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Bugfixes at pwfts and improvements on benchmarks.plot_*

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This commit is contained in:
Petrônio Cândido 2018-04-10 12:39:56 -03:00
parent 1312d96246
commit 24c4fadc08
3 changed files with 102 additions and 97 deletions
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4
pyFTS/benchmarks/Measures.py
View File

@ -5,14 +5,12 @@ pyFTS module for common benchmark metrics
"""
import time
import numba
import numpy as np
import pandas as pd
from pyFTS.common import FuzzySet,SortedCollection
from pyFTS.probabilistic import ProbabilityDistribution
def acf(data, k):
"""
Autocorrelation function estimative
@ -30,7 +28,6 @@ def acf(data, k):
return 1/((n-k)*sigma)*s
def rmse(targets, forecasts):
"""
Root Mean Squared Error
@ -45,7 +42,6 @@ def rmse(targets, forecasts):
return np.sqrt(np.nanmean((targets - forecasts) ** 2))
def rmse_interval(targets, forecasts):
"""
Root Mean Squared Error

194
pyFTS/benchmarks/benchmarks.py
View File

@ -580,100 +580,9 @@ def print_interval_statistics(original, models):
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_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5],
points=True, intervals=True, 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 typeonlegend: Add the type of forecast (point / interval) on legend
:param save: Save the picture on file
:param file: Filename to save the picture
:param tam: Size of the picture
:param points: True to plot the point forecasts, False otherwise
:param intervals: True to plot the interval forecasts, False otherwise
:param linewidth:
:return:
"""
fig = plt.figure(figsize=tam)
ax = fig.add_subplot(111)
mi = []
ma = []
legends = []
ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
for count, fts in enumerate(models, start=0):
try:
if fts.has_point_forecasting and points:
forecasts = fts.forecast(original)
if isinstance(forecasts, np.ndarray):
forecasts = forecasts.tolist()
mi.append(min(forecasts) * 0.95)
ma.append(max(forecasts) * 1.05)
for k in np.arange(0, fts.order):
forecasts.insert(0, None)
lbl = fts.shortname + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
if typeonlegend: lbl += " (Point)"
ax.plot(forecasts, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
if fts.has_interval_forecasting and intervals:
forecasts = fts.forecast_interval(original)
lbl = fts.shortname + " " + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
if not points and intervals:
ls = "-"
else:
ls = "--"
tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
color=colors[count], ls=ls, linewidth=linewidth)
mi.append(tmpmi)
ma.append(tmpma)
except ValueError as ex:
print(fts.shortname)
handles0, labels0 = ax.get_legend_handles_labels()
lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
legends.append(lgd)
# ax.set_title(fts.name)
ax.set_ylim([min(mi), max(ma)])
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.set_xlim([0, len(original)])
#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)
def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution = None, partitioners=[Grid.GridPartitioner],
partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
@ -928,14 +837,18 @@ from pyFTS.common import Transformations
def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_from):
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), 1, dt.resolution, fill=True, lw = 0)
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()
@ -949,6 +862,101 @@ def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_fr
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_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5],
points=True, intervals=True, 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 typeonlegend: Add the type of forecast (point / interval) on legend
:param save: Save the picture on file
:param file: Filename to save the picture
:param tam: Size of the picture
:param points: True to plot the point forecasts, False otherwise
:param intervals: True to plot the interval forecasts, False otherwise
:param linewidth:
:return:
"""
fig = plt.figure(figsize=tam)
ax = fig.add_subplot(111)
mi = []
ma = []
legends = []
ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
for count, fts in enumerate(models, start=0):
try:
if fts.has_point_forecasting and points:
forecasts = fts.forecast(original)
if isinstance(forecasts, np.ndarray):
forecasts = forecasts.tolist()
mi.append(min(forecasts) * 0.95)
ma.append(max(forecasts) * 1.05)
for k in np.arange(0, fts.order):
forecasts.insert(0, None)
lbl = fts.shortname + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
if typeonlegend: lbl += " (Point)"
ax.plot(forecasts, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
if fts.has_interval_forecasting and intervals:
forecasts = fts.forecast_interval(original)
lbl = fts.shortname + " " + str(fts.order if fts.is_high_order and not fts.benchmark_only else "")
if not points and intervals:
ls = "-"
else:
ls = "--"
tmpmi, tmpma = plot_interval(ax, forecasts, fts.order, label=lbl, typeonlegend=typeonlegend,
color=colors[count], ls=ls, linewidth=linewidth)
mi.append(tmpmi)
ma.append(tmpma)
except ValueError as ex:
print(fts.shortname)
handles0, labels0 = ax.get_legend_handles_labels()
lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
legends.append(lgd)
# ax.set_title(fts.name)
ax.set_ylim([min(mi), max(ma)])
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.set_xlim([0, len(original)])
#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)
def plotCompared(original, forecasts, labels, title):
fig = plt.figure(figsize=[13, 6])
ax = fig.add_subplot(111)

1
pyFTS/models/pwfts.py
View File

@ -444,6 +444,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
if 'bins' in kwargs:
_bins = kwargs.pop('bins')
nbins = len(_bins)
else:
nbins = kwargs.get("num_bins", 100)
_bins = np.linspace(uod[0], uod[1], nbins)