from pyFTS.partitioners import Grid, Util as pUtil
partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff)
from pyFTS.common import Util as cUtil
from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive
from pyFTS.models import pwfts, song, chen, ifts, hofts
from pyFTS.models.ensemble import ensemble
model = chen.ConventionalFTS(partitioner=partitioner)
#model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
model.append_transformation(tdiff)
model.fit(dataset[:800])
cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
print(model)
print("fim")
'''
model = knn.KNearestNeighbors(order=3)
#model = ensemble.AllMethodEnsembleFTS("", partitioner=partitioner)
#model = arima.ARIMA("", order=(2,0,2))
#model = quantreg.QuantileRegression("", order=2, dist=True)
#model.append_transformation(tdiff)
model.fit(dataset[:800])
print(Measures.get_distribution_statistics(dataset[800:1000], model))
model.fit(norm_train_data)
print(model)
#carregamento do conjunto de teste
df = pd.read_csv(teste)
data_test = df['Adj Close'].values
new_data = []
for i in range(len(data_test)):
if data_test[i] == data_test[i]:
new_data.append(data_test[i])
test_data = np.array(new_data)
norm_data_test = (test_data - data_min) / (data_max - data_min)
#Plota as regras de associação do conjunto
from pyFTS.common import Util
Util.plot_rules(model, size=[15, 5], rules_by_axis=fzz)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
#Prediz o resultado (Teste)
forecasts_test_norm = model.predict(norm_data_test)
forecasts_norm = model.predict(norm_train_data)
forecasts_test = np.array(forecasts_test_norm) * (data_max -
data_min) + data_min
forecasts = np.array(forecasts_norm) * (data_max - data_min) + data_min
from sklearn.metrics import mean_squared_error
#calculo da métrica
print('MSE Treinamento: ', mean_squared_error(norm_train_data, forecasts_norm))
print('MSE Teste: ', mean_squared_error(norm_data_test, forecasts_test_norm))
#Plote dos resultados de Teste e Treinamento