Metadata-Version: 2.1
Name: crosstrainer
Version: 0.1.3
Summary: CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Home-page: https://github.com/stanford-futuredata/crosstrainer
Author: Justin Yu-wei Chen
Author-email: jyc8889@gmail.com
License: MIT
Description: # CrossTrainer: Practical Domain Adaptation with Loss Reweighting
        
        This is an implementation of the method described in "CrossTrainer: Practical Domain Adaptation with Loss Reweighting" by Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, and Peter Bailis.
        
        ### Install
        The crosstrainer package can be installed using pip.
        
        ```
        pip install crosstrainer
        ```
        
        ### Usage
        
        CrossTrainer utilizes loss reweighting to train machine learning models using data from a target task with supplementary source data.
        
        ##### Inputs:
        Base model, target data, source data.
        
        ##### Outputs:
        Trained model with optimized weighting parameter alpha.
        
        ##### Example:
        
        ```python
        import crosstrainer
        from sklearn import linear_model
        
        lr = linear_model.LogisticRegression()
        ct = CrossTrainer(lr, k=5, delta=0.01)
        lr, alpha = ct.fit(X_target, y_target, X_source, y_source)
        y_pred = lr.predict(X_test)
        ```
        
        More examples can be found in the tests file: ```crosstrainer/tests/test_crosstrainer.py```.
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Description-Content-Type: text/markdown
