Prediction

Prediction

To use the model, we will reload the model from the artifacts saved in the training step. It's very easy to reload the model, e.g., ModelSet in the following example.

import os

import click
import joblib
import pandas as pd
from dotenv import load_dotenv
from haferml.blend.config import Config
from haferml.model.pipeline import ModelSetX
from haferml.sync.local import prepare_folders
from loguru import logger

load_dotenv()


class ModelSet(ModelSetX):
    """
    The core of the model including hyperparameters
    """

    def __init__(self, config, base_folder):
        super(ModelSet, self).__init__(config, base_folder)

    def reload(self):

        model_folder = self.artifacts["model"]["local_absolute"]

        logger.info("Reload models")
        self.model = joblib.load(
            os.path.join(
                self.base_folder,
                model_folder,
                self.artifacts["model"]["name"],
            )
        )

    def predict(self, data):

        return self.model.predict(data)


@click.command()
@click.option(
    "-c",
    "--config",
    type=str,
    default=os.getenv("CONFIG_FILE"),
    help="Path to config file",
)
def predict(config):

    base_folder = os.getenv("BASE_FOLDER")
    logger.debug(f"base folder is: {base_folder}")
    logger.debug(f"config: {config}")

    _CONFIG = Config(config, base_folder=base_folder)
    preprocessed_data_config = _CONFIG[["preprocessing", "dataset", "preprocessed"]]
    rf_config = _CONFIG[["model", "rf"]]

    # create folders
    prepare_folders(_CONFIG[["model", "rf", "artifacts", "model", "local"]], base_folder=base_folder)

    # load some data
    dataset_folder = _CONFIG[["model", "rf", "artifacts", "dataset", "local_absolute"]]
    df = pd.read_parquet(
        os.path.join(dataset_folder, "model_X_test.parquet")
    ).sample(1)

    # model
    logger.debug(f"Prepare modelset and dataset")
    M = ModelSet(config=rf_config, base_folder=base_folder)
    M.reload()
    logger.info(
        M.predict(df)
    )


if __name__ == "__main__":
    predict()