Refatoração dos códigos para padronizar com a rfts - Common e Chen
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
9ad5af49a4
commit
9ad28b07f2
153
benchmarks.py
153
benchmarks.py
@ -5,7 +5,7 @@ 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 import common
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from pyFTS import *
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def Teste(par):
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x = np.arange(1,par)
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@ -13,20 +13,20 @@ def Teste(par):
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plt.plot(x,y)
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# Erro quadrático médio
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def rmse(predictions,targets):
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return np.sqrt(np.mean((predictions-targets)**2))
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def rmse(forecastions,targets):
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return np.sqrt(np.mean((forecastions-targets)**2))
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# Erro Percentual médio
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def mape(predictions,targets):
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return np.mean(abs(predictions-targets)/predictions)
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def mape(forecastions,targets):
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return np.mean(abs(forecastions-targets)/forecastions)
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def plotComparedSeries(original,fts,title):
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fig = plt.figure(figsize=[20,6])
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ax = fig.add_subplot(111)
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predicted = [fts.predict(xx) for xx in original]
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error = rmse(original,predicted)
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forecasted = [fts.forecast(xx) for xx in original]
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error = rmse(original,forecasted)
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ax.plot(original,color='b',label="Original")
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ax.plot(predicted,color='r',label="Predicted")
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ax.plot(forecasted,color='r',label="Predicted")
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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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@ -35,12 +35,12 @@ def plotComparedSeries(original,fts,title):
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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 plotCompared(original,predicted,labels,title):
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def plotCompared(original,forecasted,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(predicted)):
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ax.plot(predicted[c],label=labels[c])
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for c in range(0,len(forecasted)):
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ax.plot(forecasted[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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@ -53,7 +53,7 @@ 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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predicted_best = []
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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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@ -73,29 +73,29 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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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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predicted_best_fold = []
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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 = common.GridPartitionerTrimf(train,p)
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sets = partitioner.GridPartitionerTrimf(train,p)
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fts = modelo(str(p)+ " particoes")
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fts.learn(train,sets)
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predicted = [fts.predict(xx) for xx in test]
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error = rmse(np.array(predicted),np.array(test))
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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 = 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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predicted_best_fold = predicted
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forecasted_best_fold = forecasted
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pc = pc + 1
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predicted_best_fold = [best_fold.predict(xx) for xx in original]
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ax0.plot(predicted_best_fold,label=best_fold.name)
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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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predicted_best = predicted_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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@ -108,12 +108,12 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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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(predicted_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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predictedd_best = []
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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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@ -132,15 +132,15 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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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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predicted_best_fold = []
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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 = GridPartitionerTrimf(train,p)
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sets = partitioner.GridPartitionerTrimf(train,p)
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fts = modelo(str(p)+ " particoes")
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fts.learn(train,sets)
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predicted = [fts.predictDiff(test,xx) for xx in np.arange(len(test))]
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error = rmse(np.array(predicted),np.array(test))
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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 = 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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@ -148,12 +148,12 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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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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predicted_best_fold = [best_fold.predictDiff(original, xx) for xx in np.arange(len(original))]
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ax2.plot(predicted_best_fold,label=best_fold.name)
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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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predicted_best = predicted_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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@ -166,13 +166,13 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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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(predicted_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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predicted_best = []
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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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@ -186,18 +186,18 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
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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 = common.GridPartitionerTrimf(original,p)
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sets = partitioner.GridPartitionerTrimf(original,p)
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fts = modelo(str(p)+ " particoes")
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fts.learn(original,sets)
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predicted = [fts.predict(xx) for xx in original]
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ax0.plot(predicted,label=fts.name)
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error = rmse(np.array(predicted),np.array(original))
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fts.train(original,sets)
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forecasted = [fts.forecast(xx) for xx in original]
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ax0.plot(forecasted,label=fts.name)
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error = 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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predicted_best = predicted
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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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@ -207,34 +207,35 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
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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(predicted_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 = common.differential(original)
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errors = []
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predictedd_best = []
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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_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(original,label="Original")
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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 = common.GridPartitionerTrimf(common.differential(original),p)
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sets = partitioner.GridPartitionerTrimf(difffts,p)
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fts = modelo(str(p)+ " particoes")
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fts.learn(common.differential(original),sets)
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predicted = [fts.predictDiff(original, xx) for xx in range(1,len(original))]
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predicted.insert(0,original[0])
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ax2.plot(predicted,label=fts.name)
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error = rmse(np.array(predicted),np.array(original))
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fts.train(difffts,sets)
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forecasted = [fts.forecast(xx) for xx in 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 = 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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predictedd_best = predicted
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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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@ -244,7 +245,7 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
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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(predictedd_best)
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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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@ -254,10 +255,10 @@ def compareModelsPlot(original,models_fo,models_ho):
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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["predicted"], label=model["name"])
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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["predicted"], label=model["name"])
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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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@ -268,13 +269,13 @@ def compareModelsTable(original,models_fo,models_ho):
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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 = rmse(model["predicted"],original)
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error_m = round(mape(model["predicted"],original)*100,2)
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error_r = rmse(model["forecasted"],original)
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error_m = round(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 = rmse(model["predicted"][fts.order:],original[fts.order:])
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error_m = round(mape(model["predicted"][fts.order:],original[fts.order:])*100,2)
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error_r = rmse(model["forecasted"][fts.order:],original[fts.order:])
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error_m = round(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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@ -312,7 +313,7 @@ from pyFTS import hwang
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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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predicted_best = []
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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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@ -329,20 +330,20 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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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 = common.GridPartitionerTrimf(original,p)
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sets = partitioner.GridPartitionerTrimf(original,p)
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fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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fts.learn(original,sets)
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predicted = [fts.predict(original, xx) for xx in range(o,len(original))]
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error = rmse(np.array(predicted),np.array(original[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 = rmse(np.array(forecasted),np.array(original[o:]))
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for kk in range(o):
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predicted.insert(0,None)
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ax0.plot(predicted,label=fts.name)
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forecasted.insert(0,None)
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ax0.plot(forecasted,label=fts.name)
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print(o,p,error)
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errors[oc,pc] = 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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predicted_best = predicted
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forecasted_best = forecasted
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oc = oc + 1
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pc = pc + 1
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handles0, labels0 = ax0.get_legend_handles_labels()
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@ -356,12 +357,12 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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X,Y = np.meshgrid(parameters,orders)
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surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
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ret.append(best)
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ret.append(predicted_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 range(len(parameters))] for kk in range(len(orders))])
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predictedd_best = []
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forecastedd_best = []
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ax2 = fig.add_axes([0, 0, 0.6, 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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@ -375,20 +376,20 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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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 = common.GridPartitionerTrimf(common.differential(original),p)
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sets = partitioner.GridPartitionerTrimf(common.differential(original),p)
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fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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fts.learn(original,sets)
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predicted = [fts.predictDiff(original, xx) for xx in range(o,len(original))]
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error = rmse(np.array(predicted),np.array(original[o:]))
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fts.train(original,sets)
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forecasted = [fts.forecastDiff(original, xx) for xx in range(o,len(original))]
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error = rmse(np.array(forecasted),np.array(original[o:]))
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for kk in range(o):
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predicted.insert(0,None)
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ax2.plot(predicted,label=fts.name)
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forecasted.insert(0,None)
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ax2.plot(forecasted,label=fts.name)
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print(o,p,error)
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errors[oc,pc] = 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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predictedd_best = predicted
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forecastedd_best = forecasted
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oc = oc + 1
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pc = pc + 1
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handles0, labels0 = ax2.get_legend_handles_labels()
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@ -402,5 +403,5 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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X,Y = np.meshgrid(parameters,orders)
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surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
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ret.append(bestd)
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ret.append(predictedd_best)
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ret.append(forecastedd_best)
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return ret
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50
chen.py
50
chen.py
@ -1,3 +1,4 @@
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import numpy as np
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from pyFTS import *
|
||||
|
||||
class ConventionalFLRG:
|
||||
@ -21,15 +22,18 @@ class ConventionalFLRG:
|
||||
class ConventionalFTS(fts.FTS):
|
||||
def __init__(self,name):
|
||||
super(ConventionalFTS, self).__init__(1,name)
|
||||
self.flrgs = {}
|
||||
|
||||
def forecast(self,data):
|
||||
|
||||
mv = common.fuzzyInstance(data, self.sets)
|
||||
|
||||
actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
|
||||
|
||||
actual = self.fuzzy(data)
|
||||
|
||||
if actual["fuzzyset"] not in self.flrgs:
|
||||
return self.sets[actual["fuzzyset"]].centroid
|
||||
if actual.name not in self.flrgs:
|
||||
return actual.centroid
|
||||
|
||||
flrg = self.flrgs[actual["fuzzyset"]]
|
||||
flrg = self.flrgs[actual.name]
|
||||
|
||||
count = 0.0
|
||||
denom = 0.0
|
||||
@ -39,24 +43,20 @@ class ConventionalFTS(fts.FTS):
|
||||
count = count + 1.0
|
||||
|
||||
return denom/count
|
||||
|
||||
|
||||
def generateFLRG(self, flrs):
|
||||
flrgs = {}
|
||||
for flr in flrs:
|
||||
if flr.LHS in flrgs:
|
||||
flrgs[flr.LHS].append(flr.RHS)
|
||||
else:
|
||||
flrgs[flr.LHS] = ConventionalFLRG(flr.LHS);
|
||||
flrgs[flr.LHS].append(flr.RHS)
|
||||
return (flrgs)
|
||||
|
||||
def train(self, data, sets):
|
||||
last = {"fuzzyset":"", "membership":0.0}
|
||||
actual = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for s in sets:
|
||||
self.sets[s.name] = s
|
||||
|
||||
self.flrgs = {}
|
||||
count = 1
|
||||
for inst in data:
|
||||
actual = self.fuzzy(inst)
|
||||
|
||||
if count > self.order:
|
||||
if last["fuzzyset"] not in self.flrgs:
|
||||
self.flrgs[last["fuzzyset"]] = ConventionalFLRG(last["fuzzyset"])
|
||||
|
||||
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||||
count = count + 1
|
||||
last = actual
|
||||
|
||||
self.sets = sets
|
||||
tmpdata = common.fuzzySeries(data,sets)
|
||||
flrs = common.generateNonRecurrentFLRs(tmpdata)
|
||||
self.flrgs = self.generateFLRG(flrs)
|
||||
|
||||
|
||||
48
common.py
48
common.py
@ -42,12 +42,13 @@ def sigmf(x,parameters):
|
||||
|
||||
|
||||
class FuzzySet:
|
||||
|
||||
def __init__(self,name,mf,parameters,centroid):
|
||||
self.name = name
|
||||
self.mf = mf
|
||||
self.parameters = parameters
|
||||
self.centroid = centroid
|
||||
self.lower = min(parameters)
|
||||
self.upper = max(parameters)
|
||||
|
||||
def membership(self,x):
|
||||
return self.mf(x,self.parameters)
|
||||
@ -55,16 +56,37 @@ class FuzzySet:
|
||||
def __str__(self):
|
||||
return self.name + ": " + str(self.mf) + "(" + str(self.parameters) + ")"
|
||||
|
||||
class FLR:
|
||||
def __init__(self,LHS,RHS):
|
||||
self.LHS = LHS
|
||||
self.RHS = RHS
|
||||
|
||||
def __str__(self):
|
||||
return str(self.LHS) + " -> " + str(self.RHS)
|
||||
|
||||
def GridPartitionerTrimf(data,npart,names = None,prefix = "A"):
|
||||
sets = []
|
||||
dmax = max(data)
|
||||
dmin = min(data)
|
||||
dlen = dmax - dmin
|
||||
partlen = dlen / npart
|
||||
partition = dmin
|
||||
for c in range(npart):
|
||||
sets.append( FuzzySet(prefix+str(c),trimf,[partition-partlen, partition, partition+partlen], partition ) )
|
||||
partition = partition + partlen
|
||||
|
||||
return sets
|
||||
def fuzzyInstance(inst, fuzzySets):
|
||||
mv = np.array([ fs.membership(inst) for fs in fuzzySets])
|
||||
return mv
|
||||
|
||||
|
||||
def fuzzySeries(data,fuzzySets):
|
||||
fts = []
|
||||
for item in data:
|
||||
mv = fuzzyInstance(item,fuzzySets)
|
||||
fts.append(fuzzySets[ np.argwhere(mv == max(mv) )[0,0] ])
|
||||
return fts
|
||||
|
||||
|
||||
def generateNonRecurrentFLRs(fuzzyData):
|
||||
flrs = {}
|
||||
for i in range(2,len(fuzzyData)):
|
||||
tmp = FLR(fuzzyData[i-1],fuzzyData[i])
|
||||
flrs[str(tmp)] = tmp
|
||||
ret = [value for key, value in flrs.items()]
|
||||
return ret
|
||||
|
||||
def generateRecurrentFLRs(fuzzyData):
|
||||
flrs = []
|
||||
for i in range(2,len(fuzzyData)):
|
||||
flrs[i-1] = FLR(fuzzyData[i-1],fuzzyData[i])
|
||||
return flrs
|
||||
|
||||
6
fts.py
6
fts.py
@ -24,12 +24,6 @@ class FTS:
|
||||
def train(self, data, sets):
|
||||
pass
|
||||
|
||||
def predict(self,data):
|
||||
return self.forecast(data)
|
||||
|
||||
def predictDiff(self,data,t):
|
||||
return data[t] + self.forecast(data[t-1]-data[t])
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.name + ":\n"
|
||||
for r in self.flrgs.keys():
|
||||
|
||||
17
partitioner.py
Normal file
17
partitioner.py
Normal file
@ -0,0 +1,17 @@
|
||||
import numpy as np
|
||||
from pyFTS import *
|
||||
|
||||
#print(common.__dict__)
|
||||
|
||||
def GridPartitionerTrimf(data,npart,names = None,prefix = "A"):
|
||||
sets = []
|
||||
dmax = max(data)
|
||||
dmin = min(data)
|
||||
dlen = dmax - dmin
|
||||
partlen = dlen / npart
|
||||
partition = dmin
|
||||
for c in range(npart):
|
||||
sets.append(common.FuzzySet(prefix+str(c),common.trimf,[partition-partlen, partition, partition+partlen], partition ) )
|
||||
partition = partition + partlen
|
||||
|
||||
return sets
|
||||
Loading…
Reference in New Issue
Block a user