Add data/nova-v1.parquet and restructure for HF viewer

.gitattributes

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Nova.py

@@ -1,92 +0,0 @@
-import os
-import pandas as pd
-from datasets import GeneratorBasedBuilder, SplitGenerator, Split, Value, Features, Image, DatasetInfo, Sequence
-
-
-class NovaDataset(GeneratorBasedBuilder):
-    def _info(self):
-        return DatasetInfo(
-            description="NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.",
-            features=Features({
-                "image": Image(),
-                "filename": Value("string"),
-                "caption": Value("string"),
-                "case_id": Value("string"),
-                "scan_id": Value("string"),
-                "title": Value("string"),
-                "publication_date": Value("string"),
-                "clinical_history": Value("string"),
-                "differential_diagnosis": Value("string"),
-                "final_diagnosis": Value("string"),
-                "link": Value("string"),
-                "bbox_gold": Sequence({
-                    "x": Value("float32"),
-                    "y": Value("float32"),
-                    "width": Value("float32"),
-                    "height": Value("float32"),
-                }),                
-                "bbox_raters": Sequence({
-                    "rater": Value("string"),
-                    "x": Value("float32"),
-                    "y": Value("float32"),
-                    "width": Value("float32"),
-                    "height": Value("float32"),
-                }),            }),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download_and_extract("test/Images.zip")
-        image_files = list(dl_manager.iter_files(archive_path))
-
-        # Download CSVs
-        csvs = {
-            "captions": dl_manager.download("captions.csv"),
-            "bboxes_gold": dl_manager.download("bboxes_gold.csv"),
-            "bboxes_raters": dl_manager.download("bboxes_raters.csv"),
-            "case_metadata": dl_manager.download("case_metadata.csv"),
-        }
-
-        return [
-            SplitGenerator(
-                name=Split.TEST,
-                gen_kwargs={"image_files": image_files, "csvs": csvs},
-            )
-        ]
-
-    def _generate_examples(self, image_files, csvs):
-        captions = pd.read_csv(csvs["captions"])
-        gold = pd.read_csv(csvs["bboxes_gold"])
-        raters = pd.read_csv(csvs["bboxes_raters"])
-        meta = pd.read_csv(csvs["case_metadata"])
-
-        captions["case_id"] = captions["filename"].str.extract(r"^(case\d+)_")
-
-        gold_grouped = gold.groupby("filename").apply(
-            lambda df: df[["x", "y", "width", "height"]].astype(float).to_dict(orient="records")
-        ).to_dict()
-
-        raters_grouped = raters.groupby("filename").apply(
-            lambda df: df[["rater", "x", "y", "width", "height"]].astype({"x": float, "y": float, "width": float, "height": float}).to_dict(orient="records")
-        ).to_dict()
-        meta = meta.set_index("case_id").to_dict("index")
-    
-        images_map = {os.path.basename(f): f for f in image_files}
-
-        for idx, row in captions.iterrows():
-            filename = row["filename"]
-            case_id = row["case_id"]
-            record = {
-                "image": images_map.get(filename, None),
-                "filename": filename,
-                "caption": row["caption"],
-                "case_id": case_id,
-                "scan_id": str(row["scan_id"]),
-                "bbox_gold": gold_grouped.get(filename, []),
-                "bbox_raters": raters_grouped.get(filename, []),
-            }
-            if case_id in meta:
-                record.update(meta[case_id])
-            else:
-                record.update({k: "" for k in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]})
-            yield idx, record

build_parquet.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+import os
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+# —— Inputs (adjust if paths differ)
+CAPTIONS_CSV = "captions.csv"
+META_CSV     = "case_metadata.csv"
+GOLD_CSV     = "bboxes_gold.csv"
+RATERS_CSV   = "bboxes_raters.csv"
+IMAGES_DIR   = "images"
+
+OUT_DIR      = "data"
+OUT_PARQUET  = os.path.join(OUT_DIR, "nova-v1.parquet")
+
+os.makedirs(OUT_DIR, exist_ok=True)
+
+# —— Load CSVs
+captions = pd.read_csv(CAPTIONS_CSV)
+meta     = pd.read_csv(META_CSV)
+gold     = pd.read_csv(GOLD_CSV)
+raters   = pd.read_csv(RATERS_CSV)
+
+# —— Normalize key columns as strings
+for df in (captions, meta, gold, raters):
+    if "case_id" in df.columns:
+        df["case_id"] = df["case_id"].astype(str)
+    if "scan_id" in df.columns:
+        df["scan_id"] = df["scan_id"].astype(str)
+    if "filename" in df.columns:
+        df["filename"] = df["filename"].astype(str)
+
+# —— Build the master list of images (from captions)
+# If you prefer, you can take the union of filenames from all CSVs.
+images = captions[["filename", "case_id", "scan_id", "caption"]].copy()
+images["caption"] = images["caption"].fillna("").astype(str)
+
+images.rename(columns={"caption": "caption_text"}, inplace=True)
+
+# Add image_path and split
+images["image_path"] = IMAGES_DIR + "/" + images["filename"]
+images["split"] = "test"
+
+# —— Prepare per-case metadata as a dict so we can merge by case_id
+meta_cols = [
+    "title", "publication_date", "clinical_history",
+    "differential_diagnosis", "final_diagnosis", "link"
+]
+meta = meta[["case_id"] + meta_cols].copy()
+
+# Merge per-image with per-case meta (left join by case_id)
+df = images.merge(meta, on="case_id", how="left")
+
+# —— Prepare gold bboxes
+# Required columns: filename, x, y, width, height
+gold_cols = ["filename", "x", "y", "width", "height"]
+gold_use = gold[["filename", "x", "y", "width", "height"]].copy()
+gold_use["source"] = "gold"
+gold_use["label"] = ""  # no label provided; keep empty string
+
+# —— Prepare rater bboxes
+# Required columns: filename, rater, x, y, width, height
+r_cols = ["filename", "rater", "x", "y", "width", "height"]
+raters_use = raters[r_cols].copy()
+raters_use.rename(columns={"rater": "source"}, inplace=True)
+raters_use["label"] = ""
+
+# —— Stack gold + raters, then group per filename into list[struct]
+all_boxes = pd.concat([gold_use, raters_use], ignore_index=True)
+
+def pack_boxes(group: pd.DataFrame):
+    # Convert rows -> list of dicts with fixed keys
+    return [
+        {
+            "x": float(row["x"]),
+            "y": float(row["y"]),
+            "width": float(row["width"]),
+            "height": float(row["height"]),
+            "source": str(row["source"]),
+            "label": str(row["label"]),
+        }
+        for _, row in group.iterrows()
+    ]
+
+boxes_per_file = (
+    all_boxes
+    .groupby("filename", sort=False, group_keys=False)
+    .apply(pack_boxes)
+    .rename("bboxes")
+    .reset_index()
+)
+
+# Attach bboxes to df by filename; missing -> empty list
+df = df.merge(boxes_per_file, on="filename", how="left")
+df["bboxes"] = df["bboxes"].apply(lambda v: v if isinstance(v, list) else [])
+
+# —— Build a nested Arrow schema for clean typing
+bbox_struct = pa.struct([
+    ("x", pa.float64()),
+    ("y", pa.float64()),
+    ("width", pa.float64()),
+    ("height", pa.float64()),
+    ("source", pa.string()),
+    ("label", pa.string()),
+])
+
+meta_struct = pa.struct([
+    ("title", pa.string()),
+    ("publication_date", pa.string()),  # keep as-is (e.g., 30.03.2022); can normalize later
+    ("clinical_history", pa.string()),
+    ("differential_diagnosis", pa.string()),
+    ("final_diagnosis", pa.string()),
+    ("link", pa.string()),
+])
+
+schema = pa.schema([
+    ("image_path", pa.string()),
+    ("filename", pa.string()),
+    ("split", pa.string()),
+    ("case_id", pa.string()),
+    ("scan_id", pa.string()),
+    ("caption_text", pa.string()),
+    ("bboxes", pa.list_(bbox_struct)),
+    ("meta", meta_struct),
+])
+for col in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]:
+    if col in df.columns:
+        df[col] = df[col].fillna("").astype(str)
+# —— Build Arrow arrays column by column
+def to_arrow_array_list_of_struct(list_of_dicts, struct_type):
+    # list_of_dicts: python list of dicts with keys matching struct_type
+    # returns: pa.Array(list<struct>)
+    if not list_of_dicts:
+        # represent empty with empty list
+        return pa.array([], type=pa.list_(struct_type))
+    # we will build a ListArray from a chunk; handle per row later
+    raise RuntimeError("This helper is row-wise; we will build below with pa.array on the full column.")
+
+# Build meta struct column
+meta_dicts = df[meta_cols].to_dict(orient="records")
+meta_array = pa.array(meta_dicts, type=meta_struct)
+
+# Build bboxes list<struct> column
+# Flatten per row using pa.array with explicit type
+bboxes_array = pa.array(df["bboxes"].tolist(), type=pa.list_(bbox_struct))
+
+# Build the rest as Arrow arrays
+table = pa.Table.from_arrays(
+    [
+        pa.array(df["image_path"].tolist(), type=pa.string()),
+        pa.array(df["filename"].tolist(), type=pa.string()),
+        pa.array(df["split"].tolist(), type=pa.string()),
+        pa.array(df["case_id"].tolist(), type=pa.string()),
+        pa.array(df["scan_id"].tolist(), type=pa.string()),
+        pa.array(df["caption_text"].tolist(), type=pa.string()),
+        bboxes_array,
+        meta_array,
+    ],
+    schema=schema
+)
+
+# —— Optional: sanity checks
+# - ensure files referenced actually exist
+missing = [p for p in df["image_path"] if not os.path.exists(p)]
+if missing:
+    print(f"[WARN] {len(missing)} image paths do not exist locally. First few:", missing[:5])
+
+# —— Write Parquet
+pq.write_table(table, OUT_PARQUET)
+print(f"Wrote {OUT_PARQUET} with {table.num_rows} rows.")
+

test/Images.zip → data/nova-v1.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0a87feb802ea8cd5bb5edb14c12f098268fa08e7be4f9aacc3b0ba6f4dbb4bad
-size 126289347
+oid sha256:71880c12b710b8c82100da51d574f13312cc4811fb5b20073ef4f5d98d22a319
+size 199088

images/.DS_Store

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05b3f6f9188e85e8fcd43191fea5fa2ec211705f88e40930d604e1a31b66edad
+size 6148