Source code for

import numpy as np
import pandas as pd

[docs]def generate_dataset(coeffs, n, std_dev, intercept=0., distribution='normal', binary=False, seed=None, **kwargs): """Generate an artificial dataset :param coeffs: List of coefficients to use for computing the ouytput variable. :type coeffs: :obj:`list` :param n: Number of observations to generate. :type n: :obj:`int` :param std_dev: Standard deviation of the distribution. :type std_dev: :obj:`list` :param intercept: Value of the intercept to be set, defaults to 0. :type intercept: :obj:`float`, optional :param distribution: Type of distribution to use for generating the input variables, defaults to 'normal'. Can be: * `normal`: :math:`X \\sim \\mathcal{N}(\\mu, \\sigma^{2})` * `unirform`: :math:`X \\sim \\mathcal{U}_{[\\text{low}, \\text{high}]}` :type distribution: :obj:`str`, optional :param binary: Define if output is binary, defaults to False. :type binary: :obj:`bool`, optional :param seed: Random seed, defaults to None. :type seed: :obj:`int`, optional :param \\*\\*kwargs: Arguments to be passed in the distribution function. Can be: * `normal`: :obj:`loc` = :math:`\\mu` and :obj:`scale` = :math:`\\sigma^{2}` * `uniform`: :obj:`low` and :obj:`high` :return: DataFrame with output variable named as :obj:`Y` and covariates as :obj:`X0`, :obj:`X1`, :obj:`X2`, ... :rtype: :obj:`pandas.DataFrame` """ rdm = np.random.RandomState(seed) if seed else np.random # We calculate the number of predictors, and create a coefficient matrix # With `p` rows and 1 column, for matrix multiplication p = len(coeffs) params = pd.DataFrame({'coeff': coeffs, 'std_dev': std_dev}) # Similar as before, but with `n` rows and `p` columns this time x = [] for index, row in params.iterrows(): if distribution.lower() == 'normal': x += [rdm.normal(size=n, **kwargs)] if distribution.lower() == 'uniform': x += [rdm.uniform(size=n, **kwargs)] X = np.array(x) e = rdm.normal(loc=0., scale=1., size=n) # Since x is a n*p matrix, and coefficients is a p*1 matrix # we can use matrix multiplication to get the value of y for each # set of values x1, x2 .. xp # We need to transpose it to get a 1*n array from a n*1 matrix to use in the regression model y = + e + intercept if binary: y = [1 if y_i > 0 else 0 for y_i in y] df = pd.DataFrame(X.T) # Assign column names for col in df.columns: df.rename(columns={col: 'X' + str(col)}, inplace=True) # Append Y df.loc[:, 'Y'] = y return df