svm module¶
The Support Vector Machine algorithm for classification.
This module implements the SVMClassifier class. A support vector machine (SVM) passes the data through a kernel in order to increase its dimension and thereby make the classes linearly separable.
Dependence¶
The classifier relies on the SVC class of the scikit-learn library.
- class gemseo.mlearning.classification.svm.SVMClassifier(data, transformer=mappingproxy({}), input_names=None, output_names=None, C=1.0, kernel='rbf', probability=False, random_state=0, **parameters)[source]¶
Bases:
MLClassificationAlgo
The Support Vector Machine algorithm for classification.
- 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.C (float) –
The inverse L2 regularization parameter. Higher values give less regularization.
By default it is set to 1.0.
kernel (str | Callable | None) –
The name of the kernel or a callable for the SVM. Examples: “linear”, “poly”, “rbf”, “sigmoid”, “precomputed” or a callable.
By default it is set to “rbf”.
probability (bool) –
Whether to enable the probability estimates. The algorithm is faster if set to False.
By default it is set to False.
random_state (int | None) –
The random state passed to the random number generator. Use an integer for reproducible results.
By default it is set to 0.
**parameters (int | float | bool | str | None) – The parameters of the machine learning algorithm.
- Raises:
ValueError – When both the variable and the group it belongs to have a transformer.
- DataFormatters¶
alias of
SupervisedDataFormatters
- 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)¶
Predict output data 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.
- predict_proba(input_data, hard=True)¶
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.
- 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: Final[str] = 'scikit-learn'¶
The name of the library of the wrapped machine learning algorithm.
- SHORT_ALGO_NAME: ClassVar[str] = 'SVM'¶
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 learning_samples_indices: Sequence[int]¶
The indices of the learning samples used for the training.
- resampling_results: dict[str, tuple[Resampler, list[MLAlgo], list[ndarray] | ndarray]]¶
The resampler class names bound to the resampling results.
A resampling result is formatted as
(resampler, ml_algos, predictions)
whereresampler
is aResampler
,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, 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.