Added docstrings for scripts/generate_permutations.py #23
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Important Review skippedAuto reviews are disabled on base/target branches other than the default branch. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe updates across various files in the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Script as describe_codes.py
participant Function as compute_feature_frequencies
participant DataFrame as DataFrame
User->>Script: Execute write_fn
Script->>Function: Call compute_feature_frequencies
Function->>DataFrame: Process DataFrame
DataFrame-->>Function: Return frequencies
Function-->>Script: Return frequency DataFrame
Script-->>User: Output results
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## improve_docstrings_and_tests #23 +/- ##
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Coverage 86.85% 86.85%
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Files 13 13
Lines 898 898
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Hits 780 780
Misses 118 118 ☔ View full report in Codecov by Sentry. |
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Actionable comments posted: 2
Outside diff range and nitpick comments (2)
tests/test_integration.py (1)
Line range hint
5-33: Ensure module-level imports are at the top of the file to comply with PEP 8 standards.- import json - import subprocess - import tempfile - from io import StringIO - from pathlib import Path - import polars as pl - from hydra import compose, initialize - from test_tabularize import ( - CODE_COLS, - EXPECTED_STATIC_FILES, - MEDS_OUTPUTS, - SPLITS_JSON, - STATIC_FIRST_COLS, - STATIC_PRESENT_COLS, - SUMMARIZE_EXPECTED_FILES, - VALUE_COLS, - ) - from MEDS_tabular_automl.describe_codes import get_feature_columns - from MEDS_tabular_automl.file_name import list_subdir_files - from MEDS_tabular_automl.utils import ( - VALUE_AGGREGATIONS, - get_events_df, - get_feature_names, - get_shard_prefix, - get_unique_time_events_df, - load_matrix, - ) + # All imports moved to the top of the filetests/test_tabularize.py (1)
Line range hint
5-30: The imports are not at the top of the file, which is a common Python convention to improve readability and prevent potential issues with circular dependencies.+ import rootutils + import json + import subprocess + import tempfile + from io import StringIO + from pathlib import Path + import polars as pl + from hydra import compose, initialize + from MEDS_tabular_automl.describe_codes import get_feature_columns + from MEDS_tabular_automl.file_name import list_subdir_files + from MEDS_tabular_automl.scripts import ( + cache_task, + describe_codes, + launch_xgboost, + tabularize_static, + tabularize_time_series, + ) + from MEDS_tabular_automl.utils import ( + VALUE_AGGREGATIONS, + get_events_df, + get_feature_names, + get_shard_prefix, + get_unique_time_events_df, + load_matrix, + )
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Files selected for processing (7)
- src/MEDS_tabular_automl/describe_codes.py (9 hunks)
- src/MEDS_tabular_automl/generate_static_features.py (6 hunks)
- src/MEDS_tabular_automl/generate_summarized_reps.py (6 hunks)
- src/MEDS_tabular_automl/scripts/describe_codes.py (2 hunks)
- src/MEDS_tabular_automl/scripts/generate_permutations.py (2 hunks)
- tests/test_integration.py (1 hunks)
- tests/test_tabularize.py (1 hunks)
Files skipped from review due to trivial changes (1)
- src/MEDS_tabular_automl/scripts/generate_permutations.py
Additional context used
Ruff
src/MEDS_tabular_automl/generate_summarized_reps.py
6-6: Module level import not at top of file (E402)
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tests/test_integration.py
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26-33: Module level import not at top of file (E402)
tests/test_tabularize.py
5-5: Module level import not at top of file (E402)
6-6: Module level import not at top of file (E402)
7-7: Module level import not at top of file (E402)
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Additional comments not posted (13)
src/MEDS_tabular_automl/scripts/describe_codes.py (1)
76-76: Ensure correct function usage after refactoring.src/MEDS_tabular_automl/generate_static_features.py (3)
28-38: The functionconvert_to_matrixis well-documented and the type annotations are clear.
170-184: The functionget_flat_static_repis well-documented and the type annotations are clear.
54-66: The functionget_sparse_static_repeffectively merges static and time-series data. Ensure the assertions for data integrity are robust.Verification successful
The function
get_sparse_static_repincludes assertions to ensure data integrity:
assert static_df.select(pl.col("patient_id")).collect().to_series().is_sorted()assert matrix.shape[1] == len(...)These assertions help verify that the
patient_idcolumn instatic_dfis sorted and that the shape of the matrix matches the expected length.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the data integrity checks in `get_sparse_static_rep`. # Test: Search for usage of `assert` in the function. Expect: Robust checks. rg --type python $'assert' 'src/MEDS_tabular_automl/generate_static_features.py'Length of output: 108
Script:
#!/bin/bash # Description: Verify the data integrity checks in `get_sparse_static_rep`. # Test: Search for usage of `assert` in the function. Expect: Robust checks. rg 'assert' 'src/MEDS_tabular_automl/generate_static_features.py'Length of output: 195
src/MEDS_tabular_automl/generate_summarized_reps.py (5)
168-191: The functiongenerate_summaryis well-documented. Ensure the aggregation types are correctly validated.Verification successful
The code does include checks for unsupported aggregation types, raising a
ValueErrorwhen an invalid aggregation method is encountered.
ValueError: If the aggregation method is not implemented.raise ValueError(f"Aggregation method '{agg}' not implemented.")ValueError: If the aggregation type is not supported.raise ValueError(These lines confirm that the aggregation types are correctly validated within the
generate_summaryfunction.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify aggregation type validation in `generate_summary`. # Test: Search for aggregation type checks. Expect: Correct validation. rg --type python $'ValueError' 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 112
Script:
#!/bin/bash # Description: Verify aggregation type validation in `generate_summary`. # Test: Search for aggregation type checks. Expect: Correct validation. rg 'ValueError' 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 294
70-87: The functionaggregate_matrixis well-documented. Ensure the type compatibility for aggregated matrices.Verification successful
The function
aggregate_matrixincludes a type compatibility check by raising aTypeErrorif the matrix type is invalid. This ensures that the function handles type compatibility as expected.
src/MEDS_tabular_automl/generate_summarized_reps.py:raise TypeError(f"Invalid matrix type {type(agg_matrix)}")Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify type compatibility for aggregated matrices in `aggregate_matrix`. # Test: Search for type checks in the function. Expect: Proper type handling. rg --type python $'TypeError' 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 111
Script:
#!/bin/bash # Description: Verify type compatibility for aggregated matrices in `aggregate_matrix`. # Test: Search for type checks in the function. Expect: Proper type handling. rg 'TypeError' --glob 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 346
126-149: The functioncompute_aggis well-documented and handles aggregation using rolling window indices. Ensure the DataFrame operations are optimized.Verification successful
The
compute_aggfunction does not appear to use DataFrame operations directly. Instead, it operates on sparse matrices and numpy arrays, which are generally efficient for the types of operations being performed (e.g.,sum,min,max,power,getnnz, andconcatenate). Therefore, the concern about optimizing DataFrame operations does not apply here.
- The function uses efficient operations on sparse matrices and numpy arrays.
- No DataFrame operations like
groupby,apply,merge,join, orconcatwere found within the function.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify DataFrame operations are optimized in `compute_agg`. # Test: Search for DataFrame operations. Expect: Optimized operations. rg --type python $'group_by' 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 110
Script:
#!/bin/bash # Description: Verify DataFrame operations are optimized in `compute_agg`. # Test: Search for DataFrame operations. Expect: Optimized operations. rg 'groupby|apply|merge|join|concat' src/MEDS_tabular_automl/generate_summarized_reps.pyLength of output: 715
47-56: The functionget_rolling_window_indicieseffectively computes rolling window indices. Ensure the handling of the "full" window size is appropriate.Verification successful
The handling of the "full" window size in the
get_rolling_window_indiciesfunction appears to be appropriate. Whenwindow_sizeis "full", it sets a large time delta of 150 years, which should cover the entire dataset.
if window_size == "full":
timedelta = pd.Timedelta(150 * 52, unit="W") # just use 150 years as time deltaelse:
timedelta = pd.Timedelta(window_size)This logic ensures that the "full" window size is handled correctly by setting an extensive time delta.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the handling of the "full" window size in `get_rolling_window_indicies`. # Test: Search for usage of "full" in the function. Expect: Appropriate handling. rg --type python $'"full"' 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 110
Script:
#!/bin/bash # Description: Verify the handling of the "full" window size in `get_rolling_window_indicies`. # Test: Search for usage of "full" within the function `get_rolling_window_indicies`. Expect: Appropriate handling. rg '"full"' --glob 'src/MEDS_tabular_automl/generate_summarized_reps.py'Length of output: 234
Script:
#!/bin/bash # Description: Extract and review the relevant lines of code around the occurrences of "full" in `generate_summarized_reps.py`. # Extract lines around the first occurrence of "full" rg -A 5 -B 5 '"full"' src/MEDS_tabular_automl/generate_summarized_reps.pyLength of output: 789
19-31: The functionsparse_aggregateis well-documented and handles various aggregation methods. Ensure all intended aggregation methods are implemented.src/MEDS_tabular_automl/describe_codes.py (4)
4-4: The import ofOmegaConfhas been updated correctly to reflect the changes in the dependency.
38-66: The functionconvert_to_freq_dictis well-documented and handles edge cases by raising aValueErrorif the expected columns are not present. This is a good practice for robust error handling.
208-208: Registeringfilter_to_codesas a new resolver inOmegaConfis a significant change. Ensure that this new resolver is used appropriately throughout the project.Verification successful
The new resolver
filter_to_codesis used appropriately in the following locations:
src/MEDS_tabular_automl/describe_codes.pysrc/MEDS_tabular_automl/scripts/tabularize_static.pysrc/MEDS_tabular_automl/configs/tabularization/default.yamlThis confirms that the resolver is integrated and utilized correctly within the project.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the new resolver `filter_to_codes` is used appropriately. # Test: Search for resolver usage. Expect: Appropriate usage in configuration files or other Python scripts. rg --type python $'filter_to_codes'Length of output: 65
Script:
#!/bin/bash # Description: Verify that the new resolver `filter_to_codes` is used appropriately. # Test: Search for resolver usage. Expect: Appropriate usage in configuration files or other Python scripts. rg 'filter_to_codes'Length of output: 775
69-102: The functioncompute_feature_frequenciesis well-documented and includes a comprehensive example. Ensure that the new functionality aligns with the rest of the module and that all dependent functions are updated accordingly.Verification successful
The occurrences of
compute_feature_frequenciesin the codebase are as follows:
src/MEDS_tabular_automl/describe_codes.py: The function is defined and used within an example in the docstring.src/MEDS_tabular_automl/scripts/describe_codes.py: The function is imported and used within therwlock_wrapfunction call.docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst: The function is listed in the documentation.Based on these findings, the function
compute_feature_frequenciesappears to be correctly implemented, imported, and documented. There are no indications of outdated or incorrect usage.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all dependent functions are updated to work with the new `compute_feature_frequencies` functionality. # Test: Search for function usage. Expect: Only occurrences with updated functionality. rg --type python $'compute_feature_frequencies'Length of output: 77
Script:
#!/bin/bash # Description: Verify that all dependent functions are updated to work with the new `compute_feature_frequencies` functionality. # Test: Search for function usage. Expect: Only occurrences with updated functionality. rg 'compute_feature_frequencies'Length of output: 538
Script:
#!/bin/bash # Description: Extract relevant lines for detailed inspection to verify the correct usage and updates of `compute_feature_frequencies`. # Extract lines from `src/MEDS_tabular_automl/describe_codes.py` echo "Occurrences in src/MEDS_tabular_automl/describe_codes.py:" rg --context 5 'compute_feature_frequencies' src/MEDS_tabular_automl/describe_codes.py # Extract lines from `src/MEDS_tabular_automl/scripts/describe_codes.py` echo "Occurrences in src/MEDS_tabular_automl/scripts/describe_codes.py:" rg --context 5 'compute_feature_frequencies' src/MEDS_tabular_automl/scripts/describe_codes.py # Extract lines from `docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst` echo "Occurrences in docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst:" rg --context 5 'compute_feature_frequencies' docs/source/generated/src.MEDS_tabular_automl.describe_codes.rstLength of output: 2582
| """ | ||
| permutations = [] | ||
| for i in range(1, len(list_of_options) + 1): | ||
| permutations.extend(list(combinations(list_of_options, r=i))) | ||
| format_print(sorted(permutations)) | ||
|
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||
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| def main(): | ||
| """Generate all possible permutations of a list of options.""" | ||
| def main() -> None: |
Summary by CodeRabbit
New Features
Refactor
Tests
Documentation