def project_tags(tagstoproject, tagtargets):
targetstring = ','.join(tagtargets)
projectstring = ','.join(tagstoproject)
print('First we create a model of ' + targetstring)
sizecap = 400
modelname = targetstring + 'byitself'
paths = make_paths(modelname)
sourcefolder, extension, metadatapath, outputpath1, vocabpath = paths
exclusions = make_exclusions(0, 2000, sizecap, tagstoproject)
# Note that we exclude tagstoproject from the negative contrast set, so the
# contrast sets for the two models will be identical.
positive_tags = tagtargets
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
print()
print('Then we create a model of ' + projectstring + ' and use it to predict ' + targetstring)
modelname = projectstring + 'predicts' + targetstring
paths = make_paths(modelname)
sourcefolder, extension, metadatapath, outputpath2, vocabpath = paths
exclusions = make_exclusions(0, 2000, sizecap, 'nonegatives')
positive_tags = list(tagtargets)
positive_tags.extend(tagstoproject)
testconditions = set(tagtargets)
# That's the line that actually excludes tagtarget from training.
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the second dataset at 0.5, accuracy is: ', str(rawaccuracy))
print()
# Now we compare the predictions made by these two models, comparing only
# the volumes that are in both models but excluded from the training process
# in the second model.
comparemodels.compare_untrained(outputpath1, outputpath2)
def project_gothic_beyond_date(dividedate):
print('First we create a model of gothic fiction only after ' + str(dividedate))
sizecap = 300
modelname = 'gothicjustpost' + str(dividedate)
paths = make_paths(modelname)
sourcefolder, extension, metadatapath, outputpath1, vocabpath = paths
exclusions = make_exclusions(dividedate, 2000, sizecap, 'nonegatives')
positive_tags = ['lochorror', 'pbgothic', 'locghost', 'stangothic', 'chihorror']
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
print()
print('Then we create a model of gothic fiction blindly predicting after ' + str(dividedate))
modelname = 'gothicpredictpost' + str(dividedate)
paths = make_paths(modelname)
sourcefolder, extension, metadatapath, outputpath2, vocabpath = paths
exclusions = make_exclusions(0, 2001, sizecap, 'nonegatives')
testconditions = {'1700', str(dividedate)}
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the second dataset at 0.5, accuracy is: ', str(rawaccuracy))
print()
# Now we compare the predictions made by these two models, comparing only
# the volumes that are in both models but excluded from the training process
# in the second model.
comparemodels.compare_untrained(outputpath1, outputpath2)
def the_red_and_the_black():
sizecap = 140
modelname = 'blackandthered'
paths = make_paths(modelname)
exclusions = make_exclusions(1700, 2001, sizecap, 'nonegatives')
positive_tags = ['teamred']
negative_tags = ['teamblack']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
accuracies = []
for i in range(40):
modelname = 'redandtheblack' + str(i)
paths = make_paths(modelname)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print(rawaccuracy)
accuracies.append(rawaccuracy)
with open('finalaccuracies.csv', mode = 'w', encoding = 'utf-8') as f:
for accuracy in accuracies:
f.write(str(accuracy) + '\n')
def model_taglist_within_dates(positive_tags, modelname, mindate, maxdate):
print('We are modeling these positive tags:')
for tag in positive_tags:
print(tag)
sizecap = 1000
paths = make_paths(modelname)
sourcefolder, extension, metadatapath, outputpath, vocabpath = paths
exclusions = make_exclusions(mindate, maxdate, sizecap, 'nonegatives')
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
return allvolumes
def generic():
sourcefolder = '/Volumes/TARDIS/work/characterdata/charpredict/'
extension = '.tsv'
metadatapath = '/Users/tunder/Dropbox/character/meta/predictmeta.csv'
vocabpath = '/Users/tunder/Dropbox/character/meta/predict1960-79vocab.csv'
modelname = input('Name of model? ')
outputpath = '/Users/tunder/Dropbox/character/results/' + modelname + str(datetime.date.today()) + '.csv'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
daterange = input('Range of dates to use in the model? ')
if ',' in daterange:
dates = [int(x.strip()) for x in daterange.split(',')]
dates.sort()
if len(dates) == 2:
assert dates[0] < dates[1]
excludebelow['firstpub'] = dates[0]
excludeabove['firstpub'] = dates[1]
sizecap = 1000
# CLASSIFY CONDITIONS
# We ask the user for a list of categories to be included in the positive
# set, as well as a list for the negative set. Default for the negative set
# is to include all the "random"ly selected categories. Note that random volumes
# can also be tagged with various specific genre tags; they are included in the
# negative set only if they lack tags from the positive set.
positive_tags = ['f']
negative_tags = ['m']
datetype = "firstpub"
numfeatures = 1700
regularization = .00011
testconditions = set()
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
tiltaccuracy = logisticpredict.diachronic_tilt(allvolumes, 'linear', [])
print("Divided with a line fit to the data trend, it's ", str(tiltaccuracy))
def calibrate_detective():
'''
Tests accuracy of classification for detective fiction at different sample
sizes.
'''
modelname = 'calibratedet'
paths = make_paths(modelname)
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 2020
positive_tags = ['locdetective', 'locdetmyst', 'chimyst', 'locdetmyst', 'det100']
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
sizes = [5,6,7,8,9,11,13,15,17,18,21,27,29,32,34,36,40,45,50,55,60,65,70,75,80,85,90,100]
# with open('../results/collateddetectiveaccuracies.tsv', mode = 'a', encoding = 'utf-8') as f:
# f.write('sizecap\tavgsize\trawaccuracy\n')
accuracies = []
for sizecap in sizes:
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
trainsizes = []
for vol in allvolumes:
trainsizes.append(vol[11])
# this is unfortunately dependent on the exact way
# logisticpredict formats its output
avgsize = sum(trainsizes) / len(trainsizes)
print(sizecap, avgsize, rawaccuracy)
with open('../final/collateddetaccuracies.tsv', mode = 'a', encoding = 'utf-8') as f:
f.write(str(sizecap) + '\t' + str(avgsize) + '\t' + str(rawaccuracy) + '\n')
return None
def replicate_stew():
sizecap = 140
modelname = 'replicatestew'
paths = make_paths(modelname)
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 2020
allstewgenres = {'cozy', 'hardboiled', 'det100', 'chimyst', 'locdetective', 'lockandkey', 'crime', 'locdetmyst', 'blcrime', 'anatscifi', 'locscifi', 'chiscifi', 'femscifi', 'stangothic', 'pbgothic', 'lochorror', 'chihorror', 'locghost'}
# We have to explicitly exclude genres because the category "stew" in the
# positive category wouldn't otherwise automatically exclude the constituent
# tags that were used to create it.
# I would just have put all those tags in the positive tag list, but then you'd lose
# the ability to explicitly balance equal numbers of crime, gothic,
# and science fiction, plus sensation novels. You'd get a list dominated by
# the crime categories, which are better-represented in the dataset.
excludeif['negatives'] = allstewgenres
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
positive_tags = ['stew']
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
accuracies = []
for i in range(20):
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print(rawaccuracy)
accuracies.append(rawaccuracy)
with open('stewaccuracies.csv', mode = 'a', encoding = 'utf-8') as f:
for accuracy in accuracies:
f.write(str(accuracy) + '\n')
def predictecco():
sourcefolder = '../sourcefiles/'
extension = '.tsv'
metadatapath = '../metadata/eccogenremeta.csv'
modelname = 'predictecco'
outputpath = '../modeloutput/' + modelname + str(datetime.date.today()) + '.csv'
vocabpath = '../lexicons/' + modelname + '.csv'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 1800
sizecap = 75
# CLASSIFY CONDITIONS
positive_tags = ['fic']
negative_tags = ['bio']
# testconditions = {'0', '2001', 'poe'}
testconditions = set()
datetype = "firstpub"
numfeatures = 1100
regularization = .015
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
with open('../modeloutput/eccopredicts.csv', mode = 'w', encoding = 'utf-8') as f:
f.write('row,accuracy,auc\n')
for i in range(15):
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
auc = calculate_auc(allvolumes)
print(i, rawaccuracy, auc)
with open('../modeloutput/eccopredicts.csv', mode = 'a', encoding = 'utf-8') as f:
f.write(str(i) + ',' + str(rawaccuracy) + ',' + str(auc) + '\n')
def compare(dividedate):
print('First we create a model of gender only after ' + str(dividedate))
sizecap = 500
modelname = 'post' + str(dividedate)
sourcefolder = '/Volumes/TARDIS/work/characterdata/charpredict/'
extension = '.tsv'
metadatapath = '/Users/tunder/Dropbox/character/meta/predictmeta.csv'
vocabpath = '/Users/tunder/Dropbox/character/meta/predictALLvocab.csv'
outputpath1 = '/Users/tunder/Dropbox/character/results/' + modelname + str(
datetime.date.today()) + '.csv'
paths = (sourcefolder, extension, metadatapath, outputpath1, vocabpath)
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1900
excludeabove['firstpub'] = 1950
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
positive_tags = ['f']
negative_tags = ['m']
testconditions = set()
datetype = "firstpub"
numfeatures = 2000
regularization = .00009
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures,
regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(
paths, exclusions, classifyconditions)
print(
'If we divide the dataset with a horizontal line at 0.5, accuracy is: ',
str(rawaccuracy))
print()
print(
'Then we create a model of detective fiction blindly predicting after '
+ str(dividedate))
modelname = 'predictpost' + str(dividedate)
outputpath2 = '/Users/tunder/Dropbox/character/results/' + modelname + str(
datetime.date.today()) + '.csv'
paths = (sourcefolder, extension, metadatapath, outputpath2, vocabpath)
excludebelow['firstpub'] = 1780
excludeabove['firstpub'] = 2000
sizecap = 1000
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
testconditions = {'1700', 1880}
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures,
regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(
paths, exclusions, classifyconditions)
print('If we divide the second dataset at 0.5, accuracy is: ',
str(rawaccuracy))
print()
# Now we compare the predictions made by these two models, comparing only
# the volumes that are in both models but excluded from the training process
# in the second model.
comparemodels.compare_untrained(outputpath1, outputpath2)
def generic():
sourcefolder = '/Volumes/TARDIS/work/characterdata/charpredict/'
extension = '.tsv'
metadatapath = '/Users/tunder/Dropbox/character/meta/predictmeta.csv'
vocabpath = '/Users/tunder/Dropbox/character/meta/predict1960-79vocab.csv'
modelname = input('Name of model? ')
outputpath = '/Users/tunder/Dropbox/character/results/' + modelname + str(
datetime.date.today()) + '.csv'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
daterange = input('Range of dates to use in the model? ')
if ',' in daterange:
dates = [int(x.strip()) for x in daterange.split(',')]
dates.sort()
if len(dates) == 2:
assert dates[0] < dates[1]
excludebelow['firstpub'] = dates[0]
excludeabove['firstpub'] = dates[1]
sizecap = 1000
# CLASSIFY CONDITIONS
# We ask the user for a list of categories to be included in the positive
# set, as well as a list for the negative set. Default for the negative set
# is to include all the "random"ly selected categories. Note that random volumes
# can also be tagged with various specific genre tags; they are included in the
# negative set only if they lack tags from the positive set.
positive_tags = ['f']
negative_tags = ['m']
datetype = "firstpub"
numfeatures = 1700
regularization = .00011
testconditions = set()
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures,
regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(
paths, exclusions, classifyconditions)
print(
'If we divide the dataset with a horizontal line at 0.5, accuracy is: ',
str(rawaccuracy))
tiltaccuracy = logisticpredict.diachronic_tilt(allvolumes, 'linear', [])
print("Divided with a line fit to the data trend, it's ",
str(tiltaccuracy))
def predictfic():
## PATHS.
sourcefolder = '../sourcefiles/'
extension = '.tsv'
metadatapath = 'allgenremeta.csv'
for floor in range(1720, 1920, 10):
ceiling = floor + 19
modelname = 'predictfic' + str(floor)
outputpath = '../results/' + modelname + str(datetime.date.today()) + '.csv'
vocabpath = 'vocab/predictfic' + str(floor) + '.txt'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = floor
excludeabove['firstpub'] = ceiling
# We have to explicitly exclude genres because the category "stew" in the
# positive category wouldn't otherwise automatically exclude the constituent
# tags that were used to create it.
# I would just have put all those tags in the positive tag list, but then you'd lose
# the ability to explicitly balance equal numbers of crime, gothic,
# and science fiction, plus sensation novels. You'd get a list dominated by
# the crime categories, which are better-represented in the dataset.
sizecap = 100
# CLASSIFY CONDITIONS
# We ask the user for a list of categories to be included in the positive
# set, as well as a list for the negative set. Default for the negative set
# is to include all the "random"ly selected categories. Note that random volumes
# can also be tagged with various specific genre tags; they are included in the
# negative set only if they lack tags from the positive set.
positive_tags = ['fic']
negative_tags = ['bio']
#testconditions = {'0', '2001', 'poe'}
testconditions = set()
datetype = "firstpub"
numfeatures = 3000
regularization = .00008
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
with open('../results/ficpredicts.csv', mode = 'a', encoding = 'utf-8') as f:
f.write(str(floor) + ',' + str(rawaccuracy) + '\n')
def predict2017test():
## PATHS.
sourcefolder = '/Users/tunder/Dropbox/python/nonfic/2017test/'
extension = '.tsv'
metadatapath = '2017testmeta.csv'
vocabpath = 'vocab/2017testvocab.txt'
modelname = '2017testmodel'
outputpath = '../results/' + modelname + str(datetime.date.today()) + '.csv'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 2000
# We have to explicitly exclude genres because the category "stew" in the
# positive category wouldn't otherwise automatically exclude the constituent
# tags that were used to create it.
# I would just have put all those tags in the positive tag list, but then you'd lose
# the ability to explicitly balance equal numbers of crime, gothic,
# and science fiction, plus sensation novels. You'd get a list dominated by
# the crime categories, which are better-represented in the dataset.
sizecap = 400
# CLASSIFY CONDITIONS
# We ask the user for a list of categories to be included in the positive
# set, as well as a list for the negative set. Default for the negative set
# is to include all the "random"ly selected categories. Note that random volumes
# can also be tagged with various specific genre tags; they are included in the
# negative set only if they lack tags from the positive set.
positive_tags = ['fic']
negative_tags = ['bio']
# testconditions = {'0', '2001', 'poe', 'limit==250'}
testconditions = set()
datetype = "firstpub"
numfeatures = 3200
regularization = .00008
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
tiltaccuracy = logisticpredict.diachronic_tilt(allvolumes, 'linear', [])
print("Divided with a line fit to the data trend, it's ", str(tiltaccuracy))
return allvolumes
def calibrate_stew():
'''
Tests accuracy of classification for ghastly stew at different sample
sizes.
'''
modelname = 'calibratestew'
paths = make_paths(modelname)
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 2020
allstewgenres = {'cozy', 'hardboiled', 'det100', 'chimyst', 'locdetective', 'lockandkey', 'crime', 'locdetmyst', 'blcrime', 'anatscifi', 'locscifi', 'chiscifi', 'femscifi', 'stangothic', 'pbgothic', 'lochorror', 'chihorror', 'locghost'}
# We have to explicitly exclude genres because the category "stew" in the
# positive category wouldn't otherwise automatically exclude the constituent
# tags that were used to create it.
# I would just have put all those tags in the positive tag list, but then you'd lose
# the ability to explicitly balance equal numbers of crime, gothic,
# and science fiction, plus sensation novels. You'd get a list dominated by
# the crime categories, which are better-represented in the dataset.
excludeif['negatives'] = allstewgenres
positive_tags = ['stew']
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
sizes = [5,6,7,8,9,11,13,15,17,18,21,27,29,32,34,36,40,45,50,55,60,65,70,75,80,85,90,100]
# with open('../results/collatedstewaccuracies.tsv', mode = 'a', encoding = 'utf-8') as f:
# f.write('sizecap\tavgsize\trawaccuracy\n')
accuracies = []
for sizecap in sizes:
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
trainsizes = []
for vol in allvolumes:
trainsizes.append(vol[11])
# this is unfortunately dependent on the exact way
# logisticpredict formats its output
avgsize = sum(trainsizes) / len(trainsizes)
print(sizecap, avgsize, rawaccuracy)
with open('../final/collatedstewaccuracies.tsv', mode = 'a', encoding = 'utf-8') as f:
f.write(str(sizecap) + '\t' + str(avgsize) + '\t' + str(rawaccuracy) + '\n')
return None
def ghastly_stew():
## PATHS.
sourcefolder = '../newdata/'
extension = '.fic.tsv'
metadatapath = '../meta/finalmeta.csv'
vocabpath = '../lexicon/new10k.csv'
modelname = 'ghastlystew'
outputpath = '../results/' + modelname + str(datetime.date.today()) + '.csv'
# We can simply exclude volumes from consideration on the basis on any
# metadata category we want, using the dictionaries defined below.
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1700
excludeabove['firstpub'] = 2020
allstewgenres = {'cozy', 'hardboiled', 'det100', 'chimyst', 'locdetective', 'lockandkey', 'crime', 'locdetmyst', 'blcrime', 'anatscifi', 'locscifi', 'chiscifi', 'femscifi', 'stangothic', 'pbgothic', 'lochorror', 'chihorror', 'locghost'}
# We have to explicitly exclude genres because the category "stew" in the
# positive category wouldn't otherwise automatically exclude the constituent
# tags that were used to create it.
# I would just have put all those tags in the positive tag list, but then you'd lose
# the ability to explicitly balance equal numbers of crime, gothic,
# and science fiction, plus sensation novels. You'd get a list dominated by
# the crime categories, which are better-represented in the dataset.
excludeif['negatives'] = allstewgenres
sizecap = 250
# CLASSIFY CONDITIONS
# We ask the user for a list of categories to be included in the positive
# set, as well as a list for the negative set. Default for the negative set
# is to include all the "random"ly selected categories. Note that random volumes
# can also be tagged with various specific genre tags; they are included in the
# negative set only if they lack tags from the positive set.
positive_tags = ['stew']
negative_tags = ['random', 'chirandom']
testconditions = set()
datetype = "firstpub"
numfeatures = 10000
regularization = .000075
paths = (sourcefolder, extension, metadatapath, outputpath, vocabpath)
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
def compare(dividedate):
print('First we create a model of gender only after ' + str(dividedate))
sizecap = 500
modelname = 'post' + str(dividedate)
sourcefolder = '/Volumes/TARDIS/work/characterdata/charpredict/'
extension = '.tsv'
metadatapath = '/Users/tunder/Dropbox/character/meta/predictmeta.csv'
vocabpath = '/Users/tunder/Dropbox/character/meta/predictALLvocab.csv'
outputpath1 = '/Users/tunder/Dropbox/character/results/' + modelname + str(datetime.date.today()) + '.csv'
paths = (sourcefolder, extension, metadatapath, outputpath1, vocabpath)
## EXCLUSIONS.
excludeif = dict()
excludeifnot = dict()
excludeabove = dict()
excludebelow = dict()
excludebelow['firstpub'] = 1900
excludeabove['firstpub'] = 1950
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
positive_tags = ['f']
negative_tags = ['m']
testconditions = set()
datetype = "firstpub"
numfeatures = 2000
regularization = .00009
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the dataset with a horizontal line at 0.5, accuracy is: ', str(rawaccuracy))
print()
print('Then we create a model of detective fiction blindly predicting after ' + str(dividedate))
modelname = 'predictpost' + str(dividedate)
outputpath2 = '/Users/tunder/Dropbox/character/results/' + modelname + str(datetime.date.today()) + '.csv'
paths = (sourcefolder, extension, metadatapath, outputpath2, vocabpath)
excludebelow['firstpub'] = 1780
excludeabove['firstpub'] = 2000
sizecap = 1000
exclusions = (excludeif, excludeifnot, excludebelow, excludeabove, sizecap)
testconditions = {'1700', 1880}
classifyconditions = (positive_tags, negative_tags, datetype, numfeatures, regularization, testconditions)
rawaccuracy, allvolumes, coefficientuples = logisticpredict.create_model(paths, exclusions, classifyconditions)
print('If we divide the second dataset at 0.5, accuracy is: ', str(rawaccuracy))
print()
# Now we compare the predictions made by these two models, comparing only
# the volumes that are in both models but excluded from the training process
# in the second model.
comparemodels.compare_untrained(outputpath1, outputpath2)
