def predict_anomaly(ds, model, kernel, with_skip=True):
if with_skip:
stride = kernel
else:
stride = 1
# Create set
print("Create testing set")
x_test = get_sliding_window_matrix(ds.values, kernel, stride)
# Testing
print('Testing...')
y_pred = model.predict(x_test)
# Expand results
y_pred = [val for val in y_pred for _ in range(stride)]
res = np.zeros((len(ds)))
if with_skip:
res[:len(y_pred)] = y_pred
else:
res[-len(y_pred):] = y_pred
y_pred = pd.Series(res, index=ds.index, name='features')
return y_pred
def prepare_data(ds_list, labels, kernel, stride=1):
# Create slide window matrix for each train
x_list = [
get_sliding_window_matrix(ds.values, kernel, stride) for ds in ds_list
]
# Assign labels for each matrix values
y = np.hstack([[i] * len(x) for i, x in zip(labels, x_list)])
# Concat each matrix
x = np.vstack(x_list)
return x, y
def main():
params = get_argument()
# model input
train_file = params['train']
test_file = params['test']
# feature params
features_list = params['features_list']
kernel = params['kernel']
stride = params['stride']
# feature extraction
resample_rate = params.get('resample_rate', 6400)
custom_resample = params.get('custom_resample', False)
# model params
model_type = params['model_type']
params_file = params['model_params']
# Read train file
print("Read Train File: ", os.path.basename(train_file))
ds_train = pd.read_csv(train_file)
# Select features
if features_list:
ds_train = ds_train[features_list]
# Resample
train_len = len(ds_train)
if custom_resample:
ds_train = resample_with_feature_extractor(ds_train, resample_rate)
else:
if resample_rate > 1:
ds_train = resample(ds_train, resample_rate)
print('Train Original File Length: ', train_len)
print('New File Length {} {:.02f}'.format(len(ds_train),
100 * len(ds_train) / train_len))
# Create training set
print("Create training set")
x_train = get_sliding_window_matrix(ds_train.values, kernel, stride)
print('Train shape ', x_train.shape)
# Model initialization
print("Model initialization: {}".format(model_type))
model = get_model(model_type, params_file=params_file)
# Training
print("Training...")
model.fit(x_train)
print("Read Test File: ", os.path.basename(test_file))
ds_test = pd.read_csv(test_file)
# Select features
if features_list:
ds_test = ds_test[features_list]
# Resample
test_len = len(ds_test)
if custom_resample:
ds_test = resample_with_feature_extractor(ds_test, resample_rate)
else:
if resample_rate > 1:
ds_test = resample(ds_test, resample_rate)
print('Test Original File Length: ', test_len)
print('New File Length {} {:.02f}'.format(len(ds_test),
100 * len(ds_test) / test_len))
print('Testing...')
y_pred = predict_anomaly(ds_test, model, kernel, with_skip=False)
# Encoding results into triplet formats
results = create_triplet_time_series(y_pred, with_support=True)
# Show results
print("Results:")
results = pd.DataFrame(results)
print(tabulate(results, headers='keys', tablefmt='psql'))
def main():
# Get all .CSV files in global folder
files = get_files(global_dir, ext='.CSV')
print('Found {} files'.format(len(files)))
# Get settings dataset, where each row represent a new setting entry
ds_settings = get_time_series_dataset(settings_file, sep=';', col='DT')
print('Found {} settings'.format(len(ds_settings)))
# Identify settings label
label_settings(ds_settings, ds_settings.columns[:13])
ds_settings.ltime = pd.to_datetime(ds_settings.ltime)
ds_settings.rtime = pd.to_datetime(ds_settings.rtime)
print('Found {} unique settings'.format(len(np.unique(ds_settings.label))))
settings_map = {}
setup_files = []
# Create settings map that associates a setting to each file
print('\nSettings File identification')
for file in files:
# Read dataset
ds = get_time_series_dataset(file, sep=';', col='DT')
# Get nearest left setting
setting = get_settings(ds, ds_settings)
# Update settings_map
if str(setting.label) not in settings_map:
settings_map[str(setting.label)] = [file]
else:
settings_map[str(setting.label)] += [file]
# Check if the setting start overlap with file timely interval
if check_setup(ds, setting):
print('Found setup {}: {} - {} in ds {} - {}'.format(
setting.label, setting.ltime, setting.rtime, ds.index.min(),
ds.index.max()))
setup_files += [file]
elif lazy_check_setup(ds, setting):
print('Found lazy setup {}: {} - {} in ds {} - {}'.format(
setting.label, setting.ltime, setting.rtime, ds.index.min(),
ds.index.max()))
setup_files += [file]
print('Number of timely series with setup: {}'.format(len(setup_files)))
y_pred_single = {}
y_true_single = {}
normal_files = {}
# Save settings_map and setup_files list
# with open('../results/settings_map.json', 'w') as outfile:
# json.dump(settings_map, outfile)
#
# with open('../results/setup_files.json', 'w') as outfile:
# json.dump(setup_files, outfile)
# For each state we train a models with a "normal" file and predict anomalies
print('\nTraining and Testing - {}'.format(model_type))
for k, val in settings_map.items():
print('\nState {} has {} files'.format(k, len(val)))
# Get normal file from constant_normal_files dictionary
if k not in constant_normal_files:
print('Skip, normal files founded')
continue
normal_file = constant_normal_files[k]
normal_files[k] = normal_file
if normal_file is None:
print('Impossible get normal file')
return
# Training
ds_train = get_time_series_dataset(filename=normal_file,
sep=';',
col='DT')
# Check train
if ds_train is None:
print('Impossible read train file')
return
y_pred_single[k] = {}
y_true_single[k] = {}
for col in ds_train.columns:
x_train = ds_train[[col]]
x_train = get_sliding_window_matrix(x_train.values, kernel, stride)
# Selected models
if model_type == 'pca':
model = PCA(n_components=0.95, threshold=100, c_alpha=3.2905)
elif model_type == 'clustering':
model = SetupClustering(distance="cosine",
max_dist=0.001,
anomaly_threshold=0.0001)
elif model_type == 'svm':
model = OneClassSVM(nu=0.001,
tol=0.001,
kernel="rbf",
gamma="scale")
elif model_type == 'lof':
model = LOF(n_neighbors=50,
algorithm='auto',
metric='minkowski',
contamination='auto')
elif model_type == 'if':
model = IsolationForest(n_estimators=200,
max_samples=512,
contamination=0.0003,
max_features=0.8)
else:
print("Select the wrong models")
return
# Training
print("Training... state {} col {}".format(k, col))
model.fit(x_train)
y_pred_single[k][col] = []
y_true_single[k][col] = []
print("Testing...")
for file in val:
# y_true_single is useless
# setup_files doesn't have value for label
if file in setup_files:
y_true_single[k][col].append(1)
else:
y_true_single[k][col].append(0)
x_test = get_time_series_dataset(filename=file,
sep=';',
col='DT')
# Check test
if x_test is None:
print('Impossible read test file')
return
# Create testing values
x_test = x_test[[col]]
x_test = get_sliding_window_matrix(x_test.values, kernel,
kernel)
# Testing
y_pred = model.predict(x_test)
# Save number of detected anomalies
y_pred_single[k][col].append(len(y_pred[y_pred == 1]))
# break
print('\nSelected normal files:')
for k, file in normal_files.items():
print("State {} -> {}".format(k, file))
# Create result dataset
y_pred = []
y_true = []
cols = []
files = []
states = []
for k in y_pred_single.keys():
for col in y_pred_single[k].keys():
i = 0
for pred, true in zip(y_pred_single[k][col],
y_true_single[k][col]):
y_pred.append(pred)
y_true.append(true)
cols.append(col)
files.append(settings_map[k][i])
states.append(k)
i += 1
res_ds = pd.DataFrame({
'file': files,
'cols': cols,
'states': states,
'y_pred': y_pred,
'y_true': y_true
})
# Create real ground truth
res_ds['file'] = res_ds['file'].apply(lambda x: x.split('\\')[-1])
normal_file_list = ["File ({}).CSV".format(x) for x in normal_file_id_list]
res_ds['y_true'] = 1
res_ds.loc[res_ds['file'].isin(normal_file_list), 'y_true'] = 0
# Save results
res_ds.to_csv('../results/{}_evaluation.CSV'.format(model_type),
sep=';',
index=False)
# Evaluation
print("\nEvaluation")
true_positive = len(res_ds[(res_ds['y_pred'] > 0)
& (res_ds['y_true'] > 0)])
false_positive = len(res_ds[(res_ds['y_pred'] > 0)
& (res_ds['y_true'] == 0)])
true_negative = len(res_ds[(res_ds['y_pred'] <= 0)
& (res_ds['y_true'] == 0)])
false_negative = len(res_ds[(res_ds['y_pred'] <= 0)
& (res_ds['y_true'] > 0)])
acc = 100 * (true_positive + true_negative) / len(res_ds)
print("Accuracy: {}".format(acc))
precision = 100 * true_positive / (true_positive + false_positive)
print("Precision: {}".format(precision))
recall = 100 * true_positive / (true_positive + false_negative)
print("Recall: {}".format(recall))
f_score = 2 * precision * recall / (precision + recall)
print("F-score: {}".format(f_score))
def main():
params = get_argument()
all_state_folder = params['all_state_folder']
features_list = params['features_list']
kernel = params['kernel']
stride = params['stride']
model_type = params['model_type']
resample_rate = params.get('resample_rate', 6400)
with_decision_score = params.get('with_decision_score', False)
custom_resample = params.get('custom_resample', False)
# resample_rate = 12800 # 12800 sample are 1 second
# num_sample = 1000000
with_skip = False
params_file = './params/params_{}.json'.format(model_type)
save_result = True
overwrite = True
output_dir = './results'
result_array = []
# Get list of list of files, where for each state we have a list of file
curr_files = []
# Get list of test files
test_files = []
for folder in all_state_folder:
files = get_files(folder, ext='lvm')
curr_files.append(files)
test_files += files
max_size = min([len(files) for files in curr_files[:3]])
# Get train files where each element is a list of files for a single train
train_files = []
for i in range(max_size):
train_pack = [files[i] for files in curr_files[:3]]
for j in range(1, len(train_pack)):
train_files.append(train_pack[:j + 1])
for train_pack in train_files:
if len(train_pack) < 3:
continue
print('\n' + '\\\\//' * 20)
selected_files = []
train_states = []
x_states = []
print('\n Train Pack')
for train_file in train_pack:
train_state = os.path.split(os.path.dirname(train_file))[-1]
print("State: ", train_state)
print("Read File: ", os.path.basename(train_file))
ds_train = read_ds_lvm(train_file, get_header=False)
# Check train
if ds_train is None or ds_train.empty:
print('Impossible read train file')
continue
# Select features
ds_train = ds_train[features_list]
# Resample
train_len = len(ds_train)
if custom_resample:
ds_train = resample_with_feature_extractor(
ds_train, resample_rate)
else:
ds_train = resample(ds_train, resample_rate)
# ds_train = ds_train[:num_sample]
print('Original File Length: ', train_len)
print('New File Length {} {:.02f}'.format(
len(ds_train), 100 * len(ds_train) / train_len))
# Create training set
print("Create set")
x_train = get_sliding_window_matrix(ds_train.values, kernel,
stride)
print('Shape ', x_train.shape)
selected_files.append(train_file)
train_states.append(train_state)
x_states.append(x_train)
x_states = np.vstack(x_states)
print('\n Train Size: ', x_states.shape)
print('Train state: ', train_states)
# Model initialization
print("Model initialization: {}".format(model_type))
model = get_model(model_type, params_file=params_file)
# Training
print("Training...")
model.fit(x_states)
for test_file in test_files:
test_state = os.path.split(os.path.dirname(test_file))[-1]
if test_file in selected_files:
continue
# if test_state in train_states:
# continue
print("\n State Test: ", test_state)
print("Read Test File: ", os.path.basename(test_file))
ds_test = read_ds_lvm(test_file, get_header=False)
# t1 = datetime.now()
# Check test
if ds_test is None or ds_test.empty:
print('Impossible read test file')
continue
# Select features
ds_test = ds_test[features_list]
# Resample
test_len = len(ds_test)
if custom_resample:
ds_test = resample_with_feature_extractor(
ds_test, resample_rate)
else:
ds_test = resample(ds_test, resample_rate)
# ds_test = ds_test[:num_sample]
print('Test Original File Length: ', test_len)
print('New File Length {} {:.02f}'.format(
len(ds_test), 100 * len(ds_test) / test_len))
if with_skip:
test_stride = kernel
else:
test_stride = 1
# Create set
print("Create testing set")
x_test = get_sliding_window_matrix(ds_test.values, kernel,
test_stride)
print('Test shape ', x_test.shape)
# Testing
print('Testing...')
if with_decision_score:
y_pred = model.decision_score(x_test)
else:
y_pred = model.predict(x_test)
num_error = np.sum(y_pred > 0)
mean_error = np.mean(y_pred)
if num_error > 0:
mean_only_error = np.mean(y_pred[y_pred > 0])
else:
mean_only_error = 0
if not np.sum(y_pred > 0):
print("Results: NO Anomaly founded")
else:
print("Results: {} anomalies "
"({:.05f} total {})".format(num_error, mean_error,
len(x_test)))
result_record = {
'MODEL':
model_type,
'KERNEL':
kernel,
'STRIDE':
stride,
'TRAIN_STATE':
train_states,
'TRAIN': [
os.path.basename(train_file)
for train_file in selected_files
],
'TEST_STATE':
test_state,
'TEST':
os.path.basename(test_file),
'NUM_SINGLE_ANOMALY':
num_error,
'PCT_ANOMALY':
mean_error,
'NUM_SAMPLE_ANOMALY':
mean_only_error,
'NUM_SAMPLE':
len(x_test),
'LABEL':
test_state not in train_states
}
result_array.append(result_record)
if save_result:
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
filename = os.path.join(output_dir,
'results_multi_' + model_type + '.csv')
result_ds = pd.DataFrame(result_array)
if os.path.isfile(filename) and not overwrite:
prev_result_ds = pd.read_csv(filename)
result_ds = pd.concat([prev_result_ds, result_ds],
axis=0,
ignore_index=True)
result_ds.to_csv(filename, index=False)
def main():
train_state = os.path.split(os.path.dirname(train_file))[-1]
print("\n State Train: ", train_state)
print("Read Train File: ", os.path.basename(train_file))
ds_train = read_ds_lvm(train_file, get_header=False)
# Check train
if ds_train is None or ds_train.empty:
print('Impossible read train file')
return
# Select features
ds_train = ds_train[features_list]
# Resample
train_len = len(ds_train)
if custom_resample:
ds_train = resample_with_feature_extractor(ds_train, resample_rate)
else:
ds_train = resample(ds_train, resample_rate)
# ds_train = ds_train[:num_sample]
print('Train Original File Length: ', train_len)
print('New File Length {} {:.02f}'.format(len(ds_train),
100 * len(ds_train) / train_len))
# Create training set
print("Create training set")
x_train = get_sliding_window_matrix(ds_train.values, kernel, stride)
print('Train shape ', x_train.shape)
# Model initialization
print("Model initialization: {}".format(model_type))
model = get_model(model_type, params_file=params_file)
# Training
print("Training...")
model.fit(x_train)
test_state = os.path.split(os.path.dirname(test_file))[-1]
print("\n State Test: ", test_state)
print("Read Test File: ", os.path.basename(test_file))
ds_test = read_ds_lvm(test_file, get_header=False)
# Check test
if ds_test is None or ds_test.empty:
print('Impossible read test file')
return
# Select features
ds_test = ds_test[features_list]
# Resample
test_len = len(ds_test)
if custom_resample:
ds_test = resample_with_feature_extractor(ds_test, resample_rate)
else:
ds_test = resample(ds_test, resample_rate)
# ds_test = ds_test[:num_sample]
print('Test Original File Length: ', test_len)
print('New File Length {} {:.02f}'.format(len(ds_test),
100 * len(ds_test) / test_len))
# Testing
# y_pred = predict_anomaly(ds_test, model, kernel, with_skip=with_skip)
if with_skip:
test_stride = kernel
else:
test_stride = 1
# Create set
print("Create testing set")
x_test = get_sliding_window_matrix(ds_test.values, kernel, test_stride)
print('Test shape ', x_test.shape)
# Testing
print('Testing...')
if with_decision_score:
y_pred = model.decision_score(x_test)
else:
y_pred = model.predict(x_test)
num_error = np.sum(y_pred > 0)
mean_error = np.mean(y_pred)
if num_error > 0:
mean_only_error = np.mean(y_pred[y_pred > 0])
else:
mean_only_error = 0
if not np.sum(y_pred > 0):
print("Results: NO Anomaly founded")
else:
print("Results: {} anomalies "
"({:.05f} {:.05f} total {})".format(num_error,
mean_error, mean_only_error,
len(x_test)))
# Encoding results into triplet formats
results = create_triplet_time_series(y_pred, with_support=True)
# Show results
results = pd.DataFrame(results)
if results.empty:
print("Results: NO Anomaly founded")
else:
# print(tabulate(results, headers='keys', tablefmt='psql'))
test_stride = kernel if with_skip else 1
# Number of test samples of kernel length
test_sample = int((len(ds_test) - kernel) / test_stride) + 1
# Number of single anomaly point
tot = results['support'].sum()
pct_tot = 100 * tot / (test_sample * test_stride)
print("Results: {} (record {:.02f})".format(tot, pct_tot))
if with_skip:
# Number of anomaly sample
tot_sample = int(tot / test_stride)
print("Anomaly Sample: {} (test sample {:.02f})".format(
int(tot_sample), test_sample))
def main():
output_dir = './results'
selected_files = [
"/export/static/pub/softlab/dataset_sbdio/Anomaly Detection/TEST 2/testaccelerometri.lvm",
"/export/static/pub/softlab/dataset_sbdio/Anomaly Detection/TEST 2/testaccelerometri.lvm",
"/export/static/pub/softlab/dataset_sbdio/Anomaly Detection/TEST 3/testaccelerometri_1.lvm",
"/export/static/pub/softlab/dataset_sbdio/Anomaly Detection/TEST 4/testaccelerometri.lvm",
]
features_list = [
"Acceleration_X1",
"Acceleration_Y1",
"Acceleration_Z1",
"Acceleration_X2",
"Acceleration_Y2",
"Acceleration_Z2",
"Acceleration_X3",
"Acceleration_Y3",
"Acceleration_Z3"
]
stride = 1
model_list = [
'cnn',
'lstm',
'deep',
'isolation_forest',
'setup_clustering',
'pca',
'lof',
'svm',
]
kernel_list = [180 if model_type in ['cnn', 'lstm', 'deep'] else 10 for model_type in model_list]
resample_rate = 6400
# Initialize result array to memorize performance result
result_array = []
# Model cycle
for model_type, kernel in zip(model_list, kernel_list):
print('\n\n')
print('\nModel: {}\n'.format(model_type))
params_file = './params/params_{}.json'.format(model_type)
# Train cycle
for i in range(len(selected_files)):
x_train = []
# Get train
for pos, train_file in enumerate(selected_files[:i + 1]):
if i > 0 and pos == 0:
continue
ds_train = read_ds_lvm(train_file, get_header=False)
if ds_train is None or ds_train.empty:
raise ValueError('Impossible read train file')
ds_train = ds_train[features_list]
ds_train = resample(ds_train, resample_rate)
x = get_sliding_window_matrix(ds_train.values, kernel, stride)
if pos == 0:
x = x[:len(x) // 2]
x_train.append(x)
# Train set
x_train = np.vstack(x_train)
print('\nTrain size: {}\n'.format(len(x_train)))
# Model init
model = get_model(model_type, params_file=params_file)
# Model training
train_start = datetime.now()
model.fit(x_train)
train_end = datetime.now()
# Test cycle
for j in range(len(selected_files)):
x_test = []
# Get test
for pos, test_file in enumerate(selected_files[:j + 1]):
if j > 0 and pos == 0:
continue
ds_test = read_ds_lvm(test_file, get_header=False)
if ds_test is None or ds_test.empty:
raise ValueError('Impossible read test file')
ds_test = ds_test[features_list]
ds_test = resample(ds_test, resample_rate)
x = get_sliding_window_matrix(ds_test.values, kernel, stride)
if pos == 0:
x = x[:1]
x_test.append(x)
# Test set
x_test = np.vstack(x_test)
print('\nTest size: {}\n'.format(len(x_test)))
# Model predict
test_start = datetime.now()
model.predict(x_test)
test_end = datetime.now()
result_record = {
'model': model_type,
'train_size': len(x_train),
'train_time': train_end - train_start,
'test_size': len(x_test),
'test_time': test_end - test_start,
}
result_array.append(result_record)
# Save results
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
filename = os.path.join(output_dir, 'performance.csv')
result_ds = pd.DataFrame(result_array)
result_ds.to_csv(filename, index=False)
def main():
print('Read input data')
# Get train dataset
print('train: {}'.format(train_file))
ds_train = get_time_series_dataset(filename=train_file, sep=sep, col=datetime_col)
# Check train
if ds_train is None:
raise ValueError('Impossible read train file')
# Get test dataset
print('test: {}'.format(test_file))
ds_test = get_time_series_dataset(filename=test_file, sep=sep, col=datetime_col)
# Check test
if ds_test is None:
raise ValueError('Impossible read test file')
print('from {} to {}'.format(ds_test.index.min(), ds_test.index.max()))
# Get features
print('Select features')
features = features_list
if not features:
features = ds_train.columns.to_list()
elif set(features).difference(set(ds_train.columns)):
raise ValueError('Select the wrong features')
assert np.all(ds_train.columns == ds_test.columns), 'Train and Test file have different features'
# Select features
ds_train = ds_train[features]
ds_test = ds_test[features]
# if visualize:
# fig, ax = plt.subplots(2, 1, figsize=(20, 10))
# ds_train.plot(ax=ax[0])
# ax[0].set_title('Training Data')
#
# ds_test.plot(ax=ax[1])
# ax[1].set_title('Test Data')
# plt.show()
# Model initialization
print("Model initialization: {}".format(model_type))
model = get_model(model_type, params_file=params_file)
# Create training set
print("Create training set")
x_train = get_sliding_window_matrix(ds_train.values, kernel, stride)
# Training
print("Training...")
model.fit(x_train)
# Option 1: Save trained models
if save_model:
# Create output directory
filename = os.path.join(output_dir, 'model_{}.pkl'.format(model_type))
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
# Save trained models
joblib.dump(model, filename)
# Load trained models
model = joblib.load(filename)
# Testing
print('Testing...')
y_pred = predict_anomaly(ds_test, model, kernel, with_skip=False)
# Encoding results into triplet formats
results = create_triplet_time_series(y_pred, with_support=True)
# Show results
print("Results:")
results = pd.DataFrame(results)
print(tabulate(results, headers='keys', tablefmt='psql'))
# Save results
if save:
filename = os.path.basename(test_file)
filename = os.path.join(output_dir, 'results_' + model_type + '_' + filename)
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
results.to_csv(filename, sep=sep, index=False)
def main():
params = get_argument()
all_state_folder = params['all_state_folder']
size = 3
features_list = [
"Acceleration_X1", "Acceleration_Y1", "Acceleration_Z1",
"Acceleration_X2", "Acceleration_Y2", "Acceleration_Z2",
"Acceleration_X3", "Acceleration_Y3", "Acceleration_Z3"
]
stride = 1
model_list = [
'cnn',
'lstm',
'deep',
'isolation_forest',
'setup_clustering',
'pca',
'lof',
'svm',
]
kernel_list = [
180 if model_type in ['cnn', 'lstm', 'deep'] else 10
for model_type in model_list
]
resample_rate = 6400
save_result = True
output_dir = './results'
# Initialize result array to memorize result
# for each train and test step
result_array = []
# Get files from selected folder to use for training and testing
curr_files = []
for folder in all_state_folder:
curr_files += get_files(folder, ext='lvm')[:]
test_files = curr_files
for model_type, kernel in zip(model_list, kernel_list):
print('\n' + '\\\\//' * 20)
print('\n Model: {}\n'.format(model_type))
params_file = './params/params_{}.json'.format(model_type)
for pos, train_file in enumerate(curr_files):
skip_step = False
train_state = os.path.split(os.path.dirname(train_file))[-1]
x_train = []
print("\n State Train: ", train_state)
for i in range(size):
if pos + i >= len(curr_files):
print('Not enough files')
skip_step = True
break
tmp_file = curr_files[pos + i]
tmp_state = os.path.split(os.path.dirname(tmp_file))[-1]
if tmp_state != train_state:
print('Different state and skip current train')
skip_step = True
break
print("Read {} Train File: {}".format(
i, os.path.basename(tmp_file)))
ds_tmp = read_ds_lvm(tmp_file, get_header=False)
# Check train
if ds_tmp is None or ds_tmp.empty:
print('Impossible read train file')
skip_step = True
break
# Select features
ds_tmp = ds_tmp[features_list]
# Resample
ds_tmp = resample(ds_tmp, resample_rate)
# Create training set
x_tmp = get_sliding_window_matrix(ds_tmp.values, kernel,
stride)
x_train.append(x_tmp)
if skip_step:
print('Skip current train')
continue
# Train set
x_train = np.vstack(x_train)
train_len = len(x_train)
print('\nTrain size: {}\n'.format(x_train.shape))
# Model initialization
print("Model initialization: {}".format(model_type))
model = get_model(model_type, params_file=params_file)
# Training
print("Training...")
model.fit(x_train)
for test_file in test_files:
test_state = os.path.split(os.path.dirname(test_file))[-1]
if train_state == test_state \
and test_file == train_file:
continue
print("\n State Test: ", test_state)
print("Read Test File: ", os.path.basename(test_file))
ds_test = read_ds_lvm(test_file, get_header=False)
# t1 = datetime.now()
# Check test
if ds_test is None or ds_test.empty:
print('Impossible read test file')
continue
# Select features
ds_test = ds_test[features_list]
# Resample
test_len = len(ds_test)
ds_test = resample(ds_test, resample_rate)
# ds_test = ds_test[:num_sample]
print('Test Original File Length: ', test_len)
print('New File Length {} {:.02f}'.format(
len(ds_test), 100 * len(ds_test) / test_len))
test_stride = 1
# Create set
print("Create testing set")
x_test = get_sliding_window_matrix(ds_test.values, kernel,
test_stride)
print('Test shape ', x_test.shape)
# Testing
print('Testing...')
y_pred = model.predict(x_test)
num_error = np.sum(y_pred > 0)
mean_error = np.mean(y_pred)
if num_error > 0:
mean_only_error = np.mean(y_pred[y_pred > 0])
else:
mean_only_error = 0
if not np.sum(y_pred > 0):
print("Results: NO Anomaly founded")
else:
print("Results: {} anomalies "
"({:.05f} total {})".format(num_error, mean_error,
len(x_test)))
result_record = {
'MODEL': model_type,
'KERNEL': kernel,
'STRIDE': stride,
'TRAIN_STATE': train_state,
'TRAIN': os.path.basename(train_file),
'TRAIN_SIZE': train_len,
'TEST_STATE': test_state,
'TEST': os.path.basename(test_file),
'TEST_LEN': test_len,
'NUM_SINGLE_ANOMALY': num_error,
'PCT_ANOMALY': mean_error,
'NUM_SAMPLE_ANOMALY': mean_only_error,
'NUM_SAMPLE': len(x_test),
'LABEL': train_state != test_state
}
result_array.append(result_record)
if save_result:
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
filename = os.path.join(
output_dir,
'results_single_{}'.format(size) + model_type + '.csv')
result_ds = pd.DataFrame(result_array)
result_ds.to_csv(filename, index=False)
