923 ej classification processing and ingest scripts

scripts/ej/cmr_to_models.py

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+"""
+the ej_dump is generated by running create_ej_dump.py and is scp'd to the COSMOS server
+this script is then run via the dm shell on the COSMOS server to populate the database
+"""
+
+import json
+
+from environmental_justice.models import EnvironmentalJusticeRow
+
+import urllib.parse
+
+
+def generate_source_link(doi_field):
+    authority = doi_field.get("Authority")
+    doi = doi_field.get("DOI")
+    if authority and doi:
+        return urllib.parse.urljoin(authority, doi)
+    return ""
+
+
+def concept_id_to_sinequa_id(concept_id: str) -> str:
+    return f"/SDE/CMR_API/|{concept_id}"
+
+
+def sinequa_id_to_url(sinequa_id: str) -> str:
+    base_url = "https://sciencediscoveryengine.nasa.gov/app/nasa-sba-smd/#/preview"
+    query = '{"name":"query-smd-primary","scope":"All","text":""}'
+
+    encoded_id = urllib.parse.quote(sinequa_id, safe="")
+    encoded_query = urllib.parse.quote(query, safe="")
+
+    return f"{base_url}?id={encoded_id}&query={encoded_query}"
+
+
+def categorize_processing_level(level):
+
+    advanced_analysis_levels = {"0", "Level 0", "NA", "Not Provided", "Not provided"}
+
+    basic_analysis_levels = {
+        "1",
+        "1A",
+        "1B",
+        "1C",
+        "1T",
+        "2",
+        "2A",
+        "2B",
+        "2G",
+        "2P",
+        "Level 1",
+        "Level 1A",
+        "Level 1B",
+        "Level 1C",
+        "Level 2",
+        "Level 2A",
+        "Level 2B",
+    }
+
+    exploration_levels = {"3", "4", "Level 3", "Level 4", "L2"}
+
+    if level in exploration_levels:
+        return "exploration"
+    elif level in basic_analysis_levels:
+        return "basic analysis"
+    elif level in advanced_analysis_levels:
+        return "advanced analysis"
+    else:
+        return "advanced analysis"
+
+
+# remove existing data
+EnvironmentalJusticeRow.objects.filter(destination_server=EnvironmentalJusticeRow.DestinationServerChoices.DEV).delete()
+
+ej_dump = json.load(open("backups/ej_dump_20240815_112916.json"))
+for dataset in ej_dump:
+    ej_row = EnvironmentalJusticeRow(
+        destination_server=EnvironmentalJusticeRow.DestinationServerChoices.DEV,
+        sde_link=sinequa_id_to_url(concept_id_to_sinequa_id(dataset.get("meta", {}).get("concept-id", ""))),
+        dataset=dataset.get("umm", {}).get("ShortName", ""),
+        description=dataset.get("umm", {}).get("Abstract", ""),
+        limitations=dataset.get("umm", {}).get("AccessConstraints", {}).get("Description", ""),
+        format=dataset.get("meta", {}).get("format", ""),
+        temporal_extent=", ".join(dataset.get("umm", {}).get("TemporalExtents", [{}])[0].get("SingleDateTimes", [])),
+        intended_use=categorize_processing_level(
+            dataset.get("umm", {}).get("ProcessingLevel", {}).get("Id", "advanced analysis")
+        ),
+        source_link=generate_source_link(dataset.get("umm", {}).get("DOI", {})),
+        indicators=dataset["indicators"],
+        geographic_coverage="",  # Not provided in the data
+        data_visualization="",  # dataset.get("umm", {}).get("RelatedUrls", [{}])[0].get("URL", ""),
+        latency="",  # Not provided in the data
+        spatial_resolution="",  # Not provided in the data
+        temporal_resolution="",  # Not provided in the data
+        description_simplified="",  # Not provided in the data
+        project="",  # Not provided in the data
+        strengths="",  # Not provided in the data
+    )
+    ej_row.save()

scripts/ej/create_ej_dump.py

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+"""
+inferences are supplied by the classification model. the contact point is Bishwas
+cmr is supplied by running https://github.com/NASA-IMPACT/llm-app-EJ-classifier/blob/develop/scripts/data_processing/download_cmr.py
+move to the serve like this: scp ej_dump_20240814_143036.json  sde:/home/ec2-user/sde_indexing_helper/backups/
+"""
+
+import json
+from datetime import datetime
+
+
+def load_json_file(file_path: str) -> dict:
+    with open(file_path, "r") as file:
+        return json.load(file)
+
+
+def save_to_json(data: dict | list, file_path: str) -> None:
+    with open(file_path, "w") as file:
+        json.dump(data, file, indent=2)
+
+
+def process_classifications(predictions: list[dict[str, float]], threshold: float = 0.5) -> list[str]:
+    """
+    Process the predictions and classify as follows:
+    1. If 'Not EJ' is the highest scoring prediction, return 'Not EJ' as the only classification
+    2. Filter classifications based on the threshold, excluding 'Not EJ'
+    3. Default to 'Not EJ' if no classifications meet the threshold
+    """
+    highest_prediction = max(predictions, key=lambda x: x["score"])
+
+    if highest_prediction["label"] == "Not EJ":
+        return ["Not EJ"]
+
+    classifications = [
+        pred["label"] for pred in predictions if pred["score"] >= threshold and pred["label"] != "Not EJ"
+    ]
+
+    return classifications if classifications else ["Not EJ"]
+
+
+def create_cmr_dict(cmr_data: list[dict[str, dict[str, str]]]) -> dict[str, dict[str, dict[str, str]]]:
+    """Restructure CMR data into a dictionary with 'concept-id' as the key."""
+    return {dataset["meta"]["concept-id"]: dataset for dataset in cmr_data}
+
+
+def remove_unauthorized_classifications(classifications: list[str]) -> list[str]:
+    """Filter classifications to keep only those in the authorized list."""
+
+    authorized_classifications = [
+        "Climate Change",
+        "Disasters",
+        "Extreme Heat",
+        "Food Availability",
+        "Health & Air Quality",
+        "Human Dimensions",
+        "Urban Flooding",
+        "Water Availability",
+    ]
+
+    return [cls for cls in classifications if cls in authorized_classifications]
+
+
+def update_cmr_with_classifications(
+    inferences: list[dict[str, dict]],
+    cmr_dict: dict[str, dict[str, dict]],
+    threshold: float = 0.5,
+) -> list[dict[str, dict]]:
+    """Update CMR data with valid classifications based on inferences."""
+
+    predicted_cmr = []
+
+    for inference in inferences:
+        classifications = process_classifications(predictions=inference["predictions"], threshold=threshold)
+        classifications = remove_unauthorized_classifications(classifications)
+
+        if classifications:
+            cmr_dataset = cmr_dict.get(inference["concept-id"])
+
+            if cmr_dataset:
+                cmr_dataset["indicators"] = ";".join(classifications)
+                predicted_cmr.append(cmr_dataset)
+
+    return predicted_cmr
+
+
+def main():
+    inferences = load_json_file("cmr-inference.json")
+    cmr = load_json_file("cmr_collections_umm_20240807_142146.json")
+
+    cmr_dict = create_cmr_dict(cmr)
+
+    predicted_cmr = update_cmr_with_classifications(inferences=inferences, cmr_dict=cmr_dict, threshold=0.8)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    file_name = f"ej_dump_{timestamp}.json"
+
+    save_to_json(predicted_cmr, file_name)
+
+
+if __name__ == "__main__":
+    main()

scripts/ej/thresholding.py

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+from create_ej_dump import load_json_file, create_cmr_dict, update_cmr_with_classifications
+
+inferences = load_json_file("cmr-inference.json")
+cmr = load_json_file("cmr_collections_umm_20240807_142146.json")
+
+cmr_dict = create_cmr_dict(cmr)
+
+for threshold in [0.5, 0.6, 0.7, 0.8, 0.9]:
+    predicted_cmr = update_cmr_with_classifications(inferences=inferences, cmr_dict=cmr_dict, threshold=threshold)
+    print(f"Threshold: {int(threshold*100)}%, EJ datasets: {len(predicted_cmr)}")