Source code for dlatk.DDLA

## correlates results between two csvs of results
## csvs in arguments:
## <rmatrix_csv> <rmatrix_csv>

import csv
import sys
from scipy.stats.stats import pearsonr, spearmanr
from pprint import pprint
from numpy import array, concatenate, log, sqrt, isnan, isinf,  std, clip, nan_to_num

#csv.quotechar = '\x07'
[docs]class DDLA: ignoreColumns = set(['p', 'N', 'freq', 'feature']) file1 = None file2 = None data = None header = None outputData = None def __init__(self, file1, file2, outputFile = None): self.file1 = file1 self.file2 = file2 self.outputFile = outputFile if outputFile else '-'.join([file1[:-4], file2[:-4]])+'.csv' = list() self.header = list()
[docs] def signed_r_square(self,r): r2 = float(r)**2 if r < 0: return -1 * r2 return r2
[docs] def signed_r_log(self,r): if r < 0: return -1 * log((-1*float(r))+1) return log(r+1)
[docs] def compare_correl(self,ra, na, rb, nb): if any(not isnan(i) for i in [ra, na, rb, nb]): (ra, rb) = min(1, max(-1, ra)), min(1, max(-1, rb)) raplus = 1*ra+1 raminus = 1-ra rbplus = 1*rb+1 rbminus = 1-rb za = (log(raplus)-log(raminus))/2 zb = (log(rbplus)-log(rbminus))/2 se = sqrt((1/(na-3))+(1/(nb-3))) z = (za-zb)/se z2 = abs(z) p2 = (((((.000005383*z2+.0000488906)*z2+.0000380036)*z2+.0032776263)*z2+.0211410061)*z2+.049867347)*z2+1 p2 = pow(p2, -16) return p2 else: return float('nan')
[docs] def write2CSV(self, dataDict, features): toWrite = dataDict['data'].tolist() for i in range(len(features)): toWrite[i].insert(0,features[i]) toWrite.sort(key=lambda x: x[0]) self.outputData = toWrite with open(self.outputFile,'w+') as csv_file: write = csv.writer(csv_file) write.writerow(dataDict['header']) write.writerows(toWrite)
[docs] def add2Output(self,csvOutput, outcome_data, outcome_name, featsInOrder): data = ordered = ['value', 'p', 'freq', 'N'] originalData0 = array([[data[0][outcome_name][feat][col] for col in ordered] for feat in featsInOrder]) originalData1 = array([[data[1][outcome_name][feat][col] for col in ordered] for feat in featsInOrder]) originalData = concatenate((originalData0,originalData1), axis=1) csvOutput['header'].extend([outcome_name, 'p'] + ['r_0', 'p_0', 'freq_0', 'N_0'] + ['r_1', 'p_1', 'freq_1', 'N_1']) self.header = csvOutput['header'] if 'data' in list(csvOutput.keys()): csvOutput['data'] = concatenate((csvOutput['data'], nan_to_num(outcome_data)), axis = 1) else: csvOutput['data'] = nan_to_num(outcome_data) csvOutput['data'] = concatenate((csvOutput['data'], originalData),axis=1)
[docs] def get_next(self,some_iterable, window=1): from itertools import tee, islice, zip_longest items, nexts = tee(some_iterable, 2) nexts = islice(nexts, window, None) return zip_longest(items, nexts)
[docs] def print_sorted(self, dataDict, features): data = dataDict['data'].tolist() for i in range(len(features)): data[i].insert(0,features[i]) data.sort(key=lambda x: -x[1])
[docs] def load_data(self): data = fins = [self.file1, self.file2] for split in range(len(fins)): fin = open(fins[split], 'r') reader = csv.reader(fin) data.append(dict()) #get headers: headers = None while True: headers = next(reader) if len(headers) > 1 and headers[0][:6] != 'Namesp': #remove blanks and namespace lines break #setup data dict, using column names for h in headers: h = h.strip() if h not in (self.ignoreColumns): data[split][h] = dict() #read all rows: for row in reader: if row and row[0] == 'SORTED:': print("found SORTED:", row) break if len(row) > 1: feat = row[0].strip() column_used = None for i in range(1, len(row)): # Going through the entire row, entry by entry if i < len(headers): column_name = None if headers[i] in data[split]: # New Outcome column_used = headers[i] data[split][column_used][feat] = dict() column_name = 'value' else: column_name = headers[i] cell_value = row[i].strip() if cell_value :#and not isnan(float(cell_value)): data[split][column_used][feat][column_name] = float(cell_value)
[docs] def outputForTagclouds(self, sizeField = 1, colorField = "dr"): correls = dict() outcomes = self.header[1::10] correls = {outcome: dict() for outcome in outcomes} # print self.header[1::10] # color # print self.header[sizeField+2::10] # first country correlations = size # print self.header[sizeField+6::10] # secnd country correlations = size for row in self.outputData: drs = row[1::10] rs = row[sizeField+2::10] ps = row[sizeField+3::10] Ns = row[sizeField+5::10] for i,o in enumerate(outcomes): correls[o][row[0]] = (rs[i], ps[i], Ns[i], drs[i]) return correls
[docs] def differential(self): data = self.load_data() #from pprint import pprint #pprint([(k, v) for k,v in data[1]['is_student'].iteritems() if k == 'den'][:10]) commonOutcomes = set(data[0].keys()) & set(data[1].keys()) sumRs = float(0) sumR2s = float(0) sumRhos = float(0) sumRho2s = float(0) csvOutput = {'header': ['feature',]} commonFeats = None for outcome in sorted(commonOutcomes): print("\n%s\n%s" % (outcome, '='*len(outcome))) feats0 = set(data[0][outcome].keys()) feats1 = set(data[1][outcome].keys()) commonFeats = feats0 & feats1 print("Number of feats in first results: %d" % len(feats0)) print("Number of feats in second results: %d" % len(feats1)) print("Number of feats in common: %d" % len(commonFeats)) # Getting data in the right format commonFeats = list(commonFeats) list0 = nan_to_num(array([data[0][outcome][feat]['value'] for feat in commonFeats])) list1 = nan_to_num(array([data[1][outcome][feat]['value'] for feat in commonFeats])) list0_n = array([data[0][outcome][feat]['N'] for feat in commonFeats]) list1_n = array([data[1][outcome][feat]['N'] for feat in commonFeats]) # Comparing individual correlations diffs = list0 - list1 output = array([[diffs[i], self.compare_correl(list0[i], list0_n[i], list1[i], list1_n[i])] for i in range(len(list0))]) self.add2Output(csvOutput, output, outcome, commonFeats) sorted_pairs = sorted(zip(diffs, commonFeats), key= lambda x_y: -x_y[0]) sorted_words = [x_y1[1] for x_y1 in sorted_pairs] print('Decreasing differences: <top 10 most correlated with %s> ... <top 10 most correlated with %s>' % (self.file1[:-4], self.file2[:-4] )) print(', '.join(sorted_words[:10])+' ... '+', '.join(sorted_words[-10:])) # From previous script, comparing all the r's list0r2 = array([self.signed_r_log(r) for r in list0]) list1r2 = array([self.signed_r_log(r) for r in list1]) (r, p) = pearsonr(list0, list1) (r2, p2) = pearsonr(list0r2, list1r2) print("pearson r of rs: %10.4f (%.6f)" % (r, p)) print("pearson r of signed log(r)s: %10.4f (%.6f)" % (r2, p2)) sumRs += r sumR2s += r2 (rho, p) = spearmanr(list0, list1) (rho2, p2) = spearmanr(list0r2, list1r2) print("spearman rho of rs: %10.4f (%.6f)" % (rho, p)) print("spearman rho of signed log(r)s:%10.4f (%.6f)" % (rho2, p2)) sumRhos += rho sumRho2s += rho2 self.outputData = csvOutput self.write2CSV(csvOutput, commonFeats) # print_sorted(csvOutput, commonFeats) print("\nAVERAGE RESULTS\n===============") print("pearson r of rs: %10.4f " % (sumRs / float(len(commonOutcomes)))) print("pearson r of log(r)s: %10.4f " % (sumR2s / float(len(commonOutcomes)))) print("spearman rho of rs: %10.4f " % (sumRhos / float(len(commonOutcomes)))) print("spearman rho of log(r)s: %10.4f " % (sumRho2s / float(len(commonOutcomes))))
#### TODO: calculate number in common in top 100 (i.e. as if comparing word clouds) if __name__ == '__main__': fins = sys.argv[1:3] ddla = DDLA(fins[0], fins[1]) print(dir(ddla)) ddla.differential()