# 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
import time
# 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."""
# Constant regex to get timestrings
timestamp_regex = r'^\s*(\d{4}[-/.]\d{2}[-/.]\d{2}(?:[ T]\d{2}[:]\d{2}(?:[:]\d{2}(?:[.,]\d+)?)?(?:Z|[+-]\d{2}[:]\d{2})?)?)\s*'
timestamp_pattern = re.compile(timestamp_regex, re.MULTILINE)
sacrebleu = evaluate.load("sacrebleu")
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 getLogMetric(self, pred : str, ref : str, sacrebleu):
ref = ref.strip(' \t\n\r')
pred = pred.strip(' \t\n\r')
# Find all timestrings in the log
# pred_timestrings = self.timestamp_pattern.findall(pred)
pred_split_log = self.timestamp_pattern.split(pred)
# ref_timestrings = self.timestamp_pattern.findall(ref)
ref_split_log = self.timestamp_pattern.split(ref)
# This should alwas hold (safety feature)
# TODO: remove this after testing
assert(len(pred_split_log) % 2 == len(ref_split_log) % 2 == 1)
# One logentry always consists of timestamp + log-message
pred_logentries = []
ref_logentries = []
# reorganize log into logentry-tuples, consisting of timestamp + log-message
for i in range(1, len(pred_split_log), 2):
pred_logentries.append((pred_split_log[i],pred_split_log[i+1]))
for i in range(1, len(ref_split_log), 2):
ref_logentries.append((ref_split_log[i],ref_split_log[i+1]))
# The number of logentries of the reference/prediction which has more/less entries/timestamps
max_logentries = max(len(pred_logentries), len(ref_logentries))
min_logentries = min(len(pred_logentries), len(ref_logentries))
# Case there are no timestamps in reference and none in prediction
# we can compute bleu directly from original prediction (ref will be empty, but we offload this to the bleu metric)
if (len(pred_logentries) == 0 and len(ref_logentries) == 0):
# TODO: remove this later, for testing purposes only
assert(pred == "")
# any sensible log reference is empty if there is no timestamp, hence it suffices to check exact match
logmsg_score = 100.0 if pred == ref else 0.0
return 0.3 * 100.0 + 0.7 * logmsg_score
# Case one has 0 timestamps, other has >0 timestamps
if (len(pred_logentries) == 0 or len(ref_logentries) == 0):
# It is nonsensical to compare something in this case
return 0.0
# replace all digits in the reference timestamp (first timestamp) with '/d' to get
# a regex that describes the format
pred_timestring_pattern = re.sub(r'\d', r'\\d', re.escape(pred_logentries[0][0]))
matchesPatternScore = 100.0
monotonicallyIncreasingScore = 100.0
# An array to save score per logentry
logmessage_scores = []
# TODO: Idea to penalize too long/ short logs-> add the amount of(max_len - min_len) between timestamps times score 0 at the end
# A variable to save the previous timestamp (as datetime obj) to check monotonicity
prev_datetime = None
# Convert matches to datetime objects
# TODO TODO TODO fix this:
for i in range(min_logentries):
ts, pred_lm = pred_logentries[i]
_, ref_lm = ref_logentries[i]
try:
# Check if the format matches with the format of the first timestamp
# TODO!! Check this later, maybe it is too restricting for training a llm
matchesPattern = re.fullmatch(pred_timestring_pattern, ts) is not None
# Check if the timestamps are monotonically increasing
cur_datetime = dateutil.parser.parse(ts)
monotonicallyIncreasing = True if prev_datetime == None else prev_datetime <= cur_datetime
prev_datetime = cur_datetime
# If one entry doesn't fulfill the matching pattern property or the monotinicity property, set to 0 for whole log
if (not matchesPattern):
matchesPatternScore = 0.0
if (not monotonicallyIncreasing):
monotonicallyIncreasingScore = 0.0
except Exception as e:
# e.g. date format not parsable by dateutil.parser
matchesPatternScore = 0.0
monotonicallyIncreasingScore = 0.0
logmessage_scores.append(sacrebleu.compute(predictions=[pred_lm], references=[ref_lm])["score"])
# TODO: remove later. Used only for testing purposes
assert(len(logmessage_scores) == min_logentries)
# we aggregate the bleu scores where we weight the difference in logentries with a score of 0
logmessage_aggregated_score = ((min_logentries / max_logentries) * np.mean(logmessage_scores))
# Correct amt of timestrings, monotonically increasing, consistent + (by dateutil.parser) parsable format
return 0.2 * monotonicallyIncreasingScore + 0.1 * matchesPatternScore + 0.7 * logmessage_aggregated_score
def _compute(self, predictions, references):
"""Returns the scores"""
# TODO: get separate log entries (split before timestamps), replace timestamps with token and compare the log entry with BLEU
t_before_logmetric = time.perf_counter()
timestamp_score = np.mean([self.getLogMetric(p,r, self.sacrebleu) for p,r in zip(predictions,references)])
t_after_logmetric = time.perf_counter()
logmetric_duration = f" {t_after_logmetric - t_before_logmetric:0.10f}"
return {
"score": timestamp_score,
"duration": logmetric_duration,
}