from gen_feature_data import load_json ''' Generic experiment with no summary features. ''' name = "summary" rep_id = '400404' rep_data = load_json('representatives')[rep_id] config.features_to_ignore = [] config.force_preprocess = True config.use_sparse_data = True print print '=====================================' print ' '+name print '=====================================' print # Generate our feature vectors # TODO(john): Cache this before we start to massive tests to save time gen_feature_data.genExperimentData(rep_id, experiment_name=name) # Train the SVM and print results # TODO(john): Return results in such a way that we can analyze multiple reps # or plug it into excel or something. svm.svmLearn(rep_id, C=1, gamma=1.0, kernel='rbf', debug=1, experiment_name=name)
import svm import config from gen_feature_data import load_json ''' Generic experiment with no summary features. ''' name = "no_summary" rep_id = '400003' rep_data = load_json('representatives')[rep_id] config.features_to_ignore = ['summary_word_bag'] print print '=====================================' print ' '+name print '=====================================' print # Generate our feature vectors # TODO(john): Cache this before we start to massive tests to save time gen_feature_data.genExperimentData(rep_id, experiment_name=name) # Train the SVM and print results # TODO(john): Return results in such a way that we can analyze multiple reps # or plug it into excel or something. svm.svmLearn(rep_id, C=1.0, gamma=0.0, kernel='linear', debug=1, experiment_name=name)