two version of R2R are hereHEADmaster

4 files changed, 1062 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/__init__.py
new file mode 100644
index 00000000..d34e30a8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/__init__.py
@@ -0,0 +1,144 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Contains automated machine learning classes for Azure Machine Learning SDKv2.
+
+Main areas include managing AutoML tasks.
+"""
+from azure.ai.ml.entities._job.automl import TrainingSettings
+from azure.ai.ml.entities._job.automl.image import (
+    LogTrainingMetrics,
+    LogValidationLoss,
+    ImageClassificationJob,
+    ImageClassificationMultilabelJob,
+    ImageClassificationSearchSpace,
+    ImageInstanceSegmentationJob,
+    ImageLimitSettings,
+    ImageModelSettingsClassification,
+    ImageModelSettingsObjectDetection,
+    ImageObjectDetectionJob,
+    ImageObjectDetectionSearchSpace,
+    ImageSweepSettings,
+)
+from azure.ai.ml.entities._job.automl.nlp import (
+    NlpFeaturizationSettings,
+    NlpFixedParameters,
+    NlpLimitSettings,
+    NlpSearchSpace,
+    NlpSweepSettings,
+    TextClassificationJob,
+    TextClassificationMultilabelJob,
+    TextNerJob,
+)
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.stack_ensemble_settings import (
+    StackEnsembleSettings,
+)
+from azure.ai.ml.entities._job.automl.tabular import (
+    ClassificationJob,
+    ColumnTransformer,
+    ForecastingJob,
+    ForecastingSettings,
+    RegressionJob,
+    TabularFeaturizationSettings,
+    TabularLimitSettings,
+)
+
+from .._restclient.v2023_04_01_preview.models import (
+    BlockedTransformers,
+    ClassificationModels,
+    ClassificationMultilabelPrimaryMetrics,
+    ClassificationPrimaryMetrics,
+    FeaturizationMode,
+    ForecastHorizonMode,
+    ForecastingModels,
+    ForecastingPrimaryMetrics,
+    InstanceSegmentationPrimaryMetrics,
+    LearningRateScheduler,
+    NCrossValidationsMode,
+    ObjectDetectionPrimaryMetrics,
+    RegressionModels,
+    RegressionPrimaryMetrics,
+    SamplingAlgorithmType,
+    ShortSeriesHandlingConfiguration,
+    StochasticOptimizer,
+    TargetAggregationFunction,
+    TargetLagsMode,
+    TargetRollingWindowSizeMode,
+    UseStl,
+    ValidationMetricType,
+)
+from ._automl_image import (
+    image_classification,
+    image_classification_multilabel,
+    image_instance_segmentation,
+    image_object_detection,
+)
+from ._automl_nlp import text_classification, text_classification_multilabel, text_ner
+from ._automl_tabular import classification, forecasting, regression
+
+__all__ = [
+    "ClassificationModels",
+    "RegressionModels",
+    "ForecastingModels",
+    "FeaturizationMode",
+    "NCrossValidationsMode",
+    "ForecastHorizonMode",
+    "ShortSeriesHandlingConfiguration",
+    "TargetLagsMode",
+    "TargetRollingWindowSizeMode",
+    "TargetAggregationFunction",
+    "UseStl",
+    "ClassificationPrimaryMetrics",
+    "RegressionPrimaryMetrics",
+    "ForecastingPrimaryMetrics",
+    "ClassificationMultilabelPrimaryMetrics",
+    "ObjectDetectionPrimaryMetrics",
+    "InstanceSegmentationPrimaryMetrics",
+    "ColumnTransformer",
+    "TabularFeaturizationSettings",
+    "ForecastingSettings",
+    "TabularLimitSettings",
+    "NlpFeaturizationSettings",
+    "NlpFixedParameters",
+    "NlpLimitSettings",
+    "NlpSweepSettings",
+    "NlpSearchSpace",
+    "LogTrainingMetrics",
+    "LogValidationLoss",
+    "ImageLimitSettings",
+    "ImageModelSettingsClassification",
+    "ImageModelSettingsObjectDetection",
+    "ImageSweepSettings",
+    "ImageObjectDetectionSearchSpace",
+    "ImageClassificationSearchSpace",
+    "TrainingSettings",
+    "image_classification",
+    "image_classification_multilabel",
+    "image_object_detection",
+    "image_instance_segmentation",
+    "text_classification",
+    "text_classification_multilabel",
+    "text_ner",
+    "classification",
+    "regression",
+    "forecasting",
+    "SearchSpace",
+    "StackEnsembleSettings",
+    "BlockedTransformers",
+    "ClassificationJob",
+    "ForecastingJob",
+    "RegressionJob",
+    "ImageClassificationJob",
+    "ImageClassificationMultilabelJob",
+    "ImageObjectDetectionJob",
+    "ImageInstanceSegmentationJob",
+    "LearningRateScheduler",
+    "SamplingAlgorithmType",
+    "StochasticOptimizer",
+    "TextClassificationJob",
+    "TextClassificationMultilabelJob",
+    "TextNerJob",
+    "ValidationMetricType",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_image.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_image.py
new file mode 100644
index 00000000..521c9aa2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_image.py
@@ -0,0 +1,298 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Entrypoints for creating AutoML tasks."""
+
+from typing import Optional, TypeVar, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ClassificationMultilabelPrimaryMetrics,
+    ClassificationPrimaryMetrics,
+    InstanceSegmentationPrimaryMetrics,
+    ObjectDetectionPrimaryMetrics,
+)
+from azure.ai.ml.entities._builders.base_node import pipeline_node_decorator
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.image.automl_image_object_detection_base import AutoMLImageObjectDetectionBase
+from azure.ai.ml.entities._job.automl.image.image_classification_job import ImageClassificationJob
+from azure.ai.ml.entities._job.automl.image.image_classification_multilabel_job import ImageClassificationMultilabelJob
+from azure.ai.ml.entities._job.automl.image.image_instance_segmentation_job import ImageInstanceSegmentationJob
+from azure.ai.ml.entities._job.automl.image.image_object_detection_job import ImageObjectDetectionJob
+
+TImageJob = TypeVar("TImageJob", bound=AutoMLImageObjectDetectionBase)
+
+
+def _create_image_job(
+    job_cls: TImageJob,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[Union[str, ClassificationPrimaryMetrics]] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    **kwargs,
+) -> TImageJob:
+    """Helper function to create objects for AutoML Image jobs.
+
+    :param job_cls: The job class
+    :type job_cls: TImageJob
+    :param training_data: The training input data
+    :type training_data: ~azure.ai.ml.entities.Input
+    :param target_column_name: The target column name
+    :type target_column_name: str
+    :param primary_metric: The primary metric
+    :type primary_metric: Optional[Union[str, ~azure.ai.ml.automl.ClassificationPrimaryMetrics]]
+    :param validation_data: The validation data
+    :type validation_data: Optional[~azure.ai.ml.entities.Input]
+    :param validation_data_size: The validation data size
+    :type validation_data_size: Optional[float]
+    :return: An AutoML Image Job
+    :rtype: TImageJob
+    """
+    image_job = job_cls(primary_metric=primary_metric, **kwargs)  # type: ignore[operator]
+    image_job.set_data(
+        training_data=training_data,
+        target_column_name=target_column_name,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+    )
+
+    return image_job
+
+
+@pipeline_node_decorator
+def image_classification(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[Union[str, ClassificationPrimaryMetrics]] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    **kwargs,
+) -> ImageClassificationJob:
+    """Creates an object for AutoML Image multi-class Classification job.
+
+    :keyword training_data: The training data to be used within the experiment.
+    :paramtype training_data: ~azure.ai.ml.entities.Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters.
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted,
+            and precision_score_weighted
+            Defaults to accuracy.
+    :paramtype primary_metric: Union[str, ~azure.ai.ml.automl.ClassificationPrimaryMetrics]
+    :keyword validation_data: The validation data to be used within the experiment.
+    :paramtype validation_data: Optional[~azure.ai.ml.entities.Input]
+    :keyword validation_data_size: What fraction of the data to hold out for validation when user validation data
+            is not specified. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``validation_data_size``
+            to extract validation data out of the specified training data.
+
+            Defaults to .2
+    :paramtype validation_data_size: float
+
+    :return: Image classification job object that can be submitted to an Azure ML compute for execution.
+    :rtype: ~azure.ai.ml.automl.ImageClassificationJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+                :start-after: [START automl.image_classification]
+                :end-before: [END automl.image_classification]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl image classification job
+    """
+    return _create_image_job(  # type: ignore[type-var, return-value]
+        job_cls=ImageClassificationJob,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        primary_metric=primary_metric,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        **kwargs,
+    )
+
+
+@pipeline_node_decorator
+def image_classification_multilabel(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[Union[str, ClassificationMultilabelPrimaryMetrics]] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    **kwargs,
+) -> ImageClassificationMultilabelJob:
+    """Creates an object for AutoML Image multi-label Classification job.
+
+    :keyword training_data: The training data to be used within the experiment.
+    :paramtype training_data: ~azure.ai.ml.entities.Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters.
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted,
+            precision_score_weighted, and Iou
+            Defaults to Iou.
+    :paramtype primary_metric: Union[str, ~azure.ai.ml.automl.ClassificationMultilabelPrimaryMetrics]
+    :keyword validation_data: The validation data to be used within the experiment.
+    :paramtype validation_data: Optional[~azure.ai.ml.entities.Input]
+    :keyword validation_data_size: The fraction of the training data to hold out for validation when user does not
+            provide the validation data. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``validation_data_size``
+            to extract validation data out of the specified training data.
+
+            Defaults to .2
+    :paramtype validation_data_size: float
+
+    :return: Image multi-label classification job object that can be submitted to an Azure ML compute for execution.
+    :rtype: ~azure.ai.ml.automl.ImageClassificationMultilabelJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+                :start-after: [START automl.image_classification_multilabel]
+                :end-before: [END automl.image_classification_multilabel]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl image multilabel classification job
+    """
+    return _create_image_job(  # type: ignore[type-var, return-value]
+        job_cls=ImageClassificationMultilabelJob,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        primary_metric=primary_metric,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        **kwargs,
+    )
+
+
+@pipeline_node_decorator
+def image_object_detection(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[Union[str, ObjectDetectionPrimaryMetrics]] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    **kwargs,
+) -> ImageObjectDetectionJob:
+    """Creates an object for AutoML Image Object Detection job.
+
+    :keyword training_data: The training data to be used within the experiment.
+    :paramtype training_data: ~azure.ai.ml.entities.Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters.
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: MeanAveragePrecision
+            Defaults to MeanAveragePrecision.
+    :paramtype primary_metric: Union[str, ~azure.ai.ml.automl.ObjectDetectionPrimaryMetrics]
+    :keyword validation_data: The validation data to be used within the experiment.
+    :paramtype validation_data: Optional[~azure.ai.ml.entities.Input]
+    :keyword validation_data_size: The fraction of the training data to hold out for validation when user does not
+            provide the validation data. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``validation_data_size``
+            to extract validation data out of the specified training data.
+
+            Defaults to .2
+    :paramtype validation_data_size: float
+
+    :return: Image object detection job object that can be submitted to an Azure ML compute for execution.
+    :rtype: ~azure.ai.ml.automl.ImageObjectDetectionJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+                :start-after: [START automl.image_object_detection]
+                :end-before: [END automl.image_object_detection]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl image object detection job
+    """
+    return _create_image_job(  # type: ignore[type-var, return-value]
+        job_cls=ImageObjectDetectionJob,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        primary_metric=primary_metric,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        **kwargs,
+    )
+
+
+@pipeline_node_decorator
+def image_instance_segmentation(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[Union[str, InstanceSegmentationPrimaryMetrics]] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    **kwargs,
+) -> ImageInstanceSegmentationJob:
+    """Creates an object for AutoML Image Instance Segmentation job.
+
+    :keyword training_data: The training data to be used within the experiment.
+    :paramtype training_data: ~azure.ai.ml.entities.Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters.
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: MeanAveragePrecision
+            Defaults to MeanAveragePrecision.
+    :paramtype primary_metric: Union[str, ~azure.ai.ml.automl.InstanceSegmentationPrimaryMetrics]
+    :keyword validation_data: The validation data to be used within the experiment.
+    :paramtype validation_data: Optional[~azure.ai.ml.entities.Input]
+    :keyword validation_data_size: The fraction of the training data to hold out for validation when user does not
+            provide the validation data. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``validation_data_size``
+            to extract validation data out of the specified training data.
+
+            Defaults to .2
+    :paramtype validation_data_size: float
+
+    :return: Image instance segmentation job
+    :rtype: ~azure.ai.ml.automl.ImageInstanceSegmentationJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+                :start-after: [START automl.image_instance_segmentation]
+                :end-before: [END automl.image_instance_segmentation]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl image instance segmentation job
+    """
+    return _create_image_job(  # type: ignore[type-var, return-value]
+        job_cls=ImageInstanceSegmentationJob,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        primary_metric=primary_metric,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        **kwargs,
+    )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_nlp.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_nlp.py
new file mode 100644
index 00000000..ac7ebcf6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_nlp.py
@@ -0,0 +1,175 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Entrypoints for creating AutoML tasks."""
+
+from typing import Optional
+
+from azure.ai.ml.entities._builders.base_node import pipeline_node_decorator
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.nlp.text_classification_job import TextClassificationJob
+from azure.ai.ml.entities._job.automl.nlp.text_classification_multilabel_job import TextClassificationMultilabelJob
+from azure.ai.ml.entities._job.automl.nlp.text_ner_job import TextNerJob
+
+
+@pipeline_node_decorator
+def text_classification(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    validation_data: Input,
+    primary_metric: Optional[str] = None,
+    log_verbosity: Optional[str] = None,
+    **kwargs,
+) -> TextClassificationJob:
+    """Function to create a TextClassificationJob.
+
+    A text classification job is used to train a model that can predict the class/category of a text data.
+    Input training data should include a target column that classifies the text into exactly one class.
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype training_data: Input
+    :keyword target_column_name: Name of the target column.
+    :paramtype target_column_name: str
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype validation_data: Input
+    :keyword primary_metric: Primary metric for the task.
+            Acceptable values: accuracy, AUC_weighted, precision_score_weighted
+    :paramtype primary_metric: Union[str, ClassificationPrimaryMetrics]
+    :keyword log_verbosity: Log verbosity level.
+    :paramtype log_verbosity: str
+
+    :return: The TextClassificationJob object.
+    :rtype: TextClassificationJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_classification]
+                :end-before: [END automl.text_classification]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text classification job
+    """
+
+    text_classification_job = TextClassificationJob(
+        primary_metric=primary_metric,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        validation_data=validation_data,
+        log_verbosity=log_verbosity,
+        **kwargs,
+    )
+
+    return text_classification_job
+
+
+@pipeline_node_decorator
+def text_classification_multilabel(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    validation_data: Input,
+    primary_metric: Optional[str] = None,
+    log_verbosity: Optional[str] = None,
+    **kwargs,
+) -> TextClassificationMultilabelJob:
+    """Function to create a TextClassificationMultilabelJob.
+
+    A text classification multilabel job is used to train a model that can predict the classes/categories
+    of a text data. Input training data should include a target column that classifies the text into class(es).
+    For more information on format of multilabel data, refer to:
+    https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-nlp-models#multi-label
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype training_data: Input
+    :keyword target_column_name: Name of the target column.
+    :paramtype target_column_name: str
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype validation_data: Input
+    :keyword primary_metric: Primary metric for the task.
+            Acceptable values: accuracy
+    :paramtype primary_metric: str
+    :keyword log_verbosity: Log verbosity level.
+    :paramtype log_verbosity: str
+
+    :return: The TextClassificationMultilabelJob object.
+    :rtype: TextClassificationMultilabelJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_classification_multilabel]
+                :end-before: [END automl.text_classification_multilabel]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text multilabel classification job
+    """
+
+    text_classification_multilabel_job = TextClassificationMultilabelJob(
+        primary_metric=primary_metric,
+        training_data=training_data,
+        target_column_name=target_column_name,
+        validation_data=validation_data,
+        log_verbosity=log_verbosity,
+        **kwargs,
+    )
+
+    return text_classification_multilabel_job
+
+
+@pipeline_node_decorator
+def text_ner(
+    *,
+    training_data: Input,
+    validation_data: Input,
+    primary_metric: Optional[str] = None,
+    log_verbosity: Optional[str] = None,
+    **kwargs,
+) -> TextNerJob:
+    """Function to create a TextNerJob.
+
+    A text named entity recognition job is used to train a model that can predict the named entities in the text.
+    Input training data should be a text file in CoNLL format. For more information on format of text NER data,
+    refer to:
+    https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-nlp-models#named-entity-recognition-ner
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype training_data: Input
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and a target column.
+    :paramtype validation_data: Input
+    :keyword primary_metric: Primary metric for the task.
+            Acceptable values: accuracy
+    :paramtype primary_metric: str
+    :keyword log_verbosity: Log verbosity level.
+    :paramtype log_verbosity: str
+
+    :return: The TextNerJob object.
+    :rtype: TextNerJob
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_ner]
+                :end-before: [END automl.text_ner]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text ner job
+    """
+
+    text_ner_job = TextNerJob(
+        primary_metric=primary_metric,
+        training_data=training_data,
+        validation_data=validation_data,
+        log_verbosity=log_verbosity,
+        **kwargs,
+    )
+
+    return text_ner_job
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_tabular.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_tabular.py
new file mode 100644
index 00000000..9c45e5f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/automl/_automl_tabular.py
@@ -0,0 +1,445 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Entrypoints for creating AutoML tasks."""
+from typing import List, Optional, Union
+
+from azure.ai.ml.entities._builders.base_node import pipeline_node_decorator
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.tabular import (
+    ClassificationJob,
+    ForecastingJob,
+    ForecastingSettings,
+    RegressionJob,
+)
+
+
+@pipeline_node_decorator
+def classification(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[str] = None,
+    enable_model_explainability: Optional[bool] = None,
+    weight_column_name: Optional[str] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    n_cross_validations: Optional[Union[str, int]] = None,
+    cv_split_column_names: Optional[List[str]] = None,
+    test_data: Optional[Input] = None,
+    test_data_size: Optional[float] = None,
+    **kwargs,
+) -> ClassificationJob:
+    """Function to create a ClassificationJob.
+
+    A classification job is used to train a model that best predict the class of a data sample.
+    Various models are trained using the training data. The model with the best performance on the validation data
+    based on the primary metric is selected as the final model.
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a label column (optionally a sample weights column).
+    :paramtype training_data: Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted,
+            and precision_score_weighted
+            Defaults to accuracy
+    :type primary_metric: str
+    :keyword enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML
+            training iterations.
+            The default is None. For more information, see
+            `Interpretability: model explanations in automated machine learning
+            <https://learn.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__.
+    :paramtype enable_model_explainability: bool
+    :keyword weight_column_name: The name of the sample weight column. Automated ML supports a weighted column
+            as an input, causing rows in the data to be weighted up or down.
+            If the input data is from a pandas.DataFrame which doesn't have column names,
+            column indices can be used instead, expressed as integers.
+
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters
+    :paramtype weight_column_name: str
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and label column (optionally a sample weights column).
+
+            Defaults to None
+    :paramtype validation_data: Input
+    :keyword validation_data_size: What fraction of the data to hold out for validation when user validation data
+            is not specified. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype validation_data_size: float
+    :keyword n_cross_validations: How many cross validations to perform when user validation data is not specified.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype n_cross_validations: Union[str, int]
+    :keyword cv_split_column_names: List of names of the columns that contain custom cross validation split.
+            Each of the CV split columns represents one CV split where each row are either marked
+            1 for training or 0 for validation.
+
+            Defaults to None
+    :paramtype cv_split_column_names: List[str]
+    :keyword test_data: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            The test data to be used for a test run that will automatically be started after
+            model training is complete. The test run will get predictions using the best model
+            and will compute metrics given these predictions.
+
+            If this parameter or the ``test_data_size`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+            Test data should contain both features and label column.
+            If ``test_data`` is specified then the ``target_column_name`` parameter must be specified.
+
+            Defaults to None
+    :paramtype test_data: Input
+    :keyword test_data_size: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            What fraction of the training data to hold out for test data for a test run that will
+            automatically be started after model training is complete. The test run will get
+            predictions using the best model and will compute metrics given these predictions.
+
+            This should be between 0.0 and 1.0 non-inclusive.
+            If ``test_data_size`` is specified at the same time as ``validation_data_size``,
+            then the test data is split from ``training_data`` before the validation data is split.
+            For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data has
+            1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the
+            training data will have 810 rows.
+
+            For regression based tasks, random sampling is used. For classification tasks, stratified sampling
+            is used. Forecasting does not currently support specifying a test dataset using a train/test split.
+
+            If this parameter or the ``test_data`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+
+            Defaults to None
+    :paramtype test_data_size: float
+    :return: A job object that can be submitted to an Azure ML compute for execution.
+    :rtype: ClassificationJob
+    """
+    classification_job = ClassificationJob(primary_metric=primary_metric, **kwargs)
+
+    classification_job.set_data(
+        training_data=training_data,
+        target_column_name=target_column_name,
+        weight_column_name=weight_column_name,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        n_cross_validations=n_cross_validations,
+        cv_split_column_names=cv_split_column_names,
+        test_data=test_data,
+        test_data_size=test_data_size,
+    )
+    classification_job.set_training(enable_model_explainability=enable_model_explainability)
+
+    return classification_job
+
+
+@pipeline_node_decorator
+def regression(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[str] = None,
+    enable_model_explainability: Optional[bool] = None,
+    weight_column_name: Optional[str] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    n_cross_validations: Optional[Union[str, int]] = None,
+    cv_split_column_names: Optional[List[str]] = None,
+    test_data: Optional[Input] = None,
+    test_data_size: Optional[float] = None,
+    **kwargs,
+) -> RegressionJob:
+    """Function to create a Regression Job.
+
+    A regression job is used to train a model to predict continuous values of a target variable from a dataset.
+    Various models are trained using the training data. The model with the best performance on the validation data
+    based on the primary metric is selected as the final model.
+
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a label column (optionally a sample weights column).
+    :paramtype training_data: Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: spearman_correlation, r2_score, normalized_mean_absolute_error,
+            normalized_root_mean_squared_error.
+            Defaults to normalized_root_mean_squared_error
+    :type primary_metric: str
+    :keyword enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML
+            training iterations.
+            The default is None. For more information, see
+            `Interpretability: model explanations in automated machine learning
+            <https://learn.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__.
+    :paramtype enable_model_explainability: bool
+    :keyword weight_column_name: The name of the sample weight column. Automated ML supports a weighted column
+            as an input, causing rows in the data to be weighted up or down.
+            If the input data is from a pandas.DataFrame which doesn't have column names,
+            column indices can be used instead, expressed as integers.
+
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters
+    :paramtype weight_column_name: str
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and label column (optionally a sample weights column).
+
+            Defaults to None
+    :paramtype validation_data: Input
+    :keyword validation_data_size: What fraction of the data to hold out for validation when user validation data
+            is not specified. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype validation_data_size: float
+    :keyword n_cross_validations: How many cross validations to perform when user validation data is not specified.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype n_cross_validations: Union[str, int]
+    :keyword cv_split_column_names: List of names of the columns that contain custom cross validation split.
+            Each of the CV split columns represents one CV split where each row are either marked
+            1 for training or 0 for validation.
+
+            Defaults to None
+    :paramtype cv_split_column_names: List[str]
+    :keyword test_data: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            The test data to be used for a test run that will automatically be started after
+            model training is complete. The test run will get predictions using the best model
+            and will compute metrics given these predictions.
+
+            If this parameter or the ``test_data_size`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+            Test data should contain both features and label column.
+            If ``test_data`` is specified then the ``target_column_name`` parameter must be specified.
+
+            Defaults to None
+    :paramtype test_data: Input
+    :keyword test_data_size: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            What fraction of the training data to hold out for test data for a test run that will
+            automatically be started after model training is complete. The test run will get
+            predictions using the best model and will compute metrics given these predictions.
+
+            This should be between 0.0 and 1.0 non-inclusive.
+            If ``test_data_size`` is specified at the same time as ``validation_data_size``,
+            then the test data is split from ``training_data`` before the validation data is split.
+            For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data has
+            1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows
+            and the training data will have 810 rows.
+
+            For regression based tasks, random sampling is used. For classification
+            tasks, stratified sampling is used. Forecasting does not currently
+            support specifying a test dataset using a train/test split.
+
+            If this parameter or the ``test_data`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+
+            Defaults to None
+    :paramtype test_data_size: float
+    :return: A job object that can be submitted to an Azure ML compute for execution.
+    :rtype: RegressionJob
+    """
+    regression_job = RegressionJob(primary_metric=primary_metric, **kwargs)
+    regression_job.set_data(
+        training_data=training_data,
+        target_column_name=target_column_name,
+        weight_column_name=weight_column_name,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        n_cross_validations=n_cross_validations,
+        cv_split_column_names=cv_split_column_names,
+        test_data=test_data,
+        test_data_size=test_data_size,
+    )
+    regression_job.set_training(enable_model_explainability=enable_model_explainability)
+
+    return regression_job
+
+
+@pipeline_node_decorator
+def forecasting(
+    *,
+    training_data: Input,
+    target_column_name: str,
+    primary_metric: Optional[str] = None,
+    enable_model_explainability: Optional[bool] = None,
+    weight_column_name: Optional[str] = None,
+    validation_data: Optional[Input] = None,
+    validation_data_size: Optional[float] = None,
+    n_cross_validations: Optional[Union[str, int]] = None,
+    cv_split_column_names: Optional[List[str]] = None,
+    test_data: Optional[Input] = None,
+    test_data_size: Optional[float] = None,
+    forecasting_settings: Optional[ForecastingSettings] = None,
+    **kwargs,
+) -> ForecastingJob:
+    """Function to create a Forecasting job.
+
+    A forecasting task is used to predict target values for a future time period based on the historical data.
+    Various models are trained using the training data. The model with the best performance on the validation data
+    based on the primary metric is selected as the final model.
+
+    :keyword training_data: The training data to be used within the experiment.
+            It should contain both training features and a label column (optionally a sample weights column).
+    :paramtype training_data: Input
+    :keyword target_column_name: The name of the label column.
+            This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters
+    :paramtype target_column_name: str
+    :keyword primary_metric: The metric that Automated Machine Learning will optimize for model selection.
+            Automated Machine Learning collects more metrics than it can optimize.
+            For more information on how metrics are calculated, see
+            https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.
+
+            Acceptable values: r2_score, normalized_mean_absolute_error, normalized_root_mean_squared_error
+            Defaults to normalized_root_mean_squared_error
+    :type primary_metric: str
+    :keyword enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML
+            training iterations.
+            The default is None. For more information, see
+            `Interpretability: model explanations in automated machine learning
+            <https://learn.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__.
+    :paramtype enable_model_explainability: bool
+    :keyword weight_column_name: The name of the sample weight column. Automated ML supports a weighted column
+            as an input, causing rows in the data to be weighted up or down.
+            If the input data is from a pandas.DataFrame which doesn't have column names,
+            column indices can be used instead, expressed as integers.
+
+            This parameter is applicable to ``training_data`` and ``validation_data`` parameters
+    :paramtype weight_column_name: str
+    :keyword validation_data: The validation data to be used within the experiment.
+            It should contain both training features and label column (optionally a sample weights column).
+
+            Defaults to None
+    :paramtype validation_data: Input
+    :keyword validation_data_size: What fraction of the data to hold out for validation when user validation data
+            is not specified. This should be between 0.0 and 1.0 non-inclusive.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype validation_data_size: float
+    :keyword n_cross_validations: How many cross validations to perform when user validation data is not specified.
+
+            Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or
+            ``validation_data_size`` to extract validation data out of the specified training data.
+            For custom cross validation fold, use ``cv_split_column_names``.
+
+            For more information, see
+            `Configure data splits and cross-validation in automated machine learning <https://learn.microsoft.com
+            /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__.
+
+            Defaults to None
+    :paramtype n_cross_validations: Union[str, int]
+    :keyword cv_split_column_names: List of names of the columns that contain custom cross validation split.
+            Each of the CV split columns represents one CV split where each row are either marked
+            1 for training or 0 for validation.
+
+            Defaults to None
+    :paramtype cv_split_column_names: List[str]
+    :keyword test_data: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            The test data to be used for a test run that will automatically be started after
+            model training is complete. The test run will get predictions using the best model
+            and will compute metrics given these predictions.
+
+            If this parameter or the ``test_data_size`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+            Test data should contain both features and label column.
+            If ``test_data`` is specified then the ``target_column_name`` parameter must be specified.
+
+            Defaults to None
+    :paramtype test_data: Input
+    :keyword test_data_size: The Model Test feature using test datasets or test data splits is a feature in
+            Preview state and might change at any time.
+            What fraction of the training data to hold out for test data for a test run that will
+            automatically be started after model training is complete. The test run will get
+            predictions using the best model and will compute metrics given these predictions.
+
+            This should be between 0.0 and 1.0 non-inclusive.
+            If ``test_data_size`` is specified at the same time as ``validation_data_size``,
+            then the test data is split from ``training_data`` before the validation data is split.
+            For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data
+            has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows
+            and the training data will have 810 rows.
+
+            For regression based tasks, random sampling is used. For classification
+            tasks, stratified sampling is used. Forecasting does not currently
+            support specifying a test dataset using a train/test split.
+
+            If this parameter or the ``test_data`` parameter are not specified then
+            no test run will be executed automatically after model training is completed.
+
+            Defaults to None
+    :paramtype test_data_size: float
+    :keyword forecasting_settings: The settings for the forecasting task
+    :paramtype forecasting_settings: ForecastingSettings
+    :return: A job object that can be submitted to an Azure ML compute for execution.
+    :rtype: ForecastingJob
+    """
+    forecast_job = ForecastingJob(
+        primary_metric=primary_metric,
+        forecasting_settings=forecasting_settings,
+        **kwargs,
+    )
+    forecast_job.set_data(
+        training_data=training_data,
+        target_column_name=target_column_name,
+        weight_column_name=weight_column_name,
+        validation_data=validation_data,
+        validation_data_size=validation_data_size,
+        n_cross_validations=n_cross_validations,
+        cv_split_column_names=cv_split_column_names,
+        test_data=test_data,
+        test_data_size=test_data_size,
+    )
+    forecast_job.set_training(enable_model_explainability=enable_model_explainability)
+
+    return forecast_job