def plot_signifant_region(ax1, max_mu, min_mu, max_std, min_std, max_abs):
## For the not significant region
mu_grid = np.array([-max_abs * 10, 0, max_abs * 10])
y_grid = np.abs(mu_grid) / 2
gl.fill_between(mu_grid,
10 * np.ones(mu_grid.size),
y_grid,
alpha=0.2,
color="r",
ax=ax1,
legend=["95% non-significant"])
def create_Bayesian_analysis_charts(model,
X_data_tr,
Y_data_tr,
X_data_val,
Y_data_val,
tr_loss,
val_loss,
KL_loss,
final_loss_tr,
final_loss_val,
xgrid_real_func,
ygrid_real_func,
folder_images,
epoch_i=None):
# Configurations of the plots
alpha_points = 0.2
color_points_train = "dark navy blue"
color_points_val = "amber"
color_train_loss = "cobalt blue"
color_val_loss = "blood"
color_truth = "k"
color_mean = "b"
color_most_likey = "y"
############################# Data computation #######################
if (type(X_data_tr) == type([])):
pass
else:
if (X_data_tr.shape[1] == 1): # Regression Example
x_grid, all_y_grid, most_likely_ygrid = compute_regression_1D_data(
model, X_data_tr, X_data_val, Nsamples=100)
elif (X_data_tr.shape[1] == 2): # Classification Example
xx, yy, all_y_grid, most_likely_ygrid = compute_classification_2D_data(
model, X_data_tr, X_data_val, Nsamples=100)
else: # RNN
x_grid, all_y_grid, most_likely_ygrid = compute_RNN_1D_data(
model, X_data_tr, X_data_val, Nsamples=100)
################################ Divide in plots ##############################
gl.init_figure()
ax1 = gl.subplot2grid((6, 3), (0, 0), rowspan=3, colspan=1)
ax2 = gl.subplot2grid((6, 3), (3, 0),
rowspan=3,
colspan=1,
sharex=ax1,
sharey=ax1)
ax3 = gl.subplot2grid((6, 3), (0, 1), rowspan=2, colspan=1)
ax4 = gl.subplot2grid((6, 3), (2, 1), rowspan=2, colspan=1, sharex=ax3)
ax5 = gl.subplot2grid((6, 3), (4, 1), rowspan=2, colspan=1, sharex=ax3)
ax6 = gl.subplot2grid((6, 3), (0, 2), rowspan=3, colspan=1)
ax7 = gl.subplot2grid((6, 3), (3, 2), rowspan=3, colspan=1, sharex=ax6)
if (type(X_data_tr) == type([])):
Xtrain = [
torch.tensor(X_data_tr[i],
device=model.cf_a.device,
dtype=model.cf_a.dtype) for i in range(len(X_data_tr))
]
Ytrain = torch.tensor(Y_data_tr,
device=model.cf_a.device,
dtype=torch.int64)
Xval = [
torch.tensor(X_data_val[i],
device=model.cf_a.device,
dtype=model.cf_a.dtype)
for i in range(len(X_data_val))
]
Yval = torch.tensor(Y_data_val,
device=model.cf_a.device,
dtype=torch.int64)
confusion = model.get_confusion_matrix(Xtrain, Ytrain)
plot_confusion_matrix(confusion, model.languages, ax1)
confusion = model.get_confusion_matrix(Xval, Yval)
plot_confusion_matrix(confusion, model.languages, ax2)
else:
if (X_data_tr.shape[1] == 1): # Regression Example
plot_data_regression_1d_2axes(
X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func,
X_data_val, Y_data_val, x_grid, all_y_grid, most_likely_ygrid,
alpha_points, color_points_train, color_points_val,
color_most_likey, color_mean, color_truth, ax1, ax2)
elif (X_data_tr.shape[1] == 2): # Classification Example
plot_data_classification_2d_2axes(
X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func,
X_data_val, Y_data_val, xx, yy, all_y_grid, most_likely_ygrid,
alpha_points, color_points_train, color_points_val,
color_most_likey, color_mean, color_truth, ax1, ax2)
else: # RNN example
plot_data_RNN_1d_2axes(X_data_tr, Y_data_tr, xgrid_real_func,
ygrid_real_func, X_data_val, Y_data_val,
x_grid, all_y_grid, most_likely_ygrid,
alpha_points, color_points_train,
color_points_val, color_most_likey,
color_mean, color_truth, ax1, ax2)
# gl.fill_between (x_grid, [mean_samples_grid + 2*std_samples_grid, mean_samples_grid - 2*std_samples_grid]
# , ax = ax2, alpha = 0.10, color = "b", legend = ["Mean realizaions"])
## ax2: The uncertainty of the prediction !!
# gl.plot (x_grid, std_samples_grid, ax = ax2, labels = ["Std (%i)"%(Nsamples),"X","f(X)"], legend = [" std predictions"], fill = 1, alpha = 0.3)
############## ax3 ax4 ax5: Loss Evolution !! ######################
## ax3: Evolutoin of the data loss
gl.plot([],
tr_loss,
ax=ax3,
lw=3,
labels=["Losses", "", "Data loss"],
legend=["train"],
color=color_train_loss)
gl.plot([],
val_loss,
ax=ax3,
lw=3,
legend=["validation"],
color=color_val_loss,
AxesStyle="Normal - No xaxis")
## ax4: The evolution of the KL loss
gl.plot([],
KL_loss,
ax=ax4,
lw=3,
labels=["", "", "KL loss"],
legend=["Bayesian Weights"],
AxesStyle="Normal - No xaxis",
color="k")
## ax5: Evolutoin of the total loss
gl.plot([],
final_loss_tr,
ax=ax5,
lw=3,
labels=["", "epoch", "Total Loss (Bayes)"],
legend=["train"],
color=color_train_loss)
gl.plot([],
final_loss_val,
ax=ax5,
lw=3,
legend=["validation"],
color=color_val_loss)
############## ax6 ax7: Variational Weights !! ######################
create_plot_variational_weights(model, ax6, ax7)
## Plot in chart 7 the acceptable mu = 2sigma -> sigma = |mu|/2sigma
mu_grid = np.linspace(-3, 3, 100)
y_grid = np.abs(mu_grid) / 2
gl.fill_between(mu_grid,
10 * np.ones(mu_grid.size),
y_grid,
alpha=0.2,
color="r",
ax=ax7,
legend=["95% non-significant"])
gl.set_zoom(ax=ax6, ylim=[-0.1, 10])
gl.set_zoom(ax=ax7,
xlim=[-2.5, 2.5],
ylim=[
-0.05,
np.exp(model.cf_a.input_layer_prior["log_sigma2"]) *
(1 + 0.15)
])
# gl.set_zoom (ax = ax7, xlim = [-2.5, 2.5], ylim = [-0.1,2])
# Set final properties and save figure
gl.set_fontSizes(ax=[ax1, ax2, ax3, ax4, ax5, ax6, ax7],
title=20,
xlabel=20,
ylabel=20,
legend=10,
xticks=12,
yticks=12)
gl.subplots_adjust(left=.09,
bottom=.10,
right=.90,
top=.95,
wspace=.30,
hspace=0.10)
if (type(epoch_i) == type(None)):
gl.savefig(folder_images + "../" + 'Final_values_regression_1D_' +
str(model.cf_a.eta_KL) + '.png',
dpi=100,
sizeInches=[20, 10])
else:
gl.savefig(folder_images + '%i.png' % epoch_i,
dpi=100,
sizeInches=[20, 10],
close=True,
bbox_inches="tight")
"Number of clusters (K)", "Average LL of a sample"
],
lw=3,
color="k")
gl.plot(Klusters,
mean_tr_ll + 2 * std_tr_ll,
color="k",
nf=0,
lw=1,
ls="--",
legend=["Mean Train LL +- 2std"])
gl.plot(Klusters, mean_tr_ll - 2 * std_tr_ll, color="k", nf=0, lw=1, ls="--")
gl.fill_between(Klusters,
mean_tr_ll - 2 * std_tr_ll,
mean_tr_ll + 2 * std_tr_ll,
c="k",
alpha=0.5)
for i in range(len(logl_tr_CVs)):
for k_i in range(len(Klusters)):
gl.scatter(np.ones((len(logl_tr_CVs[i][k_i]), 1)) * Klusters[k_i],
logl_tr_CVs[i][k_i],
color="k",
alpha=0.2,
lw=1)
gl.plot(Klusters,
mean_val_ll,
nf=0,
color="r",
legend=["Mean Validation LL (EM)"],
ax0 = gl.subplot2grid((1,4), (0,0), rowspan=1, colspan=3)
for i in range(Nrealizations):
f_prime = np.random.randn(N,1)
error = L.dot(f_prime)
gl.plot(tgrid,error, lw = 3, color = "b", ls = "-", alpha = 0.5,
legend = legend, labels = labels)
# gl.scatter(tgrid,f_prime, lw = 1, alpha = 0.3, color = "b")
if (flag == 1):
flag = 0
legend = []
#Variance of each prediction
v = np.diagonal(K)
gl.fill_between(tgrid, -2*np.sqrt(v), 2*np.sqrt(v), lw = 3, alpha = 0.5, color = "yellow",
legend = ["95% confidence interval"]);
gl.plot(tgrid, 2*np.sqrt(v), lw= 1, alpha = 0.5, color = "yellow", legend = ["95% confidence interval"]);
gl.plot(tgrid, 2*np.sqrt(v), lw= 1, alpha = 0.5, color = "yellow");
## Plot the covariance matrix
ax1 = gl.subplot2grid((1,4), (0,3), rowspan=1, colspan=1)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(K[0:Nshow,0:Nshow], interpolation="nearest", cmap=cmap)
# ax1.grid(True)
plt.title('Covariance matrix of Noise')
# labels=[str(x) for x in range(Nshow )]
# ax1.set_xticklabels(labels,fontsize=20)
# ax1.set_yticklabels(labels,fontsize=20)
# Add colorbar, make sure to specify tick locations to match desired ticklabels
symbol_names=[symbols[indx]])[0]
gl.init_figure()
########################## AX0 ########################
### Plot the initial Price Volume
gl.subplot2grid((Ndiv, 4), (0, 0), rowspan=HPV, colspan=4)
# gl.plot_indicator(timeData, Ndiv = Ndiv, HPV = HPV)
gl.plot(dates,
prices[:, indx],
legend=["Price"],
nf=0,
labels=["Xing Average Strategy", "Price", "Time"])
Volume = timeData.get_timeSeries(seriesNames=["Volume"])
gl.plot(dates, Volume, nf=0, na=1, lw=0, alpha=0)
gl.fill_between(dates, Volume)
axes = gl.get_axes()
axP = axes[0] # Price axes
axV = axes[1] # Volumne exes
axV.set_ylim(0, 3 * max(Volume))
gl.plot(dates,
EMAfast[:, indx],
ax=axP,
legend=["EMA = %i" % (n_fast)],
nf=0)
gl.plot(dates,
EMAslow[:, indx],
ax=axP,
legend=["EMA = %i" % (n_slow)],
labels=["Averages", "Time", "Value"],
legend=["Price", "SMA", "EMA"])
###########################################################################
# Bollinger Bands, Pivot points Resistences and Supports and ATR
BB = timeData.BBANDS(seriesNames=["Close"], n=10)
ATR = timeData.ATR(n=20)
PPSR = timeData.PPSR()
gl.set_subplots(3, 1)
gl.plot(dates, [price, BB[:, 0], BB[:, 1]],
nf=1,
labels=["Averages", "Time", "Value"],
legend=["Price", "Bollinger Bands"])
gl.fill_between(x=dates, y1=BB[:, 0], y2=BB[:, 1], alpha=0.5)
gl.plot(dates,
price,
nf=1,
labels=["Averages", "Time", "Value"],
legend=["Price"])
gl.plot(dates, PPSR, nf=0, legend=["Supports and Resistances"])
gl.plot(dates,
price,
nf=1,
labels=["Averages", "Time", "Value"],
legend=["Price"])
gl.plot(dates,
ATR,
gl.plot(dates, [price, SMA, EMA] , nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price", "SMA", "EMA"])
###########################################################################
# Bollinger Bands, Pivot points Resistences and Supports and ATR
BB = timeData.BBANDS(seriesNames = ["Close"], n = 10)
ATR = timeData.ATR(n = 20)
PPSR = timeData.PPSR()
gl.set_subplots(3,1)
gl.plot(dates, [price, BB[:,0],BB[:,1]] , nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price", "Bollinger Bands"])
gl.fill_between(x = dates, y1 = BB[:,0], y2 = BB[:,1], alpha = 0.5)
gl.plot(dates, price, nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price"])
gl.plot(dates, PPSR , nf = 0,
legend = [ "Supports and Resistances"])
gl.plot(dates, price , nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price"])
gl.plot(dates, ATR , nf = 0, na = 1,
labels = ["Averages","Time","Value"],
legend = ["ATR"], fill = 1)
pandas_lib1 = 0
X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func, X_data_val,
Y_data_val, x_grid, all_y_grid, most_likely_ygrid, alpha_points,
color_points_train, color_points_val, color_most_likey, color_mean,
color_truth, None, ax1)
pf.create_plot_variational_weights(model, ax1, ax2, plot_pdf=False)
all_axes.append(ax1)
all_axes.append(ax2)
mu_grid = np.linspace(-3, 3, 100)
y_grid = np.abs(mu_grid) / 2
gl.fill_between(mu_grid,
10 * np.ones(mu_grid.size),
y_grid,
alpha=0.2,
color="r",
ax=ax2,
legend=["95% non-significant"])
gl.set_zoom(ax=ax2,
xlim=[-2.5, 2.5],
ylim=[-0.05, model.linear1.prior.sigma1 * (1 + 0.30)])
eta_KL = eta_values[i]
ax1.set_title("Model estimations for $\zeta = " + str(eta_KL) + "$")
ax2.set_title("Variational Weights for $\zeta = " + str(eta_KL) + "$")
# gl.set_zoom (ax = ax7, xlim = [-2.5, 2.5], ylim = [-0.1,2])
# Set final properties and save figure
gl.set_fontSizes(ax=all_axes,
ls="-",
alpha=0.5,
legend=legend,
labels=labels)
# gl.scatter(tgrid,f_prime, lw = 1, alpha = 0.3, color = "b")
if (flag == 1):
flag = 0
legend = []
#Variance of each prediction
v = np.diagonal(K)
gl.fill_between(tgrid,
-2 * np.sqrt(v),
2 * np.sqrt(v),
lw=3,
alpha=0.5,
color="yellow",
legend=["95% confidence interval"])
gl.plot(tgrid,
2 * np.sqrt(v),
lw=1,
alpha=0.5,
color="yellow",
legend=["95% confidence interval"])
gl.plot(tgrid, 2 * np.sqrt(v), lw=1, alpha=0.5, color="yellow")
## Plot the covariance matrix
ax1 = gl.subplot2grid((1, 4), (0, 3), rowspan=1, colspan=1)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(K[0:Nshow, 0:Nshow], interpolation="nearest", cmap=cmap)
def plot_signifant_region(ax1, max_mu,min_mu,max_std,min_std,max_abs):
## For the not significant region
mu_grid = np.array([-max_abs *10,0,max_abs*10])
y_grid = np.abs(mu_grid)/2
gl.fill_between(mu_grid, 10*np.ones(mu_grid.size), y_grid,
alpha = 0.2, color = "r", ax = ax1, legend = ["95% non-significant"])
################################################################################################################
gl.init_figure()
title = "Validation of Number of clusters for a %i-CV. "%Nfolds;
if (clusters_relation == "MarkovChain1"):
title += "HMM"
else:
title += "EM"
ax1 = gl.plot(Klusters,mean_tr_ll, legend = ["Mean Train LL"],
labels = [title,"Number of clusters (K)","Average LL of a sample"],
lw = 3, color = "k")
gl.plot(Klusters,mean_tr_ll + 2*std_tr_ll , color = "k", nf = 0, lw = 1, ls = "--", legend = ["Mean Train LL +- 2std"])
gl.plot(Klusters,mean_tr_ll - 2*std_tr_ll , color = "k", nf = 0, lw = 1,ls = "--")
gl.fill_between(Klusters, mean_tr_ll - 2*std_tr_ll, mean_tr_ll + 2*std_tr_ll, c = "k", alpha = 0.5)
for i in range(len(logl_tr_CVs)):
for k_i in range(len(Klusters)):
gl.scatter(np.ones((len(logl_tr_CVs[i][k_i]),1))*Klusters[k_i], logl_tr_CVs[i][k_i], color = "k", alpha = 0.2, lw = 1)
gl.plot(Klusters,mean_val_ll, nf = 0, color = "r",
legend = ["Mean Validation LL"], lw = 3)
gl.plot(Klusters,mean_val_ll + 2*std_val_ll , color = "r", nf = 0, lw = 1, ls = "--", legend = ["Mean Validation LL +- 2std"])
gl.plot(Klusters,mean_val_ll - 2*std_val_ll , color = "r", nf = 0, lw = 1, ls = "--")
gl.fill_between(Klusters, mean_val_ll - 2*std_val_ll, mean_val_ll + 2*std_val_ll, c = "r", alpha = 0.1)
for i in range(len(logl_tr_CVs)):
for k_i in range(len(Klusters)):
gl.scatter(np.ones((len(logl_val_CVs[i][k_i]),1))*Klusters[k_i], logl_val_CVs[i][k_i], color = "r", alpha = 0.5, lw = 1)
gl.set_fontSizes(ax = ax1, title = 20, xlabel = 20, ylabel = 20,
if (plotting_some == 1):
Ndiv = 6; HPV = 2
### PLOT THE ORIGINAL PRICE AND MOVING AVERAGES
timeData = Cartera.get_timeDataObj(period = -1, symbol_names = [symbols[indx]])[0]
gl.init_figure()
########################## AX0 ########################
### Plot the initial Price Volume
gl.subplot2grid((Ndiv,4), (0,0), rowspan=HPV, colspan=4)
# gl.plot_indicator(timeData, Ndiv = Ndiv, HPV = HPV)
gl.plot(dates, prices[:,indx], legend = ["Price"], nf = 0,
labels = ["Xing Average Strategy","Price","Time"])
Volume = timeData.get_timeSeries(seriesNames = ["Volume"])
gl.plot(dates, Volume, nf = 0, na = 1, lw = 0, alpha = 0)
gl.fill_between(dates, Volume)
axes = gl.get_axes()
axP = axes[0] # Price axes
axV = axes[1] # Volumne exes
axV.set_ylim(0,3 * max(Volume))
gl.plot(dates, EMAfast[:,indx],ax = axP, legend = ["EMA = %i" %(n_fast)], nf = 0)
gl.plot(dates, EMAslow[:,indx],ax = axP, legend = ["EMA = %i" %(n_slow)], nf = 0)
########################## AX1 ########################
pos = 0
gl.subplot2grid((Ndiv,4), (HPV + pos,0), rowspan=1, colspan=4, sharex = axP)
gl.plot(dates, Y_data, nf = 0, legend = ["Y data filtered"])
########################## AX2 ########################
def create_Bayesian_analysis_charts(model,
X_data_tr, Y_data_tr, X_data_val, Y_data_val,
tr_loss, val_loss, KL_loss,final_loss_tr,final_loss_val,
xgrid_real_func, ygrid_real_func,
folder_images,
epoch_i = None):
# Configurations of the plots
alpha_points = 0.2
color_points_train = "dark navy blue"
color_points_val = "amber"
color_train_loss = "cobalt blue"
color_val_loss = "blood"
color_truth = "k"
color_mean = "b"
color_most_likey = "y"
############################# Data computation #######################
if(type(X_data_tr) == type([])):
pass
else:
if (X_data_tr.shape[1] == 1): # Regression Example
x_grid, all_y_grid,most_likely_ygrid = compute_regression_1D_data( model,X_data_tr,X_data_val, Nsamples = 100)
elif(X_data_tr.shape[1] == 2): # Classification Example
xx,yy , all_y_grid,most_likely_ygrid = compute_classification_2D_data( model,X_data_tr,X_data_val, Nsamples = 100)
else: # RNN
x_grid, all_y_grid,most_likely_ygrid = compute_RNN_1D_data( model,X_data_tr,X_data_val, Nsamples = 100)
################################ Divide in plots ##############################
gl.init_figure();
ax1 = gl.subplot2grid((6,3), (0,0), rowspan=3, colspan=1)
ax2 = gl.subplot2grid((6,3), (3,0), rowspan=3, colspan=1, sharex = ax1, sharey = ax1)
ax3 = gl.subplot2grid((6,3), (0,1), rowspan=2, colspan=1)
ax4 = gl.subplot2grid((6,3), (2,1), rowspan=2, colspan=1, sharex = ax3)
ax5 = gl.subplot2grid((6,3), (4,1), rowspan=2, colspan=1, sharex = ax3)
ax6 = gl.subplot2grid((6,3), (0,2), rowspan=3, colspan=1)
ax7 = gl.subplot2grid((6,3), (3,2), rowspan=3, colspan=1, sharex = ax6)
if(type(X_data_tr) == type([])):
Xtrain = [torch.tensor(X_data_tr[i],device=model.cf_a.device, dtype=model.cf_a.dtype) for i in range(len(X_data_tr))]
Ytrain = torch.tensor(Y_data_tr,device=model.cf_a.device, dtype=torch.int64)
Xval = [torch.tensor(X_data_val[i],device=model.cf_a.device, dtype=model.cf_a.dtype) for i in range(len(X_data_val))]
Yval = torch.tensor(Y_data_val,device=model.cf_a.device, dtype=torch.int64)
confusion = model.get_confusion_matrix(Xtrain, Ytrain)
plot_confusion_matrix(confusion,model.languages, ax1 )
confusion = model.get_confusion_matrix(Xval, Yval)
plot_confusion_matrix(confusion,model.languages, ax2 )
else:
if (X_data_tr.shape[1] == 1): # Regression Example
plot_data_regression_1d_2axes(X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func, X_data_val, Y_data_val,
x_grid,all_y_grid, most_likely_ygrid,
alpha_points, color_points_train, color_points_val, color_most_likey,color_mean,color_truth,
ax1,ax2)
elif(X_data_tr.shape[1] == 2): # Classification Example
plot_data_classification_2d_2axes(X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func, X_data_val, Y_data_val,
xx,yy,all_y_grid, most_likely_ygrid,
alpha_points, color_points_train, color_points_val, color_most_likey,color_mean, color_truth,
ax1,ax2)
else: # RNN example
plot_data_RNN_1d_2axes(X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func, X_data_val, Y_data_val,
x_grid,all_y_grid, most_likely_ygrid,
alpha_points, color_points_train, color_points_val, color_most_likey,color_mean,color_truth,
ax1,ax2)
# gl.fill_between (x_grid, [mean_samples_grid + 2*std_samples_grid, mean_samples_grid - 2*std_samples_grid]
# , ax = ax2, alpha = 0.10, color = "b", legend = ["Mean realizaions"])
## ax2: The uncertainty of the prediction !!
# gl.plot (x_grid, std_samples_grid, ax = ax2, labels = ["Std (%i)"%(Nsamples),"X","f(X)"], legend = [" std predictions"], fill = 1, alpha = 0.3)
############## ax3 ax4 ax5: Loss Evolution !! ######################
## ax3: Evolutoin of the data loss
gl.plot([], tr_loss, ax = ax3, lw = 3, labels = ["Losses", "","Data loss"], legend = ["train"],
color = color_train_loss)
gl.plot([], val_loss,ax = ax3, lw = 3, legend = ["validation"],
color = color_val_loss, AxesStyle = "Normal - No xaxis")
## ax4: The evolution of the KL loss
gl.plot([], KL_loss, ax = ax4, lw = 3, labels = ["", "","KL loss"], legend = ["Bayesian Weights"],
AxesStyle = "Normal - No xaxis", color = "k")
## ax5: Evolutoin of the total loss
gl.plot([], final_loss_tr, ax = ax5, lw = 3, labels = ["", "epoch","Total Loss (Bayes)"], legend = ["train"],
color = color_train_loss)
gl.plot([], final_loss_val,ax = ax5, lw = 3, legend = ["validation"], color = color_val_loss)
############## ax6 ax7: Variational Weights !! ######################
create_plot_variational_weights(model,ax6,ax7)
## Plot in chart 7 the acceptable mu = 2sigma -> sigma = |mu|/2sigma
mu_grid = np.linspace(-3,3,100)
y_grid = np.abs(mu_grid)/2
gl.fill_between(mu_grid, 10*np.ones(mu_grid.size), y_grid,
alpha = 0.2, color = "r", ax = ax7, legend = ["95% non-significant"])
gl.set_zoom (ax = ax6, ylim = [-0.1,10])
gl.set_zoom (ax = ax7, xlim = [-2.5, 2.5], ylim = [-0.05, np.exp(model.cf_a.input_layer_prior["log_sigma2"])*(1 + 0.15)])
# gl.set_zoom (ax = ax7, xlim = [-2.5, 2.5], ylim = [-0.1,2])
# Set final properties and save figure
gl.set_fontSizes(ax = [ax1,ax2,ax3,ax4,ax5,ax6,ax7], title = 20, xlabel = 20, ylabel = 20,
legend = 10, xticks = 12, yticks = 12)
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.30, hspace=0.10)
if (type(epoch_i) == type(None)):
gl.savefig(folder_images +"../"+'Final_values_regression_1D_' +str(model.cf_a.eta_KL) +'.png',
dpi = 100, sizeInches = [20, 10])
else:
gl.savefig(folder_images +'%i.png'%epoch_i,
dpi = 100, sizeInches = [20, 10], close = True, bbox_inches = "tight")