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prediction_rnnmodel_scaled_covariates_s2.py
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"""
PREDICTION WITH ALL RNN MODELS, ALL PARAMETERS, AND MAX/ MIN RESAMPLED CHUNKS WHICH ARE MIN-MAX-SCALED
(MEDIAN RESAMPLED CHUNKS ARE USED AS COVARIATES)
This script assumes that there is already the subdirectory '/RNNModel' in the directory '/data'. If you want to
adjust which input size is taken and what parameters and models are used for the prediction, have a look at the six
variables from line 29 to 43.
Lastly, you have to install some packages:
pip3 install u8darts[torch] seaborn
"""
from darts import TimeSeries
from darts.dataprocessing.transformers import Scaler, MissingValuesFiller
from darts.models import RNNModel
import os
import pandas as pd
import pickle
import sys
import time
####################
# Adjust Variables #
####################
# Model type can be {'RNN', 'LSTM', 'GRU'}
model_types = ['RNN', 'LSTM', 'GRU']
# Parameter can be {'hr', 'bp', 'o2'}
parameters = ['hr', 'bp', 'o2']
# Number of chunks can be 1000, 2000 or 15000
n_chunks = 2000
# Style can be 'all' or '20_percent'
style = 'all'
# Define input length to imitate ARIMA training size (start after this length with forecast) and predict one data point
input_length = 12
output_length = 1
###########################################
# Create Folders, Init Variables & Models #
###########################################
approach = 'RNNModel'
# Create main folder for this script
if not os.path.isdir(f'./data/{approach}/{n_chunks}_chunks'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks')
if not os.path.isdir(f'./data/{approach}/{n_chunks}_chunks/{style}'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks/{style}')
# Create model-level confusion matrix
confusion_matrix_models = pd.DataFrame(
columns=['ID', 'PARAMETER', 'MODEL', 'ENDOGENOUS', 'EXOGENOUS', 'FIRST_FORECAST', 'ALARM_TYPE',
'FP', 'TP', 'FN', 'TN', 'N_HIGH_ALARMS', 'N_LOW_ALARMS', 'N_CHUNKS', 'N_ITERATIONS'])
endogenous_input_high = 'Max'
endogenous_input_low = 'Min'
endogenous_input = endogenous_input_high + '_' + endogenous_input_low
exogenous_input = 'Median'
if style == 'all':
n_windows = 5
elif style == '20_percent':
n_windows = 1
else:
raise ValueError('The style has to be "all" or "20_percent".')
# Note: Only use filler for now, remove after resampling script is fixed
filler = MissingValuesFiller()
for model_type in model_types:
print(f'\n##############################\nCurrent Model Type: {model_type}\n##############################\n',
file=sys.stderr)
# Create sub folder for each model type
if not os.path.isdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}')
# Create model per model type
model = RNNModel(model=model_type,
input_chunk_length=input_length,
output_chunk_length=output_length,
batch_size=input_length) # batch_size must be <= input_length (bug fixed in Darts version 0.9.0)
for parameter in parameters:
print(f'\n##############################\nCurrent Parameter: {parameter.upper()}\n'
f'##############################\n', file=sys.stderr)
start_time = time.time()
# Create sub folder for each parameter
if not os.path.isdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}')
# Create sub folder for the input type (max or min as endogenous variable)
if not os.path.isdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/{endogenous_input}'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/{endogenous_input}')
###############################
# Preprocess Resampled Chunks #
###############################
print('Read resampled series and extract chunks for training and prediction...', file=sys.stderr)
# Extract first n_chunks resampled series
resampled = pd.read_parquet(f'./data/resampling/resample_output_{parameter}_first{n_chunks}.parquet',
engine='pyarrow')
# Extract relevant chunks
relevant_series_endo_high, relevant_series_endo_low, relevant_series_exo = dict(), dict(), dict()
# Collect all series with minimal length
for chunk_id in pd.unique(resampled.CHUNK_ID_FILLED_TH):
current_series = resampled[resampled['CHUNK_ID_FILLED_TH'] == chunk_id]
# At least input_chunk_length + output_chunk_length = 12 + 1 = 13 data points are required
if len(current_series) > 12:
relevant_series_endo_high[chunk_id] = filler.transform(TimeSeries.from_dataframe(
df=current_series,
time_col='CHARTTIME',
value_cols=[f'VITAL_PARAMTER_VALUE_{endogenous_input_high.upper()}_RESAMPLING'],
freq='H'))
relevant_series_endo_low[chunk_id] = filler.transform(TimeSeries.from_dataframe(
df=current_series,
time_col='CHARTTIME',
value_cols=[f'VITAL_PARAMTER_VALUE_{endogenous_input_low.upper()}_RESAMPLING'],
freq='H'))
relevant_series_exo[chunk_id] = filler.transform(TimeSeries.from_dataframe(
df=current_series,
time_col='CHARTTIME',
value_cols=[f'VITAL_PARAMTER_VALUE_{exogenous_input.upper()}_RESAMPLING'],
freq='H'))
# Note: dict with relevant exogenous series contains same IDs
relevant_chunk_ids = list(relevant_series_endo_high.keys())
# Calculate number of chunks corresponding to 20% of chunks
twenty_percent = int((20 * len(relevant_chunk_ids)) / 100)
# Iterate five times different 20% of the chunks (= 5 windows) to predict all chunks
for window_idx in range(n_windows):
print(f'{window_idx}. window\n', file=sys.stderr)
# Extract 20% of series for prediction and catch last window to avoid ignoring chunks
if window_idx == 4:
pred_series_endo_high = {chunk_id: relevant_series_endo_high[chunk_id]
for chunk_id in list(relevant_series_endo_high)[twenty_percent * window_idx:]}
pred_series_endo_low = {chunk_id: relevant_series_endo_low[chunk_id]
for chunk_id in list(relevant_series_endo_low)[twenty_percent * window_idx:]}
pred_series_exo = {chunk_id: relevant_series_exo[chunk_id]
for chunk_id in list(relevant_series_exo)[twenty_percent * window_idx:]}
else:
pred_series_endo_high = {chunk_id: relevant_series_endo_high[chunk_id]
for chunk_id in list(relevant_series_endo_high)[
twenty_percent * window_idx:twenty_percent * (window_idx + 1)]}
pred_series_endo_low = {chunk_id: relevant_series_endo_low[chunk_id]
for chunk_id in list(relevant_series_endo_low)[
twenty_percent * window_idx:twenty_percent * (window_idx + 1)]}
pred_series_exo = {chunk_id: relevant_series_exo[chunk_id]
for chunk_id in list(relevant_series_exo)[
twenty_percent * window_idx:twenty_percent * (window_idx + 1)]}
# Extract 80% of series for training
train_series_endo_high = {chunk_id: relevant_series_endo_high[chunk_id] for chunk_id in relevant_chunk_ids
if chunk_id not in list(pred_series_endo_high.keys())}
train_series_endo_low = {chunk_id: relevant_series_endo_low[chunk_id] for chunk_id in relevant_chunk_ids
if chunk_id not in list(pred_series_endo_low.keys())}
train_series_exo = {chunk_id: relevant_series_exo[chunk_id] for chunk_id in relevant_chunk_ids
if chunk_id not in list(pred_series_exo.keys())}
# Define and fit scalers for training and prediction set
pred_scalers_high, pred_scalers_low = dict(), dict()
# Normalize values
for chunk_id in train_series_endo_high.keys():
current_scaler_endo_high = Scaler()
train_series_endo_high[chunk_id] = current_scaler_endo_high.fit_transform(train_series_endo_high[chunk_id])
current_scaler_endo_low = Scaler()
train_series_endo_low[chunk_id] = current_scaler_endo_low.fit_transform(train_series_endo_low[chunk_id])
current_scaler_exo = Scaler()
train_series_exo[chunk_id] = current_scaler_exo.fit_transform(train_series_exo[chunk_id])
for chunk_id in pred_series_endo_high.keys():
current_scaler_endo_high = Scaler()
pred_series_endo_high[chunk_id] = current_scaler_endo_high.fit_transform(pred_series_endo_high[chunk_id])
pred_scalers_high[chunk_id] = current_scaler_endo_high
current_scaler_endo_low = Scaler()
pred_series_endo_low[chunk_id] = current_scaler_endo_low.fit_transform(pred_series_endo_low[chunk_id])
pred_scalers_low[chunk_id] = current_scaler_endo_low
current_scaler_exo = Scaler()
pred_series_exo[chunk_id] = current_scaler_exo.fit_transform(pred_series_exo[chunk_id])
# Note: dicts with training and prediction chunks of other series have the same lengths
print(f'#Chunks for training: {len(train_series_endo_high)}', file=sys.stderr)
print(f'#Chunks for prediction: {len(pred_series_endo_high)}', file=sys.stderr)
# Save endogenous training dicts as pickle files
train_series_endo_high_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/01_train_series_endo_high_scaled_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(train_series_endo_high, train_series_endo_high_f, protocol=pickle.HIGHEST_PROTOCOL)
train_series_endo_high_f.close()
train_series_endo_low_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/01_train_series_endo_low_scaled_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(train_series_endo_low, train_series_endo_low_f, protocol=pickle.HIGHEST_PROTOCOL)
train_series_endo_low_f.close()
# Save exogenous training dict as pickle file
train_series_exo_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/01_train_series_exo_scaled_window{window_idx}.pickle', 'wb')
pickle.dump(train_series_exo, train_series_exo_f, protocol=pickle.HIGHEST_PROTOCOL)
train_series_exo_f.close()
# Save endogenous prediction dicts as pickle files
pred_series_endo_high_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/02_pred_series_endo_high_scaled_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(pred_series_endo_high, pred_series_endo_high_f, protocol=pickle.HIGHEST_PROTOCOL)
pred_series_endo_high_f.close()
pred_series_endo_low_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/02_pred_series_endo_low_scaled_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(pred_series_endo_low, pred_series_endo_low_f, protocol=pickle.HIGHEST_PROTOCOL)
pred_series_endo_low_f.close()
# Save exogenous prediction dict as pickle file
pred_series_exo_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/02_pred_series_exo_scaled_window{window_idx}.pickle', 'wb')
pickle.dump(pred_series_exo, pred_series_exo_f, protocol=pickle.HIGHEST_PROTOCOL)
pred_series_exo_f.close()
# Save scalers for chunks to predict as pickle file
pred_scalers_high_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/03_pred_scalers_high_window{window_idx}.pickle', 'wb')
pickle.dump(pred_scalers_high, pred_scalers_high_f, protocol=pickle.HIGHEST_PROTOCOL)
pred_scalers_high_f.close()
pred_scalers_low_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/03_pred_scalers_low_window{window_idx}.pickle', 'wb')
pickle.dump(pred_scalers_low, pred_scalers_low_f, protocol=pickle.HIGHEST_PROTOCOL)
pred_scalers_low_f.close()
###################
# Pre-train Model #
###################
print('Pre-train model for high alarm forecasting...', file=sys.stderr)
# Pre-train with 80% of relevant MAX series (steady training set)
param_model_high = model
param_model_high.fit(series=list(train_series_endo_high.values()),
covariates=list(train_series_exo.values()),
verbose=True)
# Save pre-trained model as pickle file
param_model_high_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/04_pre-trained_model_high_scaled_s2_window{window_idx}.pickle',
'wb')
pickle.dump(param_model_high, param_model_high_f, protocol=pickle.HIGHEST_PROTOCOL)
param_model_high_f.close()
print('Pre-train model for low alarm forecasting...', file=sys.stderr)
# Pre-train with 80% of relevant MIN series (steady training set)
param_model_low = model
param_model_low.fit(series=list(train_series_endo_low.values()),
covariates=list(train_series_exo.values()),
verbose=True)
# Save pre-trained model as pickle file
param_model_low_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/04_pre-trained_model_low_scaled_s2_window{window_idx}.pickle',
'wb')
pickle.dump(param_model_low, param_model_low_f, protocol=pickle.HIGHEST_PROTOCOL)
param_model_low_f.close()
confusion_matrix_chunks = pd.DataFrame(
columns=['CHUNK_ID', 'SCALING', 'PARAMETER', 'MODEL', 'ENDOGENOUS', 'EXOGENOUS', 'FIRST_FORECAST',
'ALARM_TYPE', 'FP', 'TP', 'FN', 'TN', 'N_HIGH_ALARMS', 'N_LOW_ALARMS', 'N_ITERATIONS'])
# Iterate chunk IDs we want to predict
for chunk_id in pred_series_endo_high.keys():
print(f'\n##############################\nCurrent Chunk ID: {chunk_id}\n##############################\n',
file=sys.stderr)
##############################
# Hourly Predict Chunk (MAX) #
##############################
print(f'High alarm forecasting:\n', file=sys.stderr)
# Load original pre-trained model
model_original_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/04_pre-trained_model_high_scaled_s2_window{window_idx}'
f'.pickle', 'rb')
model_for_iterations_high = pickle.load(model_original_f)
model_original_f.close()
# Create empty DataFrame for prediction result
# Note: Have to use DataFrame because append() function of TimeSeries do not work
final_pred_high = pd.DataFrame(columns=['Time', 'Value'])
# Do not iterate whole series-to-predict because of start length of 12 (first prediction is for time 13)
for iteration in range(len(pred_series_endo_high[chunk_id]) - input_length):
print(f'Iteration: {iteration}', file=sys.stderr)
# Predict one measurement
current_pred_high = model_for_iterations_high.predict(
n=output_length,
series=pred_series_endo_high[chunk_id][:input_length + iteration],
covariates=pred_series_exo[chunk_id][:input_length + iteration])
# Rescale predicted measurement
current_pred_high = pred_scalers_high[chunk_id].inverse_transform(current_pred_high)
# Add intermediate prediction result to DataFrame
final_pred_high = final_pred_high.append({'Time': current_pred_high.start_time(),
'Value': current_pred_high.first_value()},
ignore_index=True)
# Save final prediction of chunk as pickle file
final_pred_high_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/05_prediction_{chunk_id}_high_scaled_s2_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(final_pred_high, final_pred_high_f, protocol=pickle.HIGHEST_PROTOCOL)
final_pred_high_f.close()
##############################
# Hourly Predict Chunk (MIN) #
##############################
print(f'Low alarm forecasting:\n', file=sys.stderr)
# Load original pre-trained model
model_original_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/04_pre-trained_model_low_scaled_s2_window{window_idx}.pickle',
'rb')
model_for_iterations_low = pickle.load(model_original_f)
model_original_f.close()
# Create empty DataFrame for prediction result
# Note: Have to use DataFrame because append() function of TimeSeries do not work
final_pred_low = pd.DataFrame(columns=['Time', 'Value'])
# Do not iterate whole series-to-predict because of start length of 12 (first prediction is for time 13)
for iteration in range(len(pred_series_endo_low[chunk_id]) - input_length):
print(f'Iteration: {iteration}', file=sys.stderr)
# Predict one measurement
current_pred_low = model_for_iterations_low.predict(
n=output_length,
series=pred_series_endo_low[chunk_id][:input_length + iteration],
covariates=pred_series_exo[chunk_id][:input_length + iteration])
# Rescale predicted measurement
current_pred_low = pred_scalers_low[chunk_id].inverse_transform(current_pred_low)
# Add intermediate prediction result to DataFrame
final_pred_low = final_pred_low.append({'Time': current_pred_low.start_time(),
'Value': current_pred_low.first_value()},
ignore_index=True)
# Save final prediction of chunk as pickle file
final_pred_low_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}/'
f'{endogenous_input}/05_prediction_{chunk_id}_low_scaled_s2_window{window_idx}'
f'.pickle', 'wb')
pickle.dump(final_pred_low, final_pred_low_f, protocol=pickle.HIGHEST_PROTOCOL)
final_pred_low_f.close()
#####################################
# Fill Chunk-level Confusion Matrix #
#####################################
# Extract original chunk
original_chunk = resampled[resampled['CHUNK_ID_FILLED_TH'] == chunk_id].sort_values('CHARTTIME')
original_chunk = original_chunk[input_length:].reset_index()
# Add boolean indicating triggered high alarm for original value
original_chunk['HIGH_ALARM_TRIGGERED'] = False
original_chunk.loc[original_chunk[f'VITAL_PARAMTER_VALUE_{endogenous_input_high.upper()}_RESAMPLING']
> original_chunk['THRESHOLD_VALUE_HIGH'],
'HIGH_ALARM_TRIGGERED'] = True
# Add boolean indicating triggered low alarm original value
original_chunk['LOW_ALARM_TRIGGERED'] = False
original_chunk.loc[original_chunk[f'VITAL_PARAMTER_VALUE_{endogenous_input_low.upper()}_RESAMPLING']
< original_chunk['THRESHOLD_VALUE_LOW'],
'LOW_ALARM_TRIGGERED'] = True
# Add columns with predicted values to chunk
original_chunk['VALUE_PREDICTION_HIGH'] = final_pred_high.Value
original_chunk['VALUE_PREDICTION_LOW'] = final_pred_low.Value
# Add boolean indicating triggered high alarm for predicted value
original_chunk['HIGH_ALARM_TRIGGERED_PREDICTION'] = False
original_chunk.loc[original_chunk['VALUE_PREDICTION_HIGH']
> original_chunk['THRESHOLD_VALUE_HIGH'],
'HIGH_ALARM_TRIGGERED_PREDICTION'] = True
# Add boolean indicating triggered low alarm for predicted value
original_chunk['LOW_ALARM_TRIGGERED_PREDICTION'] = False
original_chunk.loc[original_chunk['VALUE_PREDICTION_LOW']
< original_chunk['THRESHOLD_VALUE_LOW'],
'LOW_ALARM_TRIGGERED_PREDICTION'] = True
# Get indices where booleans are false or true for high alarms
high_triggered = set(original_chunk.index[original_chunk['HIGH_ALARM_TRIGGERED']])
high_triggered_pred = set(original_chunk.index[original_chunk['HIGH_ALARM_TRIGGERED_PREDICTION']])
high_not_triggered = set(original_chunk.index[~original_chunk['HIGH_ALARM_TRIGGERED']])
high_not_triggered_pred = set(original_chunk.index[~original_chunk['HIGH_ALARM_TRIGGERED_PREDICTION']])
# Get indices where booleans are false or true for low alarms
low_triggered = set(original_chunk.index[original_chunk['LOW_ALARM_TRIGGERED']])
low_triggered_pred = set(original_chunk.index[original_chunk['LOW_ALARM_TRIGGERED_PREDICTION']])
low_not_triggered = set(original_chunk.index[~original_chunk['LOW_ALARM_TRIGGERED']])
low_not_triggered_pred = set(original_chunk.index[~original_chunk['LOW_ALARM_TRIGGERED_PREDICTION']])
# Fill confusion matrix for high threshold analysis
confusion_matrix_chunks = confusion_matrix_chunks.append({
'CHUNK_ID': chunk_id,
'SCALING': 'Min-Max',
'PARAMETER': parameter.upper(),
'MODEL': model_type,
'ENDOGENOUS': endogenous_input_high,
'EXOGENOUS': exogenous_input,
'FIRST_FORECAST': input_length,
'ALARM_TYPE': 'High',
# Following 4 metrics look at how many indices are shared
'TP': len(high_triggered.intersection(high_triggered_pred)),
'FN': len(high_triggered.intersection(high_not_triggered_pred)),
'FP': len(high_not_triggered.intersection(high_triggered_pred)),
'TN': len(high_not_triggered.intersection(high_not_triggered_pred)),
'N_HIGH_ALARMS': len(high_triggered),
'N_LOW_ALARMS': len(low_triggered),
'N_ITERATIONS': len(pred_series_endo_high[chunk_id]) - input_length
}, ignore_index=True)
# Fill confusion matrix for low threshold analysis
confusion_matrix_chunks = confusion_matrix_chunks.append({
'CHUNK_ID': chunk_id,
'SCALING': 'Min-Max',
'PARAMETER': parameter.upper(),
'MODEL': model_type,
'ENDOGENOUS': endogenous_input_low,
'EXOGENOUS': exogenous_input,
'FIRST_FORECAST': input_length,
'ALARM_TYPE': 'Low',
# Following 4 metrics look at how many indices are shared
'TP': len(low_triggered.intersection(low_triggered_pred)),
'FN': len(low_triggered.intersection(low_not_triggered_pred)),
'FP': len(low_not_triggered.intersection(low_triggered_pred)),
'TN': len(low_not_triggered.intersection(low_not_triggered_pred)),
'N_HIGH_ALARMS': len(high_triggered),
'N_LOW_ALARMS': len(low_triggered),
'N_ITERATIONS': len(pred_series_endo_low[chunk_id]) - input_length
}, ignore_index=True)
# Save chunk-level confusion matrix after all chunks are processed
confusion_matrix_chunks_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/confusion_matrix_chunks_'
f'{model_type}_{parameter}_{endogenous_input}_scaled_window{window_idx}'
f'_s2.pickle', 'wb')
pickle.dump(confusion_matrix_chunks, confusion_matrix_chunks_f, protocol=pickle.HIGHEST_PROTOCOL)
confusion_matrix_chunks_f.close()
#####################################
# Fill Model-level Confusion Matrix #
#####################################
# Collect chunk-level confusion matrices of all five windows
confusion_matrix_chunks_concat = pd.DataFrame(
columns=['CHUNK_ID', 'PARAMETER', 'MODEL', 'ENDOGENOUS', 'EXOGENOUS', 'FIRST_FORECAST', 'ALARM_TYPE', 'FP',
'TP', 'FN', 'TN', 'N_HIGH_ALARMS', 'N_LOW_ALARMS', 'N_ITERATIONS'])
for file in os.listdir(f'./data/{approach}/{n_chunks}_chunks/{style}/'):
if os.path.isfile(os.path.join(f'./data/{approach}/{n_chunks}_chunks/{style}/', file)) and \
file.startswith(f'confusion_matrix_chunks_{model_type}_{parameter}_{endogenous_input}_scaled') and \
file.endswith('_s2.pickle'):
current_chunk_matrix_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/{file}', 'rb')
current_chunk_matrix = pickle.load(current_chunk_matrix_f)
current_chunk_matrix_f.close()
confusion_matrix_chunks_concat = pd.concat([confusion_matrix_chunks_concat, current_chunk_matrix])
confusion_matrix_chunks_concat.reset_index(inplace=True, drop=True)
runtime = time.time() - start_time
# Fill model-level confusion matrix per parameter and model type (HIGH alarm forecasting)
confusion_matrix_chunks_concat_high = \
confusion_matrix_chunks_concat[confusion_matrix_chunks_concat['ALARM_TYPE'] == 'High']
confusion_matrix_models = confusion_matrix_models.append({
# RN = Vanilla RNN, LS = LSTM, GR = GRU
# 02 = model with covariates
# s2 = scaled with min-max score
# H = High
'ID': f'{parameter.upper()}_{model_type[:2]}_02_s2_H',
'PARAMETER': parameter.upper(),
'RUNTIME': runtime,
'MODEL': model_type,
'SCALING': 'Min-Max',
'LIBRARY': 'darts',
'ENDOGENOUS': endogenous_input_high,
'EXOGENOUS': exogenous_input,
'FIRST_FORECAST': input_length,
'ALARM_TYPE': 'High',
'FP': confusion_matrix_chunks_concat_high['FP'].sum(),
'TP': confusion_matrix_chunks_concat_high['TP'].sum(),
'FN': confusion_matrix_chunks_concat_high['FN'].sum(),
'TN': confusion_matrix_chunks_concat_high['TN'].sum(),
'N_HIGH_ALARMS': confusion_matrix_chunks_concat_high['N_HIGH_ALARMS'].sum(),
'N_LOW_ALARMS': confusion_matrix_chunks_concat_high['N_LOW_ALARMS'].sum(),
'N_CHUNKS': len(confusion_matrix_chunks_concat_high),
'N_ITERATIONS': confusion_matrix_chunks_concat_high['N_ITERATIONS'].sum()
}, ignore_index=True)
# Fill model-level confusion matrix per parameter and model type (LOW alarm forecasting)
confusion_matrix_chunks_concat_low = \
confusion_matrix_chunks_concat[confusion_matrix_chunks_concat['ALARM_TYPE'] == 'Low']
confusion_matrix_models = confusion_matrix_models.append({
# RN = Vanilla RNN, LS = LSTM, GR = GRU
# 02 = model with covariates
# s2 = scaled with min-max score
# L = Low
'ID': f'{parameter.upper()}_{model_type[:2]}_02_s2_L',
'PARAMETER': parameter.upper(),
'RUNTIME': runtime,
'MODEL': model_type,
'SCALING': 'Min-Max',
'LIBRARY': 'darts',
'ENDOGENOUS': endogenous_input_low,
'EXOGENOUS': exogenous_input,
'FIRST_FORECAST': input_length,
'ALARM_TYPE': 'Low',
'FP': confusion_matrix_chunks_concat_low['FP'].sum(),
'TP': confusion_matrix_chunks_concat_low['TP'].sum(),
'FN': confusion_matrix_chunks_concat_low['FN'].sum(),
'TN': confusion_matrix_chunks_concat_low['TN'].sum(),
'N_HIGH_ALARMS': confusion_matrix_chunks_concat_low['N_HIGH_ALARMS'].sum(),
'N_LOW_ALARMS': confusion_matrix_chunks_concat_low['N_LOW_ALARMS'].sum(),
'N_CHUNKS': len(confusion_matrix_chunks_concat_low),
'N_ITERATIONS': confusion_matrix_chunks_concat_low['N_ITERATIONS'].sum()
}, ignore_index=True)
# Save model-level confusion matrix after all model types and parameter are processed
# Note: adjust path name if you want to execute this script in parallel with different parameters/ model types
confusion_matrix_models_f = open(f'./data/{approach}/{n_chunks}_chunks/{style}/confusion_matrix_models_scaled_'
f'{endogenous_input}_s2.pickle', 'wb')
pickle.dump(confusion_matrix_models, confusion_matrix_models_f, protocol=pickle.HIGHEST_PROTOCOL)
confusion_matrix_models_f.close()
print('\nFinished.', file=sys.stderr)
sys.stderr.close()