Unmaintained¶
This project is unmaintained and the checkpoint functionality has been migrated to the checkpointing project.
Assist small-scale machine learning.¶
learning-utility is a package of utilities for small-scale machine learning tasks with scikit-learn.
Installation¶
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.
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:
Heavy computation.
3
Run this script again, the with-statement will be skipped. You will get:
3
Once a value among watch
changes or the code inside the with-statement changes,
re-calculation takes place to ensure the correct output.
Please check out the details for InlineCheckpoint.
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:
id, pred
1, 0.25
2, 0.45
3, 0.56
Run:
>>> 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:
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.
Please check out the details for AutoSaver.