Getting Started

1. Loading & preprocessing the datasets

In a first step, we need to construct at least a matrix with timeseries (X_train) and a vector with labels (y_train). Additionally, test data can be loaded as well in order to evaluate the pipeline in the end.

import pandas as pd
# Read in the datafiles
train_df = pd.read_csv(<DATA_FILE>)
test_df = pd.read_csv(<DATA_FILE>)
# Split into feature matrices and label vectors
X_train = train_df.drop('target', axis=1)
y_train = train_df['target']
X_test = test_df.drop('target', axis=1)
y_test = test_df['target']

2. Creating a GeneticExtractor object

Construct the object. For a list of all possible parameters, and a description, please refer to the documentation in the code

from gendis.genetic import GeneticExtractor
genetic_extractor = GeneticExtractor(population_size=50, iterations=25, verbose=False,
                                     normed=False, add_noise_prob=0.3, add_shapelet_prob=0.3,
                                     wait=10, plot='notebook', remove_shapelet_prob=0.3,
                                     crossover_prob=0.66, n_jobs=4)

3. Fit the GeneticExtractor and construct distance matrix

shapelets =, y_train)
distances_train = genetic_extractor.transform(X_train)
distances_test = genetic_extractor.transform(X_test)

4. Fit ML classifier on constructed distance matrix

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
lr = LogisticRegression(), y_train)

print('Accuracy = {}'.format(accuracy_score(y_test, lr.predict(distances_test))))