- Adding gaussmf and trapmf support on partitioners

- Parallel util for partitioners

benchmarks/parallel_benchmarks.py

@@ -0,0 +1,9 @@
+from copy import deepcopy
+from joblib import Parallel, delayed
+import multiprocessing
+
+
+
+
+
+

common/Membership.py

@@ -5,24 +5,24 @@ from pyFTS import *
 
 def trimf(x, parameters):
     xx = round(x, 3)
-    if (xx < parameters[0]):
+    if xx < parameters[0]:
         return 0
-    elif (xx >= parameters[0] and xx < parameters[1]):
+    elif parameters[0] <= xx < parameters[1]:
         return (x - parameters[0]) / (parameters[1] - parameters[0])
-    elif (xx >= parameters[1] and xx <= parameters[2]):
+    elif parameters[1] <= xx <= parameters[2]:
         return (parameters[2] - xx) / (parameters[2] - parameters[1])
     else:
         return 0
 
 
 def trapmf(x, parameters):
-    if (x < parameters[0]):
+    if x < parameters[0]:
         return 0
-    elif (x >= parameters[0] and x < parameters[1]):
+    elif parameters[0] <= x < parameters[1]:
         return (x - parameters[0]) / (parameters[1] - parameters[0])
-    elif (x >= parameters[1] and x <= parameters[2]):
+    elif parameters[1] <= x <= parameters[2]:
         return 1
-    elif (x >= parameters[2] and x <= parameters[3]):
+    elif parameters[2] <= x <= parameters[3]:
         return (parameters[3] - x) / (parameters[3] - parameters[2])
     else:
         return 0

partitioners/Entropy.py

@@ -76,6 +76,7 @@ def bestSplit(data, npart):
     else:
         return [threshold]
 
+
 class EntropyPartitioner(partitioner.Partitioner):
     def __init__(self, data, npart, func = Membership.trimf, transformation=None):
         super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
@@ -89,7 +90,15 @@ class EntropyPartitioner(partitioner.Partitioner):
         partitions = list(set(partitions))
         partitions.sort()
         for c in np.arange(1, len(partitions) - 1):
+            if self.membership_function == Membership.trimf:
                 sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf,
                                               [partitions[c - 1], partitions[c], partitions[c + 1]],partitions[c]))
+            elif self.membership_function == Membership.trapmf:
+                b1 = (partitions[c] - partitions[c - 1])/2
+                b2 = (partitions[c + 1] - partitions[c]) / 2
+                sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trapmf,
+                                              [partitions[c - 1], partitions[c] - b1,
+                                               partitions[c] - b2, partitions[c + 1]],
+                                              partitions[c]))
 
         return sets

partitioners/Grid.py

@@ -24,6 +24,10 @@ class GridPartitioner(partitioner.Partitioner):
             elif self.membership_function == Membership.gaussmf:
                 sets.append(
                     FuzzySet.FuzzySet(self.prefix + str(count), Membership.gaussmf, [c, partlen / 3], c))
+            elif self.membership_function == Membership.trapmf:
+                q = partlen / 2
+                sets.append(
+                    FuzzySet.FuzzySet(self.prefix + str(count), Membership.trapmf, [c - partlen, c - q, c + q, c + partlen], c))
             count += 1
 
 

partitioners/Util.py

@@ -5,9 +5,10 @@ import matplotlib.colors as pltcolors
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 from pyFTS.common import Membership, Util
+from pyFTS.partitioners import Grid,Huarng,FCM,Entropy
 
 
-def plotSets(data, sets, titles, tam=[12, 10], save=False, file=None):
+def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
     num = len(sets)
     #fig = plt.figure(figsize=tam)
     maxx = max(data)
@@ -23,12 +24,40 @@ def plotSets(data, sets, titles, tam=[12, 10], save=False, file=None):
         ax.set_xlim([minx, maxx])
         for s in sets[k]:
             if s.mf == Membership.trimf:
-                ax.plot([s.parameters[0],s.parameters[1],s.parameters[2]],[0,1,0])
+                ax.plot(s.parameters,[0,1,0])
             elif s.mf == Membership.gaussmf:
                 tmpx = [ kk for kk in np.arange(s.lower, s.upper)]
                 tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)]
                 ax.plot(tmpx, tmpy)
+            elif s.mf == Membership.gaussmf:
+                ax.plot(s.parameters, [0, 1, 1, 0])
 
     plt.tight_layout()
 
     Util.showAndSaveImage(fig, file, save)
+
+
+def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None):
+    sets = [k.sets for k in objs]
+    titles = [k.name for k in objs]
+    plot_sets(data,sets,titles,tam,save,file)
+
+
+def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None):
+    all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner, Huarng.HuarngPartitioner]
+    mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
+
+    if methods is None:
+        methods = all_methods
+
+    if mf is None:
+        mf = mfs
+
+    objs = []
+
+    for p in methods:
+        for m in mf:
+            obj = p(data, npart,m)
+            objs.append(obj)
+
+    plot_partitioners(data, objs, tam, save, file)

partitioners/parallel_util.py

@@ -0,0 +1,32 @@
+from copy import deepcopy
+from joblib import Parallel, delayed
+import multiprocessing
+import  numpy as np
+
+from pyFTS.common import Membership, Util
+from pyFTS.partitioners import Grid,Huarng,FCM,Entropy
+from pyFTS.partitioners import Util
+
+
+def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None):
+    all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
+    mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
+
+    if methods is None:
+        methods = all_methods
+
+    if mf is None:
+        mf = mfs
+
+    num_cores = multiprocessing.cpu_count()
+
+    objs = []
+    for method in methods:
+        print(str(method))
+        tmp = Parallel(n_jobs=num_cores)(delayed(method)(deepcopy(data), npart, m) for m in mf)
+        objs.append(tmp)
+
+    objs = np.ravel(objs).tolist()
+
+    Util.plot_partitioners(data, objs, tam, save, file)
+

partitioners/partitioner.py

@@ -1,6 +1,7 @@
 from pyFTS.common import FuzzySet, Membership
 import  numpy as np
 
+
 class Partitioner(object):
     def __init__(self,name,data,npart,func = Membership.trimf, names=None, prefix="A", transformation=None):
         self.name = name

tests/general.py

@@ -23,13 +23,17 @@ from numpy import random
 #gauss_teste = random.normal(0,1.0,400)
 
 
-os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
+os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
-#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
-#taiex = np.array(taiexpd["avg"][:5000])
+taiex = np.array(taiexpd["avg"][:5000])
 
-nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
+from pyFTS.partitioners import parallel_util
-nasdaq = np.array(nasdaqpd["avg"][:5000])
+
+parallel_util.explore_partitioners(taiex,20)
+
+#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
+#nasdaq = np.array(nasdaqpd["avg"][:5000])
 
 #taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
 #taiex_treino = np.array(taiex["avg"][2500:3900])
@@ -49,10 +53,10 @@ diff = Transformations.Differential(1)
 #                                    gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
 
 
-bchmk.interval_sliding_window(nasdaq,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
+#bchmk.interval_sliding_window(nasdaq,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=5), #
+#                     partitions= np.arange(10,200,step=5), #
-                     dump=True, save=True, file="experiments/nasdaq_interval.csv")
+#                     dump=True, save=True, file="experiments/nasdaq_interval.csv")
 
 #3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
 #                    partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],