Removing the deprecated log_xxx_metadata calls
#28
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WalkthroughThe pull request introduces changes to the workflow configuration and updates the logging method in two functions. The Changes
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Actionable comments posted: 1
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (3)
.github/workflows/ci.yml(1 hunks)template/steps/data_preprocessor.py(2 hunks)template/steps/model_evaluator.py(2 hunks)
🔇 Additional comments (4)
template/steps/data_preprocessor.py (2)
10-10: LGTM: Import statement updated correctly
The import statement has been properly updated to use the new log_metadata function from zenml.
70-74: Verify metadata retrieval in inference pipeline
The logging call has been correctly updated to use the new log_metadata API. However, since this metadata is explicitly used for inference (as noted in the comment "Log metadata so we can load it in the inference pipeline"), we should verify that the inference pipeline can still retrieve this metadata correctly with the new API.
Let's verify the metadata usage in inference-related code:
✅ Verification successful
Metadata retrieval is working correctly in the inference pipeline
The verification shows that the metadata is being correctly retrieved in the inference pipeline. In template/run.py, the metadata is accessed using the new API format:
random_state = preprocess_pipeline_artifact.run_metadata["random_state"]
target = preprocess_pipeline_artifact.run_metadata["target"]This matches perfectly with how the metadata is being logged in the preprocessor step using log_metadata with artifact_name="preprocess_pipeline". The infer_artifact=True parameter ensures the metadata is properly associated with the artifact, making it accessible via the run_metadata property.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for metadata loading in inference-related files
rg -l "inference" | xargs rg -A 5 "(load_metadata|get_metadata)"
Length of output: 64
Script:
#!/bin/bash
# Let's try a broader search for metadata-related functions in Python files
rg -t py "(load_metadata|get_metadata|log_metadata)" -A 5
# Also search for files containing both "inference" and "metadata" to catch potential connections
rg -l "inference" | xargs rg "metadata"
Length of output: 6430
template/steps/model_evaluator.py (2)
7-7: LGTM: Import statement correctly updated
The import statement has been properly updated to reflect the new logging function.
82-85: Verify artifact inference behavior
The implementation looks correct, but let's verify the implications of infer_artifact=True:
- The metadata structure is preserved correctly
- The
sklearn_classifierartifact name is still valid
Let's verify the consistency of this change across the codebase:
✅ Verification successful
Based on the results, I can now generate the final response:
Implementation is correct and consistent
The log_metadata function is imported from zenml and used consistently across the codebase:
- Both usages in
model_evaluator.pyanddata_preprocessor.pyfollow the same pattern - The function is properly imported from zenml in both files
- No legacy
log_artifact_metadatacalls remain in the codebase
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for consistent usage of log_metadata with infer_artifact
# across the codebase
# Check for any remaining uses of log_artifact_metadata
echo "Checking for any remaining log_artifact_metadata usage:"
rg "log_artifact_metadata"
# Check for consistent usage of log_metadata with infer_artifact
echo -e "\nChecking log_metadata usage patterns:"
rg -A 2 "log_metadata\("
Length of output: 756
Script:
#!/bin/bash
# Let's check the implementation of log_metadata function and its parameters
ast-grep --pattern 'def log_metadata($$$)'
# Also check for any imports or definitions related to this function
rg "log_metadata" -B 2
Length of output: 880
bcdurak
changed the title
log_xxx_metadata calls
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (1)
template/steps/model_evaluator.py (1)
Line range hint
76-81: Fix warning messages logicThe warning messages are only displayed when test accuracy is below threshold due to incorrect
elseblock placement. This means warnings about low training accuracy might be missed.Apply this diff to fix the warning logic:
if tst_acc < min_test_accuracy: messages.append( f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !" ) - else: - for message in messages: - logger.warning(message) + + for message in messages: + logger.warning(message)🧰 Tools
🪛 Ruff (0.8.0)
84-84: Undefined name
log_metadata(F821)
84-84: Undefined name
metadata(F821)
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (1)
template/steps/model_evaluator.py(2 hunks)
🧰 Additional context used
🪛 Ruff (0.8.0)
template/steps/model_evaluator.py
7-7: zenml.log_ imported but unused
Remove unused import: zenml.log_
(F401)
84-84: Undefined name log_metadata
(F821)
84-84: Undefined name metadata
(F821)
Summary by CodeRabbit
New Features
Run testsstep to enhance workflow efficiency.Bug Fixes
Documentation