svm = SVC(kernel=kern)
headers = "Size,Time (s),Percent Correct, Percent +, Percent -, Percent + correct, Percent - correct, False + percent, False - percent\n"
if multipleTests:
file = open(approach + config.pulsar_analysis, 'w')
file.write(kern + "\n")
file.write(headers)
err_cnt = 0
for i in range(3):
trialTime = datetime.datetime.now()
svm = SVC(kernel=kern)
line = "%d," % ((i + 1) * pulsar_step)
logging.info('Getting Data............')
pulsar_data = data_handler.getPulsarData(1, (i + 1) * pulsar_step)
logging.info('Fitting Data............')
svm.fit(pulsar_data[0], pulsar_data[1])
logging.info('Testing Data............')
test_data_prediction = svm.predict(pulsar_data[2])
res = data_handler.recordResults(1, 0, config.pulsar_results,
pulsar_data[3], test_data_prediction,
approach, False)
trialTime = (datetime.datetime.now() - trialTime).seconds
line += "%.2f," % trialTime
for item in res:
headers = "Min split,Time (s),Percent Correct, Percent +, Percent -, Percent + correct, Percent - correct, False + percent, False - percent\n"
if multipleTests:
file = open("./DT/_DT_" + config.pulsar_analysis, 'w')
file.write("leaf,split = 100,depth = 9\n")
file.write(headers)
for i in range(1, 6):
trialTime = datetime.datetime.now()
clf = DecisionTreeClassifier(max_depth=best_depth,
min_samples_leaf=best_leaf,
min_samples_split=pow(10, i))
line = "%d," % (pow(10, i))
logging.info('Getting Data............')
pulsar_data = data_handler.getPulsarData(1, pulsar_size)
logging.info('Fitting Data............')
clf.fit(pulsar_data[0], pulsar_data[1])
logging.info('Testing Data............')
test_data_prediction = clf.predict(pulsar_data[2])
res = data_handler.recordResults(1, 0, config.pulsar_results,
pulsar_data[3], test_data_prediction,
"./DT/DT_", False)
trialTime = (datetime.datetime.now() - trialTime).seconds
line += "%.2f," % trialTime
for item in res: