Several Bugfixes on benchmarks; ProbabilityDistribution on arima and quantreg; Including models.seasonal on setup.py

pyFTS/benchmarks/Measures.py

@@ -227,15 +227,15 @@ def crps(targets, densities):
         Fa = heavyside_cdf(densities.columns, targets)
         for k in densities.index:
             _crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
-    elif isinstance(densities, ProbabilityDistribution):
+    elif isinstance(densities, ProbabilityDistribution.ProbabilityDistribution):
         l = len(densities.bins)
         n = 1
-        Fa = heavyside_cdf(densities.bin, targets)
+        Fa = heavyside_cdf(densities.bins, targets)
         _crps = sum([(densities.cdf(val) - Fa[val][0]) ** 2 for val in densities.bins])
     elif isinstance(densities, list):
         l = len(densities[0].bins)
         n = len(densities)
-        Fa = heavyside_cdf(densities[0].bin, targets)
+        Fa = heavyside_cdf(densities[0].bins, targets)
         for df in densities:
             _crps += sum([(df.cdf(val) - Fa[val][0]) ** 2 for val in df.bins])
 

pyFTS/benchmarks/Util.py

@@ -8,7 +8,6 @@ import matplotlib.colors as pltcolors
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from checkbox_support.parsers.tests.test_modinfo import testMultipleModinfoParser
 #from mpl_toolkits.mplot3d import Axes3D
 
 

pyFTS/benchmarks/arima.py

@@ -40,6 +40,9 @@ class ARIMA(fts.FTS):
         self.order = self.p + self.q
         self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
 
+        if self.indexer is not None:
+            data = self.indexer.get_data(data)
+
         data = self.doTransformations(data, updateUoD=True)
 
         old_fit = self.model_fit
@@ -60,6 +63,9 @@ class ARIMA(fts.FTS):
         if self.model_fit is None:
             return np.nan
 
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         ndata = np.array(self.doTransformations(data))
 
         l = len(ndata)
@@ -151,6 +157,9 @@ class ARIMA(fts.FTS):
 
     def forecastDistribution(self, data, **kwargs):
 
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         sigma = np.sqrt(self.model_fit.sigma2)
 
         l = len(data)

pyFTS/benchmarks/benchmarks.py

@@ -825,6 +825,7 @@ def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to
     cb = fig.colorbar(pc, ax=ax)
     cb.set_label('Density')
 
+from pyFTS.common import Transformations
 
 def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_from):
     from matplotlib.patches import Rectangle
@@ -836,7 +837,9 @@ def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_fr
         for y in dt.bins:
             s = Rectangle((time_from+ct, y), 1, dt.resolution, fill=True, lw = 0)
             patches.append(s)
-            colors.append(dt.distribution[y]*5)
+            colors.append(dt.density(y))
+    scale = Transformations.Scale()
+    colors = scale.apply(colors)
     pc = PatchCollection(patches=patches, match_original=True)
     pc.set_clim([0, 1])
     pc.set_cmap(cmap)

pyFTS/benchmarks/quantreg.py

@@ -7,7 +7,7 @@ from statsmodels.regression.quantile_regression import QuantReg
 from statsmodels.tsa.tsatools import lagmat
 from pyFTS import fts
 from pyFTS.common import SortedCollection
-
+from pyFTS.probabilistic import ProbabilityDistribution
 
 class QuantileRegression(fts.FTS):
     """Façade for statsmodels.regression.quantile_regression"""
@@ -32,6 +32,9 @@ class QuantileRegression(fts.FTS):
     def train(self, data, sets, order=1, parameters=None):
         self.order = order
 
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         tmp = np.array(self.doTransformations(data, updateUoD=True))
 
         lagdata, ndata = lagmat(tmp, maxlag=order, trim="both", original='sep')
@@ -75,6 +78,10 @@ class QuantileRegression(fts.FTS):
         return [lo, up]
 
     def forecast(self, data, **kwargs):
+
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         ndata = np.array(self.doTransformations(data))
         l = len(ndata)
 
@@ -91,6 +98,9 @@ class QuantileRegression(fts.FTS):
 
     def forecastInterval(self, data, **kwargs):
 
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         ndata = np.array(self.doTransformations(data))
 
         l = len(ndata)
@@ -106,6 +116,10 @@ class QuantileRegression(fts.FTS):
         return ret
 
     def forecastAheadInterval(self, data, steps, **kwargs):
+
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         ndata = np.array(self.doTransformations(data))
 
         smoothing = kwargs.get("smoothing", 0.9)
@@ -128,35 +142,50 @@ class QuantileRegression(fts.FTS):
 
         return ret[-steps:]
 
-    def forecastAheadDistribution(self, data, steps, **kwargs):
+    def forecastDistribution(self, data, **kwargs):
+
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
         ndata = np.array(self.doTransformations(data))
 
-        percentile_size = (self.original_max - self.original_min) / 100
+        ret = []
 
-        resolution = kwargs.get('resolution', percentile_size)
+        l = len(data)
 
-        grid = self.get_empty_grid(self.original_min, self.original_max, resolution)
+        for k in np.arange(self.order, l + 1):
+            dist = ProbabilityDistribution.ProbabilityDistribution(type="histogram",
+                                                                   uod=[self.original_min, self.original_max])
+            intervals = []
+            for qt in self.dist_qt:
+                sample = ndata[k - self.order: k]
+                intl = self.point_to_interval(sample, qt[0], qt[1])
+                intervals.append(intl)
 
-        index = SortedCollection.SortedCollection(iterable=grid.keys())
+            dist.appendInterval(intervals)
+
+            ret.append(dist)
+
+        return ret
+
+    def forecastAheadDistribution(self, data, steps, **kwargs):
+
+        if self.indexer is not None and isinstance(data, pd.DataFrame):
+            data = self.indexer.get_data(data)
+
+        ndata = np.array(self.doTransformations(data))
 
         ret = []
-        tmps = []
 
-        grids = {}
         for k in np.arange(self.order, steps + self.order):
-            grids[k] = self.get_empty_grid(self.original_min, self.original_max, resolution)
+            dist = ProbabilityDistribution.ProbabilityDistribution(type="histogram",
-
+                                                                   uod=[self.original_min, self.original_max])
-        for qt in self.dist_qt:
             intervals = [[k, k] for k in ndata[-self.order:]]
-            for k in np.arange(self.order, steps + self.order):
+            for qt in self.dist_qt:
                 intl = self.interval_to_interval([intervals[x] for x in np.arange(k - self.order, k)], qt[0], qt[1])
                 intervals.append(intl)
-                grids[k] = self.gridCount(grids[k], resolution, index, intl)
+            dist.appendInterval(intervals)
 
-        for k in np.arange(self.order, steps + self.order):
+            ret.append(dist)
-            tmp = np.array([grids[k][i] for i in sorted(grids[k])])
-            ret.append(tmp / sum(tmp))
 
-        grid = self.get_empty_grid(self.original_min, self.original_max, resolution)
+        return ret
-        df = pd.DataFrame(ret, columns=sorted(grid))
-        return df

pyFTS/probabilistic/ProbabilityDistribution.py

@@ -69,6 +69,12 @@ class ProbabilityDistribution(object):
 
     def density(self, values):
         ret = []
+        scalar = False
+
+        if not isinstance(values, list):
+            values = [values]
+            scalar = True
+
         for k in values:
             if self.type == "histogram":
                 v = self.index.find_ge(k)
@@ -79,6 +85,10 @@ class ProbabilityDistribution(object):
             else:
                 v = self.index.find_ge(k)
                 ret.append(self.distribution[v])
+
+        if scalar:
+            return ret[0]
+
         return ret
 
     def cdf(self, value):

setup.py

@@ -1,14 +1,14 @@
 from distutils.core import setup
 setup(
   name = 'pyFTS',
-  packages = ['pyFTS','pyFTS.benchmarks','pyFTS.common','pyFTS.data', 'pyFTS.ensemble','pyFTS.models','pyFTS.partitioners','pyFTS.probabilistic','pyFTS.tests'], 
+  packages = ['pyFTS','pyFTS.benchmarks','pyFTS.common','pyFTS.data', 'pyFTS.ensemble','pyFTS.models','pyFTS.models.seasonal','pyFTS.partitioners','pyFTS.probabilistic','pyFTS.tests'], 
-  package_data = {'benchmarks':['*'], 'common':['*'], 'data':['*'], 'ensemble':['*'], 'models':['*'], 'partitioners':['*'], 'probabilistic':['*'], 'tests':['*']},
+  package_data = {'benchmarks':['*'], 'common':['*'], 'data':['*'], 'ensemble':['*'], 'models':['*'], 'seasonal':['*'], 'partitioners':['*'], 'probabilistic':['*'], 'tests':['*']},
-  version = '1.1',
+  version = '1.2',
   description = 'Fuzzy Time Series for Python',
   author = 'Petronio Candido L. e Silva',
   author_email = 'petronio.candido@gmail.com',
   url = 'https://github.com/petroniocandido/pyFTS',
-  download_url = 'https://github.com/petroniocandido/pyFTS/archive/pkg1.1.tar.gz',
+  download_url = 'https://github.com/petroniocandido/pyFTS/archive/pkg1.2.tar.gz',
   keywords = ['forecasting', 'fuzzy time series', 'fuzzy', 'time series forecasting'],
   classifiers = [],
 )