def run_lstm_performance_plot(file_path, result_path):
df_train = pd.read_csv(f'{file_path}/without_anom.csv')
features_list = ['Time', 'Route Index', 'GPS Distance', 'Longitude']
target_features_list = [
'CINR1 OMNI', 'Radio Distance', 'Barometer Altitude'
]
input_df_train = df_train[features_list]
target_df_train = df_train[target_features_list]
window_size = 2
# Step 1 : Clean train data set
input_df_train = clean_data(input_df_train)
target_df_train = clean_data(target_df_train)
# Step 2: Normalize the data
X_train, X_train_scaler = normalize_data(data=input_df_train,
scaler="min_max")
X_train_preprocessed = get_training_data_lstm(X_train, window_size)
Y_train, Y_train_scaler = normalize_data(
data=target_df_train, # target data
scaler="min_max")
Y_train_preprocessed = get_training_data_lstm(Y_train, window_size)
# Get the model which is created by user's parameters
lstm = get_lstm_autoencoder_model(timesteps=window_size,
input_features=input_df_train.shape[1],
target_features=target_df_train.shape[1],
encoding_dimension=8,
activation='relu',
loss='mean_squared_error',
optimizer='Adam')
history = lstm.fit(X_train_preprocessed,
Y_train_preprocessed,
epochs=5,
verbose=0).history
X_pred = lstm.predict(X_train_preprocessed, verbose=0)
mean_y_train = multi_mean(Y_train_preprocessed)
mean_x_pred = multi_mean(X_pred)
assert mean_y_train.shape == mean_x_pred.shape
for i, target_feature in enumerate(target_features_list):
title = "Training performance of LSTM for " + target_feature
plot_prediction_performance(Y_train=mean_y_train[:, i],
X_pred=mean_x_pred[:, i],
results_path=result_path,
title=title,
y_label="Sensor's Mean Value")
def execute_predict(flight_route,
test_data_path=None,
similarity_score=None,
threshold=None,
svr_model=None,
X_train_scaler=None,
results_path=None,
add_plots=True,
run_new_model=False,
X_train=None,
features_list=None,
target_features_list=None,
save_model=False,
Y_train_scaler=None,
Y_train=None,
window_size=None,
event=None):
"""
Execute predictions function for a specific flight route
:param flight_route: current flight route we should train on
:param test_data_path: the path of test data directory
:param similarity_score: similarity function
:param threshold: threshold from the train
:param svr_model: SVR model
:param X_train_scaler: normalization train input scalar
:param results_path: the path of results directory
:param add_plots: indicator whether to add plots or not
:param run_new_model: indicator whether current flow is new model creation or not
:param X_train: train input data frame
:param features_list: the list of features which the user chose for the input
:param target_features_list: the list of features which the user chose for the target
:param save_model: indicator whether the user want to save the model or not
:param Y_train_scaler: normalization train target scalar
:param Y_train: train target data frame
:param window_size: window size for each instance in training
:param event: running state flag
:return: tpr scores, fpr scores, acc scores, delay scores, routes duration, attack duration
"""
tpr_scores = defaultdict(list)
fpr_scores = defaultdict(list)
acc_scores = defaultdict(list)
delay_scores = defaultdict(list)
routes_duration = defaultdict(list)
attack_duration = defaultdict(list)
# Set a threshold in new model creation flow
if run_new_model:
event.wait()
threshold = predict_train_set(svr_model, X_train, save_model,
add_plots, threshold, features_list,
target_features_list, results_path,
flight_route, similarity_score,
X_train_scaler, Y_train, Y_train_scaler)
flight_dir = os.path.join(test_data_path, flight_route)
ATTACKS = get_subdirectories(flight_dir)
figures_results_path = os.path.join(results_path, "Figures")
create_directories(figures_results_path)
attacks_figures_results_path = os.path.join(figures_results_path,
"Attacks")
create_directories(attacks_figures_results_path)
# Iterate over all attacks in order to find anomalies
for attack in ATTACKS:
event.wait()
attack_name = attack
if "_" in attack_name:
attack_name = attack_name.split("_")[0]
current_attack_figures_results_path = os.path.join(
attacks_figures_results_path, attack_name)
create_directories(current_attack_figures_results_path)
attacks_path = os.path.join(
*[str(test_data_path),
str(flight_route),
str(attack)])
for flight_csv in os.listdir(f"{attacks_path}"):
flight_attack_path = os.path.join(
*[str(attacks_path), str(flight_csv)])
df_test_source = pd.read_csv(f"{flight_attack_path}")
Y_test_labels = df_test_source[[ATTACK_COLUMN]].values
Y_test_labels_preprocessed = svr_model._preprocess(
Y_test_labels, Y_test_labels)[1]
attack_time = len(Y_test_labels)
input_df_test = df_test_source[features_list]
target_df_test = df_test_source[target_features_list]
# Step 1 : Clean test data set
input_clean_df_test = clean_data(input_df_test)
target_clean_df_test = clean_data(target_df_test)
# Step 2: Normalize the data
X_test = X_train_scaler.transform(input_clean_df_test)
# Y_test = normalize_data(data=target_clean_df_test,
# scaler="power_transform")[0]
Y_test = Y_train_scaler.transform(target_clean_df_test)
Y_test_preprocessed = svr_model._preprocess(Y_test, Y_test)[1]
X_pred = svr_model.predict(X_test)
assert len(Y_test_preprocessed) == len(X_pred)
scores_test = []
for i, pred in enumerate(X_pred):
scores_test.append(
anomaly_score(Y_test_preprocessed[i], pred,
similarity_score))
# Add reconstruction error scatter if plots indicator is true
event.wait()
if add_plots:
plot_reconstruction_error_scatter(
scores=scores_test,
labels=Y_test_labels_preprocessed,
threshold=threshold,
plot_dir=current_attack_figures_results_path,
title=
f'Outlier Score Testing for {flight_csv} in {flight_route}({attack})'
)
for i, target_feature in enumerate(target_features_list):
title = "Test performance of SVR for " + target_feature + " feature in " + flight_csv
plot_prediction_performance(
Y_train=Y_test_preprocessed[:, i],
X_pred=X_pred[:, i],
results_path=current_attack_figures_results_path,
title=title)
predictions = [1 if x >= threshold else 0 for x in scores_test]
# Add roc curve if plots indicator is true
if add_plots:
pass
# plot_roc(y_true=Y_test,y_pred=predictions, plot_dir=results_path,title=f'ROC Curve - {flight_csv} in {flight_route}({attack})')
attack_start, attack_end = get_attack_boundaries(
df_test_source[ATTACK_COLUMN])
method_scores = get_method_scores(predictions,
attack_start,
attack_end,
add_window_size=True,
window_size=window_size)
tpr_scores[attack].append(method_scores[0])
fpr_scores[attack].append(method_scores[1])
acc_scores[attack].append(method_scores[2])
delay_scores[attack].append(method_scores[3])
routes_duration[attack].append(attack_time)
attack_duration[attack].append(method_scores[4])
return tpr_scores, fpr_scores, acc_scores, delay_scores, routes_duration, attack_duration
def predict_train_set(svr_model, X_train, save_model, add_plots, threshold,
features_list, target_features_list, results_path,
flight_route, similarity_score, X_train_scaler, Y_train,
Y_train_scaler):
"""
Execute prediction on the train data set
:param svr_model: SVR model
:param X_train: train input data frame
:param save_model: indicator whether the user want to save the model or not
:param add_plots: indicator whether to add plots or not
:param threshold: threshold from the train
:param features_list: the list of features which the user chose for the input
:param target_features_list: the list of features which the user chose for the target
:param results_path: the path of results directory
:param flight_route: current flight route we are working on
:param similarity_score: similarity function
:param X_train_scaler: train input normalization scalar
:param Y_train: train target data frame
:param Y_train_scaler: train target normalization scalar
:return: threshold
"""
X_pred = svr_model.predict(X_train)
scores_train = []
Y_train_preprocessed = svr_model._preprocess(Y_train, Y_train)[1]
assert len(Y_train_preprocessed) == len(X_pred)
for i, pred in enumerate(X_pred):
scores_train.append(
anomaly_score(Y_train_preprocessed[i], pred, similarity_score))
# choose threshold for which <MODEL_THRESHOLD_FROM_TRAINING_PERCENT> % of training were lower
threshold = get_threshold(scores_train, threshold)
figures_results_path = os.path.join(results_path, "Figures")
create_directories(figures_results_path)
if add_plots:
train_figures_results_path = os.path.join(figures_results_path,
"Train")
create_directories(train_figures_results_path)
for i, target_feature in enumerate(target_features_list):
title = "Training performance of SVR for " + target_feature + " in " + flight_route
plot_prediction_performance(
Y_train=Y_train_preprocessed[:, i],
X_pred=X_pred[:, i],
results_path=train_figures_results_path,
title=title)
# Save created model if the indicator is true
if save_model:
data = {}
data['features'] = features_list
data['target_features'] = target_features_list
data['threshold'] = threshold
data['params'] = get_svr_parameters_dictionary()
model_results_path = os.path.join(results_path, "model_data")
create_directories(model_results_path)
model_data_path = os.path.join(
*[str(model_results_path), 'model_data.json'])
with open(f"{model_data_path}", 'w') as outfile:
json.dump(data, outfile)
save_model_file_path = os.path.join(model_results_path,
flight_route + "_model.pkl")
with open(save_model_file_path, 'wb') as file:
pickle.dump(svr_model, file)
save_input_scaler_file_path = os.path.join(
model_results_path, flight_route + "_train_scaler.pkl")
with open(save_input_scaler_file_path, 'wb') as file:
pickle.dump(X_train_scaler, file)
save_target_scaler_file_path = os.path.join(
model_results_path, flight_route + "_target_scaler.pkl")
with open(save_target_scaler_file_path, 'wb') as file:
pickle.dump(Y_train_scaler, file)
return threshold
def predict_train_set(lstm, X_train, save_model, add_plots, threshold,
features_list, target_features_list, results_path,
flight_route, similarity_score, X_train_scaler, Y_train,
Y_train_scaler):
"""
Execute prediction on the train data set
:param lstm: LSTM model
:param X_train: train input data frame
:param save_model: indicator whether the user want to save the model or not
:param add_plots: indicator whether to add plots or not
:param threshold: threshold from the train
:param features_list: the list of features which the user chose for the input
:param target_features_list: the list of features which the user chose for the target
:param results_path: the path of results directory
:param flight_route: current flight route we are working on
:param similarity_score: similarity function
:param X_train_scaler: train input normalization scalar
:param Y_train: train target data frame
:param Y_train_scaler: train target normalization scalar
:return: threshold
"""
X_pred = lstm.predict(X_train, verbose=0)
scores_train = []
for i, pred in enumerate(X_pred):
scores_train.append(
anomaly_score_multi(Y_train[i], pred, similarity_score))
# choose threshold for which <LSTM_THRESHOLD_FROM_TRAINING_PERCENT> % of training were lower
threshold = get_threshold(scores_train, threshold)
figures_results_path = os.path.join(results_path, "Figures")
create_directories(figures_results_path)
if add_plots:
train_figures_results_path = os.path.join(figures_results_path,
"Train")
create_directories(train_figures_results_path)
mean_x_actual = multi_mean(Y_train)
mean_x_pred = multi_mean(X_pred)
assert mean_x_actual.shape == mean_x_pred.shape
for i, target_feature in enumerate(target_features_list):
title = "Training performance of LSTM for " + target_feature + " in " + flight_route
plot_prediction_performance(
Y_train=mean_x_actual[:, i],
X_pred=mean_x_pred[:, i],
results_path=train_figures_results_path,
title=title,
y_label="Sensor's Mean Value")
# Save created model if the indicator is true
if save_model:
data = {}
data['features'] = features_list
data['target_features'] = target_features_list
data['threshold'] = threshold
data['params'] = get_lstm_parameters_dictionary()
model_results_path = os.path.join(results_path, "model_data")
create_directories(model_results_path)
model_data_path = os.path.join(
*[str(model_results_path), 'model_data.json'])
with open(f"{model_data_path}", 'w') as outfile:
json.dump(data, outfile)
lstm_model_path = os.path.join(
*[str(model_results_path),
str(flight_route) + '.h5'])
lstm.save(f"{lstm_model_path}")
save_input_scaler_file_path = os.path.join(
model_results_path, flight_route + "_train_scaler.pkl")
with open(save_input_scaler_file_path, 'wb') as file:
pickle.dump(X_train_scaler, file)
save_target_scaler_file_path = os.path.join(
model_results_path, flight_route + "_target_scaler.pkl")
with open(save_target_scaler_file_path, 'wb') as file:
pickle.dump(Y_train_scaler, file)
return threshold
