7. API Reference

This contains the API of all the functions of mclearn. The content is the same as what you would get from the docstrings, namely the parameters and the return type of each function. If this is the first time you’re using mclearn, you might like to instead go through the User Guide for an in-depth explanation of the algorithms.

As a convenience, all functions listed below can be accessed directly from the top-level module.

7.1. Classifiers

Some general standard classifier routines for astronomical data.

train_classifier	Standard classifier routine.
print_classification_result	Train the specified classifier and print out the results.
learning_curve	Compute the learning curve of a classiifer.
compute_all_learning_curves	Compute the learning curves with the most popular classifiers.
grid_search	A general grid search routine.
grid_search_svm_rbf	Do a grid search on SVM wih an RBF kernel.
grid_search_svm_sigmoid	Do a grid search on SVM wih a sigmoid kernel.
grid_search_svm_poly_degree	Do a grid search on a Linear SVM given the specified polynomial transformation.
grid_search_svm_poly	Do a grid search on SVM with polynomial transformation of the features.
grid_search_logistic_degree	Do a grid search on Logistic Regression given the specified polynomial transformation.
grid_search_logistic	Do a grid search on Logistic Regression.
predict_unlabelled_objects	Predict the classes of unlabelled objects given a classifier.

7.2. Active Learner

The main routine of all active learning algorithms.

active_learn	Conduct active learning and return a learning curve.
run_active_learning_with_heuristic	Experiment routine with a partciular classifier heuristic.
active_learning_experiment	Run an active learning experiment with specified heuristics.

7.3. Active Learning Heuristics

Heuristics used to query the most uncertain candidate out of the unlabelled pool.

random_h	Return a random candidate.
entropy_h	Return the candidate whose prediction vector displays the greatest Shannon entropy.
margin_h	Return the candidate with the smallest margin.
qbb_margin_h	Return the candidate with the smallest average margin.
qbb_kl_h	Return the candidate with the largest average KL divergence from the mean.
compute_A	Compute the A matrix in the variance estimation technique.
compute_F	Compute the F matrix in the variance estimation technqiue.
compute_pool_variance	Estimate the variance of the pool.
pool_variance_h	Return the candidate that will minimise the expected variance of the predictions.
compute_pool_entropy	Estimate the variance of the pool.
pool_entropy_h	Return the candidate that will minimise the expected entropy of the predictions.

7.4. Performance Measures

Various measures that evaluate the performance of a classifier.

naive_accuracy	Compute the naive accuracy rate.
get_beta_parameters	Extract the beta parameters from a confusion matrix.
convolve_betas	Convolves k Beta distributions.
balanced_accuracy_expected	Compute the expected value of the posterior balanced accuracy.
beta_sum_pdf	Compute the pdf of the sum of beta distributions.
beta_avg_pdf	Compute the pdf of the average of the k beta distributions.
beta_sum_cdf	Compute the cdf of the sum of the k beta distributions.
beta_avg_pdf	Compute the pdf of the average of the k beta distributions.
beta_avg_inv_cdf	Compute the inverse cdf of the average of the k beta distributions.
recall	Compute the recall from a confusion matrix.
precision	Compute the precision from a confusion matrix.
compute_balanced_accuracy	Compute the accuracy of a classifier based on some test set.

7.5. Photometric Data

Procedures specific to photometric data.

reddening_correction_sfd98	Compute the reddening values using the SFD98 correction set.
reddening_correction_sf11	Compute the reddening values using the SF11 correction set.
reddening_correction_w14	Compute the reddening values using the W14 correction set.
correct_magnitudes	Correct the values of magntidues given a correction set.
compute_colours	Compute specified combinations of colours.
fetch_sloan_data	Run an SQL query on the Sloan Sky Server.
fetch_filter	Get a filter from the internet.
fetch_spectrum	Get a spectrum from the internet.
clean_up_subclasses	Clean up the names of the subclasses in the SDSS dataset.

7.6. Data Preprocessing

Useful general-purpose preprocessing functions.

normalise_z	Normalise each feature to have zero mean and unit variance.
normalise_unit_var	Normalise each feature to have unit variance.
normalise_01	Normalise each feature to unit interval.
draw_random_sample	Split the data into a train set and test set of a given size.
balanced_train_test_split	Split the data into a balanced training set and test set of some given size.
csv_to_hdf	Convert csv files to a HDF5 table.

7.7. Visualisations

Selected plots commonly used in astronomy and active learning.

plot_class_distribution	Plot the distribution of the classes.
plot_scores	Make a barplot of the scores of some performance measure.
plot_balanced_accuracy_violin	Make a violin plot of the balanced posterior accuracy.
plot_learning_curve	Plot the learning curve.
plot_average_learning_curve	Plot the average learning curve from many trials.
plot_hex_map	Plot the density of objects on a hex map.
plot_recall_maps	Plot the recall map.
plot_filters_and_spectrum	Plot ugriz filters and spectrum in the same figure.
plot_scatter_with_classes	Plot a scater plot of the classes.
reshape_grid_socres	Reshape the scores to be used as input for the heathap.
plot_validation_accuracy_heatmap	Plot heatmap of the validation accuracy from a grid search.