"""
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 json
import logging
import os
from typing import Any
import yaml
import mlflow
from mlflow import pyfunc
from mlflow.models import Model, ModelInputExample, ModelSignature
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import _infer_signature_from_input_example
from mlflow.models.utils import _save_example
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
from mlflow.utils.environment import (
_CONDA_ENV_FILE_NAME,
_CONSTRAINTS_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_mlflow_conda_env,
_process_conda_env,
_process_pip_requirements,
_PythonEnv,
_validate_env_arguments,
)
from mlflow.utils.file_utils import get_total_file_size, write_to
from mlflow.utils.model_utils import (
_add_code_from_conf_to_system_path,
_get_flavor_configuration,
_validate_and_copy_code_paths,
_validate_and_prepare_target_save_path,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement
FLAVOR_NAME = "prophet"
_MODEL_BINARY_KEY = "data"
_MODEL_BINARY_FILE_NAME = "model.pr"
_MODEL_TYPE_KEY = "model_type"
_logger = logging.getLogger(__name__)
[docs]def get_default_pip_requirements():
"""
Returns:
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():
"""
Returns:
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,
metadata=None,
):
"""
Save a Prophet model to a path on the local file system.
Args:
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: {{ conda_env }}
code_paths: {{ code_paths }}
mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
signature: an instance of the :py:class:`ModelSignature <mlflow.models.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
:py:func:`infer_signature() <mlflow.models.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:
.. code-block:: python
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: {{ input_example }}
pip_requirements: {{ pip_requirements }}
extra_pip_requirements: {{ extra_pip_requirements }}
metadata: {{ metadata }}
"""
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()
saved_example = _save_example(mlflow_model, input_example, path)
if signature is None and saved_example is not None:
wrapped_model = _ProphetModelWrapper(pr_model)
signature = _infer_signature_from_input_example(saved_example, wrapped_model)
elif signature is False:
signature = None
if signature is not None:
mlflow_model.signature = signature
if metadata is not None:
mlflow_model.metadata = metadata
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",
conda_env=_CONDA_ENV_FILE_NAME,
python_env=_PYTHON_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,
)
if size := get_total_file_size(path):
mlflow_model.model_size_bytes = size
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
default_reqs = 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()
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: str | None = None,
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,
metadata=None,
name: str | None = None,
params: dict[str, Any] | None = None,
tags: dict[str, Any] | None = None,
model_type: str | None = None,
step: int = 0,
model_id: str | None = None,
):
"""
Logs a Prophet model as an MLflow artifact for the current run.
Args:
pr_model: Prophet model to be saved.
artifact_path: Deprecated. Use `name` instead.
conda_env: {{ conda_env }}
code_paths: {{ code_paths }}
registered_model_name: 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: An instance of the :py:class:`ModelSignature <mlflow.models.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
:py:func:`infer_signature() <mlflow.models.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:
.. code-block:: python
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: {{ input_example }}
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: {{ pip_requirements }}
extra_pip_requirements: {{ extra_pip_requirements }}
metadata: {{ metadata }}
name: {{ name }}
params: {{ params }}
tags: {{ tags }}
model_type: {{ model_type }}
step: {{ step }}
model_id: {{ model_id }}
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
"""
return Model.log(
artifact_path=artifact_path,
name=name,
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,
metadata=metadata,
params=params,
tags=tags,
model_type=model_type,
step=step,
model_id=model_id,
)
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) as f:
model = json.load(f)
return model_from_json(model)
def _load_pyfunc(path):
"""
Loads PyFunc implementation for Prophet. Called by ``pyfunc.load_model``.
Args:
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.
Args:
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>`_.
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 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 get_raw_model(self):
"""
Returns the underlying model.
"""
return self.pr_model
def predict(self, dataframe, params: dict[str, Any] | None = None):
"""
Args:
dataframe: Model input data.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictions.
"""
return self.pr_model.predict(dataframe)