Model Tooling library
Installation
Use pip to install:
pip install ml-tooling
Or use conda
conda install -c conda-forge ml_tooling
Test
We use tox for managing build and test environments, to install tox run:
pip install tox
And to run tests:
tox -e py
Example usage
Define a class using ModelData and implement the two required methods. Here we simply implement a linear regression on the Boston dataset using sklearn.datasets
from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import LinearRegression
from ml_tooling import Model
from ml_tooling.data import Dataset
# Define a new data class
class CaliforniaData(Dataset):
def load_prediction_data(self, idx):
x, _ = fetch_california_housing(return_X_y=True)
return x[idx] # Return given observation
def load_training_data(self):
return fetch_california_housing(return_X_y=True)
# Instantiate a model with an estimator
linear_california = Model(LinearRegression())
# Instantiate the data
data = CaliforniaData()
# Split training and test data
data.create_train_test()
# Score the estimator yielding a Result object
result = linear_california.score_estimator(data)
# Visualize the result
result.plot.prediction_error()
print(result)
<Result LinearRegression: {'r2': 0.68}>
Links
- Documentation: https://ml-tooling.readthedocs.io
- Releases: https://pypi.org/project/ml_tooling/
- Code: https://github.com/andersbogsnes/ml_tooling
- Issue Tracker: https://github.com/andersbogsnes/ml_tooling/issues
1 maintainer
Release files for ml-tooling 0.12.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ml_tooling-0.12.1.tar.gz | 41.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ml_tooling-0.12.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 110.5 kB
Release files / ml_tooling-0.12.1.tar.gz
| Download URL | ml_tooling-0.12.1.tar.gz |
|---|---|
| Size | 41.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d84fab1f5dc36332986e1f16b1d0b29ecb1a935e9beb8e773e5224e3aa84fa7c
|
|
BLAKE2b-256 checksum How to use checksums |
74dd561aa9e9b2f27bdec4961bef1203688d61b202e861132acd850b88434421
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
python-httpx/0.23.0
|
Release files / ml_tooling-0.12.1-py3-none-any.whl
| Download URL | ml_tooling-0.12.1-py3-none-any.whl |
|---|---|
| Size | 69.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0b82b2dc8fda470b5445d242da29a671f1c9f3df20574b122bb1bc51bc02f893
|
|
BLAKE2b-256 checksum How to use checksums |
32a62e173235a535d1fd26e6f444f2ca69a363e426d13b446a68e831c9714db8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
python-httpx/0.23.0
|
