cIV, (cognate, GSSYearsWithCognate) = random.choice(cIVCogPairs.items())
return cIV, cognate, GSSYearsWithCognate
############################################################
if __name__ == "__main__":
# define the storage containers for outputs
output = defaultdict(dict)
groups = ['group1', 'group2']
outcomes = ['propSig', 'paramSizesNormed', 'Rs', 'adjRs', 'pvalues', 'numTotal']
for group in groups:
for outcome in outcomes:
output[group][outcome] = []
articleClasses = filterArticles(articleClasses, GSSYearsUsed=True, GSSYearsPossible=False, centralIVs=True)
for article in random.sample(articleClasses, 400):
# for article in articleClasses:
# for article in [a for a in articleClasses if a.articleID == 6197]:
print 'Processing article:', article.articleID
# define the outcomes I'm interseted in for the two groups
td = defaultdict(dict)
for group in groups:
td[group]['numTotal'] = 0.0
# td[group]['coeffsSig'] = []
td[group]['numSig'] = 0.0 # proportions of significant coeffs
# td[group]['paramSizes'] = []
td[group]['paramSizesNormed'] = []
td[group]['Rs'] = []
sys.path.append('../Code/')
from articleClass import *
from filterArticleClasses import filterArticles
pathToData = '../Data/'
ALL_VARIABLE_NAMES = cp.load(open(pathToData + 'ALL_VARIABLE_NAMES.pickle'))
ALL_VARIABLE_NAMES = [str.upper(el) for el in ALL_VARIABLE_NAMES]
MISSING_VALUES_DICT = cp.load(open(pathToData + 'MISSING_VALUES_DICT.pickle', 'rb'))
MEASURE_LEVELS = cp.load(open(pathToData + 'MEASURE_LEVELS.pickle'))
articleIDAndGSSYearsUsed = cp.load(open(pathToData + 'articleIDAndGssYearsUsed-cleaned.pickle')) # load the years used
VARS_BY_YEAR = cp.load(open(pathToData + 'VARS_BY_YEAR.pickle'))
# structure of the dictionary above: { year (int) : [ set of variable names (strs), [variable_i, metadata_i] ] }
YEAR_INDICES = cp.load(open(pathToData + 'YEAR_INDICES.pickle'))
VAR_INDICES = cp.load(open(pathToData + 'VAR_INDICES_binary.pickle', 'rb'))
articleClasses = cp.load(open(pathToData + 'articleClasses.pickle', 'rb'))
articleClasses = filterArticles(articleClasses)
'''
# load GSS data
GSSFilename = 'GSS Dataset/GSS7212_R2.sav'
data = srw.SavReader(pathToData + GSSFilename)
df = pd.DataFrame(data.all(), index=data[:,0], columns=ALL_VARIABLE_NAMES)
with data: # this makes sure the file will be closed, memory cleaned up after the program is run
data = np.array(data.all()) # this makes sure the entire dataset is loaded into RAM, which makes accessing much faster
'''
from collections import defaultdict
output = defaultdict(dict)
groups = ['group1', 'group2']
outcomes = ['propSig', 'paramSizesNormed', 'Rs', 'adjRs', 'pvalues', 'numTotal']
for group in groups: