Getting ValueError: Unsupported explanation type when calling - ResponsibleAIDashboard(rai_insights) #2599

bandipavan · 2025-03-06T15:51:33Z

Describe the bug
Getting ValueError: Unsupported explanation type when calling - ResponsibleAIDashboard(rai_insights)

To Reproduce
this is part of responsibleaidashboard-blbooksgenre-binary-text-classification-model-debugging.ipynb

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See error

Stack trace
ValueError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:290, in ExplainerManager._get_interpret(self, explanation)
289 importances = FeatureImportance()
--> 290 features, scores, intercept = self._compute_global_importances(
291 explanation)
292 importances.featureNames = features

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:317, in ExplainerManager._compute_global_importances(self, explanation)
316 if is_classif_task:
--> 317 global_exp = explanation[:, :, :].mean(0)
318 features = convert_to_list(global_exp.feature_names)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\shap_explanation.py:422, in Explanation.getitem(self, item)
421 new_self = copy.copy(self)
--> 422 new_self._s = new_self._s.getitem(item)
423 new_self.op_history.append({
424 "name": "getitem",
425 "args": (item,),
426 "prev_shape": self.shape
427 })

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer.py:112, in Slicer.getitem(self, item)
111 slicer_index = index_slicer[tracked.dim]
--> 112 sliced_o = tracked[slicer_index]
113 sliced_dim = resolve_dim(index_tup, tracked.dim)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py:69, in AtomicSlicer.getitem(self, item)
68 # Slice according to object type.
---> 69 return UnifiedDataHandler.slice(self.o, index_tup, self.max_dim)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py:583, in UnifiedDataHandler.slice(cls, o, index_tup, max_dim)
582 is_element, sliced_o, cut = head_slice(o, index_tup, max_dim)
--> 583 out = tail_slice(sliced_o, index_tup[cut:], max_dim - cut, is_element)
584 return out

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py:443, in ArrayHandler.tail_slice(cls, o, tail_index, max_dim, flatten)
441 import numpy
--> 443 return numpy.array(inner)
444 elif _safe_isinstance(o, "torch", "Tensor"):

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (20,) + inhomogeneous part.

The above exception was the direct cause of the following exception:

ValueError Traceback (most recent call last)
Cell In[1], line 98
94 rai_insights.error_analysis.add()
96 rai_insights.compute()
---> 98 ResponsibleAIDashboard(rai_insights)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\raiwidgets\responsibleai_dashboard.py:40, in ResponsibleAIDashboard.init(self, analysis, public_ip, port, locale, cohort_list, is_private_link, **kwargs)
36 def init(self, analysis: RAIInsights,
37 public_ip=None, port=None, locale=None,
38 cohort_list=None, is_private_link=False,
39 **kwargs):
---> 40 self.input = ResponsibleAIDashboardInput(
41 analysis, cohort_list=cohort_list)
43 super(ResponsibleAIDashboard, self).init(
44 dashboard_type="ResponsibleAI",
45 model_data=self.input.dashboard_input,
(...) 50 is_private_link=is_private_link,
51 **kwargs)
53 def predict():

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\raiwidgets\responsibleai_dashboard_input.py:41, in ResponsibleAIDashboardInput.init(self, analysis, cohort_list)
39 model = analysis.model
40 self._is_classifier = is_classifier(model)
---> 41 self.dashboard_input = analysis.get_data()
43 self._validate_cohort_list(cohort_list)
45 self._feature_length = len(self.dashboard_input.dataset.feature_names)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\rai_text_insights\rai_text_insights.py:490, in RAITextInsights.get_data(self)
488 data = RAIInsightsData()
489 data.dataset = self._get_dataset()
--> 490 data.modelExplanationData = self.explainer.get_data()
491 data.errorAnalysisData = self.error_analysis.get_data()
492 return data

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:263, in ExplainerManager.get_data(self)
257 def get_data(self):
258 """Get explanation data
259
260 :return: A array of ModelExplanationData.
261 :rtype: List[ModelExplanationData]
262 """
--> 263 return [self._get_interpret(i) for i in self.get()]

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:263, in (.0)
257 def get_data(self):
258 """Get explanation data
259
260 :return: A array of ModelExplanationData.
261 :rtype: List[ModelExplanationData]
262 """
--> 263 return [self._get_interpret(i) for i in self.get()]

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:303, in ExplainerManager._get_interpret(self, explanation)
301 interpretation.precomputedExplanations = precomputedExplanations
302 except Exception as ex:
--> 303 raise ValueError(
304 "Unsupported explanation type") from ex
305 return interpretation

ValueError: Unsupported explanation type
Expected behavior
A clear and concise description of what you expected to happen.

Screenshots
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Desktop (please complete the following information):

OS: [e.g. iOS]
Browser [e.g. chrome, safari]
Python version: [e.g. 3.9.12]
raiwidgets and responsibleai package versions [e.g. 0.19.0]

To get the package versions please run in your command line:

pip show raiwidgets
pip show responsibleai

Additional context
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The text was updated successfully, but these errors were encountered:

bandipavan · 2025-03-06T16:01:59Z

It is part of example - notebooks/responsibleaidashboard/text/responsibleaidashboard-blbooksgenre-binary-text-classification-model-debugging.ipynb

imatiach-msft · 2025-03-06T21:29:12Z

@bandipavan sorry about the issue you are encountering. Can you please run:

pip show shap

and downgrade/upgrade shap:

pip install --upgrade "shap<=0.44.0"

There were some breaking changes in shap and this issue looks like it might be related to that package.

bandipavan · 2025-03-07T07:56:05Z

Thank you for the prompt response, applied the changes, no difference

pip install --upgrade "shap<=0.44.0"

Installing collected packages: shap
Attempting uninstall: shap
Found existing installation: shap 0.43.0
Uninstalling shap-0.43.0:
Successfully uninstalled shap-0.43.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
econml 0.15.1 requires shap<0.44.0,>=0.38.1, but you have shap 0.44.0 which is incompatible.
responsibleai 0.36.0 requires pandas<2.0.0,>=0.25.1, but you have pandas 2.2.3 which is incompatible.
Successfully installed shap-0.44.0

python .\ValidateModel.py
Model download attempt 1 of 4
Device set to use cpu
C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\transformers\pipelines\text_classification.py:106: UserWarning: `return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.
warnings.warn(
number of errors on test dataset: 1
Dataset download attempt 1 of 4
20it [00:00, 48.26it/s]
PartitionExplainer explainer: 21it [07:54, 23.73s/it]

Error Analysis
Current Status: Generating error analysis reports.
Current Status: Finished generating error analysis reports.
Time taken: 0.0 min 0.19244809999509016 sec**

Traceback (most recent call last):
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py", line 290, in _get_interpret
features, scores, intercept = self._compute_global_importances(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py", line 317, in _compute_global_importances
global_exp = explanation[:, :, :].mean(0)
~~~~~~~~~~~^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\shap_explanation.py", line 418, in getitem
new_self._s = new_self._s.getitem(item)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer.py", line 112, in getitem
sliced_o = tracked[slicer_index]
~~~~~~~^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py", line 69, in getitem
return UnifiedDataHandler.slice(self.o, index_tup, self.max_dim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py", line 583, in slice
out = tail_slice(sliced_o, index_tup[cut:], max_dim - cut, is_element)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\slicer\slicer_internal.py", line 443, in tail_slice
return numpy.array(inner)
^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (20,) + inhomogeneous part.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "C:\Users\Pavan.Bandi\AIValidator\ValidateModel.py", line 98, in
ResponsibleAIDashboard(rai_insights)
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\raiwidgets\responsibleai_dashboard.py", line 40, in init
self.input = ResponsibleAIDashboardInput(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\raiwidgets\responsibleai_dashboard_input.py", line 41, in init
self.dashboard_input = analysis.get_data()
^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\rai_text_insights\rai_text_insights.py", line 490, in get_data
data.modelExplanationData = self.explainer.get_data()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py", line 263, in get_data
return [self._get_interpret(i) for i in self.get()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py", line 263, in
return [self._get_interpret(i) for i in self.get()]
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Pavan.Bandi\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py", line 303, in _get_interpret
raise ValueError(
ValueError: Unsupported explanation type

imatiach-msft · 2025-03-10T14:55:15Z

This looks like a problem with using an older version of the interpret package.
I believe this is fixed in the latest interpret-community 0.32.0 which was released a month ago and updated to pandas>2.0 and numpy>2.0, but this repository hasn't been updated yet:
https://github.com/interpretml/interpret-community/releases/tag/v0.32.0
For now I would install:
pip install "pandas<2.0" "numpy<1.26.0"
We will upgrade this repository to support >2.0 for all workflows, no confirmed ETA yet though.

imatiach-msft · 2025-03-10T14:58:58Z

@bandipavan The key error in the stack trace above is:

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (20,) + inhomogeneous part.

this is coming from numpy>1.26 and older interpret package code.
I fixed a similar issue here:
#2594
The quick fix is to install the older version of numpy<1.26.0. I also noticed based on logs that pandas>2.0 and that might encounter some issues as well.
The other possibility is to update interpret-core package to latest but there could be other issues there so I wouldn't recommend it.

bandipavan · 2025-03-11T06:44:49Z

@imatiach-msft Applied the suggested changes, but unfortunately I am still getting the same error, here is the list of packages with their version

Package Version

aiohappyeyeballs 2.5.0
aiohttp 3.11.13
aiosignal 1.3.2
annotated-types 0.7.0
anyio 4.8.0
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
arrow 1.3.0
asttokens 3.0.0
async-lru 2.0.4
attrs 25.1.0
babel 2.17.0
beautifulsoup4 4.13.3
bidict 0.23.1
bleach 6.2.0
blinker 1.9.0
blis 1.2.0
catalogue 2.0.10
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.1
click 8.1.8
cloudpathlib 0.21.0
cloudpickle 3.1.1
colorama 0.4.6
comm 0.2.2
confection 0.1.5
cymem 2.0.11
datasets 3.3.2
debugpy 1.8.13
decorator 5.2.1
defusedxml 0.7.1
dice-ml 0.11
dill 0.3.8
econml 0.15.1
en_core_web_sm 3.8.0
erroranalysis 0.5.5
executing 2.2.0
fairlearn 0.7.0
fastjsonschema 2.21.1
filelock 3.17.0
Flask 3.1.0
flask-cors 5.0.1
Flask-SocketIO 5.5.1
fqdn 1.5.1
frozenlist 1.5.0
fsspec 2024.12.0
gender-guesser 0.4.0
gevent 24.11.1
greenlet 3.1.1
h11 0.14.0
httpcore 1.0.7
httpx 0.28.1
huggingface-hub 0.29.2
idna 3.10
interpret_community 0.32.0
interpret-core 0.6.9
ipykernel 6.29.5
ipython 9.0.1
ipython_pygments_lexers 1.1.1
isoduration 20.11.0
itsdangerous 2.2.0
jedi 0.19.2
Jinja2 3.1.6
joblib 1.4.2
json5 0.10.0
jsonpointer 3.0.0
jsonschema 4.23.0
jsonschema-specifications 2024.10.1
jupyter_client 8.6.3
jupyter_core 5.7.2
jupyter-events 0.12.0
jupyter-lsp 2.2.5
jupyter_server 2.15.0
jupyter_server_terminals 0.5.3
jupyterlab 4.3.5
jupyterlab_pygments 0.3.0
jupyterlab_server 2.27.3
langcodes 3.5.0
language_data 1.3.0
lightgbm 4.6.0
llvmlite 0.41.1
marisa-trie 1.2.1
markdown-it-py 3.0.0
MarkupSafe 3.0.2
matplotlib-inline 0.1.7
mdurl 0.1.2
mistune 3.1.2
ml_wrappers 0.6.0
mpmath 1.3.0
multidict 6.1.0
multiprocess 0.70.16
murmurhash 1.0.12
nbclient 0.10.2
nbconvert 7.16.6
nbformat 5.10.4
negspacy 1.0.4
nest-asyncio 1.6.0
networkx 2.5
nlp-feature-extractors 0.1.0
notebook 7.3.2
notebook_shim 0.2.4
numba 0.58.1
numpy 1.25.2
overrides 7.7.0
packaging 24.2
pandas 1.5.3
pandocfilters 1.5.1
parso 0.8.4
patsy 1.0.1
pillow 11.1.0
pip 25.0.1
platformdirs 4.3.6
preshed 3.0.9
prometheus_client 0.21.1
prompt_toolkit 3.0.50
propcache 0.3.0
psutil 7.0.0
pure_eval 0.2.3
pyarrow 19.0.1
pycparser 2.22
pydantic 2.10.6
pydantic_core 2.27.2
Pygments 2.19.1
python-dateutil 2.9.0.post0
python-dotenv 1.0.1
python-engineio 4.11.2
python-json-logger 3.2.1
python-socketio 5.12.1
pytz 2025.1
pywin32 308
pywinpty 2.0.15
PyYAML 6.0.2
pyzmq 26.2.1
rai_core_flask 0.7.6
raiutils 0.4.2
raiwidgets 0.36.0
referencing 0.36.2
regex 2024.11.6
requests 2.32.3
responsibleai 0.36.0
responsibleai_text 0.2.7
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rich 13.9.4
rpds-py 0.23.1
safetensors 0.5.3
scikit-learn 1.5.1
scipy 1.15.2
semver 2.13.0
Send2Trash 1.8.3
sentencepiece 0.2.0
setuptools 65.5.0
shap 0.43.0
shellingham 1.5.4
simple-websocket 1.1.0
six 1.17.0
slicer 0.0.7
smart-open 7.1.0
sniffio 1.3.1
soupsieve 2.6
spacy 3.8.4
spacy-legacy 3.0.12
spacy-loggers 1.0.5
sparse 0.15.5
srsly 2.5.1
stack-data 0.6.3
statsmodels 0.13.5
sympy 1.13.1
terminado 0.18.1
thinc 8.3.4
threadpoolctl 3.5.0
tinycss2 1.4.0
tokenizers 0.21.0
torch 2.6.0
tornado 6.4.2
tqdm 4.67.1
traitlets 5.14.3
transformers 4.49.0
typer 0.15.2
types-python-dateutil 2.9.0.20241206
typing_extensions 4.12.2
tzdata 2025.1
uri-template 1.3.0
urllib3 2.3.0
wasabi 1.1.3
wcwidth 0.2.13
weasel 0.4.1
webcolors 24.11.1
webencodings 0.5.1
websocket-client 1.8.0
Werkzeug 3.1.3
wrapt 1.17.2
wsproto 1.2.0
xxhash 3.5.0
yarl 1.18.3
zope.event 5.0
zope.interface 7.2

Stacktrace
alueError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\responsibleai_text\managers\explainer_manager.py:290, in ExplainerManager._get_interpret(self, explanation)
289 importances = FeatureImportance()
--> 290 features, scores, intercept = self._compute_global_importances(
291 explanation)
292 importances.featureNames = features