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https://github.com/vopaaz/learning-utility
Assist small-scale machine learning.
https://github.com/vopaaz/learning-utility
data-science machine-learning pandas python3 scikit-learn
Last synced: 2 months ago
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Assist small-scale machine learning.
- Host: GitHub
- URL: https://github.com/vopaaz/learning-utility
- Owner: Vopaaz
- License: gpl-3.0
- Created: 2019-08-01T13:51:44.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-07-25T00:35:13.000Z (over 2 years ago)
- Last Synced: 2024-09-30T09:20:22.391Z (3 months ago)
- Topics: data-science, machine-learning, pandas, python3, scikit-learn
- Language: Python
- Homepage: https://learning-utility.readthedocs.io/en/latest/
- Size: 155 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Unmaintained
**This project is unmaintained and the checkpoint functionality has been ported to the [checkpointing](https://github.com/Vopaaz/checkpointing) project.**
# learning-utility
**Assist small-scale machine learning.**
learning-utility is a package of utilities for small-scale machine
learning tasks with scikit-learn.
[](https://pepy.tech/project/lutil)
## Installation
```bash
pip install Lutil
```
## Key Features
### Cache Intermediate Results
`InlineCheckpoint` can cache the computation result in the first call.
Since then, if nothing has changed, it retrieves the cache and skips
computation.
Suppose you have such a .py file.
```python
from Lutil.checkpoints import InlineCheckpoint
a, b = 1, 2
with InlineCheckpoint(watch=["a", "b"], produce=["c"]):
print("Heavy computation.")
c = a + b
print(c)
```
Run the script, you will get:
```text
Heavy computation.
3
```
Run this script again, the with-statement will be skipped. You will get:
```text
3
```
Once a value among `watch` changes or the code inside the with-statement
changes, re-calculation takes place to ensure the correct output.
### Save Prediction Result According to the Given Format
Lots of machine learning competitions require a .csv file in a given format.
Most of them provide an example file.
In example.csv:
```text
id, pred
1, 0.25
2, 0.45
3, 0.56
```
Run:
```python
>>> import numpy as np
>>> from Lutil.dataIO import AutoSaver
>>> result = np.array([0.2, 0.4, 0.1, 0.5])
# Typical output of a scikit-learn predictor
>>> ac = AutoSaver(save_dir="somedir", example_path="path/to/example.csv")
>>> ac.save(result, "some_name.csv")
```
Then in your somedir/some_name.csv:
```text
id, pred
1, 0.2
2, 0.4
3, 0.1
4, 0.5
```
It also works if the `result` is a pandas DataFrame, Series, 2-dim numpy array, etc.
Also, the encoding, seperator, header, index of the example.csv will all be recognized.