diff --git a/pyFTS/benchmarks/Measures.py b/pyFTS/benchmarks/Measures.py index 2b925cf..3340ef1 100644 --- a/pyFTS/benchmarks/Measures.py +++ b/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)) diff --git a/pyFTS/benchmarks/Util.py b/pyFTS/benchmarks/Util.py index ef19e2c..c51630b 100644 --- a/pyFTS/benchmarks/Util.py +++ b/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 diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py index 506f835..1b22417 100644 --- a/pyFTS/benchmarks/benchmarks.py +++ b/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) diff --git a/pyFTS/common/Transformations.py b/pyFTS/common/Transformations.py index c04617a..fedb2d0 100644 --- a/pyFTS/common/Transformations.py +++ b/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): diff --git a/pyFTS/models/ensemble/ensemble.py b/pyFTS/models/ensemble/ensemble.py index 0e26f05..6192592 100644 --- a/pyFTS/models/ensemble/ensemble.py +++ b/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) diff --git a/pyFTS/models/ifts.py b/pyFTS/models/ifts.py index 1f1e267..ae85ae0 100644 --- a/pyFTS/models/ifts.py +++ b/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) - self.shortname = "IFTS" - self.name = "Interval FTS" + super(WeightedIntervalFTS, self).__init__(**kwargs) + self.shortname = "WIFTS" + 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 \ No newline at end of file + return ret diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py index b9b9a7f..0f92096 100644 --- a/pyFTS/tests/general.py +++ b/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] -test = data[800:1000] +datasets['TAIEX'] = TAIEX.get_data()[:5000] +datasets['NASDAQ'] = NASDAQ.get_data()[:5000] +datasets['SP500'] = SP500.get_data()[10000:15000] -#model = ensemble.SimpleEnsembleFTS(fts_method=hofts.HighOrderFTS) -#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) +methods = [ensemble.SimpleEnsembleFTS]*8 -horizon=5 - -#points = model.predict(test[:10], type='point', steps_ahead=horizon) - -intervals = model.predict(test[:10], type='point', alpha=.05, steps_ahead=horizon) -print(test[:10]) -print(intervals) -#distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, num_bins=100) - - -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}, +methods_parameters = [ + {'name': 'EnsembleFTS-HOFTS-10-.05', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05}, + {'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}, + {'name': 'EnsembleFTS-HOFTS-5-.25', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .25}, + {'name': 'EnsembleFTS-WHOFTS-10-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05}, + {'name': 'EnsembleFTS-WHOFTS-5-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .05}, + {'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}, ] -from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular -from pyFTS.benchmarks import Measures - -time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='h') - -model = granular.GranularWMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, order=2, knn=2) - -model.fit(train_mv) - -forecasts = model.predict(test_mv, type='multivariate', generators={'data': time_generator}, steps_ahead=24 ) - -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) -''' \ No newline at end of file +for dataset_name, dataset in datasets.items(): + bchmk.sliding_window_benchmarks2(dataset, 1000, train=0.8, inc=0.2, + methods=methods, + methods_parameters=methods_parameters, + benchmark_models=False, + transformations=[None], + orders=[3], + partitions=[None], + type='interval', + #distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'], + file="tmp.db", dataset=dataset_name, tag="gridsearch")