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        "author": "Vopaaz",
        "author_email": "liyifan945@163.com",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "# learning-utility\n\n**Assist small-scale machine learning.**\n\nlearning-utility is a package of utilities for small-scale machine\nlearning tasks with scikit-learn.\n\n![image](https://www.travis-ci.org/Vopaaz/learning-utility.svg?branch=master)\n![image](https://codecov.io/gh/Vopaaz/learning-utility/branch/master/graph/badge.svg)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/Lutil)\n[![Downloads](https://pepy.tech/badge/lutil)](https://pepy.tech/project/lutil)\n![PyPI](https://img.shields.io/pypi/v/Lutil)\n\n## Installation\n\n```bash\npip install Lutil\n```\n\n## Key Features\n\n### Cache Intermediate Results\n\n`InlineCheckpoint` can cache the computation result in the first call.\nSince then, if nothing has changed, it retrieves the cache and skips\ncomputation.\n\nSuppose you have such a .py file.\n\n```python\nfrom Lutil.checkpoints import InlineCheckpoint\n\na, b = 1, 2\nwith InlineCheckpoint(watch=[\"a\", \"b\"], produce=[\"c\"]):\n   print(\"Heavy computation.\")\n   c = a + b\n\nprint(c)\n```\n\nRun the script, you will get:\n\n```text\nHeavy computation.\n3\n```\n\nRun this script again, the with-statement will be skipped. You will get:\n\n```text\n3\n```\n\nOnce a value among `watch` changes or the code inside the with-statement\nchanges, re-calculation takes place to ensure the correct output.\n\n### Save Prediction Result According to the Given Format\n\nLots of machine learning competitions require a .csv file in a given format.\nMost of them provide an example file.\n\nIn example.csv:\n\n```text\nid, pred\n1, 0.25\n2, 0.45\n3, 0.56\n```\n\nRun:\n\n```python\n>>> import numpy as np\n>>> from Lutil.dataIO import AutoSaver\n\n>>> result = np.array([0.2, 0.4, 0.1, 0.5])\n       # Typical output of a scikit-learn predictor\n\n>>> ac = AutoSaver(save_dir=\"somedir\", example_path=\"path/to/example.csv\")\n>>> ac.save(result, \"some_name.csv\")\n```\n\nThen in your somedir/some_name.csv:\n\n```text\nid, pred\n1, 0.2\n2, 0.4\n3, 0.1\n4, 0.5\n```\n\nIt also works if the `result` is a pandas DataFrame, Series, 2-dim numpy array, etc.\nAlso, the encoding, seperator, header, index of the example.csv will all be recognized.",
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