diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py index f9e9cc6..2440463 100644 --- a/pyFTS/common/fts.py +++ b/pyFTS/common/fts.py @@ -321,7 +321,7 @@ class FTS(object): if 'partitioner' in kwargs: self.partitioner = kwargs.pop('partitioner') - if not self.is_wrapper and not self.benchmark_only: + if not self.is_multivariate and not self.is_wrapper and not self.benchmark_only: if self.partitioner is None: raise Exception("Fuzzy sets were not provided for the model. Use 'partitioner' parameter. ") diff --git a/pyFTS/models/multivariate/cmvfts.py b/pyFTS/models/multivariate/cmvfts.py index 8bd5167..4591869 100644 --- a/pyFTS/models/multivariate/cmvfts.py +++ b/pyFTS/models/multivariate/cmvfts.py @@ -34,12 +34,13 @@ class ClusteredMVFTS(mvfts.MVFTS): self.name = "Clustered Multivariate FTS" self.pre_fuzzyfy = kwargs.get('pre_fuzzyfy', True) + self.fuzzyfy_mode = kwargs.get('fuzzyfy_mode', 'sets') def fuzzyfy(self,data): ndata = [] for index, row in data.iterrows(): data_point = self.format_data(row) - ndata.append(self.partitioner.fuzzyfy(data_point, mode='sets')) + ndata.append(self.partitioner.fuzzyfy(data_point, mode=self.fuzzyfy_mode)) return ndata @@ -71,6 +72,50 @@ class ClusteredMVFTS(mvfts.MVFTS): return self.model.forecast(ndata, fuzzyfied=pre_fuzz, **kwargs) + def forecast_interval(self, data, **kwargs): + + if not self.model.has_interval_forecasting: + raise Exception("The internal method does not support interval forecasting!") + + data = self.check_data(data) + + pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy) + + return self.model.forecast_interval(data, fuzzyfied=pre_fuzz, **kwargs) + + def forecast_ahead_interval(self, data, steps, **kwargs): + + if not self.model.has_interval_forecasting: + raise Exception("The internal method does not support interval forecasting!") + + data = self.check_data(data) + + pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy) + + return self.model.forecast_ahead_interval(data, steps, fuzzyfied=pre_fuzz, **kwargs) + + def forecast_distribution(self, data, **kwargs): + + if not self.model.has_probability_forecasting: + raise Exception("The internal method does not support probabilistic forecasting!") + + data = self.check_data(data) + + pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy) + + return self.model.forecast_distribution(data, fuzzyfied=pre_fuzz, **kwargs) + + def forecast_ahead_distribution(self, data, steps, **kwargs): + + if not self.model.has_probability_forecasting: + raise Exception("The internal method does not support probabilistic forecasting!") + + data = self.check_data(data) + + pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy) + + return self.model.forecast_ahead_distribution(data, steps, fuzzyfied=pre_fuzz, **kwargs) + def forecast_multivariate(self, data, **kwargs): ndata = self.check_data(data) diff --git a/pyFTS/models/multivariate/common.py b/pyFTS/models/multivariate/common.py index f9b2687..edd5c39 100644 --- a/pyFTS/models/multivariate/common.py +++ b/pyFTS/models/multivariate/common.py @@ -27,18 +27,26 @@ class MultivariateFuzzySet(Composite.FuzzySet): if variable == self.target_variable.name: self.centroid = set.centroid + self.upper = set.upper + self.lower = set.lower self.name += set.name def set_target_variable(self, variable): self.target_variable = variable self.centroid = self.sets[variable.name].centroid + self.upper = self.sets[variable.name].upper + self.lower = self.sets[variable.name].lower def membership(self, x): mv = [] - for var in self.sets.keys(): - data = x[var] - mv.append(self.sets[var].membership(data)) + if isinstance(x, (dict, pd.DataFrame)): + for var in self.sets.keys(): + data = x[var] + mv.append(self.sets[var].membership(data)) + else: + mv = [self.sets[self.target_variable.name].membership(x)] + return np.nanmin(mv) diff --git a/pyFTS/models/multivariate/granular.py b/pyFTS/models/multivariate/granular.py index c7e31e5..d6db190 100644 --- a/pyFTS/models/multivariate/granular.py +++ b/pyFTS/models/multivariate/granular.py @@ -10,18 +10,19 @@ class GranularWMVFTS(cmvfts.ClusteredMVFTS): def __init__(self, **kwargs): super(GranularWMVFTS, self).__init__(**kwargs) - self.fts_method = hofts.WeightedHighOrderFTS + self.fts_method = kwargs.get('fts_method', hofts.WeightedHighOrderFTS) self.model = None """The most recent trained model""" self.knn = kwargs.get('knn', 2) self.order = kwargs.get("order", 2) self.shortname = "GranularWMVFTS" self.name = "Granular Weighted Multivariate FTS" + self.mode = kwargs.get('mode','sets') def train(self, data, **kwargs): self.partitioner = grid.IncrementalGridCluster( explanatory_variables=self.explanatory_variables, target_variable=self.target_variable, neighbors=self.knn) - super(GranularWMVFTS, self).train(data,**kwargs) + super(GranularWMVFTS, self).train(data, mode=self.mode, **kwargs) diff --git a/pyFTS/models/multivariate/grid.py b/pyFTS/models/multivariate/grid.py index 2a28416..078520c 100644 --- a/pyFTS/models/multivariate/grid.py +++ b/pyFTS/models/multivariate/grid.py @@ -31,6 +31,34 @@ class GridCluster(partitioner.MultivariatePartitioner): self.build_index() + def defuzzyfy(self, values, mode='both'): + if not isinstance(values, list): + values = [values] + + ret = [] + for val in values: + if mode == 'both': + num = [] + den = [] + for fset, mv in val: + num.append(self.sets[fset].centroid * mv) + den.append(mv) + ret.append(np.sum(num) / np.sum(den)) + elif mode == 'both': + num = np.mean([self.sets[fset].centroid for fset in val]) + ret.append(num) + elif mode == 'vector': + num = [] + den = [] + 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)) + else: + raise Exception('Unknown deffuzyfication mode') + + return ret + class IncrementalGridCluster(partitioner.MultivariatePartitioner): """ @@ -67,7 +95,8 @@ class IncrementalGridCluster(partitioner.MultivariatePartitioner): for key in fsets: mvfset = self.sets[key] ret.append((key, mvfset.membership(data))) - return ret + + return ret def incremental_search(self, data, **kwargs): alpha_cut = kwargs.get('alpha_cut', 0.) @@ -77,21 +106,30 @@ class IncrementalGridCluster(partitioner.MultivariatePartitioner): ret = [] for var in self.explanatory_variables: ac = alpha_cut if alpha_cut > 0. else var.alpha_cut - fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode='sets', alpha_cut=ac) + fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode=mode, alpha_cut=ac) - fset = [val for key, val in fsets.items()] + fsets_by_var = [fsets for var, fsets in fsets.items()] - for p in product(*fset): - key = ''.join(p) + for p in product(*fsets_by_var): + if mode == 'both': + path = [fset for fset, mv in p] + mv = [mv for fset, mv in p] + key = ''.join(path) + elif mode == 'sets': + key = ''.join(p) + path = p if key not in self.sets: mvfset = MultivariateFuzzySet(target_variable=self.target_variable) - for ct, fs in enumerate(p): + for ct, fs in enumerate(path): mvfset.append_set(self.explanatory_variables[ct].name, self.explanatory_variables[ct].partitioner[fs]) mvfset.name = key self.sets[key] = mvfset - ret.append(key) + if mode == 'sets': + ret.append(key) + elif mode == 'both': + ret.append( tuple(key,np.nanmin(mv)) ) return ret diff --git a/pyFTS/models/multivariate/mvfts.py b/pyFTS/models/multivariate/mvfts.py index dea417a..58602b3 100644 --- a/pyFTS/models/multivariate/mvfts.py +++ b/pyFTS/models/multivariate/mvfts.py @@ -302,7 +302,6 @@ class MVFTS(fts.FTS): return ret[-steps] - def clone_parameters(self, model): super(MVFTS, self).clone_parameters(model) diff --git a/pyFTS/models/multivariate/partitioner.py b/pyFTS/models/multivariate/partitioner.py index 81cf8b8..a9f666a 100644 --- a/pyFTS/models/multivariate/partitioner.py +++ b/pyFTS/models/multivariate/partitioner.py @@ -26,6 +26,11 @@ class MultivariatePartitioner(partitioner.Partitioner): self.count = {} data = kwargs.get('data', None) self.build(data) + self.uod = {} + + self.min = self.target_variable.partitioner.min + self.max = self.target_variable.partitioner.max + def format_data(self, data): ndata = {} @@ -88,8 +93,11 @@ class MultivariatePartitioner(partitioner.Partitioner): return fuzzyfy_instance_clustered(data, self, **kwargs) def change_target_variable(self, variable): + self.target_variable = variable for fset in self.sets.values(): fset.set_target_variable(variable) + self.min = variable.partitioner.min + self.max = variable.partitioner.max def build_index(self): diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py index 9f109d1..4e2bac8 100644 --- a/pyFTS/models/pwfts.py +++ b/pyFTS/models/pwfts.py @@ -41,6 +41,13 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG): return tmp + def lhs_conditional_probability_fuzzyfied(self, lhs_mv, sets, norm, uod, nbins): + pk = self.frequency_count / norm + + tmp = pk * (lhs_mv / self.partition_function(sets, uod, nbins=nbins)) + + return tmp + def rhs_unconditional_probability(self, c): return self.RHS[c] / self.frequency_count @@ -114,14 +121,54 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): def train(self, data, **kwargs): self.configure_lags(**kwargs) - parameters = kwargs.get('parameters','fuzzy') - if parameters == 'monotonic': - tmpdata = self.partitioner.fuzzyfy(data, mode='sets', method='maximum') - flrs = FLR.generate_recurrent_flrs(tmpdata) - self.generate_flrg(flrs) + if not kwargs.get('fuzzyfied',False): + self.generate_flrg2(data) else: - self.generate_flrg(data) + self.generate_flrg_fuzzyfied(data) + + def generate_flrg2(self, data): + fuzz = [] + l = len(data) + for k in np.arange(0, l): + fuzz.append(self.partitioner.fuzzyfy(data[k], mode='both', method='fuzzy', + alpha_cut=self.alpha_cut)) + + self.generate_flrg_fuzzyfied(fuzz) + + def generate_flrg_fuzzyfied(self, data): + l = len(data) + for k in np.arange(self.max_lag, l): + sample = data[k - self.max_lag: k] + set_sample = [] + for instance in sample: + set_sample.append([k for k, v in instance]) + + flrgs = self.generate_lhs_flrg_fuzzyfied(set_sample) + + for flrg in flrgs: + + if flrg.get_key() not in self.flrgs: + self.flrgs[flrg.get_key()] = flrg; + + lhs_mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, sample) + + mvs = [] + inst = data[k] + for set, mv in inst: + 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]) + + self.global_frequency_count += tmp_fq + + def pwflrg_lhs_memberhip_fuzzyfied(self, flrg, sample): + vals = [] + for ct, fuzz in enumerate(sample): + vals.append([mv for fset, mv in fuzz if fset == flrg.LHS[ct]]) + + return np.nanprod(vals) def generate_lhs_flrg(self, sample, explain=False): nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut) @@ -206,6 +253,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): pb = self.flrg_lhs_unconditional_probability(flrg) return mv * pb + def flrg_lhs_conditional_probability_fuzzyfied(self, x, flrg): + mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, x) + pb = self.flrg_lhs_unconditional_probability(flrg) + return mv * pb + def get_midpoint(self, flrg): if flrg.get_key() in self.flrgs: tmp = self.flrgs[flrg.get_key()] @@ -273,11 +325,16 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): def point_heuristic(self, sample, **kwargs): explain = kwargs.get('explain', False) + fuzzyfied = kwargs.get('fuzzyfied', False) if explain: print("Fuzzyfication \n") - flrgs = self.generate_lhs_flrg(sample, explain) + if not fuzzyfied: + flrgs = self.generate_lhs_flrg(sample, explain) + else: + fsets = self.get_sets_from_both_fuzzyfication(sample) + flrgs = self.generate_lhs_flrg_fuzzyfied(fsets, explain) mp = [] norms = [] @@ -286,16 +343,17 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): print("Rules:\n") for flrg in flrgs: - norm = self.flrg_lhs_conditional_probability(sample, flrg) + if not fuzzyfied: + norm = self.flrg_lhs_conditional_probability(sample, flrg) + else: + norm = self.flrg_lhs_conditional_probability_fuzzyfied(sample, flrg) if norm == 0: norm = self.flrg_lhs_unconditional_probability(flrg) - if explain: print("\t {} \t Midpoint: {}\t Norm: {}\n".format(str(self.flrgs[flrg.get_key()]), self.get_midpoint(flrg), norm)) - mp.append(norm * self.get_midpoint(flrg)) norms.append(norm) @@ -307,10 +365,13 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): print("Deffuzyfied value: {} \n".format(final)) return final + def get_sets_from_both_fuzzyfication(self, sample): + return [[k for k, v in inst] for inst in sample] + def point_expected_value(self, sample, **kwargs): explain = kwargs.get('explain', False) - dist = self.forecast_distribution(sample)[0] + dist = self.forecast_distribution(sample, **kwargs)[0] final = dist.expected_value() return final @@ -329,28 +390,37 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): sample = ndata[k - (self.max_lag - 1): k + 1] if method == 'heuristic': - ret.append(self.interval_heuristic(sample)) + ret.append(self.interval_heuristic(sample, **kwargs)) elif method == 'quantile': - ret.append(self.interval_quantile(sample, alpha)) + ret.append(self.interval_quantile(sample, alpha, **kwargs)) else: raise ValueError("Unknown interval forecasting method!") return ret - def interval_quantile(self, ndata, alpha): - dist = self.forecast_distribution(ndata) + def interval_quantile(self, ndata, alpha, **kwargs): + dist = self.forecast_distribution(ndata, **kwargs) itvl = dist[0].quantile([alpha, 1.0 - alpha]) return itvl - def interval_heuristic(self, sample): + def interval_heuristic(self, sample, **kwargs): + fuzzyfied = kwargs.get('fuzzyfied', False) - flrgs = self.generate_lhs_flrg(sample) + if not fuzzyfied: + flrgs = self.generate_lhs_flrg(sample) + else: + fsets = self.get_sets_from_both_fuzzyfication(sample) + flrgs = self.generate_lhs_flrg_fuzzyfied(fsets) up = [] lo = [] norms = [] for flrg in flrgs: - norm = self.flrg_lhs_conditional_probability(sample, flrg) + if not fuzzyfied: + norm = self.flrg_lhs_conditional_probability(sample, flrg) + else: + norm = self.flrg_lhs_conditional_probability_fuzzyfied(sample, flrg) + if norm == 0: norm = self.flrg_lhs_unconditional_probability(flrg) up.append(norm * self.get_upper(flrg)) @@ -370,6 +440,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): smooth = kwargs.get("smooth", "none") + fuzzyfied = kwargs.get('fuzzyfied', False) + l = len(ndata) uod = self.get_UoD() @@ -385,7 +457,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): for k in np.arange(self.max_lag - 1, l): sample = ndata[k - (self.max_lag - 1): k + 1] - flrgs = self.generate_lhs_flrg(sample) + if not fuzzyfied: + flrgs = self.generate_lhs_flrg(sample) + else: + fsets = self.get_sets_from_both_fuzzyfication(sample) + flrgs = self.generate_lhs_flrg_fuzzyfied(fsets) if 'type' in kwargs: kwargs.pop('type') @@ -398,8 +474,14 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): for s in flrgs: if s.get_key() in self.flrgs: flrg = self.flrgs[s.get_key()] - pk = flrg.lhs_conditional_probability(sample, self.partitioner.sets, self.global_frequency_count, uod, nbins) wi = flrg.rhs_conditional_probability(bin, self.partitioner.sets, uod, nbins) + if not fuzzyfied: + pk = flrg.lhs_conditional_probability(sample, self.partitioner.sets, self.global_frequency_count, uod, nbins) + else: + lhs_mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, sample) + pk = flrg.lhs_conditional_probability_fuzzyfied(lhs_mv, self.partitioner.sets, + self.global_frequency_count, uod, nbins) + num.append(wi * pk) den.append(pk) else: @@ -422,13 +504,15 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): l = len(data) + fuzzyfied = kwargs.get('fuzzyfied', False) + start = kwargs.get('start_at', 0) ret = data[start: start+self.max_lag].tolist() for k in np.arange(self.max_lag, steps+self.max_lag): - if self.__check_point_bounds(ret[-1]) : + if self.__check_point_bounds(ret[-1]) and not fuzzyfied: ret.append(ret[-1]) else: mp = self.forecast(ret[k - self.max_lag: k], **kwargs) @@ -448,11 +532,19 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): start = kwargs.get('start_at', 0) + fuzzyfied = kwargs.get('fuzzyfied', False) + sample = data[start: start + self.max_lag] - ret = [[k, k] for k in sample] + if not fuzzyfied: + ret = [[k, k] for k in sample] + else: + ret = [] + for k in sample: + kv = self.partitioner.deffuzyfy(k,mode='both') + ret.append([kv,kv]) - ret.append(self.forecast_interval(sample)[0]) + ret.append(self.forecast_interval(sample, **kwargs)[0]) for k in np.arange(self.max_lag+1, steps+self.max_lag): @@ -492,7 +584,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): tmp.set(dat, 1.0) ret.append(tmp) - dist = self.forecast_distribution(sample, bins=_bins)[0] + dist = self.forecast_distribution(sample, bins=_bins, **kwargs)[0] ret.append(dist) diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py index aafc4f2..2dd81ea 100644 --- a/pyFTS/partitioners/partitioner.py +++ b/pyFTS/partitioners/partitioner.py @@ -181,6 +181,34 @@ class Partitioner(object): sets = [(self.ordered_sets[i], mv[i]) for i in ix] return sets + def defuzzyfy(self, values, mode='both'): + if not isinstance(values, list): + values = [values] + + ret = [] + for val in values: + if mode == 'both': + num = [] + den = [] + for fset, mv in val: + num.append( self.sets[fset].centroid * mv ) + den.append(mv) + ret.append(np.sum(num)/np.sum(den)) + elif mode == 'both': + num = np.mean([self.sets[fset].centroid for fset in val ]) + ret.append(num) + elif mode == 'vector': + num = [] + den = [] + 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)) + else: + raise Exception('Unknown deffuzyfication mode') + + return ret + def check_bounds(self, data): """ Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest diff --git a/pyFTS/tests/distributed.py b/pyFTS/tests/distributed.py index f7cb7c6..250cb7f 100644 --- a/pyFTS/tests/distributed.py +++ b/pyFTS/tests/distributed.py @@ -31,7 +31,7 @@ datasets['Malaysia.load'] = malaysia["load"].values windows = [600000, 600000, 10000, 10000] -cpus = 3 +cpus = 7 for ct, (dataset_name, dataset) in enumerate(datasets.items()): bchmk.train_test_time(dataset, windowsize=windows[ct], train=0.9, inc=.5, @@ -40,6 +40,6 @@ for ct, (dataset_name, dataset) in enumerate(datasets.items()): partitions=50, steps=cpus, num_batches=cpus, - distributed='dispy', nodes=['192.168.0.110'], #, '192.168.0.107','192.168.0.106'], + distributed='dispy', nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'], file="experiments.db", dataset=dataset_name, tag="speedup") diff --git a/pyFTS/tests/pwfts.py b/pyFTS/tests/pwfts.py index 7665215..7c9f93f 100644 --- a/pyFTS/tests/pwfts.py +++ b/pyFTS/tests/pwfts.py @@ -9,70 +9,60 @@ from statsmodels.tsa.stattools import adfuller from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from pyFTS.common import Util - -from pyFTS.data import TAIEX - -taiex = TAIEX.get_data() - -train = taiex[:3000] -test = taiex[3000:3200] - -from pyFTS.common import Transformations -tdiff = Transformations.Differential(1) - from pyFTS.benchmarks import benchmarks as bchmk, Measures from pyFTS.models import pwfts,hofts,ifts +from pyFTS.models.multivariate import granular, grid from pyFTS.partitioners import Grid, Util as pUtil -fs = Grid.GridPartitioner(data=train, npart=30) #, transformation=tdiff) +from pyFTS.models.multivariate import common, variable, mvfts +from pyFTS.models.seasonal import partitioner as seasonal +from pyFTS.models.seasonal.common import DateTime +from pyFTS.common import Membership -model1 = hofts.HighOrderFTS(partitioner=fs, lags=[1,2])#lags=[0,1]) -model1.shortname = "1" -model2 = pwfts.ProbabilisticWeightedFTS(partitioner=fs, lags=[1,2]) -#model2.append_transformation(tdiff) -model2.shortname = "2" -#model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2)# lags=[1,2]) +from pyFTS.data import SONDA, Malaysia -model1.fit(train) -model2.fit(train) +df = Malaysia.get_dataframe() +df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p') -#print(model1) +train_mv = df.iloc[:8000] +test_mv = df.iloc[8000:10000] -#print(model2) +sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]} -for model in [model1, model2]: - #forecasts = model.predict(test) - print(model.shortname) - print(Measures.get_point_statistics(test, model)) +vhour = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24, + 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) -#handles, labels = ax.get_legend_handles_labels() -#ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1)) +order = 1 +knn = 1 -#print(Measures.get_point_statistics(test,model)) +model = granular.GranularWMVFTS(explanatory_variables=[vhour, vtemp, vload], target_variable=vload, + fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both', + order=order, knn=knn) +model.fit(train_mv) + +print(model) + + +print(model.predict(test_mv.iloc[:10], type='point')) +print(model.predict(test_mv.iloc[:10], type='interval')) +print(model.predict(test_mv.iloc[:10], type='distribution')) ''' -bchmk.sliding_window_benchmarks(train,1000,0.8, - methods=[pwfts.ProbabilisticWeightedFTS], #,ifts.IntervalFTS], - orders=[1,2,3], - partitions=[10]) -''' -''' +from pyFTS.data import Enrollments +train = Enrollments.get_data() -from pyFTS.common import FLR,FuzzySet,Membership,SortedCollection -taiex_fs1 = Grid.GridPartitioner(data=train, npart=30) -taiex_fs2 = Grid.GridPartitioner(data=train, npart=10, transformation=tdiff) +fs = Grid.GridPartitioner(data=train, npart=10) #, transformation=tdiff) -#pUtil.plot_partitioners(train, [taiex_fs1,taiex_fs2], tam=[15,7]) - -from pyFTS.common import fts,tree -from pyFTS.models import hofts, pwfts - -pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=taiex_fs1) -#pfts1_taiex.appendTransformation(diff) -pfts1_taiex.fit(train, save_model=True, file_path='pwfts') -pfts1_taiex.shortname = "1st Order" -print(pfts1_taiex) - -''' +model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2) +model.fit(train) +print(model) +print(model.predict(train)) +''' \ No newline at end of file