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 BaseMLClassificationAlgo
class
which inherits from the BaseMLSupervisedAlgo
class.
- class gemseo.mlearning.classification.classification.BaseMLClassificationAlgo(data, transformer=mappingproxy({}), input_names=None, output_names=None, **parameters)[source]
Bases:
BaseMLSupervisedAlgo
Classification Algorithm.
Inheriting classes shall implement the
BaseMLSupervisedAlgo._fit()
andBaseMLClassificationAlgo._predict()
methods, andBaseMLClassificationAlgo._predict_proba_soft()
method if possible.- Parameters:
data (IODataset) – The learning dataset.
transformer (TransformerType) –
The strategies to transform the variables. The values are instances of
BaseTransformer
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, theBaseTransformer
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.
- predict_proba(input_data, hard=True)[source]
Predict the probability of belonging to each cluster from input data.
The user can specify these input data either as a numpy array, e.g.
array([1., 2., 3.])
or as a dictionary, e.g.{'a': array([1.]), 'b': array([2., 3.])}
.If the numpy arrays are of dimension 2, their i-th rows represent the input data of the i-th sample; while if the numpy arrays are of dimension 1, there is a single sample.
The type of the output data and the dimension of the output arrays will be consistent with the type of the input data and the size of the input arrays.
- algo: Any
The interfaced machine learning algorithm.
- learning_set: Dataset
The learning dataset.
- n_classes: int
The number of classes.
- resampling_results: dict[str, tuple[BaseResampler, list[BaseMLAlgo], list[ndarray] | ndarray]]
The resampler class names bound to the resampling results.
A resampling result is formatted as
(resampler, ml_algos, predictions)
whereresampler
is aBaseResampler
,ml_algos
is the list of the associated machine learning algorithms built during the resampling stage andpredictions
are the predictions obtained with the latter.resampling_results
stores only one resampling result per resampler type (e.g.,"CrossValidation"
,"LeaveOneOut"
and"Boostrap"
).
- transformer: dict[str, BaseTransformer]
The strategies to transform the variables, if any.
The values are instances of
BaseTransformer
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, theBaseTransformer
will be applied to all the variables of this group.