gemseo / mlearning / core

ml_algo module

This module contains the base class for machine learning algorithms.

Machine learning is the art of building models from data, the latter being samples of properties of interest that can sometimes be sorted by group, such as inputs, outputs, categories, …

In the absence of such groups, the data can be analyzed through a study of commonalities, leading to plausible clusters. This is referred to as clustering, a branch of unsupervised learning dedicated to the detection of patterns in unlabeled data.

See also

unsupervised, cluster

When data can be separated into at least two categories by a human, supervised learning can start with classification whose purpose is to model the relations between these categories and the properties of interest. Once trained, a classification model can predict the category corresponding to new property values.

See also

supervised, classification

When the distinction between inputs and outputs can be made among the data properties, another branch of supervised learning can be considered: regression modeling. Once trained, a regression model can predict the outputs corresponding to new inputs values.

See also

supervised, regression

The quality of a machine learning algorithm can be measured using a MLQualityMeasure either with respect to the learning dataset or to a test dataset or using resampling methods, such as K-folds or leave-one-out cross-validation techniques. The challenge is to avoid over-learning the learning data leading to a loss of generality. We often want to build models that are not too dataset-dependent. For that, we want to maximize both a learning quality and a generalization quality. In unsupervised learning, a quality measure can represent the robustness of clusters definition while in supervised learning, a quality measure can be interpreted as an error, whether it is a misclassification in the case of the classification algorithms or a prediction one in the case of the regression algorithms. This quality can often be improved by building machine learning models from standardized data in such a way that the data properties have the same order of magnitude.

See also

quality_measure, transformer

Lastly, a machine learning algorithm often depends on hyperparameters to be carefully tuned in order to maximize the generalization power of the model.

See also

calibration selection

Classes:

MLAlgo(data[, transformer])

An abstract machine learning algorithm.

class gemseo.mlearning.core.ml_algo.MLAlgo(data, transformer=None, **parameters)

Bases: object

An abstract machine learning algorithm.

Such a model is built from a training dataset, data transformation options and parameters. This abstract class defines the MLAlgo.learn(), MLAlgo.save() methods and the boolean property, MLAlgo.is_trained. It also offers a string representation for end users. Derived classes shall overload the MLAlgo.learn(), MLAlgo._save_algo() and MLAlgo._load_algo() methods.

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

Initialize self. See help(type(self)) for accurate signature.

Parameters

• data (Dataset) – The learning dataset.

• transformer (Optional[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, 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.

• **parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.

Return type

None

Attributes:

ABBR
FILENAME
LIBRARY
is_trained	Return whether the algorithm is trained.
learning_samples_indices	The indices of the learning samples used for the training.

Classes:

DataFormatters()

Decorators for the internal MLAlgo methods.

Methods:

learn([samples])	Train the machine learning algorithm from the learning dataset.
load_algo(directory)	Load a machine learning algorithm from a directory.
save([directory, path, save_learning_set])	Save the machine learning algorithm.

ABBR = 'MLAlgo'

class DataFormatters

Bases: object

Decorators for the internal MLAlgo methods.

FILENAME = 'ml_algo.pkl'

LIBRARY = None

property is_trained

Return whether the algorithm is trained.

learn(samples=None)

Train the machine learning algorithm from the learning dataset.

Parameters

samples (Optional[Sequence[int]]) –

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

By default it is set to None.

Return type

None

property learning_samples_indices

The indices of the learning samples used for the training.

load_algo(directory)

Load a machine learning algorithm from a directory.

Parameters

directory (Union[str, pathlib.Path]) – The path to the directory where the machine learning algorithm is saved.

Return type

None

save(directory=None, path='.', save_learning_set=False)

Save the machine learning algorithm.

Parameters

• directory (Optional[str]) –

  The name of the directory to save the algorithm.

  By default it is set to None.

• path (Union[str, pathlib.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