From e7834e24464f6479e63760dde89cd9052e3ab1a0 Mon Sep 17 00:00:00 2001
From: Rob Davis <robsdavis473@gmail.com>
Date: Tue, 12 Sep 2023 15:29:50 +0100
Subject: [PATCH] tidy up

---
 src/synthcity/metrics/eval_performance.py | 40 -----------------------
 src/synthcity/plugins/core/dataloader.py  | 18 ----------
 2 files changed, 58 deletions(-)

diff --git a/src/synthcity/metrics/eval_performance.py b/src/synthcity/metrics/eval_performance.py
index 2061bd45..91a3a5f2 100755
--- a/src/synthcity/metrics/eval_performance.py
+++ b/src/synthcity/metrics/eval_performance.py
@@ -371,47 +371,21 @@ def _evaluate_time_series_performance(
         if self.use_cache(cache_file):
             return load_from_file(cache_file)
 
-        # print("X_gt.train().unpack()")
         (
             id_static_gt,
             id_temporal_gt,
             id_observation_times_gt,
             id_outcome_gt,
         ) = X_gt.train().unpack(as_numpy=True)
-        # print(
-        #     444444444444444444,
-        #     "temp: ",
-        #     id_temporal_gt.shape,
-        #     "obs: ",
-        #     id_observation_times_gt.shape,
-        # )
-        # print("X_gt.test().unpack()")
-        # print(X_gt.test().dataframe().shape)
         (
             ood_static_gt,
             ood_temporal_gt,
             ood_observation_times_gt,
             ood_outcome_gt,
         ) = X_gt.test().unpack(as_numpy=True)
-        # print(
-        #     444444444444444444,
-        #     "temp: ",
-        #     ood_temporal_gt.shape,
-        #     "obs: ",
-        #     ood_observation_times_gt.shape,
-        # )
-        # print("X_syn.unpack()")
-        # print(X_syn.dataframe().shape)
         static_syn, temporal_syn, observation_times_syn, outcome_syn = X_syn.unpack(
             as_numpy=True
         )
-        # print(
-        #     444444444444444444,
-        #     "temp: ",
-        #     temporal_syn.shape,
-        #     "obs: ",
-        #     observation_times_syn.shape,
-        # )
 
         skf = KFold(
             n_splits=self._n_folds, shuffle=True, random_state=self._random_state
@@ -432,7 +406,6 @@ def ts_eval_cbk(
             outcome_test: np.ndarray,
         ) -> float:
             try:
-                # print(777777777777, temporal_train.shape, observation_times_train.shape)
                 estimator = model(**model_args).fit(
                     static_train, temporal_train, observation_times_train, outcome_train
                 )
@@ -450,18 +423,12 @@ def ts_eval_cbk(
         for train_idx, test_idx in skf.split(id_static_gt):
             static_train_data = id_static_gt[train_idx]
             temporal_train_data = id_temporal_gt[train_idx]
-            # print(88888888888888888888, end=" | ")
-            # print("temp: ", id_temporal_gt.shape, end=" | ")
-            # print("obs: ", id_observation_times_gt.shape)
-            # print(id_temporal_gt[train_idx])
-            # print(id_observation_times_gt[train_idx])
             observation_times_train_data = id_observation_times_gt[train_idx]
             outcome_train_data = id_outcome_gt[train_idx]
             static_test_data = id_static_gt[test_idx]
             temporal_test_data = id_temporal_gt[test_idx]
             observation_times_test_data = id_observation_times_gt[test_idx]
             outcome_test_data = id_outcome_gt[test_idx]
-            # print("call 1")
             real_score = ts_eval_cbk(
                 static_train_data,
                 temporal_train_data,
@@ -472,7 +439,6 @@ def ts_eval_cbk(
                 observation_times_test_data,
                 outcome_test_data,
             )
-            # print("call 2")
             synth_score_id = ts_eval_cbk(
                 static_syn,
                 temporal_syn,
@@ -483,7 +449,6 @@ def ts_eval_cbk(
                 observation_times_test_data,
                 outcome_test_data,
             )
-            # print("call 3")
             synth_score_ood = ts_eval_cbk(
                 static_syn,
                 temporal_syn,
@@ -913,11 +878,6 @@ def evaluate(
         X_gt: DataLoader,
         X_syn: DataLoader,
     ) -> Dict:
-        # print("evaluate")
-        # print("gt - temp: ", len(X_gt.info()["temporal_features"]))
-        # print("gt - obs: ", X_gt.info()["window_len"])
-        # print("syn - temp: ", len(X_syn.info()["temporal_features"]))
-        # print("syn - obs: ", X_syn.info()["window_len"])
         if self._task_type == "survival_analysis":
             return self._evaluate_survival_model(
                 DeephitSurvivalAnalysis, {}, X_gt, X_syn
diff --git a/src/synthcity/plugins/core/dataloader.py b/src/synthcity/plugins/core/dataloader.py
index 9ca887fb..c01cede7 100755
--- a/src/synthcity/plugins/core/dataloader.py
+++ b/src/synthcity/plugins/core/dataloader.py
@@ -930,27 +930,10 @@ def unpack(self, as_numpy: bool = False, pad: bool = False) -> Any:
             )
         if as_numpy:
             longest_observation_seq = max([len(seq) for seq in temporal_data])
-            # print(666666666666, "temp: ", np.asarray(temporal_data).shape)
-            # print(
-            #     66666666666666,
-            #     "obs: ",
-            #     ma.vstack(
-            #         [
-            #             ma.array(
-            #                 np.resize(ot, longest_observation_seq),
-            #                 mask=[True for i in range(len(ot))]
-            #                 + [False for j in range(longest_observation_seq - len(ot))],
-            #             )
-            #             for ot in observation_times
-            #         ]
-            #     ).shape,
-            # )
             return (
                 np.asarray(static_data),
                 np.asarray(temporal_data),
-                # np.asarray(pd.concat(temporal_data)),
                 # masked array to handle variable length sequences
-                # np.asarray(observation_times),
                 ma.vstack(
                     [
                         ma.array(
@@ -963,7 +946,6 @@ def unpack(self, as_numpy: bool = False, pad: bool = False) -> Any:
                 ),
                 np.asarray(outcome),
             )
-        # print("not numpy")
         return (
             static_data,
             temporal_data,