autoqild.dataset_readers.synthetic_data_generator_distance

Generates synthetic datasets by instroducing noise with reducing the distance between gaussians of each class, simulating different distributions.

Classes

SyntheticDatasetGeneratorDistance([...])

Generator for synthetic datasets with a focus on generating data with varying class distances.

class autoqild.dataset_readers.synthetic_data_generator_distance.SyntheticDatasetGeneratorDistance(n_classes=2, n_features=2, samples_per_class=500, noise=0.1, random_state=42, fold_id=0, imbalance=0.0, gen_type='single', **kwargs)

Bases: object

Generator for synthetic datasets with a focus on generating data with varying class distances.

This class generates synthetic datasets by adjusting the distance between class distributions, allowing for the simulation of scenarios with varying levels of overlap between classes. It is designed to help in scenarios like testing classifiers on datasets with controlled class separability, with a focus on distance-based variations.

Parameters:

• n_classes (int, default=2) – Number of classes in the generated dataset.

• n_features (int, default=2) – Number of features in the generated dataset.

• samples_per_class (int or dict, default=500) – Number of samples per class. If an integer is provided, it is assumed that all classes have the same number of samples. If a dictionary is provided, the keys should be class labels and values should be the number of samples for each class.

• noise (float, default=0.1) – The level of noise to apply when generating class distributions, affecting the overlap between classes.

• random_state (int or RandomState instance, default=42) – Random state for reproducibility.

• fold_id (int, default=0) – Fold ID used for random seed generation.

• imbalance (float, default=0.0) – Proportion of the minority class in the dataset. Must be between 0 and 1.

• gen_type (str, default=`single`) – Type of generation process. It can be used to modify the dataset generation method.

• **kwargs (dict) – Additional keyword arguments.

n_classes

Number of classes in the generated dataset.

Type:

int

n_features

Number of features in the generated dataset.

Type:

int

random_state

Random state instance for reproducibility.

Type:

RandomState instance

fold_id

Fold ID used for random seed generation.

Type:

int

means

Dictionary storing the mean vectors for each class.

Type:

dict

covariances

Dictionary storing the covariance matrices for each class.

Type:

dict

seeds

Dictionary storing the random seeds used for generating each class.

Type:

dict

samples_per_class

Dictionary storing the number of samples for each class.

Type:

dict

imbalance

Proportion of the minority class in the dataset.

Type:

float

gen_type

Type of generation process.

Type:

str

n_instances

Total number of instances in the generated dataset.

Type:

int

class_labels

Array of class labels.

Type:

numpy.ndarray

y_prob

Dictionary storing the probability of each class.

Type:

dict

noise

The level of noise applied when generating class distributions.

Type:

float

logger

Logger instance for logging information.

Type:

logging.Logger

Private Methods

---------------

__generate_cov_means__

Generate the mean vectors and covariance matrices for each class. This method creates a random orthogonal matrix and generates a positive semi-definite covariance matrix. It then calculates the mean vector for each class.

bayes_predictor_mi()

Calculate the mutual information (MI) using the probability distribution function using the formulae below.

\[I(X;Y) = H(Y) - H(Y|X)\]

Returns:

mutual_information – The mutual information of the dataset.

Return type:

float

bayes_predictor_pc_softmax_mi()

Calculate the mutual information (MI) using class probabilities derived from the PDF of a class label given the input data X, applying both the Softmax and PC-Softmax functions.

\[I(X;Y) = H(Y) - H(Y|X)\]

Softmax Function:

\[S(z_k) = \frac{e^{z_k}}{\sum_{j=1}^{K} e^{z_j}}\]

where:

• ( z_k ) is the logit or raw score for class ( k ).

• ( K ) is the total number of classes.

PC-Softmax Function:

\[S_{pc}(z_k) = \frac{e^{z_k}}{\sum_{j=1}^{K} e^{z_j} \cdot p_j}\]

where:

• ( z_k ) is the logit or raw score for class ( k ).

• ( p_j = frac{text{counts}_j}{text{total samples}} ) is the prior probability of class ( j )

Returns:

• softmax_emi (float) – Estimated softmax mutual information.

• pc_softmax_emi (float) – Estimated PC-softmax mutual information.

calculate_mi()

Calculate the mutual information (MI) using the probability distribution function using the formulae below.

\[I(X;Y) = H(X) - H(X|Y)\]

Returns:

mutual_information – The mutual information of the dataset.

Return type:

float

entropy_y(y)

Calculate the entropy of the class distribution in the dataset.

Parameters:

y (array-like of shape (n_samples,)) – The labels of the dataset.

Returns:

entropy_output – The entropy of the class distribution.

Return type:

float

generate_dataset()

Generate the full synthetic dataset.

Returns:

• X (array-like of shape (n_samples, n_features)) – Feature matrix after applying sampling to create imbalance.

• y (array-like of shape (n_samples,)) – Target vector after applying sampling to create imbalance.

generate_samples_for_class(k_class)

Generate synthetic samples for a specific class.

Parameters:

k_class (int) – The class label for which to generate samples.

Returns:

• data (array-like) – A tuple containing the generated features

• labels (array-like) – A list of labels corresponding to the features

get_bayes_mi(metric_name='MCMCLogLossBayesMI')

Get the estimated mutual information based on the specified metric.

Parameters:

metric_name ({MCMCBayesMI, MCMCLogLossBayesMI, MCMCPCSoftmaxBayesMI, MCMCSoftmaxBayesMI}, default=`MCMCLogLossBayesMI`) –

The name of the metric to use for MI estimation. Must be one of:

• MCMCLogLossBayesMI: Estimate mutual information using the log loss of the bayes pedictor.

• MCMCBayesMI: Estimate mutual information using the marginal of inputs and conditionals on inputs using class labels

• MCMCPCSoftmaxBayesMI: Estimate mutual information using the MCMC PC Softmax Bayes method.

• MCMCSoftmaxBayesMI: Estimate mutual information using the MCMC Softmax Bayes method.

Returns:

mutual_information – The estimated mutual information based on the selected metric.

Return type:

float

get_prob_dist_x_given_y(k_class)

Get the multivariate normal distribution for a given class.

Parameters:

k_class (int) – The class label for which to get the distribution.

Returns:

The multivariate normal distribution for the given class.

Return type:

scipy.stats._multivariate.multivariate_normal_frozen

get_prob_fn_margx()

Get the marginal probability distribution function for the input data.

Returns:

marg_x – A function that computes the marginal probability for the input data.

Return type:

lambda function

get_prob_x_given_y(X, class_label)

Get the probability of X given a specific class label.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input data.

• class_label (int) – The class label for which to compute the probability.

Returns:

prob_x_given_y – The probability of X given the class label.

Return type:

array-like

get_prob_y_given_x(X, class_label)

Get the probability of a flipped class label given the input data X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input data.

• class_label (int) – The class label for which to compute the probability.

Returns:

prob_y_given_x – The probability of a flipped class label given the input data X.

Return type:

array-like