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Bugfixes in ResidualAnalysis

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Petrônio Cândido de Lima e Silva 2019-06-11 13:03:18 -03:00 committed by GitHub
parent 48fcf8daca
commit 16edb0deee
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1 changed files with 27 additions and 19 deletions
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46
pyFTS/benchmarks/ResidualAnalysis.py
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@ -97,15 +97,17 @@ def plotResiduals(targets, models, tam=[8, 8], save=False, file=None):
def plot_residuals(targets, models, tam=[8, 8], save=False, file=None): def plot_residuals(targets, models, tam=[8, 8], save=False, file=None):
fig, axes = plt.subplots(nrows=len(models), ncols=3, figsize=tam) import scipy as sp
fig, axes = plt.subplots(nrows=len(models), ncols=4, figsize=tam)
for c, mfts in enumerate(models, start=0): for c, mfts in enumerate(models, start=0):
if len(models) > 1: if len(models) > 1:
ax = axes[c] ax = axes[c]
else: else:
ax = axes ax = axes
forecasts = mfts.forecast(targets) forecasts = mfts.predict(targets)
res = residuals(targets, forecasts, mfts.order) res = residuals(targets, forecasts, mfts.order+1)
mu = np.mean(res) mu = np.mean(res)
sig = np.std(res) sig = np.std(res)
@ -114,40 +116,46 @@ def plot_residuals(targets, models, tam=[8, 8], save=False, file=None):
ax[0].set_xlabel(' ') ax[0].set_xlabel(' ')
ax[0].plot(res) ax[0].plot(res)
if c == 0: ax[1].set_title("Residuals Autocorrelation", size='large') if c == 0: ax[1].set_title("Autocorrelation", size='large')
ax[1].set_ylabel('ACS') ax[1].set_ylabel('ACS')
ax[1].set_xlabel('Lag') ax[1].set_xlabel('Lag')
ax[1].acorr(res) ax[1].acorr(res)
if c == 0: ax[2].set_title("Residuals Histogram", size='large') if c == 0: ax[2].set_title("Histogram", size='large')
ax[2].set_ylabel('Freq') ax[2].set_ylabel('Freq')
ax[2].set_xlabel('Bins') ax[2].set_xlabel('Bins')
ax[2].hist(res) ax[2].hist(res)
if c == 0: ax[3].set_title("QQ Plot", size='large')
_, (__, ___, r) = sp.stats.probplot(res, plot=ax[3], fit=True)
plt.tight_layout() plt.tight_layout()
Util.show_and_save_image(fig, file, save) Util.show_and_save_image(fig, file, save)
def single_plot_residuals(targets, forecasts, order, tam=[8, 8], save=False, file=None): def single_plot_residuals(targets, forecasts, order, tam=[10, 7], save=False, file=None):
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=tam) import scipy as sp
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=tam)
res = residuals(targets, forecasts, order) res = residuals(targets, forecasts, order)
ax[0].set_title("Residuals", size='large') ax[0][0].set_title("Residuals", size='large')
ax[0].set_ylabel("Model", size='large') ax[0][0].plot(res)
ax[0].set_xlabel(' ')
ax[0].plot(res)
ax[1].set_title("Residuals Autocorrelation", size='large') ax[0][1].set_title("Autocorrelation", size='large')
ax[1].set_ylabel('ACS') ax[0][1].set_ylabel('ACF')
ax[1].set_xlabel('Lag') ax[0][1].set_xlabel('Lag')
ax[1].acorr(res) ax[0][1].acorr(res)
ax[2].set_title("Residuals Histogram", size='large') ax[1][0].set_title("Histogram", size='large')
ax[2].set_ylabel('Freq') ax[1][0].set_ylabel('Freq')
ax[2].set_xlabel('Bins') ax[1][0].set_xlabel('Bins')
ax[2].hist(res) ax[1][0].hist(res)
_, (__, ___, r) = sp.stats.probplot(res, plot=ax[1][1], fit=True)
plt.tight_layout() plt.tight_layout()