CVFTS improvements and bugfixes; FTS.fit bugfix for multivariate models; Util.plot_rules high order capability

pyFTS/common/Util.py

@@ -8,42 +8,92 @@ import dill
 import numpy as np
 
 
-def plot_rules(model, size=[5, 5], axis=None):
+def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
+    if axis is None and rules_by_axis is None:
+        fig, axis = plt.subplots(nrows=1, ncols=1, figsize=size)
+    elif axis is None and rules_by_axis is not None:
+        rows = (((len(model.flrgs.keys())//rules_by_axis)) // columns)+1
+        fig, axis = plt.subplots(nrows=rows, ncols=columns, figsize=size)
+
+    if rules_by_axis is None:
+        draw_sets_on_axis(axis, model, size)
+
+    _lhs = model.partitioner.ordered_sets if not model.is_high_order else model.flrgs.keys()
+
+    for ct, key in enumerate(_lhs):
+
+        if rules_by_axis is None:
+            ax = axis
+        else:
+            colcount = (ct // rules_by_axis) % columns
+            rowcount = (ct // rules_by_axis) // columns
+
+            ax = axis[rowcount, colcount] if columns > 1 else axis[rowcount]
+
+            if ct % rules_by_axis == 0:
+                xticks = []
+                xtickslabels = []
+                draw_sets_on_axis(ax, model, size)
+
+        if not model.is_high_order:
+            if key in model.flrgs:
+                flrg = model.flrgs[key]
+                orig = model.sets[key].centroid
+                ax.plot([ct+1],[orig],'o')
+                xticks.append(ct+1)
+                xtickslabels.append(key)
+                for rhs in flrg.RHS:
+                    dest = model.sets[rhs].centroid
+                    ax.arrow(ct+1.1, orig, 0.8, dest - orig, #length_includes_head=True,
+                               head_width=0.1, head_length=0.1, shape='full', overhang=0,
+                               fc='k', ec='k')
+        else:
+            flrg = model.flrgs[key]
+            disp = (ct%rules_by_axis)*model.order + 1
+            for ct2, lhs in enumerate(flrg.LHS):
+                orig = model.sets[lhs].centroid
+                ax.plot([disp+ct2], [orig], 'o')
+                xticks.append(disp+ct2)
+                xtickslabels.append(lhs)
+            for ct2 in range(1, model.order):
+                fs1 = flrg.LHS[ct2-1]
+                fs2 = flrg.LHS[ct2]
+                orig = model.sets[fs1].centroid
+                dest = model.sets[fs2].centroid
+                ax.plot([disp+ct2-1,disp+ct2], [orig,dest],'-')
+
+            orig = model.sets[flrg.LHS[-1]].centroid
+            for rhs in flrg.RHS:
+                dest = model.sets[rhs].centroid
+                ax.arrow(disp + model.order -1 + .1, orig, 0.8, dest - orig,  # length_includes_head=True,
+                           head_width=0.1, head_length=0.1, shape='full', overhang=0,
+                           fc='k', ec='k')
+
+
+        ax.set_xticks(xticks)
+        ax.set_xticklabels(xtickslabels)
+
+    plt.tight_layout()
+    plt.show()
+
+
+def draw_sets_on_axis(axis, model, size):
     if axis is None:
         fig, axis = plt.subplots(nrows=1, ncols=1, figsize=size)
-
     for ct, key in enumerate(model.partitioner.ordered_sets):
         fs = model.sets[key]
         axis.plot([0, 1, 0], fs.parameters, label=fs.name)
         axis.axhline(fs.centroid, c="lightgray", alpha=0.5)
-
     axis.set_xlim([0, len(model.partitioner.ordered_sets)])
-    axis.set_xticks(range(0,len(model.partitioner.ordered_sets)))
+    axis.set_xticks(range(0, len(model.partitioner.ordered_sets)))
     tmp = ['']
     tmp.extend(model.partitioner.ordered_sets)
     axis.set_xticklabels(tmp)
     axis.set_ylim([model.partitioner.min, model.partitioner.max])
     axis.set_yticks([model.sets[k].centroid for k in model.partitioner.ordered_sets])
-    axis.set_yticklabels([str(round(model.sets[k].centroid,1)) + " - " + k
+    axis.set_yticklabels([str(round(model.sets[k].centroid, 1)) + " - " + k
                           for k in model.partitioner.ordered_sets])
 
-    if not model.is_high_order:
-        for ct, key in enumerate(model.partitioner.ordered_sets):
-            if key in model.flrgs:
-                flrg = model.flrgs[key]
-                orig = model.sets[key].centroid
-                axis.plot([ct+1],[orig],'o')
-                for rhs in flrg.RHS:
-                    dest = model.sets[rhs].centroid
-                    axis.arrow(ct+1.1, orig, 0.8, dest - orig, #length_includes_head=True,
-                               head_width=0.1, head_length=0.1, shape='full', overhang=0,
-                               fc='k', ec='k')
-    plt.tight_layout()
-    plt.show()
-    print("fim")
-
-
-
 
 current_milli_time = lambda: int(round(time.time() * 1000))
 

pyFTS/common/fts.py

@@ -226,8 +226,8 @@ class FTS(object):
         else:
             data = self.apply_transformations(ndata)
 
-        self.original_min = np.nanmin(data)
+            self.original_min = np.nanmin(data)
-        self.original_max = np.nanmax(data)
+            self.original_max = np.nanmax(data)
 
         if 'sets' in kwargs:
             self.sets = kwargs.pop('sets')
@@ -235,7 +235,7 @@ class FTS(object):
         if 'partitioner' in kwargs:
             self.partitioner = kwargs.pop('partitioner')
 
-        if (self.sets is None or len(self.sets) == 0) and not self.benchmark_only:
+        if (self.sets is None or len(self.sets) == 0) and not self.benchmark_only and not self.is_multivariate:
             if self.partitioner is not None:
                 self.sets = self.partitioner.sets
             else:

pyFTS/models/nonstationary/cvfts.py

@@ -1,10 +1,36 @@
 import numpy as np
-from pyFTS.models import chen
+from pyFTS.models import hofts
 from pyFTS.models.nonstationary import common,nsfts
-from pyFTS.common import FLR
+from pyFTS.common import FLR, flrg, tree
+
+class HighOrderNonstationaryFLRG(hofts.HighOrderFTS):
+    """Conventional High Order Fuzzy Logical Relationship Group"""
+    def __init__(self, order, **kwargs):
+        super(HighOrderNonstationaryFLRG, self).__init__(order, **kwargs)
+        self.LHS = []
+        self.RHS = {}
+        self.strlhs = ""
+
+    def append_rhs(self, c, **kwargs):
+        if c not in self.RHS:
+            self.RHS[c] = c
+
+    def append_lhs(self, c):
+        self.LHS.append(c)
+
+    def __str__(self):
+        tmp = ""
+        for c in sorted(self.RHS):
+            if len(tmp) > 0:
+                tmp = tmp + ","
+            tmp = tmp + c
+        return self.get_key() + " -> " + tmp
 
 
-class ConditionalVarianceFTS(chen.ConventionalFTS):
+    def __len__(self):
+        return len(self.RHS)
+
+class ConditionalVarianceFTS(hofts.HighOrderFTS):
     def __init__(self, **kwargs):
         super(ConditionalVarianceFTS, self).__init__(**kwargs)
         self.name = "Conditional Variance FTS"
@@ -17,6 +43,8 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
         self.min_stack = [0,0,0]
         self.max_stack = [0,0,0]
         self.uod_clip = False
+        self.order = 1
+        self.min_order = 1
 
     def train(self, ndata, **kwargs):
 
@@ -32,6 +60,7 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
                 self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
                 self.flrgs[flr.LHS.name].append_rhs(flr.RHS)
 
+
     def _smooth(self, a):
         return .1 * a[0] + .3 * a[1] + .6 * a[2]
 

pyFTS/tests/general.py

@@ -28,11 +28,14 @@ from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn,
 from pyFTS.models import pwfts, song, chen, ifts, hofts
 from pyFTS.models.ensemble import ensemble
 
-model = chen.ConventionalFTS(partitioner=partitioner)
+#model = chen.ConventionalFTS(partitioner=partitioner)
+model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
 model.append_transformation(tdiff)
 model.fit(dataset[:800])
 
-cUtil.plot_rules(model)
+print(model)
+
+cUtil.plot_rules(model, size=[20,20], rules_by_axis=6, columns=1)
 
 '''
 model = knn.KNearestNeighbors(order=3)

pyFTS/tests/multivariate.py

@@ -59,7 +59,7 @@ model1.target_variable = vavg
 #model.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
 
 model1.fit(train, num_batches=200, save=True, batch_save=True, file_path='mvfts_sonda', distributed=True,
-          nodes=['192.168.1.35'], batch_save_interval=10)
+          nodes=['192.168.0.110'], batch_save_interval=10)
 
 
 #model = Util.load_obj('mvfts_sonda')