path: root/bin/analyze-log.py

#!/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.model import AnalyticModel
from dfatool.validation import CrossValidator
from functools import reduce
import logging
import json
import sys
import re


def parse_logfile(filename):
    lf = dfatool.utils.Logfile()

    if filename.endswith("xz"):
        import lzma

        with lzma.open(filename, "rt") as f:
            return lf.load(f)
    with open(filename, "r") as f:
        return lf.load(f)


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
    )
    dfatool.cli.add_standard_arguments(parser)
    parser.add_argument(
        "--show-quality",
        choices=["table"],
        action="append",
        default=list(),
        help="table: show LUT, model, and static prediction error for each key and attribute.",
    )
    parser.add_argument(
        "--force-tree",
        action="store_true",
        help="Build decision tree without checking for analytic functions first",
    )
    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)

    if args.filter_param:
        args.filter_param = list(
            map(lambda x: x.split("="), args.filter_param.split(","))
        )
    else:
        args.filter_param = list()

    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(",")
        dfatool.utils.ignore_param(by_name, parameter_names, args.ignore_param)

    dfatool.utils.filter_aggregate_by_param(by_name, parameter_names, args.filter_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)] = " ".join(function_str)

    model = AnalyticModel(
        by_name,
        parameter_names,
        force_tree=args.force_tree,
        function_override=function_override,
    )

    if args.info:
        dfatool.cli.print_info_by_name(model, by_name)

    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,
        )
        xv.parameter_aware = args.parameter_aware_cross_validation
    else:
        xv_method = None

    static_model = model.get_static()
    try:
        lut_model = model.get_param_lut()
        lut_quality = model.assess(lut_model)
    except RuntimeError as e:
        if args.force_tree:
            # this is to be expected
            logging.debug(f"Skipping LUT model: {e}")
        else:
            logging.warning(f"Skipping LUT model: {e}")
        lut_model = None
        lut_quality = None

    param_model, param_info = model.get_fitted()

    if xv_method == "montecarlo":
        static_quality, _ = xv.montecarlo(
            lambda m: m.get_static(), xv_count, static=True
        )
        xv.export_filename = args.export_xv
        analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
    elif xv_method == "kfold":
        static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count, static=True)
        xv.export_filename = args.export_xv
        analytic_quality, _ = xv.kfold(lambda m: m.get_fitted()[0], xv_count)
    else:
        static_quality = model.assess(static_model)
        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)

    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,
                )

    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, model.parameters
                )

    if "table" in args.show_quality or "all" in args.show_quality:
        if xv_method is not None:
            print(
                f"Model error ({args.error_metric}) after cross validation ({xv_method}, {xv_count}):"
            )
        else:
            print(f"Model error ({args.error_metric}) on training data:")
        dfatool.cli.model_quality_table(
            lut=lut_quality,
            model=analytic_quality,
            static=static_quality,
            model_info=param_info,
            xv_method=xv_method,
            error_metric=args.error_metric,
        )

    if args.show_model_size:
        dfatool.cli.print_model_size(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)
        dfatool.cli.export_dataref(
            args.export_dref, dref, precision=args.dref_precision
        )

    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()