- Ensemble FTS - first complete version

benchmarks/quantreg.py

@@ -19,7 +19,6 @@ class QuantileRegression(fts.FTS):
         self.has_point_forecasting = True
         self.has_interval_forecasting = True
         self.has_probability_forecasting = True
-        self.has_probability_forecasting = True
         self.benchmark_only = True
         self.minOrder = 1
         self.alpha = kwargs.get("alpha", 0.05)

ensemble.py

@@ -9,11 +9,20 @@ from pyFTS.common import FLR, FuzzySet, SortedCollection
 from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
 from pyFTS.benchmarks import arima, quantreg
 from pyFTS.common import Transformations
+import scipy.stats as st
+from pyFTS import tree
+
+
+def sampler(data, quantiles):
+    ret = []
+    for qt in quantiles:
+        ret.append(np.nanpercentile(data, q=qt * 100))
+    return ret
 
 
 class EnsembleFTS(fts.FTS):
     def __init__(self, name, **kwargs):
-        super(EnsembleFTS, self).__init__("Ensemble FTS")
+        super(EnsembleFTS, self).__init__(1, "Ensemble FTS")
         self.shortname = "Ensemble FTS " + name
         self.name = "Ensemble FTS"
         self.flrgs = {}
@@ -23,51 +32,54 @@ class EnsembleFTS(fts.FTS):
         self.is_high_order = True
         self.models = []
         self.parameters = []
+        self.alpha = kwargs.get("alpha", 0.05)
+        self.max_order = 1
 
-    def build(self, data, models, partitioners, partitions, max_order=3, transformation=None, indexer=None):
+    def appendModel(self, model):
-
+        self.models.append(model)
-        methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
+        if model.order > self.max_order:
-                   ismailefendi.ImprovedWeightedFTS, sadaei.ExponentialyWeightedFTS, hwang.HighOrderFTS,
+            self.max_order = model.order
-                   hofts.HighOrderFTS, arima.ARIMA, quantreg.QuantileRegression]
-
-        transformations = [None, Transformations.Differential(1)]
-
-        for count, method in enumerate(methods, start=0):
-            mfts = method("")
-            if mfts.benchmark_only:
-                if transformation is not None:
-                    mfts.appendTransformation(transformation)
-                mfts.train(data,None, order=1, parameters=None)
-                self.models.append(mfts)
-            else:
-                for partition in partitions:
-                    for partitioner in partitioners:
-                        data_train_fs = partitioner(data, partition, transformation=transformation)
-                        mfts = method("")
-
-                        mfts.partitioner = data_train_fs
-                        if not mfts.is_high_order:
-
-                            if transformation is not None:
-                                mfts.appendTransformation(transformation)
-
-                            mfts.train(data, data_train_fs.sets)
-                            self.models.append(mfts)
-                        else:
-                            for order in np.arange(1, max_order + 1):
-                                if order >= mfts.min_order:
-                                    mfts = method("")
-                                    mfts.partitioner = data_train_fs
-
-                                    if transformation is not None:
-                                        mfts.appendTransformation(transformation)
-
-                                    mfts.train(data, data_train_fs.sets, order=order)
-                                    self.models.append(mfts)
 
     def train(self, data, sets, order=1,parameters=None):
-        #if self.models is None:
+        self.original_max = max(data)
-        pass
+        self.original_min = min(data)
+
+    def get_models_forecasts(self,data):
+        tmp = []
+        for model in self.models:
+            sample = data[-model.order:]
+            forecast = model.forecast(sample)
+            if isinstance(forecast, (list,np.ndarray)):
+                forecast = int(forecast[-1])
+            tmp.append(forecast)
+        return tmp
+
+    def get_point(self,method, forecasts, **kwargs):
+        if method == 'mean':
+            ret = np.nanmean(forecasts)
+        elif method == 'median':
+            ret = np.nanpercentile(forecasts, 50)
+        elif method == 'quantile':
+            alpha = kwargs.get("alpha",0.05)
+            ret = np.percentile(forecasts, alpha*100)
+
+        return ret
+
+    def get_interval(self, method, forecasts):
+        ret = []
+        if method == 'extremum':
+            ret.append([min(forecasts), max(forecasts)])
+        elif method == 'quantile':
+            qt_lo = np.nanpercentile(forecasts, q=self.alpha * 100)
+            qt_up = np.nanpercentile(forecasts, q=(1-self.alpha) * 100)
+            ret.append([qt_lo, qt_up])
+        elif method == 'normal':
+            mu = np.nanmean(forecasts)
+            sigma = np.sqrt(np.nanvar(forecasts))
+            ret.append(mu + st.norm.ppf(self.alpha) * sigma)
+            ret.append(mu + st.norm.ppf(1 - self.alpha) * sigma)
+
+        return ret
 
     def forecast(self, data, **kwargs):
 
@@ -76,83 +88,106 @@ class EnsembleFTS(fts.FTS):
         ndata = np.array(self.doTransformations(data))
 
         l = len(ndata)
-
         ret = []
 
-        for k in np.arange(0, l+1):
+        for k in np.arange(self.max_order, l+1):
-            tmp = []
+            sample = ndata[k - self.max_order : k ]
-            for model in self.models:
+            tmp = self.get_models_forecasts(sample)
-                if k >= model.minOrder - 1:
+            point = self.get_point(method, tmp)
-                    sample = ndata[k - model.order : k]
+            ret.append(point)
-                    tmp.append( model.forecast(sample) )
-            if method == 'mean':
-                ret.append( np.nanmean(tmp))
-            elif method == 'median':
-                ret.append(np.percentile(tmp,50))
 
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
 
         return ret
 
+
     def forecastInterval(self, data, **kwargs):
 
         method = kwargs.get('method', 'extremum')
 
+        if 'alpha' in kwargs:
+            self.alpha = kwargs.get('alpha',0.05)
+
         ndata = np.array(self.doTransformations(data))
 
         l = len(ndata)
 
         ret = []
 
-        for k in np.arange(0, l):
+        for k in np.arange(self.max_order, l+1):
-            tmp = []
+            sample = ndata[k - self.max_order : k ]
-            for model in self.models:
+            tmp = self.get_models_forecasts(sample)
-                if k >= model.minOrder - 1:
+            interval = self.get_interval(method, tmp)
-                    sample = ndata[k - model.order : k]
+            ret.append(interval)
-                    tmp.append( model.forecast(sample) )
-            if method == 'extremum':
-                ret.append( [ min(tmp), max(tmp) ] )
-            elif method == 'quantile':
-                q = kwargs.get('q', [.05, .95])
-                ret.append(np.percentile(tmp,q=q*100))
 
         return ret
 
-    def forecastAhead(self, data, steps, **kwargs):
-        pass
-
     def forecastAheadInterval(self, data, steps, **kwargs):
-        pass
 
+        method = kwargs.get('method', 'extremum')
 
-    def getGridClean(self, resolution):
+        ret = []
-        grid = {}
 
-        if len(self.transformations) == 0:
+        samples = [[k,k] for k in data[-self.max_order:]]
-            _min = self.sets[0].lower
-            _max = self.sets[-1].upper
-        else:
-            _min = self.original_min
-            _max = self.original_max
 
-        for sbin in np.arange(_min,_max, resolution):
+        for k in np.arange(self.max_order, steps):
-            grid[sbin] = 0
+            forecasts = []
+            sample = samples[k - self.max_order : k]
+            lo_sample = [i[0] for i in sample]
+            up_sample = [i[1] for i in sample]
+            forecasts.extend(self.get_models_forecasts(lo_sample) )
+            forecasts.extend(self.get_models_forecasts(up_sample))
+            interval = self.get_interval(method, forecasts)
 
-        return grid
+            if len(interval) == 1:
+                interval = interval[0]
 
-    def gridCount(self, grid, resolution, index, interval):
+            ret.append(interval)
-        #print(point_to_interval)
+            samples.append(interval)
-        for k in index.inside(interval[0],interval[1]):
-            #print(k)
-            grid[k] += 1
-        return grid
 
-    def gridCountPoint(self, grid, resolution, index, point):
+        return ret
-        k = index.find_ge(point)
+
-        # print(k)
+    def empty_grid(self, resolution):
-        grid[k] += 1
+        return self.get_empty_grid(-(self.original_max*2), self.original_max*2, resolution)
-        return grid
 
     def forecastAheadDistribution(self, data, steps, **kwargs):
-        pass
+        method = kwargs.get('method', 'extremum')
+
+        percentile_size = (self.original_max - self.original_min) / 100
+
+        resolution = kwargs.get('resolution', percentile_size)
+
+        grid = self.empty_grid(resolution)
+
+        index = SortedCollection.SortedCollection(iterable=grid.keys())
+
+        ret = []
+
+        samples = [[k] for k in data[-self.max_order:]]
+
+        for k in np.arange(self.max_order, steps + self.max_order):
+            forecasts = []
+            lags = {}
+            for i in np.arange(0, self.max_order): lags[i] = samples[k - self.max_order + i]
+
+            # Build the tree with all possible paths
+
+            root = tree.FLRGTreeNode(None)
+
+            tree.buildTreeWithoutOrder(root, lags, 0)
+
+            for p in root.paths():
+                path = list(reversed(list(filter(None.__ne__, p))))
+
+                forecasts.extend(self.get_models_forecasts(path))
+
+            samples.append(sampler(forecasts, [0.05, 0.25, 0.5, 0.75, 0.95 ]))
+
+            grid = self.gridCountPoint(grid, resolution, index, forecasts)
+
+            tmp = np.array([grid[i] for i in sorted(grid)])
+
+            ret.append(tmp / sum(tmp))
+
+        return ret
 

fts.py

@@ -210,13 +210,15 @@ class FTS(object):
     def gridCount(self, grid, resolution, index, interval):
         #print(point_to_interval)
         for k in index.inside(interval[0],interval[1]):
-            #print(k)
             grid[k] += 1
         return grid
 
     def gridCountPoint(self, grid, resolution, index, point):
-        k = index.find_ge(point)
+        if not isinstance(point, (list, np.ndarray)):
-        # print(k)
+            point = [point]
+
+        for p in point:
+            k = index.find_ge(p)
             grid[k] += 1
         return grid
 
@@ -225,4 +227,3 @@ class FTS(object):
 
 
 
-

tests/ensemble.py

@@ -0,0 +1,112 @@
+#!/usr/bin/python
+# -*- coding: utf8 -*-
+
+import os
+import numpy as np
+import pandas as pd
+import matplotlib as plt
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+import pandas as pd
+from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
+from pyFTS.common import FLR,FuzzySet,Membership,Transformations
+from pyFTS import fts,hofts,ifts,pwfts,tree, chen, ensemble, song, yu, cheng, ismailefendi, sadaei, hwang
+from pyFTS.benchmarks import naive, arima
+from numpy import random
+from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.benchmarks import arima, quantreg, Measures
+
+os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
+
+diff = Transformations.Differential(1)
+
+passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
+passengers = np.array(passengers["Passengers"])
+
+
+e = ensemble.EnsembleFTS("")
+
+fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS,
+              ismailefendi.ImprovedWeightedFTS]
+
+ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]
+
+fs = Grid.GridPartitioner(passengers, 10, transformation=diff)
+
+for method in fo_methods:
+    model = method("")
+    model.appendTransformation(diff)
+    model.train(passengers, fs.sets)
+    e.appendModel(model)
+
+
+for method in ho_methods:
+    for order in [1,2,3]:
+        model = method("")
+        model.appendTransformation(diff)
+        model.train(passengers, fs.sets, order=order)
+        e.appendModel(model)
+
+
+arima100 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima100.train(passengers, None, order=(1,0,0))
+
+arima101 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima101.train(passengers, None, order=(1,0,1))
+
+arima200 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima200.train(passengers, None, order=(2,0,0))
+
+arima201 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima201.train(passengers, None, order=(2,0,1))
+
+
+e.appendModel(arima100)
+e.appendModel(arima101)
+e.appendModel(arima200)
+e.appendModel(arima201)
+
+e.train(passengers, None)
+
+"""
+_mean = e.forecast(passengers, method="mean")
+print(_mean)
+
+_median = e.forecast(passengers, method="median")
+print(_median)
+"""
+"""
+_extremum = e.forecastInterval(passengers, method="extremum")
+print(_extremum)
+
+_quantile = e.forecastInterval(passengers, method="quantile", alpha=0.25)
+print(_quantile)
+
+
+_normal = e.forecastInterval(passengers, method="normal", alpha=0.25)
+print(_normal)
+"""
+
+"""
+_extremum = e.forecastAheadInterval(passengers, 10, method="extremum")
+print(_extremum)
+
+_quantile = e.forecastAheadInterval(passengers, 10, method="quantile", alpha=0.25)
+print(_quantile)
+
+
+_normal = e.forecastAheadInterval(passengers, 10, method="normal", alpha=0.25)
+print(_normal)
+"""
+
+dist = e.forecastAheadDistribution(passengers, 20)
+
+print(dist)
+
+
+

tests/general.py

@@ -69,10 +69,21 @@ from pyFTS.benchmarks import arima, quantreg, Measures
 
 #Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
 #"""
-tmp = arima.ARIMA("", alpha=0.25)
+arima100 = arima.ARIMA("", alpha=0.25)
 #tmp.appendTransformation(diff)
-tmp.train(enrollments, None, order=(1,0,0))
+arima100.train(passengers, None, order=(1,0,0))
-teste = tmp.forecastInterval(enrollments)
+
+arima101 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima101.train(passengers, None, order=(1,0,1))
+
+arima200 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima200.train(passengers, None, order=(2,0,0))
+
+arima201 = arima.ARIMA("", alpha=0.25)
+#tmp.appendTransformation(diff)
+arima201.train(passengers, None, order=(2,0,1))
 
 
 #tmp = quantreg.QuantileRegression("", alpha=0.25, dist=True)

tree.py

@@ -51,3 +51,15 @@ def flat(dados):
                 yield k
         else:
             yield inst
+
+
+def buildTreeWithoutOrder(node, lags, level):
+
+    if level not in lags:
+        return
+
+    for s in lags[level]:
+        node.appendChild(FLRGTreeNode(s))
+
+    for child in node.getChildren():
+        buildTreeWithoutOrder(child, lags, level + 1)