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.

Example 
import pandas as pd
import mlflow
import mlflow.pyfunc
import pmdarima
from pmdarima import auto_arima


# Define a custom model class
class PmdarimaWrapper(mlflow.pyfunc.PythonModel):
    def load_context(self, context):
        self.model = context.artifacts["model"]

    def predict(self, context, model_input):
        return self.model.predict(n_periods=model_input.shape[0])


# Specify locations of source data and the model artifact
SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
ARTIFACT_PATH = "model"

# Read data and recode columns
sales_data = pd.read_csv(SOURCE_DATA)
sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True)

# Split the data into train/test
train_size = int(0.8 * len(sales_data))
train, _ = sales_data[:train_size], sales_data[train_size:]

# Create the model
model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12)

# Log the model
with mlflow.start_run():
    wrapper = PmdarimaWrapper()
    mlflow.pyfunc.log_model(
        name="model",
        python_model=wrapper,
        artifacts={"model": mlflow.pyfunc.model_to_dict(model)},
    )
mlflow.pmdarima.get_default_conda_env()[source]
Returns

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

mlflow.pmdarima.get_default_pip_requirements()[source]
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]

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

Example 
import pandas as pd
import mlflow
from mlflow.models import infer_signature
import pmdarima
from pmdarima.metrics import smape

# Specify locations of source data and the model artifact
SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
ARTIFACT_PATH = "model"

# Read data and recode columns
sales_data = pd.read_csv(SOURCE_DATA)
sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True)

# Split the data into train/test
train_size = int(0.8 * len(sales_data))
train = sales_data[:train_size]
test = sales_data[train_size:]

with mlflow.start_run():
    # Create the model
    model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12)

    # Calculate metrics
    prediction = model.predict(n_periods=len(test))
    metrics = {"smape": smape(test["sales"], prediction)}

    # Infer signature
    input_sample = pd.DataFrame(train["sales"])
    output_sample = pd.DataFrame(model.predict(n_periods=5))
    signature = infer_signature(input_sample, output_sample)

    # Log model
    input_example = input_sample.head()
    model_info = mlflow.pmdarima.log_model(
        model, name=ARTIFACT_PATH, signature=signature, input_example=input_example
    )

# Load the model
loaded_model = mlflow.pmdarima.load_model(model_info.model_uri)
# Forecast for the next 60 days
forecast = loaded_model.predict(n_periods=60)
print(f"forecast: {forecast}")
Output 
forecast:
234    382452.397246
235    380639.458720
236    359805.611219
...
mlflow.pmdarima.log_model(pmdarima_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, **kwargs)[source]

Logs 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 – 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": [
                "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. 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 – 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 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 = 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 – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • 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.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

  • name – Model name.

  • params – A dictionary of parameters to log with the model.

  • tags – A dictionary of tags to log with the model.

  • model_type – The type of the model.

  • step – The step at which to log the model outputs and metrics

  • model_id – The ID of the model.

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

Returns

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

Example 
import pandas as pd
import mlflow
from mlflow.models import infer_signature
import pmdarima
from pmdarima.metrics import smape

# Specify locations of source data and the model artifact
SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
ARTIFACT_PATH = "model"

# Read data and recode columns
sales_data = pd.read_csv(SOURCE_DATA)
sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True)

# Split the data into train/test
train_size = int(0.8 * len(sales_data))
train = sales_data[:train_size]
test = sales_data[train_size:]

with mlflow.start_run():
    # Create the model
    model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12)

    # Calculate metrics
    prediction = model.predict(n_periods=len(test))
    metrics = {"smape": smape(test["sales"], prediction)}

    # Infer signature
    input_sample = pd.DataFrame(train["sales"])
    output_sample = pd.DataFrame(model.predict(n_periods=5))
    signature = infer_signature(input_sample, output_sample)

    # Log model
    mlflow.pmdarima.log_model(model, name=ARTIFACT_PATH, signature=signature)
mlflow.pmdarima.save_model(pmdarima_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 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 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": [
                "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. 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 – mlflow.models.Model this flavor is being added to.

  • 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 = 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 – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • 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.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

Example 
import pandas as pd
import mlflow
import pmdarima

# Specify locations of source data and the model artifact
SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
ARTIFACT_PATH = "model"

# Read data and recode columns
sales_data = pd.read_csv(SOURCE_DATA)
sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True)

# Split the data into train/test
train_size = int(0.8 * len(sales_data))
train = sales_data[:train_size]
test = sales_data[train_size:]

with mlflow.start_run():
    # Create the model
    model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12)

    # Save the model to the specified path
    mlflow.pmdarima.save_model(model, "model")