mlflow.pmdarima

The mlflow.pmdarima module provides an API for logging and loading pmdarima models. This module exports univariate pmdarima models in the following formats:

Pmdarima format

Serialized instance of a pmdarima model using pickle.

mlflow.pyfunc

Produced for use by generic pyfunc-based deployment tools and for batch auditing of historical forecasts.

mlflow.pmdarima.get_default_conda_env()[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Returns

The default Conda environment for MLflow Models produced by calls to save_model() and log_model()

mlflow.pmdarima.get_default_pip_requirements()[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Returns

A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at a minimum, contains these requirements.

mlflow.pmdarima.load_model(model_uri, dst_path=None)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Load a pmdarima ARIMA model or Pipeline object from a local file or a run.

Parameters
  • model_uri

    The location, in URI format, of the MLflow model. For example:

    • /Users/me/path/to/local/model

    • relative/path/to/local/model

    • s3://my_bucket/path/to/model

    • runs:/<mlflow_run_id>/run-relative/path/to/model

    • mlflow-artifacts:/path/to/model

    For more information about supported URI schemes, see Referencing Artifacts.

  • dst_path – The local filesystem path to which to download the model artifact. This directory must already exist. If unspecified, a local output path will be created.

Returns

A pmdarima model instance

mlflow.pmdarima.log_model(pmdarima_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, scipy.sparse.csr.csr_matrix, scipy.sparse.csc.csc_matrix]] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, **kwargs)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Log a pmdarima ARIMA or Pipeline object as an MLflow artifact for the current run.

Parameters
  • pmdarima_model – pmdarima ARIMA or Pipeline model that has been fit on a temporal series.

  • artifact_path – Run-relative artifact path to save the model instance to.

  • conda_env

    Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
        "name": "mlflow-env",
        "channels": ["conda-forge"],
        "dependencies": [
            "python=3.7.0",
            {
                "pip": [
                    "pmdarima==x.y.z"
                ],
            },
        ],
    }
    

  • code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.

  • registered_model_name – This argument may change or be removed in a future release without warning. If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.

  • signature

    Model Signature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    
    model = pmdarima.auto_arima(data)
    predictions = model.predict(n_periods=30, return_conf_int=False)
    signature = infer_signature(data, predictions)
    

    Warning

    if utilizing confidence interval generation in the predict method of a pmdarima model (return_conf_int=True), the signature will not be inferred due to the complex tuple return type when using the native ARIMA.predict() API. infer_schema will function correctly if using the pyfunc flavor of the model, though.

  • input_example – Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

  • await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["pmdarima", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

  • kwargs – Additional arguments for mlflow.models.model.Model

Returns

A ModelInfo instance that contains the metadata of the logged model.

mlflow.pmdarima.save_model(pmdarima_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, scipy.sparse.csr.csr_matrix, scipy.sparse.csc.csc_matrix]] = None, pip_requirements=None, extra_pip_requirements=None)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Save a pmdarima ARIMA model or Pipeline object to a path on the local file system.

Parameters
  • pmdarima_model – pmdarima ARIMA or Pipeline model that has been fit on a temporal series.

  • path – Local path destination for the serialized model (in pickle format) is to be saved.

  • conda_env

    Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
        "name": "mlflow-env",
        "channels": ["conda-forge"],
        "dependencies": [
            "python=3.7.0",
            {
                "pip": [
                    "pmdarima==x.y.z"
                ],
            },
        ],
    }
    

  • code_paths – A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded.

  • mlflow_modelmlflow.models.Model this flavor is being added to.

  • signature

    Model Signature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    
    model = pmdarima.auto_arima(data)
    predictions = model.predict(n_periods=30, return_conf_int=False)
    signature = infer_signature(data, predictions)
    

    Warning

    if utilizing confidence interval generation in the predict method of a pmdarima model (return_conf_int=True), the signature will not be inferred due to the complex tuple return type when using the native ARIMA.predict() API. infer_schema will function correctly if using the pyfunc flavor of the model, though.

  • input_example – Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["pmdarima", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.