- Several bugfixes in benchmarks methods and optimizations

benchmarks/arima.py

@@ -10,7 +10,7 @@ class ARIMA(fts.FTS):
     """
     Façade for statsmodels.tsa.arima_model
     """
-    def __init__(self, order, **kwargs):
+    def __init__(self, name, **kwargs):
         super(ARIMA, self).__init__(1, "ARIMA")
         self.name = "ARIMA"
         self.detail = "Auto Regressive Integrated Moving Average"
@@ -24,34 +24,41 @@ class ARIMA(fts.FTS):
         self.benchmark_only = True
         self.min_order = 1
 
-    def train(self, data, sets, order=1, parameters=None):
+    def train(self, data, sets, order=(2,1,1), parameters=None):
-        if parameters is not None:
+        self.p = order[0]
-            self.p = parameters[0]
+        self.d = order[1]
-            self.d = parameters[1]
+        self.q = order[2]
-            self.q = parameters[2]
         self.order = max([self.p, self.d, self.q])
         self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
 
         old_fit = self.model_fit
         self.model =  stats_arima(data, order=(self.p, self.d, self.q))
-        try:
         self.model_fit = self.model.fit(disp=0)
-        except:
-            try:
-                self.model = stats_arima(data, order=(self.p, self.d, self.q))
-                self.model_fit = self.model.fit(disp=1)
-            except:
-                self.model_fit = old_fit
 
-        self.trained_data = data #.tolist()
+    def ar(self, data):
+        return data.dot(self.model_fit.arparams)
+
+    def ma(self, data):
+        return data.dot(self.model_fit.maparams)
 
     def forecast(self, data, **kwargs):
         if self.model_fit is None:
             return np.nan
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
         ret = []
-        for t in data:
+
-            output = self.model_fit.forecast()
+        ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
-            ret.append( output[0] )
+
-            self.trained_data = np.append(self.trained_data, t) #.append(t)
+        residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
-            self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
+
+        ma = np.array([self.ma(residuals[k - self.q: k]) for k in np.arange(self.q, len(ar) + 1)])
+
+        ret = ar + ma
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
         return ret

benchmarks/benchmarks.py

@@ -60,73 +60,9 @@ def get_probabilistic_methods():
     return [quantreg.QuantileRegression, ensemble.EnsembleFTS, pwfts.ProbabilisticWeightedFTS]
 
 
-def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False,
-                                  save=False, file=None, sintetic=True):
-    """
-    Sliding window benchmarks for non FTS point forecasters
-    :param models: non FTS point forecasters
-    :param parameters: parameters for each model
-    :param data: data set
-    :param windowsize: size of sliding window
-    :param train: percentual of sliding window data used to train the models
-    :param dump: 
-    :param save: save results
-    :param file: file path to save the results
-    :param sintetic: if true only the average and standard deviation of the results
-    :return: DataFrame with the results
-    """
-    objs = {}
-    lcolors = {}
-    rmse = {}
-    smape = {}
-    u = {}
-    times = {}
-
-    experiments = 0
-    for ct, train, test in Util.sliding_window(data, windowsize, train):
-        experiments += 1
-        for count, method in enumerate(models, start=0):
-            model = method("")
-
-            _start = time.time()
-            model.train(train, None, parameters=parameters[count])
-            _end = time.time()
-
-            _key = model.shortname
-
-            if dump: print(ct, _key)
-
-            if _key not in objs:
-                objs[_key] = model
-                lcolors[_key] = colors[count % ncol]
-                rmse[_key] = []
-                smape[_key] = []
-                u[_key] = []
-                times[_key] = []
-
-            _tdiff = _end - _start
-
-            try:
-                _start = time.time()
-                _rmse, _smape, _u = Measures.get_point_statistics(test, model, None)
-                _end = time.time()
-                rmse[_key].append(_rmse)
-                smape[_key].append(_smape)
-                u[_key].append(_u)
-                _tdiff += _end - _start
-                times[_key].append(_tdiff)
-                if dump: print(_rmse, _smape, _u, _tdiff)
-            except:
-                rmse[_key].append(np.nan)
-                smape[_key].append(np.nan)
-                u[_key].append(np.nan)
-                times[_key].append(np.nan)
-
-    return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
-
-
 def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner],
                          partitions=[10], max_order=3,transformation=None,indexer=None,dump=False,
+                         benchmark_models=None, benchmark_models_parameters = None,
                          save=False, file=None, sintetic=True):
     """
     Sliding window benchmarks for FTS point forecasters
@@ -153,7 +89,6 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
     if models is None:
         models = get_point_methods()
 
-
     objs = {}
     lcolors = {}
     rmse = {}

benchmarks/distributed_benchmarks.py

@@ -13,7 +13,7 @@ import dispy
 import dispy.httpd
 import numpy as np
 
-from pyFTS.benchmarks import benchmarks, Util as bUtil
+from pyFTS.benchmarks import benchmarks, Util as bUtil, naive, quantreg, arima
 from pyFTS.common import Util
 from pyFTS.partitioners import Grid
 
@@ -31,17 +31,23 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
     :return: a dictionary with the benchmark results 
     """
     import time
-    from pyFTS import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei
+    from pyFTS import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei, song, cheng, hwang
     from pyFTS.partitioners import Grid, Entropy, FCM
-    from pyFTS.benchmarks import Measures
+    from pyFTS.benchmarks import Measures, naive, arima, quantreg
 
-    tmp = [yu.WeightedFTS, chen.ConventionalFTS, hofts.HighOrderFTS, ifts.IntervalFTS,
+    tmp = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
-           pwfts.ProbabilisticWeightedFTS, ismailefendi.ImprovedWeightedFTS, sadaei.ExponentialyWeightedFTS]
+            cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, hwang.HighOrderFTS,
+            pwfts.ProbabilisticWeightedFTS]
 
     tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
 
+    tmp4 = [naive.Naive, arima.ARIMA, quantreg.QuantileRegression]
+
     tmp3 = [Measures.get_point_statistics]
 
+    if mfts.benchmark_only:
+        _key = mfts.shortname + str(mfts.order if mfts.order is not None else "")
+    else:
         pttr = str(partitioner.__module__).split('.')[-1]
         _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
         mfts.partitioner = partitioner
@@ -65,6 +71,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
 
 def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=[Grid.GridPartitioner],
                          partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
+                         benchmark_models=None, benchmark_models_parameters = None,
                          save=False, file=None, sintetic=False,nodes=None, depends=None):
     """
     Distributed sliding window benchmarks for FTS point forecasters
@@ -78,6 +85,8 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
     :param transformation: data transformation
     :param indexer: seasonal indexer
     :param dump: 
+    :param benchmark_models: Non FTS models to benchmark
+    :param benchmark_models_parameters: Non FTS models parameters
     :param save: save results
     :param file: file path to save the results
     :param sintetic: if true only the average and standard deviation of the results
@@ -86,6 +95,13 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
     :return: DataFrame with the results
     """
 
+    if benchmark_models is None:
+        benchmark_models = [naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA,
+                            quantreg.QuantileRegression, quantreg.QuantileRegression]
+
+    if benchmark_models_parameters is None:
+        benchmark_models_parameters = [None, (1, 0, 1), (1, 1, 1), (2, 1, 1), (2, 1, 2), 1, 2]
+
     cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
 
     http_server = dispy.httpd.DispyHTTPServer(cluster)
@@ -116,11 +132,20 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
                     pool.append(mfts)
         else:
             pool.append(mfts)
+            mfts.order = 1
+            pool.append(mfts)
+
+    for count, model in enumerate(benchmark_models, start=0):
+        mfts = model("")
+        mfts.order = benchmark_models_parameters[count]
+        pool.append(mfts)
 
     experiments = 0
     for ct, train, test in Util.sliding_window(data, windowsize, train):
         experiments += 1
 
+        benchmarks_only = {}
+
         if dump: print('\nWindow: {0}\n'.format(ct))
 
         for partition in partitions:
@@ -129,9 +154,13 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
 
                 data_train_fs = partitioner(train, partition, transformation=transformation)
 
-                for id, m in enumerate(pool,start=0):
+                for _id, m in enumerate(pool,start=0):
+                    if m.benchmark_only and m.shortname in benchmarks_only:
+                        continue
+                    else:
+                        benchmarks_only[m.shortname] = m
                     job = cluster.submit(m, data_train_fs, train, test, ct, transformation)
-                    job.id = id  # associate an ID to identify jobs (if needed later)
+                    job.id = _id  # associate an ID to identify jobs (if needed later)
                     jobs.append(job)
 
     for job in jobs:

benchmarks/naive.py

@@ -6,7 +6,7 @@ from pyFTS import fts
 
 class Naive(fts.FTS):
     """Naïve Forecasting method"""
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(Naive, self).__init__(1, "Naive " + name)
         self.name = "Naïve Model"
         self.detail = "Naïve Model"

benchmarks/quantreg.py

@@ -9,7 +9,7 @@ from pyFTS import fts
 
 class QuantileRegression(fts.FTS):
     """Façade for statsmodels.regression.quantile_regression"""
-    def __init__(self, order, **kwargs):
+    def __init__(self, name, **kwargs):
         super(QuantileRegression, self).__init__(1, "QR")
         self.name = "QR"
         self.detail = "Quantile Regression"
@@ -27,18 +27,20 @@ class QuantileRegression(fts.FTS):
     def train(self, data, sets, order=1, parameters=None):
         self.order = order
 
-        if parameters is not None:
         self.alpha = parameters
 
         tmp = np.array(self.doTransformations(data))
 
         lagdata, ndata = lagmat(tmp, maxlag=order, trim="both", original='sep')
-        uqt = QuantReg(ndata, lagdata).fit(1 - self.alpha)
+
         mqt = QuantReg(ndata, lagdata).fit(0.5)
+        if self.alpha is not None:
+            uqt = QuantReg(ndata, lagdata).fit(1 - self.alpha)
             lqt = QuantReg(ndata, lagdata).fit(self.alpha)
 
+        self.mean_qt = [k for k in mqt.params]
+        if self.alpha is not None:
             self.upper_qt = [uqt.params[k] for k in uqt.params.keys()]
-        self.mean_qt = [mqt.params[k] for k in mqt.params.keys()]
             self.lower_qt = [lqt.params[k] for k in lqt.params.keys()]
 
     def linearmodel(self,data,params):

tests/general.py

@@ -38,9 +38,8 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 #gauss = random.normal(0,1.0,5000)
 #gauss_teste = random.normal(0,1.0,400)
 
-
+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=",")
 #nasdaq = np.array(nasdaqpd["avg"][0:5000])
@@ -62,7 +61,7 @@ from pyFTS.benchmarks import Util
 
 #Util.cast_dataframe_to_sintetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
 
-Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
+#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
 
 #tmp = arima.ARIMA("")
 #tmp.train(taiex[:1600],None,parameters=(2,0,1))
@@ -72,11 +71,11 @@ Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/ta
 
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
 
-#bchmk.point_sliding_window(gauss,2000,train=0.8, #models=[yu.WeightedFTS], # #
+bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
-#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-#                     partitions= np.arange(3,10,step=1), #transformation=diff,
+                     partitions= np.arange(10,200,step=10), #transformation=diff,
-#                     dump=True, save=True, file="experiments/gauss_point_distributed.csv",
+                     dump=True, save=True, file="experiments/taiex_point_analytic.csv",
-#                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
+                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])