Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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316 lines
12 KiB
316 lines
12 KiB
import os
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import functools
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import operator
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from distutils.version import LooseVersion
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import numpy as np
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from numpy.testing import assert_
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import pytest
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import scipy.special as sc
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__all__ = ['with_special_errors', 'assert_func_equal', 'FuncData']
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#------------------------------------------------------------------------------
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# Check if a module is present to be used in tests
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#------------------------------------------------------------------------------
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class MissingModule(object):
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def __init__(self, name):
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self.name = name
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def check_version(module, min_ver):
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if type(module) == MissingModule:
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return pytest.mark.skip(reason="{} is not installed".format(module.name))
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return pytest.mark.skipif(LooseVersion(module.__version__) < LooseVersion(min_ver),
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reason="{} version >= {} required".format(module.__name__, min_ver))
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#------------------------------------------------------------------------------
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# Enable convergence and loss of precision warnings -- turn off one by one
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#------------------------------------------------------------------------------
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def with_special_errors(func):
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"""
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Enable special function errors (such as underflow, overflow,
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loss of precision, etc.)
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"""
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@functools.wraps(func)
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def wrapper(*a, **kw):
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with sc.errstate(all='raise'):
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res = func(*a, **kw)
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return res
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return wrapper
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#------------------------------------------------------------------------------
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# Comparing function values at many data points at once, with helpful
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# error reports
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#------------------------------------------------------------------------------
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def assert_func_equal(func, results, points, rtol=None, atol=None,
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param_filter=None, knownfailure=None,
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vectorized=True, dtype=None, nan_ok=False,
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ignore_inf_sign=False, distinguish_nan_and_inf=True):
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if hasattr(points, 'next'):
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# it's a generator
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points = list(points)
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points = np.asarray(points)
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if points.ndim == 1:
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points = points[:,None]
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nparams = points.shape[1]
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if hasattr(results, '__name__'):
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# function
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data = points
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result_columns = None
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result_func = results
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else:
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# dataset
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data = np.c_[points, results]
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result_columns = list(range(nparams, data.shape[1]))
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result_func = None
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fdata = FuncData(func, data, list(range(nparams)),
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result_columns=result_columns, result_func=result_func,
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rtol=rtol, atol=atol, param_filter=param_filter,
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knownfailure=knownfailure, nan_ok=nan_ok, vectorized=vectorized,
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ignore_inf_sign=ignore_inf_sign,
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distinguish_nan_and_inf=distinguish_nan_and_inf)
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fdata.check()
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class FuncData(object):
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"""
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Data set for checking a special function.
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Parameters
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----------
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func : function
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Function to test
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data : numpy array
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columnar data to use for testing
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param_columns : int or tuple of ints
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Columns indices in which the parameters to `func` lie.
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Can be imaginary integers to indicate that the parameter
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should be cast to complex.
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result_columns : int or tuple of ints, optional
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Column indices for expected results from `func`.
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result_func : callable, optional
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Function to call to obtain results.
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rtol : float, optional
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Required relative tolerance. Default is 5*eps.
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atol : float, optional
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Required absolute tolerance. Default is 5*tiny.
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param_filter : function, or tuple of functions/Nones, optional
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Filter functions to exclude some parameter ranges.
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If omitted, no filtering is done.
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knownfailure : str, optional
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Known failure error message to raise when the test is run.
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If omitted, no exception is raised.
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nan_ok : bool, optional
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If nan is always an accepted result.
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vectorized : bool, optional
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Whether all functions passed in are vectorized.
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ignore_inf_sign : bool, optional
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Whether to ignore signs of infinities.
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(Doesn't matter for complex-valued functions.)
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distinguish_nan_and_inf : bool, optional
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If True, treat numbers which contain nans or infs as as
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equal. Sets ignore_inf_sign to be True.
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"""
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def __init__(self, func, data, param_columns, result_columns=None,
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result_func=None, rtol=None, atol=None, param_filter=None,
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knownfailure=None, dataname=None, nan_ok=False, vectorized=True,
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ignore_inf_sign=False, distinguish_nan_and_inf=True):
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self.func = func
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self.data = data
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self.dataname = dataname
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if not hasattr(param_columns, '__len__'):
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param_columns = (param_columns,)
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self.param_columns = tuple(param_columns)
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if result_columns is not None:
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if not hasattr(result_columns, '__len__'):
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result_columns = (result_columns,)
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self.result_columns = tuple(result_columns)
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if result_func is not None:
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raise ValueError("Only result_func or result_columns should be provided")
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elif result_func is not None:
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self.result_columns = None
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else:
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raise ValueError("Either result_func or result_columns should be provided")
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self.result_func = result_func
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self.rtol = rtol
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self.atol = atol
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if not hasattr(param_filter, '__len__'):
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param_filter = (param_filter,)
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self.param_filter = param_filter
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self.knownfailure = knownfailure
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self.nan_ok = nan_ok
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self.vectorized = vectorized
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self.ignore_inf_sign = ignore_inf_sign
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self.distinguish_nan_and_inf = distinguish_nan_and_inf
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if not self.distinguish_nan_and_inf:
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self.ignore_inf_sign = True
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def get_tolerances(self, dtype):
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if not np.issubdtype(dtype, np.inexact):
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dtype = np.dtype(float)
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info = np.finfo(dtype)
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rtol, atol = self.rtol, self.atol
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if rtol is None:
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rtol = 5*info.eps
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if atol is None:
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atol = 5*info.tiny
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return rtol, atol
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def check(self, data=None, dtype=None, dtypes=None):
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"""Check the special function against the data."""
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__tracebackhide__ = operator.methodcaller(
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'errisinstance', AssertionError
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)
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if self.knownfailure:
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pytest.xfail(reason=self.knownfailure)
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if data is None:
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data = self.data
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if dtype is None:
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dtype = data.dtype
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else:
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data = data.astype(dtype)
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rtol, atol = self.get_tolerances(dtype)
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# Apply given filter functions
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if self.param_filter:
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param_mask = np.ones((data.shape[0],), np.bool_)
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for j, filter in zip(self.param_columns, self.param_filter):
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if filter:
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param_mask &= list(filter(data[:,j]))
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data = data[param_mask]
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# Pick parameters from the correct columns
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params = []
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for idx, j in enumerate(self.param_columns):
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if np.iscomplexobj(j):
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j = int(j.imag)
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params.append(data[:,j].astype(complex))
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elif dtypes and idx < len(dtypes):
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params.append(data[:, j].astype(dtypes[idx]))
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else:
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params.append(data[:,j])
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# Helper for evaluating results
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def eval_func_at_params(func, skip_mask=None):
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if self.vectorized:
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got = func(*params)
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else:
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got = []
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for j in range(len(params[0])):
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if skip_mask is not None and skip_mask[j]:
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got.append(np.nan)
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continue
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got.append(func(*tuple([params[i][j] for i in range(len(params))])))
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got = np.asarray(got)
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if not isinstance(got, tuple):
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got = (got,)
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return got
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# Evaluate function to be tested
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got = eval_func_at_params(self.func)
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# Grab the correct results
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if self.result_columns is not None:
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# Correct results passed in with the data
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wanted = tuple([data[:,icol] for icol in self.result_columns])
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else:
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# Function producing correct results passed in
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skip_mask = None
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if self.nan_ok and len(got) == 1:
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# Don't spend time evaluating what doesn't need to be evaluated
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skip_mask = np.isnan(got[0])
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wanted = eval_func_at_params(self.result_func, skip_mask=skip_mask)
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# Check the validity of each output returned
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assert_(len(got) == len(wanted))
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for output_num, (x, y) in enumerate(zip(got, wanted)):
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if np.issubdtype(x.dtype, np.complexfloating) or self.ignore_inf_sign:
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pinf_x = np.isinf(x)
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pinf_y = np.isinf(y)
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minf_x = np.isinf(x)
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minf_y = np.isinf(y)
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else:
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pinf_x = np.isposinf(x)
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pinf_y = np.isposinf(y)
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minf_x = np.isneginf(x)
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minf_y = np.isneginf(y)
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nan_x = np.isnan(x)
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nan_y = np.isnan(y)
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with np.errstate(all='ignore'):
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abs_y = np.absolute(y)
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abs_y[~np.isfinite(abs_y)] = 0
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diff = np.absolute(x - y)
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diff[~np.isfinite(diff)] = 0
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rdiff = diff / np.absolute(y)
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rdiff[~np.isfinite(rdiff)] = 0
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tol_mask = (diff <= atol + rtol*abs_y)
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pinf_mask = (pinf_x == pinf_y)
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minf_mask = (minf_x == minf_y)
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nan_mask = (nan_x == nan_y)
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bad_j = ~(tol_mask & pinf_mask & minf_mask & nan_mask)
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point_count = bad_j.size
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if self.nan_ok:
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bad_j &= ~nan_x
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bad_j &= ~nan_y
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point_count -= (nan_x | nan_y).sum()
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if not self.distinguish_nan_and_inf and not self.nan_ok:
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# If nan's are okay we've already covered all these cases
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inf_x = np.isinf(x)
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inf_y = np.isinf(y)
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both_nonfinite = (inf_x & nan_y) | (nan_x & inf_y)
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bad_j &= ~both_nonfinite
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point_count -= both_nonfinite.sum()
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if np.any(bad_j):
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# Some bad results: inform what, where, and how bad
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msg = [""]
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msg.append("Max |adiff|: %g" % diff[bad_j].max())
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msg.append("Max |rdiff|: %g" % rdiff[bad_j].max())
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msg.append("Bad results (%d out of %d) for the following points (in output %d):"
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% (np.sum(bad_j), point_count, output_num,))
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for j in np.nonzero(bad_j)[0]:
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j = int(j)
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fmt = lambda x: "%30s" % np.array2string(x[j], precision=18)
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a = " ".join(map(fmt, params))
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b = " ".join(map(fmt, got))
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c = " ".join(map(fmt, wanted))
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d = fmt(rdiff)
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msg.append("%s => %s != %s (rdiff %s)" % (a, b, c, d))
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assert_(False, "\n".join(msg))
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def __repr__(self):
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"""Pretty-printing, esp. for Nose output"""
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if np.any(list(map(np.iscomplexobj, self.param_columns))):
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is_complex = " (complex)"
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else:
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is_complex = ""
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if self.dataname:
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return "<Data for %s%s: %s>" % (self.func.__name__, is_complex,
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os.path.basename(self.dataname))
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else:
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return "<Data for %s%s>" % (self.func.__name__, is_complex)
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