diff --git a/pyFTS/benchmarks/Measures.py b/pyFTS/benchmarks/Measures.py index c887a3e..3ae8685 100644 --- a/pyFTS/benchmarks/Measures.py +++ b/pyFTS/benchmarks/Measures.py @@ -288,7 +288,7 @@ def get_point_statistics(data, model, **kwargs): ret = list() if steps_ahead == 1: - forecasts = model.forecast(data, **kwargs) + forecasts = model.predict(data, **kwargs) if model.has_seasonality: nforecasts = np.array(forecasts) else: @@ -304,7 +304,7 @@ def get_point_statistics(data, model, **kwargs): tmp = model.forecast_ahead(sample, steps_ahead, **kwargs) nforecasts.append(tmp[-1]) - start = model.order + steps_ahead + start = model.order + steps_ahead -1 ret.append(np.round(rmse(ndata[start:-1:steps_ahead_sampler], nforecasts), 2)) ret.append(np.round(smape(ndata[start:-1:steps_ahead_sampler], nforecasts), 2)) ret.append(np.round(UStatistic(ndata[start:-1:steps_ahead_sampler], nforecasts), 2)) @@ -327,7 +327,7 @@ def get_interval_statistics(data, model, **kwargs): ret = list() if steps_ahead == 1: - forecasts = model.forecast_interval(data, **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)) @@ -339,10 +339,10 @@ def get_interval_statistics(data, model, **kwargs): forecasts = [] for k in np.arange(model.order, len(data) - steps_ahead): sample = data[k - model.order: k] - tmp = model.forecast_ahead_interval(sample, steps_ahead, **kwargs) + tmp = model.predict(sample, steps_ahead, **kwargs) forecasts.append(tmp[-1]) - start = model.order + steps_ahead + start = model.order + steps_ahead -1 ret.append(round(sharpness(forecasts), 2)) ret.append(round(resolution(forecasts), 2)) ret.append(round(coverage(data[model.order:], forecasts), 2)) diff --git a/pyFTS/benchmarks/Util.py b/pyFTS/benchmarks/Util.py index cecb036..05aa31f 100644 --- a/pyFTS/benchmarks/Util.py +++ b/pyFTS/benchmarks/Util.py @@ -8,6 +8,7 @@ import matplotlib.colors as pltcolors import matplotlib.pyplot as plt import numpy as np import pandas as pd +import sqlite3 #from mpl_toolkits.mplot3d import Axes3D @@ -15,6 +16,57 @@ from copy import deepcopy from pyFTS.common import Util +def open_benchmark_db(name): + conn = sqlite3.connect(name) + create_benchmark_tables(conn) + return conn + + +def create_benchmark_tables(conn): + c = conn.cursor() + + c.execute('''CREATE TABLE if not exists benchmarks( + ID integer primary key, Date int, Dataset text, Tag text, + Type text, Model text, Transformation text, 'Order' int, + Scheme text, Partitions int, + Size int, Steps int, Method text, Measure text, Value real)''') + + # Save (commit) the changes + conn.commit() + + +def insert_benchmark(data, conn): + c = conn.cursor() + + c.execute("INSERT INTO benchmarks(Date, Dataset, Tag, Type, Model, " + + "Transformation, 'Order', Scheme, Partitions, " + + "Size, Steps, Method, Measure, Value) " + + "VALUES(datetime('now'),?,?,?,?,?,?,?,?,?,?,?,?,?)", data) + conn.commit() + + +def process_common_data(dataset, tag, type, job): + model = job["obj"] + if not model.benchmark_only: + data = [dataset, tag, type, model.shortname, + str(model.partitioner.transformation) if model.partitioner.transformation is not None else None, + model.order, model.partitioner.name, str(model.partitioner.partitions), + len(model), job['steps'], job['method']] + else: + data = [tag, type, model.shortname, None, model.order, None, None, + None, job['steps'], job['method']] + return data + + +def get_dataframe_from_bd(file, filter): + con = sqlite3.connect(file) + sql = "SELECT * from benchmarks" + if filter is not None: + sql += " WHERE " + filter + return pd.read_sql_query(sql, con) + + + def extract_measure(dataframe, measure, data_columns): if not dataframe.empty: df = dataframe[(dataframe.Measure == measure)][data_columns] @@ -45,6 +97,7 @@ def find_best(dataframe, criteria, ascending): return ret + def analytic_tabular_dataframe(dataframe): experiments = len(dataframe.columns) - len(base_dataframe_columns()) - 1 models = dataframe.Model.unique() diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py index f9437c2..4b7d154 100644 --- a/pyFTS/benchmarks/benchmarks.py +++ b/pyFTS/benchmarks/benchmarks.py @@ -90,10 +90,14 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs): :return: DataFrame with the benchmark results """ + + tag = __pop('tag', None, kwargs) + dataset = __pop('dataset', None, kwargs) + distributed = __pop('distributed', False, kwargs) save = __pop('save', False, kwargs) - transformation = kwargs.get('transformation', None) + transformations = kwargs.get('transformations', [None]) progress = kwargs.get('progress', None) type = kwargs.get("type", 'point') @@ -192,13 +196,15 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs): if partitioners_models is None: - for partition in partitions: + for transformation in transformations: - for partitioner in partitioners_methods: + for partition in partitions: - data_train_fs = partitioner(data=train, npart=partition, transformation=transformation) + for partitioner in partitioners_methods: - partitioners_pool.append(data_train_fs) + data_train_fs = partitioner(data=train, npart=partition, transformation=transformation) + + partitioners_pool.append(data_train_fs) else: partitioners_pool = partitioners_models @@ -206,6 +212,10 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs): if progress: rng1 = tqdm(steps_ahead, desc="Steps") + file = kwargs.get('file', "benchmarks.db") + + conn = bUtil.open_benchmark_db(file) + for step in rng1: rng2 = partitioners_pool @@ -225,7 +235,7 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs): if not distributed: job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs) - jobs.append(job) + synthesis_method(dataset, tag, job, conn) else: job = cluster.submit(deepcopy(model), deepcopy(partitioner), train, test, **kwargs) job.id = id # associate an ID to identify jobs (if needed later) @@ -239,29 +249,29 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs): rng = jobs - cluster.wait() # wait for all jobs to finish - if progress: rng = tqdm(jobs) for job in rng: + job() if job.status == dispy.DispyJob.Finished and job is not None: - tmp = job() - jobs2.append(tmp) + 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) - jobs = deepcopy(jobs2) + cluster.wait() # wait for all jobs to finish cUtil.stop_dispy_cluster(cluster, http_server) - file = kwargs.get('file', None) + conn.close() + sintetic = kwargs.get('sintetic', False) - return synthesis_method(jobs, experiments, save, file, sintetic) + #return synthesis_method(jobs, experiments, save, file, sintetic) def get_benchmark_point_methods(): @@ -326,7 +336,6 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg tmp5 = [Transformations.Differential] - transformation = kwargs.get('transformation', None) indexer = kwargs.get('indexer', None) steps_ahead = kwargs.get('steps_ahead', 1) @@ -338,13 +347,11 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg pttr = str(partitioner.__module__).split('.')[-1] _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions) mfts.partitioner = partitioner + mfts.append_transformation(partitioner.transformation) _key += str(steps_ahead) _key += str(method) if method is not None else "" - if transformation is not None: - mfts.append_transformation(transformation) - _start = time.time() mfts.fit(train_data, order=mfts.order, **kwargs) _end = time.time() @@ -386,9 +393,6 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kw tmp3 = [Measures.get_interval_statistics] - transformation = kwargs.get('transformation', None) - indexer = kwargs.get('indexer', None) - steps_ahead = kwargs.get('steps_ahead', 1) method = kwargs.get('method', None) @@ -398,9 +402,7 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kw pttr = str(partitioner.__module__).split('.')[-1] _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions) mfts.partitioner = partitioner - - if transformation is not None: - mfts.append_transformation(transformation) + mfts.append_transformation(partitioner.transformation) _key += str(steps_ahead) _key += str(method) if method is not None else "" @@ -450,7 +452,6 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None, tmp3 = [Measures.get_distribution_statistics, SeasonalIndexer.SeasonalIndexer, SeasonalIndexer.LinearSeasonalIndexer] - transformation = kwargs.get('transformation', None) indexer = kwargs.get('indexer', None) steps_ahead = kwargs.get('steps_ahead', 1) @@ -462,13 +463,11 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None, pttr = str(partitioner.__module__).split('.')[-1] _key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions) mfts.partitioner = partitioner + mfts.append_transformation(partitioner.transformation) _key += str(steps_ahead) _key += str(method) if method is not None else "" - if transformation is not None: - mfts.append_transformation(transformation) - if mfts.has_seasonality: mfts.indexer = indexer @@ -491,126 +490,64 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None, return ret -def build_model_pool_point(models, max_order, benchmark_models, benchmark_models_parameters): - pool = [] - if models is None: - models = get_point_methods() - for model in models: - mfts = model("") +def process_point_jobs(dataset, tag, job, conn): - if mfts.is_high_order: - for order in np.arange(1, max_order + 1): - if order >= mfts.min_order: - mfts = model("") - mfts.order = order - pool.append(mfts) - else: - mfts.order = 1 - pool.append(mfts) + data = bUtil.process_common_data(dataset, tag, 'point',job) - if benchmark_models is not None: - for count, model in enumerate(benchmark_models, start=0): - par = benchmark_models_parameters[count] - mfts = model(str(par if par is not None else "")) - mfts.order = par - pool.append(mfts) - return pool + 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_point_jobs(jobs, experiments, save=False, file=None, sintetic=False): - objs = {} - rmse = {} - smape = {} - u = {} - times = {} - steps = {} - method = {} +def process_interval_jobs(dataset, tag, job, conn): - for job in jobs: - _key = job['key'] - if _key not in objs: - objs[_key] = job['obj'] - rmse[_key] = [] - smape[_key] = [] - u[_key] = [] - times[_key] = [] - steps[_key] = [] - method[_key] = [] - steps[_key] = job['steps'] - method[_key] = job['method'] - rmse[_key].append(job['rmse']) - smape[_key].append(job['smape']) - u[_key].append(job['u']) - times[_key].append(job['time']) + data = bUtil.process_common_data(dataset, tag, 'interval', job) - return bUtil.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u, steps, method) + 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) -def process_interval_jobs(jobs, experiments, save=False, file=None, sintetic=False): - objs = {} - sharpness = {} - resolution = {} - coverage = {} - q05 = {} - q25 = {} - q75 = {} - q95 = {} - times = {} - steps = {} - method = {} +def process_probabilistic_jobs(dataset, tag, job, conn): - for job in jobs: - _key = job['key'] - if _key not in objs: - objs[_key] = job['obj'] - sharpness[_key] = [] - resolution[_key] = [] - coverage[_key] = [] - times[_key] = [] - q05[_key] = [] - q25[_key] = [] - q75[_key] = [] - q95[_key] = [] - steps[_key] = [] - method[_key] = [] - - sharpness[_key].append(job['sharpness']) - resolution[_key].append(job['resolution']) - coverage[_key].append(job['coverage']) - times[_key].append(job['time']) - q05[_key].append(job['Q05']) - q25[_key].append(job['Q25']) - q75[_key].append(job['Q75']) - q95[_key].append(job['Q95']) - steps[_key] = job['steps'] - method[_key] = job['method'] - return bUtil.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, - times, q05, q25, q75, q95, steps, method) - - -def process_probabilistic_jobs(jobs, experiments, save=False, file=None, sintetic=False): - objs = {} - crps = {} - times = {} - steps = {} - method = {} - - for job in jobs: - _key = job['key'] - if _key not in objs: - objs[_key] = job['obj'] - crps[_key] = [] - times[_key] = [] - steps[_key] = [] - method[_key] = [] - - crps[_key].append(job['CRPS']) - times[_key].append(job['time']) - steps[_key] = job['steps'] - method[_key] = job['method'] - - return bUtil.save_dataframe_probabilistic(experiments, file, objs, crps, times, save, sintetic, steps, method) + data = bUtil.process_common_data(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) def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None): @@ -636,7 +573,6 @@ def print_point_statistics(data, models, externalmodels = None, externalforecast print(ret) - def print_interval_statistics(original, models): ret = "Model & Order & Sharpness & Resolution & Coverage & .05 & .25 & .75 & .95 \\\\ \n" for fts in models: @@ -653,151 +589,6 @@ def print_interval_statistics(original, models): print(ret) - - - - - -def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution = None, partitioners=[Grid.GridPartitioner], - partitions=[10], max_order=3, transformation=None, indexer=None, dump=False, - save=False, file=None, synthetic=False): - if models is None: - models = [pwfts.ProbabilisticWeightedFTS] - - objs = {} - lcolors = {} - crps_interval = {} - crps_distr = {} - times1 = {} - times2 = {} - - experiments = 0 - for ct, train,test in cUtil.sliding_window(data, windowsize, train): - experiments += 1 - for partition in partitions: - for partitioner in partitioners: - pttr = str(partitioner.__module__).split('.')[-1] - data_train_fs = partitioner(data=train, npart=partition, transformation=transformation) - - for count, model in enumerate(models, start=0): - - mfts = model("") - _key = mfts.shortname + " " + pttr+ " q = " +str(partition) - - mfts.partitioner = data_train_fs - if not mfts.is_high_order: - - if dump: print(ct,_key) - - if _key not in objs: - objs[_key] = mfts - lcolors[_key] = colors[count % ncol] - crps_interval[_key] = [] - crps_distr[_key] = [] - times1[_key] = [] - times2[_key] = [] - - if transformation is not None: - mfts.append_transformation(transformation) - - _start = time.time() - mfts.train(train, sets=data_train_fs.sets) - _end = time.time() - - _tdiff = _end - _start - - _crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test,mfts,steps=steps,resolution=resolution) - - crps_interval[_key].append_rhs(_crps1) - crps_distr[_key].append_rhs(_crps2) - times1[_key] = _tdiff + _t1 - times2[_key] = _tdiff + _t2 - - if dump: print(_crps1, _crps2, _tdiff, _t1, _t2) - - else: - for order in np.arange(1, max_order + 1): - if order >= mfts.min_order: - mfts = model("") - _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition) - mfts.partitioner = data_train_fs - - if dump: print(ct,_key) - - if _key not in objs: - objs[_key] = mfts - lcolors[_key] = colors[count % ncol] - crps_interval[_key] = [] - crps_distr[_key] = [] - times1[_key] = [] - times2[_key] = [] - - if transformation is not None: - mfts.append_transformation(transformation) - - _start = time.time() - mfts.train(train, sets=data_train_fs.sets, order=order) - _end = time.time() - - _tdiff = _end - _start - - _crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test, mfts, steps=steps, - resolution=resolution) - - crps_interval[_key].append_rhs(_crps1) - crps_distr[_key].append_rhs(_crps2) - times1[_key] = _tdiff + _t1 - times2[_key] = _tdiff + _t2 - - if dump: print(_crps1, _crps2, _tdiff, _t1, _t2) - - return bUtil.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, synthetic) - - - -def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5], - models=None, transformation=None, option=2): - if models is None: - models = [pwfts.ProbabilisticWeightedFTS] - - if resolution is None: resolution = (max(data_train) - min(data_train)) / 100 - - objs = [] - - data_train_fs = Grid.GridPartitioner(data=data_train, npart=partitions, transformation=transformation).sets - lcolors = [] - - for count, model in cUtil.enumerate2(models, start=0, step=2): - mfts = model("") - if not mfts.is_high_order: - if transformation is not None: - mfts.append_transformation(transformation) - mfts.train(data_train, sets=data_train_fs.sets) - objs.append(mfts) - lcolors.append( colors[count % ncol] ) - else: - for order in np.arange(1,max_order+1): - if order >= mfts.min_order: - mfts = model(" n = " + str(order)) - if transformation is not None: - mfts.append_transformation(transformation) - mfts.train(data_train, sets=data_train_fs.sets, order=order) - objs.append(mfts) - lcolors.append(colors[count % ncol]) - - distributions = [False for k in objs] - - distributions[0] = True - - print_distribution_statistics(data_test[start:], objs, steps, resolution) - - plot_compared_intervals_ahead(data_test, objs, lcolors, distributions=distributions, time_from=start, time_to=steps, - interpol=False, save=save, file=file, tam=tam, resolution=resolution, option=option) - - - - - def print_distribution_statistics(original, models, steps, resolution): ret = "Model & Order & Interval & Distribution \\\\ \n" for fts in models: diff --git a/pyFTS/common/Transformations.py b/pyFTS/common/Transformations.py index ce442db..6291daf 100644 --- a/pyFTS/common/Transformations.py +++ b/pyFTS/common/Transformations.py @@ -44,11 +44,15 @@ class Differential(Transformation): """ Differentiation data transform """ - def __init__(self, parameters): + def __init__(self, lag): super(Differential, self).__init__() - self.lag = parameters + self.lag = lag self.minimal_length = 2 + @property + def parameters(self): + return self.lag + def apply(self, data, param=None, **kwargs): if param is not None: self.lag = param @@ -66,7 +70,7 @@ class Differential(Transformation): def inverse(self, data, param, **kwargs): - interval = kwargs.get("point_to_interval",False) + type = kwargs.get("type","point") if isinstance(data, (np.ndarray, np.generic)): data = data.tolist() @@ -79,10 +83,14 @@ class Differential(Transformation): # print(n) # print(len(param)) - if not interval: + if type == "point": inc = [data[t] + param[t] for t in np.arange(0, n)] - else: + elif type == "interval": inc = [[data[t][0] + param[t], data[t][1] + param[t]] for t in np.arange(0, n)] + elif type == "distribution": + for t in np.arange(0, n): + data[t].differential_offset(param[t]) + inc = data if n == 1: return inc[0] @@ -103,6 +111,10 @@ class Scale(Transformation): self.transf_max = max self.transf_min = min + @property + def parameters(self): + return [self.transf_max, self.transf_min] + def apply(self, data, param=None,**kwargs): if self.data_max is None: self.data_max = np.nanmax(data) @@ -138,6 +150,10 @@ class AdaptiveExpectation(Transformation): super(AdaptiveExpectation, self).__init__(parameters) self.h = parameters + @property + def parameters(self): + return self.parameters + def apply(self, data, param=None,**kwargs): return data @@ -160,6 +176,10 @@ class BoxCox(Transformation): super(BoxCox, self).__init__() self.plambda = plambda + @property + def parameters(self): + return self.plambda + def apply(self, data, param=None, **kwargs): if self.plambda != 0: modified = [(dat ** self.plambda - 1) / self.plambda for dat in data] diff --git a/pyFTS/probabilistic/ProbabilityDistribution.py b/pyFTS/probabilistic/ProbabilityDistribution.py index ce9b78d..ce16dda 100644 --- a/pyFTS/probabilistic/ProbabilityDistribution.py +++ b/pyFTS/probabilistic/ProbabilityDistribution.py @@ -95,6 +95,25 @@ class ProbabilityDistribution(object): return ret + def differential_offset(self, value): + nbins = [] + dist = {} + + for k in self.bins: + nk = k+value + nbins.append(nk) + dist[nk] = self.distribution[k] + + self.bins = nbins + self.distribution = dist + self.labels = [str(k) for k in self.bins] + + self.bin_index = SortedCollection.SortedCollection(iterable=sorted(self.bins)) + self.quantile_index = None + self.cdf = None + self.qtl = None + + def expected_value(self): return np.nansum([v * self.distribution[v] for v in self.bins]) diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py index a947c95..ba45a18 100644 --- a/pyFTS/tests/general.py +++ b/pyFTS/tests/general.py @@ -19,19 +19,34 @@ from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil from pyFTS.models import pwfts +from pyFTS.partitioners import Grid, Util as pUtil +partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff) + +model = pwfts.ProbabilisticWeightedFTS('',partitioner=partitioner) +#model.append_transformation(tdiff) +model.fit(dataset[:800]) +print(model.predict(dataset[800:1000], type='interval')) + + ''' bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, methods=[pwfts.ProbabilisticWeightedFTS], - benchmark_models=False, orders=[1,2,3], partitions=np.arange(10,100,5), - progress=False, type='point', - #steps_ahead=[1,4,7,10], steps_ahead_sampler=10, - distributed=True, nodes=['192.168.0.102','192.168.0.106','192.168.0.110'], - save=True, file="pwfts_taiex_partitioning.csv") -''' + benchmark_models=False, + #transformations=[tdiff], + orders=[1, 2, 3], + partitions=np.arange(10, 100, 5), + progress=False, type='distribution', + #steps_ahead=[1,4,7,10], #steps_ahead=[1] + distributed=True, nodes=['192.168.0.110', '192.168.0.100','192.168.0.106'], + file="benchmarks.db", dataset="TAIEX", tag="partitioning") + #save=True, file="tmp.db") + +''' +''' dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';') print(bUtil.analytic_tabular_dataframe(dat)) #print(dat["Size"].values[0]) - +''' ''' train_split = 2000 test_length = 200