39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
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import joblib
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import xgboost as xgb
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import shap
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def processing(processed_dict):
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# model_path = r"C:\Users\abinisha\citrus\centurion-dataform-mcard\flowx\model_1\artifacts\xgboost_model_v2.joblib"
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model_path = "./xgboost_model.joblib"
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model = joblib.load(model_path)
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explainer = shap.TreeExplainer(model)
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feature_names = model.feature_names
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# build and score
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X = [processed_dict[f] for f in feature_names]
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dmatrix = xgb.DMatrix([X], feature_names=feature_names)
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pred = float(model.predict(dmatrix)[0])
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# get SHAP values as 1×n_features
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shap_matrix = explainer.shap_values(dmatrix)
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shap_vals = shap_matrix[0] if getattr(shap_matrix, "ndim", 1) > 1 else shap_matrix
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# rank & sort features by descending SHAP impact
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ranked = sorted(zip(feature_names, shap_vals),
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key=lambda fv: fv[1],
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reverse=True)
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# build a *list* of pre-sorted reason-codes
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shape_reasoncode = [
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{
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"feature": feat,
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"value": processed_dict[feat],
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"shap_rank": i+1
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}
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for i, (feat, _) in enumerate(ranked)
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]
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return {
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"prediction": pred,
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"shape_reasoncode": shape_reasoncode
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}
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