Microkernel Architecture for Machine Learning
Project description
mkml
mkml implements a Microkernel Architecture for Machine Learning Library
try it out
- Install from PyPi
pip install mkml
- By default, 3 Plug-in modules have been loaded
MKMLMetaclass.register('standardization', StandardizationPlugin)
MKMLMetaclass.register('monitoring', MonitoringPlugin)
MKMLMetaclass.register('local_datasource', LocalDataSourcePlugin)
StandardizationPlugin enforces the method signatures of the model class. In our case, the model class must have fit, predict and score methods
MonitoringPlugin monitors all the functions of the model class. In our case, it logs the input parameters, exception as well as the duration of each function in the model class
LocalDataSourcePlugin helps loading the data locally. It dynamically ingest data-loading functions into the model class to help data scientist retrieve the data without worrying how to retrieve it
- Create your own model
from mkml import BaseModel
class UserModel(BaseModel):
def __init__(self):
self._model = LinearRegression()
def fit(self, features, labels):
self._model.fit(X_train, y_train)
def predict(self, features):
self._model.predict(features)
def score(self, features, labels):
return self._model.score(features, labels)
- Instantiate the model class instantnce and load features and labels for training and prediction
um = UserModel()
features = um.get_local_data(feature_mart_location='data', group_id='train_features')
labels = um.get_local_data(feature_mart_location='data', group_id='train_labels')
um.fit(features, labels)
test_features = um.get_local_data(feature_mart_location='data', group_id='test_features')
test_labels = um.predict(test_features)
- Create and register your own Plug-in Module (ie. Remote DataSource)
from mkml import BasePlugin
class RemoteDataSourcePlugin(BasePlugin):
def __init__(self, name):
self._name = name
def apply(self, attrs, **kwargs):
logger.debug('Entering data source plugin')
attrs['get_remote_data'] = self._get_remote_data
def _get_remote_data(self, feature_mart_location, group_id):
# To be implemented
pass
## You can add additional instantiation parameters to the Plug-in class as well
MKMLMetaclass.register('remote_datasource', RemoteDataSourcePlugin, 'remote_ds_plugin')
- Use the new Remote DataSource Plug-in to retrieve features and labels
um = UserModel()
features = um.get_remote_data(feature_mart_location='http://fm', group_id='train_features')
labels = um.get_remote_data(feature_mart_location='http://fm', group_id='train_labels')
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file mkml-0.0.2.tar.gz
.
File metadata
- Download URL: mkml-0.0.2.tar.gz
- Upload date:
- Size: 3.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0.post20210125 requests-toolbelt/0.9.1 tqdm/4.56.1 CPython/3.7.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0788c316f9336fcb8e2d19e8ad34c65d03b0742098572d37c5ee9a349b4a5eb4 |
|
MD5 | a5fc943d4e1cfbfb4351e4eb755d6acc |
|
BLAKE2b-256 | f31eb7ca0db26fda439a03c2a2d0c16b87bab0ef99c6c8a8931e0a749c01211e |
File details
Details for the file mkml-0.0.2-py3-none-any.whl
.
File metadata
- Download URL: mkml-0.0.2-py3-none-any.whl
- Upload date:
- Size: 5.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0.post20210125 requests-toolbelt/0.9.1 tqdm/4.56.1 CPython/3.7.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ab9c2189d9850004cd6be063bc74840b9579543cc33d544357c985b9443c36fa |
|
MD5 | e88d5b4f9b250133796b05d3e96f1e14 |
|
BLAKE2b-256 | 78e4d4c1ab2e6ef38674a573b4cdb96a0881ef75b1c765a286f13d333dafb9b0 |