Bugfix in GridPartitioner, occured error when a BoxCox transformed series has -np.inf values

pyFTS/benchmarks/benchmarks.py

@@ -257,6 +257,7 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
                 for partition in partitions:
 
                     for partitioner in partitioners_methods:
+                        print(transformation, partition)
 
                         data_train_fs = partitioner(data=train, npart=partition, transformation=transformation)
 

pyFTS/partitioners/partitioner.py

@@ -48,7 +48,11 @@ class Partitioner(object):
             if self.indexer is not None:
                 ndata = self.indexer.get_data(ndata)
 
-            _min = min(ndata)
+            _min = np.nanmin(ndata)
+            if _min == -np.inf:
+                ndata[ndata == -np.inf] = 0
+                _min = np.nanmin(ndata)
+
             if _min < 0:
                 self.min = _min * 1.1
             else:

pyFTS/tests/nonstationary.py

@@ -8,81 +8,46 @@ import matplotlib.pyplot as plt
 from pyFTS.common import Util as cUtil
 import pandas as pd
 
-'''
-from pyFTS.data import artificial
+from pyFTS.data import TAIEX, NASDAQ, SP500, artificial
 
-lmv1 = artificial.generate_gaussian_linear(1,0.2,0.2,0.05)
-
-ts=200
-ws=35
-train1 = lmv1[:ts]
-test1 = lmv1[ts:]
-
-tmp_fs1 = Grid.GridPartitioner(data=train1[:50], npart=10)
-
-fs1 = partitioners.PolynomialNonStationaryPartitioner(train1, tmp_fs1, window_size=ws, degree=1)
-
-nsfts1 = honsfts.HighOrderNonStationaryFTS("", partitioner=fs1)
-
-nsfts1.fit(train1, order=2, parameters=ws)
-
-print(fs1)
-
-print(nsfts1.predict(test1))
-
-print(nsfts1)
-
-util.plot_sets(fs1, tam=[10, 5], start=0, end=100, step=2, data=lmv1[:100], window_size=35)
-'''
+datasets = {
+    "TAIEX": TAIEX.get_data()[:4000],
+    "SP500": SP500.get_data()[10000:14000],
+    "NASDAQ": NASDAQ.get_data()[:4000],
+    # Incremental Mean and Incremental Variance
+    "IMIV": artificial.generate_gaussian_linear(1,0.2,0.2,0.05,it=100, num=40),
+    # Incremental Mean and Incremental Variance, lower bound equals to 0
+    "IMIV0": artificial.generate_gaussian_linear(1,0.2,0.,0.05, vmin=0,it=100, num=40),
+    # Constant Mean and Incremental Variance
+    "CMIV": artificial.generate_gaussian_linear(5,0.1,0,0.02,it=100, num=40),
+    # Incremental Mean and Constant Variance
+    "IMCV": artificial.generate_gaussian_linear(1,0.6,0.1,0,it=100, num=40)
+}
 
+train_split = 2000
+test_length = 200
 
 from pyFTS.common import Transformations
+
 tdiff = Transformations.Differential(1)
 
+boxcox = Transformations.BoxCox(0)
 
-from pyFTS.common import Util
+transformations = {'None': None, 'Differential(1)': tdiff, 'BoxCox(0)': boxcox }
 
-from pyFTS.data import TAIEX
+from pyFTS.partitioners import Grid, Util as pUtil
+from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.models import chen
 
-taiex = TAIEX.get_data()
-#taiex_diff = tdiff.apply(taiex)
+tag = 'chen_partitioning'
 
-train = taiex[:600]
-test = taiex[600:800]
+for ds in ['IMIV0']: #datasets.keys():
+    dataset = datasets[ds]
 
-#fs_tmp = Grid.GridPartitioner(data=train, npart=7, transformation=tdiff)
-#fs_tmp = Entropy.EntropyPartitioner(data=train, npart=7, transformation=tdiff)
-fs_tmp = Grid.GridPartitioner(data=train, npart=20)
-
-fs = partitioners.SimpleNonStationaryPartitioner(train, fs_tmp)
-
-print(fs)
-
-model = cvfts.ConditionalVarianceFTS(partitioner=fs,memory_window=3)
-model.fit(train)
-
-print(model)
-
-#tmpp4 = model.predict(test, type='point')
-#tmp = model.predict(test, type='interval')
-
-#util.plot_sets_conditional(model, tdiff.apply(test),  step=5, size=[10, 5])
-#util.plot_sets_conditional(model, test,  step=5, size=[10, 5])
-
-fig, axes = plt.subplots(nrows=2, ncols=1, figsize=[10, 5])
-
-axes[0].plot(test[1:], label="Test Data")
-
-forecasts = model.predict(test, type='point')
-
-axes[0].plot(forecasts[:-1], label="CVFTS Forecasts")
-
-handles0, labels0 = axes[0].get_legend_handles_labels()
-lgd = axes[0].legend(handles0, labels0, loc=2)
-
-residuals = np.array(test[1:]) - np.array(forecasts[:-1])
-
-axes[1].plot(residuals)
-axes[1].set_title("Residuals")
-
-print("fim")
+    bchmk.sliding_window_benchmarks(dataset, 4000, train=0.2, inc=0.2,
+                                    methods=[chen.ConventionalFTS],
+                                    benchmark_models=False,
+                                    transformations=[boxcox], #transformations[t] for t in transformations.keys()],
+                                    partitions=np.arange(3, 40, 1),
+                                    progress=False, type='point',
+                                    file="nsfts_benchmarks.db", dataset=ds, tag=tag)