist in the data grid. However, if you attempt to pass a variable to conditional that was not specified in the formula, then you will get a KeyError.
Aaaaah, that makes sense, thanks a lot @GStechschulte !
Is that gonna work if I have more GPs though? Let's say I have 3 different GPs, on 3 different categorical variables. So now, in conditional I'm gonna have 4 keys (Fall_Year and the 3 covariates), so I have to specify average_by, which I think is gonna have the same issue. Correct?
Yeah I can confirm. If you do:
grades = np.random.choice(["A", "B", "C"], size=len(fake_data))
fake_data["Grades"] = grades
formula = "p(Campus_Living, N) ~ 0 + hsgp(Fall_Year, by=Age_Group, m=10, c=2) + hsgp(Fall_Year, by=Major, m=10, c=2) + hsgp(Fall_Year, by=Grades, m=10, c=2)"
m_bambi = bmb.Model(
formula=formula,
data=fake_data,
family="binomial",
categorical=["Age_Group", "Major", "Grades"],
)
m_bambi.build()
idata_bambi = m_bambi.fit()
# This fails
bmb.interpret.plot_predictions(
m_bambi,
idata_bambi,
conditional={
"Fall_Year": np.linspace(2012, 2025),
"Age_Group": fake_data.Age_Group.unique(),
"Major": "Science",
"Grades": "A",
},
average_by="Age_Group",
);
Now you get:
KeyError: 'Fall_Year'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
Cell In[57], line 1
----> 1 bmb.interpret.plot_predictions(
2 m_bambi,
3 idata_bambi,
4 conditional={
5 "Fall_Year": np.linspace(2012, 2025),
6 "Age_Group": fake_data.Age_Group.unique(),
7 "Major": fake_data.Major.unique(),
8 "Grades": fake_data.Grades.unique(),
9 },
10 average_by="Age_Group",
11 subplot_kwargs={
12 "main": "Fall_Year",
13 "group": "Age_Group",
14 "panel": "Age_Group"
15 },
16 fig_kwargs={"figsize": (18, 4), "sharey": True},
17 legend=False
18 );
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/bambi/interpret/plotting.py:225, in plot_predictions(model, idata, conditional, average_by, target, sample_new_groups, pps, use_hdi, prob, transforms, legend, ax, fig_kwargs, subplot_kwargs)
222 else:
223 fig = axes[0].get_figure()
--> 225 if is_numeric_dtype(cap_data[covariates.main]):
226 axes = plot_numeric(covariates, cap_data, transforms, legend, axes)
227 elif is_categorical_dtype(cap_data[covariates.main]) or is_string_dtype(
228 cap_data[covariates.main]
229 ):
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/pandas/core/frame.py:3761, in DataFrame.__getitem__(self, key)
3759 if self.columns.nlevels > 1:
3760 return self._getitem_multilevel(key)
-> 3761 indexer = self.columns.get_loc(key)
3762 if is_integer(indexer):
3763 indexer = [indexer]
File ~/mambaforge/envs/bayes-workshop/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655, in Index.get_loc(self, key)
3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:
-> 3655 raise KeyError(key) from err
3656 except TypeError:
3657 # If we have a listlike key, _check_indexing_error will raise
3658 # InvalidIndexError. Otherwise we fall through and re-raise
3659 # the TypeError.
3660 self._check_indexing_error(key)
Hope that helps!
@AlexAndorra Mmmm. For me, the three HSGP terms works with
grades = np.random.choice(["A", "B", "C"], size=len(fake_data))
fake_data["Grades"] = grades
formula = "p(Campus_Living, N) ~ 0 + hsgp(Fall_Year, by=Age_Group, m=10, c=2) + hsgp(Fall_Year, by=Major, m=10, c=2) + hsgp(Fall_Year, by=Grades, m=10, c=2)"
m_bambi = bmb.Model(
formula=formula,
data=fake_data,
family="binomial",
categorical=["Age_Group", "Major", "Grades"],
)
m_bambi.build()
idata_bambi = m_bambi.fit()
# This works
bmb.interpret.plot_predictions(
m_bambi,
idata_bambi,
conditional={
"Fall_Year": np.linspace(2012, 2025),
"Age_Group": fake_data.Age_Group.unique(),
"Major": "Science",
"Grades": "A",
},
average_by="Age_Group",
);
In the error output you provided, it looks like you were calling
Cell In[57], line 1
----> 1 bmb.interpret.plot_predictions(
2 m_bambi,
3 idata_bambi,
4 conditional={
5 "Fall_Year": np.linspace(2012, 2025),
6 "Age_Group": fake_data.Age_Group.unique(),
7 "Major": fake_data.Major.unique(),
8 "Grades": fake_data.Grades.unique(),
9 },
10 average_by="Age_Group",
11 subplot_kwargs={
12 "main": "Fall_Year",
13 "group": "Age_Group",
14 "panel": "Age_Group"
15 },
16 fig_kwargs={"figsize": (18, 4), "sharey": True},
17 legend=False
18 );
This is failing because you are only averaging by Age_Group. This is applying an aggregation function (average) for each level of Age_Group and then combining the result; effectively marginalizing over fall year, major, and grades to get the average for each age group level.
For example:
bmb.interpret.predictions(
m_bambi,
idata_bambi,
conditional={
"Fall_Year": np.linspace(2012, 2025),
"Age_Group": fake_data.Age_Group.unique(),
"Major": fake_data.Major.unique(),
"Grades": fake_data.Grades.unique(),
},
average_by="Age_Group"
)
| Age_Group | estimate | lower_3.0% | upper_97.0% |
|---|---|---|---|
| 23-26 | 0.370167 | 0.089879 | 0.650803 |
| <23 | 0.451753 | 0.149715 | 0.781734 |
| >26 | 0.439085 | 0.138099 | 0.753850 |
| Unknown | 0.361037 | 0.078200 | 0.637989 |
If you want to retain levels / values for some combination of the other covariates, you can pass more covariates to average_by. This is particular useful for plotting:
bmb.interpret.plot_predictions(
m_bambi,
idata_bambi,
conditional={
"Fall_Year": np.linspace(2012, 2025),
"Age_Group": fake_data.Age_Group.unique(),
"Major": fake_data.Major.unique(),
"Grades": fake_data.Grades.unique(),
},
average_by=["Fall_Year", "Age_Group", "Major"],
subplot_kwargs={
"main": "Fall_Year",
"group": "Age_Group",
"panel": "Major"
},
fig_kwargs={"figsize": (18, 4), "sharey": True},
legend=True
);
@tomicapretto the HSGP term component is a function call, so we can access the argument name to get Fall_Year. However, this does not return the argument to by of the HSGP term.
The HSGP component also has a levels attribute
# Putting this print inside of `get_model_covariates` of bambi/interpret/utils.py
print(vars(component))
{'call': <formulae.terms.call_resolver.LazyCall at 0x288cf53c0>,
'is_response': False,
'name': 'hsgp(Fall_Year, by=Age_Group, m=10, c=2, scale=True, centered=True)',
'contrast_matrix': None,
'env': <formulae.environment.Environment at 0x288cf7b20>,
'kind': 'numeric',
'levels': None,
'spans_intercept': None,
'value': array([[2011, 2],
[2012, 0],
[2012, 1],
[2012, 2],
[2014, 2],
[2014, 2],
[2015, 3],
[2016, 0],
[2016, 3],
[2017, 0],
[2018, 1],
[2018, 3],
[2019, 3],
[2020, 0],
[2020, 0],
[2021, 0],
[2021, 1],
[2021, 2],
[2021, 3],
[2022, 0]]),
'_intermediate_data': array([[2011, 2],
[2012, 0],
[2012, 1],
[2012, 2],
[2014, 2],
[2014, 2],
[2015, 3],
[2016, 0],
[2016, 3],
[2017, 0],
[2018, 1],
[2018, 3],
[2019, 3],
[2020, 0],
[2020, 0],
[2021, 0],
[2021, 1],
[2021, 2],
[2021, 3],
[2022, 0]])}
which I had originally thought would contain the by argument. But here it says None. Is this a bug? Or do you know how we can access the arguments to by?
If you want to retain levels / values for some combination of the other covariates, you can pass more covariates to average_by. This is particular useful for plotting
Aaaaah ok @GStechschulte , thanks for the detailed answer and help! That's my mistake then -- just wasn't using the function properly.
You know why the years are plotted as floats though? Even though they are entered as int64
@AlexAndorra anytime! Thanks for raising the questions and issues. No worries, the function signature is a bit overwhelming.
Yeah, I am looking into that. That should not be happening.
@GStechschulte
which I had originally thought would contain the by argument. But here it says None. Is this a bug? Or do you know how we can access the arguments to by?
The LazyCall instance has several attributes, one is args, which is what we are already using, and the other is kwargs, which we are not using. See for example
vars(component.call)
{'callee': 'hsgp',
'args': [<formulae.terms.call_resolver.LazyVariable at 0x7f056c63d840>],
'kwargs': {'by': <formulae.terms.call_resolver.LazyVariable at 0x7f056c63d8a0>,
'm': <formulae.terms.call_resolver.LazyValue at 0x7f056c63d960>,
'c': <formulae.terms.call_resolver.LazyValue at 0x7f056c63d9c0>,
'scale': <formulae.terms.call_resolver.LazyVariable at 0x7f056c63da20>,
'centered': <formulae.terms.call_resolver.LazyVariable at 0x7f056c63dae0>},
'stateful_transform': <bambi.transformations.HSGP at 0x7f056c63ded0>}
And to access the name of the by variable we can do component.call.kwargs["by"].name.
So, we need to check kwargs in addition to args. However, I'm realizing this won't be that simple (for now) because:
- Ideally, we only need to get the name of the kwargs that are
LazyVariables. That's not hard, it's simplykwarg.name. However... - Formulae is using
LazyVariablefor things that are not variable names, but values, such asTrue. This is not a problem for Formulae because that "name" is in the namespace where formulas are evaluated. But... - If we do
kwarg.nameon theLazyVariableof, for example,centered, the program will think there's a variable called"True", and that's not the intended behavior.
I think we can do the following:
- Update Formulae so it uses
LazyValues for things likeFalse,True, andNone(I need to check if there are more) - Update Bambi so it also uses the names passed to
kwargswhen these are instances ofLazyVariable. - In parallel, open a PR and merge the change that fixes the bug with the HSGP components
I'll work on these things now
@AlexAndorra I think it should be fixed now.
For that:
- Upgrade formulae. I released version 0.5.2
- Re-install Bambi from https://github.com/tomicapretto/bambi/tree/fix_hsgp_prediction.
I haven't tested it on my end, but I think the changes I implemented were the required ones :)
@tomicapretto ahh that makes sense. Thanks for the information and for fixing this in formulae and bambi already!