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[docs]def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, intervals = True, - save=False, file=None, tam=[20, 5], resolution=None, - cmap='Blues', linewidth=1.5): - """ - Plot the forecasts of several one step ahead models, by point or by interval - - :param original: Original time series data (list) - :param models: List of models to compare - :param colors: List of models colors - :param distributions: True to plot a distribution - :param time_from: index of data poit to start the ahead forecasting - :param time_to: number of steps ahead to forecast - :param interpol: Fill space between distribution plots - :param save: Save the picture on file - :param file: Filename to save the picture - :param tam: Size of the picture - :param resolution: - :param cmap: Color map to be used on distribution plot - :param option: Distribution type to be passed for models - :return: - """ - fig = plt.figure(figsize=tam) - ax = fig.add_subplot(111) - - cm = plt.get_cmap(cmap) - cNorm = pltcolors.Normalize(vmin=0, vmax=1) - scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) - - if resolution is None: resolution = (max(original) - min(original)) / 100 - - mi = [] - ma = [] - - for count, fts in enumerate(models, start=0): - if fts.has_probability_forecasting and distributions[count]: - density = fts.forecast_ahead_distribution(original[time_from - fts.order:time_from], time_to, - resolution=resolution) - - #plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to) - plot_density_rectange(ax, cm, density, fig, resolution, time_from, time_to) - - if fts.has_interval_forecasting and intervals: - forecasts = fts.forecast_ahead_interval(original[time_from - fts.order:time_from], time_to) - lower = [kk[0] for kk in forecasts] - upper = [kk[1] for kk in forecasts] - mi.append(min(lower)) - ma.append(max(upper)) - for k in np.arange(0, time_from - fts.order): - lower.insert(0, None) - upper.insert(0, None) - ax.plot(lower, color=colors[count], label=fts.shortname, linewidth=linewidth) - ax.plot(upper, color=colors[count], linewidth=linewidth*1.5) - - ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5) - handles0, labels0 = ax.get_legend_handles_labels() - if True in distributions: - lgd = ax.legend(handles0, labels0, loc=2) - else: - lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1)) - _mi = min(mi) - if _mi < 0: - _mi *= 1.1 - else: - _mi *= 0.9 - _ma = max(ma) - if _ma < 0: - _ma *= 0.9 - else: - _ma *= 1.1 - - ax.set_ylim([_mi, _ma]) - ax.set_ylabel('F(T)') - ax.set_xlabel('T') - ax.set_xlim([0, len(original)]) - - cUtil.show_and_save_image(fig, file, save, lgd=lgd)
-
[docs]def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to): - """ - Auxiliar function to plot_compared_intervals_ahead - """ - from matplotlib.patches import Rectangle - from matplotlib.collections import PatchCollection - patches = [] - colors = [] - for x in density.index: - for y in density.columns: - s = Rectangle((time_from + x, y), 1, resolution, fill=True, lw = 0) - patches.append(s) - colors.append(density[y][x]*5) - pc = PatchCollection(patches=patches, match_original=True) - pc.set_clim([0, 1]) - pc.set_cmap(cmap) - pc.set_array(np.array(colors)) - ax.add_collection(pc) - cb = fig.colorbar(pc, ax=ax) - cb.set_label('Density')
-
[docs]def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None): - from matplotlib.patches import Rectangle - from matplotlib.collections import PatchCollection - patches = [] - colors = [] - for ct, dt in enumerate(probabilitydist): - disp = 0.0 - if reference_data is not None: - disp = reference_data[time_from+ct] - - for y in dt.bins: - s = Rectangle((time_from+ct, y+disp), 1, dt.resolution, fill=True, lw = 0) - patches.append(s) - colors.append(dt.density(y)) - scale = Transformations.Scale() - colors = scale.apply(colors) - pc = PatchCollection(patches=patches, match_original=True) - pc.set_clim([0, 1]) - pc.set_cmap(cmap) - pc.set_array(np.array(colors)) - ax.add_collection(pc) - cb = fig.colorbar(pc, ax=ax) - cb.set_label('Density')
- - -
[docs]def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1): - lower = [kk[0] for kk in intervals] - upper = [kk[1] for kk in intervals] - mi = min(lower) * 0.95 - ma = max(upper) * 1.05 - for k in np.arange(0, order): - lower.insert(0, None) - upper.insert(0, None) - if typeonlegend: label += " (Interval)" - axis.plot(lower, color=color, label=label, ls=ls,linewidth=linewidth) - axis.plot(upper, color=color, ls=ls,linewidth=linewidth) - return [mi, ma]
-
[docs]def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1): mi = min(points) * 0.95 @@ -954,7 +812,7 @@ ls = "-" else: ls = "--" - tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend, + tmpmi, tmpma = Util.plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend, color=colors[count], ls=ls, linewidth=linewidth) mi.append(tmpmi) ma.append(tmpma) @@ -974,15 +832,7 @@ #Util.show_and_save_image(fig, file, save, lgd=legends) -
[docs]def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]): - fig = plt.figure(figsize=tam) - ax = fig.add_subplot(111) - for k,m in enumerate(pmfs,start=0): - m.plot(ax, color=lcolors[k]) - - handles0, labels0 = ax.get_legend_handles_labels() - ax.legend(handles0, labels0)
diff --git a/docs/build/html/_modules/pyFTS/common/Util.html b/docs/build/html/_modules/pyFTS/common/Util.html index c506c4d..b58221c 100644 --- a/docs/build/html/_modules/pyFTS/common/Util.html +++ b/docs/build/html/_modules/pyFTS/common/Util.html @@ -80,9 +80,199 @@ import matplotlib.pyplot as plt import dill import numpy as np +import matplotlib.cm as cmx +import matplotlib.colors as pltcolors +from pyFTS.probabilistic import ProbabilityDistribution +from pyFTS.common import Transformations + + + + +
[docs]def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, intervals = True, + save=False, file=None, tam=[20, 5], resolution=None, + cmap='Blues', linewidth=1.5): + """ + Plot the forecasts of several one step ahead models, by point or by interval + + :param original: Original time series data (list) + :param models: List of models to compare + :param colors: List of models colors + :param distributions: True to plot a distribution + :param time_from: index of data poit to start the ahead forecasting + :param time_to: number of steps ahead to forecast + :param interpol: Fill space between distribution plots + :param save: Save the picture on file + :param file: Filename to save the picture + :param tam: Size of the picture + :param resolution: + :param cmap: Color map to be used on distribution plot + :param option: Distribution type to be passed for models + :return: + """ + fig = plt.figure(figsize=tam) + ax = fig.add_subplot(111) + + cm = plt.get_cmap(cmap) + cNorm = pltcolors.Normalize(vmin=0, vmax=1) + scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) + + if resolution is None: resolution = (max(original) - min(original)) / 100 + + mi = [] + ma = [] + + for count, fts in enumerate(models, start=0): + if fts.has_probability_forecasting and distributions[count]: + density = fts.forecast_ahead_distribution(original[time_from - fts.order:time_from], time_to, + resolution=resolution) + + #plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to) + plot_density_rectange(ax, cm, density, fig, resolution, time_from, time_to) + + if fts.has_interval_forecasting and intervals: + forecasts = fts.forecast_ahead_interval(original[time_from - fts.order:time_from], time_to) + lower = [kk[0] for kk in forecasts] + upper = [kk[1] for kk in forecasts] + mi.append(min(lower)) + ma.append(max(upper)) + for k in np.arange(0, time_from - fts.order): + lower.insert(0, None) + upper.insert(0, None) + ax.plot(lower, color=colors[count], label=fts.shortname, linewidth=linewidth) + ax.plot(upper, color=colors[count], linewidth=linewidth*1.5) + + ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5) + handles0, labels0 = ax.get_legend_handles_labels() + if True in distributions: + lgd = ax.legend(handles0, labels0, loc=2) + else: + lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1)) + _mi = min(mi) + if _mi < 0: + _mi *= 1.1 + else: + _mi *= 0.9 + _ma = max(ma) + if _ma < 0: + _ma *= 0.9 + else: + _ma *= 1.1 + + ax.set_ylim([_mi, _ma]) + ax.set_ylabel('F(T)') + ax.set_xlabel('T') + ax.set_xlim([0, len(original)]) + + show_and_save_image(fig, file, save, lgd=lgd)
+ + + +
[docs]def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to): + """ + Auxiliar function to plot_compared_intervals_ahead + """ + from matplotlib.patches import Rectangle + from matplotlib.collections import PatchCollection + patches = [] + colors = [] + for x in density.index: + for y in density.columns: + s = Rectangle((time_from + x, y), 1, resolution, fill=True, lw = 0) + patches.append(s) + colors.append(density[y][x]*5) + pc = PatchCollection(patches=patches, match_original=True) + pc.set_clim([0, 1]) + pc.set_cmap(cmap) + pc.set_array(np.array(colors)) + ax.add_collection(pc) + cb = fig.colorbar(pc, ax=ax) + cb.set_label('Density')
+ + +
[docs]def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]): + fig = plt.figure(figsize=tam) + ax = fig.add_subplot(111) + + for k,m in enumerate(pmfs,start=0): + m.plot(ax, color=lcolors[k]) + + handles0, labels0 = ax.get_legend_handles_labels() + ax.legend(handles0, labels0)
+ +
[docs]def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None): + ''' + Plot forecasted ProbabilityDistribution objects on a matplotlib axis + + :param ax: matplotlib axis + :param cmap: matplotlib colormap name + :param probabilitydist: list of ProbabilityDistribution objects + :param fig: matplotlib figure + :param time_from: starting time (on x axis) to begin the plots + :param reference_data: + :return: + ''' + from matplotlib.patches import Rectangle + from matplotlib.collections import PatchCollection + patches = [] + colors = [] + for ct, dt in enumerate(probabilitydist): + disp = 0.0 + if reference_data is not None: + disp = reference_data[time_from+ct] + + for y in dt.bins: + s = Rectangle((time_from+ct, y+disp), 1, dt.resolution, fill=True, lw = 0) + patches.append(s) + colors.append(dt.density(y)) + scale = Transformations.Scale() + colors = scale.apply(colors) + pc = PatchCollection(patches=patches, match_original=True) + pc.set_clim([0, 1]) + pc.set_cmap(cmap) + pc.set_array(np.array(colors)) + ax.add_collection(pc) + cb = fig.colorbar(pc, ax=ax) + cb.set_label('Density')
+ + +
[docs]def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1): + ''' + Plot forecasted intervals on matplotlib + + :param axis: matplotlib axis + :param intervals: list of forecasted intervals + :param order: order of the model that create the forecasts + :param label: figure label + :param color: matplotlib color name + :param typeonlegend: + :param ls: matplotlib line style + :param linewidth: matplotlib width + :return: + ''' + lower = [kk[0] for kk in intervals] + upper = [kk[1] for kk in intervals] + mi = min(lower) * 0.95 + ma = max(upper) * 1.05 + for k in np.arange(0, order): + lower.insert(0, None) + upper.insert(0, None) + if typeonlegend: label += " (Interval)" + axis.plot(lower, color=color, label=label, ls=ls,linewidth=linewidth) + axis.plot(upper, color=color, ls=ls,linewidth=linewidth) + return [mi, ma]
[docs]def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1): + ''' + Plot the FLRG rules of a FTS model on a matplotlib axis + + :param model: FTS model + :param size: figure size + :param axis: matplotlib axis + :param rules_by_axis: number of rules plotted by column + :param columns: number of columns + :return: + ''' if axis is None and rules_by_axis is None: rows = 1 elif axis is None and rules_by_axis is not None: diff --git a/docs/build/html/_modules/pyFTS/data/artificial.html b/docs/build/html/_modules/pyFTS/data/artificial.html index 9f8755f..f0fa2f3 100644 --- a/docs/build/html/_modules/pyFTS/data/artificial.html +++ b/docs/build/html/_modules/pyFTS/data/artificial.html @@ -373,7 +373,7 @@
[docs]def random_walk(n=500, type='gaussian'): """ Simple random walk - + :param n: number of samples :param type: 'gaussian' or 'uniform' :return: diff --git a/docs/build/html/_modules/pyFTS/models/ensemble/ensemble.html b/docs/build/html/_modules/pyFTS/models/ensemble/ensemble.html index ccd49ac..1f7587e 100644 --- a/docs/build/html/_modules/pyFTS/models/ensemble/ensemble.html +++ b/docs/build/html/_modules/pyFTS/models/ensemble/ensemble.html @@ -117,10 +117,9 @@ self.alpha = kwargs.get("alpha", 0.05) """The quantiles """ self.point_method = kwargs.get('point_method', 'mean') - """The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile""" + """The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile, exponential""" self.interval_method = kwargs.get('interval_method', 'quantile') """The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal""" - self.order = 1
[docs] def append_model(self, model): """ @@ -137,8 +136,12 @@ self.is_multivariate = True if model.has_seasonality: - self.has_seasonality = True
+ self.has_seasonality = True + if model.original_min < self.original_min: + self.original_min = model.original_min + elif model.original_max > self.original_max: + self.original_max = model.original_max
[docs] def train(self, data, **kwargs): pass
@@ -154,9 +157,9 @@ data = self.indexer.get_data(data) sample = data[-model.order:] - forecast = model.forecast(sample) + forecast = model.predict(sample) if isinstance(forecast, (list,np.ndarray)) and len(forecast) > 0: - forecast = int(forecast[-1]) + forecast = forecast[-1] elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0: forecast = np.nan if isinstance(forecast, list): @@ -173,6 +176,13 @@ elif self.point_method == 'quantile': alpha = kwargs.get("alpha",0.05) ret = np.percentile(forecasts, alpha*100) + elif self.point_method == 'exponential': + l = len(self.models) + if l == 1: + return forecasts[0] + w = np.array([np.exp(-(l - k)) for k in range(l)]) + w = w / np.nansum(w) + ret = np.nansum([w[k] * forecasts[k] for k in range(l)]) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/hofts.html b/docs/build/html/_modules/pyFTS/models/hofts.html index cc14f55..28585bb 100644 --- a/docs/build/html/_modules/pyFTS/models/hofts.html +++ b/docs/build/html/_modules/pyFTS/models/hofts.html @@ -140,8 +140,23 @@ return self.w
[docs] def get_midpoint(self, sets): - mp = np.array([sets[c].centroid for c in self.RHS.keys()]) - return mp.dot(self.weights())
+ if self.midpoint is None: + mp = np.array([sets[c].centroid for c in self.RHS.keys()]) + self.midpoint = mp.dot(self.weights()) + + return self.midpoint + +
[docs] def get_lower(self, sets): + if self.lower is None: + lw = np.array([sets[s].lower for s in self.RHS.keys()]) + self.lower = lw.dot(self.weights()) + return self.lower
+ +
[docs] def get_upper(self, sets): + if self.upper is None: + up = np.array([sets[s].upper for s in self.RHS.keys()]) + self.upper = up.dot(self.weights()) + return self.upper
def __str__(self): _str = "" diff --git a/docs/build/html/_modules/pyFTS/models/ifts.html b/docs/build/html/_modules/pyFTS/models/ifts.html index 90b8359..32ce37c 100644 --- a/docs/build/html/_modules/pyFTS/models/ifts.html +++ b/docs/build/html/_modules/pyFTS/models/ifts.html @@ -87,7 +87,7 @@ from pyFTS.models import hofts -
[docs]class IntervalFTS(hofts.HighOrderFTS): +
[docs]class IntervalFTS(hofts.WeightedHighOrderFTS): """ High Order Interval Fuzzy Time Series """ @@ -117,9 +117,9 @@ if len(flrg.LHS) > 0: if flrg.get_key() in self.flrgs: tmp = self.flrgs[flrg.get_key()] - ret = tmp.get_lower(self.sets) + ret = tmp.get_lower(self.partitioner.sets) else: - ret = self.sets[flrg.LHS[-1]].lower + ret = self.partitioner.sets[flrg.LHS[-1]].lower return ret
[docs] def get_sequence_membership(self, data, fuzzySets): diff --git a/docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html b/docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html index 2d34de9..30a7ccd 100644 --- a/docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html +++ b/docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html @@ -112,9 +112,14 @@ self.batch_size = kwargs.get('batch_size', 10) """The batch interval between each retraining""" + self.num_models = kwargs.get('num_models', 5) + """The number of models to hold in the ensemble""" + + self.point_method = kwargs.get('point_method', 'exponential') + self.is_high_order = True self.uod_clip = False - #self.max_lag = self.window_length + self.max_lag + self.max_lag = self.window_length + self.order
[docs] def train(self, data, **kwargs): @@ -123,16 +128,9 @@ if model.is_high_order: model = self.fts_method(partitioner=partitioner, order=self.order, **self.fts_params) model.fit(data, **kwargs) - if len(self.models) > 0: - self.models.pop(0) - self.models.append(model)
- - def _point_smoothing(self, forecasts): - l = len(self.models) - - ret = np.nansum([np.exp(-(l-k)) * forecasts[k] for k in range(l)]) - - return ret + self.append_model(model) + if len(self.models) > self.num_models: + self.models.pop(0)
[docs] def forecast(self, data, **kwargs): l = len(data) @@ -143,18 +141,21 @@ for k in np.arange(self.max_lag, l): - data_window.append(data[k - self.max_lag]) + k2 = k - self.max_lag - if k >= self.window_length: + data_window.append(data[k2]) + + if k2 >= self.window_length: data_window.pop(0) - if k % self.batch_size == 0 and k >= self.window_length: + if k % self.batch_size == 0 and k2 >= self.window_length: self.train(data_window, **kwargs) - sample = data[k - self.max_lag: k] - tmp = self.get_models_forecasts(sample) - point = self._point_smoothing(tmp) - ret.append(point) + if len(self.models) > 0: + sample = data[k2: k] + tmp = self.get_models_forecasts(sample) + point = self.get_point(tmp) + ret.append(point) return ret
diff --git a/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html b/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html index 529e7d9..799339e 100644 --- a/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html +++ b/docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html @@ -111,6 +111,7 @@ self.is_high_order = True self.uod_clip = False self.max_lag = self.window_length + self.order + self.is_wrapper = True
[docs] def train(self, data, **kwargs): self.partitioner = self.partitioner_method(data=data, **self.partitioner_params) diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/flrg.html b/docs/build/html/_modules/pyFTS/models/multivariate/flrg.html index 7767f05..0c8292c 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/flrg.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/flrg.html @@ -96,7 +96,6 @@ self.LHS[var] = [] self.LHS[var].append(fset)
-
[docs] def append_rhs(self, fset, **kwargs): self.RHS.add(fset)
@@ -108,6 +107,18 @@ return np.nanmin(mvs) +
[docs] def get_lower(self, sets): + if self.lower is None: + self.lower = min([sets[rhs].lower for rhs in self.RHS]) + + return self.lower
+ +
[docs] def get_upper(self, sets): + if self.upper is None: + self.upper = max([sets[rhs].upper for rhs in self.RHS]) + + return self.upper
+ def __str__(self): _str = "" for k in self.RHS: diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html index bb11981..4ed62a7 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html @@ -219,8 +219,9 @@ mvs.append(0.) mps.append(0.) else: - mvs.append(self.flrgs[flrg.get_key()].get_membership(data_point, self.explanatory_variables)) - mps.append(self.flrgs[flrg.get_key()].get_midpoint(self.target_variable.partitioner.sets)) + _flrg = self.flrgs[flrg.get_key()] + mvs.append(_flrg.get_membership(data_point, self.explanatory_variables)) + mps.append(_flrg.get_midpoint(self.target_variable.partitioner.sets)) mv = np.array(mvs) mp = np.array(mps) @@ -275,6 +276,49 @@ return ret +
[docs] def forecast_interval(self, data, **kwargs): + ret = [] + ndata = self.apply_transformations(data) + c = 0 + for index, row in ndata.iterrows() if isinstance(ndata, pd.DataFrame) else enumerate(ndata): + data_point = self.format_data(row) + flrs = self.generate_lhs_flrs(data_point) + mvs = [] + ups = [] + los = [] + for flr in flrs: + flrg = mvflrg.FLRG(lhs=flr.LHS) + if flrg.get_key() not in self.flrgs: + #Naïve approach is applied when no rules were found + if self.target_variable.name in flrg.LHS: + fs = flrg.LHS[self.target_variable.name] + fset = self.target_variable.partitioner.sets[fs] + up = fset.upper + lo = fset.lower + mv = fset.membership(data_point[self.target_variable.name]) + mvs.append(mv) + ups.append(up) + los.append(lo) + else: + mvs.append(0.) + ups.append(0.) + los.append(0.) + else: + _flrg = self.flrgs[flrg.get_key()] + mvs.append(_flrg.get_membership(data_point, self.explanatory_variables)) + ups.append(_flrg.get_upper(self.target_variable.partitioner.sets)) + los.append(_flrg.get_lower(self.target_variable.partitioner.sets)) + + mv = np.array(mvs) + up = np.dot(mv, np.array(ups).T) / np.nansum(mv) + lo = np.dot(mv, np.array(los).T) / np.nansum(mv) + + ret.append([lo, up]) + + ret = self.target_variable.apply_inverse_transformations(ret, + params=data[self.target_variable.data_label].values) + return ret
+
[docs] def clone_parameters(self, model): super(MVFTS, self).clone_parameters(model) diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html index 753ea68..8c28d39 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/wmvfts.html @@ -107,8 +107,23 @@ return self.w
[docs] def get_midpoint(self, sets): - mp = np.array([sets[c].centroid for c in self.RHS.keys()]) - return mp.dot(self.weights())
+ if self.midpoint is None: + mp = np.array([sets[c].centroid for c in self.RHS.keys()]) + self.midpoint = mp.dot(self.weights()) + + return self.midpoint + +
[docs] def get_lower(self, sets): + if self.lower is None: + lw = np.array([sets[s].lower for s in self.RHS.keys()]) + self.lower = lw.dot(self.weights()) + return self.lower
+ +
[docs] def get_upper(self, sets): + if self.upper is None: + up = np.array([sets[s].upper for s in self.RHS.keys()]) + self.upper = up.dot(self.weights()) + return self.upper
def __str__(self): diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 4470ab1..9cb0b93 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -354,8 +354,6 @@
  • (pyFTS.models.multivariate.mvfts.MVFTS method)
    • -
  • cluster_method() (in module pyFTS.hyperparam.GridSearch) -
  • ClusteredMVFTS (class in pyFTS.models.multivariate.cmvfts)
  • CMeansPartitioner (class in pyFTS.partitioners.CMeans) @@ -445,8 +443,6 @@
  • density() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)
  • detail (pyFTS.common.fts.FTS attribute) -
    • -
  • dict_individual() (in module pyFTS.hyperparam.GridSearch)
  • Differential (class in pyFTS.common.Transformations)
    • @@ -480,12 +476,10 @@
  • enumerate2() (in module pyFTS.common.Util)
    • -
  • execute() (in module pyFTS.hyperparam.GridSearch) +
  • expected_value() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)
    • @@ -183,28 +183,8 @@ Value: the measure value

    -
    -

    pyFTS.hyperparam.GridSearch module¶

    -
    -
    -pyFTS.hyperparam.GridSearch.cluster_method(individual, train, test)[source]¶
    -
    - -
    -
    -pyFTS.hyperparam.GridSearch.dict_individual(mf, partitioner, partitions, order, lags, alpha_cut)[source]¶
    -
    - -
    -
    -pyFTS.hyperparam.GridSearch.execute(hyperparams, datasetname, train, test, **kwargs)[source]¶
    -
    - -
    -
    -pyFTS.hyperparam.GridSearch.process_jobs(jobs, datasetname, conn)[source]¶
    -
    - +
    +

    pyFTS.hyperparam.GridSearch module¶

    pyFTS.hyperparam.Evolutionary module¶

    diff --git a/docs/build/html/pyFTS.models.ensemble.html b/docs/build/html/pyFTS.models.ensemble.html index 9abd414..8f0ebac 100644 --- a/docs/build/html/pyFTS.models.ensemble.html +++ b/docs/build/html/pyFTS.models.ensemble.html @@ -319,7 +319,7 @@ XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Bra
    point_method = None¶
    -

    The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile

    +

    The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile, exponential

    diff --git a/docs/build/html/pyFTS.models.html b/docs/build/html/pyFTS.models.html index ec67bc3..c0df496 100644 --- a/docs/build/html/pyFTS.models.html +++ b/docs/build/html/pyFTS.models.html @@ -528,6 +528,22 @@ using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DO append_rhs(fset, **kwargs)[source]¶
    +
    +
    +get_lower(sets)[source]¶
    +

    Returns the lower bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:lower bound value
    +
    +
    get_midpoint(sets)[source]¶
    @@ -544,6 +560,22 @@ using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DO
    +
    +
    +get_upper(sets)[source]¶
    +

    Returns the upper bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:upper bound value
    +
    +
    weights()[source]¶
    @@ -628,7 +660,7 @@ In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016
    class pyFTS.models.ifts.IntervalFTS(**kwargs)[source]¶
    -

    Bases: pyFTS.models.hofts.HighOrderFTS

    +

    Bases: pyFTS.models.hofts.WeightedHighOrderFTS

    High Order Interval Fuzzy Time Series

    diff --git a/docs/build/html/pyFTS.models.incremental.html b/docs/build/html/pyFTS.models.incremental.html index 442b491..b937e0c 100644 --- a/docs/build/html/pyFTS.models.incremental.html +++ b/docs/build/html/pyFTS.models.incremental.html @@ -265,6 +265,12 @@

    The FTS method specific parameters

    +
    +
    +num_models = None¶
    +

    The number of models to hold in the ensemble

    +
    +
    partitioner_method = None¶
    diff --git a/docs/build/html/pyFTS.models.multivariate.html b/docs/build/html/pyFTS.models.multivariate.html index 58ebfdd..7e3e818 100644 --- a/docs/build/html/pyFTS.models.multivariate.html +++ b/docs/build/html/pyFTS.models.multivariate.html @@ -294,6 +294,22 @@ transformations and partitioners.

    append_rhs(fset, **kwargs)[source]¶
    +
    +
    +get_lower(sets)[source]¶
    +

    Returns the lower bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:lower bound value
    +
    +
    get_membership(data, variables)[source]¶
    @@ -315,6 +331,22 @@ transformations and partitioners.

    +
    +
    +get_upper(sets)[source]¶
    +

    Returns the upper bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:upper bound value
    +
    +
    set_lhs(var, fset)[source]¶
    @@ -426,6 +458,27 @@ transformations and partitioners.

    +
    +
    +forecast_interval(data, **kwargs)[source]¶
    +

    Interval forecast one step ahead

    + +++ + + + + + +
    Parameters:
      +
    • data – time series data with the minimal length equal to the max_lag of the model
      • +
    • kwargs – model specific parameters
      • +
    +
    Returns:

    a list with the prediction intervals

    +
    +
    +
    format_data(data)[source]¶
    @@ -487,6 +540,22 @@ transformations and partitioners.

    append_rhs(fset, **kwargs)[source]¶
    +
    +
    +get_lower(sets)[source]¶
    +

    Returns the lower bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:lower bound value
    +
    +
    get_midpoint(sets)[source]¶
    @@ -503,6 +572,22 @@ transformations and partitioners.

    +
    +
    +get_upper(sets)[source]¶
    +

    Returns the upper bound value for the RHS fuzzy sets

    + +++ + + + + + +
    Parameters:sets – fuzzy sets
    Returns:upper bound value
    +
    +
    weights()[source]¶
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\ No newline at end of file diff --git a/pyFTS/common/Util.py b/pyFTS/common/Util.py index 12524d0..842764f 100644 --- a/pyFTS/common/Util.py +++ b/pyFTS/common/Util.py @@ -12,6 +12,8 @@ from pyFTS.probabilistic import ProbabilityDistribution from pyFTS.common import Transformations + + def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, intervals = True, save=False, file=None, tam=[20, 5], resolution=None, cmap='Blues', linewidth=1.5): @@ -124,6 +126,17 @@ def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]): ax.legend(handles0, labels0) def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None): + ''' + Plot forecasted ProbabilityDistribution objects on a matplotlib axis + + :param ax: matplotlib axis + :param cmap: matplotlib colormap name + :param probabilitydist: list of ProbabilityDistribution objects + :param fig: matplotlib figure + :param time_from: starting time (on x axis) to begin the plots + :param reference_data: + :return: + ''' from matplotlib.patches import Rectangle from matplotlib.collections import PatchCollection patches = [] @@ -149,6 +162,19 @@ def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data= def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1): + ''' + Plot forecasted intervals on matplotlib + + :param axis: matplotlib axis + :param intervals: list of forecasted intervals + :param order: order of the model that create the forecasts + :param label: figure label + :param color: matplotlib color name + :param typeonlegend: + :param ls: matplotlib line style + :param linewidth: matplotlib width + :return: + ''' lower = [kk[0] for kk in intervals] upper = [kk[1] for kk in intervals] mi = min(lower) * 0.95 @@ -163,6 +189,16 @@ def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1): + ''' + Plot the FLRG rules of a FTS model on a matplotlib axis + + :param model: FTS model + :param size: figure size + :param axis: matplotlib axis + :param rules_by_axis: number of rules plotted by column + :param columns: number of columns + :return: + ''' if axis is None and rules_by_axis is None: rows = 1 elif axis is None and rules_by_axis is not None: