From 2cfd46dd9ad9ba40eabf73d16399fc5faba6ef14 Mon Sep 17 00:00:00 2001
From: jrzkaminski <86363785+jrzkaminski@users.noreply.github.com>
Date: Thu, 3 Aug 2023 16:58:30 +0300
Subject: [PATCH] middle state commit

---
 bamt/networks/composite_bn.py           |  2 +-
 bamt/nodes/composite_continuous_node.py | 18 +++++++++++++++---
 bamt/nodes/composite_discrete_node.py   | 16 +++++++++++++---
 3 files changed, 29 insertions(+), 7 deletions(-)

diff --git a/bamt/networks/composite_bn.py b/bamt/networks/composite_bn.py
index 4a31b45..6c52201 100644
--- a/bamt/networks/composite_bn.py
+++ b/bamt/networks/composite_bn.py
@@ -159,7 +159,7 @@ def wrapper():
                         if self.type == "Discrete":
                             pvals = [str(output[t]) for t in parents]
                         elif type(node).__name__ in ("CompositeDiscreteNode", "CompositeContinuousNode"):
-                            pvals = [str(output[t]) for t in parents]
+                            pvals = output
                         else:
                             pvals = [output[t] for t in parents]
 
diff --git a/bamt/nodes/composite_continuous_node.py b/bamt/nodes/composite_continuous_node.py
index 95b62b0..944be2e 100644
--- a/bamt/nodes/composite_continuous_node.py
+++ b/bamt/nodes/composite_continuous_node.py
@@ -4,13 +4,14 @@
 import random
 import math
 
+from numpy import array
 from .base import BaseNode
 from .gaussian_node import GaussianNode
 from .schema import GaussianParams, HybcprobParams
 
 from sklearn import linear_model
 from pandas import DataFrame
-from typing import Optional, List, Union
+from typing import Optional, List, Union, Dict
 
 from ..log import logger_nodes
 from sklearn.metrics import mean_squared_error as mse
@@ -69,14 +70,25 @@ def fit_parameters(self, data: DataFrame, **kwargs) -> GaussianParams:
                 msg="Composite Continuous Node should always have a parent"
             )
 
-    @staticmethod
-    def choose(node_info: GaussianParams, pvals: List[float]) -> float:
+    def choose(self, node_info: GaussianParams, pvals: Dict) -> float:
         """
         Return value from Logit node
         params:
         node_info: nodes info from distributions
         pvals: parent values
         """
+
+        print("ENCODERS OF", self.name, "\n", self.encoders)
+
+        for parent_key in pvals:
+            if not isinstance(pvals[parent_key], (float, int)):
+                parent_value_array = np.array(pvals[parent_key])
+                pvals[parent_key] = self.encoders[parent_key].transform(parent_value_array.reshape(1, -1))
+
+        pvals = list(pvals.values())
+
+        print("TRANSFORMED PVALS \n", pvals)
+
         if pvals:
             for el in pvals:
                 if str(el) == "nan":
diff --git a/bamt/nodes/composite_discrete_node.py b/bamt/nodes/composite_discrete_node.py
index d28152b..3a30ccd 100644
--- a/bamt/nodes/composite_discrete_node.py
+++ b/bamt/nodes/composite_discrete_node.py
@@ -10,7 +10,7 @@
 
 from sklearn import linear_model
 from pandas import DataFrame
-from typing import Optional, List, Union
+from typing import Optional, List, Union, Dict
 
 
 class CompositeDiscreteNode(BaseNode):
@@ -58,8 +58,7 @@ def fit_parameters(self, data: DataFrame, **kwargs) -> LogitParams:
             "serialization": serialization_name,
         }
 
-    @staticmethod
-    def choose(node_info: LogitParams, pvals: List[Union[float]]) -> str:
+    def choose(self, node_info: LogitParams, pvals: Dict) -> str:
         """
         Return value from Logit node
         params:
@@ -69,6 +68,17 @@ def choose(node_info: LogitParams, pvals: List[Union[float]]) -> str:
 
         rindex = 0
 
+        print("ENCODERS OF", self.name, "\n", self.encoders)
+
+        for parent_key in pvals:
+            if not isinstance(pvals[parent_key], (float, int)):
+                parent_value_array = np.array(pvals[parent_key])
+                pvals[parent_key] = self.encoders[parent_key].transform(parent_value_array.reshape(1, -1))
+
+        pvals = list(pvals.values())
+
+        print("TRANSFORMED PVALS \n", pvals)
+
         if len(node_info["classes"]) > 1:
             if node_info["serialization"] == "joblib":
                 model = joblib.load(node_info["classifier_obj"])