Metadata-Version: 2.1
Name: pbct
Version: 0.1.1
Summary: Predictive Bi-Clustering Trees in Python.
Home-page: http://github.com/pedroilidio/PCT
Author: Pedro Ilidio
Author-email: pedrilidio@gmail.com
License: GPLv3
Description: # Predictive Bi-Clustering Trees (PBCT)
        This code implements PBCTs based on its original proposal by Pliakos, Geurts and Vens in 2018. Functionality has been extended to n-dimensional interaction tensors, where n instances of n different classes would interact or not for each database instance.
        
        
        ## Installation
        The package is available at PyPI and can be installed by the usual `pip` command:
        ```
        $ pip install pbct
        ```
        Local installation can be done either by providing the `--user` flag to the above command or by cloning this repo and issuing `pip` afterwards, for example:
        ```
        $ git clone https://github.com/pedroilidio/PCT
        $ cd PCT
        $ pip install -e .
        ```
        Where the `-e` option installs it as symbolic links to the local cloned repository, so that changes in it will reflect on the installation directly.
        
        ## Usage
        Usage and input/output examples are provided in the `examples` folder.
        We provide a command-line utility to use PBCT models, that shows the following information when the `--help` option is used.
        
        ```
        $ PBCT --help
        
        usage: PBCT [-h] [--fit | --predict] [--config CONFIG] [--XX XX [XX ...]]
                    [--XX_names XX_NAMES [XX_NAMES ...]]
                    [--XX_col_names XX_COL_NAMES [XX_COL_NAMES ...]] [--Y Y]
                    [--path_model PATH_MODEL] [--max_depth MAX_DEPTH]
                    [--min_samples_leaf MIN_SAMPLES_LEAF] [--simple_mean] [--verbose]
        
        Fit a PBCT model to data or use a trained model to predict new results. Input
        files and options may be provided either with command-line options or by a
        json config file (see --config option).
        
        optional arguments:
          -h, --help            show this help message and exit
          --fit                 Use input data to train a PBCT. (default: False)
          --predict             Predict interaction between input instances. (default:
                                False)
          --config CONFIG       Load options from json file. File example:
                                {
                                    "path_model": "/path/to/save/model.json",
                                    "fit": "true",
                                    "XX": ["/path/to/X1.csv", "/path/to/X2.csv"],
                                    "Y": "/path/to/Y.csv"
                                }.
                                Multiple dicts in a list
                                will cause this script to run multiple times.
                                (default: None)
          --XX XX [XX ...]      Paths to .csv files containing rows of numerical
                                attributes for each axis' instance. (default: None)
          --XX_names XX_NAMES [XX_NAMES ...]
                                Paths to files containing string identifiers for each
                                instance for each axis, being one file for each axis.
                                (default: None)
          --XX_col_names XX_COL_NAMES [XX_COL_NAMES ...]
                                Paths to files containing string identifiers for each
                                attributecolumn, being one file for each axis.
                                (default: None)
          --Y Y                 If fitting the model to data, it represents the path
                                to a .csv file containing the known interaction matrix
                                between rows and columns data.If predicting, Y is the
                                destination path for the predicted values, formatted
                                as an interaction matrix in the same way described.
                                (default: None)
          --path_model PATH_MODEL
                                When fitting: path to the location where the model
                                will be saved. When predicting: the saved model to be
                                used. (default: trained_model.json)
          --max_depth MAX_DEPTH
                                Maximum PBCT depth allowed. -1 will disable this
                                stopping criterion. (default: -1)
          --min_samples_leaf MIN_SAMPLES_LEAF
                                Minimum number of sample pairs in the training set
                                required to arrive at each leaf. (default: 20)
          --simple_mean         If provided, the prototype function will always return
                                the mean value over the entire sub interaction matrix
                                of the leaf, not considering possible known instances.
                                (default: False)
          --verbose, -v         Show more detailed output (default: False)
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: Tree visualization
