#!/usr/bin/env python3 """analyze-log - Generate a model from performance benchmarks log files foo """ import argparse import dfatool.cli import dfatool.plotter import dfatool.utils import dfatool.functions as df from dfatool.loader import Logfile, CSVfile from dfatool.model import AnalyticModel from dfatool.validation import CrossValidator from functools import reduce import logging import json import re import sys import time def parse_logfile(filename): if ".csv" in filename: loader = CSVfile() else: loader = Logfile() if filename.endswith("xz"): import lzma with lzma.open(filename, "rt") as f: return loader.load(f) with open(filename, "r") as f: return loader.load(f) def main(): timing = dict() parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__ ) dfatool.cli.add_standard_arguments(parser) parser.add_argument( "--export-model", metavar="FILE", type=str, help="Export JSON model to FILE" ) parser.add_argument( "logfiles", nargs="+", type=str, help="Path to benchmark output (.txt or .txt.xz)", ) args = parser.parse_args() dfatool.cli.sanity_check(args) if args.log_level: numeric_level = getattr(logging, args.log_level.upper(), None) if not isinstance(numeric_level, int): print(f"Invalid log level: {args.log_level}", file=sys.stderr) sys.exit(1) logging.basicConfig( level=numeric_level, format="{asctime} {levelname}:{name}:{message}", style="{", ) observations = reduce(lambda a, b: a + b, map(parse_logfile, args.logfiles)) by_name, parameter_names = dfatool.utils.observations_to_by_name(observations) del observations if args.ignore_param: args.ignore_param = args.ignore_param.split(",") if args.filter_observation: args.filter_observation = list( map(lambda x: tuple(x.split(":")), args.filter_observation.split(",")) ) if args.filter_param: args.filter_param = list( map( lambda entry: dfatool.cli.parse_filter_string( entry, parameter_names=parameter_names ), args.filter_param.split(";"), ) ) else: args.filter_param = list() dfatool.utils.filter_aggregate_by_param(by_name, parameter_names, args.filter_param) dfatool.utils.filter_aggregate_by_observation(by_name, args.filter_observation) dfatool.utils.ignore_param(by_name, parameter_names, args.ignore_param) if args.param_shift: param_shift = dfatool.cli.parse_param_shift(args.param_shift) dfatool.utils.shift_param_in_aggregate(by_name, parameter_names, param_shift) if args.normalize_nfp: norm = dfatool.cli.parse_nfp_normalization(args.normalize_nfp) dfatool.utils.normalize_nfp_in_aggregate(by_name, norm) function_override = dict() if args.function_override: for function_desc in args.function_override.split(";"): state_or_tran, attribute, function_str = function_desc.split(":") function_override[(state_or_tran, attribute)] = function_str ts = time.time() if args.load_json: with open(args.load_json, "r") as f: model = AnalyticModel.from_json(json.load(f), by_name, parameter_names) else: model = AnalyticModel( by_name, parameter_names, force_tree=args.force_tree, compute_stats=not args.skip_param_stats, function_override=function_override, ) timing["AnalyticModel"] = time.time() - ts if args.info: dfatool.cli.print_info_by_name(model, by_name) if args.information_gain: dfatool.cli.print_information_gain_by_name(model, by_name) if args.export_csv_unparam: dfatool.cli.export_csv_unparam( model, args.export_csv_unparam, dialect=args.export_csv_dialect ) if args.export_pgf_unparam: dfatool.cli.export_pgf_unparam(model, args.export_pgf_unparam) if args.export_json_unparam: dfatool.cli.export_json_unparam(model, args.export_json_unparam) if args.plot_unparam: for kv in args.plot_unparam.split(";"): state_or_trans, attribute, ylabel = kv.split(":") fname = "param_y_{}_{}.pdf".format(state_or_trans, attribute) dfatool.plotter.plot_y( model.by_name[state_or_trans][attribute], xlabel="measurement #", ylabel=ylabel, # output=fname, show=not args.non_interactive, ) if args.boxplot_unparam: title = None if args.filter_param: title = "filter: " + ", ".join( map(lambda kv: f"{kv[0]}={kv[1]}", args.filter_param) ) for name in model.names: attr_names = sorted(model.attributes(name)) dfatool.plotter.boxplot( attr_names, [model.by_name[name][attr] for attr in attr_names], xlabel="Attribute", output=f"{args.boxplot_unparam}{name}.pdf", title=title, show=not args.non_interactive, ) for attribute in attr_names: dfatool.plotter.boxplot( [attribute], [model.by_name[name][attribute]], output=f"{args.boxplot_unparam}{name}-{attribute}.pdf", title=title, show=not args.non_interactive, ) if args.boxplot_param: dfatool.cli.boxplot_param(args, model) if args.cross_validate: xv_method, xv_count = args.cross_validate.split(":") xv_count = int(xv_count) xv = CrossValidator( AnalyticModel, by_name, parameter_names, force_tree=args.force_tree, compute_stats=not args.skip_param_stats, show_progress=args.progress, ) xv.parameter_aware = args.parameter_aware_cross_validation else: xv_method = None xv_count = None static_model = model.get_static() ts = time.time() lut_model = model.get_param_lut() timing["get lut"] = time.time() - ts if lut_model is None: lut_quality = None else: ts = time.time() lut_quality = model.assess(lut_model) timing["assess lut"] = time.time() - ts ts = time.time() param_model, param_info = model.get_fitted() timing["get model"] = time.time() - ts ts = time.time() if xv_method == "montecarlo": static_quality, _ = xv.montecarlo( lambda m: m.get_static(), xv_count, static=True, with_sum=args.add_total_observation, ) xv.export_filename = args.export_xv analytic_quality, _ = xv.montecarlo( lambda m: m.get_fitted()[0], xv_count, with_sum=args.add_total_observation ) elif xv_method == "kfold": static_quality, _ = xv.kfold( lambda m: m.get_static(), xv_count, static=True, with_sum=args.add_total_observation, ) xv.export_filename = args.export_xv analytic_quality, _ = xv.kfold( lambda m: m.get_fitted()[0], xv_count, with_sum=args.add_total_observation ) else: static_quality = model.assess(static_model, with_sum=args.add_total_observation) if args.export_raw_predictions: analytic_quality, raw_results = model.assess(param_model, return_raw=True) with open(args.export_raw_predictions, "w") as f: json.dump(raw_results, f, cls=dfatool.utils.NpEncoder) else: analytic_quality = model.assess( param_model, with_sum=args.add_total_observation ) timing["assess model"] = time.time() - ts if "static" in args.show_model or "all" in args.show_model: print("--- static model ---") for name in sorted(model.names): for attribute in sorted(model.attributes(name)): dfatool.cli.print_static( model, static_model, name, attribute, with_dependence="all" in args.show_model, precision=args.show_model_precision, ) if "param" in args.show_model or "all" in args.show_model: print("--- param model ---") for name in sorted(model.names): for attribute in sorted(model.attributes(name)): info = param_info(name, attribute) dfatool.cli.print_model( f"{name:10s} {attribute:15s}", info, precision=args.show_model_precision, ) if args.show_model_error: dfatool.cli.model_quality_table( lut=lut_quality, model=analytic_quality, static=static_quality, model_info=param_info, xv_method=xv_method, xv_count=xv_count, error_metric=args.error_metric, load_model=args.load_json, ) if args.show_model_complexity: dfatool.cli.print_model_complexity(model) if args.export_model: print(f"Exportding model to {args.export_model}") json_model = model.to_json() with open(args.export_model, "w") as f: json.dump( json_model, f, indent=2, sort_keys=True, cls=dfatool.utils.NpEncoder ) if args.export_dot: dfatool.cli.export_dot(model, args.export_dot) if args.export_dref: dref = model.to_dref(static_quality, lut_quality, analytic_quality) for key, value in timing.items(): dref[f"timing/{key}"] = (value, r"\second") if args.information_gain: for name in model.names: for attr in model.attributes(name): mutual_information = model.mutual_information(name, attr) for param in model.parameters: if param in mutual_information: dref[f"mutual information/{name}/{attr}/{param}"] = ( mutual_information[param] ) dfatool.cli.export_dataref( args.export_dref, dref, precision=args.dref_precision ) if args.export_json: with open(args.export_json, "w") as f: json.dump( model.to_json(), f, sort_keys=True, cls=dfatool.utils.NpEncoder, indent=2, ) if args.plot_param: for kv in args.plot_param.split(";"): try: state_or_trans, attribute, param_name = kv.split(":") except ValueError: print( "Usage: --plot-param='state_or_trans:attribute:param_name'", file=sys.stderr, ) sys.exit(1) dfatool.plotter.plot_param( model, state_or_trans, attribute, model.param_index(param_name), title=state_or_trans, ylabel=attribute, xlabel=param_name, output=f"{state_or_trans}-{attribute}-{param_name}.pdf", show=not args.non_interactive, ) if __name__ == "__main__": main()