Mimizuku
Mimizuku is a Python package for anomaly detection using Isolation Forest. It is designed to process log files and detect anomalies based on a variety of features.
Installation
pip install .
Usage
from mimizuku import Mimizuku
# Initialize the model
model = Mimizuku(n_estimators=500)
# Train the model with a log file or DataFrame
model.fit("./training.json")
# Save the trained model
model.save_model("./model.pkl")
# Load the model and use it for prediction
loaded_model = Mimizuku.load_model("./model.pkl")
anomalies_df = loaded_model.predict("./test.json")
# Display detected anomalies
print(anomalies_df)
1 maintainer
Metadata
1 maintainer
Release files for mimizuku 0.2.22
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| mimizuku-0.2.22.tar.gz | 3.4 kB | Without provenance | Details for mimizuku-0.2.22.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mimizuku-0.2.22-py3-none-any.whl | Python 3 | none | any | Details for mimizuku-0.2.22-py3-none-any.whl |
Total release size: 7.6 kB
Release files / mimizuku-0.2.22.tar.gz
| Download URL | mimizuku-0.2.22.tar.gz |
|---|---|
| Size | 3.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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twine/5.1.1 CPython/3.12.2
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Release files / mimizuku-0.2.22-py3-none-any.whl
| Download URL | mimizuku-0.2.22-py3-none-any.whl |
|---|---|
| Size | 4.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.2
|
Security for mimizuku 0.2.22
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