cpt 1.2.2

Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction

CPT

What is it ?

This project is a cython open-source implementation of the Compact Prediction Tree algorithm using multithreading.

CPT is a sequence prediction model. It is a highly explainable model specialized in predicting the next element of a sequence over a finite alphabet.

This implementation is based on the following research papers:

• http://www.philippe-fournier-viger.com/ADMA2013_Compact_Prediction_trees.pdf
• http://www.philippe-fournier-viger.com/spmf/PAKDD2015_Compact_Prediction_tree+.pdf

Installation

You can simply use pip install cpt.

Simple example

You can test the model with the following code:

from cpt.cpt import Cpt
model = Cpt()

model.fit([['hello', 'world'],
           ['hello', 'this', 'is', 'me'],
           ['hello', 'me']
          ])

model.predict([['hello'], ['hello', 'this']])
# Output: ['me', 'is']

For an example with the compatibility with sklearn, you should check the documentation.

Features

Train

The model can be trained with the fit method.

If needed the model can be retrained with the same method. It adds new sequences to the model and do not remove the old ones.

Multithreading

The predictions are launched by default with multithreading with OpenMP.

The predictions can also be launched in a single thread with the option multithread=False in the predict method.

You can control the number of threads by setting the following environment variable OMP_NUM_THREADS.

Pickling

You can pickle the model to save it, and load it later via pickle library.

from cpt.cpt import Cpt
import pickle


model = Cpt()
model.fit([['hello', 'world']])

dumped = pickle.dumps(model)

unpickled_model = pickle.loads(dumped)

print(model == unpickled_model)

Explainability

The CPT class has several methods to explain the predictions.

You can see which elements are considered as noise (with a low presence in sequences) with model.compute_noisy_items(noise_ratio).

You can retrieve trained sequences with model.retrieve_sequence(id).

You can find similar sequences with find_similar_sequences(sequence).

You can not yet retrieve automatically all similar sequences with the noise reduction technique.

Tuning

CPT has 3 meta parameters that need to be tuned. You can check how to tune them in the documentation. To tune you can use the model_selection module from sklearn, you can find an example here on how to.

Benchmark

The benchmark has been made on the FIFA dataset, the data can be found on the SPMF website.

Using multithreading, CPT was able to perform around 5000 predictions per second.

Without multithreading, CPT predicted around 1650 sequences per second.

Details on the benchmark can be found here.

Further reading

A study has been made on how to reduce dataset size, and so training / testing time using PageRank on the dataset.

The study has been published in IJIKM review here. An overall performance improvement of 10-40% has been observed with this technique on the prediction time without any accuracy loss.