New version of sliding_window_benchmarks; Bugfixes in benchmarks, ifts and ensemble]

pyFTS/benchmarks/Measures.py

@@ -397,7 +397,7 @@ def get_interval_statistics(data, model, **kwargs):
         forecasts = model.predict(data, **kwargs)
         ret.append(round(sharpness(forecasts), 2))
         ret.append(round(resolution(forecasts), 2))
-        ret.append(round(coverage(data[model.order:], forecasts[:-1]), 2))
+        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))
         ret.append(round(pinball_mean(0.75, data[model.max_lag:], forecasts[:-1]), 2))

pyFTS/benchmarks/Util.py

@@ -108,6 +108,30 @@ def process_common_data(dataset, tag, type, job):
     return data
 
 
+def process_common_data2(dataset, tag, type, job):
+    """
+    Wraps benchmark information on a tuple for sqlite database
+
+    :param dataset: benchmark dataset
+    :param tag: benchmark set alias
+    :param type: forecasting type
+    :param job: a dictionary with benchmark data
+    :return: tuple for sqlite database
+    """
+    data = [dataset, tag, type,
+            job['model'],
+            job['transformation'],
+            job['order'],
+            job['partitioner'],
+            job['partitions'],
+            job['size'],
+            job['steps'],
+            job['method']
+            ]
+
+    return data
+
+
 def get_dataframe_from_bd(file, filter):
     """
     Query the sqlite benchmark database and return a pandas dataframe with the results

pyFTS/benchmarks/benchmarks.py

@@ -14,6 +14,7 @@ import matplotlib as plt
 import matplotlib.pyplot as plt
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
+from itertools import product
 
 from pyFTS.common import Transformations
 from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
@@ -67,7 +68,7 @@ def get_benchmark_interval_methods():
 
 def get_interval_methods():
     """Return all FTS methods for point_to_interval forecasting"""
-    return [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
+    return [ifts.IntervalFTS, ifts.WeightedIntervalFTS, pwfts.ProbabilisticWeightedFTS]
 
 
 def get_probabilistic_methods():
@@ -80,6 +81,126 @@ def get_benchmark_probabilistic_methods():
     return [arima.ARIMA, quantreg.QuantileRegression, knn.KNearestNeighbors]
 
 
+def sliding_window_benchmarks2(data, windowsize, train=0.8, **kwargs):
+    tag = __pop('tag', None, kwargs)
+    dataset = __pop('dataset', None, kwargs)
+
+    distributed = __pop('distributed', False, kwargs)
+
+    transformations = kwargs.get('transformations', [None])
+
+    type = kwargs.get("type", 'point')
+
+    orders = __pop("orders", [1, 2, 3], kwargs)
+
+    partitioners_methods = __pop("partitioners_methods", [Grid.GridPartitioner], kwargs)
+    partitions = __pop("partitions", [10], kwargs)
+
+    partitions = [k for k in partitions]
+
+    steps_ahead = __pop('steps_ahead', [1], kwargs)
+
+    steps_ahead = [k for k in steps_ahead]
+
+    fts_methods = __pop('methods', None, kwargs)
+
+    methods_parameters = __pop('methods_parameters', None, kwargs)
+
+    if fts_methods is None:
+        if type  == 'point':
+            fts_methods = get_point_methods()
+        elif type == 'interval':
+            fts_methods = get_interval_methods()
+        elif type == 'distribution':
+            fts_methods = get_probabilistic_methods()
+
+    ix_methods = [k for k in np.arange(len(fts_methods))]
+
+    benchmark_models = __pop("benchmark_models", False, kwargs)
+    benchmark_methods = __pop("benchmark_methods", None, kwargs)
+    benchmark_methods_parameters = __pop("benchmark_methods_parameters", None, kwargs)
+
+    if type == 'point':
+        experiment_method = run_point2
+        synthesis_method = process_point_jobs2
+    elif type == 'interval':
+        experiment_method = run_interval2
+        synthesis_method = process_interval_jobs2
+    elif type == 'distribution':
+        experiment_method = 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):
+
+        if benchmark_models:
+            for bm, method in enumerate(benchmark_methods):
+                for step in steps_ahead:
+
+                    kwargs['steps_ahead'] = step
+                    kwargs['parameters'] = benchmark_methods_parameters[bm]
+
+                    if not distributed:
+                        try:
+                            job = experiment_method(method, None, None, None, train, test, ct, **kwargs)
+                            synthesis_method(dataset, tag, job, conn)
+                        except Exception as ex:
+                            print('EXCEPTION! ', method, benchmark_methods_parameters[bm])
+                            traceback.print_exc()
+                    else:
+                        job = cluster.submit(method, None, None, None, train, test, ct, **kwargs)
+                        jobs.append(job)
+        else:
+            params = [ix_methods, orders, partitioners_methods, partitions, transformations, steps_ahead]
+            for id, instance in enumerate(product(*params)):
+                fts_method = fts_methods[instance[0]]
+                kwargs['steps_ahead'] = instance[5]
+                if methods_parameters is not None:
+                    kwargs['parameters'] = methods_parameters[instance[0]]
+                if not distributed:
+                    try:
+                        job = experiment_method(fts_method, instance[1], instance[2], instance[3], instance[4], train, test, ct, **kwargs)
+                        synthesis_method(dataset, tag, job, conn)
+                    except Exception as ex:
+                        print('EXCEPTION! ', instance)
+                        traceback.print_exc()
+                else:
+                    job = cluster.submit(fts_method, instance[1], instance[2], instance[3], instance[4], 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_benchmarks(data, windowsize, train=0.8, **kwargs):
     """
     Sliding window benchmarks for FTS forecasters.
@@ -314,8 +435,6 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
     conn.close()
 
 
-
-
 def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwargs):
     """
     Run the point forecasting benchmarks
@@ -502,6 +621,159 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None,
     return ret
 
 
+def __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data, transformation):
+    mfts = fts_method(**parameters)
+    if mfts.benchmark_only or mfts.is_wrapper:
+        pttr = ''
+    else:
+        fs = partitioner_method(npart=partitions, data=train_data, transformation=transformation)
+        pttr = str(fs.__module__).split('.')[-1]
+        if order > 1:
+            mfts = fts_method(partitioner=fs, order=order, **parameters)
+        else:
+            mfts.partitioner = fs
+
+        if transformation is not None:
+            mfts.append_transformation(transformation)
+    return mfts, pttr
+
+
+def run_point2(fts_method, order, partitioner_method, partitions, transformation, train_data, test_data, window_key=None, **kwargs):
+
+    import time
+    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
+
+    tmp = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
+           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]
+
+    tmp5 = [Transformations.Differential]
+
+    indexer = kwargs.get('indexer', None)
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+    method = kwargs.get('method', None)
+    parameters = kwargs.get('parameters', {})
+
+    mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
+                               transformation)
+
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+
+    _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': pttr, 'order': order, 'partitions': partitions,
+           'transformation': '' if transformation is None else transformation.name,
+           'size': len(mfts), 'time': times,
+           'rmse': _rmse, 'smape': _smape, 'u': _u, 'window': window_key,
+           'steps': steps_ahead, 'method': method}
+
+    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
+    from pyFTS.partitioners import Grid, Entropy, FCM
+    from pyFTS.benchmarks import Measures, arima, quantreg, BSTS
+
+    tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, ifts.WeightedIntervalFTS,  pwfts.ProbabilisticWeightedFTS]
+
+    tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
+
+    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',{})
+
+    mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
+                               transformation)
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+    _start = time.time()
+    #_sharp, _res, _cov, _q05, _q25, _q75, _q95, _w05, _w25
+    metrics = Measures.get_interval_statistics(test_data, mfts, **kwargs)
+    _end = time.time()
+    times += _end - _start
+
+    ret = {'model': mfts.shortname, 'partitioner': pttr, 'order': order, 'partitions': partitions,
+           'transformation': '' if transformation is None else transformation.name,
+           'size': len(mfts), 'time': times,
+           'sharpness': metrics[0], 'resolution': metrics[1], 'coverage': metrics[2],
+           'time': times,'Q05': metrics[3], 'Q25': metrics[4], 'Q75': metrics[5], 'Q95': metrics[6],
+           'winkler05': metrics[7], 'winkler25': metrics[8],
+           'window': window_key,'steps': steps_ahead, 'method': method}
+
+    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
+    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, quantreg, knn
+    from pyFTS.models.seasonal import SeasonalIndexer
+
+    tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS, arima.ARIMA,
+           ensemble.AllMethodEnsembleFTS, knn.KNearestNeighbors]
+
+    tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
+
+    tmp3 = [Measures.get_distribution_statistics, SeasonalIndexer.SeasonalIndexer, SeasonalIndexer.LinearSeasonalIndexer]
+
+    indexer = kwargs.get('indexer', None)
+
+    steps_ahead = kwargs.get('steps_ahead', 1)
+    method = kwargs.get('method', None)
+    parameters = kwargs.get('parameters', {})
+
+    mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
+                               transformation)
+
+    if mfts.has_seasonality:
+        mfts.indexer = indexer
+
+    _start = time.time()
+    mfts.fit(train_data, **kwargs)
+    _end = time.time()
+    times = _end - _start
+
+    _crps1, _t1, _brier = Measures.get_distribution_statistics(test_data, mfts, **kwargs)
+    _t1 += times
+
+    ret = {'model': mfts.shortname, 'partitioner': pttr, 'order': order, 'partitions': partitions,
+           'transformation': '' if transformation is None else transformation.name,
+           'size': len(mfts), 'time': times,
+           'CRPS': _crps1, 'brier': _brier, 'window': window_key,
+           'steps': steps_ahead, 'method': method}
+
+    return ret
+
+
 def process_point_jobs(dataset, tag,  job, conn):
     """
     Extract information from a dictionary with point benchmark results and save it on a database
@@ -528,6 +800,32 @@ def process_point_jobs(dataset, tag,  job, conn):
     time.extend(["time", job["time"]])
     bUtil.insert_benchmark(time, conn)
 
+def process_point_jobs2(dataset, tag,  job, conn):
+    """
+    Extract information from a dictionary with point benchmark results and save it on a database
+
+    :param dataset: the benchmark dataset name
+    :param tag: alias for the benchmark group being executed
+    :param job: a dictionary with the benchmark results
+    :param conn: a connection to a Sqlite database
+    :return:
+    """
+
+    data = bUtil.process_common_data2(dataset, tag, 'point',job)
+
+    rmse = deepcopy(data)
+    rmse.extend(["rmse", job["rmse"]])
+    bUtil.insert_benchmark(rmse, conn)
+    smape = deepcopy(data)
+    smape.extend(["smape", job["smape"]])
+    bUtil.insert_benchmark(smape, conn)
+    u = deepcopy(data)
+    u.extend(["u", job["u"]])
+    bUtil.insert_benchmark(u, conn)
+    time = deepcopy(data)
+    time.extend(["time", job["time"]])
+    bUtil.insert_benchmark(time, conn)
+
 
 def process_interval_jobs(dataset, tag, job, conn):
     """
@@ -574,6 +872,42 @@ def process_interval_jobs(dataset, tag, job, conn):
     bUtil.insert_benchmark(W25, conn)
 
 
+def process_interval_jobs2(dataset, tag, job, conn):
+
+    data = bUtil.process_common_data2(dataset, tag, 'interval', job)
+
+    sharpness = deepcopy(data)
+    sharpness.extend(["sharpness", job["sharpness"]])
+    bUtil.insert_benchmark(sharpness, conn)
+    resolution = deepcopy(data)
+    resolution.extend(["resolution", job["resolution"]])
+    bUtil.insert_benchmark(resolution, conn)
+    coverage = deepcopy(data)
+    coverage.extend(["coverage", job["coverage"]])
+    bUtil.insert_benchmark(coverage, conn)
+    time = deepcopy(data)
+    time.extend(["time", job["time"]])
+    bUtil.insert_benchmark(time, conn)
+    Q05 = deepcopy(data)
+    Q05.extend(["Q05", job["Q05"]])
+    bUtil.insert_benchmark(Q05, conn)
+    Q25 = deepcopy(data)
+    Q25.extend(["Q25", job["Q25"]])
+    bUtil.insert_benchmark(Q25, conn)
+    Q75 = deepcopy(data)
+    Q75.extend(["Q75", job["Q75"]])
+    bUtil.insert_benchmark(Q75, conn)
+    Q95 = deepcopy(data)
+    Q95.extend(["Q95", job["Q95"]])
+    bUtil.insert_benchmark(Q95, conn)
+    W05 = deepcopy(data)
+    W05.extend(["winkler05", job["winkler05"]])
+    bUtil.insert_benchmark(W05, conn)
+    W25 = deepcopy(data)
+    W25.extend(["winkler25", job["winkler25"]])
+    bUtil.insert_benchmark(W25, conn)
+
+
 def process_probabilistic_jobs(dataset, tag,  job, conn):
     """
     Extract information from an dictionary with probabilistic benchmark results and save it on a database
@@ -598,6 +932,30 @@ def process_probabilistic_jobs(dataset, tag,  job, conn):
     bUtil.insert_benchmark(brier, conn)
 
 
+def process_probabilistic_jobs2(dataset, tag,  job, conn):
+    """
+    Extract information from an dictionary with probabilistic benchmark results and save it on a database
+
+    :param dataset: the benchmark dataset name
+    :param tag: alias for the benchmark group being executed
+    :param job: a dictionary with the benchmark results
+    :param conn: a connection to a Sqlite database
+    :return:
+    """
+
+    data = bUtil.process_common_data2(dataset, tag,  'density', job)
+
+    crps = deepcopy(data)
+    crps.extend(["crps",job["CRPS"]])
+    bUtil.insert_benchmark(crps, conn)
+    time = deepcopy(data)
+    time.extend(["time", job["time"]])
+    bUtil.insert_benchmark(time, conn)
+    brier = deepcopy(data)
+    brier.extend(["brier", job["brier"]])
+    bUtil.insert_benchmark(brier, conn)
+
+
 def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
     """
     Run point benchmarks on given models and data and print the results
@@ -672,13 +1030,6 @@ def print_distribution_statistics(original, models, steps, resolution):
     print(ret)
 
 
-
-
-
-
-
-
-
 def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1):
     mi = min(points) * 0.95
     ma = max(points) * 1.05
@@ -758,10 +1109,6 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
     #Util.show_and_save_image(fig, file, save, lgd=legends)
 
 
-
-
-
-
 def plotCompared(original, forecasts, labels, title):
     fig = plt.figure(figsize=[13, 6])
     ax = fig.add_subplot(111)

pyFTS/common/Transformations.py

@@ -15,6 +15,7 @@ class Transformation(object):
     def __init__(self, **kwargs):
         self.is_invertible = True
         self.minimal_length = 1
+        self.name = ''
 
     def apply(self, data, param, **kwargs):
         """
@@ -38,7 +39,7 @@ class Transformation(object):
         pass
 
     def __str__(self):
-        return self.__class__.__name__ + '(' + str(self.parameters) + ')'
+        return self.name
 
 
 class Differential(Transformation):
@@ -49,6 +50,7 @@ class Differential(Transformation):
         super(Differential, self).__init__()
         self.lag = lag
         self.minimal_length = 2
+        self.name = 'Diff'
 
     @property
     def parameters(self):
@@ -128,6 +130,7 @@ class Scale(Transformation):
         self.data_min = None
         self.transf_max = max
         self.transf_min = min
+        self.name = 'Scale'
 
     @property
     def parameters(self):
@@ -167,6 +170,7 @@ class AdaptiveExpectation(Transformation):
     def __init__(self, parameters):
         super(AdaptiveExpectation, self).__init__(parameters)
         self.h = parameters
+        self.name = 'AdaptExpect'
 
     @property
     def parameters(self):
@@ -193,6 +197,7 @@ class BoxCox(Transformation):
     def __init__(self, plambda):
         super(BoxCox, self).__init__()
         self.plambda = plambda
+        self.name = 'BoxCox'
 
     @property
     def parameters(self):

pyFTS/models/ensemble/ensemble.py

@@ -305,6 +305,8 @@ class SimpleEnsembleFTS(EnsembleFTS):
         """Possible variations of order on internal models"""
         self.uod_clip = False
 
+        self.shortname = kwargs.get('name', 'EnsembleFTS-' + str(self.method.__module__).split('.')[-1])
+
     def train(self, data, **kwargs):
         for k in self.partitions:
             fs = self.partitioner_method(data=data, npart=k)

pyFTS/models/ifts.py

@@ -62,7 +62,7 @@ class IntervalFTS(hofts.HighOrderFTS):
         if l <= self.order:
             return ndata
 
-        for k in np.arange(self.max_lag, l+1):
+        for k in np.arange(self.max_lag, l):
 
             sample = ndata[k - self.max_lag: k]
 
@@ -94,9 +94,9 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
     Weighted High Order Interval Fuzzy Time Series
     """
     def __init__(self, **kwargs):
-        super(IntervalFTS, self).__init__(**kwargs)
+        super(WeightedIntervalFTS, self).__init__(**kwargs)
-        self.shortname = "IFTS"
+        self.shortname = "WIFTS"
-        self.name = "Interval FTS"
+        self.name = "Weighted Interval FTS"
         self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)"
         self.flrgs = {}
         self.has_point_forecasting = False
@@ -138,7 +138,7 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
         if l <= self.order:
             return ndata
 
-        for k in np.arange(self.max_lag, l+1):
+        for k in np.arange(self.max_lag, l):
 
             sample = ndata[k - self.max_lag: k]
 
@@ -162,4 +162,4 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
             up_ = sum(up) / norm
             ret.append([lo_, up_])
 
-        return ret
+        return ret

pyFTS/tests/general.py

@@ -11,146 +11,41 @@ import pandas as pd
 from pyFTS.common import Util as cUtil, FuzzySet
 from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
 from pyFTS.benchmarks import benchmarks as bchmk, Measures
-from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
+from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
 from pyFTS.models.ensemble import ensemble
 from pyFTS.common import Transformations, Membership
 from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc
 from pyFTS.fcm import fts, common, GA
 
-from pyFTS.data import Enrollments, TAIEX
+from pyFTS.data import TAIEX, NASDAQ, SP500
 
-data = TAIEX.get_data()
+datasets = {}
 
-train = data[:800]
+datasets['TAIEX'] = TAIEX.get_data()[:5000]
-test = data[800:1000]
+datasets['NASDAQ'] = NASDAQ.get_data()[:5000]
+datasets['SP500'] = SP500.get_data()[10000:15000]
 
-#model = ensemble.SimpleEnsembleFTS(fts_method=hofts.HighOrderFTS)
+methods = [ensemble.SimpleEnsembleFTS]*8
-#model = quantreg.QuantileRegression(order=2, dist=True)
-#model = arima.ARIMA(order = (2,0,0))
-#model = BSTS.ARIMA(order=(2,0,0))
-model = gaussianproc.GPR(order=2)
-model.fit(train)
 
-horizon=5
+methods_parameters = [
-
+    {'name': 'EnsembleFTS-HOFTS-10-.05', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05},
-#points  = model.predict(test[:10], type='point', steps_ahead=horizon)
+    {'name': 'EnsembleFTS-HOFTS-5-.05', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .05},
-
+    {'name': 'EnsembleFTS-HOFTS-10-.25', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .25},
-intervals = model.predict(test[:10], type='point', alpha=.05,  steps_ahead=horizon)
+    {'name': 'EnsembleFTS-HOFTS-5-.25', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .25},
-print(test[:10])
+    {'name': 'EnsembleFTS-WHOFTS-10-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05},
-print(intervals)
+    {'name': 'EnsembleFTS-WHOFTS-5-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .05},
-#distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, num_bins=100)
+    {'name': 'EnsembleFTS-WHOFTS-10-.25', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .25},
-
+    {'name': 'EnsembleFTS-WHOFTS-5-.25', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .25},
-
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize=[15,5])
-
-ax.plot(test[:10], label='Original',color='black')
-cUtil.plot_interval2(intervals, test[:10], start_at=model.order, ax=ax)
-#cUtil.plot_distribution2(distributions, test[:10], start_at=model.order, ax=ax, cmap="Blues")
-
-print("")
-
-'''
-model = fts.FCM_FTS(partitioner=fs, order=1)
-
-model.fcm.weights = np.array([
-    [1, 1, 0, -1, -1],
-    [1, 1, 1, 0, -1],
-    [0, 1, 1, 1, 0],
-    [-1, 0, 1, 1, 1],
-    [-1, -1, 0, 1, 1]
-])
-
-print(data)
-print(model.forecast(data))
-'''
-'''
-dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
-
-dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
-
-train_mv = dataset.iloc[:24505]
-test_mv = dataset.iloc[24505:24605]
-
-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])
-
-print(names)
-
-from pyFTS.models.multivariate import common, variable, mvfts
-from pyFTS.models.seasonal import partitioner as seasonal
-from pyFTS.models.seasonal.common import DateTime
-
-
-
-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)
-
-
-
-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,
-                          data=train_mv, partitioner_specific=sp)
-
-
-vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
-                         partitioner=Grid.GridPartitioner, npart=35, partitioner_specific={'names': names},
-                         data=train_mv)
-
-from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
-
-parameters = [
-    {}, {},
-    {'order': 2, 'knn': 1},
-    {'order': 2, 'knn': 2},
-    {'order': 2, 'knn': 3},
 ]
 
-from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
+for dataset_name, dataset in datasets.items():
-from pyFTS.benchmarks import Measures
+    bchmk.sliding_window_benchmarks2(dataset, 1000, train=0.8, inc=0.2,
-
+                                    methods=methods,
-time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='h')
+                                    methods_parameters=methods_parameters,
-
+                                    benchmark_models=False,
-model = granular.GranularWMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, order=2, knn=2)
+                                    transformations=[None],
-
+                                    orders=[3],
-model.fit(train_mv)
+                                    partitions=[None],
-
+                                    type='interval',
-forecasts = model.predict(test_mv, type='multivariate', generators={'data': time_generator}, steps_ahead=24 )
+                                    #distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
-
+                                    file="tmp.db", dataset=dataset_name, tag="gridsearch")
-print(forecasts)
-
-'''
-'''
-from pyFTS.data import lorentz
-df = lorentz.get_dataframe(iterations=5000)
-
-train = df.iloc[:4000]
-test = df.iloc[4000:]
-
-from pyFTS.models.multivariate import common, variable, mvfts
-from pyFTS.partitioners import Grid
-
-vx = variable.Variable("x", data_label="x", alias='x', partitioner=Grid.GridPartitioner, npart=45, data=train)
-vy = variable.Variable("y", data_label="y", alias='y', partitioner=Grid.GridPartitioner, npart=45, data=train)
-vz = variable.Variable("z", data_label="z", alias='z', partitioner=Grid.GridPartitioner, npart=45, data=train)
-
-from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
-from pyFTS.benchmarks import Measures
-
-model = granular.GranularWMVFTS(explanatory_variables=[vx, vy, vz], target_variable=vx, order=5, knn=2)
-
-model.fit(train)
-
-forecasts = model.predict(test, type='multivariate', steps_ahead=20)
-
-print(forecasts)
-'''