# -*- coding: utf-8 -*-
"""
Learning Shapelets
==================

This example illustrates the use of the "Learning Shapelets" method for a time series classification task.

More information on the method can be found at: http://fs.ismll.de/publicspace/LearningShapelets/.
"""

# Author: Romain Tavenard
# License: BSD 3 clause

import numpy
from sklearn.metrics import accuracy_score
from keras.optimizers import Adagrad
import matplotlib.pyplot as plt

from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMinMax
from tslearn.shapelets import ShapeletModel, grabocka_params_to_shapelet_size_dict
from tslearn.utils import ts_size

numpy.random.seed(0)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = TimeSeriesScalerMinMax().fit_transform(X_train)
X_test = TimeSeriesScalerMinMax().fit_transform(X_test)

# Set the number of shapelets per size as done in the original paper
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=X_train.shape[0],
                                                       ts_sz=X_train.shape[1],
                                                       n_classes=len(set(y_train)),
                                                       l=0.1,
                                                       r=2)

# Define the model using parameters provided by the authors (except that we use fewer iterations here)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
                        optimizer=Adagrad(lr=.1),
                        weight_regularizer=.01,
                        max_iter=200,
                        verbose_level=0)
shp_clf.fit(X_train, y_train)
predicted_labels = shp_clf.predict(X_test)
print("Correct classification rate:", accuracy_score(y_test, predicted_labels))
print(shp_clf.model.loss)

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()

@@ -1808,34 +1808,8 @@
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Fix unrelated stuff in shapelets example