Updating and refactoring legacy code
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Petrônio Cândido
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@ -16,7 +16,7 @@ import numpy as np
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from mpl_toolkits.mplot3d import Axes3D
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from itertools import product
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from pyFTS.common import Transformations
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from pyFTS.common.transformations.differential import Differential
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from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
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from pyFTS.models.ensemble import ensemble
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@ -555,7 +555,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg
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tmp3 = [Measures.get_point_statistics]
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tmp5 = [Transformations.Differential]
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tmp5 = [Differential]
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indexer = kwargs.get('indexer', None)
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@ -806,7 +806,7 @@ def run_point2(fts_method, order, partitioner_method, partitions, transformation
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tmp3 = [Measures.get_point_statistics]
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tmp5 = [Transformations.Differential]
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tmp5 = [Differential]
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indexer = kwargs.get('indexer', None)
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@ -1384,7 +1384,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
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ls = "-"
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else:
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ls = "--"
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tmpmi, tmpma = Util.plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
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tmpmi, tmpma = cUtil.plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
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color=colors[count], ls=ls, linewidth=linewidth)
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mi.append(tmpmi)
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ma.append(tmpma)
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@ -1419,89 +1419,6 @@ def plotCompared(original, forecasts, labels, title):
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ax.set_ylim([min(original), max(original)])
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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.GridPartitioner(data=original, npart=p).sets
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fts = modelo(str(p) + " particoes")
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fts.train(original, sets=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.GridPartitioner(data=difffts, npart=p)
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fts = modelo(str(p) + " particoes")
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fts.train(difffts, sets=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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@ -6,7 +6,7 @@ Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR, Transformations, fts
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from pyFTS.common import FuzzySet, FLR, fts
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class HighOrderFTS(fts.FTS):
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@ -27,7 +27,7 @@ class HighOrderFTS(fts.FTS):
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def forecast(self, ndata, **kwargs):
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l = len(self.sets)
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l = len(self.partitioner)
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cn = np.array([0.0 for k in range(l)])
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ow = np.array([[0.0 for k in range(l)] for z in range(self.order - 1)])
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@ -40,9 +40,9 @@ class HighOrderFTS(fts.FTS):
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for ix in range(l):
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s = self.partitioner.ordered_sets[ix]
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cn[ix] = self.sets[s].membership( FuzzySet.grant_bounds(ndata[t], self.sets, self.partitioner.ordered_sets))
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cn[ix] = self.partitioner.sets[s].membership( FuzzySet.grant_bounds(ndata[t], self.partitioner.sets, self.partitioner.ordered_sets))
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for w in np.arange(self.order-1):
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ow[w, ix] = self.sets[s].membership(FuzzySet.grant_bounds(ndata[t - w], self.sets, self.partitioner.ordered_sets))
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ow[w, ix] = self.partitioner.sets[s].membership(FuzzySet.grant_bounds(ndata[t - w], self.partitioner.sets, self.partitioner.ordered_sets))
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rn[w, ix] = ow[w, ix] * cn[ix]
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ft[ix] = max(ft[ix], rn[w, ix])
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mft = max(ft)
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@ -51,15 +51,11 @@ class HighOrderFTS(fts.FTS):
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for ix in range(l):
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s = self.partitioner.ordered_sets[ix]
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if ft[ix] == mft:
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out = out + self.sets[s].centroid
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out = out + self.partitioner.sets[s].centroid
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count += 1.0
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ret.append(out / count)
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return ret
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def train(self, data, **kwargs):
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if self.sets == None:
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self.sets = self.partitioner.sets
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self.configure_lags(**kwargs)
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@ -14,20 +14,18 @@ class ConventionalFTS(fts.FTS):
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super(ConventionalFTS, self).__init__(order=1, name="FTS", **kwargs)
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self.name = "Traditional FTS"
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self.detail = "Song & Chissom"
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if self.sets is not None and self.partitioner is not None:
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self.sets = self.partitioner.sets
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self.R = None
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if self.sets is not None:
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l = len(self.sets)
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if self.partitioner.sets is not None:
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l = len(self.partitioner.sets)
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self.R = np.zeros((l,l))
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def flr_membership_matrix(self, flr):
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ordered_set = FuzzySet.set_ordered(self.sets)
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centroids = [self.sets[k].centroid for k in ordered_set]
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lm = [self.sets[flr.LHS].membership(k) for k in centroids]
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rm = [self.sets[flr.RHS].membership(k) for k in centroids]
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ordered_set = FuzzySet.set_ordered(self.partitioner.sets)
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centroids = [self.partitioner.sets[k].centroid for k in ordered_set]
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lm = [self.partitioner.sets[flr.LHS].membership(k) for k in centroids]
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rm = [self.partitioner.sets[flr.RHS].membership(k) for k in centroids]
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l = len(ordered_set)
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r = np.zeros((l, l))
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@ -38,7 +36,7 @@ class ConventionalFTS(fts.FTS):
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return r
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def operation_matrix(self, flrs):
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l = len(self.sets)
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l = len(self.partitioner)
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if self.R is None or len(self.R) == 0 :
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self.R = np.zeros((l, l))
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for k in flrs:
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@ -59,24 +57,24 @@ class ConventionalFTS(fts.FTS):
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if self.partitioner is not None:
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ordered_sets = self.partitioner.ordered_sets
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else:
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ordered_sets = FuzzySet.set_ordered(self.sets)
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ordered_sets = FuzzySet.set_ordered(self.partitioner.sets)
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l = len(ndata)
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npart = len(self.sets)
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npart = len(self.partitioner)
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ret = []
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for k in np.arange(0, l):
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mv = FuzzySet.fuzzyfy_instance(ndata[k], self.sets)
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mv = FuzzySet.fuzzyfy_instance(ndata[k], self.partitioner.sets)
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r = [max([ min(self.R[i][j], mv[j]) for j in np.arange(0,npart) ]) for i in np.arange(0,npart)]
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fs = np.ravel(np.argwhere(r == max(r)))
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if len(fs) == 1:
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ret.append(self.sets[ordered_sets[fs[0]]].centroid)
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ret.append(self.partitioner.sets[ordered_sets[fs[0]]].centroid)
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else:
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mp = [self.sets[ordered_sets[s]].centroid for s in fs]
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mp = [self.partitioner.sets[ordered_sets[s]].centroid for s in fs]
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ret.append( sum(mp)/len(mp))
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