Model-logger
Modelloger is a Python library for storing model’s profile and rapid inter model comparision. Powered by dash and SQLITE3, It’s compact ,light and interactive yet powerfull tool to gain usefull insights.
Documentaion :
Read the detailed documentation here
Installation
Using pip
Use the package manager pip to install modelloger.
pip install modelloggerUsage
from modellogger import ModelLogger
#initialise a modelloger instance
path = "c/path/to/db/databasename.db"
mlog = ModelLogger(path)
#If you are already using a db created by modelloger you can directly load it by stating it's path
#if you are creating a new project just give location where you want to store the db followed by a name.db
Functionalities
Now you are ready to rock and roll. Out of the box you will have the following functionalities:
store_model()
gboost = GradientBoostingClassifier()
gboost.fit(X_train,y_train)
mlog.store_model('my_model_name',gboost,X_train,1.0)
#alternatively
mlog.store_model('my_model_name',gboost,X_train,get_score(gboost),save_pickle = True)view_results()
mlog.view_results(True,'my_report')best_model()
mlog.best_model(smaller_the_better=True)delete_model()
mlog.delete_model(Model_name = "Mymodel")
mlog.delete_model(Model_id = 1)model_profiles()
mlog.model_profiles('All')
mlog.model_profiles(5)Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
License
1 maintainer
Metadata
1 maintainer
Release files for modellogger 0.2.3
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Release files / modellogger-0.2.3.tar.gz
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