removed preprocessing

MaastrichtU-CDS · Mar 21, 2024 · b42a817 · b42a817
1 parent 950d7c3
commit b42a817
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Showing 2 changed files with 8 additions and 21 deletions.
diff --git a/v6_logistic_regression_py/__init__.py b/v6_logistic_regression_py/__init__.py
@@ -18,8 +18,7 @@
     aggregate,
     coordinate_task,
     export_model,
-    initialize_model,
-    trash_outcomes
+    initialize_model
 )
 
 MODEL_ATTRIBUTE_KEYS = ["coef_", "intercept_", "classes_"]
@@ -35,11 +34,7 @@ def master(
     max_iter: int = 15,
     delta: float = 0.01,
     n_local_iterations: int = 1,
-    org_ids: List[int] = None,
-    trash_kwargs=dict(
-        survival_column="Survival.time",
-        event_column="deadstatus.event",
-        threshold=730)
+    org_ids: List[int] = None
 ) -> Dict[str, Any]:
     """
     Orchestrates federated logistic regression training across nodes.
@@ -90,13 +85,13 @@ def master(
         model_attrs = export_model(global_model, MODEL_ATTRIBUTE_KEYS)
         input_ = {
             'method': 'logistic_regression_partial',
-            'kwargs': {'model_attributes': model_attrs, 'predictors': predictors, 'outcome': outcome, 'trash_kwargs': trash_kwargs, "n_local_iterations": n_local_iterations}
+            'kwargs': {'model_attributes': model_attrs, 'predictors': predictors, 'outcome': outcome, "n_local_iterations": n_local_iterations}
             }
         partial_results = coordinate_task(client, input_, ids)
 
         # Aggregating updates into the global model and assessing convergence.
         global_model = aggregate(global_model, partial_results, MODEL_AGGREGATION_KEYS)
-        new_loss = compute_global_loss(client, global_model, predictors, outcome, ids, trash_kwargs)
+        new_loss = compute_global_loss(client, global_model, predictors, outcome, ids)
 
         loss_diff = abs(loss - new_loss) if loss is not None else 2 * delta
         loss = new_loss
@@ -112,14 +107,14 @@ def master(
         'iteration': iteration
     }
 
-def compute_global_loss(client, model, predictors, outcome, ids, trash_kwargs):
+def compute_global_loss(client, model, predictors, outcome, ids):
     """
     Helper function to compute global loss, abstracting detailed logging.
     """
     model_attributes = export_model(model, MODEL_ATTRIBUTE_KEYS)
     input_ = {
         'method': 'compute_loss_partial',
-        'kwargs': {'model_attributes': model_attributes, 'predictors': predictors, 'outcome': outcome, 'trash_kwargs': trash_kwargs}
+        'kwargs': {'model_attributes': model_attributes, 'predictors': predictors, 'outcome': outcome}
         }
     results = coordinate_task(client, input_, ids)
     aggregated_sample_size = np.sum([res['size'] for res in results])
@@ -134,7 +129,6 @@ def logistic_regression_partial(
     model_attributes: Dict[str, List[float]], 
     predictors: List[str], 
     outcome: str,
-    trash_kwargs: dict,
     n_local_iterations: int = 1,
 ) -> Dict[str, any]:
     """
@@ -159,9 +153,6 @@ def logistic_regression_partial(
     # Drop rows with NaNs
     df = df.dropna(how='any')
 
-    # REMOVE
-    df = trash_outcomes(df, outcome, **trash_kwargs)
-
     # Get features and outcomes
     X = df[predictors].values
     y = df[outcome].values
@@ -192,8 +183,7 @@ def compute_loss_partial(
     df: pd.DataFrame, 
     model_attributes: Dict[str, list], 
     predictors: List[str], 
-    outcome: str,
-    trash_kwargs: dict
+    outcome: str
 ) -> Dict[str, Any]:
     """
     Computes logistic regression model loss on local dataset.
@@ -217,9 +207,6 @@ def compute_loss_partial(
     # Drop rows with NaNs
     df = df.dropna(how='any')
 
-    # REMOVE
-    df = trash_outcomes(df, outcome, **trash_kwargs)
-
     # Get features and outcomes
     X = df[predictors].values
     y = df[outcome].values

diff --git a/v6_logistic_regression_py/example.py b/v6_logistic_regression_py/example.py
@@ -32,7 +32,7 @@
             'predictors': ['t', 'n', 'm'],
             'outcome': 'vital_status',
             'classes': ['alive', 'dead'],
-            'max_iter': 10,
+            'max_iter': 100,
             'delta': 0.0001
         }
     },