import contextlib import numbers from itertools import chain, product from numbers import Integral from operator import getitem import numpy as np from ..base import tokenize from ..highlevelgraph import HighLevelGraph from ..utils import derived_from, random_state_data from .core import ( Array, asarray, broadcast_shapes, broadcast_to, normalize_chunks, slices_from_chunks, ) from .creation import arange class RandomState: """ Mersenne Twister pseudo-random number generator This object contains state to deterministically generate pseudo-random numbers from a variety of probability distributions. It is identical to ``np.random.RandomState`` except that all functions also take a ``chunks=`` keyword argument. Parameters ---------- seed: Number Object to pass to RandomState to serve as deterministic seed RandomState: Callable[seed] -> RandomState A callable that, when provided with a ``seed`` keyword provides an object that operates identically to ``np.random.RandomState`` (the default). This might also be a function that returns a ``randomgen.RandomState``, ``mkl_random``, or ``cupy.random.RandomState`` object. Examples -------- >>> import dask.array as da >>> state = da.random.RandomState(1234) # a seed >>> x = state.normal(10, 0.1, size=3, chunks=(2,)) >>> x.compute() array([10.01867852, 10.04812289, 9.89649746]) See Also -------- np.random.RandomState """ def __init__(self, seed=None, RandomState=None): self._numpy_state = np.random.RandomState(seed) self._RandomState = RandomState def seed(self, seed=None): self._numpy_state.seed(seed) def _wrap( self, funcname, *args, size=None, chunks="auto", extra_chunks=(), **kwargs ): """Wrap numpy random function to produce dask.array random function extra_chunks should be a chunks tuple to append to the end of chunks """ if size is not None and not isinstance(size, (tuple, list)): size = (size,) shapes = list( { ar.shape for ar in chain(args, kwargs.values()) if isinstance(ar, (Array, np.ndarray)) } ) if size is not None: shapes.append(size) # broadcast to the final size(shape) size = broadcast_shapes(*shapes) chunks = normalize_chunks( chunks, size, # ideally would use dtype here dtype=kwargs.get("dtype", np.float64), ) slices = slices_from_chunks(chunks) def _broadcast_any(ar, shape, chunks): if isinstance(ar, Array): return broadcast_to(ar, shape).rechunk(chunks) if isinstance(ar, np.ndarray): return np.ascontiguousarray(np.broadcast_to(ar, shape)) # Broadcast all arguments, get tiny versions as well # Start adding the relevant bits to the graph dsk = {} lookup = {} small_args = [] dependencies = [] for i, ar in enumerate(args): if isinstance(ar, (np.ndarray, Array)): res = _broadcast_any(ar, size, chunks) if isinstance(res, Array): dependencies.append(res) lookup[i] = res.name elif isinstance(res, np.ndarray): name = f"array-{tokenize(res)}" lookup[i] = name dsk[name] = res small_args.append(ar[tuple(0 for _ in ar.shape)]) else: small_args.append(ar) small_kwargs = {} for key, ar in kwargs.items(): if isinstance(ar, (np.ndarray, Array)): res = _broadcast_any(ar, size, chunks) if isinstance(res, Array): dependencies.append(res) lookup[key] = res.name elif isinstance(res, np.ndarray): name = f"array-{tokenize(res)}" lookup[key] = name dsk[name] = res small_kwargs[key] = ar[tuple(0 for _ in ar.shape)] else: small_kwargs[key] = ar sizes = list(product(*chunks)) seeds = random_state_data(len(sizes), self._numpy_state) token = tokenize(seeds, size, chunks, args, kwargs) name = f"{funcname}-{token}" keys = product( [name], *([range(len(bd)) for bd in chunks] + [[0]] * len(extra_chunks)) ) blocks = product(*[range(len(bd)) for bd in chunks]) vals = [] for seed, size, slc, block in zip(seeds, sizes, slices, blocks): arg = [] for i, ar in enumerate(args): if i not in lookup: arg.append(ar) else: if isinstance(ar, Array): arg.append((lookup[i],) + block) else: # np.ndarray arg.append((getitem, lookup[i], slc)) kwrg = {} for k, ar in kwargs.items(): if k not in lookup: kwrg[k] = ar else: if isinstance(ar, Array): kwrg[k] = (lookup[k],) + block else: # np.ndarray kwrg[k] = (getitem, lookup[k], slc) vals.append( (_apply_random, self._RandomState, funcname, seed, size, arg, kwrg) ) meta = _apply_random( self._RandomState, funcname, seed, (0,) * len(size), small_args, small_kwargs, ) dsk.update(dict(zip(keys, vals))) graph = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies) return Array(graph, name, chunks + extra_chunks, meta=meta) @derived_from(np.random.RandomState, skipblocks=1) def beta(self, a, b, size=None, chunks="auto", **kwargs): return self._wrap("beta", a, b, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def binomial(self, n, p, size=None, chunks="auto", **kwargs): return self._wrap("binomial", n, p, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def chisquare(self, df, size=None, chunks="auto", **kwargs): return self._wrap("chisquare", df, size=size, chunks=chunks, **kwargs) with contextlib.suppress(AttributeError): @derived_from(np.random.RandomState, skipblocks=1) def choice(self, a, size=None, replace=True, p=None, chunks="auto"): dependencies = [] # Normalize and validate `a` if isinstance(a, Integral): # On windows the output dtype differs if p is provided or # absent, see https://github.com/numpy/numpy/issues/9867 dummy_p = np.array([1]) if p is not None else p dtype = np.random.choice(1, size=(), p=dummy_p).dtype len_a = a if a < 0: raise ValueError("a must be greater than 0") else: a = asarray(a) a = a.rechunk(a.shape) dtype = a.dtype if a.ndim != 1: raise ValueError("a must be one dimensional") len_a = len(a) dependencies.append(a) a = a.__dask_keys__()[0] # Normalize and validate `p` if p is not None: if not isinstance(p, Array): # If p is not a dask array, first check the sum is close # to 1 before converting. p = np.asarray(p) if not np.isclose(p.sum(), 1, rtol=1e-7, atol=0): raise ValueError("probabilities do not sum to 1") p = asarray(p) else: p = p.rechunk(p.shape) if p.ndim != 1: raise ValueError("p must be one dimensional") if len(p) != len_a: raise ValueError("a and p must have the same size") dependencies.append(p) p = p.__dask_keys__()[0] if size is None: size = () elif not isinstance(size, (tuple, list)): size = (size,) chunks = normalize_chunks(chunks, size, dtype=np.float64) if not replace and len(chunks[0]) > 1: err_msg = ( "replace=False is not currently supported for " "dask.array.choice with multi-chunk output " "arrays" ) raise NotImplementedError(err_msg) sizes = list(product(*chunks)) state_data = random_state_data(len(sizes), self._numpy_state) name = "da.random.choice-%s" % tokenize( state_data, size, chunks, a, replace, p ) keys = product([name], *(range(len(bd)) for bd in chunks)) dsk = { k: (_choice, state, a, size, replace, p) for k, state, size in zip(keys, state_data, sizes) } graph = HighLevelGraph.from_collections( name, dsk, dependencies=dependencies ) return Array(graph, name, chunks, dtype=dtype) # @derived_from(np.random.RandomState, skipblocks=1) # def dirichlet(self, alpha, size=None, chunks="auto"): @derived_from(np.random.RandomState, skipblocks=1) def exponential(self, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("exponential", scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def f(self, dfnum, dfden, size=None, chunks="auto", **kwargs): return self._wrap("f", dfnum, dfden, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def gamma(self, shape, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("gamma", shape, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def geometric(self, p, size=None, chunks="auto", **kwargs): return self._wrap("geometric", p, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def gumbel(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("gumbel", loc, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def hypergeometric(self, ngood, nbad, nsample, size=None, chunks="auto", **kwargs): return self._wrap( "hypergeometric", ngood, nbad, nsample, size=size, chunks=chunks, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def laplace(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("laplace", loc, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def logistic(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("logistic", loc, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def lognormal(self, mean=0.0, sigma=1.0, size=None, chunks="auto", **kwargs): return self._wrap("lognormal", mean, sigma, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def logseries(self, p, size=None, chunks="auto", **kwargs): return self._wrap("logseries", p, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def multinomial(self, n, pvals, size=None, chunks="auto", **kwargs): return self._wrap( "multinomial", n, pvals, size=size, chunks=chunks, extra_chunks=((len(pvals),),), ) @derived_from(np.random.RandomState, skipblocks=1) def negative_binomial(self, n, p, size=None, chunks="auto", **kwargs): return self._wrap("negative_binomial", n, p, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def noncentral_chisquare(self, df, nonc, size=None, chunks="auto", **kwargs): return self._wrap( "noncentral_chisquare", df, nonc, size=size, chunks=chunks, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def noncentral_f(self, dfnum, dfden, nonc, size=None, chunks="auto", **kwargs): return self._wrap( "noncentral_f", dfnum, dfden, nonc, size=size, chunks=chunks, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def normal(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("normal", loc, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def pareto(self, a, size=None, chunks="auto", **kwargs): return self._wrap("pareto", a, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def permutation(self, x): from .slicing import shuffle_slice if isinstance(x, numbers.Number): x = arange(x, chunks="auto") index = np.arange(len(x)) self._numpy_state.shuffle(index) return shuffle_slice(x, index) @derived_from(np.random.RandomState, skipblocks=1) def poisson(self, lam=1.0, size=None, chunks="auto", **kwargs): return self._wrap("poisson", lam, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def power(self, a, size=None, chunks="auto", **kwargs): return self._wrap("power", a, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def randint(self, low, high=None, size=None, chunks="auto", dtype="l", **kwargs): return self._wrap( "randint", low, high, size=size, chunks=chunks, dtype=dtype, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def random_integers(self, low, high=None, size=None, chunks="auto", **kwargs): return self._wrap( "random_integers", low, high, size=size, chunks=chunks, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def random_sample(self, size=None, chunks="auto", **kwargs): return self._wrap("random_sample", size=size, chunks=chunks, **kwargs) random = random_sample @derived_from(np.random.RandomState, skipblocks=1) def rayleigh(self, scale=1.0, size=None, chunks="auto", **kwargs): return self._wrap("rayleigh", scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def standard_cauchy(self, size=None, chunks="auto", **kwargs): return self._wrap("standard_cauchy", size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def standard_exponential(self, size=None, chunks="auto", **kwargs): return self._wrap("standard_exponential", size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def standard_gamma(self, shape, size=None, chunks="auto", **kwargs): return self._wrap("standard_gamma", shape, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def standard_normal(self, size=None, chunks="auto", **kwargs): return self._wrap("standard_normal", size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def standard_t(self, df, size=None, chunks="auto", **kwargs): return self._wrap("standard_t", df, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def tomaxint(self, size=None, chunks="auto", **kwargs): return self._wrap("tomaxint", size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def triangular(self, left, mode, right, size=None, chunks="auto", **kwargs): return self._wrap( "triangular", left, mode, right, size=size, chunks=chunks, **kwargs ) @derived_from(np.random.RandomState, skipblocks=1) def uniform(self, low=0.0, high=1.0, size=None, chunks="auto", **kwargs): return self._wrap("uniform", low, high, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def vonmises(self, mu, kappa, size=None, chunks="auto", **kwargs): return self._wrap("vonmises", mu, kappa, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def wald(self, mean, scale, size=None, chunks="auto", **kwargs): return self._wrap("wald", mean, scale, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def weibull(self, a, size=None, chunks="auto", **kwargs): return self._wrap("weibull", a, size=size, chunks=chunks, **kwargs) @derived_from(np.random.RandomState, skipblocks=1) def zipf(self, a, size=None, chunks="auto", **kwargs): return self._wrap("zipf", a, size=size, chunks=chunks, **kwargs) def _choice(state_data, a, size, replace, p): state = np.random.RandomState(state_data) return state.choice(a, size=size, replace=replace, p=p) def _apply_random(RandomState, funcname, state_data, size, args, kwargs): """Apply RandomState method with seed""" if RandomState is None: RandomState = np.random.RandomState state = RandomState(state_data) func = getattr(state, funcname) return func(*args, size=size, **kwargs) _state = RandomState() seed = _state.seed beta = _state.beta binomial = _state.binomial chisquare = _state.chisquare if hasattr(_state, "choice"): choice = _state.choice exponential = _state.exponential f = _state.f gamma = _state.gamma geometric = _state.geometric gumbel = _state.gumbel hypergeometric = _state.hypergeometric laplace = _state.laplace logistic = _state.logistic lognormal = _state.lognormal logseries = _state.logseries multinomial = _state.multinomial negative_binomial = _state.negative_binomial noncentral_chisquare = _state.noncentral_chisquare noncentral_f = _state.noncentral_f normal = _state.normal pareto = _state.pareto permutation = _state.permutation poisson = _state.poisson power = _state.power rayleigh = _state.rayleigh random_sample = _state.random_sample random = random_sample randint = _state.randint random_integers = _state.random_integers triangular = _state.triangular uniform = _state.uniform vonmises = _state.vonmises wald = _state.wald weibull = _state.weibull zipf = _state.zipf """ Standard distributions """ standard_cauchy = _state.standard_cauchy standard_exponential = _state.standard_exponential standard_gamma = _state.standard_gamma standard_normal = _state.standard_normal standard_t = _state.standard_t