MINIROCKET Pytorch

A Very Fast (Almost) Deterministic Transform for Time Series Classification.

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

Dempster, A., Schmidt, D. F., & Webb, G. I. (2020). MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification. arXiv preprint arXiv:2012.08791.

Original paper: https://arxiv.org/abs/2012.08791

Original code: https://github.com/angus924/minirocket

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MiniRocketFeatures

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

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

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get_minirocket_features

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

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

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MiniRocketHead

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

A sequential container.

Modules will be added to it in the order they are passed in the constructor. Alternatively, an OrderedDict of modules can be passed in. The forward() method of [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential) accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.

The value a [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential) provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on the [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential) applies to each of the modules it stores (which are each a registered submodule of the [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential)).

What’s the difference between a [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential) and a :class:torch.nn.ModuleList? A ModuleList is exactly what it sounds like–a list for storing Module s! On the other hand, the layers in a [Sequential](https://timeseriesAI.github.io/models.layers.html#sequential) are connected in a cascading way.

Example::

# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))

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MiniRocket

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

A sequential container.

Example::

# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))

from tsai.imports import default_device
from fastai.metrics import accuracy
from fastai.callback.tracker import ReduceLROnPlateau
from tsai.data.all import *
from tsai.learner import *

# Offline feature calculation
dsid = 'ECGFiveDays'
X, y, splits = get_UCR_data(dsid, split_data=False)
mrf = MiniRocketFeatures(c_in=X.shape[1], seq_len=X.shape[2]).to(default_device())
X_train = X[splits[0]]  # X_train may either be a np.ndarray or a torch.Tensor
mrf.fit(X_train)
X_tfm = get_minirocket_features(X, mrf)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize(by_var=True)
dls = get_ts_dls(X_tfm, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms, bs=256)
learn = ts_learner(dls, MiniRocketHead, metrics=accuracy)
learn.fit(1, 1e-4, cbs=ReduceLROnPlateau(factor=0.5, min_lr=1e-8, patience=10))

epoch	train_loss	valid_loss	accuracy	time
0	0.693147	0.530879	0.752613	00:00

# Online feature calculation
dsid = 'ECGFiveDays'
X, y, splits = get_UCR_data(dsid, split_data=False)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize()
dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms, bs=256)
learn = ts_learner(dls, MiniRocket, metrics=accuracy)
learn.fit_one_cycle(1, 1e-2)

epoch	train_loss	valid_loss	accuracy	time
0	0.693147	0.713297	0.502904	00:06