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--- |
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title: LogMetric |
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datasets: |
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- None |
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tags: |
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- evaluate |
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- metric |
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description: 'TODO: add a description here' |
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sdk: gradio |
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sdk_version: 3.19.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Metric Card for LogMetric |
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***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.* |
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## Metric Description |
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This metric is used for evaluating how good a generated log(file) is, given a reference. |
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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. |
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## How to Use |
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The metric can be just by simply giving the predicted log and the reference log as string. |
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Example with timestamps that are of correct amount, consistent, monotonically increasing (-> timestamp score of 1.0): |
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``` |
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>>> 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!"] |
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>>> references = ["2024-02-14 Hello there! \n 2024-02-14 General Kenobi! You're a bold one, aren't you?"] |
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logmetric = evaluate.load("svenwey/logmetric") |
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>>> results = logmetric.compute(predictions=predictions, |
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... references=references) |
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>>> print(results["score"]) |
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1.0 |
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``` |
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Example with timestamp missing from prediction: |
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``` |
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>>> predictions = ["You were my brother Anikin"] |
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>>> references = ["2024-01-12 You were my brother Anikin"] |
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logmetric = evaluate.load("svenwey/logmetric") |
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>>> results = logmetric.compute(predictions=predictions, |
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... references=references) |
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>>> print(results["score"]) |
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0.0 |
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``` |
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### Inputs |
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*List all input arguments in the format below* |
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- **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!*** |
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- **references** *(string list): The reference logs (ground truth)* |
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### Output Values |
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*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}* |
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*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."* |
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#### Values from Popular Papers |
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*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.* |
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### Examples |
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*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.* |
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## Limitations and Bias |
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*Note any known limitations or biases that the metric has, with links and references if possible.* |
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## Citation |
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*Cite the source where this metric was introduced.* |
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## Further References |
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*Add any useful further references.* |
