def main():
data_dir_path = 'data'
model_dir_path = 'model'
dateparser = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
satellite_data1 = pd.read_csv(data_dir_path + '/data_std.csv',
sep=',',
index_col=0,
encoding='utf-8',
parse_dates=True,
date_parser=dateparser)
column = [
'INA1_PCU输出母线电流', 'INA4_A电池组充电电流', 'INA2_A电池组放电电流', 'TNZ1PCU分流模块温度1',
'INZ6_-Y太阳电池阵电流', 'VNA2_A蓄电池整组电压', 'VNC1_蓄电池A单体1电压', 'VNZ2MEA电压(S3R)',
'VNZ4A组蓄电池BEA信号'
]
# column = ['INA1_PCU输出母线电流']#,'INA4_A电池组充电电流','INA2_A电池组放电电流','TNZ1PCU分流模块温度1','INZ6_-Y太阳电池阵电流','VNA2_A蓄电池整组电压','VNC1_蓄电池A单体1电压','VNZ2MEA电压(S3R)','VNZ4A组蓄电池BEA信号']
satellite_data = satellite_data1.loc[:, column].iloc[0:80] #96700
print(satellite_data.head())
satellite_np_data = satellite_data.as_matrix()
scaler = MinMaxScaler()
satellite_np_data = scaler.fit_transform(satellite_np_data)
print(satellite_np_data.shape)
index = satellite_data.index
columns = satellite_data.columns
time_window_size = 8
# data_std = pd.DataFrame(satellite_np_data, index=index, columns=columns)
# data_std.to_csv('data/data_scaler.csv', encoding='utf-8')
input_dataset = np.reshape(
satellite_np_data,
((int)(satellite_np_data.shape[0] / time_window_size),
time_window_size, satellite_np_data.shape[1]))
ae = LstmAutoEncoder6(index, columns)
# fit the data and save model into model_dir_path
if DO_TRAINING:
ae.fit(input_dataset,
batch_size=10,
model_dir_path=model_dir_path,
time_window_size=time_window_size,
estimated_negative_sample_ratio=0.9)
# load back the model saved in model_dir_path detect anomaly
ae.load_model(model_dir_path)
anomaly_information = ae.anomaly(input_dataset)
reconstruction_error = []
for idx, (is_anomaly, dist) in enumerate(anomaly_information):
print('# ' + str(idx) + ' is ' +
('abnormal' if is_anomaly else 'normal') + ' (dist: ' +
str(dist) + ')')
reconstruction_error.append(dist)
visualize_reconstruction_error(reconstruction_error, ae.threshold)
def main():
data_dir_path = 'data'
model_dir_path = 'model'
dateparser = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
satellite_data1 = pd.read_csv(data_dir_path + '/data_std.csv',
sep=',',
index_col=0,
encoding='utf-8',
parse_dates=True,
date_parser=dateparser)
satellite_data = satellite_data1.iloc[0:96700]
print(satellite_data.head())
satellite_np_data = satellite_data.as_matrix()
scaler = MinMaxScaler()
satellite_np_data = scaler.fit_transform(satellite_np_data)
print(satellite_np_data.shape)
index = satellite_data.index
columns = satellite_data.columns
time_window_size = 1
# data_std = pd.DataFrame(satellite_np_data, index=index, columns=columns)
# data_std.to_csv('data/data_scaler.csv', encoding='utf-8')
ae = LstmAutoEncoder5(index, columns)
# fit the data and save model into model_dir_path
if DO_TRAINING:
ae.fit(satellite_np_data,
model_dir_path=model_dir_path,
time_window_size=time_window_size,
estimated_negative_sample_ratio=0.9)
# load back the model saved in model_dir_path detect anomaly
ae.load_model(model_dir_path)
anomaly_information = ae.anomaly(satellite_np_data[:96700, :])
reconstruction_error = []
for idx, (is_anomaly, dist) in enumerate(anomaly_information):
print('# ' + str(idx) + ' is ' +
('abnormal' if is_anomaly else 'normal') + ' (dist: ' +
str(dist) + ')')
reconstruction_error.append(dist)
visualize_reconstruction_error(reconstruction_error, ae.threshold)