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model.predict_proba is not working in scoring script for classification

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
25/100
Issue type
Bug
Clarity
Needs clarification
Activity status
Stale
Tech stack
azure, jupyter-notebook, python

Research direction

Start with scoring_script.py, especially the run function and its input/output schemas, then inspect the ManagedOnlineDeployment CodeConfiguration. Reproduce the deployed request with the shown model.predict_proba call and compare its response with the local result. Done means the deployed scoring response contains class probabilities rather than only class labels.

Written by the indexing model from the issue text.

Description

This is run function. But My output of test data is class only not probabilities.
@input_schema('data', PandasParameterType(input_sample))
@output_schema(NumpyParameterType(output_sample))
def run(data):
result=model.predict_proba(data)
return result.tolist()

My deployement Function for reference
deployment = ManagedOnlineDeployment(
name=deployment_name,
endpoint_name=online_endpoint_name,
model=registered_model.id,
instance_type="Standard_F4s_v2",
instance_count=1,
code_configuration=CodeConfiguration(
code="./artifact_downloads/outputs",
scoring_script="scoring_script.py",
),
liveness_probe=ProbeSettings(
failure_threshold=30,
success_threshold=1,
timeout=2,
period=10,
initial_delay=2000,
),
readiness_probe=ProbeSettings(
failure_threshold=10,
success_threshold=1,
timeout=10,
period=10,
initial_delay=2000,
),
)

Dominant language
Jupyter Notebook
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