MINIROCKET Pytorch

class MiniRocketFeatures[source]

MiniRocketFeatures(c_in, seq_len, num_features=10000, max_dilations_per_kernel=32, random_state=None) :: Module

This is a Pytorch implementation of MiniRocket developed by Malcolm McLean and Ignacio Oguiza

MiniRocket paper citation: @article{dempster_etal_2020, author = {Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I}, title = {{MINIROCKET}: A Very Fast (Almost) Deterministic Transform for Time Series Classification}, year = {2020}, journal = {arXiv:2012.08791} } Original paper: https://arxiv.org/abs/2012.08791 Original code: https://github.com/angus924/minirocket

get_minirocket_features[source]

get_minirocket_features(o, model, chunksize=1024, use_cuda=None, to_np=False)

Function used to split a large dataset into chunks, avoiding OOM error.

class MiniRocketHead[source]

MiniRocketHead(c_in, c_out, seq_len=1, bn=True, fc_dropout=0.0) :: Sequential

A sequential container. Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in.

To make it easier to understand, here is a small example::

# Example of using Sequential
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Example of using Sequential with OrderedDict
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))

class MiniRocket[source]

MiniRocket(c_in, c_out, seq_len, num_features=10000, max_dilations_per_kernel=32, random_state=None, bn=True, fc_dropout=0) :: Sequential

A sequential container. Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in.

To make it easier to understand, here is a small example::

# Example of using Sequential
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Example of using Sequential with OrderedDict
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))