diff --git a/pyFTS/common/FuzzySet.py b/pyFTS/common/FuzzySet.py index cd38c24..492ae47 100644 --- a/pyFTS/common/FuzzySet.py +++ b/pyFTS/common/FuzzySet.py @@ -7,7 +7,7 @@ class FuzzySet(object): """ Fuzzy Set """ - def __init__(self, name, mf, parameters, centroid, alpha=1.0, type='common'): + def __init__(self, name, mf, parameters, centroid, alpha=1.0, **kwargs): """ Create a Fuzzy Set :param name: fuzzy set name @@ -20,7 +20,8 @@ class FuzzySet(object): self.parameters = parameters self.centroid = centroid self.alpha = alpha - self.type = type + self.type = kwargs.get('type', 'common') + self.variable = kwargs.get('variable',None) ":param Z: Partition function in respect to the membership function" self.Z = None if self.mf == Membership.trimf: diff --git a/pyFTS/common/flrg.py b/pyFTS/common/flrg.py index e90c1d2..2fc2dc7 100644 --- a/pyFTS/common/flrg.py +++ b/pyFTS/common/flrg.py @@ -15,14 +15,14 @@ class FLRG(object): ret = 0.0 if isinstance(self.LHS, (list, set)): assert len(self.LHS) == len(data) - ret = min([self.LHS[ct].membership(dat) for ct, dat in enumerate(data)]) + ret = np.nanmin([self.LHS[ct].membership(dat) for ct, dat in enumerate(data)]) else: ret = self.LHS.membership(data) return ret def get_midpoint(self): if self.midpoint is None: - self.midpoint = sum(self.get_midpoints())/len(self.RHS) + self.midpoint = np.nanmean(self.get_midpoints()) return self.midpoint def get_midpoints(self): diff --git a/pyFTS/common/tree.py b/pyFTS/common/tree.py index 9a15e66..239677d 100644 --- a/pyFTS/common/tree.py +++ b/pyFTS/common/tree.py @@ -53,7 +53,7 @@ def flat(dados): yield inst -def buildTreeWithoutOrder(node, lags, level): +def build_tree_without_order(node, lags, level): if level not in lags: return @@ -62,4 +62,4 @@ def buildTreeWithoutOrder(node, lags, level): node.appendChild(FLRGTreeNode(s)) for child in node.getChildren(): - buildTreeWithoutOrder(child, lags, level + 1) \ No newline at end of file + build_tree_without_order(child, lags, level + 1) diff --git a/pyFTS/data/TAIEX.py b/pyFTS/data/TAIEX.py index cd9799c..679ebd0 100644 --- a/pyFTS/data/TAIEX.py +++ b/pyFTS/data/TAIEX.py @@ -6,6 +6,13 @@ import pkg_resources def get_data(): filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv') - dat = pd.read_csv(filename, sep=";") + dat = pd.read_csv(filename, sep=",") dat = np.array(dat["avg"]) return dat + + +def get_dataframe(): + filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv') + dat = pd.read_csv(filename, sep=",") + dat["Date"] = pd.to_datetime(dat["Date"]) + return dat diff --git a/pyFTS/models/ensemble/ensemble.py b/pyFTS/models/ensemble/ensemble.py index 996fbda..2c50d75 100644 --- a/pyFTS/models/ensemble/ensemble.py +++ b/pyFTS/models/ensemble/ensemble.py @@ -154,7 +154,7 @@ class EnsembleFTS(fts.FTS): root = tree.FLRGTreeNode(None) - tree.buildTreeWithoutOrder(root, lags, 0) + tree.build_tree_without_order(root, lags, 0) for p in root.paths(): path = list(reversed(list(filter(None.__ne__, p)))) @@ -199,7 +199,7 @@ class EnsembleFTS(fts.FTS): root = tree.FLRGTreeNode(None) - tree.buildTreeWithoutOrder(root, lags, 0) + tree.build_tree_without_order(root, lags, 0) for p in root.paths(): path = list(reversed(list(filter(None.__ne__, p)))) diff --git a/pyFTS/models/multivariate/FLR.py b/pyFTS/models/multivariate/FLR.py index 8747086..2e1bd38 100644 --- a/pyFTS/models/multivariate/FLR.py +++ b/pyFTS/models/multivariate/FLR.py @@ -1,4 +1,6 @@ +import numpy as np +from pyFTS.common import flrg as flg class FLR(object): """Multivariate Fuzzy Logical Relationship""" @@ -12,11 +14,47 @@ class FLR(object): self.LHS = {} self.RHS = None - def set_lhs(self,var,set): + def set_lhs(self, var, set): self.LHS[var] = set def set_rhs(self, set): self.RHS = set def __str__(self): - return str([k.name for k in self.LHS]) + " -> " + self.RHS.name \ No newline at end of file + return str([k +":"+self.LHS[k].name for k in self.LHS.keys()]) + " -> " + self.RHS.name + + +class FLRG(flg.FLRG): + + def __init__(self, **kwargs): + super(FLRG,self).__init__(0,**kwargs) + self.LHS = kwargs.get('lhs', {}) + self.RHS = set() + self.key = None + + def set_lhs(self, var, set): + self.LHS[var] = set + + def append_rhs(self, set): + self.RHS.add(set) + + def get_key(self): + if self.key is None: + _str = "" + for k in self.LHS.keys(): + _str += "," if len(_str) > 0 else "" + _str += k + ":" + self.LHS[k].name + self.key = _str + + return self.key + + def get_membership(self, data): + return np.nanmin([self.LHS[k].membership(data[k]) for k in self.LHS.keys()]) + + def __str__(self): + _str = "" + for k in self.RHS: + _str += "," if len(_str) > 0 else "" + _str += k.name + + return self.get_key() + " -> " + _str diff --git a/pyFTS/models/multivariate/common.py b/pyFTS/models/multivariate/common.py index 934bf78..8ca879f 100644 --- a/pyFTS/models/multivariate/common.py +++ b/pyFTS/models/multivariate/common.py @@ -1,17 +1,16 @@ -from pyFTS.common import fts +from pyFTS.common import fts, FuzzySet, FLR, Membership, tree +from pyFTS.partitioners import Grid +from pyFTS.models.multivariate import FLR as MVFLR + +import numpy as np +import pandas as pd -class Variable: - def __init__(self,name, **kwargs): - self.name = name - self.alias = kwargs.get('alias', self.name) - self.data_label = kwargs.get('alias', self.name) - self.partitioner = kwargs.get('partitioner',None) - self.type = kwargs.get('type', 'common') - self.transformation = kwargs.get('transformation', None) - - def __str__(self): - return self.name +def fuzzyfy_instance(data_point, var): + mv = np.array([fs.membership(data_point) for fs in var.partitioner.sets]) + ix = np.ravel(np.argwhere(mv > 0.0)) + sets = [var.partitioner.sets[i] for i in ix] + return sets class MVFTS(fts.FTS): @@ -19,8 +18,122 @@ class MVFTS(fts.FTS): super(MVFTS, self).__init__(1, name, **kwargs) self.explanatory_variables = [] self.target_variable = None + self.flrgs = {} def append_variable(self, var): self.explanatory_variables.append(var) + def format_data(self, data): + ndata = {} + for var in self.explanatory_variables: + ndata[var.name] = data[var.data_label] + + return ndata + + def apply_transformations(self, data, params=None, updateUoD=False, **kwargs): + ndata = data.copy(deep=True) + for var in self.explanatory_variables: + ndata[var.data_label] = var.apply_transformations(data[var.data_label].values) + + return ndata + + def generate_lhs_flrs(self, data): + flrs = [] + lags = {} + for vc, var in enumerate(self.explanatory_variables): + data_point = data[var.data_label] + lags[vc] = fuzzyfy_instance(data_point, var) + + root = tree.FLRGTreeNode(None) + + tree.build_tree_without_order(root, lags, 0) + + for p in root.paths(): + path = list(reversed(list(filter(None.__ne__, p)))) + + flr = MVFLR.FLR() + + for c, e in enumerate(path, start=0): + flr.set_lhs(e.variable, e) + + flrs.append(flr) + + return flrs + + def generate_flrs(self, data): + flrs = [] + for ct in range(1, len(data.index)): + ix = data.index[ct-1] + data_point = data.loc[ix] + + tmp_flrs = self.generate_lhs_flrs(data_point) + + target_ix = data.index[ct] + target_point = data[self.target_variable.data_label][target_ix] + target = fuzzyfy_instance(target_point, self.target_variable) + + for flr in tmp_flrs: + for t in target: + flr.set_rhs(t) + flrs.append(flr) + + return flrs + + def generate_flrg(self, flrs): + flrgs = {} + + for flr in flrs: + flrg = MVFLR.FLRG(lhs=flr.LHS) + + if flrg.get_key() not in flrgs: + flrgs[flrg.get_key()] = flrg + + flrgs[flrg.get_key()].append_rhs(flr.RHS) + + return flrgs + + def train(self, data, **kwargs): + + ndata = self.apply_transformations(data) + + flrs = self.generate_flrs(ndata) + self.flrgs = self.generate_flrg(flrs) + + def forecast(self, data, **kwargs): + ret = [] + ndata = self.apply_transformations(data) + for ix in ndata.index: + data_point = ndata.loc[ix] + flrs = self.generate_lhs_flrs(data_point) + mvs = [] + mps = [] + for flr in flrs: + flrg = MVFLR.FLRG(lhs=flr.LHS) + if flrg.get_key() not in self.flrgs: + #print('hit') + mvs.append(0.) + mps.append(0.) + else: + mvs.append(self.flrgs[flrg.get_key()].get_membership(self.format_data(data_point))) + mps.append(self.flrgs[flrg.get_key()].get_midpoint()) + + #print('mv', mvs) + #print('mp', mps) + mv = np.array(mvs) + mp = np.array(mps) + + ret.append(np.dot(mv,mp.T)/np.sum(mv)) + + self.target_variable.apply_inverse_transformations(ret, + params=data[self.target_variable.data_label].values) + + return ret + + def __str__(self): + _str = self.name + ":\n" + for k in self.flrgs.keys(): + _str += str(self.flrgs[k]) + "\n" + + return _str + diff --git a/pyFTS/models/multivariate/variable.py b/pyFTS/models/multivariate/variable.py new file mode 100644 index 0000000..411d83b --- /dev/null +++ b/pyFTS/models/multivariate/variable.py @@ -0,0 +1,49 @@ +from pyFTS.common import fts, FuzzySet, FLR, Membership, tree +from pyFTS.partitioners import Grid +from pyFTS.models.multivariate import FLR as MVFLR + + +class Variable: + def __init__(self, name, **kwargs): + self.name = name + self.alias = kwargs.get('alias', self.name) + self.data_label = kwargs.get('data_label', self.name) + self.type = kwargs.get('type', 'common') + self.transformation = kwargs.get('transformation', None) + self.transformation_params = kwargs.get('transformation_params', None) + self.partitioner = None + + if kwargs.get('data', None) is not None: + self.build(**kwargs) + + def build(self, **kwargs): + fs = kwargs.get('partitioner', Grid.GridPartitioner) + mf = kwargs.get('func', Membership.trimf) + np = kwargs.get('npart', 10) + data = kwargs.get('data', None) + self.partitioner = fs(data=data[self.data_label].values, npart=np, func=mf, + transformation=self.transformation, prefix=self.alias, + variable=self.name) + + def apply_transformations(self, data, **kwargs): + + if kwargs.get('params', None) is not None: + self.transformation_params = kwargs.get('params', None) + + if self.transformation is not None: + return self.transformation.apply(data, self.transformation_params) + + return data + + def apply_inverse_transformations(self, data, **kwargs): + + if kwargs.get('params', None) is not None: + self.transformation_params = kwargs.get('params', None) + + if self.transformation is not None: + return self.transformation.inverse(data, self.transformation_params) + + return data + + def __str__(self): + return self.name diff --git a/pyFTS/models/seasonal/common.py b/pyFTS/models/seasonal/common.py index 6967352..a9ad7de 100644 --- a/pyFTS/models/seasonal/common.py +++ b/pyFTS/models/seasonal/common.py @@ -87,8 +87,8 @@ class FuzzySet(FuzzySet.FuzzySet): Temporal/Seasonal Fuzzy Set """ - def __init__(self, datepart, name, mf, parameters, centroid, alpha=1.0): - super(FuzzySet, self).__init__(name, mf, parameters, centroid, alpha) + def __init__(self, datepart, name, mf, parameters, centroid, alpha=1.0, **kwargs): + super(FuzzySet, self).__init__(name, mf, parameters, centroid, alpha, type = 'datetime', **kwargs) self.datepart = datepart def membership(self, x): diff --git a/pyFTS/models/seasonal/partitioner.py b/pyFTS/models/seasonal/partitioner.py index 11d686f..c7be376 100644 --- a/pyFTS/models/seasonal/partitioner.py +++ b/pyFTS/models/seasonal/partitioner.py @@ -9,19 +9,20 @@ import matplotlib.pylab as plt class TimeGridPartitioner(partitioner.Partitioner): """Even Length DateTime Grid Partitioner""" - def __init__(self, data, npart, season, func=Membership.trimf, names=None): + def __init__(self, **kwargs): """ Even Length Grid Partitioner + :param seasonality: Time granularity, from pyFTS.models.seasonal.common.DateTime :param data: Training data of which the universe of discourse will be extracted. The universe of discourse is the open interval between the minimum and maximum values of the training data. :param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created :param func: Fuzzy membership function (pyFTS.common.Membership) """ - super(TimeGridPartitioner, self).__init__("TimeGrid", data, npart, func=func, names=names, transformation=None, - indexer=None, preprocess=False) + super(TimeGridPartitioner, self).__init__(name="TimeGrid", **kwargs) - self.season = season + self.season = kwargs.get('seasonality', DateTime.day_of_year) + data = kwargs.get('data', None) if self.season == DateTime.year: - ndata = [strip_datepart(k, season) for k in data] + ndata = [strip_datepart(k, self.season) for k in data] self.min = min(ndata) self.max = max(ndata) else: @@ -34,6 +35,8 @@ class TimeGridPartitioner(partitioner.Partitioner): def build(self, data): sets = [] + kwargs = {'variable': self.variable} + if self.season == DateTime.year: dlen = (self.max - self.min) partlen = dlen / self.partitions @@ -49,30 +52,37 @@ class TimeGridPartitioner(partitioner.Partitioner): tmp = Composite(set_name, superset=True) 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 + 0.0000001], self.season.value, alpha=.5, + **kwargs)) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, - [c - partlen, c, c + partlen], c)) + [c - partlen, c, c + partlen], c, + **kwargs)) tmp.centroid = c sets.append(tmp) else: sets.append(FuzzySet(self.season, set_name, Membership.trimf, - [c - partlen, c, c + partlen], c)) + [c - partlen, c, c + partlen], c, + **kwargs)) elif self.membership_function == Membership.gaussmf: - sets.append(FuzzySet(self.season, set_name, Membership.gaussmf, [c, partlen / 3], c)) + sets.append(FuzzySet(self.season, set_name, Membership.gaussmf, [c, partlen / 3], c, + **kwargs)) elif self.membership_function == Membership.trapmf: q = partlen / 4 if c == self.min: tmp = Composite(set_name, superset=True) tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf, [self.season.value - pl2, self.season.value, - self.season.value + 0.0000001], 0)) + self.season.value + 0.0000001], 0, + **kwargs)) tmp.append_set(FuzzySet(self.season, set_name, Membership.trapmf, - [c - partlen, c - q, c + q, c + partlen], c)) + [c - partlen, c - q, c + q, c + partlen], c, + **kwargs)) tmp.centroid = c sets.append(tmp) else: sets.append(FuzzySet(self.season, set_name, Membership.trapmf, - [c - partlen, c - q, c + q, c + partlen], c)) + [c - partlen, c - q, c + q, c + partlen], c, + **kwargs)) count += 1 self.min = 0 diff --git a/pyFTS/partitioners/CMeans.py b/pyFTS/partitioners/CMeans.py index caba7a3..0b9519e 100644 --- a/pyFTS/partitioners/CMeans.py +++ b/pyFTS/partitioners/CMeans.py @@ -78,8 +78,8 @@ def c_means(k, dados, tam): class CMeansPartitioner(partitioner.Partitioner): - def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None): - super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation, indexer=indexer) + def __init__(self, **kwargs): + super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs) def build(self, data): sets = [] diff --git a/pyFTS/partitioners/Entropy.py b/pyFTS/partitioners/Entropy.py index 035a35c..89f8661 100644 --- a/pyFTS/partitioners/Entropy.py +++ b/pyFTS/partitioners/Entropy.py @@ -79,8 +79,8 @@ def bestSplit(data, npart): class EntropyPartitioner(partitioner.Partitioner): """Huarng Entropy Partitioner""" - def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None): - super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation, indexer=indexer) + def __init__(self, **kwargs): + super(EntropyPartitioner, self).__init__(name="Entropy", **kwargs) def build(self, data): sets = [] diff --git a/pyFTS/partitioners/FCM.py b/pyFTS/partitioners/FCM.py index 26c0648..23ed4e6 100644 --- a/pyFTS/partitioners/FCM.py +++ b/pyFTS/partitioners/FCM.py @@ -104,8 +104,8 @@ class FCMPartitioner(partitioner.Partitioner): """ """ - def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None): - super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation, indexer=indexer) + def __init__(self, **kwargs): + super(FCMPartitioner, self).__init__(name="FCM", **kwargs) def build(self,data): sets = [] diff --git a/pyFTS/partitioners/Grid.py b/pyFTS/partitioners/Grid.py index 156145b..c4248cd 100644 --- a/pyFTS/partitioners/Grid.py +++ b/pyFTS/partitioners/Grid.py @@ -9,7 +9,7 @@ from pyFTS.partitioners import partitioner class GridPartitioner(partitioner.Partitioner): """Even Length Grid Partitioner""" - def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None): + def __init__(self, **kwargs): """ Even Length Grid Partitioner :param data: Training data of which the universe of discourse will be extracted. The universe of discourse is the open interval between the minimum and maximum values of the training data. @@ -18,11 +18,13 @@ class GridPartitioner(partitioner.Partitioner): :param transformation: data transformation to be applied on data :param indexer: """ - super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation, indexer=indexer) + super(GridPartitioner, self).__init__(name="Grid", **kwargs) def build(self, data): sets = [] + kwargs = {'type': self.type, 'variable': self.variable} + dlen = self.max - self.min partlen = dlen / self.partitions @@ -30,14 +32,14 @@ class GridPartitioner(partitioner.Partitioner): for c in np.arange(self.min, self.max, partlen): if self.membership_function == Membership.trimf: sets.append( - FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c)) + FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c,**kwargs)) elif self.membership_function == Membership.gaussmf: sets.append( - FuzzySet.FuzzySet(self.prefix + str(count), Membership.gaussmf, [c, partlen / 3], c)) + FuzzySet.FuzzySet(self.prefix + str(count), Membership.gaussmf, [c, partlen / 3], c,**kwargs)) elif self.membership_function == Membership.trapmf: q = partlen / 2 sets.append( - FuzzySet.FuzzySet(self.prefix + str(count), Membership.trapmf, [c - partlen, c - q, c + q, c + partlen], c)) + FuzzySet.FuzzySet(self.prefix + str(count), Membership.trapmf, [c - partlen, c - q, c + q, c + partlen], c,**kwargs)) count += 1 self.min = self.min - partlen diff --git a/pyFTS/partitioners/Huarng.py b/pyFTS/partitioners/Huarng.py index ba56dee..9c8ef88 100644 --- a/pyFTS/partitioners/Huarng.py +++ b/pyFTS/partitioners/Huarng.py @@ -12,8 +12,8 @@ from pyFTS.partitioners import partitioner class HuarngPartitioner(partitioner.Partitioner): """Huarng Empirical Partitioner""" - def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None): - super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation, indexer=indexer) + def __init__(self, **kwargs): + super(HuarngPartitioner, self).__init__(name="Huarng", **kwargs) def build(self, data): diff = Transformations.Differential(1) diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py index eaa6f78..3a325db 100644 --- a/pyFTS/partitioners/partitioner.py +++ b/pyFTS/partitioners/partitioner.py @@ -8,8 +8,7 @@ class Partitioner(object): Universe of Discourse partitioner. Split data on several fuzzy sets """ - def __init__(self, name, data, npart, func=Membership.trimf, names=None, prefix="A", - transformation=None, indexer=None, preprocess=True): + def __init__(self, **kwargs): """ Universe of Discourse partitioner scheme. Split data on several fuzzy sets :param name: partitioner name @@ -20,24 +19,28 @@ class Partitioner(object): :param prefix: prefix of auto generated partition names :param transformation: data transformation to be applied on data """ - self.name = name - self.partitions = npart + self.name = kwargs.get('name',"") + self.partitions = kwargs.get('npart',10) self.sets = [] - self.membership_function = func - self.setnames = names - self.prefix = prefix - self.transformation = transformation - self.indexer = indexer + self.membership_function = kwargs.get('func',Membership.trimf) + self.setnames = kwargs.get('names',None) + self.prefix = kwargs.get('prefix','A') + self.transformation = kwargs.get('transformation',None) + self.indexer = kwargs.get('indexer',None) + self.variable = kwargs.get('variable', None) + self.type = kwargs.get('type', 'common') - if preprocess: + if kwargs.get('preprocess',True): + + data = kwargs.get('data',[None]) if self.indexer is not None: ndata = self.indexer.get_data(data) else: ndata = data - if transformation is not None: - ndata = transformation.apply(ndata) + if self.transformation is not None: + ndata = self.transformation.apply(ndata) else: ndata = data @@ -84,7 +87,8 @@ class Partitioner(object): self.plot_set(ax, ss) ticks.append(str(round(s.centroid,0))+'\n'+s.name) x.append(s.centroid) - plt.xticks(x,ticks) + ax.xaxis.set_ticklabels(ticks) + ax.xaxis.set_ticks(x) def plot_set(self, ax, s): if s.mf == Membership.trimf: diff --git a/pyFTS/tests/multivariate.py b/pyFTS/tests/multivariate.py new file mode 100644 index 0000000..a7d69c3 --- /dev/null +++ b/pyFTS/tests/multivariate.py @@ -0,0 +1,48 @@ +import pandas as pd +import matplotlib.pylab as plt +from pyFTS.data import TAIEX as tx +from pyFTS.common import Transformations + + +bc = Transformations.BoxCox(0) +diff = Transformations.Differential(1) + +df = tx.get_dataframe() +df = df.dropna() +#df.loc[2209] +train = df.iloc[2000:2500] +test = df.iloc[2500:3000] + +from pyFTS.partitioners import Grid, Util as pUtil +from pyFTS.models.multivariate import common, variable + +model = common.MVFTS("") + +#fig, axes = plt.subplots(nrows=5, ncols=1,figsize=[10,10]) + +vopen = variable.Variable("Open", data_label="Openly", partitioner=Grid.GridPartitioner, npart=40, data=df) +model.append_variable(vopen) +#vopen.partitioner.plot(axes[0]) +vhigh = variable.Variable("High", data_label="Highest", partitioner=Grid.GridPartitioner, npart=40, data=df)#train) +model.append_variable(vhigh) +#vhigh.partitioner.plot(axes[1]) +vlow = variable.Variable("Low", data_label="Lowermost", partitioner=Grid.GridPartitioner, npart=40, data=df)#train) +model.append_variable(vlow) +#vlow.partitioner.plot(axes[2]) +vclose = variable.Variable("Close", data_label="Close", partitioner=Grid.GridPartitioner, npart=40, data=df)#train) +model.append_variable(vclose) +#vclose.partitioner.plot(axes[3]) +vvol = variable.Variable("Volume", data_label="Volume", partitioner=Grid.GridPartitioner, npart=100, data=df, + transformation=bc)#train) +model.append_variable(vvol) +#vvol.partitioner.plot(axes[4]) + +model.target_variable = vvol + +#plt.tight_layout() +model.train(train) + +forecasted = model.forecast(test) + +print([round(k,0) for k in test['Volume'].values.tolist()]) +print([round(k,0) for k in forecasted]) \ No newline at end of file diff --git a/setup.py b/setup.py index 32384ed..2283517 100644 --- a/setup.py +++ b/setup.py @@ -4,11 +4,10 @@ setup( name='pyFTS', packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble', 'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic', - 'pyFTS.tests', 'pyFTS.models.nonstationary'], + 'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate'], #package_dir={} package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'], - 'models': ['*'], 'seasonal': ['*'], 'ensemble': ['*'], - 'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']}, + 'models': ['*'], 'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']}, #data_files=[('data', ['pyFTS/data/Enrollments.csv', 'pyFTS/data/AirPassengers.csv'])], include_package_data=True, version='1.1.1',