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How to use diff_configs method in autotest

Best Python code snippet using autotest_python

mdpp_to_cave.py

Source:mdpp_to_cave.py

1import numpy as np2import pandas as pd3import argparse, os4import json5from mdp_playground.analysis import MDPP_Analysis6from cave.cavefacade import CAVE7class MDPPToCave:8    def __init__(self):9        return10    def _create_configspace_json(self, stats_pd, var_configs):11        configspace = {12            "hyperparameters": [],13            "conditions": [],14            "forbiddens": [],15            "python_module_version": "0.4.11",16            "json_format_version": 0.1,17        }18        for param in var_configs:19            param_config = {"name": param}20            var_type = str(type(stats_pd[param].iloc[0]))21            if "int" in var_type or "bool" in var_type:22                param_config["lower"] = int(stats_pd[param].min())23                param_config["upper"] = int(stats_pd[param].max())24                param_config["default"] = int(25                    param_config["lower"] + param_config["upper"] // 226                )27                param_config["type"] = "uniform_int"28            elif "str" in var_type:29                # Categorical30                param_config["type"] = "categorical"31                param_config["choices"] = list(stats_pd["conv_filters"].unique())32                param_config["default"] = param_config["choices"][0]33            else:  # Float34                param_config["lower"] = float(stats_pd[param].min())35                param_config["upper"] = float(stats_pd[param].max())36                param_config["default"] = (37                    param_config["lower"] + param_config["upper"]38                ) / 239                param_config["type"] = "uniform_float"40            if "lr" in param:41                param_config["log"] = True42            else:43                param_config["log"] = False44            configspace["hyperparameters"].append(param_config)45        return configspace46    def _create_run_history(47        self, stats_pd, seed_idx, col_names, output_folder, var_configs48    ):49        final_rows_for_a_config = []50        for i in range(stats_pd.shape[0] - 1):51            if (52                stats_pd["timesteps_total"].iloc[i]53                > stats_pd["timesteps_total"].iloc[i + 1]54            ):55                final_rows_for_a_config.append(i)56        final_rows_for_a_config.append(57            stats_pd.shape[0] - 158        )  # Always append the last row!59        runhistory_col_names = ["cost", "time", "status", "budget", "seed"]60        runhistory_df = pd.DataFrame(columns=runhistory_col_names)61        runhistory_df["cost"] = (62            -1 * stats_pd["episode_reward_mean"].iloc[final_rows_for_a_config]63        )64        runhistory_df["time"] = stats_pd["episode_len_mean"].iloc[65            final_rows_for_a_config66        ]67        runhistory_df["budget"] = stats_pd["timesteps_total"].iloc[68            final_rows_for_a_config69        ]70        if seed_idx > 0:71            runhistory_df["seed"] = stats_pd[col_names[seed_idx]].iloc[72                final_rows_for_a_config73            ]74        else:75            runhistory_df["seed"] = 076        runhistory_df["status"] = "SUCCESS"77        for var in var_configs:78            runhistory_df[var] = stats_pd[var].iloc[final_rows_for_a_config]79        return runhistory_df80    def join_files(self, file_prefix, file_suffix):81        """Utility to join files that were written with different experiment configs"""82        with open(file_prefix + file_suffix, "ab") as combined_file:83            i = 084            num_diff_lines = []85            while True:86                if os.path.isfile(file_prefix + "_" + str(i) + file_suffix):87                    with open(88                        file_prefix + "_" + str(i) + file_suffix, "rb"89                    ) as curr_file:90                        byte_string = curr_file.read()91                        newline_count = byte_string.count(10)92                        num_diff_lines.append(newline_count)93                        combined_file.write(byte_string)94                else:95                    break96                i += 197            print(98                str(i)99                + " files were combined into 1 for file:"100                + file_prefix101                + file_suffix102            )103            # print("Files missing for config_nums:", missing_configs, ". Did you pass the right value for max_total_configs as an argument?")104            # print("Unique line count values:", np.unique(num_diff_lines))105            if i == 0:106                raise FileNotFoundError(107                    "No files to combine were present. Please check your location and/or filenames that they are correct."108                )109    def _read_stats(self, stats_file):110        if os.path.isfile(stats_file + ".csv"):111            print(112                "\033[1;31mLoading data from a sequential run/already combined runs of experiment configurations.\033[0;0m"113            )114        else:115            print(116                "\033[1;31mLoading data from a distributed run of experiment configurations. Creating a combined CSV stats file.\033[0;0m"117            )118            self.join_files(stats_file, ".csv")119            self.join_files(stats_file, "_eval.csv")120    def to_cave_csv(self, args):121        # file_path = args.file_path122        input_folder = "../mdp_files/"123        file_name = "dqn_vanilla_learning_starts"124        output_folder = "../to_cave_format/%s" % file_name125        if not os.path.exists(output_folder):126            os.makedirs(output_folder)127        ## Read current csvs ##128        stats_file = os.path.join(input_folder, file_name)129        with open(stats_file + ".csv") as file_:130            col_names = file_.readline().strip().split(", ")131            col_names[0] = col_names[0][2:]  # to remove '# ' that was written132        # print("config_names:", col_names)133        stats_pd = pd.read_csv(134            stats_file + ".csv",135            skip_blank_lines=True,136            header=None,137            names=col_names,138            comment="#",139            sep=" ",140        )141        remove_names = ["training_iteration", "algorithm", "seed"]142        parameters = col_names[:-3].copy()  # All paramaters tracked in run143        for x in col_names:144            for name in remove_names:145                if name in x:146                    parameters.remove(x)147        # Compute parameters that varied and store value in dict148        config_values = {}149        seed_idx = -1150        for i, c in enumerate(col_names):  # hardcoded 3 for no. of stats written151            if c in parameters:  # parameters we care about152                config_values[c] = stats_pd[c].unique()  # values a given parameter took153            if "seed" in c:154                seed_idx = i155        var_configs = [p for p in parameters if len(config_values[p]) > 1]156        configspace = self._create_configspace_json(stats_pd, var_configs)157        output_configspace = os.path.join(output_folder, "configspace.json")158        with open(output_configspace, "w") as fp:159            json.dump(configspace, fp, indent=2)160        scenario_str = "paramfile = ./configspace.json\nrun_obj = quality"161        output_configspace = os.path.join(output_folder, "scenario.txt")162        with open(output_configspace, "w") as fp:163            fp.write(scenario_str)164        # Runhistory and trajectory files165        runhistory_df = self._create_run_history(166            stats_pd, seed_idx, col_names, output_folder, var_configs167        )168        runhistory_df.to_csv(169            os.path.join(output_folder, "runhistory.csv"), header=True, index=False170        )171    def to_bohb_results(172        self, input_dir, exp_name, output_dir="../cave_output/", overwrite=False173    ):174        """Converts MDP Playground stats CSVs to BOHB format stats files:175        configs.json, results.json, configspace.json, in output_dir/exp_name.176        This file can be fed into cave for further analysis.177        Currently only compatible with the MDPP expt. of type: grid of configs178        exp_name : str179            Should be the expt name from MDPP, i.e., the "prefix" of the CSV stats files. A sub-directory of output_dir is created with this name to store BOHB format stats files.180        overwrite : bool181            If existing files should be overwritten.182        Returns "<output_dir>/<exp_name>"183        """184        print("Writing BOHB to cave output to %s" % (os.path.abspath(output_dir)))185        if not os.path.exists(output_dir):186            os.makedirs(output_dir)187        # file_path = args.file_path188        output_dir_final = os.path.join(output_dir, exp_name)189        if not os.path.exists(output_dir_final):190            os.makedirs(output_dir_final)191        # Read current CSVs192        ##TODO Re-use code from analyis.py to load data instead of processing it again here:193        # mdpp_analysis = MDPP_Analysis()194        # self.exp_data = mdpp_analysis.get_exp_data(dir_name=input_dir,195        #                             exp_name=exp_name,196        #                             )197        # print("exp_data:\n", self.exp_data)198        # exp_data["dims_varied"]199        # exp_data["dims_values"]200        stats_file = os.path.join(input_dir, exp_name)201        stats_file = os.path.abspath(stats_file)202        self._read_stats(stats_file)203        with open(stats_file + ".csv") as file_:204            col_names = file_.readline().strip().split(", ")205            col_names[0] = col_names[0][2:]  # to remove '# ' that was written206        # print("config_names:", col_names)207        stats_pd = pd.read_csv(208            stats_file + ".csv",209            skip_blank_lines=True,210            header=None,211            names=col_names,212            comment="#",213            sep=" ",214        )215        remove_names = ["training_iteration", "algorithm", "seed"]216        parameters = col_names[:-3].copy()  # All parameters tracked in run217        for x in col_names:218            for name in remove_names:219                if name in x:220                    parameters.remove(x)221        # Compute parameters that varied and store value in dict222        config_values = {}223        seed_idx = -1224        for i, c in enumerate(col_names):  # hardcoded 3 for no. of stats written225            if c in parameters:  # parameters we care about226                config_values[c] = stats_pd[c].unique()  # values a given parameter took227            if "seed" in c:228                seed_idx = i229        var_configs = [p for p in parameters if len(config_values[p]) > 1]230        final_rows_for_a_config = []231        for i in range(stats_pd.shape[0] - 1):232            if (233                stats_pd["timesteps_total"].iloc[i]234                > stats_pd["timesteps_total"].iloc[i + 1]235            ):236                final_rows_for_a_config.append(i)237        final_rows_for_a_config.append(238            stats_pd.shape[0] - 1239        )  # Always append the last row!240        ##------------- Start converting csv ----------------##241        # configspace and scenario file242        configspace = self._create_configspace_json(stats_pd, var_configs)243        cs_json_file = os.path.join(output_dir_final, "configspace.json")244        if os.path.exists(cs_json_file):245            if not overwrite:246                raise FileExistsError()247        with open(cs_json_file, "w") as fp:248            json.dump(configspace, fp, indent=2)249        # print("var_configs:", var_configs)250        # Trajectory and runhistory files251        # Finding end configuration training252        diff_configs = stats_pd.iloc[final_rows_for_a_config]253        # print("diff_configs:", diff_configs)254        diff_configs = diff_configs.groupby(var_configs)255        # print("grouped by", diff_configs)256        configs_mean = diff_configs.mean()257        # print("mean:", configs_mean)258        # print("diff_configs.groups:", diff_configs.groups)259        diff_configs_results = []  # results.json260        diff_configs_lst = []261        budget = stats_pd["timesteps_total"].iloc[262            final_rows_for_a_config[0]263        ]  # all have the same budget264        aux = 0265        for i, group_name in enumerate(diff_configs.groups):266            group_labels = diff_configs.groups[group_name]267            config_id = [0, 0, i]268            config_dict = {}269            # configs.json270            config_lst = [config_id]271            for name in var_configs:272                value = stats_pd[name].iloc[group_labels[0]]273                if isinstance(value, str):274                    config_dict[name] = value275                else:276                    config_dict[name] = value.item()277            config_lst.append(config_dict)278            config_lst.append({"model_based_pick": False})279            diff_configs_lst.append(config_lst)280            # results.json281            mean_reward = configs_mean["episode_reward_mean"].iloc[i]  # mean along seed282            results_lst = [283                config_id,284                budget.item(),285                {286                    "submitted": float("%.2f" % aux),287                    "started": float("%.2f" % (aux + 0.1)),288                    "finished": float("%.2f" % (aux + 1)),289                },290            ]291            aux += 1.1292            results_dict = {"loss": -mean_reward.item(), "info": {}}293            results_lst.append(results_dict)294            results_lst.append(None)295            diff_configs_results.append(results_lst)296        # configs.json297        output_configs = os.path.join(output_dir_final, "configs.json")298        if os.path.exists(output_configs):299            if not overwrite:300                raise FileExistsError()301        with open(output_configs, "w") as fout:302            for d in diff_configs_lst:303                json.dump(d, fout)304                fout.write("\n")305        # results.json306        output_results = os.path.join(output_dir_final, "results.json")307        if os.path.exists(output_results):308            if not overwrite:309                raise FileExistsError()310        with open(output_results, "w") as fout:311            for d in diff_configs_results:312                json.dump(d, fout)313                fout.write("\n")314        return output_dir_final315    def to_CAVE_object(316        self, input_dir, exp_name, output_dir="../cave_output/", overwrite=False317    ):318        """Converts MDP Playground stats CSVs to BOHB format stats files and creates319        a CAVE object from them.320        Please see to_bohb_results() for details about some of the parameters.321        """322        cave_input_file = self.to_bohb_results(323            input_dir, exp_name, output_dir, overwrite=overwrite324        )325        cave_results = os.path.join(cave_input_file, "out")326        cave = CAVE(327            folders=[cave_input_file],328            output_dir=cave_results,329            ta_exec_dir=[cave_input_file],330            file_format="BOHB",331            show_jupyter=True,332        )333        return cave334if __name__ == "__main__":335    input_dir = "../mdp_files/"336    exp_name = "dqn_seq_del"337    from cave.cavefacade import CAVE338    import os339    # The converted mdpp csvs will be stored in output_dir340    output_dir = "../mdpp_to_cave"341    mdpp_file = os.path.join(input_dir, exp_name)342    mdpp_cave = MDPPToCave()343    cave_input_file = mdpp_cave.to_bohb_results(input_dir, exp_name, output_dir)344    # cave_input_file = "../../../mdpp_to_cave/dqn_seq_del"345    # Similarly, as an example, cave will ouput it's results346    # to the same directory as cave's input files347    cave_results = os.path.join(cave_input_file, "output")348    print(os.path.abspath(cave_results))349    cave = CAVE(350        folders=[cave_input_file],351        output_dir=cave_results,352        ta_exec_dir=[cave_input_file],353        file_format="BOHB",354        show_jupyter=True,355    )356    # Common analysis357    cave.performance_table()358    cave.local_parameter_importance()359    cave.cave_fanova()  # can only be used with more than 1 dimension of hardness360    # Other analysis361    # cave.parallel_coordinates()362    # cave.cost_over_time()363    # cave.algorithm_footprints()364    # cave.pimp_comparison_table()365    # cave.cave_ablation()366    # cave.pimp_forward_selection()367    # cave.feature_importance()368    # cave.configurator_footprint()369    # cave.algorithm_footprints()370    # cave.plot_ecdf()371    # cave.plot_scatter()372    # cave.compare_default_incumbent()...

kconfig-bisect

Source:kconfig-bisect

1#!/usr/bin/env python32#3# Use binary search to find the config change that introduced a bug4#5import argparse6import os7import shutil8import subprocess9import sys10from collections import OrderedDict11from difflib import Differ12from prettytable import PrettyTable13def read_config_file(config_file):14    '''15    Read a config file into an ordered dict16    '''17    configs = OrderedDict()18    with open(config_file, encoding='utf-8') as fh:19        for line in fh:20            line = line.strip()21            if line.startswith('CONFIG_'):22                cfg, val = line.split('=', 1)23            elif line.startswith('# CONFIG_'):24                cfg = line.split(' ')[1]25                # val = '___UNSET___'26                val = None27            elif line.startswith('# Linux/'):28                arch = line.split(' ')[1].split('/')[1]29                continue30            else:31                continue32            configs[cfg] = val33    return arch, configs34def write_config_file(configs):35    '''36    Write an ordered dict to a config file37    '''38    with open('.config', 'w', encoding='utf-8') as fh:39        for cfg, val in configs.items():40            if val is None:41                fh.write(f'# {cfg} is not set\n')42            else:43                fh.write(f'{cfg}={val}\n')44def merge_configs(good_configs, bad_configs):45    '''46    Merge two ordered dicts47    '''48    differ = Differ()49    result = differ.compare(list(good_configs.keys()),50                            list(bad_configs.keys()))51    all_configs = []52    for r in list(result):53        if r[0:2] in ('- ', '+ ', '  '):54            all_configs.append(r[2:])55    merged_configs = OrderedDict()56    for cfg in all_configs:57        if cfg in merged_configs:58            print(f'-- Internal error: Duplicate config in ordered dict: {cfg}')59            sys.exit(1)60        merged_configs[cfg] = {61            'good': good_configs.get(cfg),62            'bad': bad_configs.get(cfg),63        }64    return merged_configs65def generate_config_file(arch, configs, merged_configs, prev_new_configs=None):66    '''67    Generate a new config file68    '''69    if prev_new_configs is None:70        prev_new_configs = []71    # Write the config file and determine all new config options72    write_config_file(configs)73    p = subprocess.run(['make', f'ARCH={arch}', 'listnewconfig'],74                       stdout=subprocess.PIPE, text=True, check=True)75    new_configs = [p.strip() for p in p.stdout.split('\n') if p.strip()]76    if not new_configs or new_configs == prev_new_configs:77        # All done, i.e., no new configs or new configs didn't change from the78        # previous run.79        subprocess.run(['make', f'ARCH={arch}', 'olddefconfig'], check=True)80        return81    # For the new config options, use the values from the 'good' config if they82    # exist, or the defaults otherwise83    for new in new_configs:84        cfg, val = new.split('=', 1)85        if cfg in merged_configs:86            val = merged_configs[cfg]['good']87        print(f'-- New config:   {cfg} -> {str(val)}')88        configs[cfg] = val89    # Rerun90    generate_config_file(arch, configs, merged_configs, new_configs)91def main():92    parser = argparse.ArgumentParser()93    subparsers = parser.add_subparsers(title='subcommands', required=True)94    sparser = subparsers.add_parser('start',95                                    help='Start a new bisect run')96    sparser.add_argument('good_config', metavar='good.config',97                         help='Good config file')98    sparser.add_argument('bad_config', metavar='bad.config',99                         help='Bad config file')100    sparser.set_defaults(command='start')101    sparser = subparsers.add_parser('good', help='Good test result')102    sparser.set_defaults(command='good')103    sparser = subparsers.add_parser('bad', help='Bad test result')104    sparser.set_defaults(command='bad')105    args = parser.parse_args()106    kb_dir = '.kconfig-bisect'107    good_config = os.path.join(kb_dir, 'good')108    bad_config = os.path.join(kb_dir, 'bad')109    if args.command == 'start':110        if not os.path.exists(kb_dir):111            os.mkdir(kb_dir)112        shutil.copy(args.bad_config, bad_config)113        shutil.copy(args.good_config, good_config)114    elif args.command == 'good':115        shutil.copy('.config', good_config)116    elif args.command == 'bad':117        shutil.copy('.config', bad_config)118    else:119        print(f'-- Internal errror: Unsupported command: {args.command}')120        sys.exit(1)121    # Read the config files122    arch, good_configs = read_config_file(good_config)123    _arch, bad_configs = read_config_file(bad_config)124    if arch != _arch:125        print(f'-- Config architectures don\'t match ({arch} != {_arch})')126        return 1127    print(f'-- Config arch:  {arch}')128    # Merge the config files129    merged_configs = merge_configs(good_configs, bad_configs)130    # List of different configs131    diff_configs = []132    for cfg, val in merged_configs.items():133        if val['good'] != val['bad']:134            diff_configs.append(cfg)135    if not diff_configs:136        print('-- No differences found')137        return 0138    # Print the different config settings139    print(f'-- Diff configs: {len(diff_configs)}')140    table = PrettyTable()141    table.field_names = (['Config', 'Good', 'Bad'])142    for cfg in diff_configs:143        table.add_row([cfg, merged_configs[cfg]['good'],144                       merged_configs[cfg]['bad']])145    table.align = 'l'146    print(table)147    # Create a new config148    new_configs = OrderedDict(bad_configs)149    for cfg in diff_configs[0:int((len(diff_configs) / 2))]:150        new_configs[cfg] = good_configs.get(cfg)151    generate_config_file(arch, new_configs, merged_configs)152    return 0153if __name__ == '__main__':...

kernel_config.py

Source:kernel_config.py

...30        else:31            output.write(line)32    input.close()33    output.close()34def diff_configs(old, new):35    utils.system('diff -u %s %s > %s' % (old, new, new + '.diff'),36                 ignore_status=True)37def modules_needed(config):38    return (utils.grep('CONFIG_MODULES=y', config) and utils.grep('=m', config))39def config_by_name(name, set):40    version = kernel_versions.version_choose_config(name, set[1:])41    if version:42        return set[0] + version43    return None44class kernel_config(object):45    # Build directory must be ready before init'ing config.46    #47    # Stages:48    #       1. Get original config file49    #       2. Apply overrides50    #       3. Do 'make oldconfig' to update it to current source code51    #                  (gets done implicitly during the process)52    #53    # You may specifiy the a defconfig within the tree to build,54    # or a custom config file you want, or None, to get machine's55    # default config file from the repo.56    build_dir = ''          # the directory we're building in57    config_dir = ''         # local repository for config_file data58    build_config = ''       # the config file in the build directory59    orig_config = ''        # the original config file60    over_config = ''        # config file + overrides61    def __init__(self, job, build_dir, config_dir, orig_file,62                            overrides, defconfig = False, name = None, make = None):63        self.build_dir = build_dir64        self.config_dir = config_dir65        #       1. Get original config file66        self.build_config = build_dir + '/.config'67        if (orig_file == '' and not defconfig and not make):    # use user default68            set = job.config_get("kernel.default_config_set")69            defconf = None70            if set and name:71                defconf = config_by_name(name, set)72            if not defconf:73                defconf = job.config_get("kernel.default_config")74            if defconf:75                orig_file = defconf76        if (orig_file == '' and not make and defconfig):        # use defconfig77            make = 'defconfig'78        if (orig_file == '' and make): # use the config command79            print "kernel_config: using " + make + " to configure kernel"80            os.chdir(build_dir)81            make_return = utils.system('make %s > /dev/null' % make)82            self.config_record(make)83            if (make_return):84                raise error.TestError('make % failed' % make)85        else:86            print "kernel_config: using " + orig_file + \87                                            " to configure kernel"88            self.orig_config = config_dir + '/config.orig'89            utils.get_file(orig_file, self.orig_config)90            self.update_config(self.orig_config, self.orig_config+'.new')91            diff_configs(self.orig_config, self.orig_config+'.new')92        #       2. Apply overrides93        if overrides:94            print "kernel_config: using " + overrides + \95                                            " to re-configure kernel"96            self.over_config = config_dir + '/config.over'97            overrides_local = self.over_config + '.changes'98            utils.get_file(overrides, overrides_local)99            apply_overrides(self.build_config, overrides_local, self.over_config)100            self.update_config(self.over_config, self.over_config+'.new')101            diff_configs(self.over_config, self.over_config+'.new')102        else:103            self.over_config = self.orig_config104    def update_config(self, old_config, new_config = 'None'):105        os.chdir(self.build_dir)106        shutil.copyfile(old_config, self.build_config)107        utils.system('yes "" | make oldconfig > /dev/null')108        if new_config:109            shutil.copyfile(self.build_config, new_config)110    def config_record(self, name):111        #Copy the current .config file to the config.<name>[.<n>]112        i = 1113        to = self.config_dir + '/config.%s' % name114        while os.path.exists(to):115            i += 1...

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