<filename>superbench/analyzer/data_diagnosis.py<gh_stars>0
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

"""A module for baseline-based data diagnosis."""
from typing import Callable
from pathlib import Path
import json

import pandas as pd

from superbench.common.utils import logger
from superbench.analyzer.diagnosis_rule_op import RuleOp, DiagnosisRuleType
from superbench.analyzer import file_handler
from superbench.analyzer import RuleBase
from superbench.analyzer import data_analysis


class DataDiagnosis(RuleBase):
    """The DataDiagnosis class to do the baseline-based data diagnosis."""
    def __init__(self):
        """Init function."""
        super().__init__()

    def _check_and_format_rules(self, rule, name):
        """Check the rule of the metric whether the formart is valid.

        Args:
            rule (dict): the rule
            name (str): the rule name

        Returns:
            dict: the rule for the metric
        """
        # check if rule is supported
        super()._check_and_format_rules(rule, name)
        if 'function' not in rule:
            logger.log_and_raise(exception=Exception, msg='{} lack of function'.format(name))
        if not isinstance(DiagnosisRuleType(rule['function']), DiagnosisRuleType):
            logger.log_and_raise(exception=Exception, msg='{} invalid function name'.format(name))
        # check rule format
        if 'criteria' not in rule:
            logger.log_and_raise(exception=Exception, msg='{} lack of criteria'.format(name))
        if not isinstance(eval(rule['criteria']), Callable):
            logger.log_and_raise(exception=Exception, msg='invalid criteria format')
        if rule['function'] != 'multi_rules':
            if 'metrics' not in rule:
                logger.log_and_raise(exception=Exception, msg='{} lack of metrics'.format(name))
        if 'store' in rule and not isinstance(rule['store'], bool):
            logger.log_and_raise(exception=Exception, msg='{} store must be bool type'.format(name))
        return rule

    def _get_baseline_of_metric(self, baseline, metric):
        """Get the baseline value of the metric.

        Args:
            baseline (dict): baseline defined in baseline file
            metric (str): the full name of the metric

        Returns:
            numeric: the baseline value of the metric
        """
        if metric in baseline:
            return baseline[metric]
        else:
            # exclude rank info
            short = metric.split(':')[0]
            if short in baseline:
                return baseline[short]
            # baseline not defined
            else:
                logger.warning('DataDiagnosis: get baseline - {} baseline not found'.format(metric))
                return -1

    def __get_metrics_and_baseline(self, rule, benchmark_rules, baseline):
        """Get metrics with baseline in the rule.

        Parse metric regex in the rule, and store the (baseline, metric) pair
        in _sb_rules[rule]['metrics'] and metric in _enable_metrics。

        Args:
            rule (str): the name of the rule
            benchmark_rules (dict): the dict of rules
            baseline (dict): the dict of baseline of metrics
        """
        if 'function' in self._sb_rules[rule] and self._sb_rules[rule]['function'] == 'multi_rules':
            return
        self._get_metrics(rule, benchmark_rules)
        for metric in self._sb_rules[rule]['metrics']:
            self._sb_rules[rule]['metrics'][metric] = self._get_baseline_of_metric(baseline, metric)

    def _parse_rules_and_baseline(self, rules, baseline):
        """Parse and merge rules and baseline read from file.

        Args:
            rules (dict): rules from rule yaml file
            baseline (dict): baseline of metrics from baseline json file

        Returns:
            bool: return True if successfully get the criteria for all rules, otherwise False.
        """
        try:
            if not rules:
                logger.error('DataDiagnosis: get criteria failed')
                return False
            self._sb_rules = {}
            self._enable_metrics = set()
            benchmark_rules = rules['superbench']['rules']
            for rule in benchmark_rules:
                benchmark_rules[rule] = self._check_and_format_rules(benchmark_rules[rule], rule)
                self._sb_rules[rule] = {}
                self._sb_rules[rule]['name'] = rule
                self._sb_rules[rule]['function'] = benchmark_rules[rule]['function']
                self._sb_rules[rule]['store'] = True if 'store' in benchmark_rules[
                    rule] and benchmark_rules[rule]['store'] is True else False
                self._sb_rules[rule]['criteria'] = benchmark_rules[rule]['criteria']
                self._sb_rules[rule]['categories'] = benchmark_rules[rule]['categories']
                self._sb_rules[rule]['metrics'] = {}
                self.__get_metrics_and_baseline(rule, benchmark_rules, baseline)
            self._enable_metrics = sorted(list(self._enable_metrics))
        except Exception as e:
            logger.error('DataDiagnosis: get criteria failed - {}'.format(str(e)))
            return False

        return True

    def _run_diagnosis_rules_for_single_node(self, node):
        """Use rules to diagnosis single node data.

        Use the rules defined in rule_file to diagnose the raw data of each node,
        if the node violate any rule, label as defective node and save
        the 'Category', 'Defective Details' and data summary of defective node.

        Args:
            node (str): the node to do the diagosis

        Returns:
            details_row (list): None if the node is not labeled as defective,
                otherwise details of ['Category', 'Defective Details']
            summary_data_row (dict): None if the node is not labeled as defective,
                otherwise data summary of the metrics
        """
        data_row = self._raw_data_df.loc[node]
        issue_label = False
        details = []
        categories = set()
        violation = {}
        summary_data_row = pd.Series(index=self._enable_metrics, name=node, dtype=float)
        # Check each rule
        for rule in self._sb_rules:
            # Get rule op function and run the rule
            function_name = self._sb_rules[rule]['function']
            rule_op = RuleOp.get_rule_func(DiagnosisRuleType(function_name))
            violated_num = 0
            if rule_op == RuleOp.multi_rules:
                violated_num = rule_op(self._sb_rules[rule], details, categories, violation)
            else:
                violated_num = rule_op(data_row, self._sb_rules[rule], summary_data_row, details, categories)
            # label the node as defective one
            if self._sb_rules[rule]['store']:
                violation[rule] = violated_num
            elif violated_num:
                issue_label = True
        if issue_label:
            # Add category information
            general_cat_str = ','.join(sorted(list(categories)))
            details_cat_str = ','.join(sorted((details)))
            details_row = [general_cat_str, details_cat_str]
            return details_row, summary_data_row

        return None, None

    def run_diagnosis_rules(self, rules, baseline):
        """Rule-based data diagnosis for multiple nodes' raw data.

        Use the rules defined in rules to diagnose the raw data of each node,
        if the node violate any rule, label as defective node and save
        the 'Category', 'Defective Details' and processed data of defective node.

        Args:
            rules (dict): rules from rule yaml file
            baseline (dict): baseline of metrics from baseline json file

        Returns:
            data_not_accept_df (DataFrame): defective nodes's detailed information
            label_df (DataFrame): labels for all nodes
        """
        try:
            summary_columns = ['Category', 'Defective Details']
            data_not_accept_df = pd.DataFrame(columns=summary_columns)
            summary_details_df = pd.DataFrame()
            label_df = pd.DataFrame(columns=['label'])
            if not self._parse_rules_and_baseline(rules, baseline):
                return data_not_accept_df, label_df
            # run diagnosis rules for each node
            for node in self._raw_data_df.index:
                details_row, summary_data_row = self._run_diagnosis_rules_for_single_node(node)
                if details_row:
                    data_not_accept_df.loc[node] = details_row
                    summary_details_df = summary_details_df.append(summary_data_row)
                    label_df.loc[node] = 1
                else:
                    label_df.loc[node] = 0
            # combine details for defective nodes
            if len(data_not_accept_df) != 0:
                data_not_accept_df = data_not_accept_df.join(summary_details_df)
                data_not_accept_df = data_not_accept_df.sort_values(by=summary_columns, ascending=False)

        except Exception as e:
            logger.error('DataDiagnosis: run diagnosis rules failed, message: {}'.format(str(e)))
        return data_not_accept_df, label_df

    def output_diagnosis_in_excel(self, raw_data_df, data_not_accept_df, output_path, rules):
        """Output the raw_data_df and data_not_accept_df results into excel file.

        Args:
            raw_data_df (DataFrame): raw data
            data_not_accept_df (DataFrame): defective nodes's detailed information
            output_path (str): the path of output excel file
            rules (dict): the rules of DataDiagnosis
        """
        try:
            writer = pd.ExcelWriter(output_path, engine='xlsxwriter')
            # Check whether writer is valiad
            if not isinstance(writer, pd.ExcelWriter):
                logger.error('DataDiagnosis: excel_data_output - invalid file path.')
                return
            file_handler.output_excel_raw_data(writer, raw_data_df, 'Raw Data')
            file_handler.output_excel_data_not_accept(writer, data_not_accept_df, rules)
            writer.save()
        except Exception as e:
            logger.error('DataDiagnosis: excel_data_output - {}'.format(str(e)))

    def output_diagnosis_in_json(self, data_not_accept_df, output_path):
        """Output data_not_accept_df into jsonl file.

        Args:
            data_not_accept_df (DataFrame): the DataFrame to output
            output_path (str): the path of output jsonl file
        """
        p = Path(output_path)
        try:
            data_not_accept_json = data_not_accept_df.to_json(orient='index')
            data_not_accept = json.loads(data_not_accept_json)
            if not isinstance(data_not_accept_df, pd.DataFrame):
                logger.warning('DataDiagnosis: output json data - data_not_accept_df is not DataFrame.')
                return
            if data_not_accept_df.empty:
                logger.warning('DataDiagnosis: output json data - data_not_accept_df is empty.')
                return
            with p.open('w') as f:
                for node in data_not_accept:
                    line = data_not_accept[node]
                    line['Index'] = node
                    json_str = json.dumps(line)
                    f.write(json_str + '\n')
        except Exception as e:
            logger.error('DataDiagnosis: output json data failed, msg: {}'.format(str(e)))

    def gen_md_lines(self, data_not_accept_df, rules, round):
        """Convert DataFrame into markdown lines.

        Args:
            data_not_accept_df (DataFrame): the DataFrame to output
            rules (dict): the rules of DataDiagnosis
            round (int): the number of decimal digits

        Returns:
            list: lines in markdown format
        """
        data_not_accept_df['machine'] = data_not_accept_df.index
        header = data_not_accept_df.columns.tolist()
        header = header[-1:] + header[:-1]
        data_not_accept_df = data_not_accept_df[header]
        # format precision of values to n decimal digits
        for rule in rules:
            for metric in rules[rule]['metrics']:
                if rules[rule]['function'] == 'variance':
                    if round and isinstance(round, int):
                        data_not_accept_df[metric] = data_not_accept_df[metric].map(
                            lambda x: x * 100, na_action='ignore'
                        )
                        data_not_accept_df = data_analysis.round_significant_decimal_places(
                            data_not_accept_df, round, [metric]
                        )
                    data_not_accept_df[metric] = data_not_accept_df[metric].map(
                        lambda x: '{}%'.format(x), na_action='ignore'
                    )
                elif rules[rule]['function'] == 'value':
                    if round and isinstance(round, int):
                        data_not_accept_df = data_analysis.round_significant_decimal_places(
                            data_not_accept_df, round, [metric]
                        )
        lines = file_handler.gen_md_table(data_not_accept_df, header)
        return lines

    def run(self, raw_data_file, rule_file, baseline_file, output_dir, output_format='excel', round=2):
        """Run the data diagnosis and output the results.

        Args:
            raw_data_file (str): the path of raw data jsonl file.
            rule_file (str): The path of baseline yaml file
            baseline_file (str): The path of baseline json file
            output_dir (str): the directory of output file
            output_format (str): the format of the output, 'excel' or 'json'
            round (int): the number of decimal digits
        """
        try:
            rules = self._preprocess(raw_data_file, rule_file)
            # read baseline
            baseline = file_handler.read_baseline(baseline_file)
            logger.info('DataDiagnosis: Begin to process {} nodes'.format(len(self._raw_data_df)))
            data_not_accept_df, label_df = self.run_diagnosis_rules(rules, baseline)
            logger.info('DataDiagnosis: Processed finished')
            output_path = ''
            if output_format == 'excel':
                output_path = str(Path(output_dir) / 'diagnosis_summary.xlsx')
                self.output_diagnosis_in_excel(self._raw_data_df, data_not_accept_df, output_path, self._sb_rules)
            elif output_format == 'json':
                output_path = str(Path(output_dir) / 'diagnosis_summary.jsonl')
                self.output_diagnosis_in_json(data_not_accept_df, output_path)
            elif output_format == 'md' or output_format == 'html':
                lines = self.gen_md_lines(data_not_accept_df, self._sb_rules, round)
                if output_format == 'md':
                    output_path = str(Path(output_dir) / 'diagnosis_summary.md')
                    file_handler.output_lines_in_md(lines, output_path)
                else:
                    output_path = str(Path(output_dir) / 'diagnosis_summary.html')
                    file_handler.output_lines_in_html(lines, output_path)
            else:
                logger.error('DataDiagnosis: output failed - unsupported output format')
            logger.info('DataDiagnosis: Output results to {}'.format(output_path))
        except Exception as e:
            logger.error('DataDiagnosis: run failed - {}'.format(str(e)))

<filename>superbench/analyzer/data_diagnosis.py<gh_stars>0
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

"""A module for baseline-based data diagnosis."""
from typing import Callable
from pathlib import Path
import json

import pandas as pd

from superbench.common.utils import logger
from superbench.analyzer.diagnosis_rule_op import RuleOp, DiagnosisRuleType
from superbench.analyzer import file_handler
from superbench.analyzer import RuleBase
from superbench.analyzer import data_analysis


class DataDiagnosis(RuleBase):
    """The DataDiagnosis class to do the baseline-based data diagnosis."""
    def __init__(self):
        """Init function."""
        super().__init__()

    def _check_and_format_rules(self, rule, name):
        """Check the rule of the metric whether the formart is valid.

        Args:
            rule (dict): the rule
            name (str): the rule name

        Returns:
            dict: the rule for the metric
        """
        # check if rule is supported
        super()._check_and_format_rules(rule, name)
        if 'function' not in rule:
            logger.log_and_raise(exception=Exception, msg='{} lack of function'.format(name))
        if not isinstance(DiagnosisRuleType(rule['function']), DiagnosisRuleType):
            logger.log_and_raise(exception=Exception, msg='{} invalid function name'.format(name))
        # check rule format
        if 'criteria' not in rule:
            logger.log_and_raise(exception=Exception, msg='{} lack of criteria'.format(name))
        if not isinstance(eval(rule['criteria']), Callable):
            logger.log_and_raise(exception=Exception, msg='invalid criteria format')
        if rule['function'] != 'multi_rules':
            if 'metrics' not in rule:
                logger.log_and_raise(exception=Exception, msg='{} lack of metrics'.format(name))
        if 'store' in rule and not isinstance(rule['store'], bool):
            logger.log_and_raise(exception=Exception, msg='{} store must be bool type'.format(name))
        return rule

    def _get_baseline_of_metric(self, baseline, metric):
        """Get the baseline value of the metric.

        Args:
            baseline (dict): baseline defined in baseline file
            metric (str): the full name of the metric

        Returns:
            numeric: the baseline value of the metric
        """
        if metric in baseline:
            return baseline[metric]
        else:
            # exclude rank info
            short = metric.split(':')[0]
            if short in baseline:
                return baseline[short]
            # baseline not defined
            else:
                logger.warning('DataDiagnosis: get baseline - {} baseline not found'.format(metric))
                return -1

    def __get_metrics_and_baseline(self, rule, benchmark_rules, baseline):
        """Get metrics with baseline in the rule.

        Parse metric regex in the rule, and store the (baseline, metric) pair
        in _sb_rules[rule]['metrics'] and metric in _enable_metrics。

        Args:
            rule (str): the name of the rule
            benchmark_rules (dict): the dict of rules
            baseline (dict): the dict of baseline of metrics
        """
        if 'function' in self._sb_rules[rule] and self._sb_rules[rule]['function'] == 'multi_rules':
            return
        self._get_metrics(rule, benchmark_rules)
        for metric in self._sb_rules[rule]['metrics']:
            self._sb_rules[rule]['metrics'][metric] = self._get_baseline_of_metric(baseline, metric)

    def _parse_rules_and_baseline(self, rules, baseline):
        """Parse and merge rules and baseline read from file.

        Args:
            rules (dict): rules from rule yaml file
            baseline (dict): baseline of metrics from baseline json file

        Returns:
            bool: return True if successfully get the criteria for all rules, otherwise False.
        """
        try:
            if not rules:
                logger.error('DataDiagnosis: get criteria failed')
                return False
            self._sb_rules = {}
            self._enable_metrics = set()
            benchmark_rules = rules['superbench']['rules']
            for rule in benchmark_rules:
                benchmark_rules[rule] = self._check_and_format_rules(benchmark_rules[rule], rule)
                self._sb_rules[rule] = {}
                self._sb_rules[rule]['name'] = rule
                self._sb_rules[rule]['function'] = benchmark_rules[rule]['function']
                self._sb_rules[rule]['store'] = True if 'store' in benchmark_rules[
                    rule] and benchmark_rules[rule]['store'] is True else False
                self._sb_rules[rule]['criteria'] = benchmark_rules[rule]['criteria']
                self._sb_rules[rule]['categories'] = benchmark_rules[rule]['categories']
                self._sb_rules[rule]['metrics'] = {}
                self.__get_metrics_and_baseline(rule, benchmark_rules, baseline)
            self._enable_metrics = sorted(list(self._enable_metrics))
        except Exception as e:
            logger.error('DataDiagnosis: get criteria failed - {}'.format(str(e)))
            return False

        return True

    def _run_diagnosis_rules_for_single_node(self, node):
        """Use rules to diagnosis single node data.

        Use the rules defined in rule_file to diagnose the raw data of each node,
        if the node violate any rule, label as defective node and save
        the 'Category', 'Defective Details' and data summary of defective node.

        Args:
            node (str): the node to do the diagosis

        Returns:
            details_row (list): None if the node is not labeled as defective,
                otherwise details of ['Category', 'Defective Details']
            summary_data_row (dict): None if the node is not labeled as defective,
                otherwise data summary of the metrics
        """
        data_row = self._raw_data_df.loc[node]
        issue_label = False
        details = []
        categories = set()
        violation = {}
        summary_data_row = pd.Series(index=self._enable_metrics, name=node, dtype=float)
        # Check each rule
        for rule in self._sb_rules:
            # Get rule op function and run the rule
            function_name = self._sb_rules[rule]['function']
            rule_op = RuleOp.get_rule_func(DiagnosisRuleType(function_name))
            violated_num = 0
            if rule_op == RuleOp.multi_rules:
                violated_num = rule_op(self._sb_rules[rule], details, categories, violation)
            else:
                violated_num = rule_op(data_row, self._sb_rules[rule], summary_data_row, details, categories)
            # label the node as defective one
            if self._sb_rules[rule]['store']:
                violation[rule] = violated_num
            elif violated_num:
                issue_label = True
        if issue_label:
            # Add category information
            general_cat_str = ','.join(sorted(list(categories)))
            details_cat_str = ','.join(sorted((details)))
            details_row = [general_cat_str, details_cat_str]
            return details_row, summary_data_row

        return None, None

    def run_diagnosis_rules(self, rules, baseline):
        """Rule-based data diagnosis for multiple nodes' raw data.

        Use the rules defined in rules to diagnose the raw data of each node,
        if the node violate any rule, label as defective node and save
        the 'Category', 'Defective Details' and processed data of defective node.

        Args:
            rules (dict): rules from rule yaml file
            baseline (dict): baseline of metrics from baseline json file

        Returns:
            data_not_accept_df (DataFrame): defective nodes's detailed information
            label_df (DataFrame): labels for all nodes
        """
        try:
            summary_columns = ['Category', 'Defective Details']
            data_not_accept_df = pd.DataFrame(columns=summary_columns)
            summary_details_df = pd.DataFrame()
            label_df = pd.DataFrame(columns=['label'])
            if not self._parse_rules_and_baseline(rules, baseline):
                return data_not_accept_df, label_df
            # run diagnosis rules for each node
            for node in self._raw_data_df.index:
                details_row, summary_data_row = self._run_diagnosis_rules_for_single_node(node)
                if details_row:
                    data_not_accept_df.loc[node] = details_row
                    summary_details_df = summary_details_df.append(summary_data_row)
                    label_df.loc[node] = 1
                else:
                    label_df.loc[node] = 0
            # combine details for defective nodes
            if len(data_not_accept_df) != 0:
                data_not_accept_df = data_not_accept_df.join(summary_details_df)
                data_not_accept_df = data_not_accept_df.sort_values(by=summary_columns, ascending=False)

        except Exception as e:
            logger.error('DataDiagnosis: run diagnosis rules failed, message: {}'.format(str(e)))
        return data_not_accept_df, label_df

    def output_diagnosis_in_excel(self, raw_data_df, data_not_accept_df, output_path, rules):
        """Output the raw_data_df and data_not_accept_df results into excel file.

        Args:
            raw_data_df (DataFrame): raw data
            data_not_accept_df (DataFrame): defective nodes's detailed information
            output_path (str): the path of output excel file
            rules (dict): the rules of DataDiagnosis
        """
        try:
            writer = pd.ExcelWriter(output_path, engine='xlsxwriter')
            # Check whether writer is valiad
            if not isinstance(writer, pd.ExcelWriter):
                logger.error('DataDiagnosis: excel_data_output - invalid file path.')
                return
            file_handler.output_excel_raw_data(writer, raw_data_df, 'Raw Data')
            file_handler.output_excel_data_not_accept(writer, data_not_accept_df, rules)
            writer.save()
        except Exception as e:
            logger.error('DataDiagnosis: excel_data_output - {}'.format(str(e)))

    def output_diagnosis_in_json(self, data_not_accept_df, output_path):
        """Output data_not_accept_df into jsonl file.

        Args:
            data_not_accept_df (DataFrame): the DataFrame to output
            output_path (str): the path of output jsonl file
        """
        p = Path(output_path)
        try:
            data_not_accept_json = data_not_accept_df.to_json(orient='index')
            data_not_accept = json.loads(data_not_accept_json)
            if not isinstance(data_not_accept_df, pd.DataFrame):
                logger.warning('DataDiagnosis: output json data - data_not_accept_df is not DataFrame.')
                return
            if data_not_accept_df.empty:
                logger.warning('DataDiagnosis: output json data - data_not_accept_df is empty.')
                return
            with p.open('w') as f:
                for node in data_not_accept:
                    line = data_not_accept[node]
                    line['Index'] = node
                    json_str = json.dumps(line)
                    f.write(json_str + '\n')
        except Exception as e:
            logger.error('DataDiagnosis: output json data failed, msg: {}'.format(str(e)))

    def gen_md_lines(self, data_not_accept_df, rules, round):
        """Convert DataFrame into markdown lines.

        Args:
            data_not_accept_df (DataFrame): the DataFrame to output
            rules (dict): the rules of DataDiagnosis
            round (int): the number of decimal digits

        Returns:
            list: lines in markdown format
        """
        data_not_accept_df['machine'] = data_not_accept_df.index
        header = data_not_accept_df.columns.tolist()
        header = header[-1:] + header[:-1]
        data_not_accept_df = data_not_accept_df[header]
        # format precision of values to n decimal digits
        for rule in rules:
            for metric in rules[rule]['metrics']:
                if rules[rule]['function'] == 'variance':
                    if round and isinstance(round, int):
                        data_not_accept_df[metric] = data_not_accept_df[metric].map(
                            lambda x: x * 100, na_action='ignore'
                        )
                        data_not_accept_df = data_analysis.round_significant_decimal_places(
                            data_not_accept_df, round, [metric]
                        )
                    data_not_accept_df[metric] = data_not_accept_df[metric].map(
                        lambda x: '{}%'.format(x), na_action='ignore'
                    )
                elif rules[rule]['function'] == 'value':
                    if round and isinstance(round, int):
                        data_not_accept_df = data_analysis.round_significant_decimal_places(
                            data_not_accept_df, round, [metric]
                        )
        lines = file_handler.gen_md_table(data_not_accept_df, header)
        return lines

    def run(self, raw_data_file, rule_file, baseline_file, output_dir, output_format='excel', round=2):
        """Run the data diagnosis and output the results.

        Args:
            raw_data_file (str): the path of raw data jsonl file.
            rule_file (str): The path of baseline yaml file
            baseline_file (str): The path of baseline json file
            output_dir (str): the directory of output file
            output_format (str): the format of the output, 'excel' or 'json'
            round (int): the number of decimal digits
        """
        try:
            rules = self._preprocess(raw_data_file, rule_file)
            # read baseline
            baseline = file_handler.read_baseline(baseline_file)
            logger.info('DataDiagnosis: Begin to process {} nodes'.format(len(self._raw_data_df)))
            data_not_accept_df, label_df = self.run_diagnosis_rules(rules, baseline)
            logger.info('DataDiagnosis: Processed finished')
            output_path = ''
            if output_format == 'excel':
                output_path = str(Path(output_dir) / 'diagnosis_summary.xlsx')
                self.output_diagnosis_in_excel(self._raw_data_df, data_not_accept_df, output_path, self._sb_rules)
            elif output_format == 'json':
                output_path = str(Path(output_dir) / 'diagnosis_summary.jsonl')
                self.output_diagnosis_in_json(data_not_accept_df, output_path)
            elif output_format == 'md' or output_format == 'html':
                lines = self.gen_md_lines(data_not_accept_df, self._sb_rules, round)
                if output_format == 'md':
                    output_path = str(Path(output_dir) / 'diagnosis_summary.md')
                    file_handler.output_lines_in_md(lines, output_path)
                else:
                    output_path = str(Path(output_dir) / 'diagnosis_summary.html')
                    file_handler.output_lines_in_html(lines, output_path)
            else:
                logger.error('DataDiagnosis: output failed - unsupported output format')
            logger.info('DataDiagnosis: Output results to {}'.format(output_path))
        except Exception as e:
            logger.error('DataDiagnosis: run failed - {}'.format(str(e)))

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. A module for baseline-based data diagnosis. The DataDiagnosis class to do the baseline-based data diagnosis. Init function. Check the rule of the metric whether the formart is valid.          Args:             rule (dict): the rule             name (str): the rule name          Returns:             dict: the rule for the metric # check if rule is supported # check rule format Get the baseline value of the metric.          Args:             baseline (dict): baseline defined in baseline file             metric (str): the full name of the metric          Returns:             numeric: the baseline value of the metric # exclude rank info # baseline not defined Get metrics with baseline in the rule.          Parse metric regex in the rule, and store the (baseline, metric) pair         in _sb_rules[rule]['metrics'] and metric in _enable_metrics。          Args:             rule (str): the name of the rule             benchmark_rules (dict): the dict of rules             baseline (dict): the dict of baseline of metrics Parse and merge rules and baseline read from file.          Args:             rules (dict): rules from rule yaml file             baseline (dict): baseline of metrics from baseline json file          Returns:             bool: return True if successfully get the criteria for all rules, otherwise False. Use rules to diagnosis single node data.          Use the rules defined in rule_file to diagnose the raw data of each node,         if the node violate any rule, label as defective node and save         the 'Category', 'Defective Details' and data summary of defective node.          Args:             node (str): the node to do the diagosis          Returns:             details_row (list): None if the node is not labeled as defective,                 otherwise details of ['Category', 'Defective Details']             summary_data_row (dict): None if the node is not labeled as defective,                 otherwise data summary of the metrics # Check each rule # Get rule op function and run the rule # label the node as defective one # Add category information Rule-based data diagnosis for multiple nodes' raw data.          Use the rules defined in rules to diagnose the raw data of each node,         if the node violate any rule, label as defective node and save         the 'Category', 'Defective Details' and processed data of defective node.          Args:             rules (dict): rules from rule yaml file             baseline (dict): baseline of metrics from baseline json file          Returns:             data_not_accept_df (DataFrame): defective nodes's detailed information             label_df (DataFrame): labels for all nodes # run diagnosis rules for each node # combine details for defective nodes Output the raw_data_df and data_not_accept_df results into excel file.          Args:             raw_data_df (DataFrame): raw data             data_not_accept_df (DataFrame): defective nodes's detailed information             output_path (str): the path of output excel file             rules (dict): the rules of DataDiagnosis # Check whether writer is valiad Output data_not_accept_df into jsonl file.          Args:             data_not_accept_df (DataFrame): the DataFrame to output             output_path (str): the path of output jsonl file Convert DataFrame into markdown lines.          Args:             data_not_accept_df (DataFrame): the DataFrame to output             rules (dict): the rules of DataDiagnosis             round (int): the number of decimal digits          Returns:             list: lines in markdown format # format precision of values to n decimal digits Run the data diagnosis and output the results.          Args:             raw_data_file (str): the path of raw data jsonl file.             rule_file (str): The path of baseline yaml file             baseline_file (str): The path of baseline json file             output_dir (str): the directory of output file             output_format (str): the format of the output, 'excel' or 'json'             round (int): the number of decimal digits # read baseline