def test_f1_fitness():
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,
fitness=f1_fitness)
extractor.fit(X, y)
def test_serialization():
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)
extractor.save('temp.p')
new_extractor = GeneticExtractor.load('temp.p')
new_distances = new_extractor.transform(X)
np.testing.assert_array_equal(distances, new_distances)
os.remove('temp.p')
def test_pipeline():
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)
lr = LogisticRegression()
pipeline = Pipeline([
('shapelets', extractor),
('log_reg', lr)
])
pipeline.fit(X, y)
from __future__ import print_function
import numpy
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from tslearn.generators import random_walk_blobs
from tslearn.preprocessing import TimeSeriesScalerMinMax
from tslearn.neighbors import KNeighborsTimeSeriesClassifier, KNeighborsTimeSeries
from tslearn.piecewise import SymbolicAggregateApproximation
numpy.random.seed(0)
n_ts_per_blob, sz, d, n_blobs = 20, 100, 1, 2
# Prepare data
X, y = random_walk_blobs(n_ts_per_blob=n_ts_per_blob,
sz=sz,
d=d,
n_blobs=n_blobs)
scaler = TimeSeriesScalerMinMax(min=0., max=1.) # Rescale time series
X_scaled = scaler.fit_transform(X)
indices_shuffle = numpy.random.permutation(n_ts_per_blob * n_blobs)
X_shuffle = X_scaled[indices_shuffle]
y_shuffle = y[indices_shuffle]
X_train = X_shuffle[:n_ts_per_blob * n_blobs // 2]
X_test = X_shuffle[n_ts_per_blob * n_blobs // 2:]
y_train = y_shuffle[:n_ts_per_blob * n_blobs // 2]
y_test = y_shuffle[n_ts_per_blob * n_blobs // 2:]
# Nearest neighbor search
knn = KNeighborsTimeSeries(n_neighbors=3, metric="dtw")
def _create_large_ts_dataset():
return random_walk_blobs(n_ts_per_blob=50, n_blobs=3, random_state=1,
sz=20, noise_level=0.025)
def _create_small_ts_dataset():
return random_walk_blobs(n_ts_per_blob=5, n_blobs=3, random_state=1,
sz=10, noise_level=0.025)
