531 lines
20 KiB
Python
531 lines
20 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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import pandas as pd
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import matplotlib as plt
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import matplotlib.colors as pltcolors
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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#from sklearn.cross_validation import KFold
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from pyFTS.benchmarks import Measures
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from pyFTS.partitioners import Grid
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from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
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def getIntervalStatistics(original, models):
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ret = "Model & RMSE & MAPE & Sharpness & Resolution & Coverage \\ \n"
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for fts in models:
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forecasts = fts.forecast(original)
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ret = ret + fts.shortname + " & "
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ret = ret + str(round(Measures.rmse_interval(original[fts.order - 1:], forecasts), 2)) + " & "
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ret = ret + str(round(Measures.mape_interval(original[fts.order - 1:], forecasts), 2)) + " & "
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ret = ret + str(round(Measures.sharpness(forecasts), 2)) + " & "
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ret = ret + str(round(Measures.resolution(forecasts), 2)) + " & "
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ret = ret + str(round(Measures.coverage(original[fts.order - 1:], forecasts), 2)) + " \\ \n"
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return ret
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def plotDistribution(dist):
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for k in dist.index:
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alpha = np.array([dist[x][k] for x in dist]) * 100
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x = [k for x in np.arange(0, len(alpha))]
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y = dist.columns
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plt.scatter(x, y, c=alpha, marker='s', linewidths=0, cmap='Oranges', norm=pltcolors.Normalize(vmin=0, vmax=1),
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vmin=0, vmax=1, edgecolors=None)
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def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5]):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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mi = []
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ma = []
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ax.plot(original, color='black', label="Original")
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count = 0
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for fts in models:
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if fts.hasPointForecasting:
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forecasted = fts.forecast(original)
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mi.append(min(forecasted))
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ma.append(max(forecasted))
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for k in np.arange(0, fts.order):
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forecasted.insert(0, None)
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lbl = fts.shortname
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if typeonlegend: lbl += " (Point)"
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ax.plot(forecasted, color=colors[count], label=lbl, ls="-")
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if fts.hasIntervalForecasting:
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forecasted = fts.forecastInterval(original)
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lower = [kk[0] for kk in forecasted]
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upper = [kk[1] for kk in forecasted]
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mi.append(min(lower))
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ma.append(max(upper))
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for k in np.arange(0, fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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lbl = fts.shortname
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if typeonlegend: lbl += " (Interval)"
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ax.plot(lower, color=colors[count], label=lbl,ls="--")
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ax.plot(upper, color=colors[count],ls="--")
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0, loc=2)
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count = count + 1
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# ax.set_title(fts.name)
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ax.set_ylim([min(mi), max(ma)])
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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Util.showAndSaveImage(fig,file,save)
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def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to,
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interpol=False, save=False, file=None,tam=[20, 5], resolution=None):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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if resolution is None: resolution = (max(original) - min(original))/100
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mi = []
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ma = []
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count = 0
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for fts in models:
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if fts.hasDistributionForecasting and distributions[count]:
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density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution, parameters=None)
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y = density.columns
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t = len(y)
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for k in density.index:
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alpha = np.array([density[q][k] for q in density]) * 100
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x = [time_from + k for x in np.arange(0, t)]
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for cc in np.arange(0,resolution,5):
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ax.scatter(x, y+cc, c=alpha, marker='s', linewidths=0, cmap='Oranges', edgecolors=None)
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if interpol and k < max(density.index):
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diffs = [(density[q][k + 1] - density[q][k])/50 for q in density]
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for p in np.arange(0,50):
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xx = [time_from + k + 0.02*p for q in np.arange(0, t)]
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alpha2 = np.array([density[density.columns[q]][k] + diffs[q]*p for q in np.arange(0, t)]) * 100
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ax.scatter(xx, y, c=alpha2, marker='s', linewidths=0, cmap='Oranges',
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norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
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if fts.hasIntervalForecasting:
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forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to)
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lower = [kk[0] for kk in forecasts]
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upper = [kk[1] for kk in forecasts]
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mi.append(min(lower))
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ma.append(max(upper))
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for k in np.arange(0, time_from-fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname)
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ax.plot(upper, color=colors[count])
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else:
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forecasts = fts.forecast(original)
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mi.append(min(forecasts))
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ma.append(max(forecasts))
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for k in np.arange(0, time_from):
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forecasts.insert(0, None)
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ax.plot(forecasts, color=colors[count], label=fts.shortname)
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count = count + 1
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ax.plot(original, color='black', label="Original")
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0, loc=2)
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# ax.set_title(fts.name)
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ax.set_ylim([min(mi), max(ma)])
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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Util.showAndSaveImage(fig, file, save)
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def plotCompared(original, forecasts, labels, title):
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fig = plt.figure(figsize=[13, 6])
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ax = fig.add_subplot(111)
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ax.plot(original, color='k', label="Original")
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for c in range(0, len(forecasts)):
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ax.plot(forecasts[c], label=labels[c])
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0)
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ax.set_title(title)
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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ax.set_ylim([min(original), max(original)])
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def SelecaoKFold_MenorRMSE(original, parameters, modelo):
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nfolds = 5
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ret = []
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errors = np.array([[0 for k in parameters] for z in np.arange(0, nfolds)])
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forecasted_best = []
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print("Série Original")
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fig = plt.figure(figsize=[18, 10])
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fig.suptitle("Comparação de modelos ")
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ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) # left, bottom, width, height
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ax0.set_xlim([0, len(original)])
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ax0.set_ylim([min(original), max(original)])
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ax0.set_title('Série Temporal')
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ax0.set_ylabel('F(T)')
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ax0.set_xlabel('T')
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ax0.plot(original, label="Original")
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min_rmse_fold = 100000.0
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best = None
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fc = 0 # Fold count
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kf = KFold(len(original), n_folds=nfolds)
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for train_ix, test_ix in kf:
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train = original[train_ix]
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test = original[test_ix]
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min_rmse = 100000.0
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best_fold = None
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forecasted_best_fold = []
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errors_fold = []
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pc = 0 # Parameter count
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for p in parameters:
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sets = Grid.GridPartitionerTrimf(train, p)
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fts = modelo(str(p) + " particoes")
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fts.train(train, sets)
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forecasted = [fts.forecast(xx) for xx in test]
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error = Measures.rmse(np.array(forecasted), np.array(test))
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errors_fold.append(error)
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print(fc, p, error)
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errors[fc, pc] = error
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if error < min_rmse:
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min_rmse = error
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best_fold = fts
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forecasted_best_fold = forecasted
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pc = pc + 1
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forecasted_best_fold = [best_fold.forecast(xx) for xx in original]
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ax0.plot(forecasted_best_fold, label=best_fold.name)
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if np.mean(errors_fold) < min_rmse_fold:
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min_rmse_fold = np.mean(errors)
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best = best_fold
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forecasted_best = forecasted_best_fold
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fc = fc + 1
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handles0, labels0 = ax0.get_legend_handles_labels()
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ax0.legend(handles0, labels0)
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ax1 = Axes3D(fig, rect=[0.7, 0.5, 0.3, 0.45], elev=30, azim=144)
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# ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
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ax1.set_title('Comparação dos Erros Quadráticos Médios')
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ax1.set_zlabel('RMSE')
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ax1.set_xlabel('K-fold')
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ax1.set_ylabel('Partições')
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X, Y = np.meshgrid(np.arange(0, nfolds), parameters)
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surf = ax1.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
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ret.append(best)
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ret.append(forecasted_best)
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# Modelo diferencial
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print("\nSérie Diferencial")
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errors = np.array([[0 for k in parameters] for z in np.arange(0, nfolds)])
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forecastedd_best = []
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ax2 = fig.add_axes([0, 0, 0.65, 0.45]) # left, bottom, width, height
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ax2.set_xlim([0, len(original)])
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ax2.set_ylim([min(original), max(original)])
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ax2.set_title('Série Temporal')
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ax2.set_ylabel('F(T)')
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ax2.set_xlabel('T')
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ax2.plot(original, label="Original")
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min_rmse = 100000.0
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min_rmse_fold = 100000.0
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bestd = None
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fc = 0
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diff = Transformations.differential(original)
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kf = KFold(len(original), n_folds=nfolds)
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for train_ix, test_ix in kf:
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train = diff[train_ix]
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test = diff[test_ix]
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min_rmse = 100000.0
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best_fold = None
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forecasted_best_fold = []
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errors_fold = []
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pc = 0
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for p in parameters:
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sets = Grid.GridPartitionerTrimf(train, p)
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fts = modelo(str(p) + " particoes")
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fts.train(train, sets)
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forecasted = [fts.forecastDiff(test, xx) for xx in np.arange(len(test))]
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error = Measures.rmse(np.array(forecasted), np.array(test))
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print(fc, p, error)
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errors[fc, pc] = error
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errors_fold.append(error)
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if error < min_rmse:
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min_rmse = error
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best_fold = fts
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pc = pc + 1
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forecasted_best_fold = [best_fold.forecastDiff(original, xx) for xx in np.arange(len(original))]
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ax2.plot(forecasted_best_fold, label=best_fold.name)
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if np.mean(errors_fold) < min_rmse_fold:
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min_rmse_fold = np.mean(errors)
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best = best_fold
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forecasted_best = forecasted_best_fold
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fc = fc + 1
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handles0, labels0 = ax2.get_legend_handles_labels()
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ax2.legend(handles0, labels0)
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ax3 = Axes3D(fig, rect=[0.7, 0, 0.3, 0.45], elev=30, azim=144)
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# ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
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ax3.set_title('Comparação dos Erros Quadráticos Médios')
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ax3.set_zlabel('RMSE')
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ax3.set_xlabel('K-fold')
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ax3.set_ylabel('Partições')
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X, Y = np.meshgrid(np.arange(0, nfolds), parameters)
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surf = ax3.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
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ret.append(best)
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ret.append(forecasted_best)
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return ret
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def SelecaoSimples_MenorRMSE(original, parameters, modelo):
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ret = []
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errors = []
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forecasted_best = []
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print("Série Original")
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fig = plt.figure(figsize=[20, 12])
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fig.suptitle("Comparação de modelos ")
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ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) # left, bottom, width, height
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ax0.set_xlim([0, len(original)])
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ax0.set_ylim([min(original), max(original)])
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ax0.set_title('Série Temporal')
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ax0.set_ylabel('F(T)')
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ax0.set_xlabel('T')
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ax0.plot(original, label="Original")
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min_rmse = 100000.0
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best = None
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for p in parameters:
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sets = Grid.GridPartitionerTrimf(original, p)
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fts = modelo(str(p) + " particoes")
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fts.train(original, sets)
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# print(original)
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forecasted = fts.forecast(original)
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forecasted.insert(0, original[0])
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# print(forecasted)
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ax0.plot(forecasted, label=fts.name)
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error = Measures.rmse(np.array(forecasted), np.array(original))
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print(p, error)
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errors.append(error)
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if error < min_rmse:
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min_rmse = error
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best = fts
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forecasted_best = forecasted
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handles0, labels0 = ax0.get_legend_handles_labels()
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ax0.legend(handles0, labels0)
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ax1 = fig.add_axes([0.7, 0.5, 0.3, 0.45]) # left, bottom, width, height
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ax1.set_title('Comparação dos Erros Quadráticos Médios')
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ax1.set_ylabel('RMSE')
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ax1.set_xlabel('Quantidade de Partições')
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ax1.set_xlim([min(parameters), max(parameters)])
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ax1.plot(parameters, errors)
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ret.append(best)
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ret.append(forecasted_best)
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# Modelo diferencial
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print("\nSérie Diferencial")
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difffts = Transformations.differential(original)
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errors = []
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forecastedd_best = []
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ax2 = fig.add_axes([0, 0, 0.65, 0.45]) # left, bottom, width, height
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ax2.set_xlim([0, len(difffts)])
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ax2.set_ylim([min(difffts), max(difffts)])
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ax2.set_title('Série Temporal')
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ax2.set_ylabel('F(T)')
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ax2.set_xlabel('T')
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ax2.plot(difffts, label="Original")
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min_rmse = 100000.0
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bestd = None
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for p in parameters:
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sets = Grid.GridPartitionerTrimf(difffts, p)
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fts = modelo(str(p) + " particoes")
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fts.train(difffts, sets)
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forecasted = fts.forecast(difffts)
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forecasted.insert(0, difffts[0])
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ax2.plot(forecasted, label=fts.name)
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error = Measures.rmse(np.array(forecasted), np.array(difffts))
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print(p, error)
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errors.append(error)
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if error < min_rmse:
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min_rmse = error
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bestd = fts
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forecastedd_best = forecasted
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handles0, labels0 = ax2.get_legend_handles_labels()
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ax2.legend(handles0, labels0)
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ax3 = fig.add_axes([0.7, 0, 0.3, 0.45]) # left, bottom, width, height
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ax3.set_title('Comparação dos Erros Quadráticos Médios')
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ax3.set_ylabel('RMSE')
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ax3.set_xlabel('Quantidade de Partições')
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ax3.set_xlim([min(parameters), max(parameters)])
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ax3.plot(parameters, errors)
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ret.append(bestd)
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ret.append(forecastedd_best)
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return ret
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def compareModelsPlot(original, models_fo, models_ho):
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fig = plt.figure(figsize=[13, 6])
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fig.suptitle("Comparação de modelos ")
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ax0 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height
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rows = []
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for model in models_fo:
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fts = model["model"]
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ax0.plot(model["forecasted"], label=model["name"])
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for model in models_ho:
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fts = model["model"]
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ax0.plot(model["forecasted"], label=model["name"])
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handles0, labels0 = ax0.get_legend_handles_labels()
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ax0.legend(handles0, labels0)
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def compareModelsTable(original, models_fo, models_ho):
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fig = plt.figure(figsize=[12, 4])
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fig.suptitle("Comparação de modelos ")
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columns = ['Modelo', 'Ordem', 'Partições', 'RMSE', 'MAPE (%)']
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rows = []
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for model in models_fo:
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fts = model["model"]
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error_r = Measures.rmse(model["forecasted"], original)
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error_m = round(Measures.mape(model["forecasted"], original) * 100, 2)
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rows.append([model["name"], fts.order, len(fts.sets), error_r, error_m])
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for model in models_ho:
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fts = model["model"]
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error_r = Measures.rmse(model["forecasted"][fts.order:], original[fts.order:])
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error_m = round(Measures.mape(model["forecasted"][fts.order:], original[fts.order:]) * 100, 2)
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rows.append([model["name"], fts.order, len(fts.sets), error_r, error_m])
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ax1 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height
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ax1.set_xticks([])
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ax1.set_yticks([])
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ax1.table(cellText=rows,
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colLabels=columns,
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cellLoc='center',
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bbox=[0, 0, 1, 1])
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sup = "\\begin{tabular}{"
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header = ""
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body = ""
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footer = ""
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for c in columns:
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sup = sup + "|c"
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if len(header) > 0:
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header = header + " & "
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header = header + "\\textbf{" + c + "} "
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sup = sup + "|} \\hline\n"
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header = header + "\\\\ \\hline \n"
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|
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for r in rows:
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lin = ""
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for c in r:
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|
if len(lin) > 0:
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|
lin = lin + " & "
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|
lin = lin + str(c)
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|
|
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body = body + lin + "\\\\ \\hline \n"
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|
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return sup + header + body + "\\end{tabular}"
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|
|
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from pyFTS import hwang
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|
|
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def HOSelecaoSimples_MenorRMSE(original, parameters, orders):
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ret = []
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errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
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forecasted_best = []
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print("Série Original")
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fig = plt.figure(figsize=[20, 12])
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fig.suptitle("Comparação de modelos ")
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ax0 = fig.add_axes([0, 0.5, 0.6, 0.45]) # left, bottom, width, height
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ax0.set_xlim([0, len(original)])
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ax0.set_ylim([min(original), max(original)])
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ax0.set_title('Série Temporal')
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ax0.set_ylabel('F(T)')
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ax0.set_xlabel('T')
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ax0.plot(original, label="Original")
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|
min_rmse = 100000.0
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best = None
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pc = 0
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|
for p in parameters:
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|
oc = 0
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|
for o in orders:
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|
sets = Grid.GridPartitionerTrimf(original, p)
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|
fts = hwang.HighOrderFTS(o, "k = " + str(p) + " w = " + str(o))
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|
fts.train(original, sets)
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|
forecasted = [fts.forecast(original, xx) for xx in range(o, len(original))]
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|
error = Measures.rmse(np.array(forecasted), np.array(original[o:]))
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|
for kk in range(o):
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|
forecasted.insert(0, None)
|
|
ax0.plot(forecasted, label=fts.name)
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|
print(o, p, error)
|
|
errors[oc, pc] = error
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
best = fts
|
|
forecasted_best = forecasted
|
|
oc = oc + 1
|
|
pc = pc + 1
|
|
handles0, labels0 = ax0.get_legend_handles_labels()
|
|
ax0.legend(handles0, labels0)
|
|
ax1 = Axes3D(fig, rect=[0.6, 0.5, 0.45, 0.45], elev=30, azim=144)
|
|
# ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
|
|
ax1.set_title('Comparação dos Erros Quadráticos Médios por tamanho da janela')
|
|
ax1.set_ylabel('RMSE')
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|
ax1.set_xlabel('Quantidade de Partições')
|
|
ax1.set_zlabel('W')
|
|
X, Y = np.meshgrid(parameters, orders)
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|
surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(best)
|
|
ret.append(forecasted_best)
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|
|
|
# Modelo diferencial
|
|
print("\nSérie Diferencial")
|
|
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
|
|
forecastedd_best = []
|
|
ax2 = fig.add_axes([0, 0, 0.6, 0.45]) # left, bottom, width, height
|
|
ax2.set_xlim([0, len(original)])
|
|
ax2.set_ylim([min(original), max(original)])
|
|
ax2.set_title('Série Temporal')
|
|
ax2.set_ylabel('F(T)')
|
|
ax2.set_xlabel('T')
|
|
ax2.plot(original, label="Original")
|
|
min_rmse = 100000.0
|
|
bestd = None
|
|
pc = 0
|
|
for p in parameters:
|
|
oc = 0
|
|
for o in orders:
|
|
sets = Grid.GridPartitionerTrimf(Transformations.differential(original), p)
|
|
fts = hwang.HighOrderFTS(o, "k = " + str(p) + " w = " + str(o))
|
|
fts.train(original, sets)
|
|
forecasted = [fts.forecastDiff(original, xx) for xx in range(o, len(original))]
|
|
error = Measures.rmse(np.array(forecasted), np.array(original[o:]))
|
|
for kk in range(o):
|
|
forecasted.insert(0, None)
|
|
ax2.plot(forecasted, label=fts.name)
|
|
print(o, p, error)
|
|
errors[oc, pc] = error
|
|
if error < min_rmse:
|
|
min_rmse = error
|
|
bestd = fts
|
|
forecastedd_best = forecasted
|
|
oc = oc + 1
|
|
pc = pc + 1
|
|
handles0, labels0 = ax2.get_legend_handles_labels()
|
|
ax2.legend(handles0, labels0)
|
|
ax3 = Axes3D(fig, rect=[0.6, 0.0, 0.45, 0.45], elev=30, azim=144)
|
|
# ax3 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
|
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
|
ax3.set_ylabel('RMSE')
|
|
ax3.set_xlabel('Quantidade de Partições')
|
|
ax3.set_zlabel('W')
|
|
X, Y = np.meshgrid(parameters, orders)
|
|
surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
|
ret.append(bestd)
|
|
ret.append(forecastedd_best)
|
|
return ret
|