classification module¶
This module contains the base class for classification algorithms.
The classification
module
implements classification algorithms,
whose goal is to assess the membership of input data to classes.
Classification algorithms provide methods for predicting classes of new input data, as well as predicting the probabilities of belonging to each of the classes wherever possible.
This concept is implemented through the MLClassificationAlgo
class
which inherits from the MLSupervisedAlgo
class.
- class gemseo.mlearning.classification.classification.MLClassificationAlgo(data, transformer=None, input_names=None, output_names=None, **parameters)[source]¶
Bases:
gemseo.mlearning.core.supervised.MLSupervisedAlgo
Classification Algorithm.
Inheriting classes shall implement the
MLSupervisedAlgo._fit()
andMLClassificationAlgo._predict()
methods, andMLClassificationAlgo._predict_proba_soft()
method if possible.- Parameters
data (Dataset) – The learning dataset.
transformer (Mapping[str, TransformerType] | None) –
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. If None, do not transform the variables.By default it is set to None.
input_names (Iterable[str] | None) –
The names of the input variables. If
None
, consider all the input variables of the learning dataset.By default it is set to None.
output_names (Iterable[str] | None) –
The names of the output variables. If
None
, consider all the output variables of the learning dataset.By default it is set to None.
**parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.
- Raises
ValueError – When both the variable and the group it belongs to have a transformer.
- Return type
None
- class DataFormatters¶
Bases:
gemseo.mlearning.core.ml_algo.MLAlgo.DataFormatters
Decorators for supervised algorithms.
- classmethod format_dict(predict)¶
Make an array-based function be called with a dictionary of NumPy arrays.
- Parameters
predict (Callable[[numpy.ndarray], numpy.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[[Union[numpy.ndarray, Mapping[str, numpy.ndarray]]], Union[numpy.ndarray, Mapping[str, numpy.ndarray]]]
- classmethod format_input_output(predict)¶
Make a function robust to type, array shape and data transformation.
- Parameters
predict (Callable[[numpy.ndarray], numpy.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[[Union[numpy.ndarray, Mapping[str, numpy.ndarray]]], Union[numpy.ndarray, Mapping[str, numpy.ndarray]]]
- classmethod format_samples(predict)¶
Make a 2D NumPy array-based function work with 1D NumPy array.
- Parameters
predict (Callable[[numpy.ndarray], numpy.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
Callable[[numpy.ndarray], numpy.ndarray]
- classmethod format_transform(transform_inputs=True, transform_outputs=True)¶
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
Callable[[numpy.ndarray], numpy.ndarray]
- 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)¶
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
input_data (Union[numpy.ndarray, Mapping[str, numpy.ndarray]]) – The input data.
*args – The positional arguments of the function ‘predict’.
**kwargs – The keyword arguments of the function ‘predict’.
- Returns
The output data with the same type as the input one.
- Return type
Union[numpy.ndarray, Mapping[str, numpy.ndarray]]
- predict_proba(input_data, *args, **kwargs)¶
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
input_data (Union[numpy.ndarray, Mapping[str, numpy.ndarray]]) – The input data.
*args – The positional arguments of the function ‘predict’.
**kwargs – The keyword arguments of the function ‘predict’.
- Returns
The output data with the same type as the input one.
- Return type
Union[numpy.ndarray, Mapping[str, numpy.ndarray]]
- save(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.
By default it is set to None.
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: Final[dict[str, Transformer]] = {'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>}¶
- 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: numpy.ndarray¶
The input data matrix.
- property learning_samples_indices: Sequence[int]¶
The indices of the learning samples used for the training.
- property output_data: numpy.ndarray¶
The output data matrix.
- transformer: dict[str, Transformer]¶
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. If None, do not transform the variables.