"""The ``flavor`` module provides an example for a custom model flavor for ``sktime`` library. This module exports ``sktime`` models in the following formats: sktime (native) format This is the main flavor that can be loaded back into ``sktime``, which relies on pickle internally to serialize a model. Note that pickle serialization requires using the same python environment (version) in whatever environment you're going to use this model for inference to ensure that the model will load with appropriate version of pickle. mlflow.pyfunc Produced for use by generic pyfunc-based deployment tools and batch inference. The interface for utilizing a ``sktime`` model loaded as a ``pyfunc`` type for generating forecast predictions uses a *single-row* ``Pandas DataFrame`` configuration argument. The following columns in this configuration ``Pandas DataFrame`` are supported: * ``predict_method`` (required) - specifies the ``sktime`` predict method. The supported predict methods in this example flavor are ``predict``, ``predict_interval``, ``predict_quantiles``, ``predict_var``. Additional methods (e.g. ``predict_proba``) could be added in a similar fashion. * ``fh`` (optional) - specifies the number of future periods to generate starting from the last datetime value of the training dataset, utilizing the frequency of the input training series when the model was trained. (for example, if the training data series elements represent one value per hour, in order to forecast 3 hours of future data, set the column ``fh`` to ``[1,2,3]``. If the parameter is not provided it must be passed during fit(). (Default: ``None``) * ``X`` (optional) - exogenous regressor values as a 2D numpy ndarray or list of values for future time period events. For more information, read the underlying library explanation https://www.sktime.net/en/latest/examples/AA_datatypes_and_datasets.html#Section-1:-in-memory-data-containers. (Default: ``None``) * ``coverage`` (optional) - the nominal coverage value for calculating prediction interval forecasts. Can only be provided in combination with predict method ``predict_interval``. (Default: ``0.9``) * ``alpha`` (optional) - the probability value for calculating prediction quantile forecasts. Can only be provided in combination with predict method ``predict_quantiles``. (Default: ``None``) * ``cov`` (optional) - if True, computes covariance matrix forecast. Can only be provided in combination with predict method ``predict_var``. (Default: ``False``) An example configuration for the ``pyfunc`` predict of a ``sktime`` model is shown below, using an interval forecast with nominal coverage value ``[0.9,0.95]``, a future forecast horizon of 3 periods, and no exogenous regressor elements: ====== ================= ============ ======== Index predict_method coverage fh ====== ================= ============ ======== 0 predict_interval [0.9,0.95] [1,2,3] ====== ================= ============ ======== """ import logging import os import pickle from typing import Any import flavor import numpy as np import pandas as pd import sktime import yaml from sktime.utils.multiindex import flatten_multiindex import mlflow from mlflow import pyfunc from mlflow.exceptions import MlflowException from mlflow.models import Model from mlflow.models.model import MLMODEL_FILE_NAME from mlflow.models.utils import _save_example from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS from mlflow.tracking.artifact_utils import _download_artifact_from_uri 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 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 = "sktime" SKTIME_PREDICT = "predict" SKTIME_PREDICT_INTERVAL = "predict_interval" SKTIME_PREDICT_QUANTILES = "predict_quantiles" SKTIME_PREDICT_VAR = "predict_var" SUPPORTED_SKTIME_PREDICT_METHODS = [ SKTIME_PREDICT, SKTIME_PREDICT_INTERVAL, SKTIME_PREDICT_QUANTILES, SKTIME_PREDICT_VAR, ] SERIALIZATION_FORMAT_PICKLE = "pickle" SERIALIZATION_FORMAT_CLOUDPICKLE = "cloudpickle" SUPPORTED_SERIALIZATION_FORMATS = [ SERIALIZATION_FORMAT_PICKLE, SERIALIZATION_FORMAT_CLOUDPICKLE, ] _logger = logging.getLogger(__name__) _MODEL_DATA_SUBPATH = "model.pkl" def get_default_pip_requirements(include_cloudpickle=False): """Create list of default pip requirements for MLflow Models. Returns ------- 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. """ pip_deps = [_get_pinned_requirement("sktime")] if include_cloudpickle: pip_deps += [_get_pinned_requirement("cloudpickle")] return pip_deps def get_default_conda_env(include_cloudpickle=False): """Return default Conda environment for MLflow Models. 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(include_cloudpickle)) def save_model( sktime_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature=None, input_example=None, pip_requirements=None, extra_pip_requirements=None, serialization_format=SERIALIZATION_FORMAT_PICKLE, ): """Save a ``sktime`` model to a path on the local file system. Parameters ---------- sktime_model : Fitted ``sktime`` model object. path : str Local path where the model is to be saved. conda_env : Union[dict, str], optional (default=None) Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. code_paths : array-like, optional (default=None) 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_model: mlflow.models.Model, optional (default=None) mlflow.models.Model configuration to which to add the python_function flavor. signature : mlflow.models.signature.ModelSignature, optional (default=None) Model Signature mlflow.models.ModelSignature describes model input and output :py:class:`Schema `. The model signature can be :py:func:`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: .. code-block:: py from mlflow.models import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions) .. Warning:: if performing probabilistic forecasts (``predict_interval``, ``predict_quantiles``) with a ``sktime`` model, the signature on the returned prediction object will not be correctly inferred due to the Pandas MultiIndex column type when using the these methods. ``infer_schema`` will function correctly if using the ``pyfunc`` flavor of the model, though. input_example : Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix], optional (default=None) 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 : Union[Iterable, str], optional (default=None) Either an iterable of pip requirement strings (e.g. ["sktime", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt") extra_pip_requirements : Union[Iterable, str], optional (default=None) 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") serialization_format : str, optional (default="pickle") The format in which to serialize the model. This should be one of the formats "pickle" or "cloudpickle" """ _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) if serialization_format not in SUPPORTED_SERIALIZATION_FORMATS: raise MlflowException( message=( f"Unrecognized serialization format: {serialization_format}. " "Please specify one of the following supported formats: " f"{SUPPORTED_SERIALIZATION_FORMATS}." ), error_code=INVALID_PARAMETER_VALUE, ) _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_DATA_SUBPATH) _save_model(sktime_model, model_data_path, serialization_format=serialization_format) pyfunc.add_to_model( mlflow_model, loader_module="flavor", model_path=_MODEL_DATA_SUBPATH, conda_env=_CONDA_ENV_FILE_NAME, python_env=_PYTHON_ENV_FILE_NAME, code=code_dir_subpath, ) mlflow_model.add_flavor( FLAVOR_NAME, pickled_model=_MODEL_DATA_SUBPATH, sktime_version=sktime.__version__, serialization_format=serialization_format, code=code_dir_subpath, ) mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) if conda_env is None: if pip_requirements is None: include_cloudpickle = serialization_format == SERIALIZATION_FORMAT_CLOUDPICKLE default_reqs = get_default_pip_requirements(include_cloudpickle) inferred_reqs = mlflow.models.infer_pip_requirements( path, FLAVOR_NAME, fallback=default_reqs ) default_reqs = sorted(set(inferred_reqs).union(default_reqs)) 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)) def log_model( sktime_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature=None, input_example=None, await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, pip_requirements=None, extra_pip_requirements=None, serialization_format=SERIALIZATION_FORMAT_PICKLE, **kwargs, ): """ Log a ``sktime`` model as an MLflow artifact for the current run. Parameters ---------- sktime_model : fitted ``sktime`` model Fitted ``sktime`` model object. artifact_path : str Run-relative artifact path to save the model to. conda_env : Union[dict, str], optional (default=None) Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. code_paths : array-like, optional (default=None) 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 : str, optional (default=None) 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 : mlflow.models.signature.ModelSignature, optional (default=None) Model Signature mlflow.models.ModelSignature describes model input and output :py:class:`Schema `. The model signature can be :py:func:`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: .. code-block:: py from mlflow.models import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions) .. Warning:: if performing probabilistic forecasts (``predict_interval``, ``predict_quantiles``) with a ``sktime`` model, the signature on the returned prediction object will not be correctly inferred due to the Pandas MultiIndex column type when using the these methods. ``infer_schema`` will function correctly if using the ``pyfunc`` flavor of the model, though. input_example : Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix], optional (default=None) 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 : int, optional (default=None) 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 : Union[Iterable, str], optional (default=None) Either an iterable of pip requirement strings (e.g. ["sktime", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt") extra_pip_requirements : Union[Iterable, str], optional (default=None) 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") serialization_format : str, optional (default="pickle") The format in which to serialize the model. This should be one of the formats "pickle" or "cloudpickle" kwargs: Additional arguments for :py:class:`mlflow.models.model.Model` Returns ------- A :py:class:`ModelInfo ` instance that contains the metadata of the logged model. """ return Model.log( artifact_path=artifact_path, flavor=flavor, registered_model_name=registered_model_name, sktime_model=sktime_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, serialization_format=serialization_format, **kwargs, ) def load_model(model_uri, dst_path=None): """ Load a ``sktime`` model from a local file or a run. Parameters ---------- model_uri : str 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://run-relative/path/to/model`` - ``mlflow-artifacts:/path/to/model`` For more information about supported URI schemes, see `Referencing Artifacts `_. dst_path : str, optional (default=None) 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 ``sktime`` 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) sktime_model_file_path = os.path.join(local_model_path, flavor_conf["pickled_model"]) serialization_format = flavor_conf.get("serialization_format", SERIALIZATION_FORMAT_PICKLE) return _load_model(path=sktime_model_file_path, serialization_format=serialization_format) def _save_model(model, path, serialization_format): with open(path, "wb") as out: if serialization_format == SERIALIZATION_FORMAT_PICKLE: pickle.dump(model, out) else: import cloudpickle cloudpickle.dump(model, out) def _load_model(path, serialization_format): with open(path, "rb") as pickled_model: if serialization_format == SERIALIZATION_FORMAT_PICKLE: return pickle.load(pickled_model) elif serialization_format == SERIALIZATION_FORMAT_CLOUDPICKLE: import cloudpickle return cloudpickle.load(pickled_model) def _load_pyfunc(path): """Load PyFunc implementation. Called by ``pyfunc.load_model``. Parameters ---------- path : str Local filesystem path to the MLflow Model with the ``sktime`` flavor. """ try: sktime_flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=FLAVOR_NAME) serialization_format = sktime_flavor_conf.get( "serialization_format", SERIALIZATION_FORMAT_PICKLE ) except MlflowException: _logger.warning( "Could not find sktime flavor configuration during model " "loading process. Assuming 'pickle' serialization format." ) serialization_format = SERIALIZATION_FORMAT_PICKLE pyfunc_flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=pyfunc.FLAVOR_NAME) path = os.path.join(path, pyfunc_flavor_conf["model_path"]) return _SktimeModelWrapper(_load_model(path, serialization_format=serialization_format)) class _SktimeModelWrapper: def __init__(self, sktime_model): self.sktime_model = sktime_model def predict(self, dataframe, params: dict[str, Any] | None = None) -> pd.DataFrame: df_schema = dataframe.columns.values.tolist() if len(dataframe) > 1: raise MlflowException( f"The provided prediction pd.DataFrame contains {len(dataframe)} rows. " "Only 1 row should be supplied.", error_code=INVALID_PARAMETER_VALUE, ) # Convert the configuration dataframe into a dictionary to simplify the # extraction of parameters passed to the sktime predcition methods. attrs = dataframe.to_dict(orient="index").get(0) predict_method = attrs.get("predict_method") if not predict_method: raise MlflowException( f"The provided prediction configuration pd.DataFrame columns ({df_schema}) do not " "contain the required column `predict_method` for specifying the prediction method.", error_code=INVALID_PARAMETER_VALUE, ) if predict_method not in SUPPORTED_SKTIME_PREDICT_METHODS: raise MlflowException( "Invalid `predict_method` value." f"The supported prediction methods are {SUPPORTED_SKTIME_PREDICT_METHODS}", error_code=INVALID_PARAMETER_VALUE, ) # For inference parameters 'fh', 'X', 'coverage', 'alpha', and 'cov' # the respective sktime default value is used if the value was not # provided in the configuration dataframe. fh = attrs.get("fh", None) # Any model that is trained with exogenous regressor elements will need # to provide `X` entries as a numpy ndarray to the predict method. X = attrs.get("X", None) # When the model is served via REST API the exogenous regressor must be # provided as a list to the configuration DataFrame to be JSON serializable. # Below we convert the list back to ndarray type as required by sktime # predict methods. if isinstance(X, list): X = np.array(X) # For illustration purposes only a subset of the available sktime prediction # methods is exposed. Additional methods (e.g. predict_proba) could be added # in a similar fashion. if predict_method == SKTIME_PREDICT: predictions = self.sktime_model.predict(fh=fh, X=X) if predict_method == SKTIME_PREDICT_INTERVAL: coverage = attrs.get("coverage", 0.9) predictions = self.sktime_model.predict_interval(fh=fh, X=X, coverage=coverage) if predict_method == SKTIME_PREDICT_QUANTILES: alpha = attrs.get("alpha", None) predictions = self.sktime_model.predict_quantiles(fh=fh, X=X, alpha=alpha) if predict_method == SKTIME_PREDICT_VAR: cov = attrs.get("cov", False) predictions = self.sktime_model.predict_var(fh=fh, X=X, cov=cov) # Methods predict_interval() and predict_quantiles() return a pandas # MultiIndex column structure. As MLflow signature inference does not # support MultiIndex column structure the columns must be flattened. if predict_method in [SKTIME_PREDICT_INTERVAL, SKTIME_PREDICT_QUANTILES]: predictions.columns = flatten_multiindex(predictions) return predictions