Source code for 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.
:py:mod:`mlflow.pyfunc`
    Produced for use by generic pyfunc-based deployment tools and for batch auditing
    of historical forecasts.

.. _Prophet:
    https://facebook.github.io/prophet/docs/quick_start.html#python-api
"""
import os
import yaml
import json

import mlflow
from mlflow import pyfunc
from mlflow.utils.requirements_utils import _get_pinned_requirement
from mlflow.utils.environment import (
    _mlflow_conda_env,
    _validate_env_arguments,
    _process_pip_requirements,
    _process_conda_env,
    _CONDA_ENV_FILE_NAME,
    _REQUIREMENTS_FILE_NAME,
    _CONSTRAINTS_FILE_NAME,
    _PYTHON_ENV_FILE_NAME,
    _PythonEnv,
)
from mlflow.utils.file_utils import write_to
from mlflow.utils.docstring_utils import format_docstring, LOG_MODEL_PARAM_DOCS
from mlflow.models.signature import ModelSignature
from mlflow.models.utils import _save_example
from mlflow.models import Model, ModelInputExample
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.model_utils import (
    _get_flavor_configuration,
    _validate_and_copy_code_paths,
    _add_code_from_conf_to_system_path,
    _validate_and_prepare_target_save_path,
)
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS

FLAVOR_NAME = "prophet"
_MODEL_BINARY_KEY = "data"
_MODEL_BINARY_FILE_NAME = "model.pr"
_MODEL_TYPE_KEY = "model_type"


[docs]def get_default_pip_requirements(): """ :return: A list of default pip requirements for MLflow Models produced by this flavor. Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment that, at a minimum, contains these requirements. """ # Note: Prophet's whl build process will fail due to missing dependencies, defaulting # to setup.py installation process. # If a pystan installation error occurs, ensure gcc>=8 is installed in your environment. # See: https://gcc.gnu.org/install/ return [_get_pinned_requirement("prophet")]
[docs]def get_default_conda_env(): """ :return: The default Conda environment for MLflow Models produced by calls to :func:`save_model()` and :func:`log_model()`. """ return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())
[docs]@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) def save_model( pr_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: ModelSignature = None, input_example: ModelInputExample = None, pip_requirements=None, extra_pip_requirements=None, ): """ Save a Prophet model to a path on the local file system. :param pr_model: Prophet model (an instance of Prophet() forecaster that has been fit on a temporal series. :param path: Local path where the serialized model (as JSON) is to be saved. :param conda_env: {{ conda_env }} :param 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. :param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. :param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>` describes model input and output :py:class:`Schema <mlflow.types.Schema>`. The model signature can be :py:func:`inferred <mlflow.models.infer_signature>` 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: .. code-block:: python from mlflow.models.signature import infer_signature model = Prophet().fit(df) train = model.history predictions = model.predict(model.make_future_dataframe(30)) signature = infer_signature(train, predictions) :param 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. :param pip_requirements: {{ pip_requirements }} :param extra_pip_requirements: {{ extra_pip_requirements }} """ import prophet _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) path = os.path.abspath(path) _validate_and_prepare_target_save_path(path) code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) if mlflow_model is None: mlflow_model = Model() if signature is not None: mlflow_model.signature = signature if input_example is not None: _save_example(mlflow_model, input_example, path) model_data_path = os.path.join(path, _MODEL_BINARY_FILE_NAME) _save_model(pr_model, model_data_path) model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME} pyfunc.add_to_model( mlflow_model, loader_module="mlflow.prophet", env=_CONDA_ENV_FILE_NAME, code=code_dir_subpath, **model_bin_kwargs, ) flavor_conf = { _MODEL_TYPE_KEY: pr_model.__class__.__name__, **model_bin_kwargs, } mlflow_model.add_flavor( FLAVOR_NAME, prophet_version=prophet.__version__, code=code_dir_subpath, **flavor_conf, ) mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) if conda_env is None: if pip_requirements is None: # cannot use inferred requirements due to prophet's build process # as the package installation of pystan requires Cython to be present # in the path. Prophet's installation itself requires imports of # existing libraries, preventing the execution of a batched pip install # and instead using a a strictly defined list of dependencies. # NOTE: if Prophet .whl build architecture is changed, this should be # modified to a standard inferred approach. default_reqs = get_default_pip_requirements() else: default_reqs = None conda_env, pip_requirements, pip_constraints = _process_pip_requirements( default_reqs, pip_requirements, extra_pip_requirements, ) else: conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: yaml.safe_dump(conda_env, stream=f, default_flow_style=False) if pip_constraints: write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
[docs]@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) def log_model( pr_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, pip_requirements=None, extra_pip_requirements=None, ): """ Log a Prophet model as an MLflow artifact for the current run. :param pr_model: Prophet model to be saved. :param artifact_path: Run-relative artifact path. :param conda_env: {{ conda_env }} :param 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. :param 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. :param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>` describes model input and output :py:class:`Schema <mlflow.types.Schema>`. The model signature can be :py:func:`inferred <mlflow.models.infer_signature>` 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: .. code-block:: python from mlflow.models.signature import infer_signature model = Prophet().fit(df) train = model.history predictions = model.predict(model.make_future_dataframe(30)) signature = infer_signature(train, predictions) :param 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. :param 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. :param pip_requirements: {{ pip_requirements }} :param extra_pip_requirements: {{ extra_pip_requirements }} :return: A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the metadata of the logged model. """ return Model.log( artifact_path=artifact_path, flavor=mlflow.prophet, registered_model_name=registered_model_name, pr_model=pr_model, conda_env=conda_env, code_paths=code_paths, signature=signature, input_example=input_example, await_registration_for=await_registration_for, pip_requirements=pip_requirements, extra_pip_requirements=extra_pip_requirements, )
def _save_model(model, path): from prophet.serialize import model_to_json model_ser = model_to_json(model) with open(path, "w") as f: json.dump(model_ser, f) def _load_model(path): from prophet.serialize import model_from_json with open(path, "r") as f: model = json.load(f) return model_from_json(model) def _load_pyfunc(path): """ Load PyFunc implementation for Prophet. Called by ``pyfunc.load_model``. :param path: Local filesystem path to the MLflow Model with the ``prophet`` flavor. """ return _ProphetModelWrapper(_load_model(path))
[docs]def load_model(model_uri, dst_path=None): """ Load a Prophet model from a local file or a run. :param 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`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/tracking.html# artifact-locations>`_. :param 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. :return: A Prophet model instance """ local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) _add_code_from_conf_to_system_path(local_model_path, flavor_conf) pr_model_path = os.path.join( local_model_path, flavor_conf.get(_MODEL_BINARY_KEY, _MODEL_BINARY_FILE_NAME) ) return _load_model(pr_model_path)
class _ProphetModelWrapper: def __init__(self, pr_model): self.pr_model = pr_model def predict(self, dataframe): return self.pr_model.predict(dataframe)