feat(JAQPOT-334): add jaqpot internal ID (#27)

* chore: delete comment

* feat: return all probabilities instead of the max

* feat: return jaqpotInternalId with the dataset

* refactor: predict_sklearn for jaqpotInternalId

* chore: rename to jaqpotInternalMetadata

* feat: return a predictions dict

src/handlers/predict_sklearn.py

@@ -1,30 +1,41 @@
 from ..entities.prediction_request import PredictionRequestPydantic
 from ..helpers import model_decoder, json_to_predreq
 from ..helpers.predict_methods import predict_onnx, predict_proba_onnx
+import numpy as np
 
 
 def sklearn_post_handler(request: PredictionRequestPydantic):
     model = model_decoder.decode(request.model["rawModel"])
-    data_entry_all = json_to_predreq.decode(request)
+    data_entry_all, JaqpotInternalId = json_to_predreq.decode(request)
     prediction = predict_onnx(model, data_entry_all, request)
     task = request.model["task"].lower()
     if task == "binary_classification" or task == "multiclass_classification":
         probabilities = predict_proba_onnx(model, data_entry_all, request)
     else:
         probabilities = [None for _ in range(len(prediction))]
 
-    results = {}
-    for i, feature in enumerate(request.model["dependentFeatures"]):
-        key = feature["key"]
-        values = [
-            str(item[i]) if len(request.model["dependentFeatures"]) > 1 else str(item)
-            for item in prediction
-        ]
-        results[key] = values
+    final_all = []
+    for jaqpot_id in JaqpotInternalId:
+        if len(request.model["dependentFeatures"]) == 1:
+            prediction = prediction.reshape(-1, 1)
+        jaqpot_id = int(jaqpot_id)
+        results = {
+            feature["key"]: int(prediction[jaqpot_id, i])
+            if isinstance(
+                prediction[jaqpot_id, i], (np.int16, np.int32, np.int64, np.longlong)
+            )
+            else float(prediction[jaqpot_id, i])
+            if isinstance(
+                prediction[jaqpot_id, i], (np.float16, np.float32, np.float64)
+            )
+            else prediction[jaqpot_id, i]
+            for i, feature in enumerate(request.model["dependentFeatures"])
+        }
+        results["jaqpotInternalId"] = jaqpot_id
+        results["jaqpotInternalMetadata"] = {
+            "AD": None,
+            "Probabilities": probabilities[jaqpot_id],
+        }
+        final_all.append(results)
 
-    results["Probabilities"] = [str(prob) for prob in probabilities]
-    results["AD"] = [None for _ in range(len(prediction))]
-
-    final_all = {"predictions": [dict(zip(results, t)) for t in zip(*results.values())]}
-
-    return final_all
+    return {"predictions": final_all}

src/helpers/json_to_predreq.py

@@ -5,8 +5,10 @@
 
 def decode(request):
     df = pd.DataFrame(request.dataset["input"])
+    jaqpot_Internal_Id = []
+    for i in range(len(df)):
+        jaqpot_Internal_Id.append(df.iloc[i]["jaqpotInternalId"])
     independent_features = request.model["independentFeatures"]
-    # smiles_cols = [feature['key'] for feature in independent_features if feature['featureType'] == 'SMILES']
     smiles_cols = [
         feature["key"]
         for feature in independent_features
@@ -34,4 +36,4 @@ def decode(request):
         task=request.model["task"].lower(),
         featurizer=featurizers,
     )
-    return dataset
+    return dataset, jaqpot_Internal_Id

src/helpers/predict_methods.py

@@ -61,6 +61,6 @@ def predict_proba_onnx(model, dataset: JaqpotpyDataset, request):
             )
     onnx_probs = sess.run(None, input_feed)
     onnx_probs_list = [
-        max(onnx_probs[1][instance].values()) for instance in range(len(onnx_probs[1]))
+        onnx_probs[1][instance] for instance in range(len(onnx_probs[1]))
     ]
     return onnx_probs_list