feat: data util

src/aind_exaspim_soma_detection/utils/data_util.py

@@ -0,0 +1,117 @@
+"""
+Created on Mon Jan 6 14:00:00 2025
+
+@author: Anna Grim
+@email: [email protected]
+
+Routines for loading training data.
+
+"""
+
+import ast
+import os
+
+from aind_exaspim_soma_detection.utils import img_util, util
+
+
+# --- Fetch Data ---
+def load_examples(path):
+    test_examples = list()
+    for line in util.read_txt(path):
+        idx = line.find(',')
+        brain_id = ast.literal_eval(line[1:idx])
+        xyz = ast.literal_eval(line[idx+2:-1])
+        test_examples.append((brain_id, xyz))
+    return test_examples
+
+
+def fetch_smartsheet_somas(
+    smartsheet_path, img_prefixes_path, multiscale, adjust_coords_bool=True
+):
+    # Read data
+    soma_coords = util.extract_somas_from_smartsheet(smartsheet_path)
+    img_prefixes = util.read_json(img_prefixes_path)
+
+    # Reformat data
+    data = list()
+    for brain_id, xyz_list in soma_coords.items():
+        if brain_id not in ["686955", "708373"]:
+            data.append(
+                reformat_data(brain_id, img_prefixes, multiscale, xyz_list, 1)
+            )
+    return data
+
+
+def fetch_exaspim_somas_2024(dataset_path, img_prefixes_path, multiscale):
+    """
+    Fetches and formats soma data for training from the exaSPIM dataset.
+
+    Parameters
+    ----------
+    dataset_path : str
+        Path to the dataset directory containing brain-specific subdirectories
+        with "accepts" and "rejects" folders.
+    img_prefixes_path : str
+        Path to a JSON file that maps brain IDs to image S3 prefixes.
+    multiscale : int, optional
+        Level in the image pyramid that the voxel coordinates must index into.
+
+    Returns
+    -------
+    List[tuple]
+        List of tuples where each tuple contains the following:
+            - "brain_id" (str): Unique identifier for the brain.
+            - "img_path" (str): Path to image stored in S3 image.
+            - "voxels" (list): Voxel coordinates of proposed somas.
+            - "labels" (list): Labels corresponding to voxels.
+
+    """
+    data = list()
+    img_prefixes = util.read_json(img_prefixes_path)
+    for brain_id in util.list_subdirectory_names(dataset_path):
+        # Read data
+        accepts_dir = os.path.join(dataset_path, brain_id, "accepts")
+        accepts_xyz = util.read_swc_dir(accepts_dir)
+
+        rejects_dir = os.path.join(dataset_path, brain_id, "rejects")
+        rejects_xyz = util.read_swc_dir(rejects_dir)
+
+        # Reformat data
+        data.append(
+            reformat_data(brain_id, img_prefixes, multiscale, accepts_xyz, 1)
+        )
+        data.append(
+            reformat_data(brain_id, img_prefixes, multiscale, rejects_xyz, 0)
+        )
+    return data
+
+
+def reformat_data(brain_id, img_prefixes, multiscale, xyz_list, label):
+    """
+    Reformats data for training or inference by converting xyz to voxel
+    coordinates and associates them with a brain id, image path, and labels.
+
+    Parameters
+    ----------
+    brain_id : str
+        Unique identifier for the whole brain dataset.
+    img_prefixes : dict
+        A dictionary mapping brain IDs to image S3 prefixes.
+    multiscale : int
+        Level in the image pyramid that the voxel coordinates must index into.
+    xyz_list : List[ArrayLike]
+        List 3D xyz coordinates.
+    label : int
+        Label associated with the given coordinates (i.e. 1 for "accepts" and
+        0 for "rejects").
+
+    Returns
+    -------
+    tuple
+        Tuple containing the "brain_id", "image_path", "voxels", and "labels".
+
+    """
+    img_path = img_prefixes[brain_id] + str(multiscale)
+    voxels = [img_util.to_voxels(xyz, multiscale) for xyz in xyz_list]
+    labels = len(voxels) * [label]
+    return (brain_id, img_path, voxels, labels)

src/aind_exaspim_soma_detection/utils/ml_util.py

@@ -66,9 +66,10 @@ def report_metrics(y, hat_y):
     None
 
     """
-    print("Accuracy:", accuracy_score(y, hat_y))
-    print("Accuracy Dif:", accuracy_score(y, hat_y) - np.sum(y) / len(y))
-    print("Precision:", precision_score(y, hat_y))
-    print("Recall:", recall_score(y, hat_y))
-    print("F1:", f1_score(y, hat_y))
+    accuracy = accuracy_score(y, hat_y) 
+    print("Accuracy:", round(accuracy, 4))
+    print("Accuracy Dif:", round(accuracy - np.sum(y) / len(y), 4))
+    print("Precision:", round(precision_score(y, hat_y), 4))
+    print("Recall:", round(recall_score(y, hat_y), 4))
+    print("F1:", round(f1_score(y, hat_y), 4))
     print("")