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Learning Shapelets¶
This example illustrates how the “Learning Shapelets” method can quickly find a set of shapelets that results in excellent predictive performance when used for a shapelet transform.
More information on the method can be found at: http://fs.ismll.de/publicspace/LearningShapelets/.
Correct classification rate: 1.0
# Author: Romain Tavenard
# License: BSD 3 clause
import numpy
from sklearn.metrics import accuracy_score
import tensorflow as tf
import matplotlib.pyplot as plt
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMinMax
from tslearn.shapelets import LearningShapelets, \
grabocka_params_to_shapelet_size_dict
from tslearn.utils import ts_size
# Set seed for determinism
numpy.random.seed(0)
# Load the Trace dataset
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
# Normalize each of the timeseries in the Trace dataset
X_train = TimeSeriesScalerMinMax().fit_transform(X_train)
X_test = TimeSeriesScalerMinMax().fit_transform(X_test)
# Get statistics of the dataset
n_ts, ts_sz = X_train.shape[:2]
n_classes = len(set(y_train))
# Set the number of shapelets per size as done in the original paper
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=n_ts,
ts_sz=ts_sz,
n_classes=n_classes,
l=0.1,
r=1)
# Define the model using parameters provided by the authors (except that we
# use fewer iterations here)
shp_clf = LearningShapelets(n_shapelets_per_size=shapelet_sizes,
optimizer=tf.optimizers.Adam(.01),
batch_size=16,
weight_regularizer=.01,
max_iter=200,
random_state=42,
verbose=0)
shp_clf.fit(X_train, y_train)
# Make predictions and calculate accuracy score
pred_labels = shp_clf.predict(X_test)
print("Correct classification rate:", accuracy_score(y_test, pred_labels))
# Plot the different discovered shapelets
plt.figure()
for i, sz in enumerate(shapelet_sizes.keys()):
plt.subplot(len(shapelet_sizes), 1, i + 1)
plt.title("%d shapelets of size %d" % (shapelet_sizes[sz], sz))
for shp in shp_clf.shapelets_:
if ts_size(shp) == sz:
plt.plot(shp.ravel())
plt.xlim([0, max(shapelet_sizes.keys()) - 1])
plt.tight_layout()
plt.show()
# The loss history is accessible via the `model_` that is a keras model
plt.figure()
plt.plot(numpy.arange(1, shp_clf.n_iter_ + 1), shp_clf.history_["loss"])
plt.title("Evolution of cross-entropy loss during training")
plt.xlabel("Epochs")
plt.show()
Total running time of the script: (0 minutes 10.473 seconds)