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How to use get_value_string method in Slash

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Omega2dSimulator.py

Source:Omega2dSimulator.py

...92        self.path_tail = [None]*self.path_tail_length93    def load_configs(self, config_file):94        # load some values form the config file95        self.config_reader = ConfigReader(config_file)96        self.x_lb = str2list(self.config_reader.get_value_string("system.states.first_symbol"))97        self.x_ub = str2list(self.config_reader.get_value_string("system.states.last_symbol"))98        self.x_eta = str2list(self.config_reader.get_value_string("system.states.quantizers"))99        self.u_lb = str2list(self.config_reader.get_value_string("system.controls.first_symbol"))100        self.u_ub = str2list(self.config_reader.get_value_string("system.controls.last_symbol"))101        self.u_eta = str2list(self.config_reader.get_value_string("system.controls.quantizers"))102        self.specs_formula = self.config_reader.get_value_string("specifications.ltl_formula")103        self.X_initial = self.config_reader.get_value_string("system.states.initial_set")104        self.X_initial_HR = str2hyperrects(self.X_initial)[0]105        self.SCREEN_WIDTH = int(self.config_reader.get_value_string("simulation.window_width"))106        self.SCREEN_HEIGHT = int(self.config_reader.get_value_string("simulation.window_height"))107        self.title = self.config_reader.get_value_string("simulation.widow_title")108        self.step_time = float(self.config_reader.get_value_string("system.dynamics.step_time"))109        self.skip_aps = self.config_reader.get_value_string("simulation.skip_APs").replace(" ", "").split(",")110        self.visualize_3rd_dim = ( "true" == self.config_reader.get_value_string("simulation.visualize_3rdDim"))111        self.model_image = self.config_reader.get_value_string("simulation.system_image")112        self.model_image_scale = float(self.config_reader.get_value_string("simulation.system_image_scale"))113        self.controller_file = self.config_reader.get_value_string("simulation.controller_file")114        self.use_ODE = ( "true" == self.config_reader.get_value_string("simulation.use_ode"))115        strTmp = self.config_reader.get_value_string("simulation.path_tail_length")116        if strTmp == "" or strTmp == None:117            self.path_tail_length = 0118        else:    119            self.path_tail_length = int(strTmp)120        self.model_dump_file = self.config_reader.get_value_string("simulation.model_dump_file")121        if(self.model_dump_file == "" or self.model_dump_file == None):122            self.use_model_dump = False123        else:      124            self.use_model_dump = True125        self.x_0_str = self.config_reader.get_value_string("simulation.initial_state")126        if self.x_0_str == "center":127            self.x_0 = self.X_initial_HR.get_center_element()128        elif self.x_0_str == "random":129            self.x_0 = self.X_initial_HR.get_random_element()130        else:131            self.x_0 = str2list(self.x_0_str)132        # create a quantizer and an ode solver133        self.qnt_x = Quantizer(self.x_lb, self.x_eta, self.x_ub)134        self.qnt_u = Quantizer(self.u_lb, self.u_eta, self.u_ub)135        if self.use_ODE:136            self.ode = RungeKuttaSolver(self.sys_dynamics, 5)137        # configs for the arena138        self.PADDING = 40139        self.ZERO_BASE_X = self.PADDING140        self.ZERO_BASE_Y = self.PADDING141        self.X_GRID = (self.SCREEN_WIDTH-2*self.PADDING)/self.qnt_x.get_widths()[0]142        self.Y_GRID = (self.SCREEN_HEIGHT-2*self.PADDING)/self.qnt_x.get_widths()[1]143        self.ARENA_WIDTH = (self.qnt_x.get_widths()[0])*self.X_GRID144        self.ARENA_HIGHT = (self.qnt_x.get_widths()[1])*self.Y_GRID145        self.X_SCALE_FACTOR = self.ARENA_WIDTH/(self.x_ub[0] - self.x_lb[0] + self.x_eta[0])146        self.Y_SCALE_FACTOR = self.ARENA_HIGHT/(self.x_ub[1] - self.x_lb[1] + self.x_eta[1])147        self.Z_SCALE_FACTOR = 180.0/math.pi # from rad to deg   148        self.arena_mdl_lb = self.translate_sys_to_arena(self.x_lb)149        self.arena_mdl_ub = self.translate_sys_to_arena(self.x_ub)     150        # others: subsets151        self.subset_names = self.config_reader.get_value_string("system.states.subsets.names").replace(" ","").split(",")152        self.subset_HRs = []153        for subset_name in self.subset_names:154            skip = False155            for skip_ap in self.skip_aps:156                if skip_ap == subset_name:157                    skip = True158            if skip:159                continue160            subset_str = self.config_reader.get_value_string("system.states.subsets.mapping_" + subset_name)161            if subset_str  == '':162                continue163            HRs = str2hyperrects(subset_str)164            HRs_modified = []165            for HR in HRs:166                conc_lb = HR.get_lb()167                conc_ub = HR.get_ub()168                for i in range(len(self.x_lb)):169                    if conc_lb[i] < self.x_lb[i]:170                        conc_lb[i] = self.x_lb[i] - self.x_eta[i]/2171                    if conc_ub[i] > self.x_ub[i]:172                        conc_ub[i] = self.x_ub[i] + self.x_eta[i]/2173                lb = self.translate_sys_to_arena(conc_lb)174                ub = self.translate_sys_to_arena(conc_ub)...

pmml.py

Source:pmml.py

...3"""convert LightGBM model to pmml"""4from __future__ import absolute_import5from sys import argv6from itertools import count7def get_value_string(line):8    return line[line.find('=') + 1:]9def get_array_strings(line):10    return get_value_string(line).split()11def get_array_ints(line):12    return [int(token) for token in get_array_strings(line)]13def get_field_name(node_id, prev_node_idx, is_child):14    idx = leaf_parent[node_id] if is_child else prev_node_idx15    return feature_names[split_feature[idx]]16def get_threshold(node_id, prev_node_idx, is_child):17    idx = leaf_parent[node_id] if is_child else prev_node_idx18    return threshold[idx]19def print_simple_predicate(tab_len, node_id, is_left_child, prev_node_idx, is_leaf):20    if is_left_child:21        op = 'equal' if decision_type[prev_node_idx] == 1 else 'lessOrEqual'22    else:23        op = 'notEqual' if decision_type[prev_node_idx] == 1 else 'greaterThan'24    out_('\t' * (tab_len + 1) + ("<SimplePredicate field=\"{0}\" " + " operator=\"{1}\" value=\"{2}\" />").format(25        get_field_name(node_id, prev_node_idx, is_leaf), op, get_threshold(node_id, prev_node_idx, is_leaf)))26def print_nodes_pmml(node_id, tab_len, is_left_child, prev_node_idx):27    if node_id < 0:28        node_id = ~node_id29        score = leaf_value[node_id]30        recordCount = leaf_count[node_id]31        is_leaf = True32    else:33        score = internal_value[node_id]34        recordCount = internal_count[node_id]35        is_leaf = False36    out_('\t' * tab_len + ("<Node id=\"{0}\" score=\"{1}\" " + " recordCount=\"{2}\">").format(37        next(unique_id), score, recordCount))38    print_simple_predicate(tab_len, node_id, is_left_child, prev_node_idx, is_leaf)39    if not is_leaf:40        print_nodes_pmml(left_child[node_id], tab_len + 1, True, node_id)41        print_nodes_pmml(right_child[node_id], tab_len + 1, False, node_id)42    out_('\t' * tab_len + "</Node>")43# print out the pmml for a decision tree44def print_pmml():45    # specify the objective as function name and binarySplit for46    # splitCharacteristic because each node has 2 children47    out_("\t\t\t\t<TreeModel functionName=\"regression\" splitCharacteristic=\"binarySplit\">")48    out_("\t\t\t\t\t<MiningSchema>")49    # list each feature name as a mining field, and treat all outliers as is,50    # unless specified51    for feature in feature_names:52        out_("\t\t\t\t\t\t<MiningField name=\"%s\"/>" % (feature))53    out_("\t\t\t\t\t</MiningSchema>")54    # begin printing out the decision tree55    out_("\t\t\t\t\t<Node id=\"{0}\" score=\"{1}\" recordCount=\"{2}\">".format(56        next(unique_id), internal_value[0], internal_count[0]))57    out_("\t\t\t\t\t\t<True/>")58    print_nodes_pmml(left_child[0], 6, True, 0)59    print_nodes_pmml(right_child[0], 6, False, 0)60    out_("\t\t\t\t\t</Node>")61    out_("\t\t\t\t</TreeModel>")62if len(argv) != 2:63    raise ValueError('usage: pmml.py <input model file>')64# open the model file and then process it65with open(argv[1], 'r') as model_in:66    # ignore first 6 and empty lines67    model_content = iter([line for line in model_in.read().splitlines() if line][6:])68feature_names = get_array_strings(next(model_content))69feature_infos = get_array_strings(next(model_content))70segment_id = count(1)71with open('LightGBM_pmml.xml', 'w') as pmml_out:72    def out_(string):73        pmml_out.write(string + '\n')74    out_(75        "<PMML version=\"4.3\" \n" +76        "\t\txmlns=\"http://www.dmg.org/PMML-4_3\"\n" +77        "\t\txmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n" +78        "\t\txsi:schemaLocation=\"http://www.dmg.org/PMML-4_3 http://dmg.org/pmml/v4-3/pmml-4-3.xsd\">")79    out_("\t<Header copyright=\"Microsoft\">")80    out_("\t\t<Application name=\"LightGBM\"/>")81    out_("\t</Header>")82    # print out data dictionary entries for each column83    out_("\t<DataDictionary numberOfFields=\"%d\">" % len(feature_names))84    # not adding any interval definition, all values are currently85    # valid86    for feature in feature_names:87        out_("\t\t<DataField name=\"" + feature + "\" optype=\"continuous\" dataType=\"double\"/>")88    out_("\t</DataDictionary>")89    out_("\t<MiningModel functionName=\"regression\">")90    out_("\t\t<MiningSchema>")91    # list each feature name as a mining field, and treat all outliers92    # as is, unless specified93    for feature in feature_names:94        out_("\t\t\t<MiningField name=\"%s\"/>" % (feature))95    out_("\t\t</MiningSchema>")96    out_("\t\t<Segmentation multipleModelMethod=\"sum\">")97    # read each array that contains pertinent information for the pmml98    # these arrays will be used to recreate the traverse the decision tree99    while True:100        tree_start = next(model_content, '')101        if not tree_start.startswith('Tree'):102            break103        out_("\t\t\t<Segment id=\"%d\">" % next(segment_id))104        out_("\t\t\t\t<True/>")105        tree_no = tree_start[5:]106        num_leaves = int(get_value_string(next(model_content)))107        split_feature = get_array_ints(next(model_content))108        split_gain = next(model_content)  # unused109        threshold = get_array_strings(next(model_content))110        decision_type = get_array_ints(next(model_content))111        left_child = get_array_ints(next(model_content))112        right_child = get_array_ints(next(model_content))113        leaf_parent = get_array_ints(next(model_content))114        leaf_value = get_array_strings(next(model_content))115        leaf_count = get_array_strings(next(model_content))116        internal_value = get_array_strings(next(model_content))117        internal_count = get_array_strings(next(model_content))118        shrinkage = get_value_string(next(model_content))119        has_categorical = get_value_string(next(model_content))120        unique_id = count(1)121        print_pmml()122        out_("\t\t\t</Segment>")123    out_("\t\t</Segmentation>")124    out_("\t</MiningModel>")...

evaluator.py

Source:evaluator.py

...43                self.result = const.PASS_STRING44            else:45                self.result = const.FAIL_STRING46    def __repr__(self):47        return "(" + self.get_value_string() + "," + str(self.criterion) + "," + self.result + ")"48    def is_testable(self):49        return self.value is not None and self.criterion.is_testable()50    def get_value_string(self):51        if self.value is None:52            return const.NA_STRING53        return str(self.value)54    def pretty_string(self):55        return "Metric \"%s\" = %s evaluated against range [%s, %s] and resulted in %s\n" \56               % (self.criterion.name,57                  self.get_value_string(),58                  self.criterion.min_string(),59                  self.criterion.max_string(),60                  self.result)61# Runs tests on given metrics against a given set of criteria62class QCEvaluator:63    def __init__(self, metrics_file_path, criteria_file_path):64        self._load_metrics(metrics_file_path)65        self._load_criteria(criteria_file_path)66    def _load_metrics(self, filename):67        cols = [const.METRIC_FILE_NAME_COLUMN, const.METRIC_FILE_VALUE_COLUMN]68        self.data = pd.read_csv(69            filename, sep=const.TSV_DELIMITER, names=cols, header=None)70    def _load_criteria(self, filename):71        cols = [const.CRITERIA_METRIC_COLUMN,72                const.CRITERIA_MIN_COLUMN, const.CRITERIA_MAX_COLUMN]73        raw_data = pd.read_csv(74            filename, sep=const.TSV_DELIMITER, names=cols, header=0)75        self.criteria = {}76        for i in range(raw_data.shape[0]):77            name = raw_data.at[i, const.CRITERIA_METRIC_COLUMN]78            min = raw_data.at[i, const.CRITERIA_MIN_COLUMN]79            max = raw_data.at[i, const.CRITERIA_MAX_COLUMN]80            self.criteria[name] = Criterion(name, min=min, max=max)81    def get_tests(self):82        tests = {}83        name_idx = self.data.columns.get_loc(const.METRIC_FILE_NAME_COLUMN)84        val_idx = self.data.columns.get_loc(const.METRIC_FILE_VALUE_COLUMN)85        for row in self.data.iterrows():86            name = row[1][name_idx]87            value = row[1][val_idx]88            if name not in self.criteria:89                tests[name] = Test(Criterion(name), value=value)90            else:91                criterion = self.criteria[name]92                tests[name] = Test(criterion, value=value)93        # Criteria that were defined but there were no metrics for94        missed_tests = [Test(self.criteria[x])95                        for x in self.criteria if x not in tests]96        for test in missed_tests:97            name = test.criterion.name98            tests[name] = test99        return tests.values()100    def write_tests(self, filename, tests):101        with open(filename, 'w') as f:102            f.write("#METRIC\tMIN\tMAX\tVALUE\tRESULT\n")103            f.writelines(["\t".join([x.criterion.name, x.criterion.min_string(...

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