def main():
configs = json.load(open('Configuration.json', 'r'))
epochs = configs['training']['epochs']
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path +
"TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series,
dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
##start working per outcome
for outcome in outcomes:
decision_maker = DecisionMaker()
X_train_y0, y_train1, X_valid_y0, X_valid_y1, X_valid, y_val1, X_test, y_test1, timesteps, n_features=\
process_data(normalized_timeseries, non_smotedtime_series, outcome, grouping, non_smotedtime_series[grouping], lookback)
def main():
configs = json.load(open('Configuration.json', 'r'))
epochs = configs['training']['epochs']
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
saved_models_path = configs['paths']['saved_models_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path + "TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series, dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
##start working per outcome
for outcome in outcomes:
decision_maker = DecisionMaker()
fold_ind, train_ind, test_ind = get_train_test_split(non_smotedtime_series[outcome].astype(int),
non_smotedtime_series[grouping])
X_train_y0, X_valid_y0, X_valid, y_valid, X_test, y_test, timesteps,\
n_features = \
process_data(normalized_timeseries, non_smotedtime_series, outcome, grouping, lookback,
train_ind, test_ind)
autoencoder = LSTMAutoEncoder(configs['model']['name'] + outcome, outcome, timesteps, n_features)
autoencoder.summary()
autoencoder.fit(X_train_y0, X_train_y0, epochs,lookback,X_valid_y0,X_valid_y0,2)
###save model
filename = saved_models_path+ configs['model']['name'] + outcome+ '.h5'
autoencoder.save_model(filename)
####LSTM autoencoder
autoencoder.plot_history()
test_x_predictions = autoencoder.predict(X_test)
mse = np.mean(np.power(flatten(X_test) - flatten(test_x_predictions), 2), axis=1)
test_error_df = pd.DataFrame({'Reconstruction_error' : mse,
'True_class' : y_test.tolist()})
pred_y, best_threshold, precision_rt, recall_rt= \
autoencoder.predict_binary(test_error_df.True_class, test_error_df.Reconstruction_error)
autoencoder.output_performance(test_error_df.True_class, test_error_df.Reconstruction_error,pred_y)
autoencoder.plot_reconstruction_error(test_error_df, best_threshold)
autoencoder.plot_roc(test_error_df)
autoencoder.plot_pr(precision_rt, recall_rt)
def main():
configs = json.load(open('Configuration.json', 'r'))
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
static_features = configs['data']['static_columns']
targets = configs['data']['classification_target']
timeseries_path = configs['paths']['data_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path +
"TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series,
dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
risk_score_visualiser = Visualiser(normalized_timeseries,
non_smotedtime_series, dynamic_features,
static_features)
for target in targets:
risk_score_visualiser.plot_risk_scores(target)
def main():
configs = json.load(open('Configuration.json', 'r'))
grouping = configs['data']['grouping']
static_features = configs['data']['static_columns']
dynamic_features = configs['data']['dynamic_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
##read, impute and scale dataset
##start working per outcome
for outcome in outcomes:
time_series = pd.read_csv(timeseries_path + "SMOTEDTimeSeries/" +
outcome + "StackedTimeSeries1Day.csv")
time_series[dynamic_features] = impute(time_series, dynamic_features)
normalised_series = scale(time_series, dynamic_features)
normalised_series.insert(0, grouping, time_series[grouping])
normalised_series.insert(len(normalised_series.columns), outcome,
time_series[outcome])
normalised_series = curve_shift(normalised_series,
grouping,
outcome,
shift_by=lookback - 1)
decision_maker = DecisionMaker()
#train/test and validation sets
X_cols = (normalised_series.columns).tolist()
X_cols.remove(outcome)
X_cols.remove(grouping)
input_X = normalised_series.loc[:,
normalised_series.columns.isin(
X_cols
)].values # converts the df to a numpy array
input_y = normalised_series[outcome].values
n_features = input_X.shape[1] # number of features
X, y = temporalize(X=input_X, y=input_y, lookback=lookback)
X_train, X_test, y_train, y_test = train_test_split(np.array(X),
np.array(y),
test_size=0.33,
random_state=SEED,
stratify=y)
X_train, X_valid, y_train, y_valid = train_test_split(
X_train,
y_train,
test_size=0.33,
random_state=SEED,
stratify=y_train)
X_train = X_train.reshape(X_train.shape[0], lookback, n_features)
X_valid = X_valid.reshape(X_valid.shape[0], lookback, n_features)
X_test = X_test.reshape(X_test.shape[0], lookback, n_features)
distrs_percents = [
get_distribution_percentages(
(normalised_series[outcome]).astype(int))
]
scaler = StandardScaler().fit(flatten(X_train))
a = flatten(X_train)
print('colwise mean', np.mean(a, axis=0).round(6))
print('colwise variance', np.var(a, axis=0))
X_valid_scaled = Models.LSTMAutoEncoder.Utils.scale(X_valid, scaler)
X_test_scaled = Models.LSTMAutoEncoder.Utils.scale(X_test, scaler)
timesteps = X_train.shape[1] # equal to the lookback
n_features = X_train.shape[2] # 59
epochs = 100
lr = 0.0001
lstm_autoencoder = Sequential()
# Encoder
lstm_autoencoder.add(
LSTM(32,
activation='relu',
input_shape=(timesteps, n_features),
return_sequences=True))
lstm_autoencoder.add(
LSTM(16, activation='relu', return_sequences=False))
lstm_autoencoder.add(RepeatVector(timesteps))
# Decoder
lstm_autoencoder.add(LSTM(16, activation='relu',
return_sequences=True))
lstm_autoencoder.add(LSTM(32, activation='relu',
return_sequences=True))
lstm_autoencoder.add(TimeDistributed(Dense(n_features)))
lstm_autoencoder.summary()
adam = optimizers.Adam(lr)
lstm_autoencoder.compile(loss='mse', optimizer=adam)
cp = ModelCheckpoint(filepath="lstm_autoencoder_classifier.h5",
save_best_only=True,
verbose=0)
tb = TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_images=True)
lstm_autoencoder_history = lstm_autoencoder.fit(
X_train,
X_train,
epochs=epochs,
batch_size=lookback,
validation_data=(X_valid, X_train),
verbose=2).history
#print(distrs_percents)
####LSTM autoencoder
plt.figure(figsize=(10, 10))
plt.plot(lstm_autoencoder_history['loss'], linewidth=2, label='Train')
plt.plot(lstm_autoencoder_history['val_loss'],
linewidth=2,
label='Valid')
plt.legend(loc='upper right')
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.savefig("LossOverEpochsSMOTE.pdf", bbox_inches='tight')
plt.figure(figsize=(10, 10))
valid_x_predictions = lstm_autoencoder.predict(X_valid_scaled)
mse = np.mean(np.power(
flatten(X_valid_scaled) - flatten(valid_x_predictions), 2),
axis=1)
error_df = pd.DataFrame({
'Reconstruction_error': mse,
'True_class': y_valid.tolist()
})
precision_rt, recall_rt, threshold_rt = precision_recall_curve(
error_df.True_class, error_df.Reconstruction_error)
plt.plot(threshold_rt,
precision_rt[1:],
label="Precision",
linewidth=5)
plt.plot(threshold_rt, recall_rt[1:], label="Recall", linewidth=5)
plt.title('Precision and recall for different threshold values')
plt.xlabel('Threshold')
plt.ylabel('Precision/Recall')
plt.legend()
plt.savefig(outcome + "ThresholdSMOTE.pdf", bbox_inches='tight')
test_x_predictions = lstm_autoencoder.predict(X_test_scaled)
mse = np.mean(np.power(
flatten(X_test_scaled) - flatten(test_x_predictions), 2),
axis=1)
error_df = pd.DataFrame({
'Reconstruction_error': mse,
'True_class': y_test.tolist()
})
plt.figure(figsize=(10, 10))
threshold_fixed = 0.3
groups = error_df.groupby('True_class')
fig, ax = plt.subplots()
for name, group in groups:
ax.plot(group.index,
group.Reconstruction_error,
marker='o',
ms=3.5,
linestyle='',
label="Break" if name == 1 else "Normal")
ax.hlines(threshold_fixed,
ax.get_xlim()[0],
ax.get_xlim()[1],
colors="r",
zorder=100,
label='Threshold')
ax.legend()
plt.title("Reconstruction error for different classes")
plt.ylabel("Reconstruction error")
plt.xlabel("Data point index")
plt.savefig(outcome + "ReconstructionerrorSMOTE.pdf",
bbox_inches='tight')
pred_y = [
1 if e > threshold_fixed else 0
for e in error_df.Reconstruction_error.values
]
conf_matrix = confusion_matrix(error_df.True_class, pred_y)
plt.figure(figsize=(6, 6))
sns.heatmap(conf_matrix,
xticklabels=LABELS,
yticklabels=LABELS,
annot=True,
fmt="d")
plt.title("Confusion matrix")
plt.ylabel('True class')
plt.xlabel('Predicted class')
plt.savefig(outcome + "ConfusionMatrixSMOTE.pdf", bbox_inches='tight')
false_pos_rate, true_pos_rate, thresholds = roc_curve(
error_df.True_class, error_df.Reconstruction_error)
roc_auc = auc(
false_pos_rate,
true_pos_rate,
)
plt.figure(figsize=(10, 10))
plt.plot(false_pos_rate,
true_pos_rate,
linewidth=5,
label='AUC = %0.3f' % roc_auc)
plt.plot([0, 1], [0, 1], linewidth=5)
plt.xlim([-0.01, 1])
plt.ylim([0, 1.01])
plt.legend(loc='lower right')
plt.title('Receiver operating characteristic curve (ROC)')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig(outcome + "rocSMOTE.pdf", bbox_inches='tight')
precision, recall, thresholds = precision_recall_curve(
error_df.True_class, error_df.Reconstruction_error)
pr_auc = auc(recall, precision)
plt.figure(figsize=(10, 10))
plt.plot(recall, precision, linewidth=5, label='AUC = %0.3f' % pr_auc)
plt.plot([0, 1], [0, 1], linewidth=5)
plt.xlim([-0.01, 1])
plt.ylim([0, 1.01])
plt.legend(loc='lower right')
plt.title('Receiver operating characteristic curve (ROC)')
plt.ylabel('Precision')
plt.xlabel('Recall')
plt.savefig(outcome + "precision_recall_aucSMOTE.pdf",
bbox_inches='tight')
def main():
configs = json.load(open('Configuration.json', 'r'))
epochs = configs['training']['epochs']
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
static_features = configs['data']['static_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
autoencoder_models_path = configs['paths']['autoencoder_models_path']
test_data_path = configs['paths']['test_data_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path +
"TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series,
dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
#intialise classification report which will house results of all outcomes
classification_report = ClassificationReport()
#save lstm performance for comparison with final outcome
lstm_praucs = []
##start working per outcome
for outcome in outcomes:
fold_ind, train_ind, test_ind = get_train_test_split(
non_smotedtime_series[outcome].astype(int),
non_smotedtime_series[grouping])
##Load LSTM models if they exist, otherwise train new models and save them
autoencoder_filename = autoencoder_models_path + configs['model'][
'name'] + outcome + '.h5'
X_train, X_train_y0, X_valid_y0, X_valid, y_valid, X_test, y_test, timesteps, \
n_features = \
process_data(normalized_timeseries, non_smotedtime_series, outcome, grouping, lookback,
train_ind, test_ind)
if ("3D" not in outcome):
if os.path.isfile(autoencoder_filename):
print(" Autoencoder trained model exists for oucome", outcome,
"file:", autoencoder_filename)
autoencoder = LSTMAutoEncoder(configs['model']['name'] +
outcome,
outcome,
timesteps,
n_features,
saved_model=autoencoder_filename)
autoencoder.summary()
else:
print("Autencoder trained model does not exist for outcome",
outcome, "file:", autoencoder_filename)
autoencoder = LSTMAutoEncoder(
configs['model']['name'] + outcome, outcome, timesteps,
n_features)
autoencoder.summary()
autoencoder.fit(X_train_y0, X_train_y0, epochs, lookback,
X_valid_y0, X_valid_y0, 2)
autoencoder.plot_history()
train_x_predictions = autoencoder.predict(X_train)
mse_train = np.mean(np.power(
lstm_flatten(X_train) - lstm_flatten(train_x_predictions), 2),
axis=1)
test_x_predictions = autoencoder.predict(X_test)
mse_test = np.mean(np.power(
lstm_flatten(X_test) - lstm_flatten(test_x_predictions), 2),
axis=1)
test_error_df = pd.DataFrame({
'Reconstruction_error': mse_test,
'True_class': y_test.tolist()
})
pred_y, best_threshold, precision_rt, recall_rt = \
autoencoder.predict_binary(test_error_df.True_class, test_error_df.Reconstruction_error)
autoencoder.output_performance(test_error_df.True_class, pred_y)
autoencoder.plot_reconstruction_error(test_error_df,
best_threshold)
autoencoder.plot_roc(test_error_df)
autoencoder.plot_pr(precision_rt, recall_rt)
lstm_prauc = auc(recall_rt, precision_rt)
lstm_praucs.append(lstm_prauc)
#Feature Selector
training_loc = train_ind[0] #+train_ind[1]
training_ids = non_smotedtime_series.iloc[training_loc]
training_ids = training_ids[grouping]
testing_ids = non_smotedtime_series.iloc[test_ind[1]]
testing_ids = testing_ids[grouping]
flat_df, timesteps = flatten(non_smotedtime_series,
dynamic_features, grouping,
static_features, outcome)
temporal_features = set(flat_df.columns) - set(static_features)
temporal_features = set(temporal_features) - set(
[outcome, grouping])
X_train = flat_df.loc[flat_df[grouping].isin(training_ids)]
y_train = X_train[outcome].astype(int)
training_groups = X_train[grouping]
X_train_static = X_train[static_features]
X_train_static[grouping] = training_groups
X_train = X_train[temporal_features]
X_test = flat_df.loc[flat_df[grouping].isin(testing_ids)]
y_test = X_test[outcome].astype(int)
testing_groups = X_test[grouping]
X_test_static = X_test[static_features]
X_test_static.loc[grouping] = testing_groups
X_test = X_test[temporal_features]
########
aggregate_df = generate_aggregates(X_train, temporal_features,
grouping, training_groups)
static_aggregate_train_df = pd.concat(
[aggregate_df, X_train_static], axis=1, join='inner')
static_aggregate_train_df = static_aggregate_train_df.loc[:,
~static_aggregate_train_df
.columns.
duplicated(
)]
static_aggregate_train_df.drop(columns=[grouping],
inplace=True,
axis=1)
static_aggregate_train_df['mse'] = mse_train
aggregate_df_test = generate_aggregates(X_test, temporal_features,
grouping, testing_groups)
static_aggregate_test_df = pd.concat(
[aggregate_df_test, X_test_static], axis=1, join='inner')
static_aggregate_test_df = static_aggregate_test_df.loc[:,
~static_aggregate_test_df
.columns.
duplicated(
)]
static_aggregate_test_df.drop(columns=[grouping],
inplace=True,
axis=1)
static_aggregate_test_df['mse'] = mse_test
static_aggregate_test_df.to_csv("static_aggretate.csv",
index=False)
static_baseline_classifier = XGBoostClassifier(
static_aggregate_train_df, y_train, outcome, grouping)
static_baseline_classifier.fit("aggregate_static", mse_train * 100)
y_pred_binary, best_threshold, precision_rt, recall_rt, yhat = \
static_baseline_classifier.predict(static_aggregate_test_df, y_test)
print(" CLASS WEIGHTS FOR Y ACTUAL: ", class_counts(y_test))
print(" CLASS WEIGHTS FOR Y PREDICTE: ",
class_counts(y_pred_binary))
static_baseline_classifier.output_performance(
y_test, y_pred_binary)
static_baseline_classifier.plot_pr(precision_rt, recall_rt,
"XGBoost Static")
static_baseline_classifier.plot_feature_importance(
static_aggregate_test_df.columns)
to_write_for_plotting = static_aggregate_test_df
to_write_for_plotting['outcome'] = y_test
to_write_for_plotting.to_csv(test_data_path + outcome + ".csv",
index=False)
#add to classification report
classification_report.add_model_result(outcome, y_test,
y_pred_binary,
best_threshold,
precision_rt, recall_rt,
yhat)
#delete variables
del static_aggregate_train_df
del static_aggregate_test_df
del X_train
del X_train_y0
del X_valid_y0
del X_valid
del y_valid
del X_test
del y_test
del timesteps
del train_x_predictions
del test_x_predictions
del test_error_df
#risk_score_visualiser = Visualiser(normalized_timeseries, non_smotedtime_series,
# dynamic_features, static_features
# )
#After fitting model to all outcomes, plot and get summary statistics
classification_report.plot_distributions_vs_aucs()
classification_report.plot_pr_auc()
classification_report.plot_auc()
classification_report.compare_lstim_xgboost(lstm_praucs)
def main():
configs = json.load(open('Configuration.json', 'r'))
epochs = configs['training']['epochs']
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
static_features = configs['data']['static_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
saved_models_path = configs['paths']['saved_models_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path +
"TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series,
dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
##start working per outcome
for outcome in outcomes:
decision_maker = DecisionMaker()
fold_ind, train_ind, test_ind = get_train_test_split(
non_smotedtime_series[outcome].astype(int),
non_smotedtime_series[grouping])
##Load LSTM models if they exist, otherwise train new models and save them
filename = saved_models_path + configs['model'][
'name'] + outcome + '.h5'
X_train, X_train_y0, X_valid_y0, X_valid, y_valid, X_test, y_test, timesteps, \
n_features = \
process_data(normalized_timeseries, non_smotedtime_series, outcome, grouping, lookback,
train_ind, test_ind)
if os.path.isfile(filename):
autoencoder = LSTMAutoEncoder(configs['model']['name'] + outcome,
outcome,
timesteps,
n_features,
saved_model=filename)
autoencoder.summary()
else:
autoencoder = LSTMAutoEncoder(configs['model']['name'] + outcome,
outcome, timesteps, n_features)
autoencoder.summary()
autoencoder.fit(X_train_y0, X_train_y0, epochs, lookback,
X_valid_y0, X_valid_y0, 2)
autoencoder.plot_history()
###save model
filename = saved_models_path + configs['model'][
'name'] + outcome + '.h5'
autoencoder.save_model(filename)
####Predicting using the fitted model (loaded or trained)
train_x_predictions = autoencoder.predict(X_train)
mse_train = np.mean(np.power(
lstm_flatten(X_train) - lstm_flatten(train_x_predictions), 2),
axis=1)
test_x_predictions = autoencoder.predict(X_test)
mse_test = np.mean(np.power(
lstm_flatten(X_test) - lstm_flatten(test_x_predictions), 2),
axis=1)
test_error_df = pd.DataFrame({
'Reconstruction_error': mse_test,
'True_class': y_test.tolist()
})
pred_y, best_threshold, precision_rt, recall_rt = \
autoencoder.predict_binary(test_error_df.True_class, test_error_df.Reconstruction_error)
autoencoder.output_performance(test_error_df.True_class,
test_error_df.Reconstruction_error,
pred_y)
autoencoder.plot_reconstruction_error(test_error_df, best_threshold)
autoencoder.plot_roc(test_error_df)
autoencoder.plot_pr(precision_rt, recall_rt)
#Feature Selector
training_loc = train_ind[0] #+train_ind[1]
training_ids = non_smotedtime_series.iloc[training_loc]
training_ids = training_ids[grouping]
testing_ids = non_smotedtime_series.iloc[test_ind[1]]
testing_ids = testing_ids[grouping]
flat_df, timesteps = flatten(non_smotedtime_series, dynamic_features,
grouping, static_features, outcome)
temporal_features = set(flat_df.columns) - set(static_features)
temporal_features = set(temporal_features) - set([outcome, grouping])
X_train = flat_df.loc[flat_df[grouping].isin(training_ids)]
y_train = X_train[outcome].astype(int)
training_groups = X_train[grouping]
X_train_static = X_train[static_features]
X_train_static.loc[grouping] = training_groups
X_train = X_train[temporal_features]
X_train = scale(X_train, temporal_features)
X_train['mse'] = mse_train
#X_train, y_train = smote(X_train, y_train)
X_test = flat_df.loc[flat_df[grouping].isin(testing_ids)]
y_test = X_test[outcome].astype(int)
testing_groups = X_test[grouping]
X_test_static = X_test[static_features]
X_test_static.loc[grouping] = testing_groups
X_test = X_test[temporal_features]
X_test = scale(X_test, temporal_features)
X_test['mse'] = mse_test
feature_selector = XGBoostClassifier(X_train, y_train, outcome,
grouping) #
feature_selector.fit("temporal", training_groups)
y_pred_binary, best_threshold, precision_rt, recall_rt = feature_selector.predict(
X_test, y_test)
feature_selector.plot_pr(precision_rt, recall_rt, "XGBoost Temporal")
featuredf = pd.DataFrame()
temporal_features = set(temporal_features) - set([outcome])
featuredf['features'] = list(temporal_features)
#featuredf['imp'] = fs_fi
#featuredf = featuredf[featuredf['imp'] > 0]
########
baseline_features = featuredf['features']
baseline_features = set(
[x.partition('_')[0] for x in list(baseline_features)])
baseline_features = [x + "_0" for x in list(baseline_features)]
baseline_features.insert(0, grouping)
baseline_static_features = baseline_features + static_features
slopes_df = generate_slopes(X_train, temporal_features,
static_features, grouping, training_groups)
aggregate_df = generate_aggregates(X_train, temporal_features,
grouping, training_groups)
slopes_static_baseline_train_df = pd.concat(
[slopes_df, X_train_static], axis=1, join='inner')
slopes_static_baseline_train_df = slopes_static_baseline_train_df.loc[:,
~slopes_static_baseline_train_df
.
columns
.
duplicated(
)]
slopes_static_baseline_train_groups = slopes_static_baseline_train_df[
grouping]
slopes_static_baseline_train_df.drop(columns=[grouping],
inplace=True,
axis=1)
slopes_static_baseline_train_df['mse'] = mse_train
slopes_df_test = generate_slopes(X_test, temporal_features,
static_features, grouping,
testing_groups)
slopes_static_baseline_test_df = pd.concat(
[slopes_df_test, X_test_static], axis=1, join='inner')
slopes_static_baseline_test_df = slopes_static_baseline_test_df.loc[:,
~slopes_static_baseline_test_df
.
columns
.
duplicated(
)]
slopes_static_baseline_test_groups = slopes_static_baseline_test_df[
grouping]
slopes_static_baseline_test_df.drop(columns=[grouping],
inplace=True,
axis=1)
slopes_static_baseline_test_df['mse'] = mse_test
slopes_static_baseline_classifier = XGBoostClassifier(
slopes_static_baseline_train_df, y_train, outcome, grouping)
#bs_y, bs_ths, bs_id, bs_fi = slopes_static_baseline_classifier.fit("baseline_static_slope",
# slopes_static_baseline_train_groups)
slopes_static_baseline_classifier.fit(
"baseline_static_slope", slopes_static_baseline_train_groups)
y_pred_binary, best_threshold, precision_rt, recall_rt = \
slopes_static_baseline_classifier.predict( slopes_static_baseline_test_df, y_test)
slopes_static_baseline_classifier.plot_pr(precision_rt, recall_rt,
"XGBoost Static")
def main():
configs = json.load(open('Configuration.json', 'r'))
grouping = configs['data']['grouping']
dynamic_features = configs['data']['dynamic_columns']
outcomes = configs['data']['classification_outcome']
lookback = configs['data']['batch_size']
timeseries_path = configs['paths']['data_path']
autoencoder_path = configs['paths']['autoencoder_path']
##read, impute and scale dataset
non_smotedtime_series = pd.read_csv(timeseries_path +
"TimeSeriesAggregatedUpto0.csv")
non_smotedtime_series[dynamic_features] = impute(non_smotedtime_series,
dynamic_features)
normalized_timeseries = scale(non_smotedtime_series, dynamic_features)
normalized_timeseries.insert(0, grouping, non_smotedtime_series[grouping])
##start working per outcome
for outcome in outcomes:
decision_maker = DecisionMaker()
X_train_y0, X_valid_y0, X_valid, y_valid, X_test, y_test, timesteps, n_features =\
process_data(normalized_timeseries, non_smotedtime_series, outcome, grouping, non_smotedtime_series[grouping], lookback)
epochs = 100
autoencoder = LSTMAutoEncoder(configs['model']['name'] + outcome,
outcome, timesteps, n_features)
autoencoder.summary()
cp = ModelCheckpoint(filepath="lstm_autoencoder_classifier.h5",
save_best_only=True,
verbose=0)
tb = TensorBoard(log_dir='./logs',
histogram_freq=0,
write_graph=True,
write_images=True)
autoencoder.fit(X_train_y0, X_train_y0, epochs, lookback, X_valid_y0,
X_valid_y0, 2)
####LSTM autoencoder
autoencoder.plot_history()
valid_x_predictions = autoencoder.predict(X_valid)
mse = np.mean(np.power(
flatten(X_valid) - flatten(valid_x_predictions), 2),
axis=1)
error_df = pd.DataFrame({
'Reconstruction_error': mse,
'True_class': y_valid.tolist()
})
precision_rt, recall_rt, threshold_rt = precision_recall_curve(
error_df.True_class, error_df.Reconstruction_error)
fscore = (2 * precision_rt * recall_rt) / (precision_rt + recall_rt)
ix = np.argmax(fscore)
best_threshold = threshold_rt[ix]
# print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], fscore[ix]))
pred_y = (error_df.Reconstruction_error >
best_threshold).astype('int32')
perf_df = pd.DataFrame()
perf_dict = performance_metrics(error_df.True_class, pred_y,
error_df.Reconstruction_error)
perf_df = perf_df.append(perf_dict, ignore_index=True)
perf_df.to_csv(autoencoder_path + "performancemetrics" + outcome +
".csv",
index=False)
test_x_predictions = autoencoder.predict(X_test)
mse = np.mean(np.power(
flatten(X_test) - flatten(test_x_predictions), 2),
axis=1)
error_df = pd.DataFrame({
'Reconstruction_error': mse,
'True_class': y_test.tolist()
})
plt.figure(figsize=(10, 10))
groups = error_df.groupby('True_class')
fig, ax = plt.subplots()
for name, group in groups:
ax.plot(group.index,
group.Reconstruction_error,
marker='o',
ms=3.5,
linestyle='',
label="1" if name == 1 else "0")
ax.hlines(threshold_rt[ix],
ax.get_xlim()[0],
ax.get_xlim()[1],
colors="r",
zorder=100,
label='Threshold')
ax.legend()
plt.title("Reconstruction error for different classes")
plt.ylabel("Reconstruction error")
plt.xlabel("Data point index")
plt.savefig(autoencoder_path + outcome + "Reconstructionerror.pdf",
bbox_inches='tight')
false_pos_rate, true_pos_rate, thresholds = roc_curve(
error_df.True_class, error_df.Reconstruction_error)
roc_auc = auc(
false_pos_rate,
true_pos_rate,
)
plt.figure(figsize=(10, 10))
plt.plot(false_pos_rate,
true_pos_rate,
linewidth=5,
label='AUC = %0.3f' % roc_auc)
plt.plot([0, 1], [0, 1], linewidth=5)
plt.xlim([-0.01, 1])
plt.ylim([0, 1.01])
plt.legend(loc='lower right')
plt.title('Receiver operating characteristic curve (ROC)')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig(autoencoder_path + outcome + "roc.pdf",
bbox_inches='tight')
pr_auc = auc(recall_rt, precision_rt)
plt.figure(figsize=(10, 10))
plt.plot(recall_rt,
precision_rt,
linewidth=5,
label='PR-AUC = %0.3f' % pr_auc)
plt.plot([0, 1], [1, 0], linewidth=5)
plt.xlim([-0.01, 1])
plt.ylim([0, 1.01])
plt.legend(loc='lower right')
plt.title('Precision Recall Curive')
plt.ylabel('Precision')
plt.xlabel('Recall')
plt.savefig(autoencoder_path + outcome + "precision_recall_auc.pdf",
bbox_inches='tight')
