共用方式為

ClassificationJob Class

  • Reference

Configuration for AutoML Classification Job.

Initialize a new AutoML Classification task.

Constructor

ClassificationJob(*, primary_metric: str | None = None, positive_label: str | None = None, **kwargs: Any)

Keyword-Only Parameters

Name Description
primary_metric
Optional[str]

The primary metric to use for optimization, defaults to None

Default value: None
positive_label
Optional[str]

Positive label for binary metrics calculation, defaults to None

Default value: None
featurization
Optional[TabularFeaturizationSettings]

Featurization settings. Defaults to None.
limits
Optional[TabularLimitSettings]

Limits settings. Defaults to None.
training
Optional[TrainingSettings]

Training settings. Defaults to None.
primary_metric
Optional[str]

The primary metric to use for optimization, defaults to None
positive_label
Optional[str]

Positive label for binary metrics calculation, defaults to None
featurization
Optional[TabularFeaturizationSettings]

featurization settings. Defaults to None.
limits
Optional[TabularLimitSettings]

limits settings. Defaults to None.
training
Optional[TrainingSettings]

training settings. Defaults to None.

Methods

dump

Dumps the job content into a file in YAML format.
set_data

Define data configuration.
set_featurization

Define feature engineering configuration.
set_limits

Set limits for the job.
set_training

The method to configure training related settings.

dump

Dumps the job content into a file in YAML format.

dump(dest: str | PathLike | IO, **kwargs: Any) -> None

Parameters

Name Description
dest
Required
Union[<xref:PathLike>, str, IO[AnyStr]]

The local path or file stream to write the YAML content to. If dest is a file path, a new file will be created. If dest is an open file, the file will be written to directly.

Exceptions

Type Description
FileExistsError

Raised if dest is a file path and the file already exists.
IOError

Raised if dest is an open file and the file is not writable.

set_data

Define data configuration.

set_data(*, training_data: Input, target_column_name: str, weight_column_name: str | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, n_cross_validations: str | int | None = None, cv_split_column_names: List[str] | None = None, test_data: Input | None = None, test_data_size: float | None = None) -> None

Keyword-Only Parameters

Name Description
training_data
Input

Training data.
target_column_name
str

Column name of the target column.
weight_column_name
Optional[str]

Weight column name, defaults to None

Default value: None
validation_data
Optional[Input]

Validation data, defaults to None

Default value: None
validation_data_size
Optional[float]

Validation data size, defaults to None

Default value: None
n_cross_validations
Optional[Union[str, int]]

n_cross_validations, defaults to None

Default value: None
cv_split_column_names
Optional[List[str]]

cv_split_column_names, defaults to None

Default value: None
test_data
Optional[Input]

Test data, defaults to None

Default value: None
test_data_size
Optional[float]

Test data size, defaults to None

Default value: None

set_featurization

Define feature engineering configuration.

set_featurization(*, blocked_transformers: List[BlockedTransformers | str] | None = None, column_name_and_types: Dict[str, str] | None = None, dataset_language: str | None = None, transformer_params: Dict[str, List[ColumnTransformer]] | None = None, mode: str | None = None, enable_dnn_featurization: bool | None = None) -> None

Keyword-Only Parameters

Name Description
blocked_transformers
Optional[List[Union[BlockedTransformers, str]]]

A list of transformer names to be blocked during featurization, defaults to None

Default value: None
column_name_and_types
Optional[Dict[str, str]]

A dictionary of column names and feature types used to update column purpose , defaults to None

Default value: None
dataset_language
Optional[str]

Three character ISO 639-3 code for the language(s) contained in the dataset. Languages other than English are only supported if you use GPU-enabled compute. The language_code 'mul' should be used if the dataset contains multiple languages. To find ISO 639-3 codes for different languages, please refer to https://en.wikipedia.org/wiki/List_of_ISO_639-3_codes, defaults to None

Default value: None
transformer_params
Optional[Dict[str, List[ColumnTransformer]]]

A dictionary of transformer and corresponding customization parameters , defaults to None

Default value: None
mode
Optional[str]

"off", "auto", defaults to "auto", defaults to None

Default value: None
enable_dnn_featurization
Optional[bool]

Whether to include DNN based feature engineering methods, defaults to None

Default value: None

set_limits

Set limits for the job.

set_limits(*, enable_early_termination: bool | None = None, exit_score: float | None = None, max_concurrent_trials: int | None = None, max_cores_per_trial: int | None = None, max_nodes: int | None = None, max_trials: int | None = None, timeout_minutes: int | None = None, trial_timeout_minutes: int | None = None) -> None

Keyword-Only Parameters

Name Description
enable_early_termination
Optional[bool]

Whether to enable early termination if the score is not improving in the short term, defaults to None.

Early stopping logic:

  • No early stopping for first 20 iterations (landmarks).

  • Early stopping window starts on the 21st iteration and looks for early_stopping_n_iters iterations

    (currently set to 10). This means that the first iteration where stopping can occur is the 31st.

  • AutoML still schedules 2 ensemble iterations AFTER early stopping, which might result in higher scores.

  • Early stopping is triggered if the absolute value of best score calculated is the same for past

    early_stopping_n_iters iterations, that is, if there is no improvement in score for early_stopping_n_iters iterations.

Default value: None
exit_score
Optional[float]

Target score for experiment. The experiment terminates after this score is reached. If not specified (no criteria), the experiment runs until no further progress is made on the primary metric. For for more information on exit criteria, see this article , defaults to None

Default value: None
max_concurrent_trials
Optional[int]

This is the maximum number of iterations that would be executed in parallel. The default value is 1.

  • AmlCompute clusters support one iteration running per node. For multiple AutoML experiment parent runs

    executed in parallel on a single AmlCompute cluster, the sum of the max_concurrent_trials values for all experiments should be less than or equal to the maximum number of nodes. Otherwise, runs will be queued until nodes are available.

  • DSVM supports multiple iterations per node. max_concurrent_trials should

    be less than or equal to the number of cores on the DSVM. For multiple experiments run in parallel on a single DSVM, the sum of the max_concurrent_trials values for all experiments should be less than or equal to the maximum number of nodes.

  • Databricks - max_concurrent_trials should be less than or equal to the number of

    worker nodes on Databricks.

max_concurrent_trials does not apply to local runs. Formerly, this parameter was named concurrent_iterations.

Default value: None
max_cores_per_trial
Optional[int]

The maximum number of threads to use for a given training iteration. Acceptable values:

  • Greater than 1 and less than or equal to the maximum number of cores on the compute target.

  • Equal to -1, which means to use all the possible cores per iteration per child-run.

  • Equal to 1, the default.

Default value: None
max_nodes
Optional[int]

[Experimental] The maximum number of nodes to use for distributed training.

  • For forecasting, each model is trained using max(2, int(max_nodes / max_concurrent_trials)) nodes.

  • For classification/regression, each model is trained using max_nodes nodes.

Note- This parameter is in public preview and might change in future.

Default value: None
max_trials
Optional[int]

The total number of different algorithm and parameter combinations to test during an automated ML experiment. If not specified, the default is 1000 iterations.

Default value: None
timeout_minutes
Optional[int]

Maximum amount of time in minutes that all iterations combined can take before the experiment terminates. If not specified, the default experiment timeout is 6 days. To specify a timeout less than or equal to 1 hour, make sure your dataset's size is not greater than 10,000,000 (rows times column) or an error results, defaults to None

Default value: None
trial_timeout_minutes
Optional[int]

Maximum time in minutes that each iteration can run for before it terminates. If not specified, a value of 1 month or 43200 minutes is used, defaults to None

Default value: None

set_training

The method to configure training related settings.

set_training(*, enable_onnx_compatible_models: bool | None = None, enable_dnn_training: bool | None = None, enable_model_explainability: bool | None = None, enable_stack_ensemble: bool | None = None, enable_vote_ensemble: bool | None = None, stack_ensemble_settings: StackEnsembleSettings | None = None, ensemble_model_download_timeout: int | None = None, allowed_training_algorithms: List[str] | None = None, blocked_training_algorithms: List[str] | None = None, training_mode: str | TrainingMode | None = None) -> None

Keyword-Only Parameters

Name Description
enable_onnx_compatible_models
Optional[bool]

Whether to enable or disable enforcing the ONNX-compatible models. The default is False. For more information about Open Neural Network Exchange (ONNX) and Azure Machine Learning,see this article.

Default value: None
enable_dnn_training
Optional[bool]

Whether to include DNN based models during model selection. However, the default is True for DNN NLP tasks, and it's False for all other AutoML tasks.

Default value: None
enable_model_explainability
Optional[bool]

Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. For more information, see Interpretability: model explanations in automated machine learning. , defaults to None

Default value: None
enable_stack_ensemble
Optional[bool]

Whether to enable/disable StackEnsemble iteration. If enable_onnx_compatible_models flag is being set, then StackEnsemble iteration will be disabled. Similarly, for Timeseries tasks, StackEnsemble iteration will be disabled by default, to avoid risks of overfitting due to small training set used in fitting the meta learner. For more information about ensembles, see Ensemble configuration , defaults to None

Default value: None
enable_vote_ensemble
Optional[bool]

Whether to enable/disable VotingEnsemble iteration. For more information about ensembles, see Ensemble configuration , defaults to None

Default value: None
stack_ensemble_settings
Optional[StackEnsembleSettings]

Settings for StackEnsemble iteration, defaults to None

Default value: None
ensemble_model_download_timeout
Optional[int]

During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed, defaults to None

Default value: None
allowed_training_algorithms
Optional[List[str]]

A list of model names to search for an experiment. If not specified, then all models supported for the task are used minus any specified in blocked_training_algorithms or deprecated TensorFlow models, defaults to None

Default value: None
blocked_training_algorithms
Optional[List[str]]

A list of algorithms to ignore for an experiment, defaults to None

Default value: None
training_mode
Optional[Union[str, TabularTrainingMode]]

[Experimental] The training mode to use. The possible values are-

  • distributed- enables distributed training for supported algorithms.

  • non_distributed- disables distributed training.

  • auto- Currently, it is same as non_distributed. In future, this might change.

Note: This parameter is in public preview and may change in future.

Default value: None

Attributes

base_path

The base path of the resource.

Returns

Type Description
str

The base path of the resource.

creation_context

The creation context of the resource.

Returns

Type Description
Optional[SystemData]

The creation metadata for the resource.

featurization

Get the tabular featurization settings for the AutoML job.

Returns

Type Description
TabularFeaturizationSettings

Tabular featurization settings for the AutoML job

id

The resource ID.

Returns

Type Description
Optional[str]

The global ID of the resource, an Azure Resource Manager (ARM) ID.

inputs

limits

Get the tabular limits for the AutoML job.

Returns

Type Description
TabularLimitSettings

Tabular limits for the AutoML job

log_files

Job output files.

Returns

Type Description
Optional[Dict[str, str]]

The dictionary of log names and URLs.

log_verbosity

Get the log verbosity for the AutoML job.

Returns

Type Description
<xref:LogVerbosity>

log verbosity for the AutoML job

outputs

primary_metric

The primary metric to use for optimization.

Returns

Type Description
Union[str, ClassificationPrimaryMetrics]

The primary metric to use for optimization.

status

The status of the job.

Common values returned include "Running", "Completed", and "Failed". All possible values are:

  • NotStarted - This is a temporary state that client-side Run objects are in before cloud submission.

  • Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.

  • Provisioning - On-demand compute is being created for a given job submission.

  • Preparing - The run environment is being prepared and is in one of two stages:

    • Docker image build

    • conda environment setup

  • Queued - The job is queued on the compute target. For example, in BatchAI, the job is in a queued state

    while waiting for all the requested nodes to be ready.

  • Running - The job has started to run on the compute target.

  • Finalizing - User code execution has completed, and the run is in post-processing stages.

  • CancelRequested - Cancellation has been requested for the job.

  • Completed - The run has completed successfully. This includes both the user code execution and run

    post-processing stages.

  • Failed - The run failed. Usually the Error property on a run will provide details as to why.

  • Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.

  • NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.

Returns

Type Description
Optional[str]

Status of the job.

studio_url

Azure ML studio endpoint.

Returns

Type Description
Optional[str]

The URL to the job details page.

task_type

Get task type.

Returns

Type Description
str

The type of task to run. Possible values include: "classification", "regression", "forecasting".

test_data

Get test data.

Returns

Type Description
Input

Test data input

training

Training Settings for AutoML Classification Job.

Returns

Type Description
<xref:ClassificationTrainingSettings>

Training settings used for AutoML Classification Job.

training_data

Get training data.

Returns

Type Description
Input

Training data input

type

The type of the job.

Returns

Type Description
Optional[str]

The type of the job.

validation_data

Get validation data.

Returns

Type Description
Input

Validation data input