def experiment_3(): classifier_1 = svm.LinearSVC() classifier_2 = MultinomialNB() #SENTIMENT WORDS #dataset 1 l_parser.parse_file(SOURCE_DATA_FILE,L_TARGET_DATA_FILE , 1) #dataset 2 l_parser.parse_file(SOURCE_DATA_FILE_2,L_TARGET_DATA_FILE_2 , 1) l_analyzer.cross_val(L_TARGET_DATA_FILE,classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE,classifier_2) l_analyzer.cross_val(L_TARGET_DATA_FILE_2,classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE_2,classifier_2) #ALL WORDS l_parser.parse_file(SOURCE_DATA_FILE,L_TARGET_DATA_FILE , 2) l_parser.parse_file(SOURCE_DATA_FILE_2,L_TARGET_DATA_FILE_2 , 2) l_analyzer.cross_val(L_TARGET_DATA_FILE,classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE,classifier_2) l_analyzer.cross_val(L_TARGET_DATA_FILE_2,classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE_2,classifier_2)
def experiment_3(): classifier_1 = svm.LinearSVC() classifier_2 = MultinomialNB() #SENTIMENT WORDS #dataset 1 l_parser.parse_file(SOURCE_DATA_FILE, L_TARGET_DATA_FILE, 1) #dataset 2 l_parser.parse_file(SOURCE_DATA_FILE_2, L_TARGET_DATA_FILE_2, 1) l_analyzer.cross_val(L_TARGET_DATA_FILE, classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE, classifier_2) l_analyzer.cross_val(L_TARGET_DATA_FILE_2, classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE_2, classifier_2) #ALL WORDS l_parser.parse_file(SOURCE_DATA_FILE, L_TARGET_DATA_FILE, 2) l_parser.parse_file(SOURCE_DATA_FILE_2, L_TARGET_DATA_FILE_2, 2) l_analyzer.cross_val(L_TARGET_DATA_FILE, classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE, classifier_2) l_analyzer.cross_val(L_TARGET_DATA_FILE_2, classifier_1) l_analyzer.cross_val(L_TARGET_DATA_FILE_2, classifier_2)
import cPickle as pickle import numpy as np SOURCE_DATA_FILE = "../../msd_dense_subset/mood.txt" SOURCE_DATA_FILE_2 = "../../msd_dense_subset/mood2.txt" COMBINED_TARGET_DATA_FILE = "../../msd_dense_subset/mood.pkl" LYRICS_TARGET_DATA_FILE = "../../msd_dense_subset/mood_lyrics_features_2.pkl" ECHONEST_TARGET_DATA_FILE = "../../msd_dense_subset/mood_echonest_features_2.pkl" #parse echonestfeatures echonestparser.parse_file(SOURCE_DATA_FILE_2, ECHONEST_TARGET_DATA_FILE) #parse lyricsfeatures lyricsparser.parse_file(SOURCE_DATA_FILE_2,LYRICS_TARGET_DATA_FILE,1) #get both features and combine them to a new feature space with open(ECHONEST_TARGET_DATA_FILE, 'r') as f: data = pickle.load(f) echonest_features = data['features'] echonest_tracks = data['tracks'] with open(LYRICS_TARGET_DATA_FILE, 'r') as f: data = pickle.load(f) labels = data['labels'] lyrics_features = data['features'] lyrics_tracks = data['tracks'] combined_tracks = list() combined_features = np.empty((len(lyrics_tracks),echonest_features.shape[1]+lyrics_features.shape[1]),dtype='float') combined_labels = list() for i in range(len(lyrics_tracks)):