GENDIS

class gendis.genetic.GeneticExtractor(population_size=50, iterations=25, verbose=False, normed=False, add_noise_prob=0.4, add_shapelet_prob=0.4, wait=10, plot=None, remove_shapelet_prob=0.4, crossover_prob=0.66, n_jobs=4)

Feature selection with genetic algorithm.

population_size : int

The number of individuals in our population. Increasing this parameter increases both the runtime per generation, as the probability of finding a good solution.

iterations : int

The maximum number of generations the algorithm may run.

wait : int

If no improvement has been found for wait iterations, then stop

add_noise_prob : float

The chance that gaussian noise is added to a random shapelet from a random individual every generation

add_shapelet_prob : float

The chance that a shapelet is added to a random shapelet set every gen

remove_shapelet_prob : float

The chance that a shapelet is deleted to a random shapelet set every gen

crossover_prob : float

The chance that of crossing over two shapelet sets every generation

normed : boolean

Whether we first have to normalize before calculating distances

n_jobs : int

The number of threads to use

verbose : boolean

Whether to print some statistics in every generation

plot : object

Whether to plot the individuals every generation (if the population size is smaller than or equal to 20), or to plot the fittest individual

shapelets : array-like

The fittest shapelet set after evolution

label_mapping: dict

A dictionary that maps the labels to the range [0, …, C-1]

An example showing genetic shapelet extraction on a simple dataset:

>>> from tslearn.generators import random_walk_blobs
>>> from genetic import GeneticExtractor
>>> from sklearn.linear_model import LogisticRegression
>>> import numpy as np
>>> np.random.seed(1337)
>>> X, y = random_walk_blobs(n_ts_per_blob=20, sz=64, noise_level=0.1)
>>> X = np.reshape(X, (X.shape[0], X.shape[1]))
>>> extractor = GeneticExtractor(iterations=5, n_jobs=1, population_size=10)
>>> distances = extractor.fit_transform(X, y)
>>> lr = LogisticRegression()
>>> _ = lr.fit(distances, y)
>>> lr.score(distances, y)
1.0

Methods

__init__([population_size, iterations, …])	Initialize self.
fit(X, y)	Extract shapelets from the provided timeseries and labels.
transform(X)	After fitting the Extractor, we can transform collections of timeseries in matrices with distances to each of the shapelets in the evolved shapelet set.
fit_transform(X, y)	Combine both the fit and transform method in one.
save(path)	Write away all hyper-parameters and discovered shapelets to disk
load(path)	Instantiate a saved GeneticExtractor