cpt 1.0.2

Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction

CPT

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

CPT is a sequence prediction algorithm. It is a highly explainable model and good at predicting, in a finite alphabet, next value of a sequence. However, given a sequence, CPT cannot predict an element already present in this sequence. CPT needs a tuning.

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.

For windows users, the sources are precompiled. For others, you will need to install cython.

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.