Upgraded to MEDS v0.4

pyproject.toml

@@ -26,7 +26,7 @@ dependencies = [
     "pytimeparse == 1.1.*",
     "networkx == 3.3.*",
     "pyarrow == 17.*",
-    "meds == 0.3.3",
+    "meds ~= 0.4.0",
 ]
 
 [tool.setuptools]

src/aces/run.py

@@ -11,7 +11,7 @@
 import polars as pl
 import pyarrow as pa
 import pyarrow.parquet as pq
-from meds import label_schema, prediction_time_field, subject_id_field
+from meds import LabelSchema
 from omegaconf import DictConfig, OmegaConf
 
 from . import config, predicates, query
@@ -20,15 +20,15 @@
 config_yaml = files("aces").joinpath("configs/_aces.yaml")
 
 MEDS_LABEL_MANDATORY_TYPES = {
-    subject_id_field: pl.Int64,
+    LabelSchema.subject_id_name: pl.Int64,
 }
 
 MEDS_LABEL_OPTIONAL_TYPES = {
-    "boolean_value": pl.Boolean,
-    "integer_value": pl.Int64,
-    "float_value": pl.Float64,
-    "categorical_value": pl.String,
-    prediction_time_field: pl.Datetime("us"),
+    LabelSchema.prediction_time_name: pl.Datetime("us"),
+    LabelSchema.boolean_value_name: pl.Boolean,
+    LabelSchema.integer_value_name: pl.Int64,
+    LabelSchema.float_value_name: pl.Float64,
+    LabelSchema.categorical_value_name: pl.String,
 }
 
 
@@ -56,9 +56,9 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
         >>> get_and_validate_label_schema(df)
         Traceback (most recent call last):
             ...
-        ValueError: MEDS Data DataFrame must have a 'subject_id' column of type Int64.
+        ValueError: MEDS Label DataFrame must have a 'subject_id' column of type Int64.
         >>> df = pl.DataFrame({
-        ...     subject_id_field: pl.Series([1, 3, 2], dtype=pl.UInt32),
+        ...     "subject_id": pl.Series([1, 3, 2], dtype=pl.UInt32),
         ...     "time": [datetime(2021, 1, 1), datetime(2021, 1, 2), datetime(2021, 1, 3)],
         ...     "boolean_value": [1, 0, 100],
         ... })
@@ -68,7 +68,7 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
         prediction_time: timestamp[us]
         boolean_value: bool
         integer_value: int64
-        float_value: double
+        float_value: float
         categorical_value: string
         ----
         subject_id: [[1,3,2]]
@@ -80,7 +80,7 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
     """
 
     schema = df.schema
-    if "prediction_time" not in schema:
+    if LabelSchema.prediction_time_name not in schema:
         logger.warning(
             "Output DataFrame is missing a 'prediction_time' column. If this is not intentional, add a "
             "'index_timestamp' (yes, it should be different) key to the task configuration identifying "
@@ -92,7 +92,7 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
         if col in schema and schema[col] != dtype:
             df = df.with_columns(pl.col(col).cast(dtype, strict=False))
         elif col not in schema:
-            errors.append(f"MEDS Data DataFrame must have a '{col}' column of type {dtype}.")
+            errors.append(f"MEDS Label DataFrame must have a '{col}' column of type {dtype}.")
 
     if errors:
         raise ValueError("\n".join(errors))
@@ -115,16 +115,7 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
         )
         df = df.drop(extra_cols)
 
-    df = df.select(
-        subject_id_field,
-        "prediction_time",
-        "boolean_value",
-        "integer_value",
-        "float_value",
-        "categorical_value",
-    )
-
-    return df.to_arrow().cast(label_schema)
+    return LabelSchema.align(df.to_arrow())
 
 
 @hydra.main(version_base=None, config_path=str(config_yaml.parent.resolve()), config_name=config_yaml.stem)
@@ -154,18 +145,18 @@ def main(cfg: DictConfig) -> None:  # pragma: no cover
 
     if cfg.data.standard.lower() == "meds":
         for in_col, out_col in [
-            ("subject_id", subject_id_field),
-            ("index_timestamp", "prediction_time"),
-            ("label", "boolean_value"),
+            ("subject_id", LabelSchema.subject_id_name),
+            ("index_timestamp", LabelSchema.prediction_time_name),
+            ("label", LabelSchema.boolean_value_name),
         ]:
             if in_col in result.columns:
                 result = result.rename({in_col: out_col})
-        if subject_id_field not in result.columns:
+        if LabelSchema.subject_id_name not in result.columns:
             if not result_is_empty:
                 raise ValueError("Output dataframe is missing a 'subject_id' column.")
             else:
                 logger.warning("Output dataframe is empty; adding an empty patient ID column.")
-                result = result.with_columns(pl.lit(None, dtype=pl.Int64).alias(subject_id_field))
+                result = result.with_columns(pl.lit(None, dtype=pl.Int64).alias(LabelSchema.subject_id_name))
                 result = result.head(0)
         if cfg.window_stats_dir:
             Path(cfg.window_stats_filepath).parent.mkdir(exist_ok=True, parents=True)

tests/test_meds.py

@@ -7,7 +7,7 @@
 
 import polars as pl
 import pyarrow as pa
-from meds import label_schema, subject_id_field
+from meds import DataSchema, LabelSchema
 from yaml import load as load_yaml
 
 from .utils import (
@@ -36,24 +36,23 @@
 
 # TODO: Make use meds library
 MEDS_PL_SCHEMA = {
-    subject_id_field: pl.Int64,
-    "time": pl.Datetime("us"),
-    "code": pl.Utf8,
-    "numeric_value": pl.Float32,
-    "numeric_value/is_inlier": pl.Boolean,
+    DataSchema.subject_id_name: pl.Int64,
+    DataSchema.time_name: pl.Datetime("us"),
+    DataSchema.code_name: pl.Utf8,
+    DataSchema.numeric_value_name: pl.Float32,
 }
 
 
 MEDS_LABEL_MANDATORY_TYPES = {
-    subject_id_field: pl.Int64,
+    LabelSchema.subject_id_name: pl.Int64,
 }
 
 MEDS_LABEL_OPTIONAL_TYPES = {
-    "boolean_value": pl.Boolean,
-    "integer_value": pl.Int64,
-    "float_value": pl.Float64,
-    "categorical_value": pl.String,
-    "prediction_time": pl.Datetime("us"),
+    LabelSchema.boolean_value_name: pl.Boolean,
+    LabelSchema.integer_value_name: pl.Int64,
+    LabelSchema.float_value_name: pl.Float64,
+    LabelSchema.categorical_value_name: pl.String,
+    LabelSchema.prediction_time_name: pl.Datetime("us"),
 }
 
 
@@ -113,16 +112,7 @@ def get_and_validate_label_schema(df: pl.DataFrame) -> pa.Table:
         )
         df = df.drop(extra_cols)
 
-    df = df.select(
-        subject_id_field,
-        "prediction_time",
-        "boolean_value",
-        "integer_value",
-        "float_value",
-        "categorical_value",
-    )
-
-    return df.to_arrow().cast(label_schema)
+    return LabelSchema.align(df.to_arrow())
 
 
 def parse_meds_csvs(
@@ -140,7 +130,7 @@ def reader(csv_str: str) -> pl.DataFrame:
         cols = csv_str.strip().split("\n")[0].split(",")
         read_schema = {k: v for k, v in default_read_schema.items() if k in cols}
         return pl.read_csv(StringIO(csv_str), schema=read_schema).with_columns(
-            pl.col("time").str.strptime(MEDS_PL_SCHEMA["time"], DEFAULT_CSV_TS_FORMAT)
+            pl.col("time").str.strptime(MEDS_PL_SCHEMA[DataSchema.time_name], DEFAULT_CSV_TS_FORMAT)
         )
 
     if isinstance(csvs, str):
@@ -169,9 +159,9 @@ def parse_labels_yaml(yaml_str: str) -> dict[str, pl.DataFrame]:
 
 # Data (input)
 MEDS_SHARDS = parse_shards_yaml(
-    f"""
+    """
   "train/0": |-
-    {subject_id_field},time,code,numeric_value
+    subject_id,time,code,numeric_value
     2,,SNP//rs234567,
     2,,SNP//rs345678,
     2,,GENDER//FEMALE,
@@ -196,7 +186,7 @@ def parse_labels_yaml(yaml_str: str) -> dict[str, pl.DataFrame]:
     2,6/8/1996 3:00,DEATH,
 
   "train/1": |-2
-    {subject_id_field},time,code,numeric_value
+    subject_id,time,code,numeric_value
     4,,GENDER//MALE,
     4,,SNP//rs123456,
     4,12/1/1989 12:03,ADMISSION//CARDIAC,
@@ -246,7 +236,7 @@ def parse_labels_yaml(yaml_str: str) -> dict[str, pl.DataFrame]:
     6,3/12/1996 0:00,DEATH,
 
   "held_out/0/0": |-2
-    {subject_id_field},time,code,numeric_value
+    subject_id,time,code,numeric_value
     3,,GENDER//FEMALE,
     3,,SNP//rs234567,
     3,,SNP//rs345678,
@@ -261,10 +251,10 @@ def parse_labels_yaml(yaml_str: str) -> dict[str, pl.DataFrame]:
     3,3/12/1996 0:00,DEATH,
 
   "empty_shard": |-2
-    {subject_id_field},time,code,numeric_value
+    subject_id,time,code,numeric_value
 
   "held_out": |-2
-    {subject_id_field},time,code,numeric_value
+    subject_id,time,code,numeric_value
     1,,GENDER//MALE,
     1,,SNP//rs123456,
     1,12/1/1989 12:03,ADMISSION//CARDIAC,
@@ -349,22 +339,22 @@ def parse_labels_yaml(yaml_str: str) -> dict[str, pl.DataFrame]:
 """
 
 WANT_SHARDS = parse_labels_yaml(
-    f"""
+    """
   "train/0": |-2
-    {subject_id_field},prediction_time,boolean_value,integer_value,float_value,categorical_value
+    subject_id,prediction_time,boolean_value,integer_value,float_value,categorical_value
 
   "train/1": |-2
-    {subject_id_field},prediction_time,boolean_value,integer_value,float_value,categorical_value
+    subject_id,prediction_time,boolean_value,integer_value,float_value,categorical_value
     4,1/28/1991 23:32,False,,,,
 
   "held_out/0/0": |-2
-    {subject_id_field},prediction_time,boolean_value,integer_value,float_value,categorical_value
+    subject_id,prediction_time,boolean_value,integer_value,float_value,categorical_value
 
   "empty_shard": |-2
-    {subject_id_field},prediction_time,boolean_value,integer_value,float_value,categorical_value
+    subject_id,prediction_time,boolean_value,integer_value,float_value,categorical_value
 
   "held_out": |-2
-    {subject_id_field},prediction_time,boolean_value,integer_value,float_value,categorical_value
+    subject_id,prediction_time,boolean_value,integer_value,float_value,categorical_value
     1,1/28/1991 23:32,False,,,,
     """
 )