# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# :author: Syver Doving Agdestein
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Machine learning API
--------------------
The machine learning API provides methods for creating new and loading
existing machine learning models. It also provides methods for listing
available models and options.
"""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Mapping
from gemseo.api import _get_schema
from gemseo.core.dataset import Dataset
from gemseo.mlearning.classification.classification import MLClassificationAlgo
from gemseo.mlearning.cluster.cluster import MLClusteringAlgo
from gemseo.mlearning.core.ml_algo import MLAlgo
from gemseo.mlearning.core.ml_algo import TransformerType
from gemseo.mlearning.core.supervised import MLSupervisedAlgo
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.mlearning.transform.scaler.min_max_scaler import MinMaxScaler
LOGGER = logging.getLogger(__name__)
# pylint: disable=import-outside-toplevel
[docs]def get_mlearning_models() -> list[str]:
"""Get available machine learning algorithms.
Returns:
The available machine learning algorithms.
See also
--------
import_mlearning_model
create_mlearning_model
get_mlearning_options
import_mlearning_model
"""
from gemseo.mlearning.core.factory import MLAlgoFactory
factory = MLAlgoFactory()
return factory.models
[docs]def get_regression_models() -> list[str]:
"""Get available regression models.
Returns:
The available regression models.
See also
--------
create_regression_model
get_regression_options
import_regression_model
"""
from gemseo.mlearning.regression.factory import RegressionModelFactory
factory = RegressionModelFactory()
return factory.models
[docs]def get_classification_models() -> list[str]:
"""Get available classification models.
Returns:
The available classification models.
See also
--------
create_classification_model
get_classification_options
import_classification_model
"""
from gemseo.mlearning.classification.factory import ClassificationModelFactory
factory = ClassificationModelFactory()
return factory.models
[docs]def get_clustering_models() -> list[str]:
"""Get available clustering models.
Returns:
The available clustering models.
See also
--------
create_clustering_model
get_clustering_options
import_clustering_model
"""
from gemseo.mlearning.cluster.factory import ClusteringModelFactory
factory = ClusteringModelFactory()
return factory.models
[docs]def create_mlearning_model(
name: str,
data: Dataset,
transformer: Mapping[str, TransformerType] | None = None,
**parameters,
) -> MLAlgo:
"""Create a machine learning algorithm from a learning dataset.
Args:
name: The name of the machine learning algorithm.
data: The learning dataset.
transformer: The strategies to transform the variables.
Values are instances of :class:`.Transformer`
while keys are names of either variables or groups of variables.
If None, do not transform the variables.
parameters: The parameters of the machine learning algorithm.
Returns:
A machine learning model.
See also
--------
get_mlearning_models
get_mlearning_options
import_mlearning_model
"""
from gemseo.mlearning.core.factory import MLAlgoFactory
factory = MLAlgoFactory()
return factory.create(name, data=data, transformer=transformer, **parameters)
minmax_inputs = {Dataset.INPUT_GROUP: MinMaxScaler()}
[docs]def create_regression_model(
name: str,
data: Dataset,
transformer: Mapping[str, TransformerType]
| None = MLRegressionAlgo.DEFAULT_TRANSFORMER, # noqa: B950
**parameters,
) -> MLRegressionAlgo:
"""Create a regression model from a learning dataset.
Args:
name: The name of the regression algorithm.
data: The learning dataset.
transformer: The strategies to transform the variables.
Values are instances of :class:`.Transformer`
while keys are names of either variables or groups of variables.
If None, do not transform the variables.
parameters: The parameters of the regression model.
Returns:
A regression model.
See also
--------
get_regression_models
get_regression_options
import_regression_model
"""
from gemseo.mlearning.regression.factory import RegressionModelFactory
factory = RegressionModelFactory()
if (
name == "PCERegressor"
and isinstance(transformer, dict)
and Dataset.INPUT_GROUP in transformer
):
LOGGER.warning(
"Remove input data transformation because "
"PCERegressor does not support transformers."
)
del transformer[Dataset.INPUT_GROUP]
return factory.create(name, data=data, transformer=transformer, **parameters)
[docs]def create_classification_model(
name: str,
data: Dataset,
transformer: Mapping[str, TransformerType]
| None = MLSupervisedAlgo.DEFAULT_TRANSFORMER, # noqa: B950
**parameters,
) -> MLClassificationAlgo:
"""Create a classification model from a learning dataset.
Args:
name: The name of the classification algorithm.
data: The learning dataset.
transformer: The strategies to transform the variables.
Values are instances of :class:`.Transformer`
while keys are names of either variables or groups of variables.
If None, do not transform the variables.
parameters: The parameters of the classification model.
Returns:
A classification model.
See also
--------
get_classification_models
get_classification_options
import_classification_model
"""
from gemseo.mlearning.classification.factory import ClassificationModelFactory
factory = ClassificationModelFactory()
return factory.create(name, data=data, transformer=transformer, **parameters)
[docs]def create_clustering_model(
name: str,
data: Dataset,
transformer: Mapping[str, TransformerType] | None = None,
**parameters,
) -> MLClusteringAlgo:
"""Create a clustering model from a learning dataset.
Args:
name: The name of the clustering algorithm.
data: The learning dataset.
transformer: The strategies to transform the variables.
Values are instances of :class:`.Transformer`
while keys are names of either variables or groups of variables.
If None, do not transform the variables.
parameters: The parameters of the clustering model.
Returns:
A clustering model.
See also
--------
get_clustering_models
get_clustering_options
import_clustering_model
"""
from gemseo.mlearning.cluster.factory import ClusteringModelFactory
factory = ClusteringModelFactory()
return factory.create(name, data=data, transformer=transformer, **parameters)
[docs]def import_mlearning_model(
directory: str | Path,
) -> MLAlgo:
"""Import a machine learning algorithm from a directory.
Args:
directory: The path to the directory.
Returns:
A machine learning model.
See also
--------
create_mlearning_model
get_mlearning_models
get_mlearning_options
"""
from gemseo.mlearning.core.factory import MLAlgoFactory
factory = MLAlgoFactory()
return factory.load(directory)
[docs]def import_regression_model(
directory: str | Path,
) -> MLRegressionAlgo:
"""Import a regression model from a directory.
Args:
directory: The path of the directory.
Returns:
A regression model.
See also
--------
create_regression_model
get_regression_models
get_regression_options
"""
from gemseo.mlearning.regression.factory import RegressionModelFactory
factory = RegressionModelFactory()
return factory.load(directory)
[docs]def import_classification_model(
directory: str | Path,
) -> MLClassificationAlgo:
"""Import a classification model from a directory.
Args:
directory: The path to the directory.
Returns:
A classification model.
See also
--------
create_classification_model
get_classification_models
get_classification_options
"""
from gemseo.mlearning.classification.factory import ClassificationModelFactory
factory = ClassificationModelFactory()
return factory.load(directory)
[docs]def import_clustering_model(
directory: str | Path,
) -> MLClusteringAlgo:
"""Import a clustering model from a directory.
Args:
directory: The path to the directory.
Returns:
A clustering model.
See also
--------
create_clustering_model
get_clustering_models
get_clustering_options
"""
from gemseo.mlearning.cluster.factory import ClusteringModelFactory
factory = ClusteringModelFactory()
return factory.load(directory)
[docs]def get_mlearning_options(
model_name: str,
output_json: bool = False,
pretty_print: bool = True,
) -> dict[str, str] | str:
"""Find the available options for a machine learning algorithm.
Args:
model_name: The name of the machine learning algorithm.
output_json: Whether to apply JSON format for the schema.
pretty_print: Whether to print the schema in a pretty table.
Returns:
The options schema of the machine learning algorithm.
See also
--------
create_mlearning_model
get_mlearning_models
import_mlearning_model
"""
from gemseo.mlearning.core.factory import MLAlgoFactory
factory = MLAlgoFactory().factory
grammar = factory.get_options_grammar(model_name)
return _get_schema(grammar, output_json, pretty_print)
[docs]def get_regression_options(
model_name: str,
output_json: bool = False,
pretty_print: bool = True,
) -> dict[str, str] | str:
"""Find the available options for a regression model.
Args:
model_name: The name of the regression model.
output_json: Whether to apply JSON format for the schema.
pretty_print: Print the schema in a pretty table.
Returns:
The options schema of the regression model.
See also
--------
create_regression_model
get_regression_models
import_regression_model
"""
from gemseo.mlearning.regression.factory import RegressionModelFactory
factory = RegressionModelFactory().factory
grammar = factory.get_options_grammar(model_name)
return _get_schema(grammar, output_json, pretty_print)
[docs]def get_classification_options(
model_name: str,
output_json: bool = False,
pretty_print: bool = True,
) -> dict[str, str] | str:
"""Find the available options for a classification model.
Args:
model_name: The name of the classification model.
output_json: Whether to apply JSON format for the schema.
pretty_print: Print the schema in a pretty table.
Returns:
The options schema of the classification model.
See also
--------
create_classification_model
get_classification_models
import_classification_model
"""
from gemseo.mlearning.classification.factory import ClassificationModelFactory
factory = ClassificationModelFactory().factory
grammar = factory.get_options_grammar(model_name)
return _get_schema(grammar, output_json, pretty_print)
[docs]def get_clustering_options(
model_name: str,
output_json: bool = False,
pretty_print: bool = True,
) -> dict[str, str] | str:
"""Find the available options for clustering model.
Args:
model_name: The name of the clustering model.
output_json: Whether to apply JSON format for the schema.
pretty_print: Print the schema in a pretty table.
Returns:
The options schema of the clustering model.
See also
--------
create_clustering_model
get_clustering_models
import_clustering_model
"""
from gemseo.mlearning.cluster.factory import ClusteringModelFactory
factory = ClusteringModelFactory().factory
grammar = factory.get_options_grammar(model_name)
return _get_schema(grammar, output_json, pretty_print)
