make_linear_regression.rst

make_linear_regression

Generate data by formula.

Data description

Synthetic data generated by Generate data by formula:

Y' = X1 + X2 * T + E
Y = Y', if Y' - int(Y') > eps,
Y = 0, otherwise.

Statistics for default parameters and size equals 100,000:

Features	3
Treatment	2
Samples total	size
Y not equals 0	0.49438
Y values	0 to 555.93

Parameters:

size: integer   The number of observations. x1_params : tuple(mu, sigma), default: (0, 1)   The feature with gaussian distribution and mean=mu, sd=sigma.   X1 ~ N(mu, sigma) x2_params : tuple(mu, sigma), default: (0, 0.1)   The feature with gaussian distribution and mean=mu, sd=sigma.   X2 ~ N(mu, sigma) x3_params : tuple(mu, sigma), default: (0, 1)   The feature with gaussian distribution and mean=mu, sd=sigma.   X3 ~ N(mu, sigma) t_params : tuple(mu, sigma), default: (0, 1)   The treatment with uniform distribution. Min value=min, Max value=max-1   T ~ R(min, max) e_params : tuple(mu, sigma), default: (0, 1)   The error with gaussian distribution and mean=mu, sd=sigma.   E ~ N(mu, sigma) eps : tuple(mu, sigma), default: (0, 1)   The border value. random_state : integer, default=777   random_state is the seed used by the random number generator.

Returns:

dataset: pandas DataFrame   Generated data.

Examples

from pyuplift.datasets import make_linear_regression
df = make_linear_regression(10000)
print(df)