data_diagnosis.py

# 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
import numpy as np

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 'store' not in rule:
            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]
        elif 'return_code' in metric:
            return 0
        else:
            short = metric
            # exclude rank info, for example, '.*:\d+'->'.*'
            if ':' in metric:
                short = metric.strip(metric.split(':')[-1]).strip(':')
            else:
                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.log_and_raise(exception=Exception, msg='DataDiagnosis: get criteria failed')
            self._sb_rules = {}
            self._enable_metrics = set()
            benchmark_rules = rules['superbench']['rules']
            self._raw_rules = benchmark_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
                if 'function' in benchmark_rules[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
                if 'criteria' in benchmark_rules[rule]:
                    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.log_and_raise(exception=Exception, msg='DataDiagnosis: get criteria failed - {}'.format(str(e)))

        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()
        store_values = {}
        summary_data_row = pd.Series(index=self._enable_metrics, name=node, dtype=float)
        # Check each rule
        for rule in self._sb_rules:
            # if no criteria and store is True in a rule, store the value of metrics in the rule
            if self._sb_rules[rule]['store'] and 'criteria' not in self._sb_rules[rule]:
                store_values[rule] = {}
                for metric in self._sb_rules[rule]['metrics']:
                    store_values[rule][metric] = data_row[metric]
                continue
            # 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, store_values)
            elif rule_op == RuleOp.failure_check:
                violated_num = rule_op(
                    data_row, self._sb_rules[rule], summary_data_row, details, categories, self._raw_rules[rule]
                )
            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']:
                store_values[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
        """
        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 = pd.concat(
                    [summary_details_df,
                     pd.DataFrame([summary_data_row.to_dict()], index=[summary_data_row.name])]
                )
                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)

        return data_not_accept_df, label_df

    def output_all_nodes_results(self, raw_data_df, data_not_accept_df):
        """Output diagnosis results of all nodes.

        Args:
            raw_data_df (DataFrame): raw data
            data_not_accept_df (DataFrame): defective nodes's detailed information

        Returns:
            DataFrame: all nodes' detailed information inluding ['Accept','Number Of Issues',
            'Category','Defective Details']
        """
        append_columns = ['Accept', 'Number Of Issues', 'Category', 'Defective Details']
        all_data_df = (raw_data_df).astype('float64')

        if data_not_accept_df.shape[0] == 0:
            all_data_df['Accept'] = [True for i in range(len(all_data_df))]
            all_data_df['Number Of Issues'] = [0 for i in range(len(all_data_df))]
            all_data_df['Category'] = [None for i in range(len(all_data_df))]
            all_data_df['Defective Details'] = [None for i in range(len(all_data_df))]

        elif data_not_accept_df.shape[0] > 0:
            data_not_accept_df['Accept'] = [False for i in range(len(data_not_accept_df))]
            data_not_accept_df['Number Of Issues'] = data_not_accept_df['Defective Details'].map(
                lambda x: len(x.split(','))
            )
            for index in range(len(append_columns)):
                if append_columns[index] not in data_not_accept_df:
                    logger.log_and_raise(
                        Exception,
                        msg='DataDiagnosis: output_all_nodes_results - column {} not found in data_not_accept_df.'.
                        format(append_columns[index])
                    )
                else:
                    all_data_df = all_data_df.merge(
                        data_not_accept_df[[append_columns[index]]], left_index=True, right_index=True, how='left'
                    )
            all_data_df['Accept'] = all_data_df['Accept'].replace(np.nan, True)
            all_data_df['Number Of Issues'] = all_data_df['Number Of Issues'].replace(np.nan, 0)
            all_data_df['Number Of Issues'] = all_data_df['Number Of Issues'].astype(int)

        all_data_df = all_data_df.replace(np.nan, '')

        return all_data_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.log_and_raise(exception=IOError, msg='DataDiagnosis: excel_data_output - invalid file path.')
            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.log_and_raise(exception=Exception, msg='DataDiagnosis: excel_data_output - {}'.format(str(e)))

    def output_diagnosis_in_jsonl(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.log_and_raise(
                    Exception, msg='DataDiagnosis: output json data - data_not_accept_df is not DataFrame.'
                )
            if data_not_accept_df.empty:
                with p.open('w') as f:
                    pass
                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.log_and_raise(
                exception=Exception, msg='DataDiagnosis: output json data failed, msg: {}'.format(str(e))
            )

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

        Args:
            data_not_accept_df (DataFrame): the DataFrame to output
            output_path (str): the path of output jsonl file
        """
        data_not_accept_df['Index'] = data_not_accept_df.index
        data_not_accept_df = data_not_accept_df.rename(
            columns={
                'Defective Details': 'diagnosis/issue_details',
                'Category': 'diagnosis/category',
                'Number Of Issues': 'diagnosis/issue_num',
                'Accept': 'diagnosis/accept'
            }
        )
        data_not_accept_json = data_not_accept_df.to_json(orient='records')
        data_not_accept = json.loads(data_not_accept_json)
        p = Path(output_path)
        with p.open('w') as f:
            json.dump(data_not_accept, f, indent=4)

    def generate_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
        """
        if len(data_not_accept_df) == 0:
            return []
        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.generate_md_table(data_not_accept_df, header)
        return lines

    def run(
        self, raw_data_file, rule_file, baseline_file, output_dir, output_format='excel', output_all=False, 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_all (bool): output diagnosis results for all nodes
            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)))
            output_df, label_df = self.run_diagnosis_rules(rules, baseline)
            logger.info('DataDiagnosis: Processed finished')
            output_path = str(Path(output_dir) / f'diagnosis_summary.{output_format}')
            # generate all nodes' info
            if output_all:
                output_df = self.output_all_nodes_results(self._raw_data_df, output_df)
            # output according format
            if output_format == 'excel':
                output_path = str(Path(output_dir) / 'diagnosis_summary.xlsx')
                self.output_diagnosis_in_excel(self._raw_data_df, output_df, output_path, self._sb_rules)
            elif output_format == 'json':
                self.output_diagnosis_in_json(output_df, output_path)
            elif output_format == 'jsonl':
                self.output_diagnosis_in_jsonl(output_df, output_path)
            elif output_format == 'md' or output_format == 'html':
                lines = self.generate_md_lines(output_df, self._sb_rules, round)
                if output_format == 'md':
                    file_handler.output_lines_in_md(lines, output_path)
                else:
                    file_handler.output_lines_in_html(lines, output_path)
            else:
                logger.log_and_raise(
                    exception=Exception, msg='DataDiagnosis: output failed - unsupported output format'
                )
            logger.info('DataDiagnosis: Output results to {}'.format(output_path))
        except Exception as e:
            logger.log_and_raise(exception=Exception, msg='DataDiagnosis: run failed - {}'.format(str(e)))