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old-processSPP.py
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#!/usr/bin/python
import os, re, csv, scipy, sklearn, gensim, pandas
from scipy import stats
from sklearn import preprocessing
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
dir = 'ldt'
if dir == 'ldt': t = 'LDT'
elif dir == 'naming': t = 'NT'
z200 = t+'_200ms_Z'
z1200 = t+'_1200ms_Z'
lsa = 'LSA'
w2v = 'w2v'
freq = 'LogSubFreq'
vecmodel = 0
vecmodel = gensim.models.Word2Vec.load(os.path.abspath('/Users/allysonettinger/Desktop/engw2vModel/engModelB'))
def testLM():
x = [10,9,8,7,6]
y = [2,4,6,8,10]
mod = sm.OLS(y,x)
results = mod.fit()
print results.summary()
def analyzeSPP_regr(dir):
out = open('result_summary.txt','w')
tgtlexVars = ['LogSubFreq']
targetLexDF,primeLexDF,relDF,lexDict = readSPP(dir)
tempDF = relDF[['TargetWord','Prime','Relation1',lsa,z200]]
for v in tgtlexVars:
tempDF.insert(len(tempDF.columns.values),v+'_T',np.nan)
for i in range(len(tempDF.iloc[:,1])):
tgtWord = tempDF.ix[i,'TargetWord'].lower()
tempDF.set_value(i,v+'_T',lexDict[tgtWord][v])
tempDF.insert(len(tempDF.columns.values),'w2v',np.nan)
for i in range(len(tempDF.iloc[:,1])):
tgtWord = tempDF.ix[i,'TargetWord'].lower()
primeWord = tempDF.ix[i,'Prime'].lower()
try: vecmodel.similarity(tgtWord,primeWord)
except: sim = np.nan
else: sim = vecmodel.similarity(tgtWord,primeWord)
tempDF.set_value(i,'w2v',sim)
newRelCol = []
for x in tempDF['Relation1']:
if type(x) == str:
x = x.lower()
if re.match('.*unclas',str(x)):
newRelCol.append('unclassified')
elif re.match('.*anton',str(x)):
newRelCol.append('antonym')
else: newRelCol.append(x)
tempDF.insert(len(tempDF.columns.values),'relClean',newRelCol)
print tempDF['Relation1'].unique()
print tempDF['relClean'].unique()
for rel in tempDF['relClean'].unique():
# constraint = (tempDF[z200].notnull()) & (tempDF[lsa].notnull()) & (tempDF['LogSubFreq_T'].notnull()) & (tempDF['Relation1'] == 'synonym')
constraint = (tempDF[z200].notnull()) & (tempDF[lsa].notnull()) & (tempDF['w2v'].notnull()) & (tempDF['relClean'] == rel)
try:
testDF = tempDF[['TargetWord','Prime']][constraint]
for var in [lsa,z200,w2v]:
testDF[var] = sklearn.preprocessing.scale(map(float,tempDF[var][constraint]))
except: continue
tot = len(testDF.iloc[:,0])
for pred in [lsa,w2v]:
y = testDF[z200]
X = testDF[pred]
mod = smf.OLS(y,X)
results = mod.fit()
print '\nPREDICTOR: ' + pred + '\n'
print 'RELATION: ' + rel + '\n'
print 'TOTAL ITEMS: ' + str(tot) + '\n'
print results.summary()
# def analyzeSPP_regr(dir):
# lexDict,relDict = readSPP(dir)
# lex_predictors = []
# rel_predictors = [lsa]
# depvar = z200
# x = []
# y = []
# for word in relDict:
# if vecmodel:
# try: vecmodel[word]
# except: continue
# lp = [lexDict[word][p] for p in lex_predictors]
# for prime in relDict[word]:
# if vecmodel:
# try: vecmodel[prime.lower()]
# except: continue
# else: sim = vecmodel.similarity(word,prime.lower())
# hasNone = 0
# rp = [relDict[word][prime][p] for p in rel_predictors]
# # rp.append(sim)
# item_preds = rp+lp
# for i in item_preds:
# if i == None:
# # print '\nBAD!!!!!'
# # print word
# # print ' '.join([str(item) for item in item_preds])
# # print '\n'
# hasNone = 1
# break
# predicted = relDict[word][prime][depvar]
# if predicted == None: hasNone = 1
# if hasNone: continue
# # print ' '.join([str(item) for item in item_preds])
# x.append(item_preds)
# y.append(relDict[word][prime][depvar])
#
# x = sklearn.preprocessing.scale(x)
# y = sklearn.preprocessing.scale(y)
#
# mod = sm.OLS(y,x)
# results = mod.fit()
# print results.summary()
def analyzeSPP_corr(dir,col1,col2):
targetLexDF,primeLexDF,relDF,lexDict = readSPP(dir)
constraint = (relDF[col1].notnull()) & (relDF[col2].notnull())
v1 = relDF[col1][constraint]
v2 = relDF[col2][constraint]
rho,p_rho = scipy.stats.spearmanr(v1,v2)
r,p = scipy.stats.pearsonr(v1,v2)
print rho
print p_rho
print r
print p
# def analyzeSPP_corr(dir):
# v2varname = z200
# lexDict,relDict = readSPP(dir)
# v1 = []
# v2 = []
# for word in relDict:
# if vecmodel:
# try: vecmodel[word]
# except: continue
# for prime in relDict[word]:
# if vecmodel:
# try: vecmodel[prime.lower()]
# except: continue
# else: v1var = vecmodel.similarity(word,prime.lower())
# v1var = relDict[word][prime][lsa]
# v2var = relDict[word][prime][v2varname]
# if v1var != None and v2var != None:
# v1.append(v1var)
# v2.append(v2var)
# # print ' '.join([str(x) for x in [word,prime,v1var,v2var]])
# rho,p_rho = scipy.stats.spearmanr(v1,v2)
# r,p = scipy.stats.pearsonr(v1,v2)
# print rho
# print p_rho
# print r
# print p
#
def readSPP(dir):
#need to have all the different targets, all their different primes, the lexical characteristics for those primes, and the behavioral
#measures -- could also store the priming number to sanity check
lexDict = {}
# lexList = ['Length','SubFreq','LogSubFreq','LogHAL','ELP '+t+' RT','ELP '+t+' Z','POS']
# relList = ['Relation1','FAS','BAS','LSA',t+' 200ms RT',t+' 1200ms RT',t+' 200ms Z',t+' 1200ms Z',t+' 200ms Z Priming',t+' 1200ms Z Priming',t+' 200ms RT Priming',t+' 1200ms RT Priming']
lexList = ['Length','SubFreq','LogSubFreq','LogHAL','ELP_'+t+'_RT','ELP_'+t+'_Z','POS']
relList2 = [t+'_200ms_Z_Priming',t+'_1200ms_Z_Priming',t+'_200ms_RT_Priming',t+'_1200ms_RT_Priming']
relList = ['Relation1','LSA','FAS','BAS',t+'_200ms_RT',t+'_1200ms_RT',t+'_200ms_Z',t+'_1200ms_Z','Related','Assoc']
for file in os.listdir(os.path.abspath(dir)):
if re.match('assoc_.*\.csv',file): assocRel = os.path.join(dir,file)
elif re.match('assocunrel_.*\.csv',file): assocUn = os.path.join(dir,file)
elif re.match('otherassoc_.*\.csv',file): otherAssocRel = os.path.join(dir,file)
elif re.match('otherassocunrel_.*\.csv',file): otherAssocUn = os.path.join(dir,file)
elif re.match('target_.*\.csv',file): targetF = os.path.join(dir,file)
frameTGT = pandas.read_csv(targetF)
l = len(frameTGT['TargetWord'])
frameAR = pandas.read_csv(assocRel)
frameOAR = pandas.read_csv(otherAssocRel)
frameAU = pandas.read_csv(assocUn)
frameOAU = pandas.read_csv(otherAssocUn)
for df in [frameAR,frameOAR]: df['Related'] = 'REL'
for df in [frameAU,frameOAU]:
df['Related'] = 'UNREL'
df['Relation1'] = 'norel'
df['FAS'] = None
df['BAS'] = None
for df in [frameAR,frameAU]: df['Assoc'] = 'ASSOC'
for df in [frameOAR,frameOAU]: df['Assoc'] = 'OTHER'
[frameTGT,frameAR,frameOAR,frameAU,frameOAU] = colSub([frameTGT,frameAR,frameOAR,frameAU,frameOAU])
targetLexDF = frameTGT[['TargetWord']+lexList]
primeLexDF = pandas.merge(frameAR[['Prime']+lexList],frameOAR[['Prime']+lexList], how='outer')
for i in range(len(targetLexDF['TargetWord'])):
wrd = targetLexDF.ix[i,'TargetWord'].lower()
lexDict[wrd] = {}
for it in lexList: lexDict[wrd][it] = targetLexDF.ix[i,it]
for i in range(len(primeLexDF['Prime'])):
wrd = primeLexDF.ix[i,'Prime']
lexDict[wrd] = {}
for it in lexList: lexDict[wrd][it] = primeLexDF.ix[i,it]
relPreDF1 = pandas.merge(frameAR[['TargetWord','Prime']+relList],frameOAR[['TargetWord','Prime']+relList], how='outer')
relPreDF2 = pandas.merge(frameAU[['TargetWord','Prime']+relList],frameOAU[['TargetWord','Prime']+relList], how='outer')
#
relDF = pandas.merge(relPreDF1,relPreDF2,how='outer')
return targetLexDF,primeLexDF,relDF,lexDict
def colSub(dfs):
newdfs = []
for df in dfs:
if 'Unrelated' in df.columns.values: df = df.rename(columns={'Unrelated':'Prime'})
for colName in df.columns.values:
if re.match('.*\s',colName): df = df.rename(columns={colName:re.sub(' ','_',colName)})
newdfs.append(df)
return newdfs
# return lexDict,relDict
# def readSPP_old(dir):
# #need to have all the different targets, all their different primes, the lexical characteristics for those primes, and the behavioral
# #measures -- could also store the priming number to sanity check
# lexDict = {}
# relDict = {}
#
# lexList = ['Length','SubFreq','LogSubFreq','LogHAL','ELP '+t+' RT','ELP '+t+' Z','POS']
# relList = ['Relation1','FAS','BAS','LSA',t+' 200ms RT',t+' 1200ms RT',t+' 200ms Z',t+' 1200ms Z',t+' 200ms Z Priming',t+' 1200ms Z Priming',t+' 200ms RT Priming',t+' 1200ms RT Priming']
#
# for file in os.listdir(os.path.abspath(dir)):
# if re.match('assoc_.*\.csv',file): assocRel = os.path.join(dir,file)
# elif re.match('assocunrel_.*\.csv',file): assocUn = os.path.join(dir,file)
# elif re.match('otherassoc_.*\.csv',file): otherAssocRel = os.path.join(dir,file)
# elif re.match('otherassocunrel_.*\.csv',file): otherAssocUn = os.path.join(dir,file)
# elif re.match('target_.*\.csv',file): targetF = os.path.join(dir,file)
#
# with open(targetF) as tgtFile:
# d = csv.DictReader(tgtFile)
# for row in d:
# tgt = row['TargetWord'].lower().strip()
# lexDict = updateDict(row,lexList,lexDict,tgt)
# relDict[tgt] = {}
# for f in [assocRel,otherAssocRel]:
# with open(f) as file:
# d = csv.DictReader(file)
# for row in d:
# tgt = row['TargetWord'].lower().strip()
# prime = row['Prime'].strip()
# lexDict = updateDict(row,lexList,lexDict,prime)
# relDict[tgt] = updateDict(row,relList,relDict[tgt],prime)
# for f in [assocUn,otherAssocUn]:
# with open(f) as file:
# d = csv.DictReader(file)
# for row in d:
# tgt = row['TargetWord'].lower().strip()
# prime = row['Unrelated'].strip()
# relDict[tgt] = updateDict(row,relList,relDict[tgt],prime)
#
# return lexDict,relDict
def updateDict(row,ftList,dictToUpdate,keyToUpdate):
row = {re.sub('_',' ',c):row[c] for c in row}
for c in row:
if len(row[c]) == 0:
row[c] = None
elif re.match('-*\.*[0-9]+',row[c]):
row[c] = float(row[c])
for c in ftList:
if c not in row: row[c] = None
dictToUpdate[keyToUpdate] = {c:row[c] for c in ftList}
return dictToUpdate
if __name__ == "__main__":
# readSPP(dir)
analyzeSPP_regr(dir)
# analyzeSPP_corr(dir,lsa,z200)
# testLM()