from counts.count_analysis import import_data
from counts.count_analysis import count_feature
from counts.count_analysis import return_chisquare
control_data, depressed_data, control_duration, depressed_duration = import_data()
observed, expected = count_feature('ove', control_data, depressed_data, control_duration, depressed_duration)
chisquare = return_chisquare(observed, expected)
print(chisquare)
'bac',
'cry',
'fil',
'lau',
'len',
'ove',
'sil'
]
pvalues = {}
control_data, depressed_data, control_duration, depressed_duration = import_data()
for feature in features:
obs, exp = count_feature(feature, control_data, depressed_data, control_duration, depressed_duration)
pvalue = return_chisquare(obs, exp)[1]
pvalues[feature] = pvalue
pvalues = {feature: pvalues[feature] for feature in pvalues.keys() if pvalues[feature] < 0.05}
sorted_pvalues = sorted(pvalues.items(), key=operator.itemgetter(1))
test_count = len(features)
thresholds = {}
for i in range(len(pvalues)):
threshold = ALPHA * (i+1) / test_count
thresholds[sorted_pvalues[i][0]] = threshold
for feature, pvalue in sorted_pvalues:
print('{0} {1} -- threshold: {2}'.format(feature, pvalue, thresholds[feature]))