diff --git a/config_utils.py b/config_utils.py
new file mode 100644
index 0000000..b5bc053
--- /dev/null
+++ b/config_utils.py
@@ -0,0 +1,50 @@
+import yaml
+import copy
+from typing import Any, Dict, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def load_yaml(path: str) -> Dict[str, Any]:
+    """Load a YAML file and return its contents as a dictionary."""
+    with open(path, 'r') as f:
+        return yaml.safe_load(f)
+
+
+def deep_merge(base: Dict[str, Any], overrides: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Recursively merge `overrides` into `base`. 
+    Values in `overrides` take precedence. Returns a new dict.
+    
+    Example:
+        base = {"a": 1, "b": {"c": 2, "d": 3}}
+        overrides = {"b": {"c": 99}, "e": 5}
+        result = {"a": 1, "b": {"c": 99, "d": 3}, "e": 5}
+    """
+    result = copy.deepcopy(base)
+    for key, value in overrides.items():
+        if key in result and isinstance(result[key], dict) and isinstance(value, dict):
+            result[key] = deep_merge(result[key], value)
+        else:
+            result[key] = copy.deepcopy(value)
+    return result
+
+
+def resolve_layer_config(base_config_path: str, overrides: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+    """
+    Load a layer's base YAML config file and apply any experiment-level overrides.
+    
+    Args:
+        base_config_path: Path to the layer's own config YAML.
+        overrides: Dictionary of keys to override from the experiment config.
+    
+    Returns:
+        The merged configuration dictionary.
+    """
+    base_config = load_yaml(base_config_path)
+    if overrides:
+        merged = deep_merge(base_config, overrides)
+        logger.info(f"Applied {len(overrides)} override(s) to {base_config_path}")
+        return merged
+    return base_config
\ No newline at end of file
diff --git a/data_layer/config_files/data_config_compas.yml b/data_layer/config_files/data_config_compas.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/data_layer/config_files/data_config_compas_carla.yml b/data_layer/config_files/data_config_compas_carla.yml
new file mode 100644
index 0000000..d4fe2da
--- /dev/null
+++ b/data_layer/config_files/data_config_compas_carla.yml
@@ -0,0 +1,113 @@
+# The carla version of compas, for the paper on PROBE, says to treat all their variables as continous, but that doesnt make sense?
+
+name: "compas"
+target_column: "score"
+train_split: 0.7
+balance_classes: false
+preprocessing_strategy: "standardize" # Options: normalize, standardize, min-max none
+cache_dir: "./.data_cache" # For caching, currently we pickle and save the processed datasets
+# Explicitly defines the base order of columns before encoding.
+feature_order: ["age", "two_year_recid", "priors_count", "length_of_stay", "c_charge_degree", "race", "sex"]
+# post_encoding_feat_order: ["age", "two_year_recid", "priors_count", "length_of_stay", "c_charge_degree", "race", "sex"]
+post_encoding_feat_order: ["age", "two_year_recid", "priors_count", "length_of_stay", "c_charge_degree_cat_M", "c_charge_degree_cat_F", "race_cat_African-American", "race_cat_Other", "sex_cat_Female", "sex_cat_Male"]
+
+features:
+  age:
+    short_name: "x0"
+    type: "numerical"
+    node_type: "input"
+    actionability: "same-or-increase"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: null # Numeric, no encoding needed
+    encoded_feature_names: null
+    impute: "median" # Handle missing values dynamically
+    domain: [17, 90] # Optional: can be used for validation or scaling
+    
+  two_year_recid:
+    short_name: "x1"
+    type: "numerical"
+    node_type: "input"
+    actionability: "same-or-increase"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: null # Numeric, no encoding needed
+    encoded_feature_names: null
+    impute: "median" # Handle missing values dynamically
+    
+  priors_count:
+    short_name: "x2"
+    type: "numerical"
+    node_type: "input"
+    actionability: "same-or-increase"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: null # Numeric, no encoding needed
+    encoded_feature_names: null
+    impute: "median" # Handle missing values dynamically
+
+  length_of_stay:
+    short_name: "x3"
+    type: "numerical"
+    node_type: "input"
+    actionability: "same-or-increase"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: null # Numeric, no encoding needed
+    encoded_feature_names: null
+    impute: "median" # Handle missing values dynamically
+
+  c_charge_degree:
+    short_name: "x4"
+    type: "categorical"
+    node_type: "input"
+    actionability: "any"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: "one-hot"
+    encoded_feature_names: ["c_charge_degree_cat_M", "c_charge_degree_cat_F"]
+    impute: "mode"
+
+  race:
+    short_name: "x5"
+    type: "categorical"
+    node_type: "input"
+    actionability: "any"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: "one-hot"
+    encoded_feature_names: ["race_cat_African-American", "race_cat_Other"]
+    impute: "mode"
+
+  sex:
+    short_name: "x6"
+    type: "categorical"
+    node_type: "input"
+    actionability: "any"
+    mutability: true
+    parent: null
+    parent_short: null
+    encode: "one-hot"
+    encoded_feature_names: ["sex_cat_Female", "sex_cat_Male"]
+    impute: "mode"
+
+  score:
+    short_name: "y"
+    type: "binary"
+    node_type: "output"
+    actionability: "none"
+    mutability: false
+    parent: null
+    parent_short: null
+    encode: null
+    encoded_feature_names: null
+    impute: "drop"
+    domain: [0, 1]
+
+  # etc
diff --git a/data_layer/data_module.py b/data_layer/data_object.py
similarity index 95%
rename from data_layer/data_module.py
rename to data_layer/data_object.py
index 8a919ee..94ec3b6 100644
--- a/data_layer/data_module.py
+++ b/data_layer/data_object.py
@@ -3,9 +3,9 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import normalize
 import yaml
-from typing import Dict, Tuple, List, Any
+from typing import Dict, Optional, Tuple, List, Any
 
-class DataModule:
+class DataObject:
     """
     A unified data ingestion and preprocessing pipeline for algorithmic recourse tasks.
     
@@ -16,8 +16,8 @@ class DataModule:
 
     NOTE: this module will essentially take the place of the existing data module and dataset classes,
     and all the functionality in the loadData method will be transferred here as member functions.
-    The "get_preprocessing()" acts like a controller that, based on confgs, will call appropriate util 
-    funtions. (think large if else block).
+    The "get_preprocessing()" acts like a controller that, based on configs, will call appropriate util 
+    funtions. (think large if-else block).
 
     The attributes and util member methods can be expanded on a method need bases. 
 
@@ -32,19 +32,28 @@ class DataModule:
         metadata (Dict[str, Any]): Generated bounds, constraints, and structural info for features.
     """
 
-    def __init__(self, data_path: str, config_path: str):
+    def __init__(self, data_path: str, config_path: str = None, config_override: Optional[Dict[str, Any]] = None):
         """
-        Initializes the DataModule by loading the raw data and configuration.
+        Initializes the DataObject by loading the raw data and configuration.
 
         Args:
             data_path (str): The file path to the raw CSV dataset.
             config_path (str): The file path to the YAML configuration file.
+            config_override (Optional[Dict[str, Any]]): Optional dictionary of config overrides.
         """
         self._metadata = {}
         self._raw_df = pd.read_csv(data_path)
         self._processed_df = self._raw_df.copy() # This will be transformed in place through the preprocessing pipeline.
-        with open(config_path, 'r') as file:
-            self._config = yaml.safe_load(file)
+        
+        if config_path is not None:
+            with open(config_path, 'r') as file:
+                self._config = yaml.safe_load(file)
+        else:
+            self._config = {}
+
+        # If a pre-merged config is given, use it entirely (it already contains overrides)
+        if config_override is not None:
+            self._config = config_override
 
         # drop columns not defined in the config
         columns_to_drop = [col for col in self._raw_df.columns if col not in self._config['features'].keys()]
@@ -177,7 +186,6 @@ def _apply_scaling(self) -> None:
         """
         if self._config['preprocessing_strategy'] == 'normalize':
             # NOTE: needs to be implemented
-            #scaler = normalize()
             raise NotImplementedError("Normalization strategy is not yet implemented.")
         elif self._config['preprocessing_strategy'] == 'standardize':
             scaler = StandardScaler()
diff --git a/data_layer/raw_csv/compas_carla.csv b/data_layer/raw_csv/compas_carla.csv
new file mode 100644
index 0000000..4375f01
--- /dev/null
+++ b/data_layer/raw_csv/compas_carla.csv
@@ -0,0 +1,6173 @@
+age,two_year_recid,c_charge_degree,race,sex,priors_count,length_of_stay,score
+34,1,F,African-American,Male,0,10,1
+69,0,F,Other,Male,0,0,1
+24,1,F,African-American,Male,4,1,1
+44,0,M,Other,Male,0,1,1
+41,1,F,Other,Male,14,6,1
+43,0,F,Other,Male,3,0,1
+39,0,M,Other,Female,0,2,1
+27,0,F,Other,Male,0,1,1
+23,1,M,African-American,Male,3,4,1
+37,0,M,Other,Female,0,0,1
+41,0,F,African-American,Male,0,0,1
+47,1,F,Other,Female,1,14,1
+31,1,F,African-American,Male,7,3,1
+37,0,M,Other,Male,0,0,1
+25,0,F,African-American,Male,3,1,0
+31,1,F,Other,Male,6,1,1
+31,1,F,Other,Male,5,1,1
+64,1,F,African-American,Male,13,1,1
+21,1,F,African-American,Male,1,3,0
+27,1,M,Other,Male,0,0,1
+21,0,F,Other,Female,0,1,1
+24,1,F,Other,Male,1,0,1
+43,0,M,Other,Male,1,1,1
+32,0,M,Other,Male,0,1,1
+27,1,F,African-American,Male,8,17,1
+26,1,F,African-American,Male,5,0,1
+33,1,F,African-American,Male,0,37,0
+30,1,F,Other,Male,9,44,0
+32,1,F,African-American,Male,4,1,0
+55,0,F,Other,Male,0,2,1
+49,1,F,Other,Male,7,3,1
+33,1,M,Other,Female,4,11,1
+24,1,M,Other,Male,1,1,1
+34,1,F,African-American,Male,21,33,0
+29,0,M,Other,Male,0,-1,1
+21,0,F,African-American,Female,2,79,0
+51,0,M,African-American,Male,2,1,1
+29,1,M,Other,Male,2,0,1
+51,0,F,African-American,Female,7,1,1
+29,1,F,African-American,Male,0,0,1
+25,1,M,Other,Male,9,6,0
+35,1,F,African-American,Male,13,0,0
+49,0,M,Other,Male,0,0,1
+29,0,F,African-American,Male,0,0,1
+29,1,F,Other,Male,1,0,1
+28,0,F,Other,Female,0,0,1
+31,0,F,African-American,Male,15,135,1
+24,1,F,African-American,Male,2,1,1
+30,0,M,Other,Male,0,23,1
+49,0,M,African-American,Male,4,0,1
+53,0,F,Other,Male,8,13,1
+35,0,F,African-American,Male,5,0,1
+27,1,F,African-American,Male,15,1,0
+38,1,M,Other,Male,4,1,1
+22,1,F,African-American,Male,1,1,1
+29,1,F,African-American,Male,4,18,0
+62,0,M,Other,Male,0,-1,1
+27,1,F,African-American,Male,6,30,1
+33,0,M,Other,Male,0,1,1
+56,0,M,African-American,Female,0,3,1
+45,1,F,Other,Male,6,241,1
+22,0,F,Other,Male,1,1,1
+39,0,M,African-American,Male,1,4,1
+29,1,F,African-American,Male,5,1,1
+27,1,F,Other,Male,1,14,1
+40,0,F,African-American,Male,2,3,1
+22,1,M,African-American,Male,1,16,1
+22,0,F,Other,Male,0,57,0
+26,1,F,African-American,Female,1,2,1
+32,0,F,African-American,Male,0,1,1
+41,0,M,African-American,Male,1,1,1
+30,1,M,African-American,Male,8,1,0
+50,1,F,Other,Male,0,0,1
+25,1,F,African-American,Male,14,7,0
+20,1,F,Other,Male,0,1,1
+22,1,F,African-American,Male,2,30,0
+35,0,M,African-American,Female,1,0,1
+36,1,F,African-American,Male,9,3,1
+26,1,F,Other,Male,7,0,0
+21,1,F,Other,Male,1,3,1
+25,1,F,Other,Male,9,0,0
+20,0,F,Other,Female,0,0,1
+39,1,F,Other,Male,2,-1,1
+54,0,M,Other,Female,0,0,1
+44,0,M,Other,Female,0,1,1
+19,1,M,African-American,Male,1,9,1
+30,0,F,African-American,Male,1,0,1
+39,1,F,Other,Female,0,1,1
+24,1,F,Other,Male,8,12,1
+30,0,F,Other,Male,0,5,1
+31,0,M,Other,Male,0,38,1
+39,0,F,Other,Female,1,106,1
+43,0,F,Other,Male,1,1,1
+21,1,F,African-American,Male,3,6,0
+38,1,M,Other,Male,5,2,1
+34,0,F,Other,Male,0,14,1
+33,1,F,African-American,Male,5,0,1
+37,0,F,African-American,Male,3,1,1
+42,1,F,Other,Male,1,0,1
+41,1,F,African-American,Male,10,176,0
+22,1,F,African-American,Male,0,0,1
+33,1,F,African-American,Male,28,1,0
+31,0,M,African-American,Male,1,1,1
+30,0,F,Other,Female,1,1,1
+31,1,F,Other,Male,13,471,0
+27,1,F,African-American,Male,3,0,0
+52,0,M,Other,Male,3,0,0
+33,1,F,African-American,Male,19,0,0
+42,0,F,Other,Male,6,-1,1
+25,0,F,Other,Female,2,3,1
+30,0,M,Other,Male,1,3,1
+37,0,M,Other,Female,2,1,1
+28,1,M,Other,Male,0,0,1
+24,0,M,African-American,Female,0,2,1
+29,1,F,Other,Female,10,7,1
+44,1,M,Other,Male,2,-1,1
+32,1,F,African-American,Male,3,8,1
+31,1,M,Other,Male,2,15,1
+31,0,M,African-American,Male,1,1,1
+59,1,F,Other,Male,1,0,1
+20,0,F,African-American,Female,1,2,0
+43,0,M,Other,Female,0,0,1
+38,1,F,African-American,Male,9,13,0
+32,1,F,African-American,Male,11,1,1
+33,1,M,African-American,Male,8,0,1
+31,1,F,African-American,Female,0,1,1
+33,1,F,African-American,Female,7,26,0
+27,1,F,African-American,Male,2,1,0
+20,0,F,African-American,Female,0,0,1
+26,0,M,Other,Female,0,0,1
+61,1,F,African-American,Female,1,0,1
+52,1,F,Other,Male,13,1,1
+63,0,F,Other,Male,1,14,1
+32,1,F,African-American,Female,4,6,0
+29,0,F,Other,Male,1,0,1
+26,1,M,African-American,Female,2,0,1
+39,0,M,Other,Male,1,3,1
+30,1,F,Other,Female,3,530,1
+30,0,F,African-American,Male,1,70,1
+48,0,M,Other,Male,2,0,1
+22,0,F,African-American,Male,1,0,1
+46,1,F,Other,Male,6,22,1
+31,0,F,Other,Male,0,2,1
+25,1,M,African-American,Male,1,1,1
+23,0,F,Other,Male,0,0,1
+32,0,F,African-American,Female,5,1,1
+52,0,M,Other,Male,2,3,1
+62,0,F,African-American,Male,8,15,1
+31,1,F,Other,Male,8,117,0
+28,1,M,African-American,Male,6,0,1
+54,0,F,Other,Male,2,188,1
+58,0,F,Other,Male,0,0,1
+78,1,M,Other,Male,1,0,1
+56,0,F,Other,Male,0,1,1
+24,0,F,Other,Male,0,1,1
+22,1,F,African-American,Male,1,0,1
+47,1,M,African-American,Male,23,0,0
+23,1,M,African-American,Male,0,1,1
+57,0,F,Other,Male,1,19,1
+66,0,F,African-American,Male,3,2,1
+45,1,M,Other,Male,8,71,1
+37,1,M,African-American,Male,2,0,0
+39,1,F,Other,Male,0,0,1
+26,1,F,African-American,Male,9,79,0
+45,0,F,Other,Male,2,358,1
+23,1,F,African-American,Male,0,0,1
+34,0,M,African-American,Male,2,25,1
+23,0,M,African-American,Female,1,0,1
+36,1,M,Other,Male,2,2,1
+50,0,F,Other,Male,1,1,1
+37,0,M,Other,Female,0,0,1
+44,0,M,Other,Male,4,1,1
+35,1,F,African-American,Female,23,2,0
+24,1,F,Other,Male,5,0,1
+26,1,F,African-American,Male,2,1,0
+27,0,M,African-American,Male,0,2,1
+28,0,F,African-American,Female,0,29,0
+28,1,F,Other,Female,7,1,0
+38,0,F,African-American,Male,2,1,1
+28,0,F,African-American,Female,1,38,0
+56,1,F,Other,Male,7,0,1
+38,0,F,African-American,Male,15,1,0
+28,0,M,African-American,Male,2,0,1
+51,1,M,Other,Female,0,0,1
+51,0,M,Other,Male,0,0,1
+36,1,M,Other,Male,1,1,1
+48,0,F,African-American,Male,0,1,1
+37,1,F,Other,Male,4,1,1
+30,0,F,African-American,Female,2,2,1
+41,0,M,African-American,Female,5,0,1
+27,1,F,African-American,Male,11,223,0
+37,0,M,African-American,Male,0,1,1
+33,1,F,Other,Male,4,2,1
+54,0,F,African-American,Male,11,129,0
+23,0,F,African-American,Male,2,71,0
+41,0,M,African-American,Male,0,0,1
+52,0,F,Other,Male,0,0,1
+43,1,F,Other,Male,13,51,1
+52,0,F,African-American,Male,0,13,1
+28,0,F,Other,Male,1,3,1
+30,1,M,African-American,Male,11,1,0
+28,0,F,Other,Male,1,6,1
+22,0,F,African-American,Male,0,0,1
+30,0,F,African-American,Male,4,-1,1
+25,0,F,Other,Male,4,1,1
+31,1,F,Other,Male,0,30,0
+37,1,F,Other,Male,0,35,1
+32,0,F,African-American,Male,14,1,0
+53,0,M,Other,Male,1,1,1
+28,1,F,African-American,Male,25,25,0
+44,1,M,Other,Male,8,4,1
+25,0,F,African-American,Male,0,0,1
+22,1,M,Other,Male,1,5,0
+37,0,M,Other,Female,0,1,1
+40,0,F,Other,Female,3,1,1
+31,0,M,Other,Female,0,0,1
+23,1,F,Other,Male,1,1,1
+23,1,F,Other,Female,5,0,1
+24,0,M,Other,Male,0,0,0
+29,0,M,African-American,Male,1,0,1
+20,1,F,African-American,Male,1,1,0
+30,1,F,African-American,Male,2,1,1
+39,0,F,Other,Male,0,0,1
+27,0,F,African-American,Male,4,0,1
+37,0,F,African-American,Female,2,0,1
+20,0,F,African-American,Male,0,2,0
+22,0,F,Other,Male,0,2,1
+52,0,F,African-American,Female,0,0,1
+23,1,F,African-American,Female,0,3,1
+48,0,F,African-American,Female,0,1,1
+25,0,F,Other,Male,0,0,1
+27,1,F,Other,Male,0,1,1
+30,1,F,African-American,Male,11,1,1
+22,1,M,African-American,Male,3,1,0
+22,0,F,Other,Female,1,0,1
+41,0,M,African-American,Female,2,0,1
+25,0,F,Other,Female,0,0,1
+52,1,F,African-American,Male,3,24,1
+43,0,F,African-American,Male,0,41,1
+37,0,F,Other,Male,3,2,1
+70,0,M,Other,Male,1,12,1
+46,0,F,Other,Male,3,14,1
+21,1,M,African-American,Male,0,1,0
+26,1,M,African-American,Male,1,1,0
+38,1,F,Other,Male,3,1,1
+34,0,M,Other,Male,5,1,1
+34,0,M,African-American,Male,5,0,1
+23,1,F,Other,Male,0,0,1
+34,1,F,African-American,Male,5,2,1
+39,1,F,African-American,Male,6,2,1
+24,0,F,Other,Male,0,1,1
+58,0,F,Other,Male,2,1,1
+21,0,M,African-American,Female,0,0,1
+23,1,M,African-American,Male,4,11,0
+62,0,F,Other,Male,0,1,1
+37,0,F,Other,Male,5,10,1
+43,1,F,Other,Male,2,7,1
+32,0,F,African-American,Female,5,-1,1
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+22,0,M,Other,Male,1,24,1
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+26,0,M,Other,Male,7,68,1
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+42,1,F,African-American,Female,17,8,0
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+47,0,M,Other,Female,0,0,1
+55,0,M,Other,Male,2,13,1
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+43,0,F,African-American,Male,2,-1,1
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+67,0,M,Other,Female,0,0,1
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+26,0,M,Other,Female,1,1,1
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+51,0,M,Other,Female,0,0,1
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+27,0,M,African-American,Male,4,1,1
+41,1,F,Other,Male,1,1,1
+24,0,M,African-American,Male,12,17,0
+61,0,F,African-American,Male,11,1,1
+29,0,F,Other,Female,3,0,1
+34,0,F,Other,Male,11,1,1
+45,0,F,Other,Male,0,1,1
+21,1,F,Other,Male,3,1,0
+28,1,F,African-American,Male,20,0,0
+31,0,F,Other,Male,6,3,1
+59,1,M,Other,Male,0,0,1
+40,1,M,African-American,Male,8,0,1
+22,0,M,Other,Female,1,17,0
+21,1,M,African-American,Male,1,1,1
+25,1,F,Other,Male,2,1,1
+25,0,F,African-American,Female,1,1,1
+47,1,M,African-American,Male,3,7,1
+21,1,M,African-American,Male,5,1,1
+29,1,F,African-American,Male,1,3,1
+40,1,F,Other,Male,4,0,1
+32,0,F,Other,Female,1,0,1
+39,0,M,Other,Male,0,0,1
+22,0,F,Other,Male,3,2,1
+30,1,M,African-American,Female,0,0,1
+25,0,M,Other,Male,0,0,0
+21,1,M,Other,Female,0,2,1
+25,1,M,Other,Male,0,21,1
+27,1,M,African-American,Male,5,37,0
+45,1,M,Other,Male,4,4,1
+32,1,F,African-American,Male,4,1,1
+22,1,M,African-American,Male,0,1,0
+23,1,F,Other,Male,6,84,0
+44,0,F,African-American,Female,6,2,1
+59,1,F,Other,Male,20,198,1
+23,0,F,African-American,Male,0,1,1
+46,0,F,Other,Male,0,0,1
+33,0,F,Other,Male,2,14,1
+30,1,M,Other,Male,1,0,1
+40,1,F,Other,Female,3,0,1
+54,0,F,Other,Male,7,0,1
+21,0,M,Other,Male,0,1,1
+43,1,M,Other,Male,6,0,1
+22,0,M,African-American,Male,0,3,0
+31,0,F,African-American,Male,0,-1,1
+32,1,F,African-American,Male,6,130,0
+27,1,F,Other,Male,4,3,1
+21,0,M,African-American,Male,1,1,1
+23,0,M,Other,Male,0,0,1
+33,1,M,African-American,Male,3,114,1
+64,0,M,African-American,Male,0,1,1
+42,0,F,Other,Female,0,281,1
+20,0,F,African-American,Male,0,0,1
+26,0,M,Other,Male,1,1,1
+30,1,F,African-American,Male,18,61,0
+23,1,M,Other,Male,0,1,1
+23,1,F,African-American,Male,10,4,0
+60,1,F,African-American,Male,13,2,1
+46,1,F,African-American,Male,2,64,1
+56,1,M,Other,Male,0,-1,1
+45,0,F,African-American,Female,0,0,1
+26,1,F,African-American,Male,4,0,1
+47,0,M,African-American,Female,4,0,1
+29,1,F,African-American,Female,7,1,1
+32,0,F,African-American,Male,9,56,0
+23,0,F,Other,Male,0,0,1
+29,1,F,African-American,Male,10,29,0
+33,1,F,Other,Male,11,1,1
+22,1,F,African-American,Male,1,21,1
+28,0,F,African-American,Female,1,0,1
+36,0,F,Other,Male,0,36,1
+32,0,F,Other,Male,1,0,1
+24,0,F,African-American,Male,0,2,1
+33,1,M,African-American,Male,9,2,0
+46,0,M,African-American,Male,2,1,1
+31,1,M,Other,Male,9,1,0
+49,1,F,Other,Male,3,34,0
+24,1,F,African-American,Male,0,-1,1
+57,1,F,African-American,Male,28,2,0
+36,1,F,African-American,Male,14,2,0
+43,0,F,African-American,Male,23,102,1
+42,0,M,African-American,Male,0,6,1
+32,1,F,Other,Male,4,0,0
+26,0,F,Other,Male,1,5,1
+74,0,M,Other,Male,0,1,1
+24,1,F,African-American,Male,3,1,1
+24,0,M,Other,Female,1,0,1
+22,0,F,Other,Male,0,1,1
+33,0,M,African-American,Male,2,1,1
+22,1,M,African-American,Male,1,0,1
+50,0,M,Other,Male,4,26,1
+28,1,F,Other,Female,7,6,1
+35,1,F,African-American,Female,5,7,1
+33,1,F,African-American,Male,0,0,1
+36,0,M,African-American,Female,1,0,1
+28,1,F,African-American,Male,2,1,1
+63,0,F,African-American,Male,1,1,1
+69,0,F,Other,Female,5,1,1
+41,1,F,African-American,Male,18,299,1
+44,0,M,Other,Male,1,0,1
+29,0,F,African-American,Female,0,0,1
+52,0,M,Other,Female,0,1,1
+23,1,F,African-American,Male,1,1,0
+22,0,M,Other,Female,0,0,1
+26,0,F,Other,Male,0,0,1
+24,1,M,African-American,Male,0,20,1
+45,0,M,African-American,Male,4,3,1
+22,1,F,Other,Male,0,0,1
+24,1,F,African-American,Male,4,3,1
+26,0,M,Other,Female,1,5,1
+47,0,M,African-American,Male,0,1,1
+26,0,F,African-American,Male,0,0,1
+36,1,F,African-American,Male,9,0,1
+33,1,M,Other,Male,2,1,1
+52,0,M,Other,Male,1,1,1
+51,0,F,Other,Male,0,-1,1
+47,1,F,African-American,Male,7,57,1
+38,1,M,Other,Male,1,0,1
+67,0,F,Other,Male,1,0,1
+47,0,F,African-American,Male,1,0,1
+66,0,M,Other,Male,0,1,1
+39,0,F,Other,Male,0,1,1
+49,0,M,Other,Female,2,82,1
+45,0,M,African-American,Female,9,75,1
+22,0,M,Other,Male,1,0,1
+46,1,F,Other,Female,7,3,1
+29,1,F,African-American,Male,15,1,1
+28,1,M,African-American,Male,2,2,1
+22,1,F,African-American,Female,0,1,1
+20,1,F,African-American,Male,1,4,1
+34,0,F,Other,Male,5,1,1
+22,1,F,African-American,Male,8,39,1
+25,1,M,Other,Male,3,172,0
+34,0,F,African-American,Male,1,0,1
+34,0,F,Other,Male,1,1,1
+54,1,F,African-American,Male,10,0,1
+51,1,F,Other,Male,0,1,1
+36,1,F,Other,Female,5,0,1
+20,1,F,Other,Male,2,52,0
+30,1,F,African-American,Male,8,54,1
+44,1,M,Other,Male,0,1,1
+23,1,F,African-American,Female,1,28,1
+40,0,F,Other,Male,0,1,1
+27,1,M,Other,Female,0,0,0
+23,0,F,Other,Male,2,2,1
+25,1,F,African-American,Male,3,1,1
+47,0,M,African-American,Male,14,31,1
+27,1,F,African-American,Female,3,2,1
+38,1,M,African-American,Male,16,33,0
+27,1,M,African-American,Male,0,5,1
+24,0,M,Other,Male,0,0,1
+30,1,F,Other,Male,4,4,0
+33,1,M,African-American,Male,4,17,1
+29,1,F,African-American,Female,2,10,1
+38,0,M,Other,Male,0,1,1
+56,1,F,African-American,Male,31,0,1
+26,0,F,African-American,Male,0,2,1
+28,0,F,African-American,Female,5,0,0
+43,0,M,African-American,Male,5,14,1
+29,1,F,African-American,Male,2,1,1
+22,1,F,African-American,Male,1,0,1
+74,0,F,Other,Male,2,9,1
+47,1,F,African-American,Male,4,64,0
+42,1,F,Other,Female,4,4,0
+43,0,M,Other,Female,0,0,1
+28,1,M,African-American,Female,6,2,1
+24,0,F,African-American,Female,0,0,1
+26,1,F,Other,Male,2,199,1
+56,0,M,African-American,Male,0,0,1
+47,1,M,Other,Male,3,5,1
+49,0,F,Other,Male,0,1,1
+36,0,M,Other,Female,1,0,1
+24,0,F,Other,Female,1,3,1
+30,1,F,African-American,Male,3,2,1
+28,0,F,Other,Male,11,1,1
+50,1,F,Other,Female,1,0,1
+39,1,F,African-American,Female,1,2,1
+26,1,F,African-American,Female,4,0,0
+56,0,F,African-American,Male,4,16,1
+58,0,M,Other,Male,8,1,1
+33,0,F,African-American,Male,3,0,1
+31,1,F,African-American,Female,9,0,0
+25,0,M,Other,Male,5,10,0
+25,1,M,Other,Male,7,3,1
+20,1,F,African-American,Male,1,109,0
+30,0,F,Other,Female,3,0,1
+29,1,F,Other,Male,14,0,0
+31,0,M,Other,Male,1,117,1
+21,1,F,African-American,Male,0,0,1
+44,1,F,African-American,Male,11,1,1
+30,0,F,Other,Male,2,63,1
+35,1,M,African-American,Male,12,1,1
+34,0,F,Other,Male,4,1,1
+26,0,M,African-American,Male,2,0,1
+51,0,M,Other,Male,4,0,1
+36,0,M,African-American,Male,0,2,1
+26,1,M,Other,Male,3,0,1
+21,0,F,African-American,Male,1,29,1
+59,0,M,African-American,Male,5,151,1
+29,1,F,African-American,Male,7,4,1
+28,0,F,Other,Male,1,0,1
+37,0,F,African-American,Male,4,1,1
+41,0,M,Other,Male,0,0,1
+31,1,M,Other,Male,7,1,0
+23,1,F,African-American,Male,0,1,1
+54,0,M,Other,Female,1,1,1
+30,0,M,Other,Male,2,6,0
+28,1,M,Other,Male,0,1,1
+22,0,M,African-American,Male,0,2,1
+21,1,F,African-American,Male,0,1,0
+27,0,M,African-American,Male,1,16,1
+61,1,M,African-American,Male,4,2,0
+60,1,M,Other,Male,0,3,1
+22,0,M,Other,Male,0,0,1
+22,0,F,Other,Female,1,0,1
+49,0,F,Other,Female,3,0,1
+61,0,M,Other,Female,1,-1,1
+51,1,M,African-American,Male,7,3,1
+49,0,F,Other,Female,2,10,1
+24,1,F,African-American,Female,0,0,1
+36,1,M,Other,Female,4,2,1
+42,0,F,Other,Male,5,31,1
+36,0,F,African-American,Male,9,57,1
+57,1,F,Other,Male,14,19,1
+36,0,M,Other,Male,0,0,1
+34,1,M,African-American,Male,3,2,1
+24,1,M,African-American,Male,2,33,1
+32,0,F,Other,Male,0,0,1
+25,0,F,African-American,Male,1,8,1
+30,1,M,Other,Female,2,1,1
+35,1,M,African-American,Male,8,62,1
+43,0,M,African-American,Male,0,1,1
+28,0,F,African-American,Male,1,0,1
+32,0,F,African-American,Male,4,0,1
+23,0,F,Other,Male,0,2,0
+20,1,M,African-American,Female,0,4,1
+30,1,F,African-American,Male,19,9,0
+26,0,F,Other,Male,0,173,1
+44,0,F,Other,Male,0,1,1
+30,0,M,African-American,Male,2,5,1
+21,1,M,Other,Male,0,3,1
+30,1,M,African-American,Male,0,0,1
+20,0,F,African-American,Male,0,0,0
+23,0,F,African-American,Male,0,1,1
+23,0,F,African-American,Male,0,1,1
+57,0,F,Other,Male,0,1,1
+33,0,M,African-American,Female,3,1,1
+23,1,F,Other,Female,2,1,1
diff --git a/evaluation_layer/distances.py b/evaluation_layer/distances.py
index ecaf9fa..b2fb65f 100644
--- a/evaluation_layer/distances.py
+++ b/evaluation_layer/distances.py
@@ -3,9 +3,10 @@
 import numpy as np
 import pandas as pd
 
-from evaluation_layer.evaluation_module import EvaluationModule
+from evaluation_layer.evaluation_factory import register_evaluation
+from evaluation_layer.evaluation_object import EvaluationObject
 from evaluation_layer.utils import remove_nans
-from data_layer.data_module import DataModule
+from data_layer.data_object import DataObject
 
 
 def l0_distance(delta: np.ndarray) -> List[float]:
@@ -146,13 +147,13 @@ def _get_distances(
 
     return [[d1[i], d2[i], d3[i], d4[i]] for i in range(len(d1))]
 
-
-class Distance(EvaluationModule):
+@register_evaluation("Distance")
+class Distance(EvaluationObject):
     """
     Calculates the L0, L1, L2, and L-infty distance measures.
     """
 
-    def __init__(self, data: DataModule, hyperparameters: dict = None):
+    def __init__(self, data: DataObject, hyperparameters: dict = None):
         super().__init__(data, hyperparameters)
         self.columns = ["L0_distance", "L1_distance", "L2_distance", "Linf_distance"]
 
diff --git a/evaluation_layer/evaluation_factory.py b/evaluation_layer/evaluation_factory.py
new file mode 100644
index 0000000..fc71506
--- /dev/null
+++ b/evaluation_layer/evaluation_factory.py
@@ -0,0 +1,37 @@
+from data_layer.data_object import DataObject
+from evaluation_layer.evaluation_object import EvaluationObject
+from typing import Dict, Any, List, Optional
+
+_EVAL_REGISTRY = {}
+
+
+def register_evaluation(name: str):
+    """Decorator to register an evaluation metric class by name."""
+    def decorator(cls):
+        _EVAL_REGISTRY[name] = cls
+        return cls
+    return decorator
+
+
+def create_evaluations(metrics_config: List[Dict[str, Any]], 
+                       data: DataObject) -> List[EvaluationObject]:
+    """
+    Instantiate all requested evaluation modules from the experiment config.
+    
+    Args:
+        metrics_config: List of dicts, each with "name" and optional "hyperparameters".
+        data: The DataObject instance.
+    
+    Returns:
+        List of EvaluationObject instances.
+    """
+    evaluations = []
+    for metric in metrics_config:
+        name = metric["name"]
+        hyperparams = metric.get("hyperparameters", None)
+        if name not in _EVAL_REGISTRY:
+            raise ValueError(
+                f"Evaluation '{name}' is not registered. Available: {list(_EVAL_REGISTRY.keys())}"
+            )
+        evaluations.append(_EVAL_REGISTRY[name](data, hyperparams))
+    return evaluations
\ No newline at end of file
diff --git a/evaluation_layer/evaluation_module.py b/evaluation_layer/evaluation_object.py
similarity index 68%
rename from evaluation_layer/evaluation_module.py
rename to evaluation_layer/evaluation_object.py
index 14c15d3..57fe797 100644
--- a/evaluation_layer/evaluation_module.py
+++ b/evaluation_layer/evaluation_object.py
@@ -1,16 +1,16 @@
 from abc import ABC, abstractmethod
 import pandas as pd
-from data_layer.data_module import DataModule
+from data_layer.data_object import DataObject
 
 
-class EvaluationModule(ABC):
-    def __init__(self, data: DataModule, hyperparameters: dict = None):
+class EvaluationObject(ABC):
+    def __init__(self, data: DataObject, hyperparameters: dict = None):
         """
 
         Parameters
         ----------
-        model:
-            Classification model. (optional)
+        data: DataObject
+            The data object containing the processed data and metadata.
         hyperparameters:
             Dictionary with hyperparameters, could be used to pass other things. (optional)
         """
diff --git a/evaluation_layer/utils.py b/evaluation_layer/utils.py
index c6aa994..881fea5 100644
--- a/evaluation_layer/utils.py
+++ b/evaluation_layer/utils.py
@@ -2,13 +2,13 @@
 
 import pandas as pd
 import numpy as np
-from data_layer.data_module import DataModule
-from model_layer.model_module import ModelModule
+from data_layer.data_object import DataObject
+from model_layer.model_object import ModelObject
 import logging
 
 
-def check_counterfactuals(model: ModelModule, 
-                          data: DataModule,
+def check_counterfactuals(model: ModelObject, 
+                          data: DataObject,
                           counterfactuals: pd.DataFrame,
                           factual_indices: pd.Index) -> pd.DataFrame:
     """
@@ -19,7 +19,7 @@ def check_counterfactuals(model: ModelModule,
 
     Parameters
     ----------
-    model: ModelModule
+    model: ModelObject
         The model module containing the trained model and its configuration.
     counterfactuals: pd.DataFrame
         The generated counterfactuals to be checked.
diff --git a/experiment.py b/experiment.py
index 116585d..9f7283a 100644
--- a/experiment.py
+++ b/experiment.py
@@ -1,55 +1,158 @@
-# generic example of a full end to end run of the repo
-from data_layer.data_module import DataModule
-from evaluation_layer.distances import Distance
-from model_layer.model_module import ModelModule
-from method_layer.ROAR.method import ROAR
-import numpy as np
+"""
+usage example: python -m experiment --config_path experiments/experiment_config.yml
+"""
+
+import argparse
 import pandas as pd
+import numpy as np
+import logging
 
-if __name__ == "__main__":
+from config_utils import load_yaml, resolve_layer_config
+from data_layer.data_object import DataObject
+from model_layer.model_object import ModelObject
+from method_layer.method_factory import create_method
+from evaluation_layer.evaluation_factory import create_evaluations
+
+# Force registration of all methods and evaluations
+import method_layer.ROAR.method  # noqa: F401
+import method_layer.PROBE.method  # noqa: F401
+import evaluation_layer.distances  # noqa: F401
+
+_DATA_RAW_PATH = {
+    "german": "data_layer/raw_csv/german.csv",
+    "compas_carla": "data_layer/raw_csv/compas_carla.csv",
+    # add more datasets and their raw data paths here
+}
+
+_DATA_CONFIG_PATHS = {
+    "german": "data_layer/config_files/data_config_german.yml",
+    "compas_carla": "data_layer/config_files/data_config_compas_carla.yml",
+    # add more datasets and their config paths here
+}
+
+_MODEL_CONFIG_PATHS = {
+    "mlp": "model_layer/model_config_mlp.yml",
+    # add more model types and their config paths here
+}
+
+_METHOD_CONFIG_PATHS = {
+    "ROAR": "method_layer/ROAR/library/method_config.yml",
+    "PROBE": "method_layer/PROBE/library/method_config.yml",
+    # add more method types and their config paths here
+}
 
-    data_module = DataModule(
-        data_path="data_layer/raw_csv/german.csv",
-        config_path="data_layer/config_files/data_config_german.yml")
+
+def setup_logging(name: str):
+    level = getattr(logging, name.upper(), logging.INFO)
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
+    )
+
+
+def select_factuals(model: ModelObject, data: DataObject, X_test, config) -> pd.DataFrame:
+    num_factuals = config.get("num_factuals", 5)
+    factual_selection = config.get("factual_selection", "negative_class")
+
+    if factual_selection == "negative_class":
+        prediction = model.predict(X_test)
+        neg_indices = np.where(prediction == 0)[0] # returns the indices
+        selected = X_test[neg_indices][:num_factuals]
+    elif factual_selection == "all":
+        prediction = model.predict(X_test)
+        neg_indices = np.where(prediction == 0)[0] # returns the indices
+        selected = X_test[neg_indices]
+    else:
+        raise ValueError(f"Unknown factual selection method {factual_selection}")
     
-    print("here is the processed data:")
-    print(data_module.get_processed_data().head())
+    return pd.DataFrame(selected, columns=data.get_feature_names(expanded=True))
+
+
+def run_experiment(config_path: str):
+    # load the top level experiment yaml
+
+    exp_config = load_yaml(config_path)
+    experiment = exp_config["experiment"]
 
-    model_module = ModelModule(
-        config_path="model_layer/model_config_mlp.yml",
-        data_module=data_module
+    setup_logging(experiment.get("logger", "INFO"))
+
+    logger = logging.getLogger("experiment")
+
+    logger.info(f"Running experiment {experiment['name']}")
+
+    # ---------- Data layer loading and config merging -----------
+    data_section = exp_config["data"]
+    data_config_merged = resolve_layer_config(
+        _DATA_CONFIG_PATHS[data_section["name"]],
+        data_section.get("overrides")    
+    )
+
+    data_object = DataObject(
+        data_path=_DATA_RAW_PATH[data_section["name"]],
+        config_override=data_config_merged
+    )
+
+    logger.info("Data layer loaded and configured.")
+
+    # ---------- Model layer loading and config merging -----------
+    model_section = exp_config["model"]
+    model_config_merged = resolve_layer_config(
+        _MODEL_CONFIG_PATHS[model_section["name"]],
+        model_section.get("overrides")
     )
 
-    # get model accuracy
-    train_accuracy = model_module.get_train_accuracy()
-    print(f"Model training accuracy: {train_accuracy}")
-    accuracy = model_module.get_test_accuracy()
-    print(f"Model test accuracy: {accuracy}")
+    model_object = ModelObject(
+        data_object=data_object,
+        config_override=model_config_merged
+    )
+
+    logger.info(f"Train accuracy: {model_object.get_train_accuracy():.4f}")
+    logger.info(f"Test accuracy:  {model_object.get_test_accuracy():.4f}")
+
+    # ---------- Select factuals for counterfactual generation -----------
+    X_test, y_test = model_object.get_test_data()
+    factuals = select_factuals(model_object, data_object, X_test, experiment)
+    logger.info(f"Selected {len(factuals)} factual instances.")
+
+    # ---------- Method layer loading and config merging -----------
+    method_section = exp_config["method"]
+    method_config_merged = resolve_layer_config(
+        _METHOD_CONFIG_PATHS[method_section["name"]],
+        method_section.get("overrides")
+    )
 
-    # test to see if ROAR method runs without error
-    method = ROAR(data_module, model_module)
+    method_object = create_method(
+        name=method_section["name"],
+        model=model_object,
+        data=data_object,
+        config_override=method_config_merged
+    )
 
-    # get some factuals to generate counterfactuals for
-    X_test, y_test = model_module.get_test_data()
+    counterfactuals = method_object.get_counterfactuals(factuals)
+    logger.info(f"Generated counterfactuals for {len(counterfactuals)} factual instances.")
 
-    # get the first 5 rows of the processed test data as factuals
-    # specifically, we can the ones predicted as the negative class (label 0) 
-    predictions = model_module.predict(X_test)
-    negative_class_indices = np.where(predictions == 0)[0]
+    # ---------- Evaluation layer loading and config merging -----------
+    evaluation_section = exp_config["evaluation"]
+    evaluations = create_evaluations(
+        metrics_config=evaluation_section["metrics"],
+        data=data_object
+    )
 
-    factuals = pd.DataFrame(X_test[negative_class_indices][:5], columns=data_module.get_feature_names(expanded=True))
+    results = []
+    for eval_module in evaluations:
+        eval_result = eval_module.get_evaluation(factuals, counterfactuals)
+        results.append(eval_result)
+        logger.info(f"Evaluation {eval_module.__class__.__name__} results: {eval_result}")
 
-    print("Here are the factuals we will generate counterfactuals for:")
-    print(factuals)
 
-    # now generate counterfactuals for these factuals using ROAR
-    counterfactuals = method.get_counterfactuals(factuals)
-    print("Here are the generated counterfactuals:")
-    print(counterfactuals)
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run a counterfactual explanation experiment.")
+    parser.add_argument(
+        "--config_path", 
+        type=str, 
+        required=True, 
+        help="Path to the experiment config YAML file.")
+    args = parser.parse_args()
 
-    # perform some benchmarking of the method using the evaluation module
-    evaluation_module = Distance(data_module)
+    run_experiment(args.config_path)
 
-    evaluation_results = evaluation_module.get_evaluation(factuals, counterfactuals)
-    print("Here are the evaluation results for the generated counterfactuals:")
-    print(evaluation_results)
\ No newline at end of file
diff --git a/experiments/experiment_probe_mlp_config.yml b/experiments/experiment_probe_mlp_config.yml
new file mode 100644
index 0000000..5eb3d76
--- /dev/null
+++ b/experiments/experiment_probe_mlp_config.yml
@@ -0,0 +1,53 @@
+# ============================================================
+# Top-Level Experiment Configuration
+# ============================================================
+# This file is the ONLY thing a user needs to create/modify.
+# All other layer configs can be overridden from here.
+
+experiment:
+  name: "compas_probe_mlp_experiment"
+  seed: 42
+  num_factuals: 5  # how many negative-class samples to generate counterfactuals for
+  factual_selection: "negative_class"  # Options: "negative_class", "all"
+  output_dir: "./results"
+  save_results: true
+  output_format: "csv"  # Options: "csv", "json", "both"
+  logger: "info"  # Options: "debug", "info", "warning", "error"
+
+# ---------- Data Layer ----------
+data:
+  name: "compas_carla"
+  # Override any key inside the data config without editing it directly.
+  # Keys here are merged ON TOP of whatever is in data_config_<name>.yml.
+  overrides:
+    train_split: 0.8
+    balance_classes: false
+    preprocessing_strategy: "standardize"
+
+# ---------- Model Layer ----------
+model:
+  name: "mlp" # Options: "mlp", "logistic_regression", etc.
+  overrides:
+    epochs: 40
+    learning_rate: 0.002
+    optimizer: "rms"
+    batch_size: 32
+    hidden_layers: [[50, 50]]
+    n_output: 2
+    output_activation: "softmax"
+
+# ---------- Method Layer ----------
+method:
+  name: "PROBE"  # Options: "ROAR", "PROBE", etc.
+  overrides:
+    # lambda_: 0.1
+    # loss_type: "BCE"
+
+# ---------- Evaluation Layer ----------
+evaluation:
+  metrics:
+    - name: "Distance"
+      # hyperparameters: {}  # Optional per-metric hyperparameters
+  # Future: you could add more metric objects here
+  # - name: "Sparsity"
+  # - name: "Validity"
\ No newline at end of file
diff --git a/experiments/experiment_roar_mlp_config.yml b/experiments/experiment_roar_mlp_config.yml
new file mode 100644
index 0000000..c087bf5
--- /dev/null
+++ b/experiments/experiment_roar_mlp_config.yml
@@ -0,0 +1,52 @@
+# ============================================================
+# Top-Level Experiment Configuration
+# ============================================================
+# This file is the ONLY thing a user needs to create/modify.
+# All other layer configs can be overridden from here.
+
+experiment:
+  name: "german_roar_mlp_experiment"
+  seed: 42
+  num_factuals: 5  # how many negative-class samples to generate counterfactuals for
+  factual_selection: "negative_class"  # Options: "negative_class", "all"
+  output_dir: "./results"
+  save_results: true
+  output_format: "csv"  # Options: "csv", "json", "both"
+  logger: "info"  # Options: "debug", "info", "warning", "error"
+
+# ---------- Data Layer ----------
+data:
+  name: "german"
+  # Override any key inside the data config without editing it directly.
+  # Keys here are merged ON TOP of whatever is in data_config_<name>.yml.
+  overrides:
+    train_split: 0.8
+    balance_classes: false
+    preprocessing_strategy: "standardize"
+
+# ---------- Model Layer ----------
+model:
+  name: "mlp" # Options: "mlp", "logistic_regression", etc.
+  overrides:
+    epochs: 100
+    learning_rate: 0.001
+    batch_size: 1000
+    hidden_layers: [[50, 100], [100, 200]]
+    output_activation: "sigmoid"
+
+# ---------- Method Layer ----------
+method:
+  name: "ROAR"  # Options: "ROAR", "PROBE", etc.
+  overrides:
+    lambda_: 0.1
+    delta_max: 0.1
+    loss_type: "BCE"
+
+# ---------- Evaluation Layer ----------
+evaluation:
+  metrics:
+    - name: "Distance"
+      # hyperparameters: {}  # Optional per-metric hyperparameters
+  # Future: you could add more metric objects here
+  # - name: "Sparsity"
+  # - name: "Validity"
\ No newline at end of file
diff --git a/main.py b/main.py
index aeafccf..fa11f5a 100644
--- a/main.py
+++ b/main.py
@@ -1,34 +1,55 @@
 # generic example of a full end to end run of the repo
-from data_layer.data_module import DataModule
+from data_layer.data_object import DataObject
 from evaluation_layer.distances import Distance
-from evaluation_layer.evaluation_module import EvaluationModule
+from model_layer.model_object import ModelObject
 from method_layer.ROAR.method import ROAR
-from model_layer.model_module import ModelModule
 import numpy as np
 import pandas as pd
 
 if __name__ == "__main__":
-    # Step 1: Initialize the DataModule with the path to the data config YAML
-    data_module = DataModule(config_path="data_config_adult.yml")
-    
-    # Step 2: Initialize the ModelModule with the path to the model config YAML and the processed DataModule
-    model_module = ModelModule(config_path="model_config_mlp.yml", data_module=data_module)
-    
-    # Step 3: Initialize the method module with the DataModule and ModelModule
-    method = ROAR(data_module, model_module) 
+
+    data_object = DataObject(
+        data_path="data_layer/raw_csv/german.csv",
+        config_path="data_layer/config_files/data_config_german.yml")
     
-    # Step 4: Make predictions on new data (example input)
+    print("here is the processed data:")
+    print(data_object.get_processed_data().head())
+
+    model_module = ModelObject(
+        config_path="model_layer/model_config_mlp.yml",
+        data_object=data_object
+    )
+
+    # get model accuracy
+    train_accuracy = model_module.get_train_accuracy()
+    print(f"Model training accuracy: {train_accuracy}")
+    accuracy = model_module.get_test_accuracy()
+    print(f"Model test accuracy: {accuracy}")
+
+    # test to see if ROAR method runs without error
+    method = ROAR(data_object, model_module)
+
+    # get some factuals to generate counterfactuals for
     X_test, y_test = model_module.get_test_data()
+
+    # get the first 5 rows of the processed test data as factuals
+    # specifically, we can the ones predicted as the negative class (label 0) 
     predictions = model_module.predict(X_test)
     negative_class_indices = np.where(predictions == 0)[0]
 
-    factuals = pd.DataFrame(X_test[negative_class_indices][:5], columns=data_module.get_feature_names(expanded=True))
+    factuals = pd.DataFrame(X_test[negative_class_indices][:5], columns=data_object.get_feature_names(expanded=True))
+
+    print("Here are the factuals we will generate counterfactuals for:")
+    print(factuals)
 
     # now generate counterfactuals for these factuals using ROAR
     counterfactuals = method.get_counterfactuals(factuals)
+    print("Here are the generated counterfactuals:")
+    print(counterfactuals)
 
     # perform some benchmarking of the method using the evaluation module
-    evaluation_module = Distance(data_module)
+    evaluation_module = Distance(data_object)
 
     evaluation_results = evaluation_module.get_evaluation(factuals, counterfactuals)
+    print("Here are the evaluation results for the generated counterfactuals:")
     print(evaluation_results)
\ No newline at end of file
diff --git a/method_layer/PROBE/data_config.yml b/method_layer/PROBE/data_config.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/method_layer/PROBE/library/method_config.yml b/method_layer/PROBE/library/method_config.yml
new file mode 100644
index 0000000..e971f41
--- /dev/null
+++ b/method_layer/PROBE/library/method_config.yml
@@ -0,0 +1,13 @@
+feature_cost: "_optional_"
+lr: 0.001
+lambda_: 0.01
+n_iter: 1000
+t_max_min: 0.5
+norm: 1
+clamp: True
+loss_type: "MSE"
+y_target: [0, 1]
+binary_cat_features: True
+noise_variance: 0.01
+invalidation_target: 0.45
+inval_target_eps: 0.005
\ No newline at end of file
diff --git a/method_layer/PROBE/library/method_utils.py b/method_layer/PROBE/library/method_utils.py
new file mode 100644
index 0000000..f41bebe
--- /dev/null
+++ b/method_layer/PROBE/library/method_utils.py
@@ -0,0 +1,210 @@
+import datetime
+from typing import List, Optional
+
+import numpy as np
+import math
+import torch
+import torch.optim as optim
+import torch.distributions.normal as normal_distribution
+from torch.distributions.multivariate_normal import MultivariateNormal
+from torch import nn
+from torch.autograd import Variable
+
+import logging
+
+
+DECISION_THRESHOLD = 0.5
+
+""" 
+Code is largely taken and modified from https://github.com/MartinPawelczyk/ProbabilisticallyRobustRecourse/
+"""
+
+def gradient(y, x, grad_outputs=None):
+    """Compute dy/dx @ grad_outputs"""
+    if grad_outputs is None:
+        grad_outputs = torch.tensor(1)
+    grad = torch.autograd.grad(y, [x], grad_outputs=grad_outputs, create_graph=True)[0]
+    return grad
+
+
+def compute_jacobian(inputs, output, num_classes=1):
+    """
+    :param inputs: Batch X Size (e.g. Depth X Width X Height)
+    :param output: Batch X Classes
+    :return: jacobian: Batch X Classes X Size
+    """
+    assert inputs.requires_grad
+    grad = gradient(output, inputs)
+    return grad
+
+
+def compute_invalidation_rate_closed(torch_model, x, sigma2):
+    # Compute input into CDF
+    prob = torch_model(x)
+    logit_x = torch.log(prob[0][1] / prob[0][0]).to(x.device) # logit of the positive class probability
+    Sigma2 = sigma2 * torch.eye(x.shape[0]).to(x.device) # covariance matrix of the noise
+    jacobian_x = compute_jacobian(x, logit_x, num_classes=1).reshape(-1)
+    denom = torch.sqrt(sigma2) * torch.norm(jacobian_x, 2)
+    arg = logit_x / denom
+    
+    # Evaluate Gaussian cdf
+    normal = normal_distribution.Normal(loc=0.0, scale=1.0)
+    normal_cdf = normal.cdf(arg)
+    
+    # Get invalidation rate
+    ir = 1 - normal_cdf
+    
+    return ir
+
+
+# def perturb_sample(x, n_samples, sigma2):
+#     # stack copies of this sample, i.e. n rows of x.
+#     X = x.repeat(n_samples, 1)
+#     # sample normal distributed values
+#     Sigma = torch.eye(x.shape[1]) * sigma2
+#     eps = MultivariateNormal(
+#         loc=torch.zeros(x.shape[1]), covariance_matrix=Sigma
+#     ).sample((n_samples,))
+    
+#     return X + eps
+
+
+def reparametrization_trick(mu, sigma2, n_samples):
+    #var = torch.eye(mu.shape[1]) * sigma2
+    std = torch.sqrt(sigma2).to(mu.device)
+    epsilon = MultivariateNormal(loc=torch.zeros(mu.shape[0]), covariance_matrix=torch.eye(mu.shape[0]))
+    epsilon = epsilon.sample((n_samples,)).to(mu.device)  # standard Gaussian random noise
+    ones = torch.ones_like(epsilon).to(mu.device)
+    random_samples = mu.reshape(-1) * ones + std * epsilon
+    
+    return random_samples
+
+
+def compute_invalidation_rate(torch_model, random_samples):
+    yhat = torch_model(random_samples)[:, 1]
+    hat = (yhat > 0.5).float()
+    ir = 1 - torch.mean(hat, 0)
+    return ir
+
+
+def probe_recourse(
+    model: torch.nn.Module,
+    x: np.ndarray,
+    cat_feature_indices: List[int],
+    binary_cat_features: bool = True,
+    feature_costs: Optional[List[float]] = None,
+    lr: float = 0.07,
+    lambda_param: float = 5,
+    y_target: List[int] = [0.45, 0.55],
+    n_iter: int = 500,
+    t_max_min: float = 0.15,
+    norm: int = 1,
+    clamp: bool = False,
+    loss_type: str = "MSE",
+    invalidation_target: float = 0.45,
+    inval_target_eps: float = 0.005,
+    noise_variance: float = 0.01
+) -> np.ndarray:
+    """
+    Generate counterfactual explanation for a given input sample using PROBE method.
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    torch.manual_seed(0)
+
+    noise_variance = torch.tensor(noise_variance, device=device)
+
+    x = torch.tensor(x, dtype=torch.float32, device=device)
+    y_target = torch.tensor(y_target, dtype=torch.float32, device=device)
+    lamb = torch.tensor(lambda_param, dtype=torch.float32, device=device)
+
+    x_new = Variable(x.clone(), requires_grad=True)
+
+    # NOTE: x and all its clones are in the shape (1, num_features)
+    # This is based on the original design, ill try to preseve it best I can.
+
+    # x_new_enc is a copy of x_new with reconstructed encoding constraints of x_new
+    # such that categorical data is either 0 or 1
+    # go through the list of categorical features given to us from the
+    # data module and use the list of encoded feature names to reconstruct the encoding constraints for the categorical features in x_new
+    x_new_enc = x_new.clone()
+
+    # print(f"This is the shape of x_new {x_new}")
+    # print(f"these are the cat feature indices {cat_feature_indices}")
+
+    for index in cat_feature_indices:
+        x_new_enc[0][index] = torch.round(x_new_enc[0][index])
+
+    optimizer = optim.Adam([x_new], lr=lr, amsgrad=True)
+
+    if loss_type == "MSE":
+        loss_fn = torch.nn.MSELoss()
+        f_x_new = model(x_new)[0][1]
+    else:
+        loss_fn = torch.nn.BCELoss()
+        f_x_new = model(x_new)[0][1]
+
+    t0 = datetime.datetime.now()
+    t_max = datetime.timedelta(minutes=t_max_min)
+
+    costs = []
+    ces = []
+
+    random_samples = reparametrization_trick(x_new, noise_variance, n_samples=1000)
+    invalidation_rate = compute_invalidation_rate(model, random_samples)
+
+    while (f_x_new <= DECISION_THRESHOLD) or (invalidation_rate > invalidation_target + inval_target_eps):
+
+        for _ in range(n_iter):
+
+            optimizer.zero_grad()
+
+            f_x_new_binary = model(x_new)[0]
+
+            cost = torch.dist(x_new, x, norm)
+
+            invalidation_rate_c = compute_invalidation_rate_closed(model, x_new, noise_variance)
+
+            loss_invalidation = invalidation_rate_c - invalidation_target
+
+            loss_invalidation[loss_invalidation < 0] = 0
+
+            loss = 3 * loss_invalidation + loss_fn(f_x_new_binary, y_target) + lamb * cost
+            loss.backward()
+            optimizer.step()
+
+            random_samples = reparametrization_trick(x_new, noise_variance, n_samples=10000)
+            invalidation_rate = compute_invalidation_rate(model, random_samples)
+
+            if clamp:
+                x_new.clone().clamp_(0, 1)
+
+            x_new_enc = x_new.clone()
+
+            for index in cat_feature_indices:
+                x_new_enc[0][index] = torch.round(x_new_enc[0][index])
+
+            f_x_new = model(x_new)[0][1]
+        
+        if (f_x_new > DECISION_THRESHOLD) and (invalidation_rate < invalidation_target + inval_target_eps):
+            
+            costs.append(cost)
+            ces.append(x_new)
+
+            break
+    
+        lamb -= 0.10
+
+        if datetime.datetime.now() - t0 > t_max:
+            logging.info("Timeout")
+            break
+    
+    if not ces:
+        logging.info("No Counterfactual Explanation Found at that Target Rate - Try Different Target")
+    else:
+        logging.info("Counterfactual Explanation Found")
+        costs = torch.tensor(costs)
+        min_idx = int(torch.argmin(costs).numpy())
+        x_new_enc = ces[min_idx]
+            
+    return x_new_enc.cpu().detach().numpy().squeeze(axis=0)
\ No newline at end of file
diff --git a/method_layer/PROBE/method.py b/method_layer/PROBE/method.py
index e69de29..7052cad 100644
--- a/method_layer/PROBE/method.py
+++ b/method_layer/PROBE/method.py
@@ -0,0 +1,108 @@
+
+import pandas as pd
+import numpy as np
+from typing import Any, Dict, Dict, Optional, Tuple
+from lime.lime_tabular import LimeTabularExplainer
+from sklearn.linear_model import LogisticRegression
+import yaml
+from data_layer.data_object import DataObject
+from evaluation_layer.utils import check_counterfactuals
+from method_layer.PROBE.library.method_utils import probe_recourse
+from method_layer.method_factory import register_method
+from method_layer.method_object import MethodObject
+from model_layer.model_object import ModelObject
+from config_utils import deep_merge
+import logging
+
+
+@register_method("PROBE")
+class PROBE(MethodObject):
+    """
+    Implementation of PROBE [1]_.
+
+    .. [1] [1] Martin Pawelczyk,Teresa Datta, Johan Van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju.2023
+            Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
+            https://openreview.net/pdf?id=sC-PmTsiTB(2023).
+    """
+
+    def __init__(self, 
+                data: DataObject, 
+                model: ModelObject, 
+                config_override: Optional[Dict[str, Any]] = None):
+        super().__init__(data, model, config_override=config_override)
+
+        # get configs from config file
+        self.config = yaml.safe_load(open("method_layer/PROBE/library/method_config.yml", 'r'))
+        
+        # merge configs with user specified, if they exist
+        if self._config_override is not None:
+            self.config = deep_merge(self.config, self._config_override)
+
+        # store the feature ordering
+        self._feature_order = self._data.get_feature_names(expanded=True) # ensure the feature ordering is correct for the model input
+        
+        self._feature_cost = self.config["feature_cost"]
+        self._lr = self.config["lr"]
+        self._lambda_ = self.config["lambda_"]
+        self._n_iter = self.config["n_iter"]
+        self._t_max_min = self.config["t_max_min"]
+        self._norm = self.config["norm"]
+        self._clamp = self.config["clamp"]
+        self._loss_type = self.config["loss_type"]
+        self._y_target = self.config["y_target"]
+        self._binary_cat_features = self.config["binary_cat_features"]
+        self._noise_variance = self.config["noise_variance"]
+        self._invalidation_target = self.config["invalidation_target"]
+        self._inval_target_eps = self.config["inval_target_eps"]
+
+
+    def get_counterfactuals(self, factuals: pd.DataFrame):
+        """
+        Generate counterfactual examples for given factuals.
+        """
+        factuals = factuals.reset_index()
+        factuals = factuals[self._feature_order] # ensure the feature ordering is correct for the model input
+
+        encoded_feature_names = self._data.get_categorical_features(expanded=True)
+
+        # cat_features_indeces should be a 2d array so that each row corresponds to the indices of the one-hot encoded features for a particular categorical variable.
+        cat_features_indices = []
+        for features in encoded_feature_names:
+            # Find the indices of these encoded features in the processed dataframe
+            indices = [factuals.columns.get_loc(feat) for feat in features]
+            cat_features_indices.extend(indices)
+
+        # So cat_features_indices should look something like [[3,4,5,6]] for the german dataset, 
+        # which means the 4 one-hot encoded features of "personal_status_sex" are at those positions 
+        # in the encoded dataset. 
+
+
+        cfs = []
+        for index, row in factuals.iterrows():
+            
+            counterfactual = probe_recourse(
+                self._model._model,
+                row.to_numpy().reshape((1, -1)),
+                cat_features_indices,
+                # binary_cat_features=self._binary_cat_features,
+                feature_costs=self._feature_cost,
+                lr=self._lr,
+                lambda_param=self._lambda_,
+                n_iter=self._n_iter,
+                norm=self._norm,
+                t_max_min=self._t_max_min,
+                loss_type=self._loss_type,
+                clamp=self._clamp,
+                invalidation_target=self._invalidation_target,
+                inval_target_eps=self._inval_target_eps,
+                noise_variance=self._noise_variance
+            )
+            cfs.append(counterfactual)
+
+        # Convert output into correct format
+        cfs = np.array(cfs)
+        df_cfs = pd.DataFrame(cfs, columns=self._data.get_feature_names(expanded=True)) # ensure the feature ordering is correct for the model input
+        df_cfs = check_counterfactuals(self._model, self._data, df_cfs, factuals.index) 
+        # df_cfs = self._model.get_ordered_features(df_cfs)
+
+        return df_cfs
diff --git a/method_layer/PROBE/model_config_mlp.yml b/method_layer/PROBE/model_config_mlp.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/method_layer/ROAR/library/data_config.yml b/method_layer/ROAR/library/data_config.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/method_layer/ROAR/library/method_config.yml b/method_layer/ROAR/library/method_config.yml
index 1200473..955a82f 100644
--- a/method_layer/ROAR/library/method_config.yml
+++ b/method_layer/ROAR/library/method_config.yml
@@ -1,14 +1,15 @@
-"feature_cost": Null
-"lr": 0.01
-"lambda_": 0.01
-"delta_max": 0.01
-"norm": 1
-"t_max_min": 0.5
-"loss_type": "BCE"
-"y_target": [0, 1]
-"binary_cat_features": False
-"loss_threshold": 1e-3
-"discretize": False
-"sample": True
-"lime_seed": 0
-"seed": 0
\ No newline at end of file
+feature_cost: Null
+lr: 0.001
+lambda_: 0.1
+delta_max: 0.1
+norm: 1
+t_max_min: 1
+loss_type: "BCE" # MCE, BCE
+y_target: [0, 1] # [0, 1] if BCE, [1] if MSE
+binary_cat_features: False
+loss_threshold: 0.0001
+discretize: False
+sample: True
+lime_seed: 0
+enforce_encoding: False
+seed: 0
\ No newline at end of file
diff --git a/method_layer/ROAR/library/method_utils.py b/method_layer/ROAR/library/method_utils.py
index 410a178..4bcd978 100644
--- a/method_layer/ROAR/library/method_utils.py
+++ b/method_layer/ROAR/library/method_utils.py
@@ -40,9 +40,10 @@ def _calc_max_perturbation(
         (recourse, torch.ones(1, device=recourse.device)), 0
     )  # Add 1 to the feature vector for intercept
 
-    loss_fn = torch.nn.BCELoss()
+    loss_fn = nn.BCELoss()
+
     W.requires_grad = True
-    f_x_new = torch.nn.Sigmoid()(torch.matmul(W, recourse))
+    f_x_new = nn.Sigmoid()(torch.matmul(W, recourse))
     w_loss = loss_fn(f_x_new, target_class)
 
     gradient_w_loss = grad(w_loss, W)[0]
@@ -50,7 +51,7 @@ def _calc_max_perturbation(
     bound = (-delta_max, delta_max)
     bounds = [bound] * len(gradient_w_loss)
 
-    res = linprog(c, bounds=bounds, method="simplex")
+    res = linprog(c, bounds=bounds, method="highs")
 
     if res.status != 0:
         logging.warning("Optimization with respect to delta failed to converge")
@@ -68,14 +69,15 @@ def roar_recourse(
     cat_feature_indices: List[List[int]],
     # binary_cat_features: bool = True,
     feature_costs: Optional[List[float]] = None,
-    lr: float = 0.01,
-    lambda_param: float = 0.01,
-    delta_max: float = 0.01,
+    lr: float = 1e-3,
+    lambda_param: float = 0.1,
+    delta_max: float = 0.1,
     y_target: List[int] = [0, 1],
-    t_max_min: float = 0.5,
+    t_max_min: float = 1,
     norm: int = 1,
     loss_type: str = "BCE",
-    loss_threshold: float = 1e-3,
+    loss_threshold: float = 1e-4,
+    enforce_encoding: bool = False,
     seed: int = 0,
 ) -> np.ndarray:
     """
@@ -126,13 +128,15 @@ def roar_recourse(
     intercept = torch.from_numpy(np.asarray([intercept])).float().to(device)
     x = torch.from_numpy(x).float().to(device)
     y_target = torch.tensor(y_target).float().to(device)
-    print(f"Target class for ROAR: {y_target}")
+    
     lamb = torch.tensor(lambda_param).float().to(device)
 
+    print(f"This is the value of x {x}")
+
     # x_new is used for gradient search in optimizing process
     x_new = Variable(x.clone(), requires_grad=True)
 
-    optimizer = optim.Adam([x_new], lr=lr, amsgrad=True)
+    optimizer = optim.Adam([x_new], lr=lr)
 
     if loss_type == "MSE":
         if len(y_target) != 1:
@@ -155,8 +159,9 @@ def roar_recourse(
         raise ValueError(f"loss_type {loss_type} not supported")
 
     # Placeholder values for first loop
-    loss = torch.tensor(0)
-    loss_diff = loss_threshold + 1
+    loss = torch.tensor(1)
+    loss_diff = 1
+    f_x_new = 0
 
     t0 = datetime.datetime.now()
     t_max = datetime.timedelta(minutes=t_max_min)
@@ -164,28 +169,22 @@ def roar_recourse(
     while loss_diff > loss_threshold:
         loss_prev = loss.clone().detach()
 
-        # x_new_enc is a copy of x_new with reconstructed encoding constraints of x_new
-        # such that categorical data is either 0 or 1
-        # go through the list of categorical features given to us from the
-        # data module and use the list of encoded feature names to reconstruct the encoding constraints for the categorical features in x_new
-        x_new_enc = x_new.clone()
-
-        for cat_feature_group in cat_feature_indices:
-            # We can reconstruct the encoding constraints by taking the argmax of the group of features to find the index of the feature that should be 1 (if any), and setting that feature to 1 and the rest to 0.
-            # print(f"Reconstructing encoding constraints for categorical feature group {cat_feature_group}")
-            
-            max_index = torch.argmax(x_new_enc[cat_feature_group[0]:cat_feature_group[-1]+1]).item() + cat_feature_group[0] # find the index of the maximum value in the group of features corresponding to the categorical feature
-            
-            # print(f"Reconstructing encoding constraints for categorical feature group {cat_feature_group}, max index: {max_index}")
-            for index in cat_feature_group:
-                if index != max_index:
-                    x_new_enc[index] = 0
-                else:
-                    x_new_enc[index] = 1
+        if enforce_encoding == True:
+            # x_new_enc is a copy of x_new with reconstructed encoding constraints of x_new
+            # such that categorical data is either 0 or 1
+            # go through the list of categorical features given to us from the
+            # data module and use the list of encoded feature names to reconstruct the encoding constraints for the categorical features in x_new
+            x_new_enc = x_new.clone()
+
+            for cat_feature_group in cat_feature_indices:
+
+                for index in cat_feature_group:
+                    x_new_enc[index] = torch.round(x_new_enc[index])
+                    
 
         # Calculate max delta perturbation on weights
         delta_W, delta_W0 = _calc_max_perturbation(
-            x_new_enc.squeeze(), coeff, intercept, delta_max, target_class
+            x_new.squeeze(), coeff, intercept, delta_max, target_class
         )
         delta_W, delta_W0 = (
             torch.from_numpy(delta_W).float().to(device),
@@ -196,18 +195,14 @@ def roar_recourse(
 
         # get the probability of the target class
         f_x_new = nn.Sigmoid()(
-            torch.matmul(coeff + delta_W, x_new_enc.squeeze()) + intercept + delta_W0
+            torch.matmul(coeff + delta_W, x_new.squeeze()) + intercept + delta_W0
         ).squeeze()
 
         if loss_type == "MSE":
             # single logit score for the target class for MSE loss
             f_x_new = torch.log(f_x_new / (1 - f_x_new))
 
-        cost = (
-            torch.dist(x_new_enc, x, norm)
-            # if feature_costs is None
-            # else torch.norm(feature_costs * (x_new_enc - x), norm)
-        )
+        cost = torch.dist(x_new, x, norm)
 
         loss = loss_fn(f_x_new, target_class) + lamb * cost
         loss.backward()
@@ -220,4 +215,4 @@ def roar_recourse(
             logging.info("Timeout - ROAR didn't converge")
             break
 
-    return x_new_enc.cpu().detach().numpy() #.squeeze(axis=0)
\ No newline at end of file
+    return x_new.cpu().detach().numpy() #.squeeze(axis=0)
\ No newline at end of file
diff --git a/method_layer/ROAR/library/model_config_mlp.yml b/method_layer/ROAR/library/model_config_mlp.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/method_layer/ROAR/method.py b/method_layer/ROAR/method.py
index 88884de..141f500 100644
--- a/method_layer/ROAR/method.py
+++ b/method_layer/ROAR/method.py
@@ -1,32 +1,41 @@
 
 import pandas as pd
 import numpy as np
-from typing import Optional, Tuple
+from typing import Any, Dict, Dict, Optional, Tuple
 from lime.lime_tabular import LimeTabularExplainer
 from sklearn.linear_model import LogisticRegression
 import yaml
-from data_layer.data_module import DataModule
+from data_layer.data_object import DataObject
 from evaluation_layer.utils import check_counterfactuals
 from method_layer.ROAR.library.method_utils import roar_recourse
-from method_layer.method_module import MethodModule
-from model_layer.model_module import ModelModule
+from method_layer.method_factory import register_method
+from method_layer.method_object import MethodObject
+from model_layer.model_object import ModelObject
+from config_utils import deep_merge
 import logging
 
-class ROAR(MethodModule):
+
+@register_method("ROAR")
+class ROAR(MethodObject):
     """
     Implementation of ROAR [1]_.
 
     .. [1] Upadhyay, S., Joshi, S., & Lakkaraju, H. (2021). Towards Robust and Reliable Algorithmic Recourse. NeurIPS.
     """
 
-    def __init__(self, data: DataModule, 
-                model: ModelModule, 
+    def __init__(self, data: DataObject, 
+                model: ModelObject, 
                 coeffs: Optional[np.ndarray] = None,
-                intercepts: Optional[np.ndarray] = None):
-        super().__init__(data, model)
+                intercepts: Optional[np.ndarray] = None,
+                config_override: Optional[Dict[str, Any]] = None):
+        super().__init__(data, model, config_override=config_override)
 
         # get configs from config file
         self.config = yaml.safe_load(open("method_layer/ROAR/library/method_config.yml", 'r'))
+        
+        # merge configs with user specified, if they exist
+        if self._config_override is not None:
+            self.config = deep_merge(self.config, self._config_override)
 
         # store the feature ordering
         self._feature_order = self._data.get_feature_names(expanded=True) # ensure the feature ordering is correct for the model input
@@ -44,6 +53,7 @@ def __init__(self, data: DataModule,
         self._discretize = self.config['discretize']
         self._sample = self.config['sample']
         self._lime_seed = self.config['lime_seed']
+        self._enforce_encoding = self.config['enforce_encoding']
         self._seed = self.config['seed']
 
         self._coeffs = coeffs
@@ -121,6 +131,7 @@ def get_counterfactuals(self, factuals: pd.DataFrame):
                 t_max_min=self._t_max_min,
                 loss_type=self._loss_type,
                 loss_threshold=self._loss_threshold,
+                enforce_encoding=self._enforce_encoding,
                 seed=self._seed,
             )
             cfs.append(counterfactual)
@@ -162,7 +173,6 @@ def _get_lime_coefficients(self, factuals: pd.DataFrame) -> Tuple[np.ndarray, np
                 factual,
                 self._model.predict_proba,
                 num_features=len(self._data.get_feature_names(expanded=True)),
-                # model_regressor=LogisticRegression()
             )
             intercepts.append(explanations.intercept[1])
 
diff --git a/method_layer/method_factory.py b/method_layer/method_factory.py
new file mode 100644
index 0000000..5e854aa
--- /dev/null
+++ b/method_layer/method_factory.py
@@ -0,0 +1,42 @@
+from typing import Any, Dict, Optional
+
+from data_layer.data_object import DataObject
+from method_layer.method_object import MethodObject
+from model_layer.model_object import ModelObject
+
+
+_METHOD_REGISTRY = {}
+
+
+def register_method(name: str):
+    """Decorator to register a method class by name."""
+    def decorator(cls):
+        _METHOD_REGISTRY[name.upper()] = cls
+        return cls
+    return decorator
+
+
+def create_method(name: str, 
+                  data: DataObject, 
+                  model: ModelObject,
+                  config_override: Optional[Dict[str, Any]] = None) -> MethodObject:
+    """
+    Factory function to instantiate a counterfactual method by name.
+    
+    Args:
+        name: The method name (e.g., "ROAR", "PROBE").
+        data: The DataObject instance.
+        model: The ModelObject instance.
+        config_override: Pre-merged method config to inject.
+    
+    Returns:
+        An instance of the requested MethodObject subclass.
+    """
+    name_upper = name.upper()
+    if name_upper not in _METHOD_REGISTRY:
+        raise ValueError(
+            f"Method '{name}' is not registered. Available: {list(_METHOD_REGISTRY.keys())}"
+        )
+    method_cls = _METHOD_REGISTRY[name_upper]
+    return method_cls(data, model, config_override=config_override)
+
diff --git a/method_layer/method_module.py b/method_layer/method_object.py
similarity index 57%
rename from method_layer/method_module.py
rename to method_layer/method_object.py
index bf3ae7b..dc04c25 100644
--- a/method_layer/method_module.py
+++ b/method_layer/method_object.py
@@ -1,20 +1,21 @@
 from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional
 
 import pandas as pd
 
-from data_layer.data_module import DataModule
-from model_layer.model_module import ModelModule
+from data_layer.data_object import DataObject
+from model_layer.model_object import ModelObject
 
 
-class MethodModule(ABC):
+class MethodObject(ABC):
     """
     Abstract class to implement custom recourse methods for a given black-box-model.
 
     Parameters
     ----------
-    data: data_layer.DataModule
-        The data module containing the processed data and metadata.
-    model: model_layer.ModelModule
+    data: data_layer.DataObject
+        The data object containing the processed data and metadata.
+    model: model_layer.ModelObject
         The model module containing the trained model and its configuration.
 
     Methods
@@ -24,9 +25,10 @@ class MethodModule(ABC):
    
     """
 
-    def __init__(self, data: DataModule, model: ModelModule):
+    def __init__(self, data: DataObject, model: ModelObject, config_override: Optional[Dict[str, Any]] = None):
         self._data = data
         self._model = model
+        self._config_override = config_override
 
     @abstractmethod
     def get_counterfactuals(self, factuals: pd.DataFrame):
diff --git a/model_layer/model_builder.py b/model_layer/model_builder.py
index f2f5bac..8144445 100644
--- a/model_layer/model_builder.py
+++ b/model_layer/model_builder.py
@@ -40,7 +40,7 @@ def __init__(
         self.learning_rate = params['learning_rate']
         self.optimizer = params['optimizer']
         self.loss_function = params['loss_function']
-        self.output_activation = params.get('output_activation', None)
+        self.activation = params.get('output_activation', None)
         self.device = params['device']
 
         # Dynamically build the hidden layers based on the provided configuration
@@ -55,13 +55,15 @@ def __init__(
         # Output layer
         layers.append(nn.Linear(input_size, params['n_outputs']))
 
-        self.network = nn.Sequential(*layers)
-
-        if self.output_activation == 'sigmoid':
+        if self.activation == 'sigmoid':
             self.output_activation = nn.Sigmoid()
-        elif self.output_activation == 'softmax':
+        elif self.activation == 'softmax':
             self.output_activation = nn.Softmax(dim=1)
 
+        layers.append(self.output_activation)  # Add output activation to the end of the network
+
+        self.network = nn.Sequential(*layers)
+
 
     # Predictions
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -100,6 +102,7 @@ def fit(self, x_train, y_train):
         ------
         None.
         """
+        self.train()  # Set the model to training mode
         x_train_tensor = torch.from_numpy(np.array(x_train).astype(np.float32)).to(self.device)
         y_train_tensor = torch.from_numpy(np.array(y_train)).type(torch.LongTensor).to(self.device) 
 
@@ -113,6 +116,8 @@ def fit(self, x_train, y_train):
             optimizer = optim.Adam(self.network.parameters(), lr=self.learning_rate)
         elif self.optimizer == 'sgd':
             optimizer = optim.SGD(self.network.parameters(), lr=self.learning_rate)
+        elif self.optimizer == "rms":
+            optimizer = optim.RMSprop(self.network.parameters(), lr=self.learning_rate)
         
         # defining loss function
         if self.loss_function == 'BCE':
@@ -125,12 +130,21 @@ def fit(self, x_train, y_train):
                 batch_x = batch_x.to(self.device)
                 batch_y = batch_y.to(self.device)
                 optimizer.zero_grad()
-                outputs = self.forward(batch_x)
+                outputs = self(batch_x)
                 # pass outputs through the output activation function if specified in the config
-                if self.output_activation is not None:
-                    outputs = self.output_activation(outputs)
-
-                loss = criterion(outputs, batch_y.unsqueeze(1).float())
+                # if self.output_activation is not None:
+                #     outputs = self.output_activation(outputs)
+                if self.activation == "softmax" and self.loss_function == 'BCE':
+                    batch_y = F.one_hot(batch_y, num_classes=2) # convert to one-hot encoding for BCE loss
+
+                # print(f"this is the batch_y {batch_y.unsqueeze(1).float()}")
+                # print(f"this is the outputs {outputs}")
+                if self.loss_function == 'BCE' and self.activation == "softmax":
+                    loss = criterion(outputs, batch_y.float())
+                elif self.loss_function == 'BCE' and self.activation == "sigmoid":
+                    loss = criterion(outputs, batch_y.unsqueeze(1).float())
+                else:
+                    loss = criterion(outputs, batch_y.float())
                 loss.backward()
                 optimizer.step()
 
diff --git a/model_layer/model_config_mlp.yml b/model_layer/model_config_mlp.yml
index fcde440..c4d70b5 100644
--- a/model_layer/model_config_mlp.yml
+++ b/model_layer/model_config_mlp.yml
@@ -7,7 +7,7 @@ epochs: 100
 batch_size: 1000 # make this the same as dataset size if you want to train on the whole dataset at once
 learning_rate: 0.001
 n_output: 1
-optimizer: "adam" # Options: adam, sgd
+optimizer: "adam" # Options: adam, sgd, rms
 loss_function: "BCE" # Options: BCE, MSE, etc
 hidden_layers: [[50, 100], [100, 200]] # Specific to MLP
 tree_estimators: 100    # Specific to Forest/XGBoost
diff --git a/model_layer/model_module.py b/model_layer/model_object.py
similarity index 84%
rename from model_layer/model_module.py
rename to model_layer/model_object.py
index 16ba224..e33cb75 100644
--- a/model_layer/model_module.py
+++ b/model_layer/model_object.py
@@ -1,12 +1,12 @@
 import yaml
-from typing import Any, List, Union
+from typing import Any, Dict, List, Optional, Union
 import pandas as pd
 import numpy as np
 import torch
-from data_layer.data_module import DataModule
+from data_layer.data_object import DataObject
 from model_layer.model_builder import PyTorchNeuralNetwork # make use of the existing wrapper class for pytorch models, we can add more wrapper classes for other backends as needed.
 
-class ModelModule:
+class ModelObject:
     """
     A decoupled model instantiation and routing layer.
     
@@ -21,24 +21,28 @@ class ModelModule:
     - config: The parsed YAML configuration dictionary for model architecture and training hyperparameters.
     """
 
-    def __init__(self, config_path: str, data_module: DataModule):
+    def __init__(self, config_path: str = None, data_object: DataObject = None, config_override: Optional[Dict[str, Any]] = None):
         """
-        Initializes the ModelModule without redundantly loading raw data.
+        Initializes the ModelObject without redundantly loading raw data.
         
         Args:
             config_path (str): Path to the model configuration YAML.
-            data_module (DataModule): The instantiated data layer containing 
+            data_object (DataObject): The instantiated data layer containing 
                                       the processed data, feature ordering, and bounds.
         """
-        self._data_module = data_module
-        self._config = yaml.safe_load(open(config_path, 'r'))
+        self._data_object = data_object
+        self._config = yaml.safe_load(open(config_path, 'r')) if config_path is not None else {}
         self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
+        # If a pre-merged config is given, use it entirely (it already contains overrides)
+        if config_override is not None:
+            self._config = config_override
+
         self._instantiate_model() # Dynamically instantiate the model based on the config
         self._model.to(self._device) # Move model to GPU if available
         
-        # get training data from the data module and fit the model
-        X_train, X_test, y_train, y_test = self._data_module.get_train_test_split()
+        # get training data from the data object and fit the model
+        X_train, X_test, y_train, y_test = self._data_object.get_train_test_split()
 
         self._x_train = X_train
         self._y_train = y_train
@@ -52,14 +56,14 @@ def _instantiate_model(self) -> None:
         Maps the requested architecture and backend from the YAML config to the 
         corresponding wrapper class (e.g., PyTorchNeuralNetwork, XGBClassifier).
         
-        Dynamically fetches input dimensions directly via `self.data_module.get_feature_names(expanded=True)` 
+        Dynamically fetches input dimensions directly via `self.data_object.get_feature_names(expanded=True)` 
         to ensure the input layer precisely matches the encoded dataset.
         """
         architecture = self._config['architecture']
         backend = self._config['backend']
 
         params = {
-            "n_inputs" : len(self._data_module.get_feature_names(expanded=True)), # Dynamically determine input size
+            "n_inputs" : len(self._data_object.get_feature_names(expanded=True)), # Dynamically determine input size
             "n_outputs" : self._config.get('n_output', 2), # Default to 2 for binary classification, can be overridden in config
             "layers" : self._config['hidden_layers'], # describes the number of input and output neurons in each hidden layer, e.g., [[10,100], [100,10]] for two hidden layers with 10 neurons each
             "batch_size" : self._config.get('batch_size', 1000),
@@ -92,9 +96,9 @@ def get_train_accuracy(self) -> float:
         are ordered correctly according to the DataModule's specifications before 
         making predictions and calculating accuracy.
         """
-        # ensure X_train is in the correct feature order as specified by the DataModule
+        # ensure X_train is in the correct feature order as specified by the DataObject
         if isinstance(self._x_train, pd.DataFrame):
-            feature_names = self._data_module.get_feature_names(expanded=True)
+            feature_names = self._data_object.get_feature_names(expanded=True)
             self._x_train = self._x_train[feature_names].values # reorder columns to match the expected feature order
 
         predictions = self.predict(self._x_train)
@@ -110,9 +114,9 @@ def get_test_accuracy(self) -> float:
         are ordered correctly according to the DataModule's specifications before 
         making predictions and calculating accuracy.
         """
-        # ensure X_test is in the correct feature order as specified by the DataModule
+        # ensure X_test is in the correct feature order as specified by the DataObject
         if isinstance(self._x_test, pd.DataFrame):
-            feature_names = self._data_module.get_feature_names(expanded=True)
+            feature_names = self._data_object.get_feature_names(expanded=True)
             self._x_test = self._x_test[feature_names].values # reorder columns to match the expected feature order
 
         predictions = self.predict(self._x_test)
@@ -124,15 +128,15 @@ def predict(self, x: Union[np.ndarray, pd.DataFrame, torch.Tensor]) -> Union[np.
         Returns raw predictions in the correct format for counterfactual search algorithms.
         
         This method ensures that the input features are ordered according to the 
-        DataModule's specifications before passing them to the underlying model. 
+        DataObject's specifications before passing them to the underlying model. 
         The output is returned in a consistent format (e.g., numpy array or tensor) 
         regardless of the backend.
         """
 
-        # ensure input is in tensor format for PyTorch models, and in the correct feature order as specified by the DataModule
+        # ensure input is in tensor format for PyTorch models, and in the correct feature order as specified by the DataObject
         # should return a list of 1s or 0s.
         if isinstance(x, pd.DataFrame):
-            feature_names = self._data_module.get_feature_names(expanded=True)
+            feature_names = self._data_object.get_feature_names(expanded=True)
             x = x[feature_names].values # reorder columns to match the expected feature order
         
         x_tensor = torch.tensor(x, dtype=torch.float32, device=self._device)
@@ -154,9 +158,9 @@ def predict_proba(self, x: Union[np.ndarray, pd.DataFrame, torch.Tensor]) -> Uni
         Automatically enforces the correct feature input order before passing data 
         to the underlying model.
         """
-        # ensure input is in tensor format for PyTorch models, and in the correct feature order as specified by the DataModule
+        # ensure input is in tensor format for PyTorch models, and in the correct feature order as specified by the DataObject
         if isinstance(x, pd.DataFrame):
-            feature_names = self._data_module.get_feature_names(expanded=True)
+            feature_names = self._data_object.get_feature_names(expanded=True)
             x = x[feature_names].values # reorder columns to match the expected feature order
         
         x_tensor = torch.tensor(x, dtype=torch.float32, device=self._device)