Is it possible execute the ARIMA model with multiprocessing in python? I've got an error in the following code I'm using:
import warnings
from pandas import read_csv
from pandas import datetime
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import multiprocessing
from multiprocessing import pool
# evaluate an ARIMA model for a given order (p,d,q)
def evaluate_arima_model(X, arima_order):
# prepare training dataset
train_size = int(len(X) * 0.66)
train, test = X[0:train_size], X[train_size:]
history = [x for x in train]
# make predictions
predictions = list()
for t in range(len(test)):
model = ARIMA(history, order=arima_order)
model_fit = model.fit(disp=0)
yhat = model_fit.forecast()[0]
predictions.append(yhat)
history.append(test[t])
# calculate out of sample error
error = mean_squared_error(test, predictions)
return error
# evaluate combinations of p, d and q values for an ARIMA model
def evaluate_models(dataset, p_values, d_values, q_values):
dataset = dataset.astype('float32')
best_score, best_cfg = float("inf"), None
for p in p_values:
for d in d_values:
for q in q_values:
order = (p,d,q)
start = time.time()
try:
mse = evaluate_arima_model(dataset, order)
if mse < best_score:
best_score, best_cfg = mse, order
print('ARIMA%s MSE=%.3f' % (order,mse))
except:
continue
end = time.time()
cycletime = end - start
print('Time in secs to complete calcultion : %.2f' % (cycletime))
print('Best ARIMA%s MSE=%.3f' % (best_cfg, best_score))
return best_cfg
class Bar(object):
def __init__(self,x):
self.x = x
def parser(x):
return datetime.strptime(''+x, '%Y-%m-%d %H:%M:%S')
series = read_csv('clean_cpu.csv', usecols=['SAMPLE_TIME','VALUE'],header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# evaluate parameters
p_values = [0]
d_values = range(0, 3)
q_values = range(0, 1)
warnings.filterwarnings("ignore")
res = pool().map(evaluate_models,[Bar(series.value),Bar(p_values),Bar(d_values),Bar(q_values)])
#arima_lag = evaluate_models(series.values, p_values, d_values, q_values)
print('Best LAG is %s' % str(arima_lag))
The error I got is:
res = pool().map(evaluate_models,[Bar(series.value),Bar(p_values),Bar(d_values),Bar(q_values)]) TypeError: 'module' object is not callable
Any help would be greatly appreciated.
PS : I'm using Python v3.5.2
This is all related to one line :
In [1]: from multiprocessing import pool
In [2]: pool
Out[2]: <module 'multiprocessing.pool' from '/usr/lib/python3.6/multiprocessing/pool.py'>
In [3]: pool()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-c16521659604> in <module>()
----> 1 pool()
TypeError: 'module' object is not callable
You are trying to call a module as if it was a function.
You have to read the documentation of the multiprocessing module .
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.