apply lint

src/nlpsig/data_preparation.py

@@ -328,9 +328,9 @@ def _check_feature_exists(self, feature: str) -> bool:
             # not in ._feature_list, but is a valid column name in self.df,
             # so add to feature list
             self._feature_list += [feature]
-            
+
         return feature in self._feature_list
-    
+
     def _obtain_feature_columns(
         self,
         features: list[str] | str | None,
@@ -369,8 +369,8 @@ def _obtain_feature_columns(
             # convert to list of strings
             if isinstance(features, str):
                 features = [features]
-            
-            if isinstance(features, list):    
+
+            if isinstance(features, list):
                 # check each item in features is in self._feature_list
                 # if it isn't, but is a column in self.df, it will add
                 # it to self._feature_list
@@ -777,9 +777,7 @@ def pad(
             raise ValueError("`method` must be either 'k_last' or 'max'.")
 
         # obtain feature colnames
-        feature_colnames = self._obtain_feature_columns(
-            features=features
-        )
+        feature_colnames = self._obtain_feature_columns(features=features)
         if len(feature_colnames) > 0:
             if isinstance(standardise_method, str):
                 standardise_method = [standardise_method] * len(feature_colnames)
@@ -881,9 +879,7 @@ def get_time_feature(
             (can be found in `._feature_list` attribute).
         """
         if time_feature not in self._feature_list:
-            raise ValueError(
-                f"`time_feature` should be in {self._feature_list}."
-            )
+            raise ValueError(f"`time_feature` should be in {self._feature_list}.")
 
         if not self.time_features_added:
             self.set_time_features()

tests/conftest.py

@@ -29,7 +29,7 @@ def test_df_with_datetime():
     return pd.DataFrame(
         {
             "text": [f"text_{i}" for i in range(n_entries)],
-            "binary_var": [rng.choice([0,1]) for i in range(n_entries)],
+            "binary_var": [rng.choice([0, 1]) for i in range(n_entries)],
             "continuous_var": rng.random(n_entries),
             "id_col": [0 for i in range(100)]
             + [rng.integers(1, 5) for i in range(n_entries - 100)],
@@ -45,7 +45,7 @@ def test_df_no_time():
     return pd.DataFrame(
         {
             "text": [f"text_{i}" for i in range(n_entries)],
-            "binary_var": [rng.choice([0,1]) for i in range(n_entries)],
+            "binary_var": [rng.choice([0, 1]) for i in range(n_entries)],
             "continuous_var": rng.random(n_entries),
             "id_col": [0 for i in range(100)]
             + [rng.integers(1, 5) for i in range(n_entries - 100)],
@@ -60,7 +60,7 @@ def test_df_to_pad():
     return pd.DataFrame(
         {
             "text": [f"text_{i}" for i in range(n_entries)],
-            "binary_var": [rng.choice([0,1]) for i in range(n_entries)],
+            "binary_var": [rng.choice([0, 1]) for i in range(n_entries)],
             "continuous_var": rng.random(n_entries),
             "id_col": 0,
             "label_col": [rng.integers(0, 4) for i in range(n_entries)],

tests/test_data_preparation.py

@@ -28,11 +28,7 @@ def test_default_initialisation_datetime(
     # 1 dummy id column
     assert obj.df.shape == (
         len(obj.original_df.index),
-        1
-        + len(obj.original_df.columns)
-        + emb.shape[1]
-        + len(obj._feature_list)
-        + 1,
+        1 + len(obj.original_df.columns) + emb.shape[1] + len(obj._feature_list) + 1,
     )
     assert obj.pooled_embeddings is None
     assert set(obj._feature_list) == {
@@ -67,10 +63,7 @@ def test_default_initialisation_no_time(
     # 1 dummy id column
     assert obj.df.shape == (
         len(obj.original_df.index),
-        len(obj.original_df.columns)
-        + emb.shape[1]
-        + len(obj._feature_list)
-        + 1,
+        len(obj.original_df.columns) + emb.shape[1] + len(obj._feature_list) + 1,
     )
     assert obj.pooled_embeddings is None
     assert obj._feature_list == ["timeline_index"]
@@ -105,10 +98,7 @@ def test_initialisation_with_id_and_label_datetime(
     # 3 time features
     assert obj.df.shape == (
         len(obj.original_df.index),
-        1
-        + len(obj.original_df.columns)
-        + emb.shape[1]
-        + len(obj._feature_list),
+        1 + len(obj.original_df.columns) + emb.shape[1] + len(obj._feature_list),
     )
     assert obj.pooled_embeddings is None
     assert set(obj._feature_list) == {
@@ -516,7 +506,10 @@ def test_obtain_colnames_both(test_df_with_datetime, emb, emb_reduced):
     )
     assert obj._obtain_embedding_colnames(embeddings="full") == emb_names
     assert obj._obtain_embedding_colnames(embeddings="dim_reduced") == emb_reduced_names
-    assert obj._obtain_embedding_colnames(embeddings="both") == emb_reduced_names + emb_names
+    assert (
+        obj._obtain_embedding_colnames(embeddings="both")
+        == emb_reduced_names + emb_names
+    )
 
 
 def test_obtain_feature_columns_string(test_df_with_datetime, emb):
@@ -548,9 +541,11 @@ def test_obtain_feature_columns_string_additional_binary(test_df_with_datetime,
         "timeline_index",
         "binary_var",
     }
-
 
-def test_obtain_feature_columns_string_additional_continuous(test_df_with_datetime, emb):
+
+def test_obtain_feature_columns_string_additional_continuous(
+    test_df_with_datetime, emb
+):
     # default initialisation
     obj = PrepareData(original_df=test_df_with_datetime, embeddings=emb)
     # originally only have the time features
@@ -592,7 +587,9 @@ def test_obtain_feature_columns_list_additional(test_df_with_datetime, emb):
         "time_diff",
         "timeline_index",
     }
-    assert obj._obtain_feature_columns(["time_encoding", "timeline_index", "binary_var", "continuous_var"]) == [
+    assert obj._obtain_feature_columns(
+        ["time_encoding", "timeline_index", "binary_var", "continuous_var"]
+    ) == [
         "time_encoding",
         "timeline_index",
         "binary_var",
@@ -734,6 +731,7 @@ def test_standardise_pd_wrong_method(vec_to_standardise, test_df_no_time, emb):
     obj = PrepareData(original_df=test_df_no_time, embeddings=emb)
     incorrect_method = "fake_method"
     with pytest.raises(
-        ValueError, match=re.escape(f"`method`: {incorrect_method} must be in {implemented}.")
+        ValueError,
+        match=re.escape(f"`method`: {incorrect_method} must be in {implemented}."),
     ):
         obj._standardise_pd(vec=vec_to_standardise, method=incorrect_method)

tests/test_padding.py

@@ -1250,7 +1250,7 @@ def test_pad_by_id_k_last_additional(test_df_with_datetime, emb):
     assert type(obj.array_padded) == np.ndarray
     assert np.array_equal(padded_array, obj.array_padded)
     assert obj.array_padded.shape == (len(obj.original_df["id_col"].unique()), k, ncol)
-    
+
 
 def test_pad_by_id_max(test_df_with_datetime, emb):
     obj = PrepareData(
@@ -1278,7 +1278,7 @@ def test_pad_by_id_max(test_df_with_datetime, emb):
     assert type(obj.array_padded) == np.ndarray
     assert np.array_equal(padded_array, obj.array_padded)
     assert obj.array_padded.shape == (len(obj.original_df["id_col"].unique()), k, ncol)
-    
+
 
 def test_pad_by_id_max_additional(test_df_with_datetime, emb):
     obj = PrepareData(