mlflow.prophet

The mlflow.prophet module provides an API for logging and loading Prophet models. This module exports univariate Prophet models in the following flavors

Prophet (native) format

This is the main flavor that can be accessed with Prophet APIs.

mlflow.pyfunc

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

mlflow.prophet.get_default_conda_env()[source]
返回

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

mlflow.prophet.get_default_pip_requirements()[source]
返回

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

Load a Prophet model from a local file or a run.

参数

  • model_uri –

    MLflow 模型在 URI 格式中的位置。例如：

    • /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

    有关支持的 URI 方案的更多信息，请参阅 引用 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.

返回

A Prophet model instance

mlflow.prophet.log_model(pr_model, artifact_path: str | None = None, conda_env=None, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None, name: str | None = None, params: dict[str, typing.Any] | None = None, tags: dict[str, typing.Any] | None = None, model_type: str | None = None, step: int = 0, model_id: str | None = None)[source]

Logs a Prophet model as an MLflow artifact for the current run.

参数

  • pr_model – Prophet model to be saved.

  • artifact_path – Deprecated. Use name instead.

  • 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 a 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=3.8.15",
        {
            "pip": [
                "prophet==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. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

    For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.

  • registered_model_name – 如果提供，则在 registered_model_name 下创建一个模型版本，如果给定名称的注册模型不存在，也会创建该注册模型。

  • signature –

    An instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example

    from mlflow.models import infer_signature

model = Prophet().fit(df)
train = model.history
predictions = model.predict(model.make_future_dataframe(30))
signature = infer_signature(train, predictions)

  • input_example – 一个或多个有效的模型输入实例。输入示例用作要馈送给模型的数据的提示。它将被转换为 Pandas DataFrame，然后使用 Pandas 的面向拆分（split-oriented）格式序列化为 json，或者转换为 numpy 数组，其中示例将通过转换为列表来序列化为 json。字节将进行 base64 编码。当 signature 参数为 None 时，输入示例用于推断模型签名。

  • await_registration_for – 等待模型版本完成创建并处于 READY 状态的秒数。默认情况下，函数等待五分钟。指定 0 或 None 可跳过等待。

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["prophet", "-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.

    警告

    以下参数不能同时指定

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    此示例演示了如何使用 pip_requirements 和 extra_pip_requirements 指定 pip requirements。

  • metadata – 传递给模型并存储在 MLmodel 文件中的自定义元数据字典。

  • name – 模型名称。

  • params – 要与模型一起记录的参数字典。

  • tags – 要与模型一起记录的标签字典。

  • model_type – 模型的类型。

  • step – 记录模型输出和指标的步骤

  • model_id – 模型的 ID。

返回

一个 ModelInfo 实例，其中包含已记录模型的元数据。

mlflow.prophet.save_model(pr_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

Save a Prophet model to a path on the local file system.

参数

  • pr_model – Prophet model (an instance of Prophet() forecaster that has been fit on a temporal series.

  • path – Local path where the serialized model (as JSON) 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 a 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=3.8.15",
        {
            "pip": [
                "prophet==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. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

    For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.

  • mlflow_model – 要添加此 flavor 的 mlflow.models.Model。

  • signature –

    an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example

    from mlflow.models import infer_signature

model = Prophet().fit(df)
train = model.history
predictions = model.predict(model.make_future_dataframe(30))
signature = infer_signature(train, predictions)

  • input_example – 一个或多个有效的模型输入实例。输入示例用作要馈送给模型的数据的提示。它将被转换为 Pandas DataFrame，然后使用 Pandas 的面向拆分（split-oriented）格式序列化为 json，或者转换为 numpy 数组，其中示例将通过转换为列表来序列化为 json。字节将进行 base64 编码。当 signature 参数为 None 时，输入示例用于推断模型签名。

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["prophet", "-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.

    警告

    以下参数不能同时指定

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    此示例演示了如何使用 pip_requirements 和 extra_pip_requirements 指定 pip requirements。

  • metadata – 传递给模型并存储在 MLmodel 文件中的自定义元数据字典。