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:
line += '%.5f,' % item
line = line[:-1] + "\n"
file.write(line)
logging.info("FINISHED TEST #%d/10 time: %d" % ((i + 1), trialTime))
file.close()
else:
pulsar_data = data_handler.getPulsarData(1, pulsar_size)
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:
line += '%.5f,' % item
line = line[:-1] + "\n"
file.write(line)
logging.info("FINISHED TEST #%d/20 time: %d" % ((i + 1), trialTime))
file.close()
else:
pulsar_data = data_handler.getPulsarData(1, pulsar_size)
learning_rate_init=learn,
hidden_layer_sizes=(wide, deep),
random_state=1)
line = "%d," % ((i + 1) * dota_step)
logging.info('Getting Data............')
dota_data = data_handler.getDotaData(1, (i + 1) * dota_step)
logging.info('Fitting Data............')
clf.fit(dota_data[0], dota_data[1])
logging.info('Testing Data............')
test_data_prediction = clf.predict(dota_data[2])
res = data_handler.recordResults(1, -1, config.dota_results,
dota_data[3], test_data_prediction,
"./ANN/ANN_", False)
trialTime = (datetime.datetime.now() - trialTime).seconds
line += "%.2f," % trialTime
for item in res:
line += "%.5f%%," % item
line = line[:-1] + "\n"
file.write(line)
logging.info("FINISHED TEST #%d/10 time: %d" % ((i + 1), (trialTime)))
file.close()
else:
dota_data = data_handler.getDotaData(1, dota_size)
clf.fit(dota_data[0], dota_data[1])
for i in range(3, 5): # for testing
trialTime = datetime.datetime.now()
svm = SVC(kernel = kern)
line = "%d," % ((i + 1) * dota_step)
logging.info('Getting Data............')
dota_data = data_handler.getDotaData(1, (i + 1) * dota_step)
logging.info('Fitting Data............')
svm.fit(dota_data[0], dota_data[1])
logging.info('Testing Data............')
test_data_prediction = svm.predict(dota_data[2])
res = data_handler.recordResults(1, -1, config.dota_results,dota_data[3], test_data_prediction, approach, False)
trialTime = (datetime.datetime.now() - trialTime).seconds
line += "%.2f," %trialTime
for item in res:
line += "%.5f%%," % item
line = line[:-1] + "\n"
file.write(line)
logging.info("FINISHED TEST #%d/10 time: %d" % ((i + 1), trialTime))
file.close()
else:
logging.info('getting data.............')
dota_data = data_handler.getDotaData(1, dota_size)