BOSSVS not working with a single feature

[roman-4erkasov]

BOSSVS not working with a single feature

[johannfaouzi]

Sorry for the delayed response.

In order to train a classification algorithm, one needs several samples. In our case, a sample is a time series. This set of training samples is often called the training set.

The expected format for the training set is similar to the one used in scikit-learn (if you are familiar with it):

• X_train is a 2D-array with shape (n_samples, n_timestamps): the first dimension corresponds to the samples (time series), while the second dimension corresponds to the time.
• y_train is a 1D-array with shape (n_samples,): it contains the label associated with each sample (time series).

The format is identical for the test set.

Let's load a toy dataset to illustrate this:

>>> from pyts.datasets import load_gunpoint
>>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
>>> X_train.shape
(50, 150)  # there are 50 time series, each with 150 values.
>>> y_train.shape
(50,)  # there are 50 labels because there are 50 time series in the training set.
>>> y_train
array([2, 2, 1, 1, 2, ...])  # there are 2 labels (denoted as the integers 1 and 2).
>>> X_test.shape
(150, 150)  # there are 150 time series, each with 150 values.
>>> y_test.shape
(150,)  # there are 150 labels because there are 150 time series in the test set.

Now, one can perform classification using BOSSVS on this dataset:

>>> from pyts.classification import BOSSVS
>>> clf = BOSSVS()
>>> clf.fit(X_train, y_train)
BOSSVS()
>>> clf.score(X_test, y_test)
0.82  # accuracy score of 0.82 on the test set

Back to your example, I don't understand your data. It seems that you have 300 time series, but each time series has a single value. You cannot use BOSSVS with such data. You cannot do any time series analysis if the time series have a single value. It probably does not make sense to consider this kind of data as time series.

Hope this helps you a bit and I would be happy to give you more info if needed, but I'm not sure to understand your data.