ipython bugfix

ads/automl/provider.py

@@ -1,38 +1,33 @@
 #!/usr/bin/env python
-# -*- coding: utf-8; -*-
 
-# Copyright (c) 2020, 2023 Oracle and/or its affiliates.
+# Copyright (c) 2020, 2025 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 time
 import sys
+import time
 import warnings
-from abc import ABC, abstractmethod, abstractproperty
-import math
-import pandas as pd
+from abc import ABC, abstractmethod
+
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 from sklearn import set_config
 from sklearn.dummy import DummyClassifier, DummyRegressor
 
-import matplotlib.pyplot as plt
-
 import ads
+from ads.common import logger, utils
+from ads.common.decorator.deprecate import deprecated
+from ads.common.decorator.runtime_dependency import (
+    OptionalDependency,
+    runtime_dependency,
+)
 from ads.common.utils import (
+    is_notebook,
     ml_task_types,
     wrap_lines,
-    is_documentation_mode,
-    is_notebook,
 )
-from ads.common.decorator.runtime_dependency import (
-    runtime_dependency,
-    OptionalDependency,
-)
-from ads.common.decorator.deprecate import deprecated
 from ads.dataset.label_encoder import DataFrameLabelEncoder
-from ads.dataset.helper import is_text_data
-
-from ads.common import logger, utils
 
 
 class AutoMLProvider(ABC):
@@ -141,7 +136,7 @@ def get_transformer_pipeline(self):
         pass
 
 
-class BaselineModel(object):
+class BaselineModel:
     """
     A BaselineModel object that supports fit/predict/predict_proba/transform
     interface. Labels (y) are encoded using DataFrameLabelEncoder.
@@ -156,7 +151,6 @@ def __init__(self, est):
         self.df_label_encoder = DataFrameLabelEncoder()
 
     def predict(self, X):
-
         """
         Runs the Baselines predict function and returns the result.
 
@@ -174,7 +168,6 @@ def predict(self, X):
         return self.est.predict(X)
 
     def predict_proba(self, X):
-
         """
         Runs the Baselines predict_proba function and returns the result.
 
@@ -192,7 +185,6 @@ def predict_proba(self, X):
         return self.est.predict_proba(X)
 
     def fit(self, X, y):
-
         """
         Fits the baseline estimator.
 
@@ -213,7 +205,6 @@ def fit(self, X, y):
         return self
 
     def transform(self, X):
-
         """
         Runs the Baselines transform function and returns the result.
 
@@ -304,16 +295,15 @@ def decide_estimator(self, **kwargs):
         """
         if self.est is not None:
             return self.est
-        else:
-            if self.ml_task_type == ml_task_types.REGRESSION:
-                return BaselineModel(DummyRegressor())
-            elif self.ml_task_type in [
-                ml_task_types.BINARY_CLASSIFICATION,
-                ml_task_types.MULTI_CLASS_CLASSIFICATION,
-                ml_task_types.BINARY_TEXT_CLASSIFICATION,
-                ml_task_types.MULTI_CLASS_TEXT_CLASSIFICATION,
-            ]:
-                return BaselineModel(DummyClassifier())
+        elif self.ml_task_type == ml_task_types.REGRESSION:
+            return BaselineModel(DummyRegressor())
+        elif self.ml_task_type in [
+            ml_task_types.BINARY_CLASSIFICATION,
+            ml_task_types.MULTI_CLASS_CLASSIFICATION,
+            ml_task_types.BINARY_TEXT_CLASSIFICATION,
+            ml_task_types.MULTI_CLASS_TEXT_CLASSIFICATION,
+        ]:
+            return BaselineModel(DummyClassifier())
 
 
 # An installation of oracle labs automl is required only for this class
@@ -483,8 +473,11 @@ def print_summary(
                 0, "Rank based on Performance", np.arange(2, len(sorted_summary_df) + 2)
             )
 
-            from IPython.core.display import display, HTML
+            from IPython.display import HTML
 
+            from ads.common.utils import get_display
+
+            display = get_display()
             with pd.option_context(
                 "display.max_colwidth",
                 1000,
@@ -595,9 +588,7 @@ def _decide_estimator(self, **kwargs):
         if (
             self.ml_task_type == ml_task_types.BINARY_CLASSIFICATION
             or self.ml_task_type == ml_task_types.BINARY_TEXT_CLASSIFICATION
-        ):
-            test_model_list = ["LogisticRegression"]
-        elif (
+        ) or (
             self.ml_task_type == ml_task_types.MULTI_CLASS_CLASSIFICATION
             or self.ml_task_type == ml_task_types.MULTI_CLASS_TEXT_CLASSIFICATION
         ):
@@ -712,7 +703,7 @@ def visualize_algorithm_selection_trials(self, ylabel=None):
         for f in mean_scores_ser.keys():
             se = scipy.stats.sem(scores_ser[f], ddof=1)
             y_error.append(se)
-            if f == "{}_AS".format(self.est.selected_model_):
+            if f == f"{self.est.selected_model_}_AS":
                 colors.append("orange")
             elif mean_scores_ser[f] >= mean_scores_ser.mean():
                 colors.append("teal")
@@ -741,7 +732,7 @@ def visualize_adaptive_sampling_trials(self):
             _log_visualize_no_trials("adaptive sampling")
             return
         fig, ax = plt.subplots(1, figsize=(6, 3))
-        ax.set_title("Adaptive Sampling ({})".format(trials[0][0]))
+        ax.set_title(f"Adaptive Sampling ({trials[0][0]})")
         ax.set_xlabel("Dataset sample size")
         ax.set_ylabel(r"Predicted model score")
         scores = [
@@ -882,7 +873,7 @@ def visualize_tuning_trials(self, ylabel=None):
             plt.show()
 
 
-class AutoMLPreprocessingTransformer(object):  # pragma: no cover
+class AutoMLPreprocessingTransformer:  # pragma: no cover
     @deprecated(
         details="Working with AutoML has moved from within ADS to working directly with the AutoMLx library. AutoMLx are preinstalled in conda pack automlx_p38_cpu_v2 and later, and can now be updated independently of ADS. AutoMLx documentation may be found at https://docs.oracle.com/en-us/iaas/tools/automlx/latest/html/multiversion/v23.1.1/index.html. Notebook examples are in Oracle's samples repository: https://github.com/oracle-samples/oci-data-science-ai-samples/tree/master/notebook_examples and a migration tutorial can be found at https://accelerated-data-science.readthedocs.io/en/latest/user_guide/model_training/automl/quick_start.html .",
         raise_error=True,
@@ -931,7 +922,7 @@ def __repr__(self):
         return self.msg
 
 
-class AutoMLFeatureSelection(object):  # pragma: no cover
+class AutoMLFeatureSelection:  # pragma: no cover
     @deprecated(
         details="Working with AutoML has moved from within ADS to working directly with the AutoMLx library. AutoMLx are preinstalled in conda pack automlx_p38_cpu_v2 and later, and can now be updated independently of ADS. AutoMLx documentation may be found at https://docs.oracle.com/en-us/iaas/tools/automlx/latest/html/multiversion/v23.1.1/index.html. Notebook examples are in Oracle's samples repository: https://github.com/oracle-samples/oci-data-science-ai-samples/tree/master/notebook_examples and a migration tutorial can be found at https://accelerated-data-science.readthedocs.io/en/latest/user_guide/model_training/automl/quick_start.html .",
         raise_error=True,

ads/catalog/model.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
-# -*- coding: utf-8; -*-
 
-# Copyright (c) 2020, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2020, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import warnings
@@ -27,20 +26,30 @@
 
 import pandas as pd
 import yaml
+from oci.data_science.data_science_client import DataScienceClient
+from oci.data_science.models import (
+    ArtifactExportDetailsObjectStorage,
+    ArtifactImportDetailsObjectStorage,
+    CreateModelDetails,
+    ExportModelArtifactDetails,
+    ImportModelArtifactDetails,
+    ModelSummary,
+    WorkRequest,
+)
+from oci.data_science.models import Model as OCIModel
+from oci.data_science.models.model_provenance import ModelProvenance
+from oci.data_science.models.update_model_details import UpdateModelDetails
+from oci.exceptions import ServiceError
+from oci.identity import IdentityClient
+
 from ads.catalog.summary import SummaryList
 from ads.common import auth, logger, oci_client, utils
 from ads.common.decorator.deprecate import deprecated
 from ads.common.decorator.runtime_dependency import (
-    runtime_dependency,
     OptionalDependency,
+    runtime_dependency,
 )
 from ads.common.model_artifact import ConflictStrategy, ModelArtifact
-from ads.model.model_metadata import (
-    METADATA_SIZE_LIMIT,
-    MetadataSizeTooLarge,
-    ModelCustomMetadata,
-    ModelTaxonomyMetadata,
-)
 from ads.common.object_storage_details import ObjectStorageDetails
 from ads.common.oci_resource import SEARCH_TYPE, OCIResource
 from ads.config import (
@@ -51,22 +60,14 @@
 )
 from ads.dataset.progress import TqdmProgressBar
 from ads.feature_engineering.schema import Schema
-from ads.model.model_version_set import ModelVersionSet, _extract_model_version_set_id
 from ads.model.deployment.model_deployer import ModelDeployer
-from oci.data_science.data_science_client import DataScienceClient
-from oci.data_science.models import (
-    ArtifactExportDetailsObjectStorage,
-    ArtifactImportDetailsObjectStorage,
-    CreateModelDetails,
-    ExportModelArtifactDetails,
-    ImportModelArtifactDetails,
+from ads.model.model_metadata import (
+    METADATA_SIZE_LIMIT,
+    MetadataSizeTooLarge,
+    ModelCustomMetadata,
+    ModelTaxonomyMetadata,
 )
-from oci.data_science.models import Model as OCIModel
-from oci.data_science.models import ModelSummary, WorkRequest
-from oci.data_science.models.model_provenance import ModelProvenance
-from oci.data_science.models.update_model_details import UpdateModelDetails
-from oci.exceptions import ServiceError
-from oci.identity import IdentityClient
+from ads.model.model_version_set import ModelVersionSet, _extract_model_version_set_id
 
 _UPDATE_MODEL_DETAILS_ATTRIBUTES = [
     "display_name",
@@ -391,8 +392,9 @@ def show_in_notebook(self, display_format: str = "dataframe") -> None:
             Nothing.
         """
         if display_format == "dataframe":
-            from IPython.core.display import display
+            from ads.common.utils import get_display
 
+            display = get_display()
             display(self.to_dataframe())
         elif display_format == "yaml":
             print(self._to_yaml())
@@ -454,9 +456,9 @@ def commit(self, force: bool = True) -> None:
         if hasattr(self, "metadata_custom"):
             attributes["custom_metadata_list"] = self.metadata_custom._to_oci_metadata()
         if hasattr(self, "metadata_taxonomy"):
-            attributes[
-                "defined_metadata_list"
-            ] = self.metadata_taxonomy._to_oci_metadata()
+            attributes["defined_metadata_list"] = (
+                self.metadata_taxonomy._to_oci_metadata()
+            )
 
         update_model_details = UpdateModelDetails(**attributes)
         # freeform_tags=self._model.freeform_tags, defined_tags=self._model.defined_tags)
@@ -558,7 +560,7 @@ def load_model(
 
         try:
             provenance_response = cls._get_provenance_metadata(ds_client, model_id)
-        except Exception as e:
+        except Exception:
             raise ValueError(
                 f"Unable to fetch model provenance metadata for model {model_id}"
             )
@@ -1071,7 +1073,7 @@ def _download_large_artifact(
         None
             Nothing.
         """
-        progress.update(f"Importing model artifacts from model catalog")
+        progress.update("Importing model artifacts from model catalog")
         self._import_model_artifact(model_id=model_id, bucket_uri=bucket_uri)
 
         progress.update("Copying model artifacts to the artifact directory")
@@ -1360,7 +1362,7 @@ def upload_model(
                 raise ValueError("project_id needs to be specified.")
             schema_file = os.path.join(model_artifact.artifact_dir, "schema.json")
             if os.path.exists(schema_file):
-                with open(schema_file, "r") as schema:
+                with open(schema_file) as schema:
                     metadata = json.load(schema)
                     freeform_tags = {"problem_type": metadata["problem_type"]}
 
@@ -1475,7 +1477,7 @@ def _export_model_artifact(
         3. Exports artifact from the user's object storage bucket to the system one.
         """
         artifact_zip_path = self._prepare_model_artifact(model_artifact, progress)
-        progress.update(f"Copying model artifact to the Object Storage bucket")
+        progress.update("Copying model artifact to the Object Storage bucket")
 
         try:
             bucket_uri_file_name = os.path.basename(bucket_uri)

ads/catalog/notebook.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
-# -*- coding: utf-8; -*-
 
-# Copyright (c) 2020, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2020, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import warnings
@@ -14,31 +13,31 @@
     stacklevel=2,
 )
 
-from pandas import DataFrame
-import oci
+from types import MethodType
+
 from oci.data_science.models import (
-    NotebookSessionSummary,
-    UpdateNotebookSessionDetails,
     CreateNotebookSessionDetails,
     NotebookSession,
     NotebookSessionConfigurationDetails,
+    NotebookSessionSummary,
+    UpdateNotebookSessionDetails,
 )
 from oci.exceptions import ServiceError
-from types import MethodType
+from pandas import DataFrame
 
 from ads.catalog.summary import SummaryList
+from ads.common import auth as authutil
+from ads.common import oci_client as oc
 from ads.common import utils
 from ads.common.decorator.runtime_dependency import (
-    runtime_dependency,
     OptionalDependency,
+    runtime_dependency,
 )
-from ads.common import auth as authutil
-from ads.common import oci_client as oc
 from ads.config import (
-    OCI_IDENTITY_SERVICE_ENDPOINT,
     NB_SESSION_COMPARTMENT_OCID,
-    PROJECT_OCID,
+    OCI_IDENTITY_SERVICE_ENDPOINT,
     OCI_ODSC_SERVICE_ENDPOINT,
+    PROJECT_OCID,
 )
 
 create_notebook_details_attributes = CreateNotebookSessionDetails().swagger_types.keys()
@@ -210,8 +209,9 @@ def show_in_notebook(notebook_self):
             """
             Describe the project by showing it's properties
             """
-            from IPython.core.display import display
+            from ads.common.utils import get_display
 
+            display = get_display()
             display(notebook_self)
 
         def _repr_html_(notebook_self):