def runFactorAnalyzer(self, cols_to_norm, result): fa = FactorAnalyzer(rotation="varimax", n_factors=2) df = result[cols_to_norm] result = result.dropna() df = df.dropna() fa.fit(df) ev = fa.get_eigenvalues() kmo_all, kmo_model = calculate_kmo(df) if (kmo_model < 0.6): print("kmo_model: %s " % kmo_model) array = fa.transform(df) #print("Factors: %s" % (array)) #print("loadings: %s " % fa.loadings_) #print("eigenvalues: %s " % ev[0]) dataframe = pd.DataFrame(columns=[ 'Player', 'Session', 'Time', 'NegativeEmotion', 'PositiveEmotion' ]) print("T session: %s " % len(result['Session'])) dataframe['Session'] = result['Session'] dataframe['Player'] = result['Player'] dataframe['Time'] = result['ts'] dataframe['NegativeEmotion'] = np.around(array[:, 0], 2) dataframe['PositiveEmotion'] = np.around(array[:, 1], 2) dataframe.to_csv('/home/elton/Desktop/Dataset/MetricsEmotion.csv', sep=',', mode='a', header=False)
def get_factor_eigenvalues(df): fa = FactorAnalyzer(rotation=None) fa.fit(df) ev, v = fa.get_eigenvalues() return ev
def main(): """ Run the script. """ # set up an argument parser parser = argparse.ArgumentParser(prog='factor_analyzer.py') parser.add_argument( dest='feature_file', help="Input file containing the pre-processed features " "for the training data") parser.add_argument( dest='output_dir', help="Output directory to save " "the output files", ) parser.add_argument('-f', '--factors', dest="num_factors", type=int, default=3, help="Number of factors to use (Default 3)", required=False) parser.add_argument('-r', '--rotation', dest="rotation", type=str, default='none', help="The rotation to perform (Default 'none')", required=False) parser.add_argument('-m', '--method', dest="method", type=str, default='minres', help="The method to use (Default 'minres')", required=False) # parse given command line arguments args = parser.parse_args() method = args.method factors = args.num_factors rotation = None if args.rotation == 'none' else args.rotation file_path = args.feature_file if not file_path.lower().endswith('.csv'): raise ValueError('The feature file must be in CSV format.') data = pd.read_csv(file_path) # get the logger logger = logging.getLogger(__name__) logging.setLevel(logging.INFO) # log some useful messages so that the user knows logger.info( "Starting exploratory factor analysis on: {}.".format(file_path)) # run the analysis analyzer = FactorAnalyzer() analyzer.analyze(data, factors, rotation, method) # create paths to loadings loadings, eigenvalues, communalities, variance path_loadings = os.path.join(args.output_dir, 'loadings.csv') path_eigen = os.path.join(args.output_dir, 'eigenvalues.csv') path_communalities = os.path.join(args.output_dir, 'communalities.csv') path_variance = os.path.join(args.output_dir, 'variance.csv') # retrieve loadings, eigenvalues, communalities, variance loadings = analyzer.loadings eigen, _ = analyzer.get_eigenvalues() communalities = analyzer.get_communalities() variance = analyzer.get_factor_variance() # save the files logger.info("Saving files...") loadings.to_csv(path_loadings) eigen.to_csv(path_eigen) communalities.to_csv(path_communalities) variance.to_csv(path_variance)