Supervised learning¶
This module contains the base class for the supervised machine learning algorithms.
Supervised machine learning is a task of learning relationships between input and output variables based on an input-output dataset. One usually distinguishes between two types of supervised machine learning algorithms, based on the nature of the outputs. For a continuous output variable, a regression is performed, while for a discrete output variable, a classification is performed.
Given a set of input variables \(x \in \mathbb{R}^{n_{\text{samples}}\times n_{\text{inputs}}}\) and a set of output variables \(y\in \mathbb{K}^{n_{\text{samples}}\times n_{\text{outputs}}}\), where \(n_{\text{inputs}}\) is the dimension of the input variable, \(n_{\text{outputs}}\) is the dimension of the output variable, \(n_{\text{samples}}\) is the number of training samples and \(\mathbb{K}\) is either \(\mathbb{R}\) or \(\mathbb{N}\) for regression and classification tasks respectively, a supervised learning algorithm seeks to find a function \(f: \mathbb{R}^{n_{\text{inputs}}} \to \mathbb{K}^{n_{\text{outputs}}}\) such that \(y=f(x)\).
In addition, we often want to impose some additional constraints on the function \(f\), mainly to ensure that it has a generalization capacity beyond the training data, i.e. it is able to correctly predict output values of new input values. This is called regularization. Assuming \(f\) is parametrized by a set of parameters \(\theta\), and denoting \(f_\theta\) the parametrized function, one typically seeks to minimize a function of the form
where \(\mu\) is a distance-like measure, typically a mean squared error, a cross entropy in the case of a regression, or a probability to be maximized in the case of a classification, and \(\Omega\) is a regularization term that limits the parameters from over-fitting, typically some norm of its argument.
The supervised
module implements this concept
through the MLSupervisedAlgo
class based on a IODataset
.
- class gemseo.mlearning.core.supervised.MLSupervisedAlgo(data, transformer=mappingproxy({}), input_names=None, output_names=None, **parameters)[source]
Supervised machine learning algorithm.
Inheriting classes shall overload the
MLSupervisedAlgo._fit()
andMLSupervisedAlgo._predict()
methods.- Parameters:
data (IODataset) – The learning dataset.
transformer (TransformerType) –
The strategies to transform the variables. The values are instances of
Transformer
while the keys are the names of either the variables or the groups of variables, e.g."inputs"
or"outputs"
in the case of the regression algorithms. If a group is specified, theTransformer
will be applied to all the variables of this group. IfIDENTITY
, do not transform the variables.By default it is set to {}.
input_names (Iterable[str] | None) – The names of the input variables. If
None
, consider all the input variables of the learning dataset.output_names (Iterable[str] | None) – The names of the output variables. If
None
, consider all the output variables of the learning dataset.**parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.
- Raises:
ValueError – When both the variable and the group it belongs to have a transformer.
- class DataFormatters[source]
Decorators for supervised algorithms.
- classmethod format_dict(predict)[source]
Make an array-based function be called with a dictionary of NumPy arrays.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function to be called; it takes a NumPy array in input and returns a NumPy array.
- Returns:
A function making the function ‘predict’ work with either a NumPy data array or a dictionary of NumPy data arrays indexed by variables names. The evaluation will have the same type as the input data.
- Return type:
Callable[[ndarray | Mapping[str, ndarray]], ndarray | Mapping[str, ndarray]]
- classmethod format_input_output(predict)[source]
Make a function robust to type, array shape and data transformation.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function of interest to be called.
- Returns:
A function calling the function of interest ‘predict’, while guaranteeing consistency in terms of data type and array shape, and applying input and/or output data transformation if required.
- Return type:
Callable[[ndarray | Mapping[str, ndarray]], ndarray | Mapping[str, ndarray]]
- classmethod format_samples(predict)[source]
Make a 2D NumPy array-based function work with 1D NumPy array.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function to be called; it takes a 2D NumPy array in input and returns a 2D NumPy array. The first dimension represents the samples while the second one represents the components of the variables.
- Returns:
A function making the function ‘predict’ work with either a 1D NumPy array or a 2D NumPy array. The evaluation will have the same dimension as the input data.
- Return type:
- classmethod format_transform(transform_inputs=True, transform_outputs=True)[source]
Force a function to transform its input and/or output variables.
- Parameters:
- Returns:
A function evaluating a function of interest, after transforming its input data and/or before transforming its output data.
- Return type:
- learn(samples=None, fit_transformers=True)
Train the machine learning algorithm from the learning dataset.
- load_algo(directory)
Load a machine learning algorithm from a directory.
- Parameters:
directory (str | Path) – The path to the directory where the machine learning algorithm is saved.
- Return type:
None
- predict(input_data, *args, **kwargs)[source]
Evaluate ‘predict’ with either array or dictionary-based input data.
Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.
Then, the processing evaluates the function ‘predict’ from this NumPy input data array.
Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.
- Parameters:
- Returns:
The output data with the same type as the input one.
- Return type:
- to_pickle(directory=None, path='.', save_learning_set=False)
Save the machine learning algorithm.
- Parameters:
directory (str | None) – The name of the directory to save the algorithm.
path (str | Path) –
The path to parent directory where to create the directory.
By default it is set to “.”.
save_learning_set (bool) –
Whether to save the learning set or get rid of it to lighten the saved files.
By default it is set to False.
- Returns:
The path to the directory where the algorithm is saved.
- Return type:
- DEFAULT_TRANSFORMER: DefaultTransformerType = mappingproxy({'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>})
The default transformer for the input and output data, if any.
- IDENTITY: Final[DefaultTransformerType] = mappingproxy({})
A transformer leaving the input and output variables as they are.
- LIBRARY: ClassVar[str] = ''
The name of the library of the wrapped machine learning algorithm.
- SHORT_ALGO_NAME: ClassVar[str] = 'MLSupervisedAlgo'
The short name of the machine learning algorithm, often an acronym.
Typically used for composite names, e.g.
f"{algo.SHORT_ALGO_NAME}_{dataset.name}"
orf"{algo.SHORT_ALGO_NAME}_{discipline.name}"
.
- algo: Any
The interfaced machine learning algorithm.
- property input_data: ndarray
The input data matrix.
- property input_dimension: int
The input space dimension.
- property is_trained: bool
Return whether the algorithm is trained.
- property learning_samples_indices: Sequence[int]
The indices of the learning samples used for the training.
- learning_set: IODataset
The learning dataset.
- property output_data: ndarray
The output data matrix.
- property output_dimension: int
The output space dimension.
- transformer: dict[str, Transformer]
The strategies to transform the variables, if any.
The values are instances of
Transformer
while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, theTransformer
will be applied to all the variables of this group.