EVOLUTION-MANAGER

Edit File: common_tests.py

from __future__ import division, print_function, absolute_import import pickle import numpy as np import numpy.testing as npt from numpy.testing import assert_allclose, assert_equal from scipy._lib._numpy_compat import suppress_warnings from pytest import raises as assert_raises import numpy.ma.testutils as ma_npt from scipy._lib._util import getargspec_no_self as _getargspec from scipy import stats def check_named_results(res, attributes, ma=False): for i, attr in enumerate(attributes): if ma: ma_npt.assert_equal(res[i], getattr(res, attr)) else: npt.assert_equal(res[i], getattr(res, attr)) def check_normalization(distfn, args, distname): norm_moment = distfn.moment(0, *args) npt.assert_allclose(norm_moment, 1.0) # this is a temporary plug: either ncf or expect is problematic; # best be marked as a knownfail, but I've no clue how to do it. if distname == "ncf": atol, rtol = 1e-5, 0 else: atol, rtol = 1e-7, 1e-7 normalization_expect = distfn.expect(lambda x: 1, args=args) npt.assert_allclose(normalization_expect, 1.0, atol=atol, rtol=rtol, err_msg=distname, verbose=True) _a, _b = distfn.support(*args) normalization_cdf = distfn.cdf(_b, *args) npt.assert_allclose(normalization_cdf, 1.0) def check_moment(distfn, arg, m, v, msg): m1 = distfn.moment(1, *arg) m2 = distfn.moment(2, *arg) if not np.isinf(m): npt.assert_almost_equal(m1, m, decimal=10, err_msg=msg + ' - 1st moment') else: # or np.isnan(m1), npt.assert_(np.isinf(m1), msg + ' - 1st moment -infinite, m1=%s' % str(m1)) if not np.isinf(v): npt.assert_almost_equal(m2 - m1 * m1, v, decimal=10, err_msg=msg + ' - 2ndt moment') else: # or np.isnan(m2), npt.assert_(np.isinf(m2), msg + ' - 2nd moment -infinite, m2=%s' % str(m2)) def check_mean_expect(distfn, arg, m, msg): if np.isfinite(m): m1 = distfn.expect(lambda x: x, arg) npt.assert_almost_equal(m1, m, decimal=5, err_msg=msg + ' - 1st moment (expect)') def check_var_expect(distfn, arg, m, v, msg): if np.isfinite(v): m2 = distfn.expect(lambda x: x*x, arg) npt.assert_almost_equal(m2, v + m*m, decimal=5, err_msg=msg + ' - 2st moment (expect)') def check_skew_expect(distfn, arg, m, v, s, msg): if np.isfinite(s): m3e = distfn.expect(lambda x: np.power(x-m, 3), arg) npt.assert_almost_equal(m3e, s * np.power(v, 1.5), decimal=5, err_msg=msg + ' - skew') else: npt.assert_(np.isnan(s)) def check_kurt_expect(distfn, arg, m, v, k, msg): if np.isfinite(k): m4e = distfn.expect(lambda x: np.power(x-m, 4), arg) npt.assert_allclose(m4e, (k + 3.) * np.power(v, 2), atol=1e-5, rtol=1e-5, err_msg=msg + ' - kurtosis') elif not np.isposinf(k): npt.assert_(np.isnan(k)) def check_entropy(distfn, arg, msg): ent = distfn.entropy(*arg) npt.assert_(not np.isnan(ent), msg + 'test Entropy is nan') def check_private_entropy(distfn, args, superclass): # compare a generic _entropy with the distribution-specific implementation npt.assert_allclose(distfn._entropy(*args), superclass._entropy(distfn, *args)) def check_entropy_vect_scale(distfn, arg): # check 2-d sc = np.asarray([[1, 2], [3, 4]]) v_ent = distfn.entropy(*arg, scale=sc) s_ent = [distfn.entropy(*arg, scale=s) for s in sc.ravel()] s_ent = np.asarray(s_ent).reshape(v_ent.shape) assert_allclose(v_ent, s_ent, atol=1e-14) # check invalid value, check cast sc = [1, 2, -3] v_ent = distfn.entropy(*arg, scale=sc) s_ent = [distfn.entropy(*arg, scale=s) for s in sc] s_ent = np.asarray(s_ent).reshape(v_ent.shape) assert_allclose(v_ent, s_ent, atol=1e-14) def check_edge_support(distfn, args): # Make sure that x=self.a and self.b are handled correctly. x = distfn.support(*args) if isinstance(distfn, stats.rv_discrete): x = x[0]-1, x[1] npt.assert_equal(distfn.cdf(x, *args), [0.0, 1.0]) npt.assert_equal(distfn.sf(x, *args), [1.0, 0.0]) if distfn.name not in ('skellam', 'dlaplace'): # with a = -inf, log(0) generates warnings npt.assert_equal(distfn.logcdf(x, *args), [-np.inf, 0.0]) npt.assert_equal(distfn.logsf(x, *args), [0.0, -np.inf]) npt.assert_equal(distfn.ppf([0.0, 1.0], *args), x) npt.assert_equal(distfn.isf([0.0, 1.0], *args), x[::-1]) # out-of-bounds for isf & ppf npt.assert_(np.isnan(distfn.isf([-1, 2], *args)).all()) npt.assert_(np.isnan(distfn.ppf([-1, 2], *args)).all()) def check_named_args(distfn, x, shape_args, defaults, meths): ## Check calling w/ named arguments. # check consistency of shapes, numargs and _parse signature signature = _getargspec(distfn._parse_args) npt.assert_(signature.varargs is None) npt.assert_(signature.keywords is None) npt.assert_(list(signature.defaults) == list(defaults)) shape_argnames = signature.args[:-len(defaults)] # a, b, loc=0, scale=1 if distfn.shapes: shapes_ = distfn.shapes.replace(',', ' ').split() else: shapes_ = '' npt.assert_(len(shapes_) == distfn.numargs) npt.assert_(len(shapes_) == len(shape_argnames)) # check calling w/ named arguments shape_args = list(shape_args) vals = [meth(x, *shape_args) for meth in meths] npt.assert_(np.all(np.isfinite(vals))) names, a, k = shape_argnames[:], shape_args[:], {} while names: k.update({names.pop(): a.pop()}) v = [meth(x, *a, **k) for meth in meths] npt.assert_array_equal(vals, v) if 'n' not in k.keys(): # `n` is first parameter of moment(), so can't be used as named arg npt.assert_equal(distfn.moment(1, *a, **k), distfn.moment(1, *shape_args)) # unknown arguments should not go through: k.update({'kaboom': 42}) assert_raises(TypeError, distfn.cdf, x, **k) def check_random_state_property(distfn, args): # check the random_state attribute of a distribution *instance* # This test fiddles with distfn.random_state. This breaks other tests, # hence need to save it and then restore. rndm = distfn.random_state # baseline: this relies on the global state np.random.seed(1234) distfn.random_state = None r0 = distfn.rvs(*args, size=8) # use an explicit instance-level random_state distfn.random_state = 1234 r1 = distfn.rvs(*args, size=8) npt.assert_equal(r0, r1) distfn.random_state = np.random.RandomState(1234) r2 = distfn.rvs(*args, size=8) npt.assert_equal(r0, r2) # can override the instance-level random_state for an individual .rvs call distfn.random_state = 2 orig_state = distfn.random_state.get_state() r3 = distfn.rvs(*args, size=8, random_state=np.random.RandomState(1234)) npt.assert_equal(r0, r3) # ... and that does not alter the instance-level random_state! npt.assert_equal(distfn.random_state.get_state(), orig_state) # finally, restore the random_state distfn.random_state = rndm def check_meth_dtype(distfn, arg, meths): q0 = [0.25, 0.5, 0.75] x0 = distfn.ppf(q0, *arg) x_cast = [x0.astype(tp) for tp in (np.int_, np.float16, np.float32, np.float64)] for x in x_cast: # casting may have clipped the values, exclude those distfn._argcheck(*arg) x = x[(distfn.a < x) & (x < distfn.b)] for meth in meths: val = meth(x, *arg) npt.assert_(val.dtype == np.float_) def check_ppf_dtype(distfn, arg): q0 = np.asarray([0.25, 0.5, 0.75]) q_cast = [q0.astype(tp) for tp in (np.float16, np.float32, np.float64)] for q in q_cast: for meth in [distfn.ppf, distfn.isf]: val = meth(q, *arg) npt.assert_(val.dtype == np.float_) def check_cmplx_deriv(distfn, arg): # Distributions allow complex arguments. def deriv(f, x, *arg): x = np.asarray(x) h = 1e-10 return (f(x + h*1j, *arg)/h).imag x0 = distfn.ppf([0.25, 0.51, 0.75], *arg) x_cast = [x0.astype(tp) for tp in (np.int_, np.float16, np.float32, np.float64)] for x in x_cast: # casting may have clipped the values, exclude those distfn._argcheck(*arg) x = x[(distfn.a < x) & (x < distfn.b)] pdf, cdf, sf = distfn.pdf(x, *arg), distfn.cdf(x, *arg), distfn.sf(x, *arg) assert_allclose(deriv(distfn.cdf, x, *arg), pdf, rtol=1e-5) assert_allclose(deriv(distfn.logcdf, x, *arg), pdf/cdf, rtol=1e-5) assert_allclose(deriv(distfn.sf, x, *arg), -pdf, rtol=1e-5) assert_allclose(deriv(distfn.logsf, x, *arg), -pdf/sf, rtol=1e-5) assert_allclose(deriv(distfn.logpdf, x, *arg), deriv(distfn.pdf, x, *arg) / distfn.pdf(x, *arg), rtol=1e-5) def check_pickling(distfn, args): # check that a distribution instance pickles and unpickles # pay special attention to the random_state property # save the random_state (restore later) rndm = distfn.random_state distfn.random_state = 1234 distfn.rvs(*args, size=8) s = pickle.dumps(distfn) r0 = distfn.rvs(*args, size=8) unpickled = pickle.loads(s) r1 = unpickled.rvs(*args, size=8) npt.assert_equal(r0, r1) # also smoke test some methods medians = [distfn.ppf(0.5, *args), unpickled.ppf(0.5, *args)] npt.assert_equal(medians[0], medians[1]) npt.assert_equal(distfn.cdf(medians[0], *args), unpickled.cdf(medians[1], *args)) # restore the random_state distfn.random_state = rndm def check_freezing(distfn, args): # regression test for gh-11089: freezing a distribution fails # if loc and/or scale are specified if isinstance(distfn, stats.rv_continuous): locscale = {'loc': 1, 'scale': 2} else: locscale = {'loc': 1} rv = distfn(*args, **locscale) assert rv.a == distfn(*args).a assert rv.b == distfn(*args).b def check_rvs_broadcast(distfunc, distname, allargs, shape, shape_only, otype): np.random.seed(123) with suppress_warnings() as sup: # frechet_l and frechet_r are deprecated, so all their # methods generate DeprecationWarnings. sup.filter(category=DeprecationWarning, message=".*frechet_") sample = distfunc.rvs(*allargs) assert_equal(sample.shape, shape, "%s: rvs failed to broadcast" % distname) if not shape_only: rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype) np.random.seed(123) expected = rvs(*allargs) assert_allclose(sample, expected, rtol=1e-15)