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xgboost.py

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+import os
+from pathlib import Path
+import numpy as np
+import scipy
+import xgboost as xgb
+
+
+ROOT = Path(os.path.realpath(os.path.expanduser(__file__))).parents[0]
+MODEL_PATH = str(ROOT / os.environ.get("MODEL_NAME"))
+
+
+class Model(object):
+    """
+    Main class for the model server to use for predictions
+    """
+
+    classifier = xgb.XGBClassifier()
+    classifier.load_model(MODEL_PATH)
+
+    @staticmethod
+    def metadata():
+        """
+        Returns the metadata for the model
+        """
+        return {
+            'signature_name': 'serving_default',
+            'inputs': {
+                'input_data': {
+                    'dtype': 'nd.array(float)'
+                }
+            },
+            'outputs': {
+                'class_proba': {
+                    'dtype': 'nd.array(float)'
+                }, 
+                'predictions': {
+                    "dtype": "nd.array(float)"
+                }
+            }
+        }
+
+    @classmethod
+    def predict(cls, data):
+        """
+        Scores the model with the provided input data
+        """
+        input_data = data['input_data']
+
+        if isinstance(input_data, list):
+            input_data = np.matrix(input_data)
+
+        probs = cls.classifier.predict_proba(input_data)
+        predictions = probs[:, 1] > 0.2
+        data_dict = {
+            "softmax_probs" : probs.tolist(),
+            "classification" : predictions.tolist()
+        }
+        return data_dict

xgboost_llm_as_a_judge_20092024.json

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