Improvements for numerical stability

pyFTS/benchmarks/Measures.py

@@ -89,7 +89,7 @@ def smape(targets, forecasts, type=2):
     elif type == 2:
         return np.nanmean(np.abs(forecasts - targets) / (np.abs(forecasts) + abs(targets))) * 100
     else:
-        return np.sum(np.abs(forecasts - targets)) / np.sum(forecasts + targets)
+        return np.nansum(np.abs(forecasts - targets)) / np.nansum(forecasts + targets)
 
 
 def mape_interval(targets, forecasts):
@@ -124,7 +124,7 @@ def UStatistic(targets, forecasts):
     for k in np.arange(0, l - 1):
         y.append(np.subtract(forecasts[k], targets[k]) ** 2)
         naive.append(np.subtract(targets[k + 1], targets[k]) ** 2)
-    return np.sqrt(np.divide(np.sum(y), np.sum(naive)))
+    return np.sqrt(np.divide(np.nansum(y), np.nansum(naive)))
 
 
 def TheilsInequality(targets, forecasts):
@@ -137,9 +137,9 @@ def TheilsInequality(targets, forecasts):
     """
     res = targets - forecasts
     t = len(res)
-    us = np.sqrt(sum([u ** 2 for u in res]))
+    us = np.sqrt(np.nansum([u ** 2 for u in res]))
-    ys = np.sqrt(sum([y ** 2 for y in targets]))
+    ys = np.sqrt(np.nansum([y ** 2 for y in targets]))
-    fs = np.sqrt(sum([f ** 2 for f in forecasts]))
+    fs = np.sqrt(np.nansum([f ** 2 for f in forecasts]))
     return us / (ys + fs)
 
 
@@ -255,12 +255,12 @@ def brier_score(targets, densities):
         try:
             v = d.bin_index.find_le(targets[ct])
 
-            score = sum([d.density(k) ** 2 for k in d.bins if k != v])
+            score = np.nansum([d.density(k) ** 2 for k in d.bins if k != v])
             score += (d.density(v) - 1) ** 2
             ret.append(score)
         except ValueError as ex:
-            ret.append(sum([d.density(k) ** 2 for k in d.bins]))
+            ret.append(np.nansum([d.density(k) ** 2 for k in d.bins]))
-    return sum(ret) / len(ret)
+    return np.nansum(ret) / len(ret)
 
 
 def logarithm_score(targets, densities):
@@ -301,7 +301,7 @@ def crps(targets, densities):
 
     n = len(densities)
     for ct, df in enumerate(densities):
-        _crps += sum([(df.cumulative(bin) - (1 if bin >= targets[ct] else 0)) ** 2 for bin in df.bins])
+        _crps += np.nansum([(df.cumulative(bin) - (1 if bin >= targets[ct] else 0)) ** 2 for bin in df.bins])
 
     return _crps / n
 
@@ -419,6 +419,8 @@ def get_interval_statistics(data, model, **kwargs):
         forecasts = model.predict(data, **kwargs)
         ret.append(round(sharpness(forecasts), 2))
         ret.append(round(resolution(forecasts), 2))
+        if model.is_multivariate:
+            data = data[model.target_variable.data_label].values
         ret.append(round(coverage(data[model.max_lag:], forecasts[:-1]), 2))
         ret.append(round(pinball_mean(0.05, data[model.max_lag:], forecasts[:-1]), 2))
         ret.append(round(pinball_mean(0.25, data[model.max_lag:], forecasts[:-1]), 2))
@@ -436,6 +438,8 @@ def get_interval_statistics(data, model, **kwargs):
         start = model.max_lag + steps_ahead - 1
         ret.append(round(sharpness(forecasts), 2))
         ret.append(round(resolution(forecasts), 2))
+        if model.is_multivariate:
+            data = data[model.target_variable.data_label].values
         ret.append(round(coverage(data[model.max_lag:], forecasts), 2))
         ret.append(round(pinball_mean(0.05, data[start:], forecasts), 2))
         ret.append(round(pinball_mean(0.25, data[start:], forecasts), 2))
@@ -502,6 +506,8 @@ def get_distribution_statistics(data, model, **kwargs):
         _s1 = time.time()
         forecasts = model.predict(data, **kwargs)
         _e1 = time.time()
+        if model.is_multivariate:
+            data = data[model.target_variable.data_label].values
         ret.append(round(crps(data[model.max_lag:], forecasts[:-1]), 3))
         ret.append(round(_e1 - _s1, 3))
         ret.append(round(brier_score(data[model.max_lag:], forecasts[:-1]), 3))
@@ -516,6 +522,8 @@ def get_distribution_statistics(data, model, **kwargs):
         _e1 = time.time()
 
         start = model.max_lag + steps_ahead
+        if model.is_multivariate:
+            data = data[model.target_variable.data_label].values
         ret.append(round(crps(data[start:-1:skip], forecasts), 3))
         ret.append(round(_e1 - _s1, 3))
         ret.append(round(brier_score(data[start:-1:skip], forecasts), 3))

pyFTS/benchmarks/benchmarks.py

@@ -81,6 +81,105 @@ def get_benchmark_probabilistic_methods():
     return [arima.ARIMA, quantreg.QuantileRegression, knn.KNearestNeighbors]
 
 
+def multivariate_sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs):
+    from pyFTS.models.multivariate import common, variable, mvfts
+
+    tag = __pop('tag', None, kwargs)
+    dataset = __pop('dataset', None, kwargs)
+
+    distributed = __pop('distributed', False, kwargs)
+
+    variables = __pop('variables', {}, kwargs)
+
+    target_variable = __pop('target_variable', '', kwargs)
+
+    type = kwargs.get("type", 'point')
+
+    steps_ahead = __pop('steps_ahead', [1], kwargs)
+
+    steps_ahead = [k for k in steps_ahead]
+
+    fts_methods = __pop('methods', [], kwargs)
+
+    if fts_methods is not None:
+        methods_parameters = __pop('methods_parameters', None, kwargs)
+
+    if type == 'point':
+        experiment_method = mv_run_point2
+        synthesis_method = process_point_jobs2
+    elif type == 'interval':
+        experiment_method = mv_run_interval2
+        synthesis_method = process_interval_jobs2
+    elif type == 'distribution':
+        experiment_method = mv_run_probabilistic2
+        synthesis_method = process_probabilistic_jobs2
+    else:
+        raise ValueError("Type parameter has a unkown value!")
+
+    if distributed:
+        import pyFTS.distributed.dispy as dispy
+
+        nodes = kwargs.get("nodes", ['127.0.0.1'])
+        cluster, http_server = dispy.start_dispy_cluster(experiment_method, nodes)
+
+    inc = __pop("inc", 0.1, kwargs)
+
+    file = kwargs.get('file', "benchmarks.db")
+
+    conn = bUtil.open_benchmark_db(file)
+
+    jobs = []
+    for ct, train, test in cUtil.sliding_window(data, windowsize, train, inc=inc, **kwargs):
+        for id, fts_method in enumerate(fts_methods):
+
+            kwargs['steps_ahead'] = max(steps_ahead)
+            parameters = {}
+            if methods_parameters is not None:
+                parameters = methods_parameters[id]
+
+            vars = []
+            tvar = None
+
+            for key, value in variables.items():
+                var = variable.Variable(key, data=train, **value)
+                vars.append(var)
+                if key == target_variable:
+                    tvar = var
+
+
+            model = fts_method(explanatory_variables=vars, target_variable=tvar,
+                               **parameters)
+
+            if not distributed:
+                try:
+                    job = experiment_method(model, train, test, ct, **kwargs)
+                    synthesis_method(dataset, tag, job, conn)
+                except Exception as ex:
+                    print('EXCEPTION! ', fts_method)
+                    traceback.print_exc()
+            else:
+                job = cluster.submit(model, train, test, ct, **kwargs)
+                job.id = id
+                jobs.append(job)
+
+    if distributed:
+        for job in jobs:
+            job()
+            if job.status == dispy.dispy.DispyJob.Finished and job is not None:
+                tmp = job.result
+                synthesis_method(dataset, tag, tmp, conn)
+            else:
+                print("status", job.status)
+                print("result", job.result)
+                print("stdout", job.stdout)
+                print("stderr", job.exception)
+
+        cluster.wait()  # wait for all jobs to finish
+        dispy.stop_dispy_cluster(cluster, http_server)
+
+    conn.close()
+
+
 def sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs):
     tag = __pop('tag', None, kwargs)
     dataset = __pop('dataset', None, kwargs)
@@ -630,6 +729,63 @@ def __build_model(fts_method, order, parameters, partitioner_method, partitions,
     return mfts, pttr
 
 
+def mv_run_point2(mfts, train_data, test_data, window_key=None, **kwargs):
+    import time
+    from pyFTS.models import hofts, ifts, pwfts
+    from pyFTS.models.multivariate import mvfts, wmvfts, granular
+    from pyFTS.partitioners import Grid, Entropy, FCM
+    from pyFTS.benchmarks import Measures, arima, quantreg, BSTS, benchmarks
+
+    tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, ifts.WeightedIntervalFTS, pwfts.ProbabilisticWeightedFTS]
+
+    tmp2 = [Grid.GridPartitioner, mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
+
+    tmp4 = [arima.ARIMA, quantreg.QuantileRegression, BSTS.ARIMA]
+
+    tmp3 = [Measures.get_interval_statistics]
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+    method = kwargs.get('method', None)
+
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+    if steps_ahead == 1:
+
+        _start = time.time()
+        _rmse, _smape, _u = Measures.get_point_statistics(test_data, mfts, **kwargs)
+        _end = time.time()
+        times += _end - _start
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'rmse': _rmse, 'smape': _smape, 'u': _u, 'window': window_key,
+               'steps': steps_ahead, 'method': method}
+    else:
+        _start = time.time()
+        forecasts = mfts.predict(test_data, **kwargs)
+        _end = time.time()
+        times += _end - _start
+
+        eval = Measures.get_point_ahead_statistics(test_data[mfts.order:mfts.order + steps_ahead], forecasts)
+
+        for key in eval.keys():
+            eval[key]["time"] = times
+            eval[key]["method"] = method
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'window': window_key, 'steps': steps_ahead, 'method': method,
+               'ahead_results': eval
+               }
+
+    return ret
+
+
 def run_point2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs):
 
     import time
@@ -698,6 +854,67 @@ def run_point2(fts_method, order, partitioner_method, partitions, transformation
     return ret
 
 
+def mv_run_interval2(mfts,train_data, test_data, window_key=None, **kwargs):
+    import time
+    from pyFTS.models import hofts,ifts,pwfts
+    from pyFTS.models.multivariate import mvfts, wmvfts, granular
+    from pyFTS.partitioners import Grid, Entropy, FCM
+    from pyFTS.benchmarks import Measures, arima, quantreg, BSTS, benchmarks
+
+    tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, ifts.WeightedIntervalFTS,  pwfts.ProbabilisticWeightedFTS]
+
+    tmp2 = [Grid.GridPartitioner, mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS ]
+
+    tmp4 = [arima.ARIMA, quantreg.QuantileRegression, BSTS.ARIMA]
+
+    tmp3 = [Measures.get_interval_statistics]
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+    method = kwargs.get('method', None)
+    parameters = kwargs.get('parameters',{})
+
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+    if steps_ahead == 1:
+
+        _start = time.time()
+        metrics = Measures.get_interval_statistics(test_data, mfts, **kwargs)
+        _end = time.time()
+        times += _end - _start
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'sharpness': metrics[0], 'resolution': metrics[1], 'coverage': metrics[2],
+               'time': times,'pinball05': metrics[3], 'pinball25': metrics[4], 'pinball75': metrics[5], 'pinball95': metrics[6],
+               'winkler05': metrics[7], 'winkler25': metrics[8],
+               'window': window_key,'steps': steps_ahead, 'method': method}
+    else:
+        _start = time.time()
+        intervals = mfts.predict(test_data, **kwargs)
+        _end = time.time()
+        times += _end - _start
+
+        eval = Measures.get_interval_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], intervals)
+
+        for key in eval.keys():
+            eval[key]["time"] = times
+            eval[key]["method"] = method
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'window': window_key, 'steps': steps_ahead, 'method': method,
+               'ahead_results': eval
+               }
+
+
+    return ret
+
+
 def run_interval2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs):
     import time
     from pyFTS.models import hofts,ifts,pwfts
@@ -760,6 +977,63 @@ def run_interval2(fts_method, order, partitioner_method, partitions, transformat
     return ret
 
 
+def mv_run_probabilistic2(mfts, train_data, test_data, window_key=None, **kwargs):
+    import time
+    from pyFTS.models import hofts, ifts, pwfts
+    from pyFTS.models.multivariate import mvfts, wmvfts, granular
+    from pyFTS.partitioners import Grid, Entropy, FCM
+    from pyFTS.benchmarks import Measures, arima, quantreg, BSTS, benchmarks
+
+    tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, ifts.WeightedIntervalFTS, pwfts.ProbabilisticWeightedFTS]
+
+    tmp2 = [Grid.GridPartitioner, mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
+
+    tmp4 = [arima.ARIMA, quantreg.QuantileRegression, BSTS.ARIMA]
+
+    tmp3 = [Measures.get_interval_statistics]
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+    method = kwargs.get('method', None)
+    parameters = kwargs.get('parameters', {})
+
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+    if steps_ahead == 1:
+
+        _crps1, _t1, _brier = Measures.get_distribution_statistics(test_data, mfts, **kwargs)
+        times += _t1
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'crps': _crps1, 'brier': _brier, 'window': window_key,
+               'steps': steps_ahead, 'method': method}
+    else:
+        _start = time.time()
+        distributions = mfts.predict(test_data, **kwargs)
+        _end = time.time()
+        times += _end - _start
+
+        eval = Measures.get_distribution_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], distributions)
+
+        for key in eval.keys():
+            eval[key]["time"] = times
+            eval[key]["method"] = method
+
+        ret = {'model': mfts.shortname, 'partitioner': None, 'order': mfts.order, 'partitions': None,
+               'transformation': None,
+               'size': len(mfts), 'time': times,
+               'window': window_key, 'steps': steps_ahead, 'method': method,
+               'ahead_results': eval
+               }
+
+    return ret
+
+
+
 def run_probabilistic2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs):
     import time
     import numpy as np

pyFTS/common/Util.py

@@ -202,6 +202,7 @@ def plot_distribution2(probabilitydist, data, **kwargs):
     scalarMap = cm.ScalarMappable(norm=normal, cmap=cmap)
 
     for ct in np.arange(1, int(lq / 2) + 1):
+        try:
             y = [[data[start_at], data[start_at]]]
             for pd in probabilitydist:
                 qts = pd.quantile([qt[ct - 1], qt[-ct]])
@@ -209,6 +210,8 @@ def plot_distribution2(probabilitydist, data, **kwargs):
 
             ax.fill_between(x, [k[0] for k in y], [k[1] for k in y],
                             facecolor=scalarMap.to_rgba(ct / lq))
+        except:
+            pass
 
     if kwargs.get('median',True):
         y = [data[start_at]]

pyFTS/models/ensemble/ensemble.py

@@ -110,7 +110,7 @@ class EnsembleFTS(fts.FTS):
             ret = np.nanpercentile(forecasts, 50)
         elif self.point_method == 'quantile':
             alpha = kwargs.get("alpha",0.05)
-            ret = np.percentile(forecasts, alpha*100)
+            ret = np.nanpercentile(forecasts, alpha*100)
         elif self.point_method == 'exponential':
             l = len(self.models)
             if l == 1:

pyFTS/models/ifts.py

@@ -80,9 +80,9 @@ class IntervalFTS(hofts.HighOrderFTS):
                     affected_flrgs_memberships.append(mv)
 
             # gerar o intervalo
-            norm = sum(affected_flrgs_memberships)
+            norm = np.nansum(affected_flrgs_memberships)
-            lo_ = sum(lo) / norm
+            lo_ = np.nansum(lo) / norm
-            up_ = sum(up) / norm
+            up_ = np.nansum(up) / norm
             ret.append([lo_, up_])
 
         return ret
@@ -165,9 +165,9 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
                     affected_flrgs_memberships.append(mv)
 
             # gerar o intervalo
-            norm = sum(affected_flrgs_memberships)
+            norm = np.nansum(affected_flrgs_memberships)
-            lo_ = sum(lo) / norm
+            lo_ = np.nansum(lo) / norm
-            up_ = sum(up) / norm
+            up_ = np.nansum(up) / norm
             ret.append([lo_, up_])
 
         return ret

pyFTS/models/multivariate/grid.py

@@ -43,7 +43,7 @@ class GridCluster(partitioner.MultivariatePartitioner):
                 for fset, mv in val:
                     num.append(self.sets[fset].centroid * mv)
                     den.append(mv)
-                ret.append(np.sum(num) / np.sum(den))
+                ret.append(np.nansum(num) / np.nansum(den))
             elif mode == 'both':
                 num = np.mean([self.sets[fset].centroid for fset in val])
                 ret.append(num)
@@ -53,7 +53,7 @@ class GridCluster(partitioner.MultivariatePartitioner):
                 for fset, mv in enumerate(val):
                     num.append(self.sets[self.ordered_sets[fset]].centroid * mv)
                     den.append(mv)
-                ret.append(np.sum(num) / np.sum(den))
+                ret.append(np.nansum(num) / np.nansum(den))
             else:
                 raise Exception('Unknown deffuzyfication mode')
 

pyFTS/models/multivariate/mvfts.py

@@ -152,7 +152,7 @@ class MVFTS(fts.FTS):
             mv = np.array(mvs)
             mp = np.array(mps)
 
-            ret.append(np.dot(mv,mp.T)/np.sum(mv))
+            ret.append(np.dot(mv,mp.T)/np.nansum(mv))
 
         ret = self.target_variable.apply_inverse_transformations(ret,
                                                            params=data[self.target_variable.data_label].values)

pyFTS/models/pwfts.py

@@ -73,21 +73,21 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
     def get_midpoint(self, sets):
         '''Return the expectation of the PWFLRG, the weighted sum'''
         if self.midpoint is None:
-            self.midpoint = np.sum(np.array([self.rhs_unconditional_probability(s) * sets[s].centroid
+            self.midpoint = np.nansum(np.array([self.rhs_unconditional_probability(s) * sets[s].centroid
                                              for s in self.RHS.keys()]))
 
         return self.midpoint
 
     def get_upper(self, sets):
         if self.upper is None:
-            self.upper = np.sum(np.array([self.rhs_unconditional_probability(s) * sets[s].upper
+            self.upper = np.nansum(np.array([self.rhs_unconditional_probability(s) * sets[s].upper
                                           for s in self.RHS.keys()]))
 
         return self.upper
 
     def get_lower(self, sets):
         if self.lower is None:
-            self.lower = np.sum(np.array([self.rhs_unconditional_probability(s) * sets[s].lower
+            self.lower = np.nansum(np.array([self.rhs_unconditional_probability(s) * sets[s].lower
                                           for s in self.RHS.keys()]))
 
         return self.lower
@@ -159,7 +159,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                     self.flrgs[flrg.get_key()].append_rhs(set, count=lhs_mv * mv)
                     mvs.append(mv)
 
-                tmp_fq = sum([lhs_mv * kk for kk in mvs if kk > 0])
+                tmp_fq = np.nansum([lhs_mv * kk for kk in mvs if kk > 0])
 
                 self.global_frequency_count += tmp_fq
 
@@ -226,7 +226,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                     self.flrgs[flrg.get_key()].append_rhs(set, count=lhs_mv * mv)
                     mvs.append(mv)
 
-                tmp_fq = sum([lhs_mv*kk for kk in mvs if kk > 0])
+                tmp_fq = np.nansum([lhs_mv*kk for kk in mvs if kk > 0])
 
                 self.global_frequency_count += tmp_fq
 
@@ -268,7 +268,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         else:
             if len(flrg.LHS) > 0:
                 pi = 1 / len(flrg.LHS)
-                ret = sum(np.array([pi * self.partitioner.sets[s].centroid for s in flrg.LHS]))
+                ret = np.nansum(np.array([pi * self.partitioner.sets[s].centroid for s in flrg.LHS]))
             else:
                 ret = np.nan
         return ret
@@ -282,10 +282,10 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 _set = self.partitioner.sets[s]
                 tmp = _flrg.rhs_unconditional_probability(s) * (_set.membership(x) / _set.partition_function(uod=self.get_UoD()))
                 cond.append(tmp)
-            ret = sum(np.array(cond))
+            ret = np.nansum(np.array(cond))
         else:
             pi = 1 / len(flrg.LHS)
-            ret = sum(np.array([pi * self.partitioner.sets[s].membership(x) for s in flrg.LHS]))
+            ret = np.nansum(np.array([pi * self.partitioner.sets[s].membership(x) for s in flrg.LHS]))
         return ret
 
     def get_upper(self, flrg):
@@ -307,12 +307,12 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
     def forecast(self, data, **kwargs):
         method = kwargs.get('method','heuristic')
 
-        l = len(data)
+        l = len(data)+1
 
         ret = []
 
-        for k in np.arange(self.max_lag - 1, l):
+        for k in np.arange(self.max_lag, l):
-            sample = data[k - (self.max_lag - 1): k + 1]
+            sample = data[k - self.max_lag: k]
 
             if method == 'heuristic':
                 ret.append(self.point_heuristic(sample, **kwargs))
@@ -360,9 +360,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             mp.append(norm * self.get_midpoint(flrg))
             norms.append(norm)
 
-        norm = sum(norms)
+        norm = np.nansum(norms)
 
-        final = sum(mp) / norm if norm != 0 else 0
+        final = np.nansum(mp) / norm if norm != 0 else 0
 
         if explain:
             print("Deffuzyfied value: {} \n".format(final))
@@ -431,12 +431,12 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             norms.append(norm)
 
             # gerar o intervalo
-        norm = sum(norms)
+        norm = np.nansum(norms)
         if norm == 0:
             return [0, 0]
         else:
-            lo_ = sum(lo) / norm
+            lo_ = np.nansum(lo) / norm
-            up_ = sum(up) / norm
+            up_ = np.nansum(up) / norm
             return [lo_, up_]
 
     def forecast_distribution(self, ndata, **kwargs):
@@ -496,7 +496,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                         else:
                             num.append(0.0)
                             den.append(0.000000001)
-                    pf = sum(num) / sum(den)
+                    pf = np.nansum(num) / np.nansum(den)
 
                     dist.set(bin, pf)
 

pyFTS/partitioners/partitioner.py

@@ -202,9 +202,9 @@ class Partitioner(object):
                 raise Exception('Unknown deffuzyfication mode')
 
         if mode in ('both','vector'):
-            return np.sum(num) / np.sum(den)
+            return np.nansum(num) / np.nansum(den)
         else:
-            return np.mean(num)
+            return np.nanmean(num)
 
     def check_bounds(self, data):
         """

pyFTS/probabilistic/ProbabilityDistribution.py

@@ -209,11 +209,17 @@ class ProbabilityDistribution(object):
         if isinstance(values, list):
             ret = []
             for val in values:
+                try:
                     k = self.bin_index.find_ge(val)
                     ret.append(self.cdf[k])
+                except:
+                    ret.append(np.nan)
         else:
+            try:
                 k = self.bin_index.find_ge(values)
                 return self.cdf[values]
+            except:
+                return np.nan
 
     def quantile(self, values):
         """
@@ -229,11 +235,17 @@ class ProbabilityDistribution(object):
         if isinstance(values, list):
             ret = []
             for val in values:
+                try:
                     k = self.quantile_index.find_ge(val)
                     ret.append(self.qtl[str(k)][0])
+                except:
+                    ret.append(np.nan)
         else:
+            try:
                 k = self.quantile_index.find_ge(values)
                 ret = self.qtl[str(k)]
+            except:
+                return np.nan
 
         return ret
 
@@ -243,7 +255,7 @@ class ProbabilityDistribution(object):
 
         :return:the entropy of the probability distribution
         """
-        h = -sum([self.distribution[k] * np.log(self.distribution[k]) if self.distribution[k] > 0 else 0
+        h = -np.nansum([self.distribution[k] * np.log(self.distribution[k]) if self.distribution[k] > 0 else 0
                   for k in self.bins])
         return h
 
@@ -255,7 +267,7 @@ class ProbabilityDistribution(object):
         :param q: a probabilistic.ProbabilityDistribution object
         :return: Cross entropy between this probability distribution and the given distribution
         """
-        h = -sum([self.distribution[k] * np.log(q.distribution[k]) if self.distribution[k] > 0 else 0
+        h = -np.nansum([self.distribution[k] * np.log(q.distribution[k]) if self.distribution[k] > 0 else 0
                   for k in self.bins])
         return h
 
@@ -267,7 +279,7 @@ class ProbabilityDistribution(object):
         :param q:  a probabilistic.ProbabilityDistribution object
         :return: Kullback-Leibler divergence
         """
-        h = sum([self.distribution[k] * np.log(self.distribution[k]/q.distribution[k]) if self.distribution[k] > 0 else 0
+        h = np.nansum([self.distribution[k] * np.log(self.distribution[k]/q.distribution[k]) if self.distribution[k] > 0 else 0
                   for k in self.bins])
         return h
 

pyFTS/tests/multivariate.py

@@ -7,13 +7,12 @@ from pyFTS.partitioners import Grid, Simple, Entropy
 from pyFTS.common import Util
 
 from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
-from pyFTS.benchmarks import Measures
+from pyFTS.benchmarks import benchmarks as bchmk, Measures
 from pyFTS.common import Util as cUtil
-
+from pyFTS.models import pwfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
-
 from pyFTS.models.multivariate import common, variable, mvfts
 from pyFTS.partitioners import Grid
 from pyFTS.common import Membership
@@ -22,54 +21,45 @@ from pyFTS.common import Membership
 import os
 
 
-dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
+from pyFTS.data import SONDA, Malaysia
-
-dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
-
-train_uv = dataset['glo_avg'].values[:24505]
-test_uv = dataset['glo_avg'].values[24505:]
-
-train_mv = dataset.iloc[:24505]
-test_mv = dataset.iloc[24505:]
-
-from pyFTS.models.multivariate import common, variable, mvfts
-from pyFTS.models.seasonal import partitioner as seasonal
-from pyFTS.models.seasonal.common import DateTime
-
-from itertools import product
-
-levels = ['VL', 'L', 'M', 'H', 'VH']
-sublevels = [str(k) for k in np.arange(0, 7)]
-names = []
-for combination in product(*[levels, sublevels]):
-    names.append(combination[0] + combination[1])
-
-sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May',
-                                                      'Jun','Jul', 'Aug','Sep','Oct',
-                                                      'Nov','Dec']}
-
-vmonth = variable.Variable("Month", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=12,
-                           data=train_mv, partitioner_specific=sp)
 
+df = Malaysia.get_dataframe()
+df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
 
 sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
 
-vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
+variables = {
-                          data=train_mv, partitioner_specific=sp)
+    "Hour": dict(data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
+                          partitioner_specific=sp, alpha_cut=.3),
+    "Temperature": dict(data_label="temperature", alias='temp',
+                        partitioner=Grid.GridPartitioner, npart=10, func=Membership.gaussmf,
+                        alpha_cut=.25),
+    "Load": dict(data_label="load", alias='load',
+                         partitioner=Grid.GridPartitioner, npart=10, func=Membership.gaussmf,
+                         alpha_cut=.25)
+}
 
-vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
+methods = [mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
-                         partitioner=Grid.GridPartitioner, npart=35, partitioner_specific={'names': names},
+#methods = [granular.GranularWMVFTS]
-                         data=train_mv)
-fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
-model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, partitioner=fs,
-                              order=2, knn=1)
-#model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
 
-model.fit(train_mv)
+parameters = [
-generator = lambda x : x + pd.to_timedelta(1, unit='h')
+    {},{},
-forecasts = model.predict(test_mv.iloc[:3], steps_ahead=48, generators={'data': generator} )
+    dict(fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
+                order=1, knn=1)
+]
 
-print(forecasts)
+bchmk.multivariate_sliding_window_benchmarks2(df, 10000, train=0.9, inc=0.25,
+                                              methods=methods,
+                                              methods_parameters=parameters,
+                                              variables=variables,
+                                              target_variable='Load',
+                                              type='interval',
+                                              steps_ahead=[1],
+                                              distributed=False,
+                                              nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
+                                              file="experiments.db", dataset='Malaysia',
+                                              tag="experiments"
+                                              )
 
 
 '''

pyFTS/tests/pwfts.py

@@ -14,71 +14,68 @@ from pyFTS.models import pwfts,hofts,ifts
 from pyFTS.models.multivariate import granular, grid
 from pyFTS.partitioners import Grid, Util as pUtil
 
-from pyFTS.models.multivariate import common, variable, mvfts
+from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 from pyFTS.common import Membership
 
-from pyFTS.data import SONDA, Malaysia
+def sample_by_hour(data):
+    return [np.nanmean(data[k:k+60]) for k in np.arange(0,len(data),60)]
 
-df = Malaysia.get_dataframe()
+def sample_date_by_hour(data):
-df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
+    return [data[k] for k in np.arange(0,len(data),60)]
 
-train_mv = df.iloc[:8000]
+from pyFTS.data import SONDA
-test_mv = df.iloc[8000:10000]
 
-sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
+sonda = SONDA.get_dataframe()[['datahora','glo_avg','ws_10m']]
 
-vhour = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
+sonda = sonda.drop(sonda.index[np.where(sonda["ws_10m"] <= 0.01)])
+sonda = sonda.drop(sonda.index[np.where(sonda["glo_avg"] <= 0.01)])
+sonda = sonda.dropna()
+sonda['datahora'] = pd.to_datetime(sonda["datahora"], format='%Y-%m-%d %H:%M:%S')
+
+
+var = {
+    'datahora': sample_date_by_hour(sonda['datahora'].values),
+    'glo_avg': sample_by_hour(sonda['glo_avg'].values),
+    'ws_10m': sample_by_hour(sonda['ws_10m'].values),
+}
+
+df = pd.DataFrame(var)
+
+train_mv = df.iloc[:9000]
+test_mv = df.iloc[9000:10000]
+
+fig, ax = plt.subplots(nrows=2, ncols=1, figsize=[10,3])
+
+sp = {'seasonality': DateTime.month, 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']}
+
+vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12,
                           data=train_mv, partitioner_specific=sp, alpha_cut=.3)
-vtemp = variable.Variable("Temperature", data_label="temperature", alias='temp',
-                         partitioner=Grid.GridPartitioner, npart=5, func=Membership.gaussmf,
-                         data=train_mv, alpha_cut=.3)
-vload = variable.Variable("Load", data_label="load", alias='load',
-                         partitioner=Grid.GridPartitioner, npart=5, func=Membership.gaussmf,
-                         data=train_mv, alpha_cut=.3)
 
-order = 1
+vmonth.partitioner.plot(ax[0])
-knn = 1
 
-model = granular.GranularWMVFTS(explanatory_variables=[vhour, vtemp, vload], target_variable=vload,
+vwin = variable.Variable("Wind", data_label="ws_10m", alias='wind',
+                         partitioner=Grid.GridPartitioner, npart=15, func=Membership.gaussmf,
+                         data=train_mv, alpha_cut=.25)
+
+vwin.partitioner.plot(ax[1])
+
+plt.tight_layout()
+
+order = 3
+knn = 2
+
+model = granular.GranularWMVFTS(explanatory_variables=[vmonth, vwin], target_variable=vwin,
                                 fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
                                 order=order, knn=knn)
 
 model.fit(train_mv)
 
+fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,3])
+ax.plot(test_mv['ws_10m'].values[:100], label='original')
 
-temp_generator = pwfts.ProbabilisticWeightedFTS(partitioner=vtemp.partitioner, order=1)
+forecasts = model.predict(test_mv.iloc[:100], type='distribution')
-temp_generator.fit(train_mv['temperature'].values)
 
-#print(model)
+Util.plot_distribution2(forecasts, test_mv['ws_10m'].values[:100], start_at=model.order-1, ax=ax)
 
-time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='h')
-#temp_generator = lambda x : x
-
-generators = {'time': time_generator, 'temperature': temp_generator}
-
-#print(model.predict(test_mv.iloc[:10], type='point', steps_ahead=10, generators=generators))
-#print(model.predict(test_mv.iloc[:10], type='interval', steps_ahead=10, generators=generators))
-print(model.predict(test_mv.iloc[:10], type='distribution', steps_ahead=10, generators=generators))
-
-
-#
-
-#forecasts1 = model.predict(test_mv, type='multivariate')
-#forecasts2 = model.predict(test, type='multivariate', generators={'date': time_generator},
-#                           steps_ahead=200)
-
-
-'''
-from pyFTS.data import Enrollments
-train = Enrollments.get_data()
-
-fs = Grid.GridPartitioner(data=train, npart=10) #, transformation=tdiff)
-
-model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2)
-model.fit(train)
-print(model)
-
-print(model.predict(train))
-'''