Measure the quality of a machine learning algorithm¶
Measuring the quality of a machine learning algorithm.
- class gemseo.mlearning.qual_measure.quality_measure.MLQualityMeasure(algo, fit_transformers=False)[source]
An abstract quality measure to assess a machine learning algorithm.
This measure can be minimized (e.g.
MSEMeasure
) or maximized (e.g.R2Measure
).It can be evaluated from the learning dataset, from a test dataset or using resampling techniques such as boostrap, cross-validation or leave-one-out.
The machine learning algorithm is usually trained. If not but required by the evaluation technique, the quality measure will train it.
Lastly, the transformers of the algorithm fitted from the learning dataset can be used as is by the resampling methods or re-fitted for each algorithm trained on a subset of the learning dataset.
- Parameters
algo (gemseo.mlearning.core.ml_algo.MLAlgo) – A machine learning algorithm.
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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)[source]
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- evaluate_bootstrap(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_learn(samples=None, multioutput=True)[source]
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_loo(samples=None, multioutput=True)[source]
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_test(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
NoReturn
- classmethod is_better(val1, val2)[source]
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
- BOOTSTRAP: ClassVar[str] = 'bootstrap'
The name of the method to evaluate the measure by bootstrap.
- KFOLDS: ClassVar[str] = 'kfolds'
The name of the method to evaluate the measure by cross-validation.
- LEARN: ClassVar[str] = 'learn'
The name of the method to evaluate the measure on the learning dataset.
- LOO: ClassVar[str] = 'loo'
The name of the method to evaluate the measure by leave-one-out.
- SMALLER_IS_BETTER: ClassVar[bool] = True
Whether to minimize or maximize the measure.
- TEST: ClassVar[str] = 'test'
The name of the method to evaluate the measure on a test dataset.
- algo: gemseo.mlearning.core.ml_algo.MLAlgo
The machine learning algorithm usually trained.
- class gemseo.mlearning.qual_measure.quality_measure.MLQualityMeasureFactory(*args, **kwargs)[source]
A factory of
MLQualityMeasure
.- Parameters
base_class – The base class to be considered.
module_names – The fully qualified modules names to be searched.
- create(class_name, **options)
Return an instance of a class.
- get_class(name)
Return a class from its name.
- Parameters
name (str) – The name of the class.
- Returns
The class.
- Raises
ImportError – If the class is not available.
- Return type
- get_default_options_values(name)
Return the constructor kwargs default values of a class.
- get_default_sub_options_values(name, **options)
Return the default values of the sub options of a class.
- Parameters
- Returns
The JSON grammar.
- Return type
- get_library_name(name)
Return the name of the library related to the name of a class.
- get_options_doc(name)
Return the constructor documentation of a class.
- get_options_grammar(name, write_schema=False, schema_path=None)
Return the options JSON grammar for a class.
Attempt to generate a JSONGrammar from the arguments of the __init__ method of the class.
- Parameters
name (str) – The name of the class.
write_schema (bool) –
If True, write the JSON schema to a file.
By default it is set to False.
schema_path (str | None) –
The path to the JSON schema file. If None, the file is saved in the current directory in a file named after the name of the class.
By default it is set to None.
- Returns
The JSON grammar.
- Return type
- get_sub_options_grammar(name, **options)
Return the JSONGrammar of the sub options of a class.
- Parameters
- Returns
The JSON grammar.
- Return type
- is_available(name)
Return whether a class can be instantiated.
- update()
Search for the classes that can be instantiated.
- The search is done in the following order:
The fully qualified module names
The plugin packages
The packages from the environment variables
- Return type
None
Here is the baseclass to measure the error of machine learning algorithms.
The concept of error measure is implemented with the MLErrorMeasure
class and
proposes different evaluation methods.
- class gemseo.mlearning.qual_measure.error_measure.MLErrorMeasure(algo, fit_transformers=False)[source]
An abstract error measure for machine learning.
- Parameters
algo (gemseo.mlearning.core.supervised.MLSupervisedAlgo) – A machine learning algorithm for supervised learning.
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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- evaluate_bootstrap(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (None | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)[source]
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_test(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
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.qual_measure.cluster_measure.MLClusteringMeasure(algo, fit_transformers=False)[source]
An abstract clustering measure for clustering algorithms.
- Parameters
algo (gemseo.mlearning.cluster.cluster.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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_learn(samples=None, multioutput=True)[source]
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
NoReturn
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
- class gemseo.mlearning.qual_measure.cluster_measure.MLPredictiveClusteringMeasure(algo, fit_transformers=False)[source]
An abstract clustering measure for predictive clustering algorithms.
- Parameters
algo (gemseo.mlearning.cluster.cluster.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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- evaluate_bootstrap(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_test(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
The mean squared error to measure the quality of a regression algorithm.
The mse_measure
module
implements the concept of mean squared error measures
for machine learning algorithms.
This concept is implemented through the MSEMeasure
class
and overloads the MLErrorMeasure._compute_measure()
method.
The mean squared error (MSE) is defined by
where \(\hat{y}\) are the predictions and \(y\) are the data points.
- class gemseo.mlearning.qual_measure.mse_measure.MSEMeasure(algo, fit_transformers=False)[source]
The Mean Squared Error measure for machine learning.
- Parameters
algo (gemseo.mlearning.regression.regression.MLRegressionAlgo) – A machine learning algorithm for regression.
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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (None | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
The R2 to measure the quality of a regression algorithm.
The r2_measure
module
implements the concept of R2 measures for machine learning algorithms.
This concept is implemented through the R2Measure
class
and overloads the MLErrorMeasure._compute_measure()
method.
The R2 is defined by
where \(\hat{y}\) are the predictions, \(y\) are the data points and \(\bar{y}\) is the mean of \(y\).
- class gemseo.mlearning.qual_measure.r2_measure.R2Measure(algo, fit_transformers=False)[source]
The R2 measure for machine learning.
- Parameters
algo (gemseo.mlearning.regression.regression.MLRegressionAlgo) – A machine learning algorithm for regression.
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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- evaluate_bootstrap(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.
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
NoReturn
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
The F1 to measure the quality of a classification algorithm.
The F1 is defined by
where \(\mathit{precision}\) is the number of correctly predicted positives divided by the total number of predicted positives and \(\mathit{recall}\) is the number of correctly predicted positives divided by the total number of true positives.
- class gemseo.mlearning.qual_measure.f1_measure.F1Measure(algo, fit_transformers=False)[source]
The F1 measure for machine learning.
- Parameters
algo (gemseo.mlearning.classification.classification.MLClassificationAlgo) – A machine learning algorithm for classification.
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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (None | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.
The silhouette coefficient to measure the quality of a clustering algorithm.
The silhouette
module implements the
concept of silhouette coefficient measure for machine learning algorithms.
This concept is implemented through the
SilhouetteMeasure
class and
overloads the MLClusteringMeasure._compute_measure()
method.
The silhouette coefficient is defined for each point as the difference between the average distance from the point to each of the other points in its cluster and the average distance from the point to each of the points in the nearest cluster different from its own.
More formally, the silhouette coefficient \(s_i\) of a point \(x_i\) is given by
where \(k_i\) is the index of the cluster to which \(x_i\) belongs, \(K\) is the number of clusters, \(C_k\) is the set of indices of points belonging to the cluster \(k\) (\(k=1,\\cdots,K\)), and \(|C_k| = \\sum_{j\\in C_k} 1\) is the number of points in the cluster \(k\), \(k=1,\\cdots,K\).
- class gemseo.mlearning.qual_measure.silhouette.SilhouetteMeasure(algo, fit_transformers=False)[source]
The silhouette coefficient measure for machine learning.
- Parameters
algo (gemseo.mlearning.cluster.cluster.MLPredictiveClusteringAlgo) – 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 False.
- Return type
None
- evaluate(method='learn', samples=None, **options)
Evaluate the quality measure.
- Parameters
method (str) –
The name of the method to evaluate the quality measure.
By default it is set to learn.
samples (Sequence[int] | None) –
The indices of the learning samples. If
None
, use the whole learning dataset.By default it is set to None.
**options (OptionType | None) – The options of the estimation method (e.g.
test_data
for the test method,n_replicates
for the bootstrap one, …).
- Returns
The value of the quality measure.
- Raises
ValueError – When the name of the method is unknown.
- Return type
float | ndarray
- evaluate_bootstrap(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_kfolds(n_folds=5, samples=None, multioutput=True, randomize=False, 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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.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 False.
seed (int | None) –
The seed of the pseudo-random number generator. If
None
, then an unpredictable generator will be used.By default it is set to None.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_learn(samples=None, multioutput=True)
Evaluate the quality measure from the learning dataset.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_loo(samples=None, multioutput=True)
Evaluate the quality measure using the leave-one-out technique.
- Parameters
- Returns
The value of the quality measure.
- Return type
float | ndarray
- evaluate_test(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.By default it is set to None.
multioutput (bool) –
If
True
, return the quality measure for each output component. Otherwise, average these measures.By default it is set to True.
- Returns
The value of the quality measure.
- Return type
float | ndarray
- classmethod is_better(val1, val2)
Compare the quality between two values.
This methods returns
True
if the first one is better than the second one.For most measures, a smaller value is “better” than a larger one (MSE etc.). But for some, like an R2-measure, higher values are better than smaller ones. This comparison method correctly handles this, regardless of the type of measure.