Cleaning up formatting warnings

ads/opctl/operator/lowcode/anomaly/model/anomaly_merlion.py

@@ -4,9 +4,11 @@
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import importlib
+import logging
 
 import numpy as np
 import pandas as pd
+import report_creator as rc
 from merlion.post_process.threshold import AggregateAlarms
 from merlion.utils import TimeSeries
 
@@ -21,6 +23,8 @@
 from .anomaly_dataset import AnomalyOutput
 from .base_model import AnomalyOperatorBaseModel
 
+logging.getLogger("root").setLevel(logging.WARNING)
+
 
 class AnomalyMerlionOperatorModel(AnomalyOperatorBaseModel):
     """Class representing Merlion Anomaly Detection operator model."""
@@ -84,7 +88,7 @@ def _build_model(self) -> AnomalyOutput:
         for target, df in self.datasets.full_data_dict.items():
             data = df.set_index(date_column)
             data = TimeSeries.from_pd(data)
-            for model_name, (model_config, model) in model_config_map.items():
+            for _, (model_config, model) in model_config_map.items():
                 if self.spec.model == SupportedModels.BOCPD:
                     model_config = model_config(**self.spec.model_kwargs)
                 else:
@@ -115,7 +119,7 @@ def _build_model(self) -> AnomalyOutput:
                     y_pred = (y_pred.to_pd().reset_index()["anom_score"] > 0).astype(
                         int
                     )
-                except Exception as e:
+                except Exception:
                     y_pred = (
                         scores["anom_score"]
                         > np.percentile(
@@ -135,15 +139,12 @@ def _build_model(self) -> AnomalyOutput:
                         OutputColumns.SCORE_COL: scores["anom_score"],
                     }
                 ).reset_index(drop=True)
-                # model_objects[model_name].append(model)
 
                 anomaly_output.add_output(target, anomaly, score)
         return anomaly_output
 
     def _generate_report(self):
         """Genreates a report for the model."""
-        import report_creator as rc
-
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),
             rc.Text(

ads/opctl/operator/lowcode/anomaly/model/automlx.py

@@ -1,16 +1,21 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
+
 import pandas as pd
+import report_creator as rc
 
 from ads.common.decorator.runtime_dependency import runtime_dependency
-from .anomaly_dataset import AnomalyOutput
+from ads.opctl import logger
+from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
 
+from .anomaly_dataset import AnomalyOutput
 from .base_model import AnomalyOperatorBaseModel
-from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
+
+logging.getLogger("root").setLevel(logging.WARNING)
 
 
 class AutoMLXOperatorModel(AnomalyOperatorBaseModel):
@@ -25,16 +30,17 @@ class AutoMLXOperatorModel(AnomalyOperatorBaseModel):
         ),
     )
     def _build_model(self) -> pd.DataFrame:
-        from automlx import init
         import logging
 
+        import automlx
+
         try:
-            init(
+            automlx.init(
                 engine="ray",
                 engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}},
                 loglevel=logging.CRITICAL,
             )
-        except Exception as e:
+        except Exception:
             logger.info("Ray already initialized")
         date_column = self.spec.datetime_column.name
         anomaly_output = AnomalyOutput(date_column=date_column)
@@ -73,8 +79,6 @@ def _build_model(self) -> pd.DataFrame:
         return anomaly_output
 
     def _generate_report(self):
-        import report_creator as rc
-
         """The method that needs to be implemented on the particular model level."""
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),

ads/opctl/operator/lowcode/anomaly/model/autots.py

@@ -1,9 +1,12 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
+
+import report_creator as rc
+
 from ads.common.decorator.runtime_dependency import runtime_dependency
 from ads.opctl import logger
 from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
@@ -12,6 +15,8 @@
 from .anomaly_dataset import AnomalyOutput
 from .base_model import AnomalyOperatorBaseModel
 
+logging.getLogger("root").setLevel(logging.WARNING)
+
 
 class AutoTSOperatorModel(AnomalyOperatorBaseModel):
     """Class representing AutoTS Anomaly Detection operator model."""
@@ -91,8 +96,6 @@ def _build_model(self) -> AnomalyOutput:
         return anomaly_output
 
     def _generate_report(self):
-        import report_creator as rc
-
         """The method that needs to be implemented on the particular model level."""
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),

ads/opctl/operator/lowcode/anomaly/model/base_model.py

@@ -3,6 +3,7 @@
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
 import os
 import tempfile
 import time
@@ -12,6 +13,7 @@
 import fsspec
 import numpy as np
 import pandas as pd
+import report_creator as rc
 from sklearn import linear_model
 
 from ads.common.object_storage_details import ObjectStorageDetails
@@ -33,6 +35,8 @@
 from ..operator_config import AnomalyOperatorConfig, AnomalyOperatorSpec
 from .anomaly_dataset import AnomalyDatasets, AnomalyOutput, TestData
 
+logging.getLogger("root").setLevel(logging.WARNING)
+
 
 class AnomalyOperatorBaseModel(ABC):
     """The base class for the anomaly detection operator models."""
@@ -59,8 +63,8 @@ def __init__(self, config: AnomalyOperatorConfig, datasets: AnomalyDatasets):
     def generate_report(self):
         """Generates the report."""
         import matplotlib.pyplot as plt
-        plt.rcParams.update({'figure.max_open_warning': 0})
-        import report_creator as rc
+
+        plt.rcParams.update({"figure.max_open_warning": 0})
 
         start_time = time.time()
         # fallback using sklearn oneclasssvm when the sub model _build_model fails
@@ -84,7 +88,13 @@ def generate_report(self):
                 anomaly_output, test_data, elapsed_time
             )
         table_blocks = [
-            rc.DataTable(df.head(SUBSAMPLE_THRESHOLD) if self.spec.subsample_report_data and len(df) > SUBSAMPLE_THRESHOLD else df, label=col, index=True)
+            rc.DataTable(
+                df.head(SUBSAMPLE_THRESHOLD)
+                if self.spec.subsample_report_data and len(df) > SUBSAMPLE_THRESHOLD
+                else df,
+                label=col,
+                index=True,
+            )
             for col, df in self.datasets.full_data_dict.items()
         ]
         data_table = rc.Select(blocks=table_blocks)
@@ -144,7 +154,9 @@ def generate_report(self):
             else:
                 figure_blocks = None
 
-            blocks.append(rc.Group(*figure_blocks, label=target)) if figure_blocks else None
+            blocks.append(
+                rc.Group(*figure_blocks, label=target)
+            ) if figure_blocks else None
         plots = rc.Select(blocks)
 
         report_sections = []
@@ -154,7 +166,9 @@ def generate_report(self):
         yaml_appendix = rc.Yaml(self.config.to_dict())
         summary = rc.Block(
             rc.Group(
-                rc.Text(f"You selected the **`{self.spec.model}`** model.\n{model_description.text}\n"),
+                rc.Text(
+                    f"You selected the **`{self.spec.model}`** model.\n{model_description.text}\n"
+                ),
                 rc.Text(
                     "Based on your dataset, you could have also selected "
                     f"any of the models: `{'`, `'.join(SupportedModels.keys() if self.spec.datetime_column else NonTimeADSupportedModels.keys())}`."
@@ -285,8 +299,6 @@ def _save_report(
         test_metrics: pd.DataFrame,
     ):
         """Saves resulting reports to the given folder."""
-        import report_creator as rc
-
         unique_output_dir = self.spec.output_directory.url
 
         if ObjectStorageDetails.is_oci_path(unique_output_dir):

ads/opctl/operator/lowcode/anomaly/model/isolationforest.py

@@ -1,17 +1,21 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
+
 import numpy as np
 import pandas as pd
+import report_creator as rc
 
 from ads.common.decorator.runtime_dependency import runtime_dependency
+from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
 
-from .base_model import AnomalyOperatorBaseModel
 from .anomaly_dataset import AnomalyOutput
-from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
+from .base_model import AnomalyOperatorBaseModel
+
+logging.getLogger("root").setLevel(logging.WARNING)
 
 
 class IsolationForestOperatorModel(AnomalyOperatorBaseModel):
@@ -36,13 +40,9 @@ def _build_model(self) -> AnomalyOutput:
         for target, df in self.datasets.full_data_dict.items():
             model = IsolationForest(**model_kwargs)
             model.fit(df)
-            y_pred = np.vectorize(self.outlier_map.get)(
-                model.predict(df)
-            )
+            y_pred = np.vectorize(self.outlier_map.get)(model.predict(df))
 
-            scores = model.score_samples(
-                df
-            )
+            scores = model.score_samples(df)
 
             index_col = df.columns[0]
 
@@ -59,7 +59,6 @@ def _build_model(self) -> AnomalyOutput:
 
     def _generate_report(self):
         """Generates the report."""
-        import report_creator as rc
 
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),

ads/opctl/operator/lowcode/anomaly/model/oneclasssvm.py

@@ -1,17 +1,21 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
+
 import numpy as np
 import pandas as pd
+import report_creator as rc
 
 from ads.common.decorator.runtime_dependency import runtime_dependency
+from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
 
-from .base_model import AnomalyOperatorBaseModel
 from .anomaly_dataset import AnomalyOutput
-from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
+from .base_model import AnomalyOperatorBaseModel
+
+logging.getLogger("root").setLevel(logging.WARNING)
 
 
 class OneClassSVMOperatorModel(AnomalyOperatorBaseModel):
@@ -36,13 +40,9 @@ def _build_model(self) -> AnomalyOutput:
         for target, df in self.datasets.full_data_dict.items():
             model = OneClassSVM(**model_kwargs)
             model.fit(df)
-            y_pred = np.vectorize(self.outlier_map.get)(
-                model.predict(df)
-            )
+            y_pred = np.vectorize(self.outlier_map.get)(model.predict(df))
 
-            scores = model.score_samples(
-                df
-            )
+            scores = model.score_samples(df)
 
             index_col = df.columns[0]
 
@@ -54,12 +54,11 @@ def _build_model(self) -> AnomalyOutput:
             ).reset_index(drop=True)
 
             anomaly_output.add_output(target, anomaly, score)
-            
+
         return anomaly_output
 
     def _generate_report(self):
         """Generates the report."""
-        import report_creator as rc
 
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),

ads/opctl/operator/lowcode/anomaly/model/randomcutforest.py

@@ -3,8 +3,11 @@
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import logging
+
 import numpy as np
 import pandas as pd
+import report_creator as rc
 
 from ads.common.decorator.runtime_dependency import runtime_dependency
 from ads.opctl import logger
@@ -13,6 +16,8 @@
 from .anomaly_dataset import AnomalyOutput
 from .base_model import AnomalyOperatorBaseModel
 
+logging.getLogger("root").setLevel(logging.WARNING)
+
 
 class RandomCutForestOperatorModel(AnomalyOperatorBaseModel):
     """
@@ -27,7 +32,7 @@ class RandomCutForestOperatorModel(AnomalyOperatorBaseModel):
         ),
     )
     def _build_model(self) -> AnomalyOutput:
-        from rrcf import RCTree
+        import rrcf
 
         model_kwargs = self.spec.model_kwargs
 
@@ -96,7 +101,6 @@ def _build_model(self) -> AnomalyOutput:
 
     def _generate_report(self):
         """Generates the report."""
-        import report_creator as rc
 
         other_sections = [
             rc.Heading("Selected Models Overview", level=2),