Classification

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.

Classes:

MLClassificationAlgo(data[, transformer, ...])

Classification Algorithm.

class gemseo.mlearning.classification.classification.MLClassificationAlgo(data, transformer=None, input_names=None, output_names=None, **parameters)

Classification Algorithm.

Inheriting classes shall implement the MLSupervisedAlgo._fit() and MLClassificationAlgo._predict() methods, and MLClassificationAlgo._predict_proba_soft() method if possible.

learning_set

The learning dataset.

Type

Dataset

parameters

The parameters of the machine learning algorithm.

Type

Dict[str,MLAlgoParameterType]

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, the Transformer will be applied to all the variables of this group. If None, do not transform the variables.

Type

Dict[str,Transformer]

algo

The interfaced machine learning algorithm.

Type

Any

input_names

The names of the input variables.

Type

List[str]

input_space_center

The center of the input space.

Type

Dict[str, ndarray]

output_names

The names of the output variables.

Type

List[str]

n_classes

The number of classes.

Type

int

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, the Transformer 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

Classes:

DataFormatters()

Decorators for supervised algorithms.

Attributes:

input_data	The input data matrix.
input_dimension	The input space dimension.
is_trained	Return whether the algorithm is trained.
learning_samples_indices	The indices of the learning samples used for the training.
output_data	The output data matrix.
output_dimension	The output space dimension.

Methods:

learn([samples, fit_transformers])	Train the machine learning algorithm from the learning dataset.
load_algo(directory)	Load a machine learning algorithm from a directory.
predict(input_data, *args, **kwargs)	Evaluate 'predict' with either array or dictionary-based input data.
predict_proba(input_data, *args, **kwargs)	Evaluate 'predict' with either array or dictionary-based input data.
save([directory, path, save_learning_set])	Save the machine learning algorithm.

class DataFormatters

Decorators for supervised algorithms.

Methods:

format_dict(predict)	Make an array-based function be called with a dictionary of NumPy arrays.
format_input_output(predict)	Make a function robust to type, array shape and data transformation.
format_samples(predict)	Make a 2D NumPy array-based function work with 1D NumPy array.
format_transform([transform_inputs, ...])	Force a function to transform its input and/or output variables.

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

• transform_inputs (bool) –

  Whether to transform the input variables.

  By default it is set to True.

• transform_outputs (bool) –

  Whether to transform the output variables.

  By default it is set to True.

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]

property input_data: numpy.ndarray

The input data matrix.

property input_dimension: int

The input space dimension.

property is_trained: bool

Return whether the algorithm is trained.

learn(samples=None, fit_transformers=True)

Train the machine learning algorithm from the learning dataset.

Parameters

• samples (Sequence[int] | None) –

  The indices of the learning samples. If None, use the whole learning dataset.

  By default it is set to None.

• fit_transformers (bool) –

  Whether to fit the variable transformers.

  By default it is set to True.

Return type

None

property learning_samples_indices: Sequence[int]

The indices of the learning samples used for the training.

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

property output_data: numpy.ndarray

The output data matrix.

property output_dimension: int

The output space dimension.

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

str

Available classification models are:

• Dependence
• Dependence
• Dependence

Examples

Development