cluster_measure module¶
Here is the baseclass to measure the quality of machine learning algorithms.
The concept of clustering quality measure is implemented with the
MLClusteringMeasure
class and proposes different evaluation methods.
- class gemseo.mlearning.quality_measures.cluster_measure.MLClusteringMeasure(algo, fit_transformers=True)[source]
Bases:
MLQualityMeasure
An abstract clustering measure for clustering algorithms.
- Parameters:
algo (MLClusteringAlgo) – A machine learning algorithm for clustering.
fit_transformers (bool) –
Whether to re-fit the transformers when using resampling techniques. If
False
, use the transformers of the algorithm fitted from the whole learning dataset.By default it is set to True.
- compute_learning_measure(samples=None, multioutput=True)[source]
Evaluate the quality measure from the learning dataset.
- Parameters:
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters:
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- algo: MLAlgo
The machine learning algorithm usually trained.
- class gemseo.mlearning.quality_measures.cluster_measure.MLPredictiveClusteringMeasure(algo, fit_transformers=True)[source]
Bases:
MLClusteringMeasure
An abstract clustering measure for predictive clustering algorithms.
- Parameters:
algo (MLPredictiveClusteringAlgo) – A machine learning algorithm for predictive clustering.
fit_transformers (bool) –
Whether to re-fit the transformers when using resampling techniques. If
False
, use the transformers of the algorithm fitted from the whole learning dataset.By default it is set to True.
- compute_bootstrap_measure(n_replicates=100, samples=None, multioutput=True, seed=None)[source]
Evaluate the quality measure using the bootstrap technique.
- Parameters:
n_replicates (int) –
The number of bootstrap replicates.
By default it is set to 100.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
seed (int | None) – The seed of the pseudo-random number generator. If
None
, an unpredictable generator will be used.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- compute_cross_validation_measure(n_folds=5, samples=None, multioutput=True, randomize=True, seed=None)[source]
Evaluate the quality measure using the k-folds technique.
- Parameters:
n_folds (int) –
The number of folds.
By default it is set to 5.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
randomize (bool) –
Whether to shuffle the samples before dividing them in folds.
By default it is set to True.
seed (int | None) – The seed of the pseudo-random number generator. If
None
, an unpredictable generator is used.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- compute_test_measure(test_data, samples=None, multioutput=True)[source]
Evaluate the quality measure using a test dataset.
- Parameters:
test_data (Dataset) – The test dataset.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- evaluate_bootstrap(n_replicates=100, samples=None, multioutput=True, seed=None)
Evaluate the quality measure using the bootstrap technique.
- Parameters:
n_replicates (int) –
The number of bootstrap replicates.
By default it is set to 100.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
seed (int | None) – The seed of the pseudo-random number generator. If
None
, an unpredictable generator will be used.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=True, seed=None)
Evaluate the quality measure using the k-folds technique.
- Parameters:
n_folds (int) –
The number of folds.
By default it is set to 5.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
randomize (bool) –
Whether to shuffle the samples before dividing them in folds.
By default it is set to True.
seed (int | None) – The seed of the pseudo-random number generator. If
None
, an unpredictable generator is used.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- evaluate_test(test_data, samples=None, multioutput=True)
Evaluate the quality measure using a test dataset.
- Parameters:
test_data (Dataset) – The test dataset.
samples (Sequence[int] | None) – The indices of the learning samples. If
None
, use the whole learning dataset.multioutput (bool) –
Whether the quality measure is returned for each component of the outputs. Otherwise, the average quality measure.
By default it is set to True.
- Returns:
The value of the quality measure.
- Return type:
MeasureType
- algo: MLAlgo
The machine learning algorithm usually trained.