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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_classification_tutorial.py:


Time Series Classification with InceptionTime
=============================================

**Author**: `Vincent Scharf <vincent.scharf@smail.inf.h-brs.de>`__

In this tutorial, we are going to learn how to train an InceptionTime style classifier.

For this tutorial, we will use the Beef dataset available at the *UEA & UCR Time Series
Classification Repository* :cite:`Dau2019UCR`.
The beef dataset consists of four classes of beef spectrograms, from pure beef and beef
adulterated with varying degrees of offal.
The spectrograms are univariate time series of length 470. The train- and test set consist
of 30 sampels each.

.. note::
    ``torchtime`` provides easy access to common, publicly accessible
    datasets. Please refer to the official documentation for the list of
    available datasets.

We will do the following steps in order:

1. Load the Beef training and test datasets using ``torchtime`` and
    impute potential missing values
2. Define an InceptionTime style classifier
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data

1. Load and Normalize Beef
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Using ``torchtime``, it’s extremely easy to load datasets contained in the UCR & UEA
time series classification repository.

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.. code-block:: default

    import torch
    import torch.utils.data as data
    import torchtime
    import torchtime.transforms as transforms








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The output of torchtime datasets can contain NaN values.
We impute those missing values using ``transforms.Nan2Value()``.

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. note::
    If running on Windows and you get a BrokenPipeError, try setting
    the num_worker of torch.utils.data.DataLoader() to 0.

.. GENERATED FROM PYTHON SOURCE LINES 50-67

.. code-block:: default


    transform = transforms.Compose(
        [transforms.Nan2Value()])

    batch_size = 4

    trainset = torchtime.datasets.UCR(root='./data', name="Beef", train=True,
                                      download=True, transform=transform)
    trainloader = data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)

    testset = torchtime.datasets.UCR(root='./data', name="Beef", train=False,
                                     download=True, transform=transform)
    testloader = data.DataLoader(testset, batch_size=batch_size,
                                 shuffle=False, num_workers=2)

    classes = ('unadulterated', 'heart', 'kidney', 'liver', 'tripe')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Downloading http://www.timeseriesclassification.com/Downloads/Beef.zip
    Downloading http://www.timeseriesclassification.com/Downloads/Beef.zip to ./data/UCR/Beef/Beef.zip

      0%|          | 0/432784 [00:00<?, ?it/s]
    433152it [00:00, 4466983.11it/s]          
    Extracting ./data/UCR/Beef/Beef.zip to ./data/UCR/Beef

    /home/vincent/miniconda3/envs/torchtime/lib/python3.9/site-packages/torchtime-0.0.1+8eabcd0-py3.9.egg/torchtime/io/ts.py:459: UserWarning: Meta information for '@dimensions' is missing. Inferring from data.
      self.parse_body(line)
    /home/vincent/miniconda3/envs/torchtime/lib/python3.9/site-packages/torchtime-0.0.1+8eabcd0-py3.9.egg/torchtime/datasets/ucruea.py:135: DataConversionWarning: Labels are not numeric and no explicit class mapping is provided. Please pass an explicit class mapping to the constructor. Using class labels parsed from dataset.
      self.data, self.targets, self.classes = self._load_data()




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Let us show some of the training spectrograms, for fun.

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.. code-block:: default


    import matplotlib.pyplot as plt

    def seriesshow(sequences, labels):
        """ Plot a univariate time series.
        """
        fig, ax = plt.subplots()
        for series, label in zip(sequences, labels):
            for dimension in series:
                ax.plot(dimension, label=classes[label])
        ax.legend()
        ax.grid()
        fig.show()


    # get some random training sequences
    dataiter = iter(trainloader)
    sequences, labels = next(dataiter)

    # show sequences
    seriesshow(sequences, labels)
    # print labels
    print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))




.. image-sg:: /tutorials/images/sphx_glr_classification_tutorial_001.png
   :alt: classification tutorial
   :srcset: /tutorials/images/sphx_glr_classification_tutorial_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    unadulterated liver heart tripe




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2. Define an InceptionTime style classifier
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We import the InceptionTime Model :cite:`IsmailFawaz2020InceptionTime` available through
the ``torchtime.models`` package and initialize it such that it takes a 1-channel time series
as an input and maps it onto one of the five classes defined above.

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.. code-block:: default


    import torchtime.models as models

    net = models.InceptionTime(n_inputs=1, n_classes=5)








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3. Define a Loss function and optimizer
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Let's use a Classification Cross-Entropy loss and SGD with momentum.

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.. code-block:: default


    import torch.nn as nn
    import torch.optim as optim

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)








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4. Train the network
^^^^^^^^^^^^^^^^^^^^

This is when things start to get interesting.
We simply have to loop over our data iterator, and feed the inputs to the
network and optimize.

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.. code-block:: default


    for epoch in range(10):  # loop over the dataset multiple times

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
                running_loss = 0.0

    print('Finished Training')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/vincent/miniconda3/envs/torchtime/lib/python3.9/site-packages/torch/autograd/__init__.py:173: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484806139/work/c10/cuda/CUDAFunctions.cpp:109.)
      Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    Finished Training




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Let's quickly save our trained model:

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.. code-block:: default


    PATH = './beef_classifier.pth'
    torch.save(net.state_dict(), PATH)








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See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_
for more details on saving PyTorch models.

5. Test the network on the test data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

We have trained the network for 10 passes over the training dataset (as it is a relatively
small dataset with a 1:1 train/test split).
But we need to check if the network has learnt anything at all.

We will check this by predicting the class label that the neural network
outputs, and checking it against the ground-truth. If the prediction is
correct, we add the sample to the list of correct predictions.

Okay, first step. Let us display a series from the test set to get familiar.

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.. code-block:: default


    dataiter = iter(testloader)
    sequences, labels = next(dataiter)

    # print sequences
    seriesshow(sequences, labels)
    print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))




.. image-sg:: /tutorials/images/sphx_glr_classification_tutorial_002.png
   :alt: classification tutorial
   :srcset: /tutorials/images/sphx_glr_classification_tutorial_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    GroundTruth:  unadulterated unadulterated unadulterated unadulterated




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Next, let's load back in our saved model (note: saving and re-loading the model
wasn't necessary here, we only did it to illustrate how to do so):

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.. code-block:: default


    net = models.InceptionTime(n_inputs=1, n_classes=5)
    net.load_state_dict(torch.load(PATH))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <All keys matched successfully>



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Okay, now let us see what the neural network thinks these examples above are:

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.. code-block:: default


    outputs = net(sequences)








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The outputs are energies for the 5 classes.
The higher the energy for a class, the more the network
thinks that the sequence is of the particular class.
So, let's get the index of the highest energy:

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.. code-block:: default

    _, predicted = torch.max(outputs, 1)

    print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'
                                  for j in range(4)))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Predicted:  heart unadulterated unadulterated unadulterated




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The results seem pretty good.

Let us look at how the network performs on the whole dataset.

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.. code-block:: default


    correct = 0
    total = 0
    # since we're not training, we don't need to calculate the gradients for our outputs
    with torch.no_grad():
        for data in testloader:
            sequences, labels = data
            # calculate outputs by running sequences through the network
            outputs = net(sequences)
            # the class with the highest energy is what we choose as prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print(f'Accuracy of the network on the 30 test sequences: {100 * correct // total} %')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Accuracy of the network on the 30 test sequences: 26 %




.. GENERATED FROM PYTHON SOURCE LINES 220-226

That looks way better than chance, which is 20% accuracy (randomly picking
a class out of 5 classes).
Seems like the network learnt something.

Hmmm, what are the classes that performed well, and the classes that did
not perform well:

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.. code-block:: default


    # prepare to count predictions for each class
    correct_pred = {classname: 0 for classname in classes}
    total_pred = {classname: 0 for classname in classes}

    # again no gradients needed
    with torch.no_grad():
        for data in testloader:
            sequences, labels = data
            outputs = net(sequences)
            _, predictions = torch.max(outputs, 1)
            # collect the correct predictions for each class
            for label, prediction in zip(labels, predictions):
                if label == prediction:
                    correct_pred[classes[label]] += 1
                total_pred[classes[label]] += 1

    # print accuracy for each class
    for classname, correct_count in correct_pred.items():
        accuracy = 100 * float(correct_count) / total_pred[classname]
        print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Accuracy for class: unadulterated is 83.3 %
    Accuracy for class: heart is 33.3 %
    Accuracy for class: kidney is 0.0 %
    Accuracy for class: liver is 0.0 %
    Accuracy for class: tripe is 16.7 %




.. GENERATED FROM PYTHON SOURCE LINES 249-260

Okay, so what next?

How do we run these neural networks on the GPU?

Training on GPU
^^^^^^^^^^^^^^^
Just like how you transfer a Tensor onto the GPU, you transfer the neural
net onto the GPU.

Let's first define our device as the first visible cuda device if we have
CUDA available:

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.. code-block:: default


    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    # Assuming that we are on a CUDA machine, this should print a CUDA device:

    print(device)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    cpu




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The rest of this section assumes that ``device`` is a CUDA device.

Then these methods will recursively go over all modules and convert their
parameters and buffers to CUDA tensors:

.. code:: python

    net.to(device)


Remember that you will have to send the inputs and targets at every step
to the GPU too:

.. code:: python

        inputs, labels = data[0].to(device), data[1].to(device)

Why don't I notice MASSIVE speedup compared to CPU? Because your network
is tiny.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  8.902 seconds)


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