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BCCasebook_binary.py

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+import pandas as pd
+from pathlib import Path
+from datasets import Features, Value, Sequence
+from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split
+import pickle
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+
+class FcsParquetIO:
+    """A class including all methods to write and read parquets transformed from one fcs by FileTransformer.fcs_to_parquet.
+    """
+    @staticmethod
+    def read_parquets(read_dir: Path) -> tuple[dict, pd.DataFrame]:
+        """A method to read a group of parquets, which are transformed and partitioned from one fcs by FileTransformer.fcs_to_parquet,  from the given directory. 
+
+        Args:
+            read_dir (Path): The directory including all parquets transformed and partitioned from one fcs.
+
+        Returns:
+            tuple[dict, pd.DataFrame]: The tuple includes two elements. The first one is the meta data from the original fcs. The second one is the event data from the original fcs.
+        """
+        parquet_filepaths = list(read_dir.glob("*.parquet"))
+        table = {"meta": None, "event": pd.DataFrame()}
+        for parquet_filepath in parquet_filepaths:
+            table = pq.read_table(parquet_filepath)
+
+            parquet_filename = str(
+                parquet_filepath.name).replace(".parquet", '')
+            if "part_001" in parquet_filename:
+                raw_meta = table.schema.metadata or {}
+                table["meta"] = {k.decode('utf8'): v.decode('utf8')
+                                 for k, v in raw_meta.items()}
+
+            table["event"] = pd.concat(
+                [table["event"], table.to_pandas()], axis=0, ignore_index=True)
+
+        return table["meta"], table["event"]
+
+
+feature_fields = {
+    "flow_id": Value("string"),
+    "original_id": Value("string"),
+    "specimen": Value("string"),
+    "purpose": Value("string"),
+    "site": Value("string"),
+    "machine": Value("string"),
+    "color_counts": Value("int32"),
+    "sampling_date": Value("date32"),
+    "measuring_date": Value("date32"),
+    "panel": Value("string"),
+    "label": Value("string"),
+    "sublabel": Sequence(Value("string")),
+    "RDP": Value("float32"),
+
+    "tube_counts": Value("int32"),
+    "tube_event_counts": Value("string"),
+    "tube_channels": Value("string"),
+
+    "data": Value("binary")
+}
+features = Features(feature_fields)
+
+
+class BCCasebook_binary(GeneratorBasedBuilder):
+    def _info(self):
+        return DatasetInfo(
+            features=features,
+            description="This dataset is a project of BC Casebook data labeled for Malignant and Non-malignant.",
+            homepage="",
+            citation="",
+            license=""
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = Path(self.base_path) / "fcs"
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            )
+        ]
+
+    def _generate_examples(self, data_dir: Path):
+        sample_metas = pd.read_csv(Path(self.base_path) / "sample_metas.csv")
+
+        for column in sample_metas.columns.to_list():
+            if column in feature_fields.keys():
+                feature_field = column
+                feature_type = feature_fields[feature_field]
+                if feature_type is Value("int32"):
+                    sample_metas[feature_field] = sample_metas[feature_field].astype(
+                        "Int32")
+                elif feature_type is Value("float32"):
+                    sample_metas[feature_field] = sample_metas[feature_field].astype(
+                        "float32")
+                elif feature_type is Value("date32"):
+                    sample_metas[feature_field] = pd.to_datetime(
+                        sample_metas[feature_field], format="%m/%d/%Y").dt.date
+                elif feature_type is Value("string"):
+                    sample_metas[feature_field] = sample_metas[feature_field].astype(
+                        "string")
+
+        for column in feature_fields.keys():
+            if column not in sample_metas.columns.to_list():
+                sample_metas[column] = None
+
+        sublabel_columns = [
+            column for column in sample_metas.columns.to_list() if "sublabel" in column]
+        if len(sublabel_columns) > 0:
+            sample_metas["sublabel"] = sample_metas.apply(
+                lambda row: [str(row[col]) for col in sublabel_columns], axis=1)
+            sample_metas = sample_metas.drop(columns=sublabel_columns)
+
+        for sample_index, sample_dir in enumerate(sorted(list(data_dir.glob('*')))):
+            if sample_dir.is_dir():
+                sample_meta = sample_metas[sample_metas["flow_id"]
+                                           == str(sample_dir.name)].to_dict(orient="records")[0]
+                sample_tubes = {}
+                for tube_dir in sorted(list(sample_dir.glob('*'))):
+                    if tube_dir.is_dir():
+                        meta, event = FcsParquetIO.read_parquets(tube_dir)
+                        sample_tubes[tube_dir.name] = {
+                            "meta": meta, "event": event}
+                sample_meta["data"] = pickle.dumps(sample_tubes)
+
+                yield sample_index, sample_meta

README.md

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+---
+language:
+- en
+tags:
+- flow cytometry
+- fcs
+- BC
+- malignant
+- binary
+
+---
+
+This dataset is a project of BC Casebook data labeled for Malignant and Non-malignant.

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