diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 1a0e74f..da696ee 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/pyFTS.common.doctree b/docs/build/doctrees/pyFTS.common.doctree index c2780e4..8fad2c8 100644 Binary files a/docs/build/doctrees/pyFTS.common.doctree and b/docs/build/doctrees/pyFTS.common.doctree differ diff --git a/docs/build/doctrees/pyFTS.models.multivariate.doctree b/docs/build/doctrees/pyFTS.models.multivariate.doctree index bb8bf09..aee6cbb 100644 Binary files a/docs/build/doctrees/pyFTS.models.multivariate.doctree and b/docs/build/doctrees/pyFTS.models.multivariate.doctree differ diff --git a/docs/build/doctrees/pyFTS.models.seasonal.doctree b/docs/build/doctrees/pyFTS.models.seasonal.doctree index f39997e..0489a14 100644 Binary files a/docs/build/doctrees/pyFTS.models.seasonal.doctree and b/docs/build/doctrees/pyFTS.models.seasonal.doctree differ diff --git a/docs/build/doctrees/pyFTS.partitioners.doctree b/docs/build/doctrees/pyFTS.partitioners.doctree index b34e2a2..85a7655 100644 Binary files a/docs/build/doctrees/pyFTS.partitioners.doctree and b/docs/build/doctrees/pyFTS.partitioners.doctree differ diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index f392560..5cb4b33 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -127,6 +127,8 @@
  • pyFTS.models.multivariate.cmvfts
  • pyFTS.models.multivariate.common
  • pyFTS.models.multivariate.flrg
    • +
  • pyFTS.models.multivariate.granular
    • +
  • pyFTS.models.multivariate.grid
  • pyFTS.models.multivariate.mvfts
  • pyFTS.models.multivariate.variable
  • pyFTS.models.multivariate.wmvfts
    • diff --git a/docs/build/html/_modules/pyFTS/common/FuzzySet.html b/docs/build/html/_modules/pyFTS/common/FuzzySet.html index ae6cf0b..2fea767 100644 --- a/docs/build/html/_modules/pyFTS/common/FuzzySet.html +++ b/docs/build/html/_modules/pyFTS/common/FuzzySet.html @@ -81,6 +81,7 @@ """ Fuzzy Set """ + def __init__(self, name, mf, parameters, centroid, alpha=1.0, **kwargs): """ Create a Fuzzy Set @@ -97,15 +98,15 @@ """The alpha cut value""" self.type = kwargs.get('type', 'common') """The fuzzy set type (common, composite, nonstationary, etc)""" - self.variable = kwargs.get('variable',None) + self.variable = kwargs.get('variable', None) """In multivariate time series, indicate for which variable this fuzzy set belogs""" self.Z = None """Partition function in respect to the membership function""" if parameters is not None: if self.mf == Membership.gaussmf: - self.lower = parameters[0] - parameters[1]*3 - self.upper = parameters[0] + parameters[1]*3 + self.lower = parameters[0] - parameters[1] * 3 + self.upper = parameters[0] + parameters[1] * 3 elif self.mf == Membership.sigmf: k = (parameters[1] / (2 * parameters[0])) self.lower = parameters[1] - k @@ -135,7 +136,7 @@ """ return self.mf(self.transform(x), self.parameters) * self.alpha -
    [docs] def partition_function(self,uod=None, nbins=100): +
    [docs] def partition_function(self, uod=None, nbins=100): """ Calculate the partition function over the membership function. @@ -175,7 +176,7 @@ fs2 = ordered_sets[midpoint + 1] if midpoint < max_len else ordered_sets[max_len] if fuzzy_sets[fs1].centroid <= fuzzy_sets[fs].transform(x) <= fuzzy_sets[fs2].centroid: - return (midpoint-1, midpoint, midpoint+1) + return (midpoint - 1, midpoint, midpoint + 1) elif midpoint <= 1: return [0] elif midpoint >= max_len: @@ -194,13 +195,12 @@ :param data: input value to be fuzzyfied :param partitioner: a trained pyFTS.partitioners.Partitioner object :param kwargs: dict, optional arguments - :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets') :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership) :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) ) - :returns a list with the fuzzyfied values, depending on the mode + """ alpha_cut = kwargs.get('alpha_cut', 0.) mode = kwargs.get('mode', 'sets') @@ -369,6 +369,7 @@ else: return data
    +
    [docs]def check_bounds(data, fuzzy_sets, ordered_sets): if data < fuzzy_sets[ordered_sets[0]].lower: return fuzzy_sets[ordered_sets[0]] diff --git a/docs/build/html/_modules/pyFTS/common/fts.html b/docs/build/html/_modules/pyFTS/common/fts.html index ae79ca3..2a27d6d 100644 --- a/docs/build/html/_modules/pyFTS/common/fts.html +++ b/docs/build/html/_modules/pyFTS/common/fts.html @@ -145,6 +145,8 @@ self.max_lag = self.order """A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags needed to forecast a single step ahead""" + self.log = pd.DataFrame([],columns=["Datetime","Operation","Value"]) + """"""
    [docs] def fuzzy(self, data): """ @@ -222,7 +224,7 @@ elif type == 'distribution': ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs) elif type == 'multivariate': - ret = self.forecast_ahead_multivariate(ndata, **kwargs) + ret = self.forecast_ahead_multivariate(ndata, steps_ahead, **kwargs) if not ['point', 'interval', 'distribution', 'multivariate'].__contains__(type): raise ValueError('The argument \'type\' has an unknown value.') @@ -630,7 +632,10 @@ """ for flrg in self.flrgs.keys(): - self.flrgs[flrg].reset_calculated_values()
    + self.flrgs[flrg].reset_calculated_values()
    + +
    [docs] def append_log(self,operation, value): + pass
    diff --git a/docs/build/html/_modules/pyFTS/models/chen.html b/docs/build/html/_modules/pyFTS/models/chen.html index 7fc1d9b..7c735d8 100644 --- a/docs/build/html/_modules/pyFTS/models/chen.html +++ b/docs/build/html/_modules/pyFTS/models/chen.html @@ -124,7 +124,7 @@
    [docs] def train(self, data, **kwargs): - tmpdata = FuzzySet.fuzzyfy(data, partitioner=self.partitioner, method='maximum', mode='sets') + tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets') flrs = FLR.generate_non_recurrent_flrs(tmpdata) self.generate_flrg(flrs)
    diff --git a/docs/build/html/_modules/pyFTS/models/hofts.html b/docs/build/html/_modules/pyFTS/models/hofts.html index 28585bb..f1797e9 100644 --- a/docs/build/html/_modules/pyFTS/models/hofts.html +++ b/docs/build/html/_modules/pyFTS/models/hofts.html @@ -200,6 +200,9 @@ nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut) for k in sample] + if explain: + self.append_log("Fuzzyfication","{} -> {}".format(sample, nsample)) + return self.generate_lhs_flrg_fuzzyfied(nsample, explain)
    [docs] def generate_lhs_flrg_fuzzyfied(self, sample, explain=False): @@ -211,7 +214,7 @@ lags.append(lhs) if explain: - print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + self.append_log("Ordering Lags", "Lag {} Value {}".format(o, lhs)) # Trace the possible paths for path in product(*lags): @@ -291,17 +294,11 @@ sample = ndata[k - self.max_lag: k] - if explain: - print("Fuzzyfication \n") - if not fuzzyfied: flrgs = self.generate_lhs_flrg(sample, explain) else: flrgs = self.generate_lhs_flrg_fuzzyfied(sample, explain) - if explain: - print("Rules:\n") - midpoints = [] memberships = [] for flrg in flrgs: @@ -314,7 +311,7 @@ memberships.append(mv) if explain: - print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1], + self.append_log("Rule Matching", "{} -> {} (Naïve) Midpoint: {}".format(str(flrg.LHS), flrg.LHS[-1], mp)) else: flrg = self.flrgs[flrg.get_key()] @@ -324,19 +321,17 @@ memberships.append(mv) if explain: - print("\t {} \t Midpoint: {}\n".format(str(flrg), mp)) - print("\t {} \t Membership: {}\n".format(str(flrg), mv)) + self.append_log("Rule Matching", "{}, Midpoint: {} Membership: {}".format(flrg.get_key(), mp, mv)) if mode == "mean" or fuzzyfied: final = np.nanmean(midpoints) + if explain: self.append_log("Deffuzyfication", "By Mean: {}".format(final)) else: final = np.dot(midpoints, memberships) + if explain: self.append_log("Deffuzyfication", "By Memberships: {}".format(final)) ret.append(final) - if explain: - print("Deffuzyfied value: {} \n".format(final)) - return ret
    diff --git a/docs/build/html/_modules/pyFTS/models/ismailefendi.html b/docs/build/html/_modules/pyFTS/models/ismailefendi.html index d8877f6..66d8d08 100644 --- a/docs/build/html/_modules/pyFTS/models/ismailefendi.html +++ b/docs/build/html/_modules/pyFTS/models/ismailefendi.html @@ -137,7 +137,7 @@
    [docs] def train(self, ndata, **kwargs): - tmpdata = FuzzySet.fuzzyfy(ndata, partitioner=self.partitioner, method='maximum', mode='sets') + tmpdata = self.partitioner.fuzzyfy(ndata, method='maximum', mode='sets') flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_flrg(flrs)
    diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html index 88c65d6..486bce4 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html @@ -78,11 +78,12 @@ from pyFTS.common import FuzzySet, FLR, fts, flrg from pyFTS.models import hofts from pyFTS.models.multivariate import mvfts, grid, common +from types import LambdaType
    [docs]class ClusteredMVFTS(mvfts.MVFTS): """ - Meta model for multivariate, high order, clustered multivariate FTS + Meta model for high order, clustered multivariate FTS """ def __init__(self, **kwargs): super(ClusteredMVFTS, self).__init__(**kwargs) @@ -112,16 +113,15 @@ ndata = [] for index, row in data.iterrows(): data_point = self.format_data(row) - ndata.append(common.fuzzyfy_instance_clustered(data_point, self.partitioner, - alpha_cut=self.alpha_cut)) + ndata.append(self.partitioner.fuzzyfy(data_point, mode='sets')) return ndata
    [docs] def train(self, data, **kwargs): + self.fts_params['order'] = self.order + self.model = self.fts_method(partitioner=self.partitioner, **self.fts_params) - if self.model.is_high_order: - self.model.order = self.order ndata = self.check_data(data) @@ -147,18 +147,53 @@ ndata = self.check_data(data) - ret = {} - for var in self.explanatory_variables: - if self.target_variable.name != var.name: - self.target_variable = var - self.partitioner.change_target_variable(var) - self.model.partitioner = self.partitioner - self.model.reset_calculated_values() + generators = kwargs.get('generators', {}) - ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs) + already_processed_cols = [] + + ret = {} + + ret[self.target_variable.data_label] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs) + + for var in self.explanatory_variables: + if var.data_label not in already_processed_cols: + if var.data_label in generators: + if isinstance(generators[var.data_label], LambdaType): + fx = generators[var.data_label] + if len(data[var.data_label].values) > self.order: + ret[var.data_label] = [fx(k) for k in data[var.data_label].values[self.order:]] + else: + ret[var.data_label] = [fx(data[var.data_label].values[-1])] + elif isinstance(generators[var.data_label], fts.FTS): + model = generators[var.data_label] + if not model.is_multivariate: + ret[var.data_label] = model.forecast(data[var.data_label].values) + else: + ret[var.data_label] = model.forecast(data) + elif self.target_variable.name != var.name: + self.target_variable = var + self.partitioner.change_target_variable(var) + self.model.partitioner = self.partitioner + self.model.reset_calculated_values() + ret[var.data_label] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs) + + already_processed_cols.append(var.data_label) return pd.DataFrame(ret, columns=ret.keys())
    +
    [docs] def forecast_ahead_multivariate(self, data, steps, **kwargs): + + ndata = self.apply_transformations(data) + + ret = ndata.iloc[:self.order] + + for k in np.arange(0, steps): + sample = ret.iloc[k:self.order+k] + tmp = self.forecast_multivariate(sample, **kwargs) + ret = ret.append(tmp, ignore_index=True) + + return ret
    + def __str__(self): """String representation of the model""" return str(self.model) diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/common.html b/docs/build/html/_modules/pyFTS/models/multivariate/common.html index bd6ae09..1b9f502 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/common.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/common.html @@ -117,7 +117,7 @@
    [docs]def fuzzyfy_instance(data_point, var, tuples=True): - fsets = FuzzySet.fuzzyfy(data_point, var.partitioner, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut) + fsets = var.partitioner.fuzzyfy(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut) if tuples: return [(var.name, fs) for fs in fsets] else: @@ -128,7 +128,7 @@ alpha_cut = kwargs.get('alpha_cut', 0.0) mode = kwargs.get('mode', 'sets') fsets = [] - for fset in cluster.knn(data_point): + for fset in cluster.search(data_point, type='name'): if cluster.sets[fset].membership(data_point) > alpha_cut: if mode == 'sets': fsets.append(fset) diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html index 4ed62a7..63fc3ee 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html @@ -119,7 +119,6 @@
    [docs] def format_data(self, data): ndata = {} for var in self.explanatory_variables: - #ndata[var.name] = data[var.data_label] ndata[var.name] = var.partitioner.extractor(data[var.data_label]) return ndata
    diff --git a/docs/build/html/_modules/pyFTS/models/sadaei.html b/docs/build/html/_modules/pyFTS/models/sadaei.html index 0c9bda6..1b00d0a 100644 --- a/docs/build/html/_modules/pyFTS/models/sadaei.html +++ b/docs/build/html/_modules/pyFTS/models/sadaei.html @@ -141,7 +141,7 @@ self.flrgs[flr.LHS].append_rhs(flr.RHS)
    [docs] def train(self, data, **kwargs): - tmpdata = FuzzySet.fuzzyfy(data, partitioner=self.partitioner, method='maximum', mode='sets') + tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets') flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_flrg(flrs, self.c)
    diff --git a/docs/build/html/_modules/pyFTS/models/seasonal/common.html b/docs/build/html/_modules/pyFTS/models/seasonal/common.html index 13ba11d..3e4ff7f 100644 --- a/docs/build/html/_modules/pyFTS/models/seasonal/common.html +++ b/docs/build/html/_modules/pyFTS/models/seasonal/common.html @@ -93,9 +93,9 @@ day_of_month = 30 day_of_year = 364 day_of_week = 7 - hour = 6 - minute = 7 - second = 8 + hour = 24 + minute = 60 + second = 60 hour_of_day = 24 hour_of_week = 168 hour_of_month = 744 diff --git a/docs/build/html/_modules/pyFTS/models/seasonal/partitioner.html b/docs/build/html/_modules/pyFTS/models/seasonal/partitioner.html index afeaa93..4217b13 100644 --- a/docs/build/html/_modules/pyFTS/models/seasonal/partitioner.html +++ b/docs/build/html/_modules/pyFTS/models/seasonal/partitioner.html @@ -78,6 +78,7 @@ from pyFTS.models.seasonal.common import DateTime, FuzzySet, strip_datepart import numpy as np import matplotlib.pylab as plt +from scipy.spatial import KDTree
    [docs]class TimeGridPartitioner(partitioner.Partitioner): @@ -130,6 +131,8 @@ partlen = dlen / self.partitions elif self.season == DateTime.day_of_week: self.min, self.max, partlen, pl2 = 0, 7, 1, 1 + elif self.season == DateTime.minute: + self.min, self.max, partlen, pl2 = 0, 60, 1, 1 elif self.season == DateTime.hour: self.min, self.max, partlen, pl2 = 0, 24, 1, 1 elif self.season == DateTime.month: @@ -148,7 +151,7 @@ tmp = Composite(set_name, superset=True, **kwargs) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, [self.season.value - pl2, self.season.value, - self.season.value + 0.0000001], self.season.value, alpha=.5, + self.season.value + pl2], self.season.value, alpha=1, **kwargs)) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, [c - partlen, c, c + partlen], c, @@ -158,8 +161,8 @@ elif c == self.max - partlen: tmp = Composite(set_name, superset=True, **kwargs) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, - [0.0000001, 0.0, - pl2], 0.0, alpha=.5, + [-pl2, 0.0, + pl2], 0.0, alpha=1, **kwargs)) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, [c - partlen, c, c + partlen], c, @@ -196,6 +199,57 @@ return sets
    +
    [docs] def build_index(self): + points = [] + + fset = self.sets[self.ordered_sets[0]] + points.append([fset.sets[1].lower, fset.sets[1].centroid, fset.sets[1].upper]) + + for ct, key in enumerate(self.ordered_sets[1:-1]): + fset = self.sets[key] + points.append([fset.lower, fset.centroid, fset.upper]) + + fset = self.sets[self.ordered_sets[-1]] + points.append([fset.sets[1].lower, fset.sets[1].centroid, fset.sets[1].upper]) + + import sys + sys.setrecursionlimit(100000) + + self.kdtree = KDTree(points) + + sys.setrecursionlimit(1000)
    + +
    [docs] def search(self, data, **kwargs): + ''' + Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several + overlapped fuzzy sets. + + :param data: the value to search for the nearest fuzzy sets + :param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names. + :param results: the number of nearest fuzzy sets to return + :return: a list with the nearest fuzzy sets + ''' + + type = kwargs.get('type','index') + results = kwargs.get('results',3) + + if self.kdtree is None: + self.build_index() + + _, ix = self.kdtree.query([data, data, data], results) + + ix = ix.tolist() + + if 0 in ix: + ix.insert(0, self.partitions-1) + elif self.partitions-1 in ix: + ix.insert(0, 0) + + if type == 'name': + return [self.ordered_sets[k] for k in sorted(ix)] + else: + return sorted(ix)
    +
    [docs] def plot(self, ax): """ diff --git a/docs/build/html/_modules/pyFTS/models/song.html b/docs/build/html/_modules/pyFTS/models/song.html index 309e7e5..63c7124 100644 --- a/docs/build/html/_modules/pyFTS/models/song.html +++ b/docs/build/html/_modules/pyFTS/models/song.html @@ -124,7 +124,7 @@
    [docs] def train(self, data, **kwargs): - tmpdata = FuzzySet.fuzzyfy(data, partitioner=self.partitioner, method='maximum', mode='sets') + tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets') flrs = FLR.generate_non_recurrent_flrs(tmpdata) self.operation_matrix(flrs)
    diff --git a/docs/build/html/_modules/pyFTS/models/yu.html b/docs/build/html/_modules/pyFTS/models/yu.html index 5a430ed..efb976a 100644 --- a/docs/build/html/_modules/pyFTS/models/yu.html +++ b/docs/build/html/_modules/pyFTS/models/yu.html @@ -133,7 +133,7 @@ self.flrgs[flr.LHS].append_rhs(flr.RHS)
    [docs] def train(self, ndata, **kwargs): - tmpdata = FuzzySet.fuzzyfy(ndata, partitioner=self.partitioner, method='maximum', mode='sets') + tmpdata = self.partitioner.fuzzyfy(ndata, method='maximum', mode='sets') flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_FLRG(flrs)
    diff --git a/docs/build/html/_modules/pyFTS/partitioners/partitioner.html b/docs/build/html/_modules/pyFTS/partitioners/partitioner.html index 3b9d77b..b04bce3 100644 --- a/docs/build/html/_modules/pyFTS/partitioners/partitioner.html +++ b/docs/build/html/_modules/pyFTS/partitioners/partitioner.html @@ -74,6 +74,7 @@

    Source code for pyFTS.partitioners.partitioner

     from pyFTS.common import FuzzySet, Membership
 import numpy as np
+from scipy.spatial import KDTree
 import matplotlib.pylab as plt
 
 
@@ -108,6 +109,8 @@
         """Anonymous function used to extract a single primitive type from an object instance"""
         self.ordered_sets = None
         """A ordered list of the fuzzy sets names, sorted by their middle point"""
+        self.kdtree = None
+        """A spatial index to help in fuzzyfication"""
 
         if kwargs.get('preprocess',True):
 
@@ -179,8 +182,112 @@
         """
         return self.sets[self.ordered_sets[-1]]
    +
    [docs] def build_index(self): + points = [] + + #self.index = {} + + for ct, key in enumerate(self.ordered_sets): + fset = self.sets[key] + points.append([fset.lower, fset.centroid, fset.upper]) + #self.index[ct] = fset.name + + import sys + sys.setrecursionlimit(100000) + + self.kdtree = KDTree(points) + + sys.setrecursionlimit(1000)
    +
    [docs] def fuzzyfy(self, data, **kwargs): - return FuzzySet.fuzzyfy(data, self, **kwargs)
    + """ + Fuzzyfy the input data according to this partitioner fuzzy sets. + + :param data: input value to be fuzzyfied + :keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets') + :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership) + :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership + values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) ) + + :returns a list with the fuzzyfied values, depending on the mode + """ + + if isinstance(data, (list, np.ndarray)): + ret = [] + for inst in data: + mv = self.fuzzyfy(inst, **kwargs) + ret.append(mv) + return ret + + alpha_cut = kwargs.get('alpha_cut', 0.) + mode = kwargs.get('mode', 'sets') + method = kwargs.get('method', 'fuzzy') + + nearest = self.search(data, type='index') + + mv = np.zeros(self.partitions) + + for ix in nearest: + tmp = self[ix].membership(data) + mv[ix] = tmp if tmp >= alpha_cut else 0. + + ix = np.ravel(np.argwhere(mv > 0.)) + + if ix.size == 0: + mv[self.check_bounds(data)] = 1. + + if method == 'fuzzy' and mode == 'vector': + return mv + elif method == 'fuzzy' and mode == 'sets': + ix = np.ravel(np.argwhere(mv > 0.)) + sets = [self.ordered_sets[i] for i in ix] + return sets + elif method == 'maximum' and mode == 'sets': + mx = max(mv) + ix = np.ravel(np.argwhere(mv == mx)) + return self.ordered_sets[ix[0]] + elif mode == 'both': + ix = np.ravel(np.argwhere(mv > 0.)) + sets = [(self.ordered_sets[i], mv[i]) for i in ix] + return sets + +
    [docs] 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 + fuzzy set. + + :param data: input data to be verified + :return: the index of the closest fuzzy set when data is outside de universe of discourse or None if + the data is inside the UoD. + ''' + if data < self.min: + return 0 + elif data > self.max: + return self.partitions-1
    + +
    [docs] def search(self, data, **kwargs): + ''' + Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several + overlapped fuzzy sets. + + :param data: the value to search for the nearest fuzzy sets + :param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names. + :param results: the number of nearest fuzzy sets to return + :return: a list with the nearest fuzzy sets + ''' + if self.kdtree is None: + self.build_index() + + type = kwargs.get('type','index') + results = kwargs.get('results', 3) + + _, ix = self.kdtree.query([data, data, data], results) + + if type == 'name': + return [self.ordered_sets[k] for k in sorted(ix)] + else: + return sorted(ix)
    +
    [docs] def plot(self, ax, rounding=0): """ @@ -240,7 +347,36 @@ :return: number of partitions """ - return self.partitions
    + return self.partitions + + def __getitem__(self, item): + """ + Return a fuzzy set by its order or its name. + + :param item: If item is an integer then it represents the fuzzy set index (order), if it was a string then + it represents the fuzzy set name. + :return: the fuzzy set + """ + if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)): + if item < 0 or item >= self.partitions: + raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions)) + return self.sets[self.ordered_sets[item]] + elif isinstance(item, str): + if item not in self.sets: + raise ValueError("The fuzzy set with name {} does not exist.".format(item)) + return self.sets[item] + else: + raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item))) + + def __iter__(self): + """ + Iterate over the fuzzy sets, ordered by its midpoints. + + :return: An iterator over the fuzzy sets. + """ + for key in self.ordered_sets: + yield self.sets[key] + diff --git a/docs/build/html/_sources/pyFTS.models.multivariate.rst.txt b/docs/build/html/_sources/pyFTS.models.multivariate.rst.txt index 64ada4d..3ca4fb3 100644 --- a/docs/build/html/_sources/pyFTS.models.multivariate.rst.txt +++ b/docs/build/html/_sources/pyFTS.models.multivariate.rst.txt @@ -45,6 +45,22 @@ pyFTS.models.multivariate.flrg module :undoc-members: :show-inheritance: +pyFTS.models.multivariate.partitioner module +--------------------------------------------- + +.. automodule:: pyFTS.models.multivariate.partitioner + :members: + :undoc-members: + :show-inheritance: + + pyFTS.models.multivariate.grid module +------------------------------------------ + +.. automodule:: pyFTS.models.multivariate.grid + :members: + :undoc-members: + :show-inheritance: + pyFTS.models.multivariate.mvfts module -------------------------------------- @@ -68,4 +84,12 @@ pyFTS.models.multivariate.cmvfts module :members: :undoc-members: :show-inheritance: + +pyFTS.models.multivariate.granular module +--------------------------------------------- +.. automodule:: pyFTS.models.multivariate.granular + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 9cb0b93..37a989e 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -154,6 +154,8 @@
  • (pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG method)
    • +
  • append_log() (pyFTS.common.fts.FTS method) +
  • append_model() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)
  • append_rhs() (pyFTS.common.flrg.FLRG method) @@ -278,11 +280,11 @@
  • brier_score() (in module pyFTS.benchmarks.Measures)
    • - - + @@ -330,6 +340,8 @@
  • check_bounds_index() (in module pyFTS.common.FuzzySet) @@ -633,7 +645,11 @@
  • forecast_ahead_multivariate() (pyFTS.common.fts.FTS method) + +
  • forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method) @@ -354,6 +356,73 @@ transformations and partitioners.

    + +
    +

    pyFTS.models.multivariate.partitioner module

    +
    +
    +
    +class pyFTS.models.multivariate.grid.GridCluster(**kwargs)[source]
    +

    Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

    +

    A cartesian product of all fuzzy sets of all variables

    +
    +
    +build(data)[source]
    +

    Perform the partitioning of the Universe of Discourse

    + +++ + + + + + +
    Parameters:data – training data
    Returns:
    +
    + +
    + +
    +
    +class pyFTS.models.multivariate.grid.IncrementalGridCluster(**kwargs)[source]
    +

    Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

    +

    Create combinations of fuzzy sets of the variables on demand, incrementally increasing the +multivariate fuzzy set base.

    +
    +
    +fuzzyfy(data, **kwargs)[source]
    +

    Fuzzyfy the input data according to this partitioner fuzzy sets.

    + +++ + + + +
    Parameters:
      +
    • data – input value to be fuzzyfied
      • +
    • alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
      • +
    • method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
      • +
    • mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
      • +
    +
    +

    values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )

    +

    :returns a list with the fuzzyfied values, depending on the mode

    +
    + +
    + +
    + +
    +
    +prune()[source]
    +
    + +
    +

    pyFTS.models.multivariate.mvfts module

    @@ -614,7 +683,7 @@ transformations and partitioners.

    class pyFTS.models.multivariate.cmvfts.ClusteredMVFTS(**kwargs)[source]

    Bases: pyFTS.models.multivariate.mvfts.MVFTS

    -

    Meta model for multivariate, high order, clustered multivariate FTS

    +

    Meta model for high order, clustered multivariate FTS

    check_data(data)[source]
    @@ -641,6 +710,28 @@ transformations and partitioners.

    +
    +
    +forecast_ahead_multivariate(data, steps, **kwargs)[source]
    +

    Multivariate forecast n step ahead

    + +++ + + + + + +
    Parameters:
      +
    • data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model
      • +
    • steps – the number of steps ahead to forecast
      • +
    • kwargs – model specific parameters
      • +
    +
    Returns:

    a Pandas Dataframe object representing the forecasted values for each variable

    +
    +
    +
    forecast_multivariate(data, **kwargs)[source]
    @@ -705,6 +796,40 @@ transformations and partitioners.

    +
    +
    +

    pyFTS.models.multivariate.granular module

    +
    +
    +class pyFTS.models.multivariate.granular.GranularWMVFTS(**kwargs)[source]
    +

    Bases: pyFTS.models.multivariate.cmvfts.ClusteredMVFTS

    +

    Granular multivariate weighted high order FTS

    +
    +
    +model = None
    +

    The most recent trained model

    +
    + +
    +
    +train(data, **kwargs)[source]
    +

    Method specific parameter fitting

    + +++ + + + +
    Parameters:
      +
    • data – training time series data
      • +
    • kwargs – Method specific parameters
      • +
    +
    +
    + +
    +
    diff --git a/docs/build/html/pyFTS.models.seasonal.html b/docs/build/html/pyFTS.models.seasonal.html index 0ab1f4b..164eae6 100644 --- a/docs/build/html/pyFTS.models.seasonal.html +++ b/docs/build/html/pyFTS.models.seasonal.html @@ -381,8 +381,8 @@
    -
    -hour_of_day = 24
    +
    +hour = 24
    @@ -401,13 +401,13 @@
    -
    -minute_of_day = 1440
    +
    +minute = 60
    -
    -minute_of_hour = 60
    +
    +minute_of_day = 1440
    @@ -435,11 +435,6 @@ quarter = 4
    -
    -
    -second = 8
    -
    -
    second_of_day = 86400
    @@ -600,6 +595,11 @@
    +
    +
    +build_index()[source]
    +
    +
    mask = None
    @@ -614,6 +614,29 @@ :return:

    +
    +
    +search(data, **kwargs)[source]
    +

    Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several +overlapped fuzzy sets.

    + +++ + + + + + +
    Parameters:
      +
    • data – the value to search for the nearest fuzzy sets
      • +
    • type – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.
      • +
    • results – the number of nearest fuzzy sets to return
      • +
    +
    Returns:

    a list with the nearest fuzzy sets

    +
    +
    +
    season = None
    diff --git a/docs/build/html/pyFTS.partitioners.html b/docs/build/html/pyFTS.partitioners.html index b34c9b6..a613d95 100644 --- a/docs/build/html/pyFTS.partitioners.html +++ b/docs/build/html/pyFTS.partitioners.html @@ -144,6 +144,29 @@
    +
    +
    +build_index()[source]
    +
    + +
    +
    +check_bounds(data)[source]
    +

    Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest +fuzzy set.

    + +++ + + + + + +
    Parameters:data – input data to be verified
    Returns:the index of the closest fuzzy set when data is outside de universe of discourse or None if
    +

    the data is inside the UoD.

    +
    +
    extractor = None
    @@ -153,7 +176,24 @@
    fuzzyfy(data, **kwargs)[source]
    -
    +

    Fuzzyfy the input data according to this partitioner fuzzy sets.

    + +++ + + + +
    Parameters:
      +
    • data – input value to be fuzzyfied
      • +
    • alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
      • +
    • method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
      • +
    • mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
      • +
    +
    +

    values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )

    +

    :returns a list with the fuzzyfied values, depending on the mode

    +
    @@ -171,6 +211,12 @@
    +
    +
    +kdtree = None
    +

    A spatial index to help in fuzzyfication

    +
    +
    lower_set()[source]
    @@ -247,6 +293,29 @@

    prefix of auto generated partition names

    +
    +
    +search(data, **kwargs)[source]
    +

    Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several +overlapped fuzzy sets.

    + +++ + + + + + +
    Parameters:
      +
    • data – the value to search for the nearest fuzzy sets
      • +
    • type – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.
      • +
    • results – the number of nearest fuzzy sets to return
      • +
    +
    Returns:

    a list with the nearest fuzzy sets

    +
    +
    +
    setnames = None
    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 4300155..9fbac75 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ 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- Fuzzy Time Series for Python","pyFTS","pyFTS package","pyFTS.benchmarks package","pyFTS.common package","pyFTS.data package","pyFTS.distributed package","pyFTS.hyperparam package","pyFTS.models package","pyFTS.models.ensemble package","pyFTS.models.incremental package","pyFTS.models.multivariate package","pyFTS.models.nonstationary package","pyFTS.models.seasonal package","pyFTS.partitioners package","pyFTS.probabilistic package","pyFTS Quick Start"],titleterms:{FTS:16,airpasseng:5,arima:3,artifici:5,benchmark:3,bitcoin:5,chaotic:5,chen:8,cheng:8,cmean:14,cmsft:13,cmvft:11,common:[4,5,11,12,13],composit:4,conf:2,content:[2,3,4,5,6,7,8,9,10,11,12,13,14,15],cvft:12,data:5,dataset:5,dispi:6,distribut:6,dowjon:5,enrol:5,ensembl:9,entropi:14,ethereum:5,eur:5,evolutionari:7,exampl:16,fcm:14,flr:[4,11],flrg:[4,11,12],fts:4,fuzzi:[0,16],fuzzyset:4,gbp:5,gener:5,glass:5,granular:11,grid:14,gridsearch:7,henon:5,hoft:8,honsft:12,how:[0,16],huarng:14,hwang:8,hyperparam:7,ift:8,increment:10,incrementalensembl:10,index:0,inmet:5,instal:16,ismailefendi:8,kde:15,knn:3,librari:0,logistic_map:5,lorentz:5,mackei:5,malaysia:5,measur:3,membership:4,model:[8,9,10,11,12,13],modul:[2,3,4,5,6,7,8,9,10,11,12,13,14,15],msft:13,multiseason:9,multivari:11,mvft:11,naiv:3,nasdaq:5,nonstationari:12,nsft:12,packag:[2,3,4,5,6,7,8,9,10,11,12,13,14,15],parallel_util:14,partition:[11,12,13,14],perturb:12,probabilist:15,probabilitydistribut:15,pwft:8,pyft:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],python:0,quantreg:3,quick:16,refer:[0,16],residualanalysi:3,rossler:5,sadaei:8,season:13,seasonalindex:13,seri:[0,5,16],sft:13,simpl:14,singleton:14,sonda:5,song:8,sortedcollect:4,spark:6,start:16,submodul:[2,3,4,5,6,7,8,9,10,11,12,13,14,15],subpackag:[2,8],sunspot:5,synthet:5,taiex:5,time:[0,5,16],timevari:10,transform:4,tree:4,usag:16,usd:5,util:[3,4,7,12,14],variabl:11,what:[0,16],wmvft:11}}) \ No newline at end of file diff --git a/docs/pyFTS.models.multivariate.rst b/docs/pyFTS.models.multivariate.rst index 64ada4d..3ca4fb3 100644 --- a/docs/pyFTS.models.multivariate.rst +++ b/docs/pyFTS.models.multivariate.rst @@ -45,6 +45,22 @@ pyFTS.models.multivariate.flrg module :undoc-members: :show-inheritance: +pyFTS.models.multivariate.partitioner module +--------------------------------------------- + +.. automodule:: pyFTS.models.multivariate.partitioner + :members: + :undoc-members: + :show-inheritance: + + pyFTS.models.multivariate.grid module +------------------------------------------ + +.. automodule:: pyFTS.models.multivariate.grid + :members: + :undoc-members: + :show-inheritance: + pyFTS.models.multivariate.mvfts module -------------------------------------- @@ -68,4 +84,12 @@ pyFTS.models.multivariate.cmvfts module :members: :undoc-members: :show-inheritance: + +pyFTS.models.multivariate.granular module +--------------------------------------------- +.. automodule:: pyFTS.models.multivariate.granular + :members: + :undoc-members: + :show-inheritance: + diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py index ca45049..6620f20 100644 --- a/pyFTS/common/fts.py +++ b/pyFTS/common/fts.py @@ -71,6 +71,8 @@ class FTS(object): self.max_lag = self.order """A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags needed to forecast a single step ahead""" + self.log = pd.DataFrame([],columns=["Datetime","Operation","Value"]) + """""" def fuzzy(self, data): """ @@ -558,6 +560,9 @@ class FTS(object): for flrg in self.flrgs.keys(): self.flrgs[flrg].reset_calculated_values() + def append_log(self,operation, value): + pass + diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py index 9e395a9..a299543 100644 --- a/pyFTS/models/hofts.py +++ b/pyFTS/models/hofts.py @@ -126,6 +126,9 @@ class HighOrderFTS(fts.FTS): nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut) for k in sample] + if explain: + self.append_log("Fuzzyfication","{} -> {}".format(sample, nsample)) + return self.generate_lhs_flrg_fuzzyfied(nsample, explain) def generate_lhs_flrg_fuzzyfied(self, sample, explain=False): @@ -137,7 +140,7 @@ class HighOrderFTS(fts.FTS): lags.append(lhs) if explain: - print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + self.append_log("Ordering Lags", "Lag {} Value {}".format(o, lhs)) # Trace the possible paths for path in product(*lags): @@ -217,17 +220,11 @@ class HighOrderFTS(fts.FTS): sample = ndata[k - self.max_lag: k] - if explain: - print("Fuzzyfication \n") - if not fuzzyfied: flrgs = self.generate_lhs_flrg(sample, explain) else: flrgs = self.generate_lhs_flrg_fuzzyfied(sample, explain) - if explain: - print("Rules:\n") - midpoints = [] memberships = [] for flrg in flrgs: @@ -240,7 +237,7 @@ class HighOrderFTS(fts.FTS): memberships.append(mv) if explain: - print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1], + self.append_log("Rule Matching", "{} -> {} (Naïve) Midpoint: {}".format(str(flrg.LHS), flrg.LHS[-1], mp)) else: flrg = self.flrgs[flrg.get_key()] @@ -250,19 +247,17 @@ class HighOrderFTS(fts.FTS): memberships.append(mv) if explain: - print("\t {} \t Midpoint: {}\n".format(str(flrg), mp)) - print("\t {} \t Membership: {}\n".format(str(flrg), mv)) + self.append_log("Rule Matching", "{}, Midpoint: {} Membership: {}".format(flrg.get_key(), mp, mv)) if mode == "mean" or fuzzyfied: final = np.nanmean(midpoints) + if explain: self.append_log("Deffuzyfication", "By Mean: {}".format(final)) else: final = np.dot(midpoints, memberships) + if explain: self.append_log("Deffuzyfication", "By Memberships: {}".format(final)) ret.append(final) - if explain: - print("Deffuzyfied value: {} \n".format(final)) - return ret diff --git a/pyFTS/models/multivariate/grid.py b/pyFTS/models/multivariate/grid.py index 0561c6c..4dc4b88 100644 --- a/pyFTS/models/multivariate/grid.py +++ b/pyFTS/models/multivariate/grid.py @@ -33,6 +33,10 @@ class GridCluster(partitioner.MultivariatePartitioner): class IncrementalGridCluster(partitioner.MultivariatePartitioner): + """ + Create combinations of fuzzy sets of the variables on demand, incrementally increasing the + multivariate fuzzy set base. + """ def __init__(self, **kwargs): super(IncrementalGridCluster, self).__init__(**kwargs) self.name="IncrementalGridCluster" diff --git a/pyFTS/models/multivariate/mvfts.py b/pyFTS/models/multivariate/mvfts.py index bbbb8a9..73de64d 100644 --- a/pyFTS/models/multivariate/mvfts.py +++ b/pyFTS/models/multivariate/mvfts.py @@ -9,11 +9,11 @@ import pandas as pd def product_dict(**kwargs): - ''' + """ Code by Seth Johnson :param kwargs: :return: - ''' + """ keys = kwargs.keys() vals = kwargs.values() for instance in product(*vals): @@ -244,7 +244,6 @@ class MVFTS(fts.FTS): params=data[self.target_variable.data_label].values) return ret - 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 d244929..81cf8b8 100644 --- a/pyFTS/models/multivariate/partitioner.py +++ b/pyFTS/models/multivariate/partitioner.py @@ -53,14 +53,15 @@ class MultivariatePartitioner(partitioner.Partitioner): self.build_index() def search(self, data, **kwargs): - ''' + """ Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several overlapped fuzzy sets. :param data: the value to search for the nearest fuzzy sets :param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names. :return: a list with the nearest fuzzy sets - ''' + """ + if self.kdtree is None: self.build_index() @@ -83,9 +84,6 @@ class MultivariatePartitioner(partitioner.Partitioner): elif type == 'index': return sorted(ix) - - - def fuzzyfy(self, data, **kwargs): return fuzzyfy_instance_clustered(data, self, **kwargs)