# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TODO: Add a description here.""" import evaluate import datasets import re import dateutil.parser import numpy as np from typing import List, Dict, Any # Constant regex to get timestrings timestamp_regex = r'^\s*\[?\s*(\d{4}[-/.]\d{2}[-/.]\d{2}(?:[ T]\d{2}[:]\d{2}(?:[:]\d{2}(?:[.,]\d+)?)?(?:Z|[+-]\d{2}[:]\d{2})?)?)\s*\]?\s*' TIMESTAMP_PATTERN = re.compile(timestamp_regex, re.MULTILINE) INT_PATTERN = re.compile(r'(-?\d+)') FLOAT_PATTERN = re.compile(r'(-?\d+\.\d+)') SACREBLEU_METRIC = evaluate.load("evaluate-metric/sacrebleu") # TODO: Add BibTeX citation _CITATION = """\ @InProceedings{huggingface:module, title = {A great new module}, authors={huggingface, Inc.}, year={2020} } """ # TODO: Add description of the module here _DESCRIPTION = """\ This new module is designed to solve this great ML task and is crafted with a lot of care. """ # TODO: Add description of the arguments of the module here _KWARGS_DESCRIPTION = """ Calculates how good are predictions given some references, using certain scores Args: predictions: list of predictions to score. Each predictions should be a string with tokens separated by spaces. references: list of reference for each prediction. Each reference should be a string with tokens separated by spaces. Returns: accuracy: description of the first score, another_score: description of the second score, Examples: Examples should be written in doctest format, and should illustrate how to use the function. >>> my_new_module = evaluate.load("my_new_module") >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) >>> print(results) {'accuracy': 1.0} """ # TODO: Define external resources urls if needed BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class LogMetric(evaluate.Metric): """TODO: Short description of my evaluation module.""" def _info(self): # TODO: Specifies the evaluate.EvaluationModuleInfo object return evaluate.MetricInfo( # This is the description that will appear on the modules page. module_type="metric", description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference # Both prediction and reference are strings features=datasets.Features({ "predictions": datasets.Value("string", id="sequence"), "references": datasets.Value("string", id="sequence"), }), # Homepage of the module for documentation homepage="http://module.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_module"], reference_urls=["http://path.to.reference.url/new_module"] ) def _download_and_prepare(self, dl_manager): """Optional: download external resources useful to compute the scores""" # TODO: Download external resources if needed pass def _compute(self, predictions, references): # TODO: get separate log entries (split before timestamps), replace timestamps with token and compare the log entry with BLEU metric_dicts = [PredRefScore(p,r).run() for p,r in zip(predictions,references)] # Extract keys (assuming all dictionaries have the same keys) keys = metric_dicts[0].keys() # Convert list of dictionaries into a 2D numpy array values = np.array([list(d.values()) for d in metric_dicts]) # Calculate the mean along the vertical axis (axis=0) mean_values = np.mean(values, axis=0) # a dictionary, matching the keys with their corresponding mean values metric_result = dict(zip(keys, mean_values)) return metric_result class PredRefScore: scores : Dict[str, float]= {} def __init__(self, prediction : str, reference: str) -> Dict[str, float]: self.reference = reference.strip(' \t\n\r') self.prediction = prediction.strip(' \t\n\r') def run(self): self.getLogMetric() return self.scores ##### Convenience Methods ##### # TODO: also set pred_ts, ref_ts, pred_msgs and ref_msgs as fields # A score depending on the difference in length of two sentences def get_length_score(self, preds_split : List[Any], refs_split : List[Any]) -> float: pred_content_lengths = np.vectorize(len)(preds_split) ref_content_lengths = np.vectorize(len)(refs_split) return self.smapeScore(pred_content_lengths, ref_content_lengths) # helper function that computes the smape_score either between two numbers or two lists of numbers (must be the same length) def smapeScore(self, P, R) -> float: P_isnumber = isinstance(P, (int, float)) R_isnumber = isinstance(R, (int, float)) # either both must be numbers or both must be no number assert P_isnumber == R_isnumber if not P_isnumber: assert(len(P) == len(R)) if P_isnumber and R_isnumber: if P == 0 and R == 0: return 1.0 # since this leads to (|R| + |P|) = 0 return 1 - (np.sum(np.abs(R - P) / (np.abs(R) + np.abs(P)))) # (n = 1) else: if len(P) == 0 and len(R) == 0: return 1.0 # since this leads to n = 0 n = len(P) P = np.array(P) R = np.array(R) denominator = np.abs(R) + np.abs(P) # Replace zeros in the denominator with 1 to avoid division by zero. # the denominator[i] = 0 is only possible if R[i] == P[i] == 0, hence we can set denominator[i] = 1 and still achieve the result of 0 after division at index i denominator[denominator == 0] = 1 return 1 - (1.0/n * np.sum(np.abs(R - P) / denominator)) # Replaces numbers in a string with a placeholder def replaceNumbers(self, text : str) -> str: text = INT_PATTERN.sub(r'<|INT|>', text) text = FLOAT_PATTERN.sub(r'<|FLOAT|>', text) return text # Split all log-entries in timestamps and log-messages def split_log_entry(self, pred : str, ref: str): pred_lines = pred.splitlines() ref_lines = ref.splitlines() # One logentry always consists of timestamp + log-message pred_timestamps, pred_logMessages = [], [] ref_timestamps, ref_logMessages = [], [] for i in range(len(pred_lines)): if TIMESTAMP_PATTERN.match(pred_lines[i]) is not None: # try to match timestamp _, pred_ts, pred_msg = TIMESTAMP_PATTERN.split(pred_lines[i]) pred_timestamps.append(pred_ts) pred_logMessages.append(pred_msg) else: # 0. space heuristic pred_msg = pred_lines[i] pred_logMessages.append(pred_msg) for i in range(len(ref_lines)): if TIMESTAMP_PATTERN.match(ref_lines[i]) is None: raise ValueError("The provided regex can't parse a timestamp in a reference log. Please make sure that the regex can parse a provided reference log format. Line: " + ref_lines[i]) _, ref_ts, ref_msg = TIMESTAMP_PATTERN.split(ref_lines[i]) ref_timestamps.append(ref_ts) ref_logMessages.append(ref_msg) # We extend the shorter list to the length of the longer one max_logentries = max(len(pred_logMessages), len(ref_logMessages)) pred_logMessages += (max_logentries - len(pred_logMessages)) * [" "] ref_logMessages += (max_logentries- len(ref_logMessages)) * [" "] return pred_timestamps, pred_logMessages, ref_timestamps, ref_logMessages ##### Individual Setter Methods for Scores ##### # splits both strings at \n and then computes the smape_score of their lengths def set_linecount_score(self, pred : str, ref : str) -> None: pred_lines_amt = len(pred.splitlines()) ref_lines_amt = len(ref.splitlines()) self.scores["linecount_difference_SMAPE_score"] = self.smapeScore(pred_lines_amt, ref_lines_amt) def set_sacrebleu_score(self, pred_log_messages : List[str], ref_log_messages : List[str]) -> None: sacrebleu_score = SACREBLEU_METRIC.compute(predictions=pred_log_messages, references=ref_log_messages)["score"] / 100.0 self.scores["linecontent_sacrebleu_score"] = sacrebleu_score def set_smape_length_score(self, pred_log_messages : List[str], ref_log_messages : List[str]) -> None: smape_length_score = self.get_length_score(pred_log_messages, ref_log_messages) self.scores["linecontentlength_difference_SMAPE_score"] = smape_length_score def set_sacrebleu_withoutexplnumbers_score(self, pred_log_messages : List[str], ref_log_messages : List[str]): vectorized_replaceNumbers = np.vectorize(self.replaceNumbers) cleaned_pred_logMessages = vectorized_replaceNumbers(pred_log_messages) cleaned_ref_logMessages = vectorized_replaceNumbers(ref_log_messages) sacrebleu_withoutExplicitNumbers_score = SACREBLEU_METRIC.compute(predictions=cleaned_pred_logMessages, references=cleaned_ref_logMessages)["score"] / 100.0 self.scores["linecontent_sacrebleu_withoutExplicitNumbers_score"] = sacrebleu_withoutExplicitNumbers_score # Get differenct scores regarding the content of a log-message def all_linecontent_scores(self, pred_logMessages : List[str], ref_logMessages: List[str]) -> None: if pred_logMessages == [] and ref_logMessages == []: pred_logMessages = [""] ref_logMessages = [""] self.set_sacrebleu_score(pred_logMessages, ref_logMessages) self.set_smape_length_score(pred_logMessages, ref_logMessages) self.set_sacrebleu_withoutexplnumbers_score(pred_logMessages, ref_logMessages) def set_timestamp_amt_score(self, pred_timestamps : List[str], ref_timestamps : List[str]): timestamp_amt_score = self.smapeScore(len(pred_timestamps), len(ref_timestamps)) self.scores["timestamps_SMAPE_difference_score"] = timestamp_amt_score def set_timestamp_format_consistency_score(self, pred_timestamps, ref_timestamps): if (len(pred_timestamps) == 0): self.scores["timestamps_formatConsistency_score"] = 1.0 return pred_timestring_pattern = re.sub(r'\d', r'\\d', re.escape(pred_timestamps[0])).strip() all_consistent = all(re.fullmatch(pred_timestring_pattern, ts.strip()) is not None for ts in ref_timestamps) self.scores["timestamps_formatConsistency_score"] = 1.0 if all_consistent else 0.0 def set_timestamp_monotonicity_score(self, pred_timestamps) -> None: try: parsed_times = [dateutil.parser.parse(ts) for ts in pred_timestamps] # Parse all timestamps except dateutil.parser.ParserError: self.scores["timestamps_monotinicity_score"] = 0.0 return # Check if the timestamps are monotonically increasing all_monotone = all(t1 <= t2 for t1, t2 in zip(parsed_times, parsed_times[1:])) self.scores["timestamps_monotinicity_score"] = 1.0 if all_monotone else 0.0 # get different scores regarding the timestamp def all_timestamp_scores(self, pred_timestamps, ref_timestamps) -> None: self.set_timestamp_amt_score(pred_timestamps, ref_timestamps) self.set_timestamp_format_consistency_score(pred_timestamps, ref_timestamps) self.set_timestamp_monotonicity_score(pred_timestamps) # driver method for different score computations def getLogMetric(self): self.set_linecount_score(self.prediction, self.reference) # Split log on timestamps pred_timestamps, pred_logMessages, ref_timestamps, ref_logMessages = self.split_log_entry(self.prediction, self.reference) self.all_linecontent_scores(pred_logMessages, ref_logMessages) self.all_timestamp_scores(pred_timestamps, ref_timestamps)