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
|
|||||||
import traceback
|
import traceback
|
||||||
|
|
||||||
import matplotlib as plt
|
import matplotlib as plt
|
||||||
import matplotlib.cm as cmx
|
|
||||||
import matplotlib.colors as pltcolors
|
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from mpl_toolkits.mplot3d import Axes3D
|
from mpl_toolkits.mplot3d import Axes3D
|
||||||
|
|
||||||
from pyFTS.probabilistic import ProbabilityDistribution
|
|
||||||
from pyFTS.common import Transformations
|
from pyFTS.common import Transformations
|
||||||
from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
|
from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
|
||||||
from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
|
from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
|
||||||
@ -26,12 +24,6 @@ from pyFTS.benchmarks import Util as bUtil
|
|||||||
from pyFTS.common import Util as cUtil
|
from pyFTS.common import Util as cUtil
|
||||||
# from sklearn.cross_validation import KFold
|
# from sklearn.cross_validation import KFold
|
||||||
from pyFTS.partitioners import Grid
|
from pyFTS.partitioners import Grid
|
||||||
from matplotlib import rc
|
|
||||||
|
|
||||||
#rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
|
|
||||||
## for Palatino and other serif fonts use:
|
|
||||||
#rc('font',**{'family':'serif','serif':['Palatino']})
|
|
||||||
#rc('text', usetex=True)
|
|
||||||
|
|
||||||
colors = ['grey', 'darkgrey', 'rosybrown', 'maroon', 'red','orange', 'gold', 'yellow', 'olive', 'green',
|
colors = ['grey', 'darkgrey', 'rosybrown', 'maroon', 'red','orange', 'gold', 'yellow', 'olive', 'green',
|
||||||
'darkgreen', 'cyan', 'lightblue','blue', 'darkblue', 'purple', 'darkviolet' ]
|
'darkgreen', 'cyan', 'lightblue','blue', 'darkblue', 'purple', 'darkviolet' ]
|
||||||
@ -681,145 +673,11 @@ def print_distribution_statistics(original, models, steps, resolution):
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
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')
|
|
||||||
|
|
||||||
|
|
||||||
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')
|
|
||||||
|
|
||||||
|
|
||||||
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]
|
|
||||||
|
|
||||||
|
|
||||||
def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1):
|
def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1):
|
||||||
mi = min(points) * 0.95
|
mi = min(points) * 0.95
|
||||||
@ -880,7 +738,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
|
|||||||
ls = "-"
|
ls = "-"
|
||||||
else:
|
else:
|
||||||
ls = "--"
|
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)
|
color=colors[count], ls=ls, linewidth=linewidth)
|
||||||
mi.append(tmpmi)
|
mi.append(tmpmi)
|
||||||
ma.append(tmpma)
|
ma.append(tmpma)
|
||||||
@ -900,15 +758,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
|
|||||||
#Util.show_and_save_image(fig, file, save, lgd=legends)
|
#Util.show_and_save_image(fig, file, save, lgd=legends)
|
||||||
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -6,6 +6,160 @@ import time
|
|||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import dill
|
import dill
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import matplotlib.cm as cmx
|
||||||
|
import matplotlib.colors as pltcolors
|
||||||
|
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):
|
||||||
|
"""
|
||||||
|
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)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
|
||||||
|
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]
|
||||||
|
|
||||||
|
|
||||||
def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
|
def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user