Implementation of state-of-the-art distance metrics from research papers which can handle mixed-type data and missing values.
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.
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Example - HEOM
# 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.reshape(1, -1), n_neighbors = 5) print(result)
The code have implemented based on the following literature: HEOM, VDM and HVDM: https://arxiv.org/pdf/cs/9701101.pdf
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