Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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324 lines
11 KiB
324 lines
11 KiB
import pickle
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import numpy as np
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import numpy.testing as npt
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from numpy.testing import assert_allclose, assert_equal, suppress_warnings
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from pytest import raises as assert_raises
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import numpy.ma.testutils as ma_npt
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from scipy._lib._util import getfullargspec_no_self as _getfullargspec
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from scipy import stats
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def check_named_results(res, attributes, ma=False):
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for i, attr in enumerate(attributes):
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if ma:
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ma_npt.assert_equal(res[i], getattr(res, attr))
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else:
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npt.assert_equal(res[i], getattr(res, attr))
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def check_normalization(distfn, args, distname):
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norm_moment = distfn.moment(0, *args)
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npt.assert_allclose(norm_moment, 1.0)
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# this is a temporary plug: either ncf or expect is problematic;
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# best be marked as a knownfail, but I've no clue how to do it.
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if distname == "ncf":
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atol, rtol = 1e-5, 0
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else:
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atol, rtol = 1e-7, 1e-7
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normalization_expect = distfn.expect(lambda x: 1, args=args)
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npt.assert_allclose(normalization_expect, 1.0, atol=atol, rtol=rtol,
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err_msg=distname, verbose=True)
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_a, _b = distfn.support(*args)
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normalization_cdf = distfn.cdf(_b, *args)
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npt.assert_allclose(normalization_cdf, 1.0)
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def check_moment(distfn, arg, m, v, msg):
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m1 = distfn.moment(1, *arg)
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m2 = distfn.moment(2, *arg)
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if not np.isinf(m):
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npt.assert_almost_equal(m1, m, decimal=10, err_msg=msg +
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' - 1st moment')
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else: # or np.isnan(m1),
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npt.assert_(np.isinf(m1),
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msg + ' - 1st moment -infinite, m1=%s' % str(m1))
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if not np.isinf(v):
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npt.assert_almost_equal(m2 - m1 * m1, v, decimal=10, err_msg=msg +
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' - 2ndt moment')
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else: # or np.isnan(m2),
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npt.assert_(np.isinf(m2),
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msg + ' - 2nd moment -infinite, m2=%s' % str(m2))
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def check_mean_expect(distfn, arg, m, msg):
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if np.isfinite(m):
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m1 = distfn.expect(lambda x: x, arg)
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npt.assert_almost_equal(m1, m, decimal=5, err_msg=msg +
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' - 1st moment (expect)')
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def check_var_expect(distfn, arg, m, v, msg):
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if np.isfinite(v):
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m2 = distfn.expect(lambda x: x*x, arg)
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npt.assert_almost_equal(m2, v + m*m, decimal=5, err_msg=msg +
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' - 2st moment (expect)')
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def check_skew_expect(distfn, arg, m, v, s, msg):
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if np.isfinite(s):
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m3e = distfn.expect(lambda x: np.power(x-m, 3), arg)
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npt.assert_almost_equal(m3e, s * np.power(v, 1.5),
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decimal=5, err_msg=msg + ' - skew')
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else:
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npt.assert_(np.isnan(s))
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def check_kurt_expect(distfn, arg, m, v, k, msg):
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if np.isfinite(k):
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m4e = distfn.expect(lambda x: np.power(x-m, 4), arg)
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npt.assert_allclose(m4e, (k + 3.) * np.power(v, 2), atol=1e-5, rtol=1e-5,
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err_msg=msg + ' - kurtosis')
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elif not np.isposinf(k):
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npt.assert_(np.isnan(k))
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def check_entropy(distfn, arg, msg):
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ent = distfn.entropy(*arg)
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npt.assert_(not np.isnan(ent), msg + 'test Entropy is nan')
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def check_private_entropy(distfn, args, superclass):
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# compare a generic _entropy with the distribution-specific implementation
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npt.assert_allclose(distfn._entropy(*args),
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superclass._entropy(distfn, *args))
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def check_entropy_vect_scale(distfn, arg):
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# check 2-d
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sc = np.asarray([[1, 2], [3, 4]])
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v_ent = distfn.entropy(*arg, scale=sc)
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s_ent = [distfn.entropy(*arg, scale=s) for s in sc.ravel()]
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s_ent = np.asarray(s_ent).reshape(v_ent.shape)
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assert_allclose(v_ent, s_ent, atol=1e-14)
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# check invalid value, check cast
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sc = [1, 2, -3]
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v_ent = distfn.entropy(*arg, scale=sc)
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s_ent = [distfn.entropy(*arg, scale=s) for s in sc]
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s_ent = np.asarray(s_ent).reshape(v_ent.shape)
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assert_allclose(v_ent, s_ent, atol=1e-14)
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def check_edge_support(distfn, args):
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# Make sure that x=self.a and self.b are handled correctly.
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x = distfn.support(*args)
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if isinstance(distfn, stats.rv_discrete):
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x = x[0]-1, x[1]
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npt.assert_equal(distfn.cdf(x, *args), [0.0, 1.0])
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npt.assert_equal(distfn.sf(x, *args), [1.0, 0.0])
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if distfn.name not in ('skellam', 'dlaplace'):
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# with a = -inf, log(0) generates warnings
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npt.assert_equal(distfn.logcdf(x, *args), [-np.inf, 0.0])
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npt.assert_equal(distfn.logsf(x, *args), [0.0, -np.inf])
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npt.assert_equal(distfn.ppf([0.0, 1.0], *args), x)
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npt.assert_equal(distfn.isf([0.0, 1.0], *args), x[::-1])
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# out-of-bounds for isf & ppf
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npt.assert_(np.isnan(distfn.isf([-1, 2], *args)).all())
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npt.assert_(np.isnan(distfn.ppf([-1, 2], *args)).all())
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def check_named_args(distfn, x, shape_args, defaults, meths):
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## Check calling w/ named arguments.
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# check consistency of shapes, numargs and _parse signature
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signature = _getfullargspec(distfn._parse_args)
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npt.assert_(signature.varargs is None)
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npt.assert_(signature.varkw is None)
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npt.assert_(not signature.kwonlyargs)
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npt.assert_(list(signature.defaults) == list(defaults))
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shape_argnames = signature.args[:-len(defaults)] # a, b, loc=0, scale=1
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if distfn.shapes:
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shapes_ = distfn.shapes.replace(',', ' ').split()
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else:
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shapes_ = ''
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npt.assert_(len(shapes_) == distfn.numargs)
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npt.assert_(len(shapes_) == len(shape_argnames))
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# check calling w/ named arguments
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shape_args = list(shape_args)
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vals = [meth(x, *shape_args) for meth in meths]
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npt.assert_(np.all(np.isfinite(vals)))
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names, a, k = shape_argnames[:], shape_args[:], {}
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while names:
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k.update({names.pop(): a.pop()})
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v = [meth(x, *a, **k) for meth in meths]
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npt.assert_array_equal(vals, v)
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if 'n' not in k.keys():
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# `n` is first parameter of moment(), so can't be used as named arg
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npt.assert_equal(distfn.moment(1, *a, **k),
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distfn.moment(1, *shape_args))
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# unknown arguments should not go through:
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k.update({'kaboom': 42})
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assert_raises(TypeError, distfn.cdf, x, **k)
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def check_random_state_property(distfn, args):
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# check the random_state attribute of a distribution *instance*
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# This test fiddles with distfn.random_state. This breaks other tests,
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# hence need to save it and then restore.
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rndm = distfn.random_state
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# baseline: this relies on the global state
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np.random.seed(1234)
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distfn.random_state = None
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r0 = distfn.rvs(*args, size=8)
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# use an explicit instance-level random_state
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distfn.random_state = 1234
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r1 = distfn.rvs(*args, size=8)
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npt.assert_equal(r0, r1)
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distfn.random_state = np.random.RandomState(1234)
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r2 = distfn.rvs(*args, size=8)
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npt.assert_equal(r0, r2)
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# check that np.random.Generator can be used (numpy >= 1.17)
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if hasattr(np.random, 'default_rng'):
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# obtain a np.random.Generator object
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rng = np.random.default_rng(1234)
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distfn.rvs(*args, size=1, random_state=rng)
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# can override the instance-level random_state for an individual .rvs call
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distfn.random_state = 2
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orig_state = distfn.random_state.get_state()
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r3 = distfn.rvs(*args, size=8, random_state=np.random.RandomState(1234))
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npt.assert_equal(r0, r3)
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# ... and that does not alter the instance-level random_state!
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npt.assert_equal(distfn.random_state.get_state(), orig_state)
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# finally, restore the random_state
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distfn.random_state = rndm
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def check_meth_dtype(distfn, arg, meths):
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q0 = [0.25, 0.5, 0.75]
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x0 = distfn.ppf(q0, *arg)
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x_cast = [x0.astype(tp) for tp in
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(np.int_, np.float16, np.float32, np.float64)]
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for x in x_cast:
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# casting may have clipped the values, exclude those
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distfn._argcheck(*arg)
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x = x[(distfn.a < x) & (x < distfn.b)]
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for meth in meths:
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val = meth(x, *arg)
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npt.assert_(val.dtype == np.float_)
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def check_ppf_dtype(distfn, arg):
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q0 = np.asarray([0.25, 0.5, 0.75])
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q_cast = [q0.astype(tp) for tp in (np.float16, np.float32, np.float64)]
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for q in q_cast:
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for meth in [distfn.ppf, distfn.isf]:
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val = meth(q, *arg)
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npt.assert_(val.dtype == np.float_)
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def check_cmplx_deriv(distfn, arg):
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# Distributions allow complex arguments.
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def deriv(f, x, *arg):
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x = np.asarray(x)
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h = 1e-10
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return (f(x + h*1j, *arg)/h).imag
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x0 = distfn.ppf([0.25, 0.51, 0.75], *arg)
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x_cast = [x0.astype(tp) for tp in
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(np.int_, np.float16, np.float32, np.float64)]
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for x in x_cast:
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# casting may have clipped the values, exclude those
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distfn._argcheck(*arg)
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x = x[(distfn.a < x) & (x < distfn.b)]
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pdf, cdf, sf = distfn.pdf(x, *arg), distfn.cdf(x, *arg), distfn.sf(x, *arg)
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assert_allclose(deriv(distfn.cdf, x, *arg), pdf, rtol=1e-5)
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assert_allclose(deriv(distfn.logcdf, x, *arg), pdf/cdf, rtol=1e-5)
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assert_allclose(deriv(distfn.sf, x, *arg), -pdf, rtol=1e-5)
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assert_allclose(deriv(distfn.logsf, x, *arg), -pdf/sf, rtol=1e-5)
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assert_allclose(deriv(distfn.logpdf, x, *arg),
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deriv(distfn.pdf, x, *arg) / distfn.pdf(x, *arg),
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rtol=1e-5)
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def check_pickling(distfn, args):
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# check that a distribution instance pickles and unpickles
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# pay special attention to the random_state property
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# save the random_state (restore later)
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rndm = distfn.random_state
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distfn.random_state = 1234
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distfn.rvs(*args, size=8)
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s = pickle.dumps(distfn)
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r0 = distfn.rvs(*args, size=8)
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unpickled = pickle.loads(s)
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r1 = unpickled.rvs(*args, size=8)
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npt.assert_equal(r0, r1)
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# also smoke test some methods
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medians = [distfn.ppf(0.5, *args), unpickled.ppf(0.5, *args)]
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npt.assert_equal(medians[0], medians[1])
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npt.assert_equal(distfn.cdf(medians[0], *args),
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unpickled.cdf(medians[1], *args))
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# restore the random_state
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distfn.random_state = rndm
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def check_freezing(distfn, args):
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# regression test for gh-11089: freezing a distribution fails
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# if loc and/or scale are specified
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if isinstance(distfn, stats.rv_continuous):
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locscale = {'loc': 1, 'scale': 2}
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else:
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locscale = {'loc': 1}
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rv = distfn(*args, **locscale)
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assert rv.a == distfn(*args).a
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assert rv.b == distfn(*args).b
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def check_rvs_broadcast(distfunc, distname, allargs, shape, shape_only, otype):
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np.random.seed(123)
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with suppress_warnings() as sup:
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# frechet_l and frechet_r are deprecated, so all their
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# methods generate DeprecationWarnings.
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sup.filter(category=DeprecationWarning, message=".*frechet_")
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sample = distfunc.rvs(*allargs)
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assert_equal(sample.shape, shape, "%s: rvs failed to broadcast" % distname)
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if not shape_only:
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rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
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np.random.seed(123)
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expected = rvs(*allargs)
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assert_allclose(sample, expected, rtol=1e-13)
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