def load_model(self):
"""
Compute the coefficients of the AutoReg models in model_dir_path
Returns:
df_coefs_all: Coefficients arrays of the AutoReg models as a DataFrame
"""
coefs_all = []
col_names = []
for fname in os.listdir(self.model_dir_path):
if fname.endswith(".pkl"):
trace_id, coord = os.path.splitext(fname)[0].split('-')
if trace_id == os.path.splitext(self.train_trace)[0]:
col_names.append(coord)
file = open(os.path.join(self.model_dir_path, fname), "rb")
model = AutoRegResults.load(file)
coefs = model.params
coefs_all.append(coefs)
coefs_all = np.array(coefs_all).T
df_coefs_all = pd.DataFrame(coefs_all, columns=col_names)
coords = self.coords
df_coefs_all = df_coefs_all.reindex(columns=coords)
return df_coefs_all
def AutoRegPredict():
#load the saved model
model = AutoRegResults.load('models/ar_model.pkl')
data = np.load('models/ar_data.npy')
last_observation = np.load('models/ar_observation.npy')
#make the prediction
predictions = model.predict(start=len(data), end=len(data)) #predict the next step out of the sample
#transform the prediction
yhat = predictions[0] + last_observation[0]
#print(f'Prediction: {yhat}')
updateDataSet(yhat)
return yhat
def predict():
data=pd.read_csv('wind_data.csv')
model = AutoRegResults.load('var_model.pkl')
data['dt']=pd.to_datetime(data['dt'])
data.set_index('dt', inplace=True)
data['month'] = data.index.month
data['hour'] = data.index.hour
data = cycle_encode(data, ['month','hour'])
data.drop(['month','hour'], axis=1, inplace=True)
period = 24
data_diff = data.diff(period).dropna()
final_pred = retrive_prediction(model, data_diff, data, 72)
wind_speed=np.power(final_pred[:,3],3)
pressure=final_pred[:,1]
temp=final_pred[:,0]+273
R=2.90515100
air_density=np.multiply(temp,R).astype(float)
air_density=np.divide(pressure,air_density).astype(float)
A=3.14*58.5*58.5
constant=A*0.5*1.173*0.5
power_op=np.multiply(wind_speed,constant)
power_data=pd.DataFrame(list(zip(power_op,final_pred[:,3])), columns=['Calc_power','Wind_speed'])
power_dict=power_data.to_dict()
power_dict = {'Points': power_dict}
with open("db.json", "w") as wf:
json.dump(power_dict,wf)
return
X = difference(series.values)
# fit model
model = AutoReg(X, lags=6)
model_fit = model.fit()
# save model to file
model_fit.save('petrol_model.pkl')
# save the differenced dataset
numpy.save('petrol_data.npy', X)
# save the last ob
numpy.save('petrol_obs.npy', [series.values[-1]])
# load AR model from file and make a one-step prediction
# load model
model = AutoRegResults.load('petrol_model.pkl')
data = numpy.load('petrol_data.npy')
last_ob = numpy.load('petrol_obs.npy')
# make prediction
predictions = model.predict(start=len(data), end=len(data))
# transform prediction
yhat = predictions[0] + last_ob[0]
print('Prediction for next week: %f' % yhat)
# # update the data for the manual model with a new observation once available
# import numpy
# # get real observation
# observation = 48
# # update and save differenced observation
# lag = numpy.load('man_data.npy')
# last_ob = numpy.load('man_obs.npy')
# load the AR model from file
from statsmodels.tsa.ar_model import AutoRegResults
import numpy
loaded = AutoRegResults.load('ar_model.pkl')
print(loaded.params)
data = numpy.load('ar_data.npy')
last_ob = numpy.load('ar_obs.npy')
print(last_ob)
def pred_forecast(sales):
# values, dates, tmp = tuple_to_list(sales)
csv_file, csv_columns, values = sales_to_csv(sales)
try:
with open(csv_file, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
for data in values:
writer.writerow(data)
except IOError:
print("I/O error")
series = read_csv(csv_file, header=0, index_col=0)
# print(series.head())
# series.plot()
# pyplot.show()
x = difference(series.values)
size = int(len(x) * 0.66)
train, test = x[0:size], x[size:]
# train autoregression
# AR
window = 7
model_ar = AutoReg(train, lags=window)
model_fit_ar = model_ar.fit()
# save model to file
model_fit_ar.save("manast_site/static/predictions/ar_model.pkl")
# save the differenced dataset
numpy.save("manast_site/static/predictions/ar_data.npy", x)
# save the last ob
numpy.save("manast_site/static/predictions/ar_obs.npy",
[series.values[-1]])
# save coefficients
coef = model_fit_ar.params
numpy.save("manast_site/static/predictions/ar_man_model.npy", coef)
# save lag
lag = x[-window:]
numpy.save("manast_site/static/predictions/ar_man_data.npy", lag)
# save the last ob
numpy.save("manast_site/static/predictions/ar_man_obs.npy",
[series.values[-1]])
# load model
model = AutoRegResults.load("manast_site/static/predictions/ar_model.pkl")
data = numpy.load("manast_site/static/predictions/ar_data.npy")
last_ob = numpy.load("manast_site/static/predictions/ar_obs.npy")
# make prediction
predictions = model.predict(start=len(data), end=len(data))
# transform prediction
yhat_ar = predictions[0] + last_ob[0]
rmse_ar = sqrt(mean_squared_error(test,
predictions[:len(predictions) - 1]))
direction = "manast_site/static/predictions/predictionAR.png"
direction_ar = "predictions/predictionAR.png"
pyplot.close()
pyplot.plot(test, color='blue', label=_("Results"))
pyplot.plot(predictions, color='red', label=_("Predictions"))
pyplot.legend()
pyplot.savefig(direction)
# MA
model_ma = ARMA(train, order=(0, 0))
model_fit_ma = model_ma.fit(disp=False)
# save model to file
model_fit_ma.save("manast_site/static/predictions/ma_model.pkl")
# save the differenced dataset
numpy.save("manast_site/static/predictions/ma_data.npy", x)
# save the last ob
numpy.save("manast_site/static/predictions/ma_obs.npy",
[series.values[-1]])
# save coefficients
coef = model_fit_ma.params
numpy.save("manast_site/static/predictions/ma_man_model.npy", coef)
# save lag
lag = x[-window:]
numpy.save("manast_site/static/predictions/ma_man_data.npy", lag)
# save the last ob
numpy.save("manast_site/static/predictions/ma_man_obs.npy",
[series.values[-1]])
# load model
model = AutoRegResults.load("manast_site/static/predictions/ma_model.pkl")
data = numpy.load("manast_site/static/predictions/ma_data.npy")
last_ob = numpy.load("manast_site/static/predictions/ma_obs.npy")
# make prediction
predictions = model.predict(start=len(data), end=len(data))
# transform prediction
yhat_ma = predictions[0] + last_ob[0]
rmse_ma = sqrt(mean_squared_error(test,
predictions[:len(predictions) - 1]))
# ARMA
model_arma = ARMA(train, order=(window, 0))
model_fit_arma = model_arma.fit(disp=False)
# save model to file
model_fit_arma.save("manast_site/static/predictions/arma_model.pkl")
# save the differenced dataset
numpy.save("manast_site/static/predictions/arma_data.npy", x)
# save the last ob
numpy.save("manast_site/static/predictions/arma_obs.npy",
[series.values[-1]])
# save coefficients
coef = model_fit_arma.params
numpy.save("manast_site/static/predictions/arma_man_model.npy", coef)
# save lag
lag = x[-window:]
numpy.save("manast_site/static/predictions/arma_man_data.npy", lag)
# save the last ob
numpy.save("manast_site/static/predictions/arma_man_obs.npy",
[series.values[-1]])
# load model
model = AutoRegResults.load(
"manast_site/static/predictions/arma_model.pkl")
data = numpy.load("manast_site/static/predictions/arma_data.npy")
last_ob = numpy.load("manast_site/static/predictions/arma_obs.npy")
# make prediction
predictions = model.predict(start=len(data), end=len(data))
# transform prediction
yhat_arma = predictions[0] + last_ob[0]
rmse_arma = sqrt(
mean_squared_error(test, predictions[:len(predictions) - 1]))
prev_week = []
error_prev_week = 0
actual_week = []
for v in range(6, len(series.values)):
prev_week.append(float(series.values[v - 7]))
actual_week.append(float(series.values[v]))
error = float(series.values[v]) - float(series.values[v - 7])
error_prev_week += abs(error)
epd_week = error_prev_week / (len(series.values) - 7)
# print(epd_week)
return direction_ar, yhat_ar, rmse_ar, yhat_ma, rmse_ma, yhat_arma, rmse_arma, prev_week, actual_week, error_prev_week, epd_week