Metadata-Version: 2.1
Name: naive_feature_selection
Version: 0.0.1
Summary: Naive Feature Selection
Home-page: https://github.com/aspremon/NaiveFeatureSelection
Author: Armin Askari, Alexandre d'Aspremont, Laurent El Ghaoui
Author-email: aspremon@ens.fr
License: UNKNOWN
Description: 
        NFS: Naive Feature Selection
        =======
        
        This package solves the Naive Feature Selection problem described in [the paper](https://arxiv.org/abs/1905.09884).
        
        # Installation
        
        ```
        pip install git+https://github.com/aspremon/NaiveFeatureSelection
        ```
        
        # Usage
        
        ## Minimal usage script
        
        The [DemoNFS.py](DemoNFS.py) script loads the *20 newsgroups* text data set from *scikit-learn* and reports accuracy of Naive Feature Selection, followed by SVC using the selected features.
        
        The package is compatible with *scikit-learn*'s *Fit-Transform* paradigm. To demonstrate this, [DemoNFS.py](DemoNFS.py) runs the same test using the *pipeline* package from *scikit-learn* and performs cross validation using *GridSearchCV* from *sklearn.model_selection*.
        
        To run the `DemoNFS.py` script, type
        ```
        python DemoNFS.py
        ```
        
        This should produce the following output
        
        ```
        Testing NFS ...
        Loading 20 newsgroups dataset for categories:
        ['sci.med', 'sci.space']
        
        Extracting features from the training data using a sparse vectorizer
        n_samples: 1187, n_features: 21368
        
        Extracting features from the test data using the same vectorizer
        n_samples: 790, n_features: 21368
        
        NFS accuracy:   0.843
        
        Space features:
        ['aerospace', 'allen', 'ames', 'apollo', 'astronomy', 'billion', 'built', 'centaur', 'comet', 'command', 'commercial', 'cost', 'data', 'dc', 'dryden', 'earth', 'flight', 'funding', 'government', 'gravity', 'jupiter', 'landing', 'launch', 'launched', 'launches', 'lunar', 'mars', 'mary', 'mining', 'mission', 'missions', 'moon', 'nasa', 'orbit', 'orbital', 'pat', 'payload', 'planetary', 'program', 'project', 'proton', 'rocket', 'rockets', 'russian', 'satellite', 'satellites', 'shafer', 'shuttle', 'software', 'solar', 'space', 'spacecraft', 'ssto', 'station', 'sun', 'titan', 'vehicle']
        
        Med features:
        ['allergic', 'banks', 'blood', 'brain', 'cadre', 'cancer', 'candida', 'chastity', 'diagnosed', 'diet', 'disease', 'diseases', 'doctor', 'doctors', 'drug', 'drugs', 'dsl', 'food', 'foods', 'geb', 'gordon', 'health', 'intellect', 'lyme', 'med', 'medical', 'medicine', 'msg', 'n3jxp', 'pain', 'patient', 'patients', 'pitt', 'seizures', 'shameful', 'skepticism', 'soon', 'surrender', 'symptoms', 'syndrome', 'therapy', 'treatment', 'yeast']
        
        Pipeline accuracy:      0.843
        
        Best cross validated k: 500
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
