""" from matplotlib import mlab import outlierdetect import pandas as pd DATA_FILE = 'CHW_toydata.csv' def print_scores(scores): for interviewer in scores.keys(): print "%s" % interviewer for column in scores[interviewer].keys(): print "\t%s:\t%.2f" % (column, scores[interviewer][column]) if __name__ == '__main__': data = pd.read_csv(DATA_FILE) # Uncomment to load as pandas.DataFrame. # data = mlab.csv2rec(DATA_FILE) # Uncomment to load as numpy.recarray. # Compute SVA outlier scores #Uncomment to run SVA in addition to MMA #sva_scores = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever']) #print "SVA outlier scores" #print_scores(sva_scores) # Compute MMA outlier scores. Will work only if scipy is installed. mma_scores = outlierdetect.run_mma(data, 'interviewer_ID', ['blood_pressure', 'abdominal_exam', 'ifa_tablets_issued']) print "\nMMA outlier scores" print_scores(mma_scores)
""" from matplotlib import mlab import outlierdetect import pandas as pd DATA_FILE = 'example_data.csv' def print_scores(scores): for interviewer in scores.keys(): print "%s" % interviewer for column in scores[interviewer].keys(): print "\t%s:\t%.2f" % (column, scores[interviewer][column]) if __name__ == '__main__': data = pd.read_csv(DATA_FILE) # Uncomment to load as pandas.DataFrame. # data = mlab.csv2rec(DATA_FILE) # Uncomment to load as numpy.recarray. # Compute SVA outlier scores. sva_scores = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever']) print "SVA outlier scores" print_scores(sva_scores) # Compute MMA outlier scores. Will work only if scipy is installed. mma_scores = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever']) print "\nMMA outlier scores" print_scores(mma_scores)
""" from matplotlib import mlab import outlierdetect import pandas as pd DATA_FILE = 'example_data.csv' def print_scores(scores): for interviewer in scores.keys(): print "%s" % interviewer for column in scores[interviewer].keys(): print "\t%s:\t%.2f" % (column, scores[interviewer][column]) if __name__ == '__main__': data = pd.read_csv(DATA_FILE) # Uncomment to load as pandas.DataFrame. # data = mlab.csv2rec(DATA_FILE) # Uncomment to load as numpy.recarray. # Compute SVA outlier scores. (sva_scores, agg_col_to_data) = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever']) print "SVA outlier scores" print_scores(sva_scores) # Compute MMA outlier scores. Will work only if scipy is installed. (mma_scores, agg_col_to_data) = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever']) print "\nMMA outlier scores" print_scores(mma_scores)
def print_scores(scores): for interviewer in scores.keys(): print("%s" % interviewer) for column in scores[interviewer].keys(): score = scores[interviewer][column]['score'] observed_frequencies = scores[interviewer][column]['observed_freq'] expected_frequencies = scores[interviewer][column]['expected_freq'] p_value = scores[interviewer][column]['p_value'] print("Observed Frequencies: %s" % observed_frequencies) print("Expected Frequencies: %s" % expected_frequencies) print("P-Value: %d" % p_value) if __name__ == '__main__': data = pd.read_csv(DATA_FILE) # Uncomment to load as pandas.DataFrame. # data = mlab.csv2rec(DATA_FILE) # Uncomment to load as numpy.recarray. # Compute SVA outlier scores. (sva_scores, _) = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever']) print("SVA outlier scores") print_scores(sva_scores) # Compute MMA outlier scores. Will work only if scipy is installed. if hasattr(outlierdetect, 'run_mma'): (mma_scores, _) = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever']) print("\nMMA outlier scores") print_scores(mma_scores)