Metadata-Version: 2.1
Name: distython
Version: 0.0.3
Summary: Implementation of state-of-the-art distance metrics from research papers which can handle mixed-type data and missing values.
Home-page: https://github.com/KacperKubara/distython
Author: Kacper Kubara
Author-email: kacper.kubara.ai@gmail.com
License: UNKNOWN
Description: # distython
        Implementation of state-of-the-art distance metrics from research papers which can handle mixed-type data and missing values.
        At the moment, HEOM, HVDM and VDM are tested and working. VDM and HVDM has been released recently so please report bugs, if there are any.
        Please feel free to help and contribute to the project as there is a lack of existing implementations of hetergeneous distance metrics.
        # Installation
        Clone the repository with `git clone`.
        Install the necessary packages with `pipenv install`
        
        # Example - HEOM
        ```python
        # Example code of how the HEOM metric can be used together with Scikit-Learn
        import numpy as np
        from sklearn.neighbors import NearestNeighbors
        from sklearn.datasets import load_boston
        # Importing a custom metric class
        from HEOM import HEOM
        
        # Load the dataset from sklearn
        boston = load_boston()
        boston_data = boston["data"]
        # Categorical variables in the data
        categorical_ix = [3, 8]
        # The problem here is that NearestNeighbors can't handle np.nan
        # So we have to set up the NaN equivalent
        nan_eqv = 12345
        
        # Introduce some missingness to the data for the purpose of the example
        row_cnt, col_cnt = boston_data.shape
        for i in range(row_cnt):
            for j in range(col_cnt):
                rand_val = np.random.randint(20, size=1)
                if rand_val == 10:
                    boston_data[i, j] = nan_eqv
        
        # Declare the HEOM with a correct NaN equivalent value
        heom_metric = HEOM(boston_data, categorical_ix, nan_equivalents = [nan_eqv])
        
        # Declare NearestNeighbor and link the metric
        neighbor = NearestNeighbors(metric = heom_metric.heom)
        
        # Fit the model which uses the custom distance metric 
        neighbor.fit(boston_data)
        
        # Return 5-Nearest Neighbors to the 1st instance (row 1)
        result = neighbor.kneighbors(boston_data[0].reshape(1, -1), n_neighbors = 5)
        print(result)
        ```
        # Research Papers
        The code have implemented based on the following literature:
        HEOM, VDM and HVDM: https://arxiv.org/pdf/cs/9701101.pdf
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
