Fixes lint failures on main

CHANGELOG.md

@@ -48,6 +48,7 @@ Inspired from [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 - Fix pandas dependency issue in nox session by installing pandas package to python directly by @thanawan-atc in ([#266](https://github.com/opensearch-project/opensearch-py-ml/pull/266))
 - Fix conditional job execution issue in model upload workflow by @thanawan-atc in ([#294](https://github.com/opensearch-project/opensearch-py-ml/pull/294))
 - fix bug in `MLCommonClient_client.upload_model` by @rawwar in ([#336](https://github.com/opensearch-project/opensearch-py-ml/pull/336))
+- fix lint issues on main by @rawwar in ([#374](https://github.com/opensearch-project/opensearch-py-ml/pull/374))
 
 ## [1.1.0]
 

docs/source/conf.py

@@ -22,7 +22,6 @@
 import os
 import sys
 
-
 sys.path.insert(0, os.path.abspath("../../"))
 
 # -- Project information -----------------------------------------------------

opensearch_py_ml/dataframe.py

@@ -978,8 +978,10 @@ def _sizeof_fmt(num: float, size_qualifier: str) -> str:
         elif verbose is False:  # specifically set to False, not nesc None
             _non_verbose_repr()
         else:
-            _non_verbose_repr() if exceeds_info_cols else _verbose_repr(
-                number_of_columns
+            (
+                _non_verbose_repr()
+                if exceeds_info_cols
+                else _verbose_repr(number_of_columns)
             )
 
         # pandas 0.25.1 uses get_dtype_counts() here. This

opensearch_py_ml/field_mappings.py

@@ -445,9 +445,9 @@ def find_aggregatable(row, df):
                 try:
                     series = df.loc[df.os_field_name == os_field_name_keyword]
                     if not series.empty and series.is_aggregatable.squeeze():
-                        row_as_dict[
-                            "aggregatable_os_field_name"
-                        ] = os_field_name_keyword
+                        row_as_dict["aggregatable_os_field_name"] = (
+                            os_field_name_keyword
+                        )
                     else:
                         row_as_dict["aggregatable_os_field_name"] = None
                 except KeyError:

opensearch_py_ml/ml_commons/model_uploader.py

@@ -95,9 +95,9 @@ def _register_model(
         model_meta_json[TOTAL_CHUNKS_FIELD] = total_num_chunks
 
         if MODEL_CONTENT_SIZE_IN_BYTES_FIELD not in model_meta_json:
-            model_meta_json[
-                MODEL_CONTENT_SIZE_IN_BYTES_FIELD
-            ] = model_content_size_in_bytes
+            model_meta_json[MODEL_CONTENT_SIZE_IN_BYTES_FIELD] = (
+                model_content_size_in_bytes
+            )
         if MODEL_CONTENT_HASH_VALUE not in model_meta_json:
             # Generate the sha1 hash for the model zip file
             hash_val_model_file = _generate_model_content_hash_value(model_path)

opensearch_py_ml/ml_models/metrics_correlation/event_detection.py

@@ -94,9 +94,9 @@ def merge_events(
     send = ends[ix]
     sevents = candidates[ix, :]
 
-    merged: List[
-        Dict[str, torch.Tensor]
-    ] = []  # merge in linear pass over time dimension
+    merged: List[Dict[str, torch.Tensor]] = (
+        []
+    )  # merge in linear pass over time dimension
     currstart = torch.tensor([-1])
     currend = torch.tensor([-1])
     currevent = torch.ones(T) * -1.0

opensearch_py_ml/ml_models/sentencetransformermodel.py

@@ -1304,9 +1304,9 @@ def make_model_config_json(
                 model_config_content["model_content_size_in_bytes"] = os.stat(
                     model_zip_file_path
                 ).st_size
-                model_config_content[
-                    "model_content_hash_value"
-                ] = _generate_model_content_hash_value(model_zip_file_path)
+                model_config_content["model_content_hash_value"] = (
+                    _generate_model_content_hash_value(model_zip_file_path)
+                )
 
         if verbose:
             print("generating ml-commons_model_config.json file...\n")

opensearch_py_ml/operations.py

@@ -1159,9 +1159,11 @@ def _map_pd_aggs_to_os_aggs(
         # piggy-back on that single aggregation.
         if extended_stats_calls >= 2:
             os_aggs = [
-                ("extended_stats", os_agg)
-                if os_agg in extended_stats_os_aggs
-                else os_agg
+                (
+                    ("extended_stats", os_agg)
+                    if os_agg in extended_stats_os_aggs
+                    else os_agg
+                )
                 for os_agg in os_aggs
             ]
 

tests/dataframe/test_iterrows_itertuples_pytest.py

@@ -61,9 +61,11 @@ def assert_tuples_almost_equal(left, right):
             # Shim which uses pytest.approx() for floating point values inside tuples.
             assert len(left) == len(right)
             assert all(
-                (lt == rt)  # Not floats? Use ==
-                if not isinstance(lt, float) and not isinstance(rt, float)
-                else (lt == pytest.approx(rt))  # If both are floats use pytest.approx()
+                (
+                    (lt == rt)  # Not floats? Use ==
+                    if not isinstance(lt, float) and not isinstance(rt, float)
+                    else (lt == pytest.approx(rt))
+                )  # If both are floats use pytest.approx()
                 for lt, rt in zip(left, right)
             )
 

utils/model_uploader/update_models_upload_history_md.py

@@ -76,9 +76,9 @@ def create_model_json_obj(
         "Model ID": model_id,
         "Model Version": model_version,
         "Model Format": model_format,
-        "Embedding Dimension": str(embedding_dimension)
-        if embedding_dimension is not None
-        else "N/A",
+        "Embedding Dimension": (
+            str(embedding_dimension) if embedding_dimension is not None else "N/A"
+        ),
         "Pooling Mode": pooling_mode if pooling_mode is not None else "N/A",
         "Workflow Run ID": workflow_id if workflow_id is not None else "-",
     }