Source code for sasmodels.compare_many

#!/usr/bin/env python
Program to compare results from many random parameter sets for a given model.

The result is a comma separated value (CSV) table that can be redirected
from standard output into a file and loaded into a spreadsheet.

The models are compared for each parameter set and if the difference is
greater than expected for that precision, the parameter set is labeled
as bad and written to the output, along with the random seed used to
generate that parameter value.  This seed can be used with :mod:`.compare`
to reload and display the details of the model.
from __future__ import print_function

import sys
import traceback

import numpy as np  # type: ignore

from . import core
from .compare import (
    randomize_pars, suppress_pd, make_data, make_engine, get_pars,

# pylint: disable=unused-import
    from typing import Tuple, Any, Dict, List
except ImportError:
    from .data import Data
# pylint: enable=unused-import

MODELS = core.list_models()

[docs]def calc_stats(target, value, index): # type: (np. ndarray, np.ndarray, Any) -> Tuple[float, float, float, float] """ Calculate statistics between the target value and the computed value. *target* and *value* are the vectors being compared, with the difference normalized by *target* to get relative error. Only the elements listed in *index* are used, though index may be and empty slice defined by *slice(None, None)*. Returns: *maxrel* the maximum relative difference *rel95* the relative difference with the 5% biggest differences ignored *maxabs* the maximum absolute difference for the 5% biggest differences *maxval* the maximum value for the 5% biggest differences """ resid = abs(value-target)[index] relerr = resid/target[index] sorted_rel_index = np.argsort(relerr) #p90 = int(len(relerr)*0.90) p95 = int(len(relerr)*0.95) maxrel = np.max(relerr) rel95 = relerr[sorted_rel_index[p95]] maxabs = np.max(resid[sorted_rel_index[p95:]]) maxval = np.max(value[sorted_rel_index[p95:]]) return maxrel, rel95, maxabs, maxval
# Target 'good' value for various precision levels. PRECISION = { 'fast': 1e-3, 'half': 1e-3, 'single': 5e-5, 'double': 5e-14, 'single!': 5e-5, 'double!': 5e-14, 'quad!': 5e-18, }
[docs]def compare_instance(name, data, index, N=1, mono=True, cutoff=1e-5, base='single', comp='double'): # type: (str, Data, Any, int, bool, float, str, str) -> None r""" Compare the model under different calculation engines. *name* is the name of the model. *data* is the data object giving $q, \Delta q$ calculation points. *index* is the active set of points. *N* is the number of comparisons to make. *cutoff* is the polydispersity weight cutoff to make the calculation a little bit faster. *base* and *comp* are the names of the calculation engines to compare. """ is_2d = hasattr(data, 'qx_data') model_info = core.load_model_info(name) pars = get_pars(model_info) header = ('\n"Model","%s","Count","%d","Dimension","%s"' % (name, N, "2D" if is_2d else "1D")) if not mono: header += ',"Cutoff",%g'%(cutoff,) print(header) if is_2d: if not model_info.parameters.has_2d: print(',"1-D only"') return # Some not very clean macros for evaluating the models and checking the # results. They freely use variables from the current scope, even some # which have not been defined yet, complete with abuse of mutable lists # to allow them to update values in the current scope since nonlocal # declarations are not available in python 2.7. def try_model(fn, pars): """ Return the model evaluated at *pars*. If there is an exception, print it and return NaN of the right shape. """ try: result = fn(**pars) except Exception: traceback.print_exc() print("when comparing %s for %d"%(name, seed)) if hasattr(data, 'qx_data'): result = np.NaN* else: result = np.NaN*data.x return result def check_model(pars): """ Run the two calculators against *pars*, returning statistics on the differences. See :func:`calc_stats` for the list of stats. """ base_value = try_model(calc_base, pars) comp_value = try_model(calc_comp, pars) stats = calc_stats(base_value, comp_value, index) max_diff[0] = max(max_diff[0], stats[0]) good[0] = good[0] and (stats[0] < expected) return list(stats) try: calc_base = make_engine(model_info, data, base, cutoff) calc_comp = make_engine(model_info, data, comp, cutoff) except Exception as exc: #raise print('"Error: %s"'%str(exc).replace('"', "'")) print('"good","%d of %d","max diff",%g' % (0, N, np.NaN)) return expected = max(PRECISION[base], PRECISION[comp]) num_good = 0 first = True max_diff = [0] for k in range(N): print("Model %s %d"%(name, k+1), file=sys.stderr) seed = np.random.randint(1e6) # type: int np.random.seed(seed) pars_i = randomize_pars(model_info, pars) constrain_pars(model_info, pars_i) if mono: pars_i = suppress_pd(pars_i) good = [True] columns = check_model(pars_i) columns += [v for _, v in sorted(pars_i.items())] if first: labels = [" vs. ".join((calc_base.engine, calc_comp.engine))] print_column_headers(pars_i, labels) first = False if good[0]: num_good += 1 else: print(("%d,"%seed)+','.join("%s"%v for v in columns)) print('"good","%d of %d","max diff",%g'%(num_good, N, max_diff[0]))
[docs]def main(argv): # type: (List[str]) -> None """ Main program. """ if len(argv) not in (3, 4, 5, 6): print_help() return target = argv[0] try: model_list = [target] if target in MODELS else core.list_models(target) except ValueError: msg = """\ Bad model {target}. Use available model or model type. available models: ./sascomp -models model types: all, py, c, double, single, opencl, 1d, 2d, nonmagnetic, magnetic\ """.format(target=target) print(msg, file=sys.stderr) return try: count = int(argv[1]) is2D = argv[2].startswith('2d') assert argv[2][1] == 'd' Nq = int(argv[2][2:]) mono = len(argv) <= 3 or argv[3] == 'mono' cutoff = float(argv[3]) if not mono else 0 base = argv[4] if len(argv) > 4 else "single" comp = argv[5] if len(argv) > 5 else "double!" except Exception: traceback.print_exc() print_usage() return data, index = make_data({ 'qmin': 0.001, 'qmax': 1.0, 'is2d': is2D, 'nq': Nq, 'res': 0., 'accuracy': 'Low', 'view':'log', 'zero': False }) for model in model_list: compare_instance(model, data, index, N=count, mono=mono, cutoff=cutoff, base=base, comp=comp)
if __name__ == "__main__": #from .compare import push_seed #with push_seed(1): main(sys.argv[1:]) main(sys.argv[1:])