tisc: pytorch-time-series-classification

Simple model creation and training framework for time series classification in Pytorch.

What can you do with tisc?

tisc is a simple framework for time series classification in Pytorch.

• You can create a Pytorch model for time series classification with just one function.
• You can choose the model from many supported models.
• You can train the model with just one method.
• You can evaluate or predict with the trained model with just one method.

Setup

1. Install tisc

bash pip install tisc

2. Install Pytorch

If Pytorch is not installed to your environment, you have to install Pytorch that matches your environment from the official website: https://pytorch.org/get-started/locally/

example (this command is for my environment): bash pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Usage

0. Prepare the dataset / dataloader

Time series data

The time series data should be a 3D tensor with the shape of (number_of_samples, timestep, dimentions).

For example, if you have a dataset with 1000 samples, each sample has 20 timesteps, and each timestep has 100 dimentions, the shape of the dataset should be (1000, 20, 100).

The label should be a 1D tensor with the shape of (number_of_samples,).

For example, if you have a dataset with 1000 samples, the shape of the label should be (1000,).

```python import torch

The shape of the time series data should be (numberofsamples, timestep, dimentions)

print(train_data.shape) # (1000, 20, 100)

The shape of the label should be (numberofsamples,)

print(train_label.shape) # (1000,) ```

Dataset

tisc supports the dataset that is a subclass of torch.utils.data.Dataset.

The dataset should return a tuple of (data, label) in the __getitem__ method.

You can use TensorDataset from torch.utils.data to create a dataset from the time series data and the label easily.

```python import torch from torch.utils.data import TensorDataset

Prepare the dataset with TensorDataset

traindataset = TensorDataset(traindata, trainlabel) valdataset = TensorDataset(valdata, vallabel) testdataset = TensorDataset(testdata, test_label)

Check the type of the dataset

print(type(train_dataset)) # ```

Dataloader

You have to use torch.utils.data.DataLoader to load the dataset.

```python import torch from torch.utils.data import DataLoader

Prepare the dataset

trainloader = DataLoader(traindataset, batchsize=512, shuffle=True) valloader = DataLoader(valdataset, batchsize=512, shuffle=False) testloader = DataLoader(testdataset, batch_size=512, shuffle=False)

Check the type of the dataloader

print(type(train_loader)) # ```

1. Create a classifier

You can create a classifier with the build_classifier function.

The build_classifier function returns a tisc.Classifier object.

A Classifier object contains the model, the optimizer, the loss function, and the training and evaluation methods.

When you create a classifier, you have to pass the following arguments:

• model_name: The name of the model. The model should be one of the supported models. (e.g., 'LSTM', 'BiLSTM', 'Transformer')
• timestep: The number of timesteps in the time series data.
• dimentions: The number of dimentions in each timestep.
• num_classes: The number of classes in the dataset.

```python import tisc

Create a classifier

classifier = tisc.buildclassifier(modelname='LSTM', timestep=20, dimentions=100, num_classes=10)

Check the type of the classifier

print(type(classifier)) # ```

2. Train the classifier

You can train the classifier with the train method.

The train method requires the following arguments:

• epochs: The number of epochs to train the classifier.
• train_loader: The dataloader for the training dataset.

you can pass val_loader to train the classifier with validation.

```python classifier.train(train_loader, epochs=100)

If the val_loader argument is passed, you can train the classifier with validation.

classifier.train(trainloader, valloader=val_loader, epochs=100) ```

3. Evaluate the classifier

You can evaluate the classifier with the evaluate method.

The evaluate method requires the following arguments:

• test_loader: The dataloader for the test dataset.

The evaluate method can return the classification report and the confusion matrix if you pass the return_report and return_confusion_matrix arguments as True.

If with_best_model argument is True, the classifier will use the best model that marked the best result about the model saving strategy.

python classifier.evaluate(test_loader, return_report=True, return_confusion_matrix=True, with_best_model=True)

Supported models

The models that can be used in version 0.1.2:

• LSTM
• BiLSTM
• Transformer

and more! (More models will be added.)