def get_predictions(context,model,X,y,train_scaler,batch_size,look_ahead,look_back,epochs,experiment_id):
predictions = model.predict(X, batch_size=batch_size)
# rescale
predictions = train_scaler.inverse_transform(predictions)
y = train_scaler.inverse_transform(y)
# extract first timestep for true values
y_true = y[:, 0].flatten()
# diagonals contains a reading's values calculated at different points in time
diagonals = util.get_diagonals(predictions)
# the top left and bottom right predictions do not contain predictions for all timesteps
# fill the missing prediction values in diagonals. curenttly using the first predicted value for all missing timesteps
for idx, diagonal in enumerate(diagonals):
diagonal = diagonal.flatten()
# missing value filled with the first value
diagonals[idx] = np.hstack((diagonal, np.full(look_ahead - len(diagonal), diagonal[0])))
predictions_timesteps = np.asarray(diagonals)
shifted_1 = util.shift_time_series(y_true, 1)
title = "Prediction on %s data. %d epochs, look back %d, look_ahead %d & batch_size %d." % (
context, epochs, look_back, look_ahead, batch_size)
path = "%s/%s/"%("imgs",experiment_id)
make_plots(context,predictions_timesteps,y_true,look_ahead,title,path,cfg.run_config['save_figure'])
return predictions_timesteps, y_true
def get_predictions(context, model, X, y, train_scaler, batch_size, look_ahead,
look_back, epochs, experiment_id):
predictions = model.predict(X, batch_size=batch_size)
# print predictions.shape
# rescale
predictions = train_scaler.inverse_transform(predictions)
y = train_scaler.inverse_transform(y)
print('predictions:', predictions.shape)
print('y:', y.shape)
# extract first timestep for true values
y_true = y[:, 0].flatten()
print('y_true:', y_true.shape)
# diagonals contains a reading's values calculated at different points in time
diagonals = util.get_diagonals(predictions)
print('diagonals:', len(diagonals))
# the top left and bottom right predictions do not contain predictions for all timesteps
# fill the missing prediction values in diagonals. curenttly using the first predicted value for all missing timesteps
for idx, diagonal in enumerate(diagonals):
diagonal = diagonal.flatten()
# missing value filled with the first value
diagonals[idx] = np.hstack(
(diagonal, np.full(look_ahead - len(diagonal), diagonal[0])))
predictions_timesteps = np.asarray(diagonals)
print('y_true: ', y_true.shape)
print('predictions_timesteps:', predictions_timesteps.shape)
for i in range(look_ahead):
logging.info(
"%s RMSE on %d timestep prediction %f" %
(context, (i + 1),
mean_squared_error(y_true, predictions_timesteps[:, i])**0.5))
shifted_1 = util.shift_time_series(y_true, 1)
logging.info(" %s RMSE Naive One Timestep Shift %f", context,
mean_squared_error(y_true[1:], shifted_1[1:])**0.5)
title = "Prediction on %s data. %d epochs, look back %d, look_ahead %d & batch_size %d." % (
context, epochs, look_back, look_ahead, batch_size)
path = "%s/%s/" % ("imgs", experiment_id)
make_plots(context, predictions_timesteps, y_true, look_ahead, title, path,
cfg.run_config['save_figure'], cfg.run_config['Xserver'])
return predictions_timesteps, y_true
def get_predictions(context,model,X,y,train_scaler,batch_size,look_ahead,look_back,epochs,experiment_id):
predictions = model.predict(X, batch_size=batch_size)
print predictions.shape
# rescale
predictions = train_scaler.inverse_transform(predictions)
y = train_scaler.inverse_transform(y)
# extract first timestep for true values
y_true = y[:, 0].flatten()
# diagonals contains a reading's values calculated at different points in time
diagonals = util.get_diagonals(predictions)
# the top left and bottom right predictions do not contain predictions for all timesteps
# fill the missing prediction values in diagonals. curenttly using the first predicted value for all missing timesteps
for idx, diagonal in enumerate(diagonals):
diagonal = diagonal.flatten()
# missing value filled with the first value
diagonals[idx] = np.hstack((diagonal, np.full(look_ahead - len(diagonal), diagonal[0])))
predictions_timesteps = np.asarray(diagonals)
for i in range(look_ahead):
logging.info("%s RMSE on %d timestep prediction %f" % ( context,
(i + 1), mean_squared_error(y_true, predictions_timesteps[:, i]) ** 0.5))
shifted_1 = util.shift_time_series(y_true, 1)
logging.info(" %s RMSE Naive One Timestep Shift %f",context,
mean_squared_error(y_true[1:], shifted_1[1:]) ** 0.5)
title = "Prediction on %s data. %d epochs, look back %d, look_ahead %d & batch_size %d." % (
context, epochs, look_back, look_ahead, batch_size)
path = "%s/%s/"%("imgs",experiment_id)
make_plots(context,predictions_timesteps,y_true,look_ahead,title,path,cfg.run_config['save_figure'],
cfg.run_config['Xserver'])
return predictions_timesteps, y_true
