MALT: Machine Learning for Transients¶
MALT is a classification pipeline based on the paper “Classification of Multiwavelength Transients with Machine Learning” by Sooknunan et al. (2018). It is a framework which allows the user to classify time series data. The user is free to choose the interpolation technique, feature extraction method, and the machine learning classifier to use.
The default pipeline is shown below. It uses Gaussian processes to interpolate the data, a wavelet feature extraction method and a random forest classifier.
How to install MALT¶
First clone the git repo and install virtualenv if not already installed:
git clone https://github.com/kimeels/MALT.git
python3 -m pip install --user virtualenv
Change directories into MALT and create a virtual environment:
cd MALT
python3 -m venv malt_env
Start the virtual env and install the necessary packages using the requirements file:
source malt_env/bin/activate
pip3 install -r requirements.txt
Example Notebooks¶
Install Jupyter Notebooks/Labs if not already installed.
Lightcurve Example¶
Change directories into Examples and start the MALT_lightcurve_class_example notebook:
cd MALT/Examples
jupyter notebook MALT_lightcurve_class_example notebook.ipynb
Dataset Example¶
Change directories into Examples and start the MALT_dataset_class_example notebook:
cd MALT/Examples
jupyter notebook MALT_dataset_class_example notebook.ipynb
The MALT API reference¶
The Lightcurve class¶
-
class
malt.Lightcurve(filepath, interpolate=False, interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, obj_type=None)¶ -
extract_features(feat_ex_method=<function get_wavelet_feature>)¶ Extracts features from the given lightcurve with assigned feature extraction method.
- self: Lightcurve object
- An instance of the Lightcurve class.
- feat_ex_method: python function
- Function to use for the feature extraction.
-
interpolate(interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, aug_num=1)¶ Interpolates the given lightcurve with assigned interpolation function
- self: Lightcurve object
- An instance of the Lightcurve class.
- interp_func: python function
- A python function that takes in a lightcurve and interpolates it.
- ini_t: str or float
- Initial time to start sampling.
- obs_time: float
- The total length of the interpolated lightcurve.
- sample_size: int
- Number of data points in interpolated lightcurve.
- aug_num: int
- Number of lightcurves to augment to.
-
loadfile(filename)¶ Loads file to extract time, flux, flux_err ra_dec and class
filename: path to dataset
-
The Dataset class¶
-
class
malt.Dataset(configFile='', feat_ex_method=<function get_wavelet_feature>, interpolate=True, interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, aug_num=1, ml_method=<class 'malt.machine_learning.RFclassifier'>, hyperparams={'criterion': ['gini', 'entropy'], 'n_estimators': array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])}, n_jobs=-1, pca=True, n_components=20)¶ -
add(new_lightcurve)¶ - Adds new lightcurve to the Dataset then retrains Dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- lightcurve: Lightcurve object
- Lightcurve object to add to dataset.
-
extract_features()¶ Extracts features from all the lightcurves in the given dataset with assigned feature extraction method.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
get_pca()¶ Performs PCA decomposition of a feature array X.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
interpolate()¶ Interpolates all the lightcurves in the given dataset with assigned interpolation function.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
classmethod
load_from_save(filename)¶ - Returns a saved Dataset instance using pickle
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- filename: str
- filename under which the Dataset instance was saved.
-
populate(filepaths)¶ Initialises an instance of the Dataset class.
- self: Database object
- An instance of the Database class.
- filepaths: list
- List containing the paths to the data files.
-
predict(lightcurve, show_prob=False)¶ - Predicts the type of given lightcurve object using classifier trained on Dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- lightcurve: Lightcurve object
- Lightcurve object for which to predict
- show_prob: boolean.
- If True will print full output from predict_proba()
-
project_pca(lightcurve=None)¶ Projects self.features onto calculated PCA axis from self.pca
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
run_diagnostic()¶ Runs the Diagnostic test which trains n classifiers on different subsets of the Dataset to test how well it can classify objects.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
save(filename='saved_dataset')¶ - Saves a Dataset instance using a pickle dump
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- filename: str
- filename under which to store the Dataset instance
-
train(verbose=1)¶ - Trains a ML algorithm on the Dataset with the parameters specified on initialisation.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
verbose: How much information to print out.
-
types(show_aug_num=False)¶ Prints out the counts of each object type stored in the dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- show_aug_num: boolean
- Use augmented lightcurve when counting type numbers.
-
MALT interpolator¶
-
malt.interpolator.get_gp(lightcurve, t0, obs_time, sample_size, aug_num)¶ Returns a Gaussian Process (george) object marginalised on the data in file.
- lightcurve: Lightcurve object
- An instance of the Lightcurve class.
- t0: float
- Initial time to start sampling.
- obs_time: float
- The total length of the interpolated lightcurve.
- sample_size: int
- Number of data points in interpolated lightcurve.