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