def build_default_model(oneslice_pred, ch4_pred, sa_predict, p_1=0.6):
#p_1 one slice model percentage
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'build_default_model',
dhl.START_FUNCTION, ' - ')
default_pred = {}
def _bdm_ave(x1, x2, x0):
if np.isnan(x1[0]):
return x0 if np.isnan(x2[0]) else x2
elif np.isnan(x2[0]):
return x1
return np.asarray([x1[0] * p_1 + x2[0] * (1 - p_1),
min(x1[1], x2[1])])
for case, value in sa_predict.iteritems():
pred1 = oneslice_pred.get(case)
pred2 = ch4_pred.get(case)
if pred1 is None:
pred1 = np.zeros(4)
pred1[:] = np.nan
if pred2 is None:
pred2 = np.zeros(4)
pred2[:] = np.nan
x = np.zeros(4)
x[0:2] = _bdm_ave(pred1[0:2], pred2[0:2], value[0:2])
x[2:4] = _bdm_ave(pred1[2:4], pred2[2:4], value[2:4])
default_pred[case] = x
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'build_default_model',
dhl.END_FUNCTION, ' - ')
return default_pred
def test_method2(self, par1):
turn = dhl.turn()
dhl.log_time_stamp(turn, 'log_test', 'TestClass', 'test_method',
dhl.START_FUNCTION, 'function explicity')
dhl.log_complexity(turn, 'log_test', 'TestClass', 'test_method', 'K')
print par1
dhl.log_time_stamp(turn, 'log_test', 'TestClass', 'test_method',
dhl.END_FUNCTION, 'function explicity')
dhl.inc_turn()
def train_oneslice_model(areas_all, train_true):
#train OneSliceModel using CNN_B result
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_oneslice_model',
dhl.START_FUNCTION, ' - ')
oneslice_model = OneSliceModel()
oneslice_model.fit(areas_all, train_true)
oneslice_predict = oneslice_model.predict(areas_all)
analysis.evaluate_pred(oneslice_predict, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_oneslice_model',
dhl.END_FUNCTION, ' - ')
return oneslice_predict
def train_sex_age_model(info, train_true):
##train sex_age model
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sex_age_model',
dhl.START_FUNCTION, ' - ')
sa_model = SexAgeModel()
sa_model.fit(info, train_true)
sa_predict = sa_model.predict(info)
analysis.evaluate_pred(sa_predict, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sex_age_model',
dhl.END_FUNCTION, ' - ')
return sa_predict
def train_sax_model(areas_all, train_true, version, cleaner=[]):
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sax_model',
dhl.START_FUNCTION, ' - ')
sax_model = SaxModel(version=version)
result = analysis.get_preliminary_volume(areas_all, cleaner=cleaner)
sax_model.fit(result, train_true)
sax_predict = sax_model.predict(result)
analysis.evaluate_pred(sax_predict, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sax_model',
dhl.END_FUNCTION, ' - ')
return sax_predict
def train_ch4_model(ch4_data, train_true):
#tencia's 4-ch model, implemented 02/27
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_ch4_model',
dhl.START_FUNCTION, ' - ')
ch4_model = Ch4Model()
ch4_model.fit(ch4_data, train_true)
ch4_pred = ch4_model.predict(ch4_data)
analysis.evaluate_pred(ch4_pred, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_ch4_model',
dhl.END_FUNCTION, ' - ')
return ch4_pred
def train_sax_cnt_filter_model(areas_all, cont_all, train_true, cleaner=[]):
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-',
'train_sax_cnt_filter_model', dhl.START_FUNCTION, ' - ')
cnt_result = analysis.get_preliminary_volume_cnt_filter(areas_all,
cont_all,
cleaner=cleaner)
cnt_sax_model = SaxFilterModel()
cnt_sax_model.fit(cnt_result, train_true)
cnt_sax_predict = cnt_sax_model.predict(cnt_result)
analysis.evaluate_pred(cnt_sax_predict, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-',
'train_sax_cnt_filter_model', dhl.END_FUNCTION, ' - ')
return cnt_sax_predict
def train_sax_cnt_model(areas_all,
cont_all,
train_true,
version=2,
cleaner=[]):
#sax model method2, use contour compeleteness to filter result
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sax_cnt_model',
dhl.START_FUNCTION, ' - ')
cnt_sax_model = SaxModel(version=version)
cnt_result = analysis.get_preliminary_volume_cnt(areas_all,
cont_all,
cleaner=cleaner)
cnt_sax_model.fit(cnt_result, train_true)
cnt_sax_predict = cnt_sax_model.predict(cnt_result)
analysis.evaluate_pred(cnt_sax_predict, train_true)
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'train_sax_cnt_model',
dhl.END_FUNCTION, ' - ')
return cnt_sax_predict
def test_method(self, par1):
turn = dhl.turn()
for i in range(par1):
dhl.log_time_stamp(turn, 'log_test', 'TestClass', 'test_method', i,
'iter ' + str(i))
print i
pred1 = np.zeros(4)
pred1[:] = np.nan
if pred2 is None:
pred2 = np.zeros(4)
pred2[:] = np.nan
x = np.zeros(4)
x[0:2] = _bdm_ave(pred1[0:2], pred2[0:2], value[0:2])
x[2:4] = _bdm_ave(pred1[2:4], pred2[2:4], value[2:4])
default_pred[case] = x
dhl.log_time_stamp(turn, 'train_pred.py', '-', 'build_default_model',
dhl.END_FUNCTION, ' - ')
return default_pred
if __name__ == '__main__':
turn = dhl.turn()
dhl.log_time_stamp(turn, 'train_pred.py', '-', '__main__',
dhl.START_FUNCTION, ' - ')
cleaner = [0, 1, 2]
#set to [] to turn it off
#####Load all data, same in the test code, but we can filter out train in train and test for test
info = pd.read_csv(sts.output_dir + '/info.csv')
ch4_data = {
int(r[0]): (r[1], r[2])
for _, r in pd.read_csv(sts.tencia_output_dir + '/ch4_volumes_map.csv',
header=False).iterrows()
}
#CNN_B results
tencia_files = ['pMS', 'p1090']
tencia_areas = [
analysis.get_cnn_results(sts.tencia_output_dir +