330 lines
7.9 KiB
Plaintext
330 lines
7.9 KiB
Plaintext
This rule raises an issue when legacy `numpy.random.RandomState` is used.
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== Why is this an issue?
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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.
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`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.
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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.
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Below is the list of legacy functions and their alternatives:
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[cols="1,1"]
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|===
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|Legacy function name
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|numpy.random.Generator alternative
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|numpy.random.RandomState.seed
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|numpy.random.default_rng
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|numpy.random.RandomState.rand
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|numpy.random.Generator.random
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|numpy.random.RandomState.randn
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|numpy.random.Generator.standard_normal
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|numpy.random.RandomState.randint
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|numpy.random.Generator.integers
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|numpy.random.RandomState.random_integers
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|numpy.random.Generator.integers
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|numpy.random.RandomState.random_sample
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|numpy.random.Generator.random
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|numpy.random.RandomState.choice
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|numpy.random.Generator.choice
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|numpy.random.RandomState.bytes
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|numpy.random.Generator.bytes
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|numpy.random.RandomState.shuffle
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|numpy.random.Generator.shuffle
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|numpy.random.RandomState.permutation
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|numpy.random.Generator.permutation
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|numpy.random.RandomState.beta
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|numpy.random.Generator.beta
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|numpy.random.RandomState.binomial
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|numpy.random.Generator.binomial
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|numpy.random.RandomState.chisquare
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|numpy.random.Generator.chisquare
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|numpy.random.RandomState.dirichlet
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|numpy.random.Generator.dirichlet
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|numpy.random.RandomState.exponential
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|numpy.random.Generator.exponential
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|numpy.random.RandomState.f
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|numpy.random.Generator.f
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|numpy.random.RandomState.gamma
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|numpy.random.Generator.gamma
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|numpy.random.RandomState.geometric
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|numpy.random.Generator.geometric
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|numpy.random.RandomState.gumbel
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|numpy.random.Generator.gumbel
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|numpy.random.RandomState.hypergeometric
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|numpy.random.Generator.hypergeometric
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|numpy.random.RandomState.laplace
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|numpy.random.Generator.laplace
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|numpy.random.RandomState.logistic
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|numpy.random.Generator.logistic
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|numpy.random.RandomState.lognormal
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|numpy.random.Generator.lognormal
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|numpy.random.RandomState.logseries
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|numpy.random.Generator.logseries
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|numpy.random.RandomState.multinomial
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|numpy.random.Generator.multinomial
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|numpy.random.RandomState.multivariate_normal
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|numpy.random.Generator.multivariate_normal
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|numpy.random.RandomState.negative_binomial
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|numpy.random.Generator.negative_binomial
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|numpy.random.RandomState.noncentral_chisquare
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|numpy.random.Generator.noncentral_chisquare
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|numpy.random.RandomState.noncentral_f
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|numpy.random.Generator.noncentral_f
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|numpy.random.RandomState.normal
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|numpy.random.Generator.normal
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|numpy.random.RandomState.pareto
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|numpy.random.Generator.pareto
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|numpy.random.RandomState.poisson
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|numpy.random.Generator.poisson
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|numpy.random.RandomState.power
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|numpy.random.Generator.power
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|numpy.random.RandomState.rayleigh
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|numpy.random.Generator.rayleigh
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|numpy.random.RandomState.standard_cauchy
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|numpy.random.Generator.standard_cauchy
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|numpy.random.RandomState.standard_exponential
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|numpy.random.Generator.standard_exponential
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|numpy.random.RandomState.standard_gamma
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|numpy.random.Generator.standard_gamma
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|numpy.random.RandomState.standard_normal
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|numpy.random.Generator.standard_normal
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|numpy.random.RandomState.standard_t
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|numpy.random.Generator.standard_t
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|numpy.random.RandomState.triangular
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|numpy.random.Generator.triangular
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|numpy.random.RandomState.uniform
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|numpy.random.Generator.uniform
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|numpy.random.RandomState.vonmises
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|numpy.random.Generator.vonmises
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|numpy.random.RandomState.wald
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|numpy.random.Generator.wald
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|numpy.random.RandomState.weibull
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|numpy.random.Generator.weibull
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|numpy.random.RandomState.zipf
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|numpy.random.Generator.zipf
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|numpy.random.beta
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|numpy.random.Generator.beta
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|numpy.random.binomial
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|numpy.random.Generator.binomial
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|numpy.random.bytes
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|numpy.random.Generator.bytes
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|numpy.random.chisquare
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|numpy.random.Generator.chisquare
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|numpy.random.choice
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|numpy.random.Generator.choice
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|numpy.random.dirichlet
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|numpy.random.Generator.dirichlet
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|numpy.random.exponential
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|numpy.random.Generator.exponential
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|numpy.random.f
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|numpy.random.Generator.f
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|numpy.random.gamma
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|numpy.random.Generator.gamma
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|numpy.random.geometric
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|numpy.random.Generator.geometric
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|numpy.random.gumbel
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|numpy.random.Generator.gumbel
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|numpy.random.hypergeometric
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|numpy.random.Generator.hypergeometric
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|numpy.random.laplace
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|numpy.random.Generator.laplace
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|numpy.random.logistic
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|numpy.random.Generator.logistic
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|numpy.random.lognormal
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|numpy.random.Generator.lognormal
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|numpy.random.logseries
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|numpy.random.Generator.logseries
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|numpy.random.multinomial
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|numpy.random.Generator.multinomial
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|numpy.random.multivariate_normal
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|numpy.random.Generator.multivariate_normal
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|numpy.random.negative_binomial
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|numpy.random.Generator.negative_binomial
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|numpy.random.noncentral_chisquare
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|numpy.random.Generator.noncentral_chisquare
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|numpy.random.noncentral_f
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|numpy.random.Generator.noncentral_f
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|numpy.random.normal
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|numpy.random.Generator.normal
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|numpy.random.pareto
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|numpy.random.Generator.pareto
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|numpy.random.permutation
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|numpy.random.Generator.permutation
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|numpy.random.poisson
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|numpy.random.Generator.poisson
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|numpy.random.power
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|numpy.random.Generator.power
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|numpy.random.rand
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|numpy.random.Generator.random
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|numpy.random.randint
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|numpy.random.Generator.integers
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|numpy.random.randn
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|numpy.random.Generator.standard_normal
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|numpy.random.random
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|numpy.random.Generator.random
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|numpy.random.random_integers
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|numpy.random.Generator.integers
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|numpy.random.random_sample
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|numpy.random.Generator.random
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|numpy.random.ranf
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|numpy.random.Generator.random
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|numpy.random.rayleigh
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|numpy.random.Generator.rayleigh
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|numpy.random.sample
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|numpy.random.Generator.random
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|numpy.random.seed
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|numpy.random.default_rng
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|numpy.random.shuffle
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|numpy.random.Generator.shuffle
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|numpy.random.standard_cauchy
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|numpy.random.Generator.standard_cauchy
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|numpy.random.standard_exponential
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|numpy.random.Generator.standard_exponential
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|numpy.random.standard_gamma
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|numpy.random.Generator.standard_gamma
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|numpy.random.standard_normal
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|numpy.random.Generator.standard_normal
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|numpy.random.standard_t
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|numpy.random.Generator.standard_t
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|numpy.random.triangular
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|numpy.random.Generator.triangular
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|numpy.random.uniform
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|numpy.random.Generator.uniform
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|numpy.random.vonmises
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|numpy.random.Generator.vonmises
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|numpy.random.wald
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|numpy.random.Generator.wald
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|numpy.random.weibull
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|numpy.random.Generator.weibull
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|numpy.random.zipf
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|numpy.random.Generator.zipf
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|===
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== How to fix it
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To fix this issue, replace usages of `numpy.random.RandomState` to `numpy.random.Generator`.
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=== Code examples
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==== Noncompliant code example
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[source,python,diff-id=1,diff-type=noncompliant]
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----
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import numpy as np
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np.random.seed(42)
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x = np.random.randn() # Noncompliant: this relies on numpy.random.RandomState, which is deprecated
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----
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==== Compliant solution
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[source,python,diff-id=1,diff-type=compliant]
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----
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import numpy as np
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generator = np.random.default_rng(42)
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x = generator.standard_normal()
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----
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== Resources
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=== Documentation
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* NumPy Documentation - https://numpy.org/doc/stable/reference/random/generator.html#random-generator[Random Generator]
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* NumPy Documentation - https://numpy.org/doc/stable/reference/random/legacy.html#legacy-random-generation[Legacy Random Generation]
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* NumPy Documentation - https://numpy.org/neps/nep-0019-rng-policy.html[NEP19 RNG Policy]
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