Source code for monai.auto3dseg.algo_gen

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from monai.config import PathLike
from monai.transforms import Randomizable




class Algo:
    """
    An algorithm in this context is loosely defined as a data processing pipeline consisting of multiple components
    such as image preprocessing, followed by deep learning model training and evaluation.
    """

    template_path: PathLike | None = None



    def set_data_stats(self, *args, **kwargs):
        """Provide dataset (and summaries) so that the model creation can depend on the input datasets."""
        pass




    def train(self, *args, **kwargs):
        """Read training/validation data and output a model."""
        pass




    def predict(self, *args, **kwargs):
        """Read test data and output model predictions."""
        pass




    def get_score(self, *args, **kwargs):
        """Returns the model quality measurement based on training and validation datasets."""
        pass




    def get_output_path(self, *args, **kwargs):
        """Returns the algo output paths for scripts location"""
        pass






class AlgoGen(Randomizable):
    """
    A data-driven algorithm generator. It optionally takes the following inputs:

        - training dataset properties (such as data statistics from ``monai.auto3dseg.analyzer``),
        - previous algorithm's scores measuring the model quality,
        - computational budgets,

    and generates ``Algo`` instances. The generated algos are to be trained with the training datasets::

                                  scores
                        +------------------------+
                        |   +---------+          |
        +-----------+   +-->|         |    +-----+----+
        | Dataset,  |       | AlgoGen |--->|   Algo   |
        | summaries |------>|         |    +----------+
        +-----+-----+       +---------+          ^
              |                                  |
              +----------------------------------+

    This class also maintains a history of previously generated Algo and their corresponding validation scores.
    The Algo generation process may be stochastic (using ``Randomizable.R`` as the source random state).
    """



    def set_data_stats(self, *args, **kwargs):  # type ignore
        """Provide dataset summaries/properties so that the generator can be conditioned on the input datasets."""
        pass




    def set_budget(self, *args, **kwargs):
        """Provide computational budget so that the generator outputs algorithms that requires reasonable resources."""
        pass




    def set_score(self, *args, **kwargs):
        """Feedback from the previously generated algo, the score can be used for new Algo generations."""
        pass




    def get_data_stats(self, *args, **kwargs):
        """Get current dataset summaries."""
        pass




    def get_budget(self, *args, **kwargs):
        """Get the current computational budget."""
        pass




    def get_history(self, *args, **kwargs):
        """Get the previously generated algo."""
        pass




    def generate(self):
        """Generate new Algo -- based on data_stats, budget, and history of previous algo generations."""
        pass




    def run_algo(self, *args, **kwargs):
        """
        Launch the Algos. This is useful for light-weight Algos where there's no need to distribute the training jobs.

        If the generated Algos require significant scheduling of parallel executions, a job scheduler/controller
        implemented separately is preferred to run them. In this case the controller should also report back the
        scores and the algo history, so that the future ``AlgoGen.generate`` can leverage the information.
        """
        pass