def singleshot():
fn = str(random.randint(1, 25))
filename = 'rt-data-io/' + fn + '.csv'
weightFile = 'Data/weights.h5'
predFile = 'Data/singleCSV/singlePred.h5'
columns = [
'stresses_full_xx', 'stresses_full_yy', 'stresses_full_xy',
'stresses_full_xz', 'stresses_full_yz', 'stresses_full_zz'
]
testFile = 'single.h5'
dictionary = defaultdict(list)
print('working with {},...'.format(filename.split('/')[-1]))
data = helper_functions.csv2dict(filename)
grid_aftershock_count = np.double(data['aftershocksyn'])
temp = grid_aftershock_count.tolist()
dictionary['aftershocksyn'].extend(temp)
for column in columns:
dictionary[column].extend(np.double(data[column]))
columns.append('aftershocksyn')
helper_functions.dict2HDF('single.h5', columns, dictionary)
features_in = [
'stresses_full_xx', 'stresses_full_yy', 'stresses_full_xy',
'stresses_full_xz', 'stresses_full_yz', 'stresses_full_zz'
]
features_out = 'aftershocksyn'
model = helper_functions.createModel()
model.load_weights(weightFile)
X, y = helper_functions.loadDataFromHDF(testFile, features_in,
features_out)
y_pred = model.predict(X)
helper_functions.writeHDF(predFile, X, y)
auc = sklearn.metrics.roc_auc_score(y, y_pred)
return auc
import numpy as np
import sklearn as sklearn
import helper_functions
# name of weights
weightFile = 'Data/weights.h5'
predFile = 'predicted.h5'
testFile = 'Data/testing.h5'
# name of features in our dataset
features_in = [
'stresses_full_xx', 'stresses_full_yy', 'stresses_full_xy',
'stresses_full_xz', 'stresses_full_yz', 'stresses_full_zz'
]
# name of label
features_out = 'aftershocksyn'
# load the model
model = helper_functions.createModel()
# load the weights and evaluate on test file
model.load_weights(weightFile)
X, y = helper_functions.loadDataFromHDF(testFile, features_in, features_out)
y_pred = model.predict(X)
helper_functions.writeHDF(predFile, X, y)
auc = sklearn.metrics.roc_auc_score(y, y_pred)
print('merged AUC on testing data set: ' + str(auc))