def fit(self, X, y, sample_weight=None):
sklearn_X = _prepare_ts_datasets_sklearn(X)
if self.kernel == "gak" and self.gamma == "auto":
self.gamma = gamma_soft_dtw(to_time_series_dataset(X))
self.kernel = _sparse_kernel_func_gak(sz=self.sz, d=self.d, gamma=self.gamma)
super(TimeSeriesSVC, self).fit(sklearn_X, y, sample_weight=sample_weight)
self.kernel = _sparse_kernel_func_gak(sz=self.sz, d=self.d, gamma=self.gamma, slice_support_vectors=self.support_)
return self
def _preprocess_sklearn(self, X, y=None, fit_time=False):
force_all_finite = self.kernel not in VARIABLE_LENGTH_METRICS
if y is None:
X = check_array(X,
allow_nd=True,
force_all_finite=force_all_finite)
else:
X, y = check_X_y(X,
y,
allow_nd=True,
force_all_finite=force_all_finite)
X = to_time_series_dataset(X)
if fit_time:
self._X_fit = X
if self.gamma == "auto":
self.gamma_ = gamma_soft_dtw(X)
else:
self.gamma_ = self.gamma
self.classes_ = numpy.unique(y)
else:
check_is_fitted(self, ['svm_estimator_', '_X_fit'])
X = check_dims(X,
X_fit_dims=self._X_fit.shape,
extend=True,
check_n_features_only=(self.kernel
in VARIABLE_LENGTH_METRICS))
if self.kernel in VARIABLE_LENGTH_METRICS:
assert self.kernel == "gak"
self.estimator_kernel_ = "precomputed"
if fit_time:
sklearn_X = cdist_gak(X,
sigma=numpy.sqrt(self.gamma_ / 2.),
n_jobs=self.n_jobs,
verbose=self.verbose)
else:
sklearn_X = cdist_gak(X,
self._X_fit,
sigma=numpy.sqrt(self.gamma_ / 2.),
n_jobs=self.n_jobs,
verbose=self.verbose)
else:
self.estimator_kernel_ = self.kernel
sklearn_X = to_sklearn_dataset(X)
if y is None:
return sklearn_X
else:
return sklearn_X, y
def _preprocess_sklearn(self, X, y=None, fit_time=False):
force_all_finite = self.kernel not in VARIABLE_LENGTH_METRICS
if y is None:
X = check_array(X,
allow_nd=True,
force_all_finite=force_all_finite)
else:
X, y = check_X_y(X,
y,
allow_nd=True,
force_all_finite=force_all_finite)
X = check_dims(X, X_fit=None)
X = to_time_series_dataset(X)
if fit_time:
self._X_fit = X
self.gamma_ = gamma_soft_dtw(X)
self.classes_ = numpy.unique(y)
if self.kernel in VARIABLE_LENGTH_METRICS:
assert self.kernel == "gak"
self.estimator_kernel_ = "precomputed"
if fit_time:
sklearn_X = cdist_gak(X,
sigma=numpy.sqrt(self.gamma_ / 2.),
n_jobs=self.n_jobs,
verbose=self.verbose)
else:
sklearn_X = cdist_gak(X,
self._X_fit,
sigma=numpy.sqrt(self.gamma_ / 2.),
n_jobs=self.n_jobs,
verbose=self.verbose)
else:
self.estimator_kernel_ = self.kernel
sklearn_X = _prepare_ts_datasets_sklearn(X)
if y is None:
return sklearn_X
else:
return sklearn_X, y
def softdtw_augment_train_set(x_train,
y_train,
classes,
num_synthetic_ts,
max_neighbors=5):
from tslearn.neighbors import KNeighborsTimeSeries
from tslearn.barycenters import softdtw_barycenter
from tslearn.metrics import gamma_soft_dtw
# synthetic train set and labels
synthetic_x_train = []
synthetic_y_train = []
# loop through each class
for c in classes:
# get the MTS for this class
c_x_train = x_train[np.where(y_train == 0)[0]]
if len(c_x_train) == 1:
# skip if there is only one time series per set
continue
# compute appropriate gamma for softdtw for the entire class
class_gamma = gamma_soft_dtw(c_x_train)
# loop through the number of synthtectic examples needed
generated_samples = 0
while generated_samples < num_synthetic_ts:
# Choose a random representative for the class
representative_indices = np.arange(len(c_x_train))
random_representative_index = np.random.choice(
representative_indices, size=1, replace=False)
random_representative = c_x_train[random_representative_index]
# Choose a random number of neighbors (between 1 and one minus the total number of class representatives)
random_number_of_neighbors = int(
np.random.uniform(1, max_neighbors, size=1))
knn = KNeighborsTimeSeries(n_neighbors=random_number_of_neighbors +
1,
metric='softdtw',
metric_params={
'gamma': class_gamma
}).fit(c_x_train)
random_neighbor_distances, random_neighbor_indices = knn.kneighbors(
X=random_representative, return_distance=True)
random_neighbor_indices = random_neighbor_indices[0]
random_neighbor_distances = random_neighbor_distances[0]
nearest_neighbor_distance = np.sort(random_neighbor_distances)[1]
# random_neighbors = np.zeros((random_number_of_neighbors+1, c_x_train.shape[1]), dtype=float)
random_neighbors = np.zeros(
(random_number_of_neighbors + 1, c_x_train.shape[1],
c_x_train.shape[2]),
dtype=float)
for j, neighbor_index in enumerate(random_neighbor_indices):
random_neighbors[j, :] = c_x_train[neighbor_index]
# Choose a random weight vector (and then normalize it)
weights = np.exp(
np.log(0.5) * random_neighbor_distances /
nearest_neighbor_distance)
weights /= np.sum(weights)
# Compute tslearn.barycenters.softdtw_barycenter with weights=random weights and gamma value specific to neighbors
random_neighbors_gamma = gamma_soft_dtw(random_neighbors)
generated_sample = softdtw_barycenter(random_neighbors,
weights=weights,
gamma=random_neighbors_gamma)
synthetic_x_train.append(generated_sample)
synthetic_y_train.append(c)
# Repeat until you have the desired number of synthetic samples for each class
generated_samples += 1
# return the synthetic set
return np.array(synthetic_x_train), np.array(synthetic_y_train)
