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| import pandas as pd | |
| import numpy as np | |
| def get_entire_subset(): | |
| """ | |
| Returns the entire subset, which is an empty dictionary | |
| :return: empty dictionary | |
| """ | |
| return {} | |
| def get_random_subset(coordinates: pd.DataFrame, prob: float, min_elements: int = 0): | |
| """ | |
| Returns a random subset | |
| :param coordinates: data frame containing having as columns the features | |
| :param prob: probability to select a value of a feature | |
| :param min_elements: minimum number of elements to be included in the randomly generated sub-population | |
| :return: dictionary representing a random sub-population | |
| """ | |
| subset_random_values = {} | |
| shuffled_column_names = np.random.permutation(coordinates.columns.values) | |
| # consider each column once, in random order | |
| for column_name in shuffled_column_names: | |
| # get unique values of the current column | |
| temp = coordinates[column_name].unique() | |
| # include each attribute value with probability = prob | |
| mask_values = np.random.rand(len(temp)) < prob | |
| if mask_values.sum() < len(temp): | |
| # set values for the current column | |
| subset_random_values[column_name] = temp[mask_values].tolist() | |
| # compute the remaining records | |
| mask_subset = coordinates[subset_random_values.keys()].isin(subset_random_values).all(axis=1) | |
| remaining_records = len(coordinates.loc[mask_subset]) | |
| # only filter on this attribute if at least min_elements records would be kept | |
| if remaining_records < min_elements: | |
| del subset_random_values[column_name] | |
| return subset_random_values |