Reference / API¶

Datasets¶

Loading/saving a dataset

`Dataset`(name, metadata[, observations, …])	A dataset of many astronomical objects.
`Dataset.load`(name[, metadata_only, chunk, …])	Load a dataset that has been saved in HDF5 format in the data directory.
`Dataset.from_objects`(name, objects, **kwargs)	Load a dataset from a list of AstronomicalObject instances.
`Dataset.path`	Return the path to where this dataset should lie on disk
`Dataset.write`([overwrite])	Write the dataset out to disk.

Retrieving objects from the dataset

Dataset.get_object([index, object_class, …]) Parse keywords to pull a specific object out of the dataset

Plotting lightcurves of objects in the dataset

`Dataset.plot_light_curve`(args, *kwargs)	Plot the light curve for an object in the dataset.
`Dataset.plot_interactive`()	Make an interactive plot of the light curves in the dataset.

Extracting features from objects in the dataset

`Dataset.extract_raw_features`(featurizer[, …])	Extract raw features from the dataset.
`Dataset.get_raw_features_path`([tag])	Return the path to where the raw features for this dataset should lie on disk
`Dataset.write_raw_features`([tag])	Write the raw features out to disk.
`Dataset.load_raw_features`([tag])	Load the raw features from disk.
`Dataset.select_features`(featurizer)	Select features from the dataset for classification.

Classifying objects in the dataset

`Dataset.predict`(classifier)	Generate predictions using a classifier.
`Dataset.get_predictions_path`([classifier])	Return the path to where the predicitons for this dataset for a given classifier should lie on disk.
`Dataset.write_predictions`([classifier])	Write predictions for this classifier to disk.
`Dataset.load_predictions`([classifier])	Load the predictions for a classifier from disk.
`Dataset.label_folds`([num_folds, random_state])	Separate the dataset into groups for k-folding

`AstronomicalObject`(metadata, observations)	An astronomical object, with metadata and a lightcurve.
`AstronomicalObject.bands`	Return a list of bands that this object has observations in
`AstronomicalObject.subtract_background`()	Subtract the background levels from each band.
`AstronomicalObject.preprocess_observations`([…])	Apply preprocessing to the observations.
`AstronomicalObject.fit_gaussian_process`([…])	Fit a Gaussian Process model to the light curve.
`AstronomicalObject.get_default_gaussian_process`()	Get the default Gaussian Process.
`AstronomicalObject.predict_gaussian_process`(…)	Predict the Gaussian process in a given set of bands and at a given set of times.
`AstronomicalObject.plot_light_curve`([…])	Plot the object’s light curve
`AstronomicalObject.print_metadata`()	Print out the object’s metadata in a nice format.

Augmentor API

`Augmentor`(**cosmology_kwargs)	Class used to augment a dataset.
`Augmentor.augment_object`(reference_object[, …])	Generate an augmented version of an object.
`Augmentor.augment_dataset`(augment_name, …)	Generate augmented versions of all objects in a dataset.

Augmentor Implementations

plasticc.PlasticcAugmentor() Implementation of an Augmentor for the PLAsTiCC dataset

Augmentor methods to implement in subclasses

`Augmentor._augment_metadata`(reference_object)	Generate new metadata for the augmented object.
`Augmentor._choose_sampling_times`(…[, …])	Choose the times at which to sample for a new augmented object.
`Augmentor._choose_target_observation_count`(…)	Choose the target number of observations for a new augmented light curve.
`Augmentor._simulate_light_curve_uncertainties`(…)	Simulate the observation-related noise for a light curve.
`Augmentor._simulate_detection`(observations, …)	Simulate the detection process for a light curve.

Classifier API

`Classifier`(name)	Classifier used to classify the different objects in a dataset.
`Classifier.train`(dataset)	Train the classifier on a dataset
`Classifier.predict`(dataset)	Generate predictions for a dataset
`Classifier.path`	Get the path to where a classifier should be stored on disk
`Classifier.write`([overwrite])	Write a trained classifier to disk
`Classifier.load`(name)	Load a classifier that was previously saved to disk

Classifier Implementations

LightGBMClassifier(name, featurizer[, …]) Feature based classifier using LightGBM to classify objects.

Weights and metrics

`evaluate_weights_flat`(dataset[, class_weights])	Evaluate the weights to use for classification on a dataset.
`evaluate_weights_redshift`(dataset[, …])	Evaluate redshift-weighted weights to use to generate a rates-independent classifier.
`weighted_multi_logloss`(true_classes, predictions)	Evaluate a weighted multi-class logloss function.

Featurizer API

`Featurizer`	Class used to extract features from objects.
`Featurizer.extract_raw_features`(…[, …])	Extract raw features from an object
`Featurizer.select_features`(raw_features)	Select features to use for classification
`Featurizer.extract_features`(astronomical_object)	Extract features from an object.

Featurizer Implementations

plasticc.PlasticcFeaturizer Class used to extract features for the PLAsTiCC dataset.

AvocadoException The base class for all exceptions raised in avocado.