Update README.md

README.md

@@ -44,6 +44,7 @@ pretty_name: LILA Camera Traps
   - [Discussion of Biases](#discussion-of-biases)
   - [Other Known Limitations](#other-known-limitations)
 - [Additional Information](#additional-information)
+  - [Usage](#dataset-usage)
   - [Dataset Curators](#dataset-curators)
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
@@ -474,6 +475,55 @@ N/A
 
 ## Additional Information
 
+### Working with Taxonomies
+
+All the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the "Caltech Camera Traps" dataset to find all the entries with a "felis catus" as the species for the first annotation.
+
+```python
+dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
+taxonomy = dataset.features["annotations"].feature["taxonomy"]
+
+# Filters to show only cats
+cats = dataset.filter(lambda x: x["annotations"]["taxonomy"][0]["species"] == taxonomy["species"].str2int("felis catus"))
+```
+
+The original common names have been saved with their taxonomy mappings in this repository in `common_names_to_tax.json`. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate.
+
+The following example loads the first "sea turtle" in the "Island Conservation Camera Traps" dataset.
+
+```python
+LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
+dataset = load_dataset("society-ethics/lila_camera_traps", "Island Conservation Camera Traps", split="train")
+taxonomy = dataset.features["annotations"].feature["taxonomy"]
+
+sea_turtle = LILA_COMMON_NAMES_TO_TAXONOMY.loc["sea turtle"].to_dict()
+sea_turtle = {k: taxonomy[k].str2int(v) if v is not None else v for k, v in sea_turtle.items()}  # Map to ClassLabel integers
+
+sea_turtle_dataset = ds.filter(lambda x: x["annotations"]["taxonomy"][0] == sea_turtle)
+```
+
+The example below selects a random item from the dataset, and then maps from the taxonomy to a common name:
+
+```python
+LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
+
+dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
+taxonomy = dataset.features["annotations"].feature["taxonomy"]
+
+random_entry = dataset.shuffle()[0]
+filter_taxonomy = random_entry["annotations"]["taxonomy"][0]
+
+filter_keys = list(map(lambda x: (x[0], taxonomy[x[0]].int2str(x[1])), filter(lambda x: x[1] is not None, list(filter_taxonomy.items()))))
+
+if len(filter_keys) > 0:
+    print(LILA_COMMON_NAMES_TO_TAXONOMY[np.logical_and.reduce([
+        LILA_COMMON_NAMES_TO_TAXONOMY[k] == v for k,v in filter_keys
+    ])])
+else:
+    print("No common name found for the item.")
+```
+
+
 ### Dataset Curators
 
 LILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth.