README.md

---
title: LogMetric
datasets:
- None
tags:
- evaluate
- metric
description: 'TODO: add a description here'
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---

# Metric Card for LogMetric

***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*

## Metric Description
This metric is used for evaluating how good a generated log(file) is, given a reference. 

The metric evaluates if the predicted log has the correct amount of timestamps, if timestamps are monotonically increasing and if the timestamps are consistent in their format.

## How to Use
The metric can be just by simply giving the predicted log and the reference log as string.

Example with timestamps that are of correct amount, consistent, monotonically increasing (-> timestamp score of 1.0):
```
>>> predictions = ["2024-01-12 11:23 It's over Anikin, I have the high ground \n 2024-01-12 11:24 You underestimate my power!"]
>>> references = ["2024-02-14 Hello there! \n 2024-02-14 General Kenobi! You're a bold one, aren't you?"]
logmetric = evaluate.load("svenwey/logmetric")
>>> results = logmetric.compute(predictions=predictions, 
...                             references=references)
>>> print(results["score"])
1.0
```

Example with timestamp missing from prediction:
```
>>> predictions = ["You were my brother Anikin"]
>>> references = ["2024-01-12 You were my brother Anikin"]
logmetric = evaluate.load("svenwey/logmetric")
>>> results = logmetric.compute(predictions=predictions, 
...                             references=references)
>>> print(results["score"])
0.0
```


### Inputs
*List all input arguments in the format below*
- **predictions** *(string list): The logs, as predicted/generated by the ML model. **Important: Every logfile is only one string, even if it contains multiple lines!***
- **references** *(string list): The reference logs (ground truth)*
### Output Values

*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*

*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*

#### Values from Popular Papers
*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*

### Examples
*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*

## Limitations and Bias
*Note any known limitations or biases that the metric has, with links and references if possible.*

## Citation
*Cite the source where this metric was introduced.*

## Further References
*Add any useful further references.*