diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index b127453..f613efa 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 12205ff..e91bc38 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.doctree b/docs/build/doctrees/pyFTS.models.doctree index 9714acb..7eb4aaa 100644 Binary files a/docs/build/doctrees/pyFTS.models.doctree and b/docs/build/doctrees/pyFTS.models.doctree differ diff --git a/docs/build/doctrees/pyFTS.models.ensemble.doctree b/docs/build/doctrees/pyFTS.models.ensemble.doctree index 881c1d5..ed22af5 100644 Binary files a/docs/build/doctrees/pyFTS.models.ensemble.doctree and b/docs/build/doctrees/pyFTS.models.ensemble.doctree differ diff --git a/docs/build/doctrees/pyFTS.models.nonstationary.doctree b/docs/build/doctrees/pyFTS.models.nonstationary.doctree index 1bf8bfd..c5f45c3 100644 Binary files a/docs/build/doctrees/pyFTS.models.nonstationary.doctree and b/docs/build/doctrees/pyFTS.models.nonstationary.doctree differ diff --git a/docs/build/doctrees/pyFTS.partitioners.doctree b/docs/build/doctrees/pyFTS.partitioners.doctree index 9753960..8024bc1 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/pyFTS/common/fts.html b/docs/build/html/_modules/pyFTS/common/fts.html index ae66fd4..f6e7fcc 100644 --- a/docs/build/html/_modules/pyFTS/common/fts.html +++ b/docs/build/html/_modules/pyFTS/common/fts.html @@ -167,6 +167,7 @@ :keyword start: in the multi step forecasting, the index of the data where to start forecasting :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster :keyword nodes: a list with the dispy cluster nodes addresses + :keyword explain: try to explain, step by step, the one-step-ahead point forecasting result given the input data. :return: a numpy array with the forecasted data """ @@ -416,6 +417,7 @@ if save: Util.persist_obj(self, file_path) +
[docs] def clone_parameters(self, model): """ Import the parameters values from other model diff --git a/docs/build/html/_modules/pyFTS/models/chen.html b/docs/build/html/_modules/pyFTS/models/chen.html index 4623c94..781e000 100644 --- a/docs/build/html/_modules/pyFTS/models/chen.html +++ b/docs/build/html/_modules/pyFTS/models/chen.html @@ -130,22 +130,36 @@
[docs] def forecast(self, ndata, **kwargs): - l = len(ndata) + explain = kwargs.get('explain',False) + + l = len(ndata) if not explain else 1 ret = [] for k in np.arange(0, l): - mv = FuzzySet.fuzzyfy_instance(ndata[k], self.sets) + actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets) - actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets) #self.sets[np.argwhere(mv == max(mv))[0, 0]] + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: _flrg = self.flrgs[actual.name] - ret.append(_flrg.get_midpoint(self.sets)) + mp = _flrg.get_midpoint(self.sets) + + ret.append(mp) + + if explain: + print("Rules:\n\n {} \t Midpoint: {} \n".format(str(_flrg), mp)) + + print("Deffuzyfied value: {} \n".format(mp)) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/hofts.html b/docs/build/html/_modules/pyFTS/models/hofts.html index d1dbefe..c956567 100644 --- a/docs/build/html/_modules/pyFTS/models/hofts.html +++ b/docs/build/html/_modules/pyFTS/models/hofts.html @@ -136,7 +136,7 @@ self.max_lag = self.order self.lags = np.arange(1, self.order+1) -
[docs] def generate_lhs_flrg(self, sample): +
[docs] def generate_lhs_flrg(self, sample, explain=False): lags = {} flrgs = [] @@ -145,6 +145,9 @@ lhs = FuzzySet.fuzzyfy(sample[o-1], partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut) lags[ct] = lhs + if explain: + print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + root = tree.FLRGTreeNode(None) tree.build_tree_without_order(root, lags, 0) @@ -185,27 +188,50 @@
[docs] def forecast(self, ndata, **kwargs): + explain = kwargs.get('explain', False) + ret = [] - l = len(ndata) + l = len(ndata) if not explain else self.max_lag + 1 if l < self.max_lag: return ndata for k in np.arange(self.max_lag, l+1): - flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k]) + + if explain: + print("Fuzzyfication \n") + + flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k], explain) + + if explain: + print("Rules:\n") tmp = [] for flrg in flrgs: if flrg.get_key() not in self.flrgs: if len(flrg.LHS) > 0: - tmp.append(self.sets[flrg.LHS[-1]].centroid) + mp = self.sets[flrg.LHS[-1]].centroid + tmp.append(mp) + + if explain: + print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1], + mp)) + else: flrg = self.flrgs[flrg.get_key()] - tmp.append(flrg.get_midpoint(self.sets)) + mp = flrg.get_midpoint(self.sets) + tmp.append(mp) - ret.append(np.nanmean(tmp)) + if explain: + print("\t {} \t Midpoint: {}\n".format(str(flrg), mp)) + + final = np.nanmean(tmp) + ret.append(final) + + if explain: + print("Deffuzyfied value: {} \n".format(final)) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/ifts.html b/docs/build/html/_modules/pyFTS/models/ifts.html index 1115246..c9ee781 100644 --- a/docs/build/html/_modules/pyFTS/models/ifts.html +++ b/docs/build/html/_modules/pyFTS/models/ifts.html @@ -148,7 +148,7 @@ for flrg in flrgs: if len(flrg.LHS) > 0: - # achar o os bounds de cada FLRG, ponderados pela pertinência + mv = flrg.get_membership(sample, self.sets) up.append(mv * self.get_upper(flrg)) lo.append(mv * self.get_lower(flrg)) diff --git a/docs/build/html/_modules/pyFTS/models/ismailefendi.html b/docs/build/html/_modules/pyFTS/models/ismailefendi.html index 007804a..f544092 100644 --- a/docs/build/html/_modules/pyFTS/models/ismailefendi.html +++ b/docs/build/html/_modules/pyFTS/models/ismailefendi.html @@ -131,7 +131,7 @@ if flr.LHS in self.flrgs: self.flrgs[flr.LHS].append_rhs(flr.RHS) else: - self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS); + self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS) self.flrgs[flr.LHS].append_rhs(flr.RHS)
[docs] def train(self, ndata, **kwargs): @@ -141,7 +141,8 @@ self.generate_flrg(flrs)
[docs] def forecast(self, ndata, **kwargs): - l = 1 + + explain = kwargs.get('explain', False) if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets @@ -149,7 +150,8 @@ ordered_sets = FuzzySet.set_ordered(self.sets) ndata = np.array(ndata) - l = len(ndata) + + l = len(ndata) if not explain else 1 ret = [] @@ -157,13 +159,28 @@ actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights())) + final = mp.dot(flrg.weights()) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/nonstationary/util.html b/docs/build/html/_modules/pyFTS/models/nonstationary/util.html index c52b00c..ba215f5 100644 --- a/docs/build/html/_modules/pyFTS/models/nonstationary/util.html +++ b/docs/build/html/_modules/pyFTS/models/nonstationary/util.html @@ -129,7 +129,7 @@
[docs]def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, - save=False, file=None, axes=None): + save=False, file=None, axes=None, fig=None): range = np.arange(0, len(data), step) ticks = [] if axes is None: @@ -151,11 +151,11 @@ else: axes.plot([t, t + 1, t], param,c=set.metadata['color']) - ticks.extend(["t+"+str(t),""]) + #ticks.extend(["t+"+str(t),""]) axes.set_ylabel("Universe of Discourse") axes.set_xlabel("Time") - plt.xticks([k for k in range], ticks, rotation='vertical') + #plt.xticks([k for k in range], ticks, rotation='vertical') handles0, labels0 = axes.get_legend_handles_labels() lgd = axes.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1)) diff --git a/docs/build/html/_modules/pyFTS/models/pwfts.html b/docs/build/html/_modules/pyFTS/models/pwfts.html index 6182a93..cf63345 100644 --- a/docs/build/html/_modules/pyFTS/models/pwfts.html +++ b/docs/build/html/_modules/pyFTS/models/pwfts.html @@ -196,7 +196,7 @@ else: self.generate_flrg(data)
-
[docs] def generate_lhs_flrg(self, sample): +
[docs] def generate_lhs_flrg(self, sample, explain=False): lags = {} flrgs = [] @@ -206,6 +206,9 @@ lags[ct] = lhs + if explain: + print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + root = tree.FLRGTreeNode(None) tree.build_tree_without_order(root, lags, 0) @@ -300,9 +303,6 @@ cond.append(tmp) ret = sum(np.array(cond)) else: - ########################################## - # this may be the problem! TEST IT!!! - ########################################## pi = 1 / len(flrg.LHS) ret = sum(np.array([pi * self.sets[s].membership(x) for s in flrg.LHS])) return ret
@@ -346,27 +346,48 @@
[docs] def point_heuristic(self, sample, **kwargs): - flrgs = self.generate_lhs_flrg(sample) + explain = kwargs.get('explain', False) + + if explain: + print("Fuzzyfication \n") + + flrgs = self.generate_lhs_flrg(sample, explain) mp = [] norms = [] + + if explain: + print("Rules:\n") + for flrg in flrgs: norm = self.flrg_lhs_conditional_probability(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) norm = sum(norms) - if norm == 0: - return 0 - else: - return sum(mp) / norm
+ final = sum(mp) / norm if norm != 0 else 0 + + if explain: + print("Deffuzyfied value: {} \n".format(final)) + return final
[docs] def point_expected_value(self, sample, **kwargs): - return self.forecast_distribution(sample)[0].expected_value()
+ explain = kwargs.get('explain', False) + dist = self.forecast_distribution(sample)[0] + + final = dist.expected_value() + return final
[docs] def forecast_interval(self, ndata, **kwargs): diff --git a/docs/build/html/_modules/pyFTS/models/sadaei.html b/docs/build/html/_modules/pyFTS/models/sadaei.html index de65e0a..f924f41 100644 --- a/docs/build/html/_modules/pyFTS/models/sadaei.html +++ b/docs/build/html/_modules/pyFTS/models/sadaei.html @@ -145,7 +145,8 @@ self.generate_flrg(flrs, self.c)
[docs] def forecast(self, ndata, **kwargs): - l = 1 + + explain = kwargs.get('explain', False) if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets @@ -162,13 +163,28 @@ actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights())) + final = mp.dot(flrg.weights()) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/yu.html b/docs/build/html/_modules/pyFTS/models/yu.html index bd54588..b2303f4 100644 --- a/docs/build/html/_modules/pyFTS/models/yu.html +++ b/docs/build/html/_modules/pyFTS/models/yu.html @@ -138,6 +138,8 @@
[docs] def forecast(self, ndata, **kwargs): + explain = kwargs.get('explain', False) + if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets else: @@ -145,7 +147,7 @@ ndata = np.array(ndata) - l = len(ndata) + l = len(ndata) if not explain else 1 ret = [] @@ -153,13 +155,28 @@ actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n\n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights(self.sets))) + final = mp.dot(flrg.weights(self.sets)) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret
diff --git a/docs/build/html/_modules/pyFTS/partitioners/partitioner.html b/docs/build/html/_modules/pyFTS/partitioners/partitioner.html index 4425f6a..5c705a6 100644 --- a/docs/build/html/_modules/pyFTS/partitioners/partitioner.html +++ b/docs/build/html/_modules/pyFTS/partitioners/partitioner.html @@ -150,19 +150,35 @@ pass
[docs] def get_name(self, counter): + """ + Find the name of the fuzzy set given its counter id. + + :param counter: The number of the fuzzy set + :return: String + """ return self.prefix + str(counter) if self.setnames is None else self.setnames[counter]
[docs] def lower_set(self): + """ + Return the fuzzy set on lower bound of the universe of discourse. + + :return: Fuzzy Set + """ return self.sets[self.ordered_sets[0]]
[docs] def upper_set(self): + """ + Return the fuzzy set on upper bound of the universe of discourse. + + :return: Fuzzy Set + """ return self.sets[self.ordered_sets[-1]]
[docs] def plot(self, ax): """ - Plot the - :param ax: - :return: + Plot the partitioning using the Matplotlib axis ax + + :param ax: Matplotlib axis """ ax.set_title(self.name) ax.set_ylim([0, 1]) @@ -182,6 +198,12 @@ ax.xaxis.set_ticks(x)
[docs] def plot_set(self, ax, s): + """ + Plot an isolate fuzzy set on Matplotlib axis + + :param ax: Matplotlib axis + :param s: Fuzzy Set + """ if s.mf == Membership.trimf: ax.plot([s.parameters[0], s.parameters[1], s.parameters[2]], [0, s.alpha, 0]) elif s.mf == Membership.gaussmf: @@ -191,12 +213,24 @@ elif s.mf == Membership.trapmf: ax.plot(s.parameters, [0, s.alpha, s.alpha, 0])
- def __str__(self): + """ + Return a string representation of the partitioner, the list of fuzzy sets and their parameters + + :return: + """ tmp = self.name + ":\n" for key in self.sets.keys(): tmp += str(self.sets[key])+ "\n" - return tmp + return tmp + + def __len__(self): + """ + Return the number of partitions + + :return: number of partitions + """ + return self.partitions diff --git a/docs/build/html/pyFTS.common.html b/docs/build/html/pyFTS.common.html index 613ee7e..68647fd 100644 --- a/docs/build/html/pyFTS.common.html +++ b/docs/build/html/pyFTS.common.html @@ -1818,6 +1818,7 @@ needed to forecast a single step ahead

  • start – in the multi step forecasting, the index of the data where to start forecasting
  • distributed – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster
  • nodes – a list with the dispy cluster nodes addresses
    • +
  • explain – try to explain, step by step, the one-step-ahead point forecasting result given the input data.
    • diff --git a/docs/build/html/pyFTS.models.ensemble.html b/docs/build/html/pyFTS.models.ensemble.html index 0b82334..cbca2b1 100644 --- a/docs/build/html/pyFTS.models.ensemble.html +++ b/docs/build/html/pyFTS.models.ensemble.html @@ -239,7 +239,7 @@ -Returns:

    a list with the forecasted Probability Distributions

    +Returns:

    a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

    @@ -260,7 +260,7 @@ -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -345,7 +345,7 @@ -Returns:

    a list with the forecasted Probability Distributions

    +Returns:

    a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

    diff --git a/docs/build/html/pyFTS.models.html b/docs/build/html/pyFTS.models.html index 015e18e..702bd01 100644 --- a/docs/build/html/pyFTS.models.html +++ b/docs/build/html/pyFTS.models.html @@ -471,7 +471,7 @@ using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DO
    -generate_lhs_flrg(sample)[source]
    +generate_lhs_flrg(sample, explain=False)[source]
    @@ -575,7 +575,7 @@ In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016 -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -893,7 +893,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, -Returns:

    a list with the forecasted Probability Distributions

    +Returns:

    a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions

    @@ -914,7 +914,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -928,7 +928,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
    -generate_lhs_flrg(sample)[source]
    +generate_lhs_flrg(sample, explain=False)[source]
    diff --git a/docs/build/html/pyFTS.models.nonstationary.html b/docs/build/html/pyFTS.models.nonstationary.html index d0911e3..3c9a9f7 100644 --- a/docs/build/html/pyFTS.models.nonstationary.html +++ b/docs/build/html/pyFTS.models.nonstationary.html @@ -299,7 +299,7 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.

    -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -507,7 +507,7 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.

    -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -605,7 +605,7 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.

    -Returns:

    a list with the forecasted intervals

    +Returns:

    a list with the prediction intervals

    @@ -745,7 +745,7 @@ IEEE Transactions on Fuzzy Systems, v. 16, n. 4, p. 1072-1086, 2008.

    -pyFTS.models.nonstationary.util.plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, save=False, file=None, axes=None)[source]
    +pyFTS.models.nonstationary.util.plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, save=False, file=None, axes=None, fig=None)[source]
    diff --git a/docs/build/html/pyFTS.partitioners.html b/docs/build/html/pyFTS.partitioners.html index 2e29378..1a47cf2 100644 --- a/docs/build/html/pyFTS.partitioners.html +++ b/docs/build/html/pyFTS.partitioners.html @@ -146,12 +146,32 @@
    get_name(counter)[source]
    -
    +

    Find the name of the fuzzy set given its counter id.

    + +++ + + + + + +
    Parameters:counter – The number of the fuzzy set
    Returns:String
    +
    lower_set()[source]
    -
    +

    Return the fuzzy set on lower bound of the universe of discourse.

    + +++ + + + +
    Returns:Fuzzy Set
    +
    @@ -174,15 +194,34 @@
    plot(ax)[source]
    -

    Plot the -:param ax: -:return:

    +

    Plot the partitioning using the Matplotlib axis ax

    + +++ + + + +
    Parameters:ax – Matplotlib axis
    plot_set(ax, s)[source]
    -
    +

    Plot an isolate fuzzy set on Matplotlib axis

    + +++ + + + +
    Parameters:
      +
    • ax – Matplotlib axis
      • +
    • s – Fuzzy Set
      • +
    +
    +
    @@ -205,7 +244,16 @@
    upper_set()[source]
    -
    +

    Return the fuzzy set on upper bound of the universe of discourse.

    + +++ + + + +
    Returns:Fuzzy Set
    +
    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index aeb5a16..e61d3d8 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py index 3783857..3e3134e 100644 --- a/pyFTS/common/fts.py +++ b/pyFTS/common/fts.py @@ -93,6 +93,7 @@ class FTS(object): :keyword start: in the multi step forecasting, the index of the data where to start forecasting :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster :keyword nodes: a list with the dispy cluster nodes addresses + :keyword explain: try to explain, step by step, the one-step-ahead point forecasting result given the input data. :return: a numpy array with the forecasted data """ @@ -342,6 +343,7 @@ class FTS(object): if save: Util.persist_obj(self, file_path) + def clone_parameters(self, model): """ Import the parameters values from other model diff --git a/pyFTS/models/chen.py b/pyFTS/models/chen.py index 23be024..e53be8d 100644 --- a/pyFTS/models/chen.py +++ b/pyFTS/models/chen.py @@ -56,21 +56,35 @@ class ConventionalFTS(fts.FTS): def forecast(self, ndata, **kwargs): - l = len(ndata) + explain = kwargs.get('explain',False) + + l = len(ndata) if not explain else 1 ret = [] for k in np.arange(0, l): - mv = FuzzySet.fuzzyfy_instance(ndata[k], self.sets) + actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets) - actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets) #self.sets[np.argwhere(mv == max(mv))[0, 0]] + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: _flrg = self.flrgs[actual.name] - ret.append(_flrg.get_midpoint(self.sets)) + mp = _flrg.get_midpoint(self.sets) + + ret.append(mp) + + if explain: + print("Rules:\n\n {} \t Midpoint: {} \n".format(str(_flrg), mp)) + + print("Deffuzyfied value: {} \n".format(mp)) return ret diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py index c6b724b..9aa4bbd 100644 --- a/pyFTS/models/hofts.py +++ b/pyFTS/models/hofts.py @@ -62,7 +62,7 @@ class HighOrderFTS(fts.FTS): self.max_lag = self.order self.lags = np.arange(1, self.order+1) - def generate_lhs_flrg(self, sample): + def generate_lhs_flrg(self, sample, explain=False): lags = {} flrgs = [] @@ -71,6 +71,9 @@ class HighOrderFTS(fts.FTS): lhs = FuzzySet.fuzzyfy(sample[o-1], partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut) lags[ct] = lhs + if explain: + print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + root = tree.FLRGTreeNode(None) tree.build_tree_without_order(root, lags, 0) @@ -111,26 +114,49 @@ class HighOrderFTS(fts.FTS): def forecast(self, ndata, **kwargs): + explain = kwargs.get('explain', False) + ret = [] - l = len(ndata) + l = len(ndata) if not explain else self.max_lag + 1 if l < self.max_lag: return ndata for k in np.arange(self.max_lag, l+1): - flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k]) + + if explain: + print("Fuzzyfication \n") + + flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k], explain) + + if explain: + print("Rules:\n") tmp = [] for flrg in flrgs: if flrg.get_key() not in self.flrgs: if len(flrg.LHS) > 0: - tmp.append(self.sets[flrg.LHS[-1]].centroid) + mp = self.sets[flrg.LHS[-1]].centroid + tmp.append(mp) + + if explain: + print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1], + mp)) + else: flrg = self.flrgs[flrg.get_key()] - tmp.append(flrg.get_midpoint(self.sets)) + mp = flrg.get_midpoint(self.sets) + tmp.append(mp) - ret.append(np.nanmean(tmp)) + if explain: + print("\t {} \t Midpoint: {}\n".format(str(flrg), mp)) + + final = np.nanmean(tmp) + ret.append(final) + + if explain: + print("Deffuzyfied value: {} \n".format(final)) return ret diff --git a/pyFTS/models/ifts.py b/pyFTS/models/ifts.py index 99b2c8b..269cc8f 100644 --- a/pyFTS/models/ifts.py +++ b/pyFTS/models/ifts.py @@ -74,7 +74,7 @@ class IntervalFTS(hofts.HighOrderFTS): for flrg in flrgs: if len(flrg.LHS) > 0: - # achar o os bounds de cada FLRG, ponderados pela pertinência + mv = flrg.get_membership(sample, self.sets) up.append(mv * self.get_upper(flrg)) lo.append(mv * self.get_lower(flrg)) diff --git a/pyFTS/models/ismailefendi.py b/pyFTS/models/ismailefendi.py index d88f628..ff0f0d5 100644 --- a/pyFTS/models/ismailefendi.py +++ b/pyFTS/models/ismailefendi.py @@ -57,7 +57,7 @@ class ImprovedWeightedFTS(fts.FTS): if flr.LHS in self.flrgs: self.flrgs[flr.LHS].append_rhs(flr.RHS) else: - self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS); + self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS) self.flrgs[flr.LHS].append_rhs(flr.RHS) def train(self, ndata, **kwargs): @@ -67,7 +67,8 @@ class ImprovedWeightedFTS(fts.FTS): self.generate_flrg(flrs) def forecast(self, ndata, **kwargs): - l = 1 + + explain = kwargs.get('explain', False) if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets @@ -75,7 +76,8 @@ class ImprovedWeightedFTS(fts.FTS): ordered_sets = FuzzySet.set_ordered(self.sets) ndata = np.array(ndata) - l = len(ndata) + + l = len(ndata) if not explain else 1 ret = [] @@ -83,12 +85,27 @@ class ImprovedWeightedFTS(fts.FTS): actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights())) + final = mp.dot(flrg.weights()) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret diff --git a/pyFTS/models/nonstationary/util.py b/pyFTS/models/nonstationary/util.py index 69a29c6..0d99d7f 100644 --- a/pyFTS/models/nonstationary/util.py +++ b/pyFTS/models/nonstationary/util.py @@ -55,7 +55,7 @@ def plot_sets(partitioner, start=0, end=10, step=1, tam=[5, 5], colors=None, def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, - save=False, file=None, axes=None): + save=False, file=None, axes=None, fig=None): range = np.arange(0, len(data), step) ticks = [] if axes is None: @@ -77,11 +77,11 @@ def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None, else: axes.plot([t, t + 1, t], param,c=set.metadata['color']) - ticks.extend(["t+"+str(t),""]) + #ticks.extend(["t+"+str(t),""]) axes.set_ylabel("Universe of Discourse") axes.set_xlabel("Time") - plt.xticks([k for k in range], ticks, rotation='vertical') + #plt.xticks([k for k in range], ticks, rotation='vertical') handles0, labels0 = axes.get_legend_handles_labels() lgd = axes.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1)) diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py index 25630bc..32e0fbc 100644 --- a/pyFTS/models/pwfts.py +++ b/pyFTS/models/pwfts.py @@ -122,7 +122,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): else: self.generate_flrg(data) - def generate_lhs_flrg(self, sample): + def generate_lhs_flrg(self, sample, explain=False): lags = {} flrgs = [] @@ -132,6 +132,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): lags[ct] = lhs + if explain: + print("\t (Lag {}) {} -> {} \n".format(o, sample[o-1], lhs)) + root = tree.FLRGTreeNode(None) tree.build_tree_without_order(root, lags, 0) @@ -226,9 +229,6 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): cond.append(tmp) ret = sum(np.array(cond)) else: - ########################################## - # this may be the problem! TEST IT!!! - ########################################## pi = 1 / len(flrg.LHS) ret = sum(np.array([pi * self.sets[s].membership(x) for s in flrg.LHS])) return ret @@ -272,27 +272,48 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): def point_heuristic(self, sample, **kwargs): - flrgs = self.generate_lhs_flrg(sample) + explain = kwargs.get('explain', False) + + if explain: + print("Fuzzyfication \n") + + flrgs = self.generate_lhs_flrg(sample, explain) mp = [] norms = [] + + if explain: + print("Rules:\n") + for flrg in flrgs: norm = self.flrg_lhs_conditional_probability(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) norm = sum(norms) - if norm == 0: - return 0 - else: - return sum(mp) / norm + final = sum(mp) / norm if norm != 0 else 0 + + if explain: + print("Deffuzyfied value: {} \n".format(final)) + return final def point_expected_value(self, sample, **kwargs): - return self.forecast_distribution(sample)[0].expected_value() + explain = kwargs.get('explain', False) + dist = self.forecast_distribution(sample)[0] + + final = dist.expected_value() + return final def forecast_interval(self, ndata, **kwargs): diff --git a/pyFTS/models/sadaei.py b/pyFTS/models/sadaei.py index 6d3d43a..6bac656 100644 --- a/pyFTS/models/sadaei.py +++ b/pyFTS/models/sadaei.py @@ -71,7 +71,8 @@ class ExponentialyWeightedFTS(fts.FTS): self.generate_flrg(flrs, self.c) def forecast(self, ndata, **kwargs): - l = 1 + + explain = kwargs.get('explain', False) if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets @@ -88,12 +89,27 @@ class ExponentialyWeightedFTS(fts.FTS): actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights())) + final = mp.dot(flrg.weights()) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret diff --git a/pyFTS/models/yu.py b/pyFTS/models/yu.py index aebc804..01187d3 100644 --- a/pyFTS/models/yu.py +++ b/pyFTS/models/yu.py @@ -64,6 +64,8 @@ class WeightedFTS(fts.FTS): def forecast(self, ndata, **kwargs): + explain = kwargs.get('explain', False) + if self.partitioner is not None: ordered_sets = self.partitioner.ordered_sets else: @@ -71,7 +73,7 @@ class WeightedFTS(fts.FTS): ndata = np.array(ndata) - l = len(ndata) + l = len(ndata) if not explain else 1 ret = [] @@ -79,12 +81,27 @@ class WeightedFTS(fts.FTS): actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets) + if explain: + print("Fuzzyfication:\n\n {} -> {} \n\n".format(ndata[k], actual.name)) + if actual.name not in self.flrgs: ret.append(actual.centroid) + + if explain: + print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid)) + else: flrg = self.flrgs[actual.name] mp = flrg.get_midpoints(self.sets) - ret.append(mp.dot(flrg.weights(self.sets))) + final = mp.dot(flrg.weights(self.sets)) + + ret.append(final) + + if explain: + print("Rules:\n\n {} \n\n ".format(str(flrg))) + print("Midpoints: \n\n {}\n\n".format(mp)) + + print("Deffuzyfied value: {} \n".format(final)) return ret diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py index e1ffbb1..6275daa 100644 --- a/pyFTS/partitioners/partitioner.py +++ b/pyFTS/partitioners/partitioner.py @@ -76,19 +76,35 @@ class Partitioner(object): pass def get_name(self, counter): + """ + Find the name of the fuzzy set given its counter id. + + :param counter: The number of the fuzzy set + :return: String + """ return self.prefix + str(counter) if self.setnames is None else self.setnames[counter] def lower_set(self): + """ + Return the fuzzy set on lower bound of the universe of discourse. + + :return: Fuzzy Set + """ return self.sets[self.ordered_sets[0]] def upper_set(self): + """ + Return the fuzzy set on upper bound of the universe of discourse. + + :return: Fuzzy Set + """ return self.sets[self.ordered_sets[-1]] def plot(self, ax): """ - Plot the - :param ax: - :return: + Plot the partitioning using the Matplotlib axis ax + + :param ax: Matplotlib axis """ ax.set_title(self.name) ax.set_ylim([0, 1]) @@ -108,6 +124,12 @@ class Partitioner(object): ax.xaxis.set_ticks(x) def plot_set(self, ax, s): + """ + Plot an isolate fuzzy set on Matplotlib axis + + :param ax: Matplotlib axis + :param s: Fuzzy Set + """ if s.mf == Membership.trimf: ax.plot([s.parameters[0], s.parameters[1], s.parameters[2]], [0, s.alpha, 0]) elif s.mf == Membership.gaussmf: @@ -117,9 +139,21 @@ class Partitioner(object): elif s.mf == Membership.trapmf: ax.plot(s.parameters, [0, s.alpha, s.alpha, 0]) - def __str__(self): + """ + Return a string representation of the partitioner, the list of fuzzy sets and their parameters + + :return: + """ tmp = self.name + ":\n" for key in self.sets.keys(): tmp += str(self.sets[key])+ "\n" return tmp + + def __len__(self): + """ + Return the number of partitions + + :return: number of partitions + """ + return self.partitions diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py index f6db17a..e3e2199 100644 --- a/pyFTS/tests/general.py +++ b/pyFTS/tests/general.py @@ -11,7 +11,7 @@ import pandas as pd from pyFTS.common import Util as cUtil, FuzzySet from pyFTS.partitioners import Grid, Util as pUtil from pyFTS.benchmarks import benchmarks as bchmk -from pyFTS.models import chen +from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts from pyFTS.common import Transformations tdiff = Transformations.Differential(1) @@ -19,15 +19,18 @@ tdiff = Transformations.Differential(1) from pyFTS.data import TAIEX, SP500, NASDAQ dataset = TAIEX.get_data() -''' -partitioner = Grid.GridPartitioner(data=dataset[:800], npart=3, transformation=tdiff) -model = chen.ConventionalFTS(partitioner=partitioner) -model.append_transformation(tdiff) +partitioner = Grid.GridPartitioner(data=dataset[:800], npart=30) #, transformation=tdiff) -model.fit([0, 90.00]) +model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2) +#model.append_transformation(tdiff) + +model.fit(dataset[:800]) print(model) + +ret = model.predict([5000.00, 5200.00, 5400.00], explain=True) + ''' #dataset = SP500.get_data()[11500:16000] #dataset = NASDAQ.get_data() @@ -57,7 +60,7 @@ bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, #steps_ahead=[1,2,4,6,8,10], distributed=False, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'], file="benchmarks.db", dataset="NASDAQ", tag="comparisons") -''' + ''' ''' from pyFTS.partitioners import Grid, Util as pUtil diff --git a/pyFTS/tests/nonstationary.py b/pyFTS/tests/nonstationary.py index 6c71bd9..ea48c37 100644 --- a/pyFTS/tests/nonstationary.py +++ b/pyFTS/tests/nonstationary.py @@ -10,7 +10,7 @@ import pandas as pd from pyFTS.data import TAIEX, NASDAQ, SP500, artificial, mackey_glass -mackey_glass.get_data() +#mackey_glass.get_data() datasets = { "TAIEX": TAIEX.get_data()[:4000], @@ -54,14 +54,31 @@ partitions = {'CMIV': {'BoxCox(0)': 36, 'Differential(1)': 11, 'None': 8}, 'TAIEX': {'BoxCox(0)': 39, 'Differential(1)': 31, 'None': 33}} from pyFTS.models.nonstationary import partitioners as nspart, cvfts, util as nsUtil +''' +#fs = nspart.simplenonstationary_gridpartitioner_builder(data=datasets['SP500'][:300], +# npart=partitions['SP500']['None'], +# transformation=None) +fs = Grid.GridPartitioner(data=datasets['SP500'][:300], + npart=15, + transformation=None) +fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15, 5]) + +fs.plot(axes) + +from pyFTS.common import Util + +Util.show_and_save_image(fig, "fig2.png", True) + +#nsUtil.plot_sets(fs) +''' def model_details(ds, tf, train_split, test_split): data = datasets[ds] train = data[:train_split] test = data[train_split:test_split] transformation = transformations[tf] - fs = nspart.simplenonstationary_gridpartitioner_builder(data=train, npart=partitions[ds][tf], + fs = nspart.simplenonstationary_gridpartitioner_builder(data=train, npart=15, #partitions[ds][tf], transformation=transformation) model = nsfts.NonStationaryFTS(partitioner=fs) model.fit(train) @@ -69,21 +86,24 @@ def model_details(ds, tf, train_split, test_split): forecasts = model.predict(test) residuals = np.array(test[1:]) - np.array(forecasts[:-1]) - fig, axes = plt.subplots(nrows=2, ncols=1, figsize=[15, 10]) + fig, axes = plt.subplots(nrows=3, ncols=1, figsize=[15, 10]) axes[0].plot(test[1:], label="Original") axes[0].plot(forecasts[:-1], label="Forecasts") + axes[0].set_ylabel("Univ. of Discourse") - axes[1].set_title("Residuals") + #axes[1].set_title("Residuals") axes[1].plot(residuals) + axes[1].set_ylabel("Error") handles0, labels0 = axes[0].get_legend_handles_labels() lgd = axes[0].legend(handles0, labels0, loc=2) - nsUtil.plot_sets_conditional(model, test, step=10, size=[12, 5]) - -model_details('NASDAQ','None',200,2000) - + nsUtil.plot_sets_conditional(model, test, step=10, size=[10, 7], + save=True,file="fig.png", axes=axes[2], fig=fig) +model_details('SP500','None',200,400) +#''' +print("ts") ''' tag = 'benchmarks' diff --git a/setup.py b/setup.py index 4fba10d..0accc15 100644 --- a/setup.py +++ b/setup.py @@ -5,16 +5,12 @@ setup( 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.models.multivariate'], - #package_dir={} - #package_data={'pyFTS.data': ['*.csv','*.csv.bz2']}, - #data_files=[('data', ['pyFTS/data/Enrollments.csv', 'pyFTS/data/AirPassengers.csv'])], - #include_package_data=True, - version='1.2.2', + version='1.2.3', description='Fuzzy Time Series for Python', author='Petronio Candido L. e Silva', author_email='petronio.candido@gmail.com', - url='https://github.com/petroniocandido/pyFTS', - download_url='https://github.com/petroniocandido/pyFTS/archive/pkg1.2.3.tar.gz', + url='https://pyfts.github.io/pyFTS/', + download_url='https://github.com/PYFTS/pyFTS/archive/docSphinx.tar.gz', keywords=['forecasting', 'fuzzy time series', 'fuzzy', 'time series forecasting'], classifiers=[ 'Programming Language :: Python :: 3', @@ -22,4 +18,5 @@ setup( 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], + install_requires=['dill'], )