config.use_sparse_data = True
config.normalize_data = False
config.normalize_type = 'unit_length' # Other valid option: 'center' (centers/scales distribution around zero)
config.validate= True
# Generate our feature vectors
gen_feature_data.genAllExperimentData(experiment_name=name) #********************************************************************** seeeeeee)
#Train the SVM and print results
#try the C values in Cs for each kernel in kernels
kernels= ['linear']
Cs= [0.001,0.01,0.003]
stats = svm.svmLearnAll(C=.01, gamma=0.00, kernel='linear', experiment_name=name, debug=1, rep_max=None,Clist=Cs,kernelList=kernels,mcnemar=True)
mcnemardata={}
for (rep_id,repstats) in stats.items():
if ('WrongPredictions') in repstats.keys():
bills=repstats['WrongPredictions']
mcnemardata[rep_id]=bills
del(repstats['WrongPredictions'])
mcn= open("mcnemar_data/"+name,'w')
mcn.write(json.dumps(mcnemardata))
mcn.close()
print "Done with all reps"
config.use_sparse_data = True
config.normalize_data = False
config.normalize_type = 'unit_length' # Other valid option: 'center' (centers/scales distribution around zero)
config.validate= True
# Generate our feature vectors
gen_feature_data.genAllExperimentData(experiment_name=name) #********************************************************************** seeeeeee)
#Train the SVM and print results
#try the C values in Cs for each kernel in kernels
kernels= ['linear']
Cs= [0.1, 0.3, 0.5, 0.7, 1.2, 2.5, 3.6,10]
stats = svm.svmLearnAll(C=.01, gamma=0.00, kernel='linear', experiment_name=name, debug=3, rep_max=2,Clist=Cs,kernelList=kernels)
print "Done with all reps"
f = open('experiment_results/'+name+'.json', 'w')
f.write(json.dumps(stats))
f.close()
raw_input("Press Enter to continue... \nAbout to write .csv. Make sure to close the results file if you have it open.")
# Format stats for excel:
f = open('experiment_results/'+name+'.csv', 'w')
#Write headers:
for stat_name in stats[stats.keys()[0]]:
f.write(','+stat_name)
name = "all_with_summary"
config.features_to_ignore = []
config.use_sparse_data = True
config.normalize_data = False
config.normalize_type = 'unit_length' # Other valid option: 'center' (centers/scales distribution around zero)
# Generate our feature vectors
gen_feature_data.genAllExperimentData(experiment_name=name)
# Train the SVM and print results
# TODO(john): Return results in such a way that we can analyze multiple reps
# or plug it into excel or something.
stats = svm.svmLearnAll(C=.01, gamma=0.00, kernel='linear', experiment_name=name, debug=1, rep_max=430)
print "Done with all reps"
f = open('experiment_results/'+name+'.json', 'w')
f.write(json.dumps(stats))
f.close()
raw_input("Press Enter to continue... \nAbout to write .csv. Make sure to close the results file if you have it open.")
# Format stats for excel:
f = open('experiment_results/'+name+'.csv', 'w')
#Write headers:
for stat_name in stats[stats.keys()[0]]:
f.write(','+stat_name)
