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        "author": "Bluesheeptoken",
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        "description": "# CPT\n\nThis project is a cython open-source implementation of the Compact Prediction Tree algorithm using multithreading.\n\nCPT 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][2].\n\nThis implementation is based on the following research papers\n\nhttp://www.philippe-fournier-viger.com/ADMA2013_Compact_Prediction_trees.pdf\n\nhttp://www.philippe-fournier-viger.com/spmf/PAKDD2015_Compact_Prediction_tree+.pdf\n\n## Installation\n\nYou can simply use `pip install cpt`.\n\nFor windows users, the sources are precompiled. For others, you will need to install `cython`.\n\n## Simple example\n\nYou can test the model with the following code\n\n```python\nfrom cpt.cpt import Cpt\nmodel = Cpt()\n\nmodel.fit([['hello', 'world'],\n           ['hello', 'this', 'is', 'me'],\n           ['hello', 'me']\n          ])\n\nmodel.predict([['hello'], ['hello', 'this']])\n# Output: ['me', 'is']\n```\nFor an example with the compatibility with sklearn, you should check the [documentation][1].\n\n## Features\n### Train\n\nThe model can be trained with the `fit` method.\n\nIf needed the model can be retrained with the same method. It adds new sequences to the model and do not remove the old ones.\n\n### Multithreading\n\nThe predictions are launched by default with multithreading with OpenMP.\n\nThe predictions can also be launched in a single thread with the option `multithread=False` in the `predict` method.\n\nYou can control the number of threads by setting the following environment variable `OMP_NUM_THREADS`.\n\n### Pickling\n\nYou can pickle the model to save it, and load it later via pickle library.\n```python\nfrom cpt.cpt import Cpt\nimport pickle\n\n\nmodel = Cpt()\nmodel.fit([['hello', 'world']])\n\ndumped = pickle.dumps(model)\n\nunpickled_model = pickle.loads(dumped)\n\nprint(model == unpickled_model)\n```\n\n### Explainability\n\nThe CPT class has several methods to explain the predictions.\n\nYou can see which elements are considered as `noise` (with a low presence in sequences) with `model.compute_noisy_items(noise_ratio)`.\n\nYou can retrieve trained sequences with `model.retrieve_sequence(id)`.\n\nYou can find similar sequences with `find_similar_sequences(sequence)`.\n\nYou can not yet retrieve automatically all similar sequences with the noise reduction technique.\n\n### Tuning\n\nCPT has 3 meta parameters that need to be tuned. You can check how to tune them in the [documentation][1]. To tune you can use the `model_selection` module from `sklearn`, you can find an example [here][3] on how to.\n\n[1]: https://cpt.readthedocs.io/en/latest/\n[2]: https://github.com/bluesheeptoken/CPT#tuning\n[3]: https://cpt.readthedocs.io/en/latest/example.html#sklearn-example\n\n\n",
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