Support sklearn.neighbors.KNeighborsClassifier

mlModelSaver/__init__.py

@@ -4,6 +4,7 @@ import json
 import os
 
 from functools import partial
+import numpy as np
 
 def ensure_directory_exists(directory_path):
     """
@@ -35,11 +36,17 @@ def check_file_exists(file_path):
 
 supportedModels = {
     "sm.OLS": {
-        "supported": True
+        "supported": True,
+        "normalPredictorFunction": "predict"
     },
     "sm.Logit": {
-        "supported": True
-    }
+        "supported": True,
+        "normalPredictorFunction": "predict"
+    },
+    "sklearn.neighbors.KNeighborsClassifier": {
+        "supported": True,
+        "normalPredictorFunction": "predict_proba"
+    },
 }
 
 supportedDataType = {
@@ -66,13 +73,26 @@ def mlModelSavePredict(self, df, typeOfPredict = 'normal'):
     output = []
     outputsName = self.mlModelSaverConfig.get("outputs", [{"name": "result"}])
     outputsName = [item["name"] for item in outputsName]
+    modelType = self.mlModelSaverConfig['modelType']
+    modelTypeConfig = supportedModels[modelType]
     if typeOfPredict == 'normal':
-        results = self.predict(dfAfterTransformation)
+        results = getattr(self, modelTypeConfig['normalPredictorFunction'])(dfAfterTransformation)
+        print(results)
         for  value in results:
-            output.append({
-                outputsName[0]: value,
-            })
+            if isinstance(value, np.ndarray):
+                res = {}
+                for index, val in enumerate(value):
+                    res[outputsName[index]] = val
+                output.append(res)
+            else:
+                output.append({
+                    outputsName[0]: value,
+                })
+            print(type(value))
+            print(value)
+            print(outputsName)
         return output
+    return output
 
 class MlModelSaver: