RandCraft is a Python library for object-oriented combination and manipulation of univariate random variables, built on top of the scipy.stats module.
Have you ever wanted to add together random variables but can't be bothered working out an analytical solution? Randcraft makes it simple.
from randcraft import make_normal, make_coin_flip
coin_flip = make_coin_flip()
# <RandomVariable(discrete): mean=0.5, var=0.25>
norm = make_normal(mean=0, std_dev=0.2)
# <RandomVariable(normal): mean=0.0, var=0.04>
combined = coin_flip + norm
# <RandomVariable(mixture): mean=0.5, var=0.29>
combined.sample_one()
# 0.8678903828104276
combined.plot()
- Object-oriented random variables: Wrap and combine distributions as Python objects.
- Distribution composition: Add, multiply, and transform random variables.
- Sampling and statistics: Easily sample from composed distributions and compute statistics.
- Extensible: Supports custom distributions via subclassing.
- Integration with scipy.stats: Use any frozen continuous distribution from scipy stats
RandCraft currently supports the following distributions:
- Normal, Uniform, Beta, Gamma, Lognormal + any other parametric continuous distribution from scipy.stats
- Discrete
- DiracDelta
- Gaussian kde distribution from provided observations
- Mixture distributions
- Anonymous distribution functions based on a provided sampler function
Distributions can all be combined arbitrarily with addition and subtraction. The library will simplify the new distribution analytically where possible, and use numerical approaches otherwise.
You can also extend RandCraft with your own custom distributions.
pip install randcraftmake_normal,make_uniform...etc: Create a random variable- Addition subtraction with constants or other RVs:
+,- - Division by constant to scale RV values
.sample_numpy(size): Draw samples.get_mean(),.get_variance(): Get statistics.cdf(x): Evaluate cdf at points.ppf(x): Evaluate inverse of cdf at points.plot(): Take a look at your distribution
from randcraft.constructors import make_die_roll
die = make_die_roll(sides=6)
# <RandomVariable(discrete): mean=3.5, var=2.92>
three_dice = die.multi_sample(3)
# <RandomVariable(discrete): mean=10.5, var=8.75>
three_dice.cdf(10.0)
# 0.5
three_dice.ppf(0.5)
# 10.0from scipy.stats import uniform
from randcraft.constructors import make_scipy
rv = make_scipy(uniform, loc=1, scale=2)
# <RandomVariable(scipy-uniform): mean=2.0, var=0.333>
b = rv.scale(2.0)
# <RandomVariable(scipy-uniform): mean=4.0, var=1.33>You have observations of two independent random variables. You want to use kernal density estimation to create continuous random variables for each and then add them together.
import numpy as np
from randcraft.observations import make_gaussian_kde
observations_a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
observations_b = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0])
rv_a = make_gaussian_kde(observations=observations_a, bw_method=0.1)
# <RandomVariable(multi): mean=3.0, var=2.42>
rv_b = make_gaussian_kde(observations=observations_b)
# <RandomVariable(multi): mean=0.5, var=0.676>
rv_joined = rv_a + rv_b
# <RandomVariable(multi): mean=3.5, var=3.1>Uses gaussian_kde by scipy.stats under the hood. You also have the option to pass arguments for gaussian_kde, or provide your own kernel as a RandomVariable.
from randcraft import make_uniform
rv = make_uniform(low=0, high=1)
# <RandomVariable(scipy-uniform): mean=0.5, var=0.0833>
rv_sample_mean = rv.multi_sample(n=30)/30
# <RandomVariable(multi): mean=0.5, var=0.00278>
rv_sample_mean.plot()
You have observations of two independent random variables. You want to use kernal density estimation to create continuous random variables for each and then add them together.
from randcraft.constructors import make_normal, make_uniform, make_discrete
from randcraft.misc import mix_rvs
rv1 = make_normal(mean=0, std_dev=1)
# <RandomVariable(scipy-norm): mean=0.0, var=1.0>
rv2 = make_uniform(low=-1, high=1)
# <RandomVariable(scipy-uniform): mean=-0.0, var=0.333>
combined = rv1 + rv2
# <RandomVariable(multi): mean=0.0, var=1.33>
discrete = make_discrete(values=[1, 2, 3])
# <RandomVariable(discrete): mean=2.0, var=0.667>
# Make a new rv which has a random chance of drawing from one of the other 4 rvs
mixed = mix_rvs([rv1, rv2, combined, discrete])
# <RandomVariable(mixture): mean=0.5, var=1.58>
mixed.plot()
You can create custom random variable classes by subclassing the base RV class and implementing required methods.
The library is designed to work with univariate random variables only. Multi-dimensional rvs or correlations etc are not supported.
MIT License
Built on scipy.stats.