def main(crowdflower_output, num_judgments):
"""
this script computes the agreement of judgments given in the crowdflower
job using a metric called Fleiss kappa
"""
cf_results = read_full_results(crowdflower_output)
mat = compute_matrix(cf_results, num_judgments)
print computeFleissKappa(mat)
def main(crowdflower_output, num_judgments):
"""
this script computes the agreement of judgments given in the crowdflower
job using a metric called Fleiss kappa
"""
cf_results = read_full_results(crowdflower_output)
mat = compute_matrix(cf_results, num_judgments)
print computeFleissKappa(mat)
def main(crowdflower_output, num_judgments):
"""
this script computes the agreement of judgments given in the crowdflower
job using a metric called Fleiss kappa
:param file crowdflower_output: CSV file containing the results from crowdflower
:param int num_judgments: Consider only this number of results, skip if not enough
"""
cf_results = read_full_results(crowdflower_output)
mat = compute_matrix(cf_results, num_judgments)
print computeFleissKappa(mat)
def main(crowdflower_output, num_judgments):
"""
this script computes the agreement of judgments given in the crowdflower
job using a metric called Fleiss kappa
:param file crowdflower_output: CSV file containing the results from crowdflower
:param int num_judgments: Consider only this number of results, skip if not enough
"""
cf_results = read_full_results(crowdflower_output)
mat = compute_matrix(cf_results, num_judgments)
print computeFleissKappa(mat)
def main(crowdflower_output, num_judgments):
cf_results = read_full_results(crowdflower_output)
mat = compute_matrix(cf_results, num_judgments)
print computeFleissKappa(mat)