org.sonar.l10n.py.rules.python.S6711.html Maven / Gradle / Ivy
This rule raises an issue when legacy numpy.random.RandomState is used.
Why is this an issue?
Using a predictable seed is a common best practice when using NumPy to create reproducible results. To that end, using
np.random.seed(number) to set the seed of the global numpy.random.RandomState has been the privileged solution for a long
time.
numpy.random.RandomState and its associated methods rely on a global state, which may be problematic when threads or other forms of
concurrency are involved. The global state may be altered and the global seed may be reset at various points in the program (for instance, through an
imported package or script), which would lead to irreproducible results.
Instead, the preferred best practice to generate reproducible pseudorandom numbers is to instantiate a numpy.random.Generator object
with a seed and reuse it in different parts of the code. This avoids the reliance on a global state. Whenever a new seed is needed, a new generator
may be created instead of mutating a global state.
Below is the list of legacy functions and their alternatives:
Legacy function name
numpy.random.Generator alternative
numpy.random.RandomState.seed
numpy.random.default_rng
numpy.random.RandomState.rand
numpy.random.Generator.random
numpy.random.RandomState.randn
numpy.random.Generator.standard_normal
numpy.random.RandomState.randint
numpy.random.Generator.integers
numpy.random.RandomState.random_integers
numpy.random.Generator.integers
numpy.random.RandomState.random_sample
numpy.random.Generator.random
numpy.random.RandomState.choice
numpy.random.Generator.choice
numpy.random.RandomState.bytes
numpy.random.Generator.bytes
numpy.random.RandomState.shuffle
numpy.random.Generator.shuffle
numpy.random.RandomState.permutation
numpy.random.Generator.permutation
numpy.random.RandomState.beta
numpy.random.Generator.beta
numpy.random.RandomState.binomial
numpy.random.Generator.binomial
numpy.random.RandomState.chisquare
numpy.random.Generator.chisquare
numpy.random.RandomState.dirichlet
numpy.random.Generator.dirichlet
numpy.random.RandomState.exponential
numpy.random.Generator.exponential
numpy.random.RandomState.f
numpy.random.Generator.f
numpy.random.RandomState.gamma
numpy.random.Generator.gamma
numpy.random.RandomState.geometric
numpy.random.Generator.geometric
numpy.random.RandomState.gumbel
numpy.random.Generator.gumbel
numpy.random.RandomState.hypergeometric
numpy.random.Generator.hypergeometric
numpy.random.RandomState.laplace
numpy.random.Generator.laplace
numpy.random.RandomState.logistic
numpy.random.Generator.logistic
numpy.random.RandomState.lognormal
numpy.random.Generator.lognormal
numpy.random.RandomState.logseries
numpy.random.Generator.logseries
numpy.random.RandomState.multinomial
numpy.random.Generator.multinomial
numpy.random.RandomState.multivariate_normal
numpy.random.Generator.multivariate_normal
numpy.random.RandomState.negative_binomial
numpy.random.Generator.negative_binomial
numpy.random.RandomState.noncentral_chisquare
numpy.random.Generator.noncentral_chisquare
numpy.random.RandomState.noncentral_f
numpy.random.Generator.noncentral_f
numpy.random.RandomState.normal
numpy.random.Generator.normal
numpy.random.RandomState.pareto
numpy.random.Generator.pareto
numpy.random.RandomState.poisson
numpy.random.Generator.poisson
numpy.random.RandomState.power
numpy.random.Generator.power
numpy.random.RandomState.rayleigh
numpy.random.Generator.rayleigh
numpy.random.RandomState.standard_cauchy
numpy.random.Generator.standard_cauchy
numpy.random.RandomState.standard_exponential
numpy.random.Generator.standard_exponential
numpy.random.RandomState.standard_gamma
numpy.random.Generator.standard_gamma
numpy.random.RandomState.standard_normal
numpy.random.Generator.standard_normal
numpy.random.RandomState.standard_t
numpy.random.Generator.standard_t
numpy.random.RandomState.triangular
numpy.random.Generator.triangular
numpy.random.RandomState.uniform
numpy.random.Generator.uniform
numpy.random.RandomState.vonmises
numpy.random.Generator.vonmises
numpy.random.RandomState.wald
numpy.random.Generator.wald
numpy.random.RandomState.weibull
numpy.random.Generator.weibull
numpy.random.RandomState.zipf
numpy.random.Generator.zipf
numpy.random.beta
numpy.random.Generator.beta
numpy.random.binomial
numpy.random.Generator.binomial
numpy.random.bytes
numpy.random.Generator.bytes
numpy.random.chisquare
numpy.random.Generator.chisquare
numpy.random.choice
numpy.random.Generator.choice
numpy.random.dirichlet
numpy.random.Generator.dirichlet
numpy.random.exponential
numpy.random.Generator.exponential
numpy.random.f
numpy.random.Generator.f
numpy.random.gamma
numpy.random.Generator.gamma
numpy.random.geometric
numpy.random.Generator.geometric
numpy.random.gumbel
numpy.random.Generator.gumbel
numpy.random.hypergeometric
numpy.random.Generator.hypergeometric
numpy.random.laplace
numpy.random.Generator.laplace
numpy.random.logistic
numpy.random.Generator.logistic
numpy.random.lognormal
numpy.random.Generator.lognormal
numpy.random.logseries
numpy.random.Generator.logseries
numpy.random.multinomial
numpy.random.Generator.multinomial
numpy.random.multivariate_normal
numpy.random.Generator.multivariate_normal
numpy.random.negative_binomial
numpy.random.Generator.negative_binomial
numpy.random.noncentral_chisquare
numpy.random.Generator.noncentral_chisquare
numpy.random.noncentral_f
numpy.random.Generator.noncentral_f
numpy.random.normal
numpy.random.Generator.normal
numpy.random.pareto
numpy.random.Generator.pareto
numpy.random.permutation
numpy.random.Generator.permutation
numpy.random.poisson
numpy.random.Generator.poisson
numpy.random.power
numpy.random.Generator.power
numpy.random.rand
numpy.random.Generator.random
numpy.random.randint
numpy.random.Generator.integers
numpy.random.randn
numpy.random.Generator.standard_normal
numpy.random.random
numpy.random.Generator.random
numpy.random.random_integers
numpy.random.Generator.integers
numpy.random.random_sample
numpy.random.Generator.random
numpy.random.ranf
numpy.random.Generator.random
numpy.random.rayleigh
numpy.random.Generator.rayleigh
numpy.random.sample
numpy.random.Generator.random
numpy.random.seed
numpy.random.default_rng
numpy.random.shuffle
numpy.random.Generator.shuffle
numpy.random.standard_cauchy
numpy.random.Generator.standard_cauchy
numpy.random.standard_exponential
numpy.random.Generator.standard_exponential
numpy.random.standard_gamma
numpy.random.Generator.standard_gamma
numpy.random.standard_normal
numpy.random.Generator.standard_normal
numpy.random.standard_t
numpy.random.Generator.standard_t
numpy.random.triangular
numpy.random.Generator.triangular
numpy.random.uniform
numpy.random.Generator.uniform
numpy.random.vonmises
numpy.random.Generator.vonmises
numpy.random.wald
numpy.random.Generator.wald
numpy.random.weibull
numpy.random.Generator.weibull
numpy.random.zipf
numpy.random.Generator.zipf
How to fix it
To fix this issue, replace usages of numpy.random.RandomState to numpy.random.Generator.
Code examples
Noncompliant code example
import numpy as np
np.random.seed(42)
x = np.random.randn() # Noncompliant: this relies on numpy.random.RandomState, which is deprecated
Compliant solution
import numpy as np
generator = np.random.default_rng(42)
x = generator.standard_normal()
Resources
Documentation
- NumPy Documentation - Random Generator
- NumPy Documentation - Legacy Random Generation
- NumPy Documentation - NEP19 RNG Policy