fully remove ta features

pdr_backend/aimodel/aimodel_data_factory.py

@@ -1,3 +1,7 @@
+#
+# Copyright 2024 Ocean Protocol Foundation
+# SPDX-License-Identifier: Apache-2.0
+#
 import logging
 import sys
 from typing import List, Optional, Tuple
@@ -112,8 +116,6 @@ def create_xy(
         x_dim_len = len(train_feeds_list) * ss.autoregressive_n
         diff = 0 if ss.transform == "None" else 1
 
-        features: List[pd.Series] = []
-
         # main work
         xcol_list = []  # [col_i] : name_str
         x_list = []  # [col_i] : Series. Build this up. Not df here (slow)
@@ -150,16 +152,6 @@ def create_xy(
                     x_col = hist_col + f":(z(t-{ds1})-z(t-{ds11}))/z(t-{ds11})"
                 xcol_list += [x_col]
 
-                for i, feature in enumerate(features):
-                    assert type(feature) == pd.Series  # type check for mypy
-                    feature_np = list(feature.values)
-                    features_shifted = pd.Series(
-                        _slice(feature_np, -shift - N_train - 1, -shift)
-                    )
-                    x_list += [features_shifted]
-                    xrecent_list += [pd.Series(_slice(feature_np, -shift, -shift + 1))]
-                    xcol_list.append(f"{feature.name}_t-{ds1}-{i}")
-
         # convert x lists to dfs, all at once. Faster than building up df.
         assert len(x_list) == len(xrecent_list) == len(xcol_list)
         x_df = pd.concat(x_list, keys=xcol_list, axis=1)
@@ -189,8 +181,7 @@ def create_xy(
         # postconditions
         assert X.shape[0] == yraw.shape[0] == ytran.shape[0]
         assert X.shape[0] <= (N_train + 1)
-        feature_dims = len(features) * len(train_feeds_list) * ss.autoregressive_n
-        assert X.shape[1] == x_dim_len + feature_dims
+        assert X.shape[1] == x_dim_len
         assert isinstance(x_df, pd.DataFrame)
 
         assert "timestamp" not in x_df.columns