# Our pipeline consists of two phases. First, data will be normalized using
# min-max normalization. Afterwards, it is fed to a KNN classifier. For the
# KNN classifier, we tune the n_neighbors and weights hyper-parameters.
n_splits = 3
pipeline = GridSearchCV(Pipeline([('normalize', TimeSeriesScalerMinMax()),
('knn', KNeighborsTimeSeriesClassifier())]),
{
'knn__n_neighbors': [5, 25],
'knn__weights': ['uniform', 'distance']
},
cv=StratifiedKFold(n_splits=n_splits,
shuffle=True,
random_state=42))
X_train, y_train, _, _ = CachedDatasets().load_dataset("Trace")
# Keep only timeseries of class 1, 2, 3
X_train = X_train[y_train > 0]
y_train = y_train[y_train > 0]
# Keep only the first 50 timeseries of both train and
# retain only a small amount of each of the timeseries
X_train, y_train = X_train[:50, 50:150], y_train[:50]
# Plot our timeseries
colors = ['g', 'b', 'r']
plt.figure()
for ts, label in zip(X_train, y_train):
plt.plot(ts, c=colors[label - 2], alpha=0.5)
plt.title('The timeseries in the dataset')
"""
# Author: Romain Tavenard
# License: BSD 3 clause
import numpy
import matplotlib.pyplot as plt
from tslearn_cuda.not_used.tslearn.clustering import GlobalAlignmentKernelKMeans
from tslearn_cuda.not_used.tslearn import sigma_gak
from tslearn_cuda.not_used.tslearn.datasets import CachedDatasets
from tslearn_cuda.not_used.tslearn.preprocessing import TimeSeriesScalerMeanVariance
seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
# Keep first 3 classes
X_train = X_train[y_train < 4]
numpy.random.shuffle(X_train)
# Keep only 50 time series
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50])
sz = X_train.shape[1]
gak_km = GlobalAlignmentKernelKMeans(n_clusters=3,
sigma=sigma_gak(X_train),
n_init=20,
verbose=True,
random_state=seed)
y_pred = gak_km.fit_predict(X_train)
plt.figure()