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mmcdermott / MEDS_transforms

A simple set of MEDS polars-based ETL and transformation functions
MIT License
15 stars 3 forks source link

Fixes and expands tests for `aggregate_code_metadata` across various aggregations #166

Closed mmcdermott closed 1 month ago

mmcdermott commented 1 month ago

Adds a full integration test to the aggregation and makes the mapper work properly in both do_summarize_all and not cases with quantile reduction.

Closes #163 and #165

coderabbitai[bot] commented 1 month ago

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codecov-commenter commented 1 month ago

Test Failures Detected: Due to failing tests, we cannot provide coverage reports at this time.

:x: Failed Test Results:

Completed 77 tests with 1 failed, 76 passed and 0 skipped.

View the full list of failed tests ## pytest - **Class name:** tests.test_aggregate_code_metadata
**Test name:** test_aggregate_code_metadata

want = shape: (12, 13)
┌───────────────────────┬────────────────────┬─────────────────┬──────────────────────┬───────────────...─────┴────────────┴────────────┴─────────────────────────────────┴──────────────────┴─────────────────────────────────┘
got = shape: (12, 13)
┌───────────────────────┬────────────────────┬─────────────────┬──────────────────────┬───────────────...─────┴────────────┴────────────┴─────────────────────────────────┴─────────────────────────────────┴──────────────────┘
msg = 'Expected the dataframe at .../output_cohort/metadata/codes.parquet to be equal to the target.\nScript st...etadata\x1b[0m:\x1b[36mrun_map_reduce\x1b[0m:\x1b[36m721\x1b[0m - \x1b[1mFinished reduction in 0:00:00.035312\x1b[0m\n'
kwargs = {'check_column_order': False, 'check_row_order': True}

    def assert_df_equal(want: pl.DataFrame, got: pl.DataFrame, msg: str = None, **kwargs):
        try:
>           assert_frame_equal(want, got, **kwargs)

tests/utils.py:172: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (shape: (12, 13)
┌───────────────────────┬────────────────────┬─────────────────┬──────────────────────┬──────────────...────┴────────────┴────────────┴─────────────────────────────────┴─────────────────────────────────┴──────────────────┘)
kwargs = {'check_column_order': False, 'check_row_order': True}

    @wraps(function)
    def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
        _rename_keyword_argument(
            old_name, new_name, kwargs, function.__qualname__, version
        )
>       return function(*args, **kwargs)
E       AssertionError: DataFrames are different (dtypes do not match)
E       [left]:  {'code': String, 'code/n_occurrences': UInt8, 'code/n_patients': UInt8, 'values/n_occurrences': UInt8, 'values/n_patients': UInt8, 'values/sum': Float32, 'values/sum_sqd': Float32, 'values/n_ints': UInt8, 'values/min': Float32, 'values/max': Float32, 'description': String, 'parent_codes': List(String), 'values/quantiles': Struct({'values/quantile/0.25': Float64, 'values/quantile/0.5': Float64, 'values/quantile/0.75': Float64})}
E       [right]: {'code': String, 'code/n_occurrences': UInt8, 'code/n_patients': UInt8, 'values/n_occurrences': UInt8, 'values/n_patients': UInt8, 'values/sum': Float32, 'values/sum_sqd': Float32, 'values/n_ints': UInt8, 'values/min': Float32, 'values/max': Float32, 'values/quantiles': Struct({'values/quantile/0.25': Float32, 'values/quantile/0.5': Float32, 'values/quantile/0.75': Float32}), 'description': String, 'parent_codes': List(String)}

.../hostedtoolcache/Python/3.12.4....../x64/lib/python3.12.../polars/_utils/deprecation.py:91: AssertionError

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

    def test_aggregate_code_metadata():
>       single_stage_transform_tester(
            transform_script=AGGREGATE_CODE_METADATA_SCRIPT,
            stage_name="aggregate_code_metadata",
            transform_stage_kwargs={"aggregations": AGGREGATIONS, "do_summarize_over_all_codes": True},
            want_outputs=WANT_OUTPUT_CODE_METADATA_FILE,
            code_metadata=MEDS_CODE_METADATA_FILE,
            do_use_config_yaml=True,
        )

tests/test_aggregate_code_metadata.py:179: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/transform_tester_base.py:391: in single_stage_transform_tester
    check_df_output(cohort_metadata_dir / "codes.parquet", want_outputs, stderr, stdout)
tests/transform_tester_base.py:289: in check_df_output
    assert_df_equal(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

want = shape: (12, 13)
┌───────────────────────┬────────────────────┬─────────────────┬──────────────────────┬───────────────...─────┴────────────┴────────────┴─────────────────────────────────┴──────────────────┴─────────────────────────────────┘
got = shape: (12, 13)
┌───────────────────────┬────────────────────┬─────────────────┬──────────────────────┬───────────────...─────┴────────────┴────────────┴─────────────────────────────────┴─────────────────────────────────┴──────────────────┘
msg = 'Expected the dataframe at .../output_cohort/metadata/codes.parquet to be equal to the target.\nScript st...etadata\x1b[0m:\x1b[36mrun_map_reduce\x1b[0m:\x1b[36m721\x1b[0m - \x1b[1mFinished reduction in 0:00:00.035312\x1b[0m\n'
kwargs = {'check_column_order': False, 'check_row_order': True}

    def assert_df_equal(want: pl.DataFrame, got: pl.DataFrame, msg: str = None, **kwargs):
        try:
            assert_frame_equal(want, got, **kwargs)
        except AssertionError as e:
            pl.Config.set_tbl_rows(-1)
            print(f"DFs are not equal: {msg}\nwant:")
            print(want)
            print("got:")
            print(got)
>           raise AssertionError(f"{msg}\n{e}") from e
E           AssertionError: Expected the dataframe at .../output_cohort/metadata/codes.parquet to be equal to the target.
E           Script stdout:
E           
E           Script stderr:
E           .../hostedtoolcache/Python/3.12.4....../x64/lib/python3.12.../hydra/_internal/defaults_list.py:251: UserWarning: In 'config': Defaults list is missing `_self_`. See https://hydra.cc/docs/1.2/upgrades/1.0_to_1.1/default_composition_order for more information
E             warnings.warn(msg, UserWarning)
E           #x1B[32m2024-08-14 14:37:24.412#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.utils#x1B[0m:#x1B[36mstage_init#x1B[0m:#x1B[36m72#x1B[0m - #x1B[1mRunning aggregate_code_metadata with the following configuration:
E           input_dir: .../tmp/tmpug3vkmt6/MEDS_cohort
E           cohort_dir: .../tmp/tmpug3vkmt6/output_cohort
E           _default_description: 'This is a MEDS pipeline ETL. Please set a more detailed description
E             at the top of your specific pipeline
E           
E             configuration file.'
E           log_dir: ${stage_cfg.output_dir}/.logs
E           do_overwrite: false
E           seed: 1
E           stages:
E           - aggregate_code_metadata
E           stage_configs:
E             reshard_to_split:
E               n_patients_per_shard: 50000
E             filter_patients:
E               min_events_per_patient: null
E               min_measurements_per_patient: null
E             add_time_derived_measurements:
E               age:
E                 DOB_code: MEDS_BIRTH
E                 age_code: AGE
E                 age_unit: years
E               time_of_day:
E                 time_of_day_code: TIME_OF_DAY
E                 endpoints:
E                 - 6
E                 - 12
E                 - 18
E                 - 24
E             count_code_occurrences:
E               aggregations:
E               - code/n_occurrences
E               - code/n_patients
E               do_summarize_over_all_codes: true
E             filter_measurements:
E               min_patients_per_code: null
E               min_occurrences_per_code: null
E             fit_outlier_detection:
E               aggregations:
E               - values/n_occurrences
E               - values/sum
E               - values/sum_sqd
E             occlude_outliers:
E               stddev_cutoff: 4.5
E             fit_normalization:
E               aggregations:
E               - code/n_occurrences
E               - code/n_patients
E               - values/n_occurrences
E               - values/sum
E               - values/sum_sqd
E             fit_vocabulary_indices:
E               is_metadata: true
E               ordering_method: lexicographic
E               output_dir: ${cohort_dir}
E             reorder_measurements:
E               ordered_code_patterns: &&&
E             aggregate_code_metadata:
E               aggregations:
E               - code/n_occurrences
E               - code/n_patients
E               - values/n_occurrences
E               - values/n_patients
E               - values/sum
E               - values/sum_sqd
E               - values/n_ints
E               - values/min
E               - values/max
E               - name: values/quantiles
E                 quantiles:
E                 - 0.25
E                 - 0.5
E                 - 0.75
E               do_summarize_over_all_codes: true
E           worker: 0
E           polling_time: 300
E           stage: ${current_script_name:}
E           stage_cfg: ${oc.create:${populate_stage:${stage}, ${input_dir}, ${cohort_dir}, ${stages},
E             ${stage_configs}}}
E           etl_metadata:
E             pipeline_name: &&&
E             dataset_name: &&&
E             dataset_version: &&&
E             package_name: ${get_package_name:}
E             package_version: ${get_package_version:}
E           etl_metadata.pipeline_name: preprocess
E           code_modifiers: &&&
E           hydra.verbose: true
E           #x1B[0m
E           #x1B[32m2024-08-14 14:37:24.448#x1B[0m | #x1B[34m#x1B[1mDEBUG   #x1B[0m | #x1B[36mMEDS_transforms.utils#x1B[0m:#x1B[36mstage_init#x1B[0m:#x1B[36m96#x1B[0m - #x1B[34m#x1B[1mStage config:
E           aggregations:
E           - code/n_occurrences
E           - code/n_patients
E           - values/n_occurrences
E           - values/n_patients
E           - values/sum
E           - values/sum_sqd
E           - values/n_ints
E           - values/min
E           - values/max
E           - name: values/quantiles
E             quantiles:
E             - 0.25
E             - 0.5
E             - 0.75
E           do_summarize_over_all_codes: true
E           is_metadata: true
E           data_input_dir: ....../tmpug3vkmt6/MEDS_cohort/data
E           metadata_input_dir: ....../tmpug3vkmt6/MEDS_cohort/metadata
E           output_dir: ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata
E           reducer_output_dir: ....../tmpug3vkmt6/output_cohort/metadata
E           train_only: true
E           
E           Paths: (checkbox indicates if it exists)
E             - input_dir: ✅ ....../tmpug3vkmt6/MEDS_cohort/data
E             - output_dir: ✅ ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata
E             - metadata_input_dir: ✅ ....../tmpug3vkmt6/MEDS_cohort/metadata#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.483#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mshard_iterator#x1B[0m:#x1B[36m493#x1B[0m - #x1B[1mMapping computation over a maximum of 2 shards#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.483#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.mapper#x1B[0m:#x1B[36mmap_over#x1B[0m:#x1B[36m589#x1B[0m - #x1B[1mProcessing train split only via shard prefix. Not filtering with ....../tmpug3vkmt6/MEDS_cohort/metadata/patient_split.parquet.#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.500#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.mapper#x1B[0m:#x1B[36mmap_over#x1B[0m:#x1B[36m617#x1B[0m - #x1B[1mProcessing ....../tmpug3vkmt6/MEDS_cohort/data/train/1.parquet into ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/1.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.502#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m217#x1B[0m - #x1B[1mRegistered lock at 2024-08-14 14:37:24.502294. Double checking no earlier locks have been registered.#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.508#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m224#x1B[0m - #x1B[1mReading input dataframe from ....../tmpug3vkmt6/MEDS_cohort/data/train/1.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.509#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m226#x1B[0m - #x1B[1mRead dataset#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.511#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m238#x1B[0m - #x1B[1mWriting final output to ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/1.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.516#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m240#x1B[0m - #x1B[1mSucceeded in 0:00:00.013849#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.516#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m241#x1B[0m - #x1B[1mLeaving cache directory ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/.1.parquet_cache, but clearing lock at ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/.1.parquet_cache/locks/2024-08-14T14:37:24.502294.json#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.516#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.mapper#x1B[0m:#x1B[36mmap_over#x1B[0m:#x1B[36m617#x1B[0m - #x1B[1mProcessing ....../tmpug3vkmt6/MEDS_cohort/data/train/0.parquet into ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/0.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.517#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m217#x1B[0m - #x1B[1mRegistered lock at 2024-08-14 14:37:24.517531. Double checking no earlier locks have been registered.#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.518#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m224#x1B[0m - #x1B[1mReading input dataframe from ....../tmpug3vkmt6/MEDS_cohort/data/train/0.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.518#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m226#x1B[0m - #x1B[1mRead dataset#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.519#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m238#x1B[0m - #x1B[1mWriting final output to ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/0.parquet#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.521#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m240#x1B[0m - #x1B[1mSucceeded in 0:00:00.003860#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.521#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.utils#x1B[0m:#x1B[36mrwlock_wrap#x1B[0m:#x1B[36m241#x1B[0m - #x1B[1mLeaving cache directory ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/.0.parquet_cache, but clearing lock at ....../tmpug3vkmt6/output_cohort/aggregate_code_metadata/train/.0.parquet_cache/locks/2024-08-14T14:37:24.517531.json#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.521#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.mapreduce.mapper#x1B[0m:#x1B[36mmap_over#x1B[0m:#x1B[36m628#x1B[0m - #x1B[1mFinished mapping in 0:00:00.072798#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.522#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.aggregate_code_metadata#x1B[0m:#x1B[36mrun_map_reduce#x1B[0m:#x1B[36m695#x1B[0m - #x1B[1mStarting reduction process#x1B[0m
E           #x1B[32m2024-08-14 14:37:24.522#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.aggregate_code_metadata#x1B[0m:#x1B[36mrun_map_reduce#x1B[0m:#x1B[36m702#x1B[0m - #x1B[1mAll map shards complete! Starting code metadata reduction computation.#x1B[0m
E           .../src/MEDS_transforms/aggregate_code_metadata.py:672: PerformanceWarning: Determining the column names of a LazyFrame requires resolving its schema, which is a potentially expensive operation. Use `LazyFrame.collect_schema().names()` to get the column names without this warning.
E             if agg not in df.columns:
E           #x1B[32m2024-08-14 14:37:24.551#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.aggregate_code_metadata#x1B[0m:#x1B[36mrun_map_reduce#x1B[0m:#x1B[36m715#x1B[0m - #x1B[1mJoining to existing code metadata at ....../tmpug3vkmt6/MEDS_cohort/metadata/codes.parquet#x1B[0m
E           .../src/MEDS_transforms/aggregate_code_metadata.py:717: PerformanceWarning: Determining the column names of a LazyFrame requires resolving its schema, which is a potentially expensive operation. Use `LazyFrame.collect_schema().names()` to get the column names without this warning.
E             existing = existing.drop(*[c for c in existing.columns if c in set(reduced.columns) - set(join_cols)])
E           #x1B[32m2024-08-14 14:37:24.558#x1B[0m | #x1B[1mINFO    #x1B[0m | #x1B[36mMEDS_transforms.aggregate_code_metadata#x1B[0m:#x1B[36mrun_map_reduce#x1B[0m:#x1B[36m721#x1B[0m - #x1B[1mFinished reduction in 0:00:00.035312#x1B[0m
E           
E           DataFrames are different (dtypes do not match)
E           [left]:  {'code': String, 'code/n_occurrences': UInt8, 'code/n_patients': UInt8, 'values/n_occurrences': UInt8, 'values/n_patients': UInt8, 'values/sum': Float32, 'values/sum_sqd': Float32, 'values/n_ints': UInt8, 'values/min': Float32, 'values/max': Float32, 'description': String, 'parent_codes': List(String), 'values/quantiles': Struct({'values/quantile/0.25': Float64, 'values/quantile/0.5': Float64, 'values/quantile/0.75': Float64})}
E           [right]: {'code': String, 'code/n_occurrences': UInt8, 'code/n_patients': UInt8, 'values/n_occurrences': UInt8, 'values/n_patients': UInt8, 'values/sum': Float32, 'values/sum_sqd': Float32, 'values/n_ints': UInt8, 'values/min': Float32, 'values/max': Float32, 'values/quantiles': Struct({'values/quantile/0.25': Float32, 'values/quantile/0.5': Float32, 'values/quantile/0.75': Float32}), 'description': String, 'parent_codes': List(String)}

tests/utils.py:179: AssertionError