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Pipeline and Dataset helpers for complex algorithm evaluation.

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tpcp - Tiny Pipelines for Complex Problems

A generic way to build object-oriented datasets and algorithm pipelines and tools to evaluate them.

Easily install tpcp via pip:

pip install tpcp

Or add it to your project with poetry:

poetry add tpcp


Evaluating Algorithms - in particular when they contain machine learning - is hard. Besides understanding required concepts (cross validation, bias, overfitting, ...), you need to implement the required steps and make them work together with your algorithms and data. If you are doing something "regular" like training an SVM on tabular data, amazing libraries like sklearn, tslearn, pytorch, and many others, have your back. By using their built-in tools (e.g. sklearn.evaluation.GridSearchCV) you prevent implementation errors, and you are provided with a sensible structure to organize your code that is well understood in the community.

However, often the problems we are trying to solve are not regular. They are complex. As an example, here is the summary of the method from one of our recent papers:

  • We have continuous multi-dimensional sensor recordings from multiple participants from a hospital visit and multiple days at home
  • For each participant we have global metadata (age, diagnosis) and daily annotations
  • We want to train a Hidden-Markov-Model that can find events in the data streams
  • We need to tune hyper-parameters of the algorithm using a participant-wise cross validation
  • We want to evaluate the final performance of the algorithm for the settings trained on the hospital data -> tested on home data and trained on home data -> tested on home data
  • Using the same structure we want to evaluate a state-of-the-art algorithm to compare the results

None of the standard frameworks can easily abstract this problem, because here we have none-tabular data, multiple data sources per participant, a non-traditional ML algorithm, and a complex train-test split logic.

With tpcp we want to provide a flexible framework to approach such complex problems with structure and confidence.


To make tpcp easy to use, we try to focus on a couple of key ideas:

  • Datasets are Python classes (think of pytorch.datasets, but more flexible) that can be split, iterated over, and queried.
  • Algorithms and Pipelines are Python classes with a simple run and optimize interface, that can be implemented to fit any problem.
  • Everything is a parameter and everything is optimization: In regular ML we differentiate training and hyper-parameter optimization. In tpcp we consider everything that modifies parameters or weights as an optimization. This allows to use the same concepts and code interfaces from simple algorithms that just require a grid search to optimize a parameter to neuronal network pipelines with hyperparameter tuning.
  • Provide what is difficult, allow to change everything else: tpcp implements complicated constructs like cross validation and grid search and, whenever possible, tries to catch obvious errors in your approach. However, for the actual algorithm and dataset you are free to do whatever is required to solve your current research question.

Projects that use tpcp


Dev Setup

We are using poetry to manage dependencies and poethepoet to run and manage dev tasks.

To set up the dev environment including the required dependencies for using tpcp together with optuna run the following commands:

git clone
cd tpcp
poetry install -E optuna -E torch # This might take a while

Afterwards you can start to develop and change things. If you want to run tests, format your code, build the docs, ..., you can run one of the following poethepoet commands

  lint           Lint all files with Prospector.
  check          Check all potential format and linting issues.
  test           Run Pytest with coverage.
  docs           Build the html docs using Sphinx.

by calling

poetry run poe <command name>

If you installed poethepoet globally, you can skip the poetry run part at the beginning.


The entire development is managed via GitHub. If you run into any issues, want to discuss certain decisions, want to contribute features or feature requests, just reach out to us by opening a new issue.

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