Moving plot_interval and plot_density functions from benchmarks to common.Util
This commit is contained in:
Petrônio Cândido
parent
2e0e86b747
commit
7ee6c444b1
@ -10,13 +10,11 @@ from copy import deepcopy
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import traceback
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import matplotlib as plt
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import matplotlib.cm as cmx
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import matplotlib.colors as pltcolors
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import matplotlib.pyplot as plt
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D
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from pyFTS.probabilistic import ProbabilityDistribution
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from pyFTS.common import Transformations
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from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
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@ -26,12 +24,6 @@ from pyFTS.benchmarks import Util as bUtil
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from pyFTS.common import Util as cUtil
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# from sklearn.cross_validation import KFold
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from pyFTS.partitioners import Grid
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from matplotlib import rc
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#rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
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## for Palatino and other serif fonts use:
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#rc('font',**{'family':'serif','serif':['Palatino']})
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#rc('text', usetex=True)
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colors = ['grey', 'darkgrey', 'rosybrown', 'maroon', 'red','orange', 'gold', 'yellow', 'olive', 'green',
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'darkgreen', 'cyan', 'lightblue','blue', 'darkblue', 'purple', 'darkviolet' ]
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@ -681,145 +673,11 @@ def print_distribution_statistics(original, models, steps, resolution):
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def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, intervals = True,
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save=False, file=None, tam=[20, 5], resolution=None,
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cmap='Blues', linewidth=1.5):
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"""
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Plot the forecasts of several one step ahead models, by point or by interval
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:param original: Original time series data (list)
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:param models: List of models to compare
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:param colors: List of models colors
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:param distributions: True to plot a distribution
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:param time_from: index of data poit to start the ahead forecasting
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:param time_to: number of steps ahead to forecast
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:param interpol: Fill space between distribution plots
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:param save: Save the picture on file
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:param file: Filename to save the picture
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:param tam: Size of the picture
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:param resolution:
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:param cmap: Color map to be used on distribution plot
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:param option: Distribution type to be passed for models
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:return:
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"""
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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cm = plt.get_cmap(cmap)
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cNorm = pltcolors.Normalize(vmin=0, vmax=1)
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scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
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if resolution is None: resolution = (max(original) - min(original)) / 100
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mi = []
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ma = []
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for count, fts in enumerate(models, start=0):
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if fts.has_probability_forecasting and distributions[count]:
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density = fts.forecast_ahead_distribution(original[time_from - fts.order:time_from], time_to,
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resolution=resolution)
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#plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to)
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plot_density_rectange(ax, cm, density, fig, resolution, time_from, time_to)
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if fts.has_interval_forecasting and intervals:
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forecasts = fts.forecast_ahead_interval(original[time_from - fts.order:time_from], time_to)
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lower = [kk[0] for kk in forecasts]
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upper = [kk[1] for kk in forecasts]
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mi.append(min(lower))
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ma.append(max(upper))
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for k in np.arange(0, time_from - fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname, linewidth=linewidth)
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ax.plot(upper, color=colors[count], linewidth=linewidth*1.5)
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ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
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handles0, labels0 = ax.get_legend_handles_labels()
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if True in distributions:
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lgd = ax.legend(handles0, labels0, loc=2)
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else:
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lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
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_mi = min(mi)
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if _mi < 0:
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_mi *= 1.1
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else:
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_mi *= 0.9
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_ma = max(ma)
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if _ma < 0:
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_ma *= 0.9
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else:
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_ma *= 1.1
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ax.set_ylim([_mi, _ma])
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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cUtil.show_and_save_image(fig, file, save, lgd=lgd)
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def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to):
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"""
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Auxiliar function to plot_compared_intervals_ahead
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"""
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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patches = []
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colors = []
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for x in density.index:
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for y in density.columns:
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s = Rectangle((time_from + x, y), 1, resolution, fill=True, lw = 0)
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patches.append(s)
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colors.append(density[y][x]*5)
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pc = PatchCollection(patches=patches, match_original=True)
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pc.set_clim([0, 1])
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pc.set_cmap(cmap)
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pc.set_array(np.array(colors))
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ax.add_collection(pc)
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cb = fig.colorbar(pc, ax=ax)
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cb.set_label('Density')
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def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None):
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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patches = []
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colors = []
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for ct, dt in enumerate(probabilitydist):
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disp = 0.0
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if reference_data is not None:
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disp = reference_data[time_from+ct]
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for y in dt.bins:
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s = Rectangle((time_from+ct, y+disp), 1, dt.resolution, fill=True, lw = 0)
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patches.append(s)
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colors.append(dt.density(y))
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scale = Transformations.Scale()
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colors = scale.apply(colors)
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pc = PatchCollection(patches=patches, match_original=True)
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pc.set_clim([0, 1])
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pc.set_cmap(cmap)
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pc.set_array(np.array(colors))
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ax.add_collection(pc)
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cb = fig.colorbar(pc, ax=ax)
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cb.set_label('Density')
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def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1):
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lower = [kk[0] for kk in intervals]
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upper = [kk[1] for kk in intervals]
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mi = min(lower) * 0.95
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ma = max(upper) * 1.05
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for k in np.arange(0, order):
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lower.insert(0, None)
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upper.insert(0, None)
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if typeonlegend: label += " (Interval)"
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axis.plot(lower, color=color, label=label, ls=ls,linewidth=linewidth)
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axis.plot(upper, color=color, ls=ls,linewidth=linewidth)
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return [mi, ma]
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def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1):
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mi = min(points) * 0.95
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@ -880,7 +738,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
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ls = "-"
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else:
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ls = "--"
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tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
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tmpmi, tmpma = Util.plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
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color=colors[count], ls=ls, linewidth=linewidth)
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mi.append(tmpmi)
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ma.append(tmpma)
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@ -900,15 +758,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
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#Util.show_and_save_image(fig, file, save, lgd=legends)
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def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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for k,m in enumerate(pmfs,start=0):
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m.plot(ax, color=lcolors[k])
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0)
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@ -6,6 +6,160 @@ import time
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import matplotlib.pyplot as plt
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import dill
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import numpy as np
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import matplotlib.cm as cmx
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import matplotlib.colors as pltcolors
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from pyFTS.probabilistic import ProbabilityDistribution
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from pyFTS.common import Transformations
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def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, intervals = True,
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save=False, file=None, tam=[20, 5], resolution=None,
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cmap='Blues', linewidth=1.5):
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"""
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Plot the forecasts of several one step ahead models, by point or by interval
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:param original: Original time series data (list)
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:param models: List of models to compare
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:param colors: List of models colors
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:param distributions: True to plot a distribution
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:param time_from: index of data poit to start the ahead forecasting
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:param time_to: number of steps ahead to forecast
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:param interpol: Fill space between distribution plots
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:param save: Save the picture on file
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:param file: Filename to save the picture
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:param tam: Size of the picture
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:param resolution:
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:param cmap: Color map to be used on distribution plot
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:param option: Distribution type to be passed for models
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:return:
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"""
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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cm = plt.get_cmap(cmap)
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cNorm = pltcolors.Normalize(vmin=0, vmax=1)
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scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
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if resolution is None: resolution = (max(original) - min(original)) / 100
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mi = []
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ma = []
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for count, fts in enumerate(models, start=0):
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if fts.has_probability_forecasting and distributions[count]:
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density = fts.forecast_ahead_distribution(original[time_from - fts.order:time_from], time_to,
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resolution=resolution)
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#plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to)
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plot_density_rectange(ax, cm, density, fig, resolution, time_from, time_to)
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if fts.has_interval_forecasting and intervals:
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forecasts = fts.forecast_ahead_interval(original[time_from - fts.order:time_from], time_to)
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lower = [kk[0] for kk in forecasts]
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upper = [kk[1] for kk in forecasts]
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mi.append(min(lower))
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ma.append(max(upper))
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for k in np.arange(0, time_from - fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname, linewidth=linewidth)
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ax.plot(upper, color=colors[count], linewidth=linewidth*1.5)
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ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
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handles0, labels0 = ax.get_legend_handles_labels()
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if True in distributions:
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lgd = ax.legend(handles0, labels0, loc=2)
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else:
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lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
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_mi = min(mi)
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if _mi < 0:
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_mi *= 1.1
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else:
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_mi *= 0.9
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_ma = max(ma)
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if _ma < 0:
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_ma *= 0.9
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else:
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_ma *= 1.1
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ax.set_ylim([_mi, _ma])
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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show_and_save_image(fig, file, save, lgd=lgd)
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def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to):
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"""
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Auxiliar function to plot_compared_intervals_ahead
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"""
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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patches = []
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colors = []
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for x in density.index:
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for y in density.columns:
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s = Rectangle((time_from + x, y), 1, resolution, fill=True, lw = 0)
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patches.append(s)
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colors.append(density[y][x]*5)
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pc = PatchCollection(patches=patches, match_original=True)
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pc.set_clim([0, 1])
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pc.set_cmap(cmap)
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pc.set_array(np.array(colors))
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ax.add_collection(pc)
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cb = fig.colorbar(pc, ax=ax)
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cb.set_label('Density')
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def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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for k,m in enumerate(pmfs,start=0):
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m.plot(ax, color=lcolors[k])
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0)
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def plot_distribution(ax, cmap, probabilitydist, fig, time_from, reference_data=None):
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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patches = []
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colors = []
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for ct, dt in enumerate(probabilitydist):
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disp = 0.0
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if reference_data is not None:
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disp = reference_data[time_from+ct]
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for y in dt.bins:
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s = Rectangle((time_from+ct, y+disp), 1, dt.resolution, fill=True, lw = 0)
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patches.append(s)
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colors.append(dt.density(y))
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scale = Transformations.Scale()
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colors = scale.apply(colors)
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pc = PatchCollection(patches=patches, match_original=True)
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pc.set_clim([0, 1])
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pc.set_cmap(cmap)
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pc.set_array(np.array(colors))
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ax.add_collection(pc)
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cb = fig.colorbar(pc, ax=ax)
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cb.set_label('Density')
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def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1):
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lower = [kk[0] for kk in intervals]
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upper = [kk[1] for kk in intervals]
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mi = min(lower) * 0.95
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ma = max(upper) * 1.05
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for k in np.arange(0, order):
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lower.insert(0, None)
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upper.insert(0, None)
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if typeonlegend: label += " (Interval)"
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axis.plot(lower, color=color, label=label, ls=ls,linewidth=linewidth)
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axis.plot(upper, color=color, ls=ls,linewidth=linewidth)
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return [mi, ma]
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def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
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