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Python for loop within function not returning values

I wrote this fairly simple Python function but for some reason after the for loop ends, nothing returns or is able to be printed out within the function. I can call the function just fine and I called prints within the for loop to ensure the values were correct and they were. Am I missing anything obvious here? The print statement at the bottom prints nothing.

def evaluate_arima_model(X, arima_order, s_arima_order):
scores =
train_steps = [36, 48, 60, 72, 84]
for i in train_steps:
Train = X[0:i]
Test = X[i:i + 12]
model = SARIMAX(Train, order=arima_order, seasonal_order=s_arima_order)
model_fit = model.fit(trend='nc', disp=0)
yhat = model_fit.forecast(12)
rmse = sqrt(mean_squared_error(numpy.exp(Test), numpy.exp(yhat)))
scores.append(rmse)
print(scores)
return scores


This is how the function is called (by another function with nested loops

def evaluate_models(dataset, p_values, d_values, q_values, sp_values, sd_values, sq_values, s_values):
dataset = dataset.astype('float32')
best_score, best_cfg, best_cfg2 = float("inf"), None, None
for p in p_values:
for d in d_values:
for q in q_values:
order = (p,d,q)
for sp in sp_values:
for sd in sd_values:
for sq in sq_values:
for s in s_values:
sorder = (sp,sd,sq,s)
try:
rmse = evaluate_arima_model(dataset, order, sorder)
if rmse < best_score:
best_score, best_cfg, best_cfg2 = rmse, order, sorder
print('ARIMA%s SARIMA%s RMSE=%.3f' % (order,sorder,rmse))
except:
continue
print('n','Best ARIMA%s SARIMA%s RMSE=%.3f' % (best_cfg, best_cfg2, best_score))


series = read_csv('dataset.csv', header=None, index_col=0, parse_dates=True, squeeze=True)
series = numpy.log(series)

# Evaluate parameters
p_values = range(0, 2)
d_values = range(0, 2)
q_values = range(0, 2)

# Evaluate seasonal parameters
sp_values = range(0, 2)
sd_values = range(0, 2)
sq_values = range(0, 2)

#Set seasonality
s_values = [12]

#Call grid loop
evaluate_models(series, p_values, d_values, q_values, sp_values, sd_values, sq_values, s_values)


Output: Best ARIMANone SARIMANone RMSE=inf

New version still not working:

def evaluate_arima_model(X, arima_order, s_arima_order):
scores =
train_steps = [36, 48, 60, 72, 84]
for i in train_steps:
Train = X[0:i]
Test = X[i:i + 12]
model = SARIMAX(Train, order=arima_order, seasonal_order=s_arima_order)
model_fit = model.fit(trend='nc', disp=0)
yhat = model_fit.forecast(12)
rmse = None
rmse = sqrt(mean_squared_error(numpy.exp(Test), numpy.exp(yhat)))
scores.append(rmse)
print(scores)
print(scores)
return scores


None

3 Answers
3

Your print() statement must print something. However, because you do not have a return statement, your function does not return anything (well, it returns None). If you want your function to return something, add one last line:

print()

return

None

return scores


Simplify code:

In [1]: def evaluate_arima_model(X, arima_order, s_arima_order):
...: scores =
...: train_steps = [36, 48, 60, 72, 84]
...: for i in train_steps:
...: rmse = None
...: scores.append(rmse)
...: print(scores)
...: return scores
...:
...:

In [2]: evaluate_arima_model(1,1,1)
[None, None, None, None, None]
Out[2]: [None, None, None, None, None]


I do not see a reason for this to not work.

print(scores)

You need to write the return statement instead of print.

def evaluate_arima_model(X, arima_order, s_arima_order):
scores =
train_steps = [36, 48, 60, 72, 84]
for i in train_steps:
Train = X[0:i]
Test = X[i:i + 12]
model = SARIMAX(Train, order=arima_order, seasonal_order=s_arima_order)
model_fit = model.fit(trend='nc', disp=0)
yhat = model_fit.forecast(12)
rmse = sqrt(mean_squared_error(numpy.exp(Test), numpy.exp(yhat)))
scores.append(rmse)
return(scores)


println

Finally sorted this out, the issue was returning a list vs a scalar, which is what I needed. So "return scores[0]" fixed it.

def evaluate_arima_model(X, arima_order, s_arima_order):
scores =
train_steps = [36]
for i in train_steps:
Train = X[0:i]
Test = X[i:i + 12]
model = SARIMAX(Train, order=arima_order, seasonal_order=s_arima_order)
model_fit = model.fit(trend='nc', disp=0)
yhat = model_fit.forecast(12)
rmse = sqrt(mean_squared_error(numpy.exp(Test), numpy.exp(yhat)))
scores.append(rmse)
return scores[0]


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