mlem.api.apply()

Apply provided model against provided data

def apply(
    model: Union[str, MlemModel],
    *data: Union[str, MlemData, Any],
    method: str = None,
    output: str = None,
    target_project: str = None,
    index: bool = None,
    external: bool = None,
    batch_size: Optional[int] = None,
) -> Optional[Any]

Usage:

from mlem.api import apply

y_pred = apply("rf", "data", method="predict_proba")

Description

This API is the underlying mechanism for the mlem apply command and facilitates running inferences on entire datasets. The API applies i.e. calls a model's method (eg: predict) and returns the output (as a MLEM object) while also saving it if required.

Parameters

• model (required) - MLEM model (a MlemModel object).
• data (required) - Input to the model.
• method (optional) - Which model method to use. If None, use the only method model has. If more than one is available, will fail.
• output (optional) - If value is provided, assume its path and save output there.
• target_project (optional) - The path to project to save the results to.
• index (optional) - Whether to index saved output in MLEM root folder.
• external (optional) - Whether to save result outside mlem dir.
• batch_size (optional) - If data is to be loaded and applied in batches.

Exceptions

• WrongMethodError - Thrown if wrong method name for model is provided
• NotImplementedError - Saving several input data objects is not implemented yet

Examples

from numpy import ndarray
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from mlem.core.objects import MlemData, MlemModel
from mlem.api import apply

train, target = load_iris(return_X_y=True)
model = DecisionTreeClassifier().fit(train, target)
d = MlemData.from_data(train)
m = MlemModel.from_obj(model)
res = apply(m, d, method="predict")
assert isinstance(res, ndarray)