- More data files and artificial data generators

MANIFEST

@@ -1,2 +1,6 @@
 include data/Enrollments.csv
 include data/AirPassengers.csv
+include data/NASDAQ.csv
+include data/SP500.csv
+include data/sunspots.csv
+include data/TAIEX.csv

pyFTS/benchmarks/ResidualAnalysis.py

@@ -17,7 +17,7 @@ def residuals(targets, forecasts, order=1):
     return np.array(targets[order:]) - np.array(forecasts[:-1])
 
 
-def ChiSquared(q,h):
+def chi_squared(q, h):
     """
     Chi-Squared value
     :param q: 
@@ -28,7 +28,7 @@ def ChiSquared(q,h):
     return p
 
 
-def compareResiduals(data, models):
+def compare_residuals(data, models):
     """
     Compare residual's statistics of several models
     :param data: 
@@ -49,7 +49,7 @@ def compareResiduals(data, models):
         ret += str(round(q1,2)) + "		& "
         q2 = Measures.BoxLjungStatistic(res, 10)
         ret += str(round(q2,2)) + "		& "
-        ret += str(ChiSquared(q2, 10))
+        ret += str(chi_squared(q2, 10))
         ret += "	\\\\ \n"
     return ret
 
@@ -131,9 +131,8 @@ def plot_residuals(targets, models, tam=[8, 8], save=False, file=None):
 
 
 def single_plot_residuals(targets, forecasts, order, tam=[8, 8], save=False, file=None):
-    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=tam)
+    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=tam)
 
-    ax = axes
     res = residuals(targets, forecasts, order)
 
     ax[0].set_title("Residuals", size='large')

pyFTS/benchmarks/benchmarks.py

@@ -13,7 +13,7 @@ import matplotlib.cm as cmx
 import matplotlib.colors as pltcolors
 import matplotlib.pyplot as plt
 import numpy as np
-#from mpl_toolkits.mplot3d import Axes3D
+from mpl_toolkits.mplot3d import Axes3D
 
 from pyFTS.probabilistic import ProbabilityDistribution
 from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
@@ -30,8 +30,8 @@ rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
 #rc('font',**{'family':'serif','serif':['Palatino']})
 rc('text', usetex=True)
 
-colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
-          'cyan', 'blue', 'darkblue', 'purple', 'darkviolet']
+colors = ['grey', 'darkgrey', 'rosybrown', 'maroon', 'red','orange', 'gold', 'yellow', 'olive', 'green',
+          'darkgreen', 'cyan', 'lightblue','blue', 'darkblue', 'purple', 'darkviolet' ]
 
 ncol = len(colors)
 
@@ -281,7 +281,7 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
         print_point_statistics(data_test, objs)
 
     if residuals:
-        print(ResidualAnalysis.compareResiduals(data_test, objs))
+        print(ResidualAnalysis.compare_residuals(data_test, objs))
         ResidualAnalysis.plot_residuals(data_test, objs, save=save, file=file, tam=tam)
 
     if series:
@@ -303,6 +303,8 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
 
         plot_probability_distributions(pmfs, lcolors, tam=tam)
 
+    return models
+
 
 def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
     ret = "Model		& Order     & RMSE		& SMAPE      & Theil's U		\\\\ \n"
@@ -539,29 +541,32 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
     ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
 
     for count, fts in enumerate(models, start=0):
-        if fts.has_point_forecasting and points:
-            forecasts = fts.forecast(original)
-            if isinstance(forecasts, np.ndarray):
-                forecasts = forecasts.tolist()
-            mi.append(min(forecasts) * 0.95)
-            ma.append(max(forecasts) * 1.05)
-            for k in np.arange(0, fts.order):
-                forecasts.insert(0, None)
-            lbl = fts.shortname + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
-            if typeonlegend: lbl += " (Point)"
-            ax.plot(forecasts, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
+        try:
+            if fts.has_point_forecasting and points:
+                forecasts = fts.forecast(original)
+                if isinstance(forecasts, np.ndarray):
+                    forecasts = forecasts.tolist()
+                mi.append(min(forecasts) * 0.95)
+                ma.append(max(forecasts) * 1.05)
+                for k in np.arange(0, fts.order):
+                    forecasts.insert(0, None)
+                lbl = fts.shortname + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
+                if typeonlegend: lbl += " (Point)"
+                ax.plot(forecasts, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
 
-        if fts.has_interval_forecasting and intervals:
-            forecasts = fts.forecast_interval(original)
-            lbl = fts.shortname + " " + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
-            if not points and intervals:
-                ls = "-"
-            else:
-                ls = "--"
-            tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
-                                         color=colors[count], ls=ls, linewidth=linewidth)
-            mi.append(tmpmi)
-            ma.append(tmpma)
+            if fts.has_interval_forecasting and intervals:
+                forecasts = fts.forecast_interval(original)
+                lbl = fts.shortname + " " + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
+                if not points and intervals:
+                    ls = "-"
+                else:
+                    ls = "--"
+                tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
+                                             color=colors[count], ls=ls, linewidth=linewidth)
+                mi.append(tmpmi)
+                ma.append(tmpma)
+        except ValueError as ex:
+            print(fts.shortname)
 
         handles0, labels0 = ax.get_legend_handles_labels()
         lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
@@ -573,7 +578,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
     ax.set_xlabel('T')
     ax.set_xlim([0, len(original)])
 
-    Util.show_and_save_image(fig, file, save, lgd=legends)
+    #Util.show_and_save_image(fig, file, save, lgd=legends)
 
 
 def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
@@ -1022,7 +1027,10 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
                       partitioner=Grid.GridPartitioner,transformation=None,indexer=None):
     _3d = len(orders) > 1
     ret = []
-    errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))])
+    if _3d:
+        errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))])
+    else:
+        errors = []
     forecasted_best = []
     fig = plt.figure(figsize=tam)
     # fig.suptitle("Comparação de modelos ")
@@ -1055,7 +1063,10 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
             else:
                 forecasted = fts.forecast_interval(test)
                 error = 1.0 - Measures.rmse_interval(np.array(test[o:]), np.array(forecasted[:-1]))
-            errors[oc, pc] = error
+            if _3d:
+                errors[oc, pc] = error
+            else:
+                errors.append( error )
             if error < min_rmse:
                 min_rmse = error
                 best = fts
@@ -1067,9 +1078,8 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
         # ax0.legend(handles0, labels0)
         ax0.plot(test, label="Original", linewidth=3.0, color="black")
         if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
-    if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
-    # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
-    if _3d:
+    if _3d and not plotforecasts:
+        ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
         ax1.set_title('Error Surface')
         ax1.set_ylabel('Model order')
         ax1.set_xlabel('Number of partitions')
@@ -1079,9 +1089,9 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
     else:
         ax1 = fig.add_axes([0, 1, 0.9, 0.9])
         ax1.set_title('Error Curve')
-        ax1.set_ylabel('Number of partitions')
-        ax1.set_xlabel('RMSE')
-        ax0.plot(errors,partitions)
+        ax1.set_xlabel('Number of partitions')
+        ax1.set_ylabel('RMSE')
+        ax1.plot(partitions, errors)
     ret.append(best)
     ret.append(forecasted_best)
     ret.append(min_rmse)

pyFTS/benchmarks/distributed_benchmarks.py

@@ -31,8 +31,7 @@ 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, hofts, pwfts,ismailefendi,sadaei, song, cheng, hwang
-    from pyFTS.models import chen
+    from pyFTS.models import yu, chen, hofts, pwfts,ismailefendi,sadaei, song, cheng, hwang
     from pyFTS.partitioners import Grid, Entropy, FCM
     from pyFTS.benchmarks import Measures, naive, arima, quantreg
     from pyFTS.common import Transformations
@@ -223,7 +222,7 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
     :return: a dictionary with the benchmark results 
     """
     import time
-    from pyFTS import hofts,ifts,pwfts
+    from pyFTS.models import hofts,ifts,pwfts
     from pyFTS.partitioners import Grid, Entropy, FCM
     from pyFTS.benchmarks import Measures, arima, quantreg
 
@@ -424,8 +423,8 @@ def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, windo
     """
     import time
     import numpy as np
-    from pyFTS import hofts, ifts, pwfts
-    from pyFTS.models import ensemble
+    from pyFTS.models import hofts, ifts, pwfts
+    from pyFTS.models.ensemble import ensemble
     from pyFTS.partitioners import Grid, Entropy, FCM
     from pyFTS.benchmarks import Measures, arima
     from pyFTS.models.seasonal import SeasonalIndexer

pyFTS/benchmarks/parallel_benchmarks.py

@@ -10,7 +10,7 @@ from copy import deepcopy
 import numpy as np
 from joblib import Parallel, delayed
 
-from pyFTS.benchmarks import benchmarks, Util
+from pyFTS.benchmarks import benchmarks, Util as bUtil
 from pyFTS.common import Util
 from pyFTS.partitioners import Grid
 

pyFTS/common/FLR.py

@@ -35,6 +35,7 @@ class IndexedFLR(FLR):
     def __str__(self):
         return str(self.index) + ": "+ self.LHS.name + " -> " + self.RHS.name
 
+
 def generate_high_order_recurrent_flr(fuzzyData):
     """
     Create a ordered FLR set from a list of fuzzy sets with recurrence
@@ -55,7 +56,8 @@ def generate_high_order_recurrent_flr(fuzzyData):
             flrs.append(tmp)
     return flrs
 
-def generateRecurrentFLRs(fuzzyData):
+
+def generate_recurrent_flrs(fuzzyData):
     """
     Create a ordered FLR set from a list of fuzzy sets with recurrence
     :param fuzzyData: ordered list of fuzzy sets
@@ -84,20 +86,20 @@ def generateRecurrentFLRs(fuzzyData):
     return flrs
 
 
-def generateNonRecurrentFLRs(fuzzyData):
+def generate_non_recurrent_flrs(fuzzyData):
     """
     Create a ordered FLR set from a list of fuzzy sets without recurrence
     :param fuzzyData: ordered list of fuzzy sets
     :return: ordered list of FLR
     """
-    flrs = generateRecurrentFLRs(fuzzyData)
+    flrs = generate_recurrent_flrs(fuzzyData)
     tmp = {}
     for flr in flrs: tmp[str(flr)] = flr
     ret = [value for key, value in tmp.items()]
     return ret
 
 
-def generateIndexedFLRs(sets, indexer, data, transformation=None):
+def generate_indexed_flrs(sets, indexer, data, transformation=None):
     """
     Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence
     :param sets: fuzzy sets

pyFTS/common/Transformations.py

@@ -46,7 +46,7 @@ class Differential(Transformation):
         for t in np.arange(0, self.lag): diff.insert(0, 0)
         return diff
 
-    def inverse(self,data, param, **kwargs):
+    def inverse(self, data, param, **kwargs):
 
         interval = kwargs.get("point_to_interval",False)
 
@@ -56,11 +56,11 @@ class Differential(Transformation):
         if not isinstance(data, list):
             data = [data]
 
-        if not isinstance(param, list):
-            param = [param]
-
         n = len(data)
 
+#        print(n)
+#        print(len(param))
+
         if not interval:
             inc = [data[t] + param[t] for t in np.arange(0, n)]
         else:

pyFTS/common/fts.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pandas as pd
-from pyFTS import tree
-from pyFTS.common import FuzzySet, SortedCollection
+from pyFTS.common import FuzzySet, SortedCollection, tree
 
 
 class FTS(object):
@@ -34,6 +33,8 @@ class FTS(object):
         self.original_max = 0
         self.original_min = 0
         self.partitioner = kwargs.get("partitioner", None)
+        if self.partitioner != None:
+            self.sets = self.partitioner.sets
         self.auto_update = False
         self.benchmark_only = False
         self.indexer = None
@@ -199,7 +200,6 @@ class FTS(object):
                 params = [None for k in self.transformations]
 
             for c, t in enumerate(reversed(self.transformations), start=0):
-                print(c)
                 ndata = t.inverse(data, params[c], **kwargs)
 
             return ndata

pyFTS/data/AirPassengers.py

@@ -1,8 +1,10 @@
 import pandas as pd
 import numpy as np
+import pkg_resources
 
 
 def get_data():
-    passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
+    filename = pkg_resources.resource_filename('pyFTS', 'data/AirPassengers.csv')
+    passengers = pd.read_csv(filename, sep=",")
     passengers = np.array(passengers["Passengers"])
     return passengers

pyFTS/data/Enrollments.py

@@ -3,9 +3,8 @@ import numpy as np
 import os
 import pkg_resources
 
+
 def get_data():
-    #data_path = os.path.dirname(__file__)
-    #filename = os.path.join(data_path,"Enrollments.csv")
     filename = pkg_resources.resource_filename('pyFTS', 'data/Enrollments.csv')
     enrollments = pd.read_csv(filename, sep=";")
     enrollments = np.array(enrollments["Enrollments"])

pyFTS/data/NASDAQ.py

@@ -0,0 +1,11 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_data():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/NASDAQ.csv')
+    dat = pd.read_csv(filename, sep=";")
+    dat = np.array(dat["avg"])
+    return dat

pyFTS/data/SP500.py

@@ -0,0 +1,11 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_data():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/SP500.csv')
+    dat = pd.read_csv(filename, sep=",")
+    dat = np.array(dat["Avg"])
+    return dat

pyFTS/data/TAIEX.py

@@ -0,0 +1,11 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_data():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv')
+    dat = pd.read_csv(filename, sep=";")
+    dat = np.array(dat["avg"])
+    return dat

pyFTS/data/artificial.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+
+def generate_gaussian_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=10, vmin=None, vmax=None):
+    """
+    Generate data sampled from Gaussian distribution, with constant or linear changing parameters
+    :param mu_ini: Initial mean
+    :param sigma_ini: Initial variance
+    :param mu_inc:  Mean increment after 'num' samples
+    :param sigma_inc: Variance increment after 'num' samples
+    :param it: Number of iterations
+    :param num: Number of samples generated on each iteration
+    :param vmin: Lower bound value of generated data
+    :param vmax: Upper bound value of generated data
+    :return: A list of it*num float values
+    """
+    mu = mu_ini
+    sigma = sigma_ini
+    ret = []
+    for k in np.arange(0,it):
+        tmp = np.random.normal(mu, sigma, num)
+        if vmin is not None:
+            tmp = np.maximum(np.full(num, vmin), tmp)
+        if vmax is not None:
+            tmp = np.minimum(np.full(num, vmax), tmp)
+        ret.extend(tmp)
+        mu += mu_inc
+        sigma += sigma_inc
+    return ret
+
+def generate_uniform_linear(min_ini, max_ini, min_inc, max_inc, it=100, num=10, vmin=None, vmax=None):
+    """
+    Generate data sampled from Uniform distribution, with constant or  linear changing bounds
+    :param mu_ini: Initial mean
+    :param sigma_ini: Initial variance
+    :param mu_inc:  Mean increment after 'num' samples
+    :param sigma_inc: Variance increment after 'num' samples
+    :param it: Number of iterations
+    :param num: Number of samples generated on each iteration
+    :param vmin: Lower bound value of generated data
+    :param vmax: Upper bound value of generated data
+    :return: A list of it*num float values
+    """
+    _min = min_ini
+    _max = max_ini
+    ret = []
+    for k in np.arange(0,it):
+        tmp = np.random.uniform(_min, _max, num)
+        if vmin is not None:
+            tmp = np.maximum(np.full(num, vmin), tmp)
+        if vmax is not None:
+            tmp = np.minimum(np.full(num, vmax), tmp)
+        ret.extend(tmp)
+        _min += min_inc
+        _max += max_inc
+    return ret

pyFTS/data/sunspots.csv

@@ -0,0 +1,310 @@
+YEAR,SUNACTIVITY
+1700,5
+1701,11
+1702,16
+1703,23
+1704,36
+1705,58
+1706,29
+1707,20
+1708,10
+1709,8
+1710,3
+1711,0
+1712,0
+1713,2
+1714,11
+1715,27
+1716,47
+1717,63
+1718,60
+1719,39
+1720,28
+1721,26
+1722,22
+1723,11
+1724,21
+1725,40
+1726,78
+1727,122
+1728,103
+1729,73
+1730,47
+1731,35
+1732,11
+1733,5
+1734,16
+1735,34
+1736,70
+1737,81
+1738,111
+1739,101
+1740,73
+1741,40
+1742,20
+1743,16
+1744,5
+1745,11
+1746,22
+1747,40
+1748,60
+1749,80.9
+1750,83.4
+1751,47.7
+1752,47.8
+1753,30.7
+1754,12.2
+1755,9.6
+1756,10.2
+1757,32.4
+1758,47.6
+1759,54
+1760,62.9
+1761,85.9
+1762,61.2
+1763,45.1
+1764,36.4
+1765,20.9
+1766,11.4
+1767,37.8
+1768,69.8
+1769,106.1
+1770,100.8
+1771,81.6
+1772,66.5
+1773,34.8
+1774,30.6
+1775,7
+1776,19.8
+1777,92.5
+1778,154.4
+1779,125.9
+1780,84.8
+1781,68.1
+1782,38.5
+1783,22.8
+1784,10.2
+1785,24.1
+1786,82.9
+1787,132
+1788,130.9
+1789,118.1
+1790,89.9
+1791,66.6
+1792,60
+1793,46.9
+1794,41
+1795,21.3
+1796,16
+1797,6.4
+1798,4.1
+1799,6.8
+1800,14.5
+1801,34
+1802,45
+1803,43.1
+1804,47.5
+1805,42.2
+1806,28.1
+1807,10.1
+1808,8.1
+1809,2.5
+1810,0
+1811,1.4
+1812,5
+1813,12.2
+1814,13.9
+1815,35.4
+1816,45.8
+1817,41.1
+1818,30.1
+1819,23.9
+1820,15.6
+1821,6.6
+1822,4
+1823,1.8
+1824,8.5
+1825,16.6
+1826,36.3
+1827,49.6
+1828,64.2
+1829,67
+1830,70.9
+1831,47.8
+1832,27.5
+1833,8.5
+1834,13.2
+1835,56.9
+1836,121.5
+1837,138.3
+1838,103.2
+1839,85.7
+1840,64.6
+1841,36.7
+1842,24.2
+1843,10.7
+1844,15
+1845,40.1
+1846,61.5
+1847,98.5
+1848,124.7
+1849,96.3
+1850,66.6
+1851,64.5
+1852,54.1
+1853,39
+1854,20.6
+1855,6.7
+1856,4.3
+1857,22.7
+1858,54.8
+1859,93.8
+1860,95.8
+1861,77.2
+1862,59.1
+1863,44
+1864,47
+1865,30.5
+1866,16.3
+1867,7.3
+1868,37.6
+1869,74
+1870,139
+1871,111.2
+1872,101.6
+1873,66.2
+1874,44.7
+1875,17
+1876,11.3
+1877,12.4
+1878,3.4
+1879,6
+1880,32.3
+1881,54.3
+1882,59.7
+1883,63.7
+1884,63.5
+1885,52.2
+1886,25.4
+1887,13.1
+1888,6.8
+1889,6.3
+1890,7.1
+1891,35.6
+1892,73
+1893,85.1
+1894,78
+1895,64
+1896,41.8
+1897,26.2
+1898,26.7
+1899,12.1
+1900,9.5
+1901,2.7
+1902,5
+1903,24.4
+1904,42
+1905,63.5
+1906,53.8
+1907,62
+1908,48.5
+1909,43.9
+1910,18.6
+1911,5.7
+1912,3.6
+1913,1.4
+1914,9.6
+1915,47.4
+1916,57.1
+1917,103.9
+1918,80.6
+1919,63.6
+1920,37.6
+1921,26.1
+1922,14.2
+1923,5.8
+1924,16.7
+1925,44.3
+1926,63.9
+1927,69
+1928,77.8
+1929,64.9
+1930,35.7
+1931,21.2
+1932,11.1
+1933,5.7
+1934,8.7
+1935,36.1
+1936,79.7
+1937,114.4
+1938,109.6
+1939,88.8
+1940,67.8
+1941,47.5
+1942,30.6
+1943,16.3
+1944,9.6
+1945,33.2
+1946,92.6
+1947,151.6
+1948,136.3
+1949,134.7
+1950,83.9
+1951,69.4
+1952,31.5
+1953,13.9
+1954,4.4
+1955,38
+1956,141.7
+1957,190.2
+1958,184.8
+1959,159
+1960,112.3
+1961,53.9
+1962,37.6
+1963,27.9
+1964,10.2
+1965,15.1
+1966,47
+1967,93.8
+1968,105.9
+1969,105.5
+1970,104.5
+1971,66.6
+1972,68.9
+1973,38
+1974,34.5
+1975,15.5
+1976,12.6
+1977,27.5
+1978,92.5
+1979,155.4
+1980,154.6
+1981,140.4
+1982,115.9
+1983,66.6
+1984,45.9
+1985,17.9
+1986,13.4
+1987,29.4
+1988,100.2
+1989,157.6
+1990,142.6
+1991,145.7
+1992,94.3
+1993,54.6
+1994,29.9
+1995,17.5
+1996,8.6
+1997,21.5
+1998,64.3
+1999,93.3
+2000,119.6
+2001,111
+2002,104
+2003,63.7
+2004,40.4
+2005,29.8
+2006,15.2
+2007,7.5
+2008,2.9

pyFTS/data/sunspots.py

@@ -0,0 +1,11 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_data():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/sunspots.csv')
+    dat = pd.read_csv(filename, sep=",")
+    dat = np.array(dat["SUNACTIVITY"])
+    return dat

pyFTS/models/chen.py

@@ -50,7 +50,7 @@ class ConventionalFTS(fts.FTS):
         self.sets = sets
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
-        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_non_recurrent_flrs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
     def forecast(self, data, **kwargs):

pyFTS/models/ismailefendi.py

@@ -68,7 +68,7 @@ class ImprovedWeightedFTS(fts.FTS):
         ndata = self.apply_transformations(data)
 
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
-        flrs = FLR.generateRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_recurrent_flrs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
     def forecast(self, data, **kwargs):

pyFTS/models/nonstationary/cvfts.py

@@ -34,7 +34,7 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
         self.max_tx = max(ndata)
 
         tmpdata = common.fuzzySeries(ndata, self.sets, method='fuzzy', const_t=0)
-        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_non_recurrent_flrs(tmpdata)
         self.flrgs = self.generate_flrg(flrs)
 
     def generate_flrg(self, flrs, **kwargs):

pyFTS/models/nonstationary/honsfts.py

@@ -105,7 +105,7 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
 
         ndata = self.apply_transformations(data)
         #tmpdata = common.fuzzyfy_series_old(ndata, self.sets)
-        #flrs = FLR.generateRecurrentFLRs(ndata)
+        #flrs = FLR.generate_recurrent_flrs(ndata)
         window_size = parameters if parameters is not None else 1
         self.flrgs = self.generate_flrg(ndata, window_size=window_size)
 

pyFTS/models/nonstationary/nsfts.py

@@ -54,7 +54,7 @@ class NonStationaryFTS(fts.FTS):
         window_size = parameters if parameters is not None else 1
         tmpdata = common.fuzzySeries(ndata, self.sets, window_size, method=self.method)
         #print([k[0].name for k in tmpdata])
-        flrs = FLR.generateRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_recurrent_flrs(tmpdata)
         #print([str(k) for k in flrs])
         self.flrgs = self.generate_flrg(flrs)
 

pyFTS/models/pwfts.py

@@ -125,7 +125,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         for s in self.sets:    self.setsDict[s.name] = s
         if parameters == 'Monotonic':
             tmpdata = FuzzySet.fuzzyfy_series_old(data, sets)
-            flrs = FLR.generateRecurrentFLRs(tmpdata)
+            flrs = FLR.generate_recurrent_flrs(tmpdata)
             self.flrgs = self.generateFLRG(flrs)
         else:
             self.flrgs = self.generate_flrg(data)

pyFTS/models/sadaei.py

@@ -67,7 +67,7 @@ class ExponentialyWeightedFTS(fts.FTS):
         self.sets = sets
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
-        flrs = FLR.generateRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_recurrent_flrs(tmpdata)
         self.flrgs = self.generateFLRG(flrs, self.c)
 
     def forecast(self, data, **kwargs):

pyFTS/models/seasonal/cmsfts.py

@@ -59,7 +59,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
     def train(self, data, sets, order=1, parameters=None):
         self.sets = sets
         self.seasonality = parameters
-        flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
+        flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
         self.flrgs = self.generateFLRG(flrs)
 
     def getMidpoints(self, flrg, data):

pyFTS/models/seasonal/msfts.py

@@ -36,7 +36,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
         self.sets = sets
         self.seasonality = parameters
         #ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
-        flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
+        flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
         self.flrgs = self.generateFLRG(flrs)
 
     def forecast(self, data, **kwargs):

pyFTS/models/seasonal/sfts.py

@@ -64,7 +64,7 @@ class SeasonalFTS(fts.FTS):
         self.sets = sets
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
-        flrs = FLR.generateRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_recurrent_flrs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
     def forecast(self, data, **kwargs):

pyFTS/models/song.py

@@ -38,10 +38,11 @@ class ConventionalFTS(fts.FTS):
         return r
 
     def train(self, data, sets,order=1,parameters=None):
-        self.sets = sets
+        if sets != None:
+            self.sets = sets
         ndata = self.apply_transformations(data)
-        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
-        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
+        flrs = FLR.generate_non_recurrent_flrs(tmpdata)
         self.R = self.operation_matrix(flrs)
 
     def forecast(self, data, **kwargs):
@@ -67,6 +68,6 @@ class ConventionalFTS(fts.FTS):
 
                 ret.append( sum(mp)/len(mp))
 
-        ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])
+        ret = self.apply_inverse_transformations(ret, params=[data])
 
         return ret

pyFTS/models/yu.py

@@ -60,7 +60,7 @@ class WeightedFTS(fts.FTS):
         self.sets = sets
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
-        flrs = FLR.generateRecurrentFLRs(tmpdata)
+        flrs = FLR.generate_recurrent_flrs(tmpdata)
         self.flrgs = self.generate_FLRG(flrs)
 
     def forecast(self, data, **kwargs):

pyFTS/partitioners/Util.py

@@ -21,11 +21,11 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
     #print(h)
     fig, axes = plt.subplots(nrows=num, ncols=1,figsize=tam)
     for k in np.arange(0,num):
-        #ax = fig.add_axes([0.05, 1-(k*h), 0.9, h*0.7])  # left, bottom, width, height
+        ticks = []
+        x = []
         ax = axes[k]
         ax.set_title(titles[k])
         ax.set_ylim([0, 1.1])
-        ax.set_xlim([minx, maxx])
         for s in sets[k]:
             if s.mf == Membership.trimf:
                 ax.plot(s.parameters,[0,1,0])
@@ -35,6 +35,10 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
                 ax.plot(tmpx, tmpy)
             elif s.mf == Membership.trapmf:
                 ax.plot(s.parameters, [0, 1, 1, 0])
+            ticks.append(str(round(s.centroid, 0)) + '\n' + s.name)
+            x.append(s.centroid)
+        ax.xaxis.set_ticklabels(ticks)
+        ax.xaxis.set_ticks(x)
 
     plt.tight_layout()
 
@@ -44,7 +48,7 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
 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)
+    plot_sets(data, sets, titles, tam, save, file)
 
 
 def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None):
@@ -59,6 +63,9 @@ def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=
     for p in methods:
         for m in mf:
             obj = p(data, npart,m)
+            obj.name = obj.name  + " - " + obj.membership_function.__name__
             objs.append(obj)
 
     plot_partitioners(data, objs, tam, save, file)
+
+    return objs

pyFTS/partitioners/partitioner.py

@@ -1,5 +1,6 @@
 from pyFTS.common import FuzzySet, Membership
 import numpy as np
+import matplotlib.pylab as plt
 
 
 class Partitioner(object):
@@ -70,6 +71,8 @@ class Partitioner(object):
         ax.set_title(self.name)
         ax.set_ylim([0, 1])
         ax.set_xlim([self.min, self.max])
+        ticks = []
+        x = []
         for s in self.sets:
             if s.mf == Membership.trimf:
                 ax.plot([s.parameters[0], s.parameters[1], s.parameters[2]], [0, 1, 0])
@@ -77,6 +80,10 @@ class Partitioner(object):
                 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)
+            ticks.append(str(round(s.centroid,0))+'\n'+s.name)
+            x.append(s.centroid)
+        plt.xticks(x,ticks)
+
 
     def __str__(self):
         tmp = self.name + ":\n"

pyFTS/probabilistic/ProbabilityDistribution.py

@@ -1,10 +1,9 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from pyFTS.common import FuzzySet,SortedCollection
+from pyFTS.common import FuzzySet,SortedCollection,tree
 from pyFTS.probabilistic import kde
-from pyFTS import tree
-from pyFTS.common import SortedCollection
+
 
 class ProbabilityDistribution(object):
     """