def plot_weights_network(model, folder_images):
#
weights = model.linear1.weight.detach().numpy()
biases = model.linear1.bias.detach().numpy().reshape(-1, 1)
neurons = np.concatenate((weights, biases), axis=1)
weights2 = model.W2.detach().numpy()
biases2 = model.b2.detach().numpy().reshape(-1, 1)
neurons2 = np.concatenate((weights2, biases2), axis=0).T
gl.init_figure()
ax1 = gl.subplot2grid((1, 4), (0, 0), rowspan=1, colspan=2)
ax2 = gl.subplot2grid((1, 4), (0, 3), rowspan=1, colspan=4)
cmap = cm.get_cmap('coolwarm', 30)
cax = ax1.imshow(neurons, interpolation="nearest", cmap=cmap)
cax2 = ax2.imshow(neurons2, interpolation="nearest", cmap=cmap)
# plt.xticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='vertical')
# plt.yticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='horizontal')
plt.colorbar(cax)
# plt.colorbar(cax2)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Weights ')
# 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
plt.show()
gl.savefig(folder_images + 'Weights.png',
dpi=100,
sizeInches=[2 * 8, 2 * 2])
def create_image_training_epoch(X_data_tr, Y_data_tr, X_data_val, Y_data_val,
tr_loss, val_loss, x_grid, y_grid, cf_a,
video_fotograms_folder, epoch_i):
"""
Creates the image of the training and validation accuracy
"""
gl.init_figure();
ax1 = gl.subplot2grid((2,1), (0,0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((2,1), (1,0), rowspan=1, colspan=1)
plt.title("Training")
## First plot with the data and predictions !!!
ax1 = gl.scatter(X_data_tr, Y_data_tr, ax = ax1, lw = 3,legend = ["tr points"], labels = ["Analysis of training", "X","Y"])
gl.scatter(X_data_val, Y_data_val, lw = 3,legend = ["val points"])
gl.plot (x_grid, y_grid, legend = ["Prediction function"])
gl.set_zoom(xlimPad = [0.2, 0.2], ylimPad = [0.2,0.2], X = X_data_tr, Y = Y_data_tr)
## Second plot with the evolution of parameters !!!
ax2 = gl.plot([], tr_loss, ax = ax2, lw = 3, labels = ["RMSE. lr: %.3f"%cf_a.lr, "epoch","RMSE"], legend = ["train"])
gl.plot([], val_loss, lw = 3, legend = ["validation"], loc = 3)
gl.set_fontSizes(ax = [ax1,ax2], title = 20, xlabel = 20, ylabel = 20,
legend = 20, xticks = 12, yticks = 12)
# Set final properties and save figure
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.30, hspace=0.30)
gl.savefig(video_fotograms_folder +'%i.png'%epoch_i,
dpi = 100, sizeInches = [14, 10], close = True, bbox_inches = None)
def plot_weights_network(model, folder_images):
#
weights = model.linear1.weight.detach().numpy()
biases = model.linear1.bias.detach().numpy().reshape(-1,1)
neurons = np.concatenate((weights, biases), axis = 1)
weights2 = model.W2.detach().numpy()
biases2 = model.b2.detach().numpy().reshape(-1,1)
neurons2 = np.concatenate((weights2, biases2), axis =0).T
gl.init_figure();
ax1 = gl.subplot2grid((1,4), (0,0), rowspan=1, colspan=2)
ax2 = gl.subplot2grid((1,4), (0,3), rowspan=1, colspan=4)
cmap = cm.get_cmap('coolwarm', 30)
cax = ax1.imshow(neurons, interpolation="nearest", cmap=cmap)
cax2 = ax2.imshow(neurons2, interpolation="nearest", cmap=cmap)
# plt.xticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='vertical')
# plt.yticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='horizontal')
plt.colorbar(cax)
# plt.colorbar(cax2)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Weights ')
# 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
plt.show()
gl.savefig(folder_images +'Weights.png',
dpi = 100, sizeInches = [2*8, 2*2])
def create_image_training_epoch(X_data_tr, Y_data_tr, X_data_val, Y_data_val,
tr_loss, val_loss, x_grid, y_grid, cf_a,
video_fotograms_folder, epoch_i):
"""
Creates the image of the training and validation accuracy
"""
gl.init_figure()
ax1 = gl.subplot2grid((2, 1), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((2, 1), (1, 0), rowspan=1, colspan=1)
plt.title("Training")
## First plot with the data and predictions !!!
ax1 = gl.scatter(X_data_tr,
Y_data_tr,
ax=ax1,
lw=3,
legend=["tr points"],
labels=["Analysis of training", "X", "Y"])
gl.scatter(X_data_val, Y_data_val, lw=3, legend=["val points"])
gl.plot(x_grid, y_grid, legend=["Prediction function"])
gl.set_zoom(xlimPad=[0.2, 0.2],
ylimPad=[0.2, 0.2],
X=X_data_tr,
Y=Y_data_tr)
## Second plot with the evolution of parameters !!!
ax2 = gl.plot([],
tr_loss,
ax=ax2,
lw=3,
labels=["RMSE. lr: %.3f" % cf_a.lr, "epoch", "RMSE"],
legend=["train"])
gl.plot([], val_loss, lw=3, legend=["validation"], loc=3)
gl.set_fontSizes(ax=[ax1, ax2],
title=20,
xlabel=20,
ylabel=20,
legend=20,
xticks=12,
yticks=12)
# Set final properties and save figure
gl.subplots_adjust(left=.09,
bottom=.10,
right=.90,
top=.95,
wspace=.30,
hspace=0.30)
gl.savefig(video_fotograms_folder + '%i.png' % epoch_i,
dpi=100,
sizeInches=[14, 10],
close=True,
bbox_inches=None)
Z = R.dot(Y - mu_Y) + mu_Y
SigmaZ = R.dot(SigmaY.dot(R.T))
mu_Z = mu_Y
## TODO: AS we can see, the angle is actually positive, but when proejcting that is what you lose.
############################################################
################# PLOT DATA ###############################
############################################################
if(distribution_graph):
# Get the histogram and gaussian estimations !
## Scatter plot of the points
gl.init_figure()
ax1 = gl.subplot2grid((4,4), (1,0), rowspan=3, colspan=3)
gl.scatter(Y[0,:],Y[1,:], alpha = 0.5, ax = ax1, lw = 4, AxesStyle = "Normal",
labels = ["","U1","U2"],
legend = ["%i points"%Nsam])
# Plot the projection vectors
n = 6;
gl.plot([-n*R[0,0],n*R[0,0]],[- n*R[0,1],n*R[0,1]],color = "y", lw = 3);
gl.plot([-n*R[1,0],n*R[1,0]],[-n*R[1,1],n*R[1,1]],color = "r", lw = 3);
## Plot the projections !!
# V1_pr= []
# V2_pr= []
#
# for i in range(Y.shape[1]):
# V1_pr.append([Y[:,i],Y[:,i]- R[0,:].dot(Y[:,i]) * R[0,:]])
def generate_images_iterations_ll(Xs,mus,covs, Ks ,myDManager, logl,theta_list,model_theta_list,folder_images_gif):
# os.remove(folder_images_gif) # Remove previous images if existing
"""
WARNING: MEANT FOR ONLY 3 Distributions due to the color RGB
"""
import shutil
ul.create_folder_if_needed(folder_images_gif)
shutil.rmtree(folder_images_gif)
ul.create_folder_if_needed(folder_images_gif)
######## Plot the original data #####
Xdata = np.concatenate(Xs,axis = 1).T
colors = ["r","b","g"]
K_G,K_W,K_vMF = Ks
### FOR EACH ITERATION
for i in range(len(theta_list)): # theta_list
indx = i
gl.init_figure()
ax1 = gl.subplot2grid((1,2), (0,0), rowspan=1, colspan=1)
## Get the relative ll of the Gaussian denoising cluster.
ll = myDManager.pdf_log_K(Xdata,theta_list[indx])
N,K = ll.shape
# print ll.shape
for j in range(N): # For every sample
#TODO: Can this not be done without a for ?
# Normalize the probability of the sample being generated by the clusters
Marginal_xi_probability = gf.sum_logs(ll[j,:])
ll[j,:] = ll[j,:]- Marginal_xi_probability
ax1 = gl.scatter(Xdata[j,0],Xdata[j,1], labels = ['EM Evolution. Kg:'+str(K_G)+ ', Kw:' + str(K_W) + ', K_vMF:' + str(K_vMF), "X1","X2"],
color = (np.exp(ll[j,1]), np.exp(ll[j,0]), np.exp(ll[j,2])) , ### np.exp(ll[j,2])
alpha = 1, nf = 0)
# Only doable if the clusters dont die
for k_c in myDManager.clusterk_to_Dname.keys():
k = myDManager.clusterk_to_thetak[k_c]
distribution_name = myDManager.clusterk_to_Dname[k_c] # G W
if (distribution_name == "Gaussian"):
## Plot the ecolution of the mu
#### Plot the Covariance of the clusters !
mean,w,h,theta = bMA.get_gaussian_ellipse_params( mu = theta_list[indx][k][0], Sigma = theta_list[indx][k][1], Chi2val = 2.4477)
r_ellipse = bMA.get_ellipse_points(mean,w,h,theta)
gl.plot(r_ellipse[:,0], r_ellipse[:,1], ax = ax1, ls = "-.", lw = 3,
AxesStyle = "Normal2",
legend = ["Kg(%i). pi:%0.2f"%(k, float(model_theta_list[indx][0][0,k]))])
elif(distribution_name == "Watson"):
#### Plot the pdf of the distributino !
## Distribution parameters for Watson
kappa = float(theta_list[indx][k][1]); mu = theta_list[-1][k][0]
Nsa = 1000
# Draw 2D samples as transformation of the angle
Xalpha = np.linspace(0, 2*np.pi, Nsa)
Xgrid= np.array([np.cos(Xalpha), np.sin(Xalpha)])
probs = [] # Vector with probabilities
for i in range(Nsa):
probs.append(np.exp(Wad.Watson_pdf_log(Xgrid[:,i],[mu,kappa]) ))
probs = np.array(probs)
# Plot it in polar coordinates
X1_w = (1 + probs) * np.cos(Xalpha)
X2_w = (1 + probs) * np.sin(Xalpha)
gl.plot(X1_w,X2_w,
alpha = 1, lw = 3, ls = "-.", legend = ["Kw(%i). pi:%0.2f"%(k, float(model_theta_list[indx][0][0,k]))])
elif(distribution_name == "vonMisesFisher"):
#### Plot the pdf of the distributino !
## Distribution parameters for Watson
kappa = float(theta_list[indx][k][1]); mu = theta_list[indx][k][0]
Nsa = 1000
# Draw 2D samples as transformation of the angle
Xalpha = np.linspace(0, 2*np.pi, Nsa)
Xgrid= np.array([np.cos(Xalpha), np.sin(Xalpha)])
probs = [] # Vector with probabilities
for i in range(Nsa):
probs.append(np.exp(vMFd.vonMisesFisher_pdf_log(Xgrid[:,i],[mu,kappa]) ))
probs = np.array(probs)
probs = probs.reshape((probs.size,1)).T
# Plot it in polar coordinates
X1_w = (1 + probs) * np.cos(Xalpha)
X2_w = (1 + probs) * np.sin(Xalpha)
# print X1_w.shape, X2_w.shape
gl.plot(X1_w,X2_w,
alpha = 1, lw = 3, ls = "-.", legend = ["Kvmf(%i). pi:%0.2f"%(k, float(model_theta_list[indx][0][0,k]))])
gl.set_zoom(xlim = [-6,6], ylim = [-6,6], ax = ax1)
ax2 = gl.subplot2grid((1,2), (0,1), rowspan=1, colspan=1)
if (indx == 0):
gl.add_text(positionXY = [0.1,.5], text = r' Initilization Incomplete LogLike: %.2f'%(logl[0]),fontsize = 15)
pass
elif (indx >= 1):
gl.plot(range(1,np.array(logl).flatten()[1:].size +1),np.array(logl).flatten()[1:(indx+1)], ax = ax2,
legend = ["Iteration %i, Incom LL: %.2f"%(indx, logl[indx])], labels = ["Convergence of LL with generated data","Iterations","LL"], lw = 2)
gl.scatter(1, logl[1], lw = 2)
pt = 0.05
gl.set_zoom(xlim = [0,len(logl)], ylim = [logl[1] - (logl[-1]-logl[1])*pt,logl[-1] + (logl[-1]-logl[1])*pt], ax = ax2)
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.2, hspace=0.01)
gl.savefig(folder_images_gif +'gif_'+ str(indx) + '.png',
dpi = 100, sizeInches = [16, 8], close = "yes",bbox_inches = None)
gl.close("all")
def create_Bayesian_analysis_charts_simplified(model, train_dataset, validation_dataset,
tr_loss, val_loss, KL_loss,
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"
################################ 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)
####### ax1, ax2: Get confusion matrices ##########
labels_classes, confusion = model.get_confusion_matrix(train_dataset)
plot_confusion_matrix(confusion,labels_classes, ax1 )
labels_classes, confusion = model.get_confusion_matrix(validation_dataset)
plot_confusion_matrix(confusion,labels_classes, ax2 )
############## ax3 ax4 ax5: Loss Evolution !! ######################
## ax3: Evolutoin of the data loss
gl.plot([], tr_loss, ax = ax3, lw = 3, labels = ["Losses", "","Data loss (MSE)"], 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([], tr_loss, ax = ax5, lw = 3, labels = ["", "epoch","Total Loss (Bayes)"], legend = ["train"],
color = color_train_loss)
gl.plot([], val_loss,ax = ax5, lw = 3, legend = ["validation"], color = color_val_loss)
############## ax6 ax7: Variational Weights !! ######################
create_plot_variational_weights(model,ax6,ax7)
gl.set_zoom (ax = ax6, ylim = [-0.1,10])
gl.set_zoom (ax = ax7, xlim = [-2.5, 2.5], ylim = [-0.1,0.5])
# 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 +'Training_Example_Data_Bayesian.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")
def create_image_weights_epoch(model, video_fotograms_folder2, epoch_i):
"""
Creates the image of the training and validation accuracy
"""
N_Bayesian_layers = len(model.VBmodels)
N_Normal_layers = len(model.LinearModels)
# Compute the number of squares we will need:
# 1 x linear layers, 2 x LSTMS
gl.init_figure()
cmap = cm.get_cmap('coolwarm', 30)
all_axes = []
for i in range(N_Bayesian_layers):
layer = model.VBmodels[i]
# if (layer.type_layer == "linear"):
if ("linear" in type(layer).__name__.lower()):
ax = gl.subplot2grid((1, N_Bayesian_layers + N_Normal_layers),
(0, i),
rowspan=1,
colspan=1)
weights = layer.weight.detach().cpu().numpy()
biases = layer.bias.detach().cpu().numpy().reshape(-1, 1)
neurons = np.concatenate((weights, biases), axis=1)
cax = ax.imshow(neurons,
interpolation="nearest",
cmap=cmap,
vmin=-2,
vmax=2)
all_axes.append(ax)
else:
ax = gl.subplot2grid((1, N_Bayesian_layers + N_Normal_layers),
(0, i),
rowspan=1,
colspan=1)
weights_ih = layer.weight_ih.detach().cpu().numpy()
biases_ih = layer.bias_ih.detach().cpu().numpy().reshape(-1, 1)
weights_hh = layer.weight_hh.detach().cpu().numpy()
biases_hh = layer.bias_hh.detach().cpu().numpy().reshape(-1, 1)
weights = np.concatenate((weights_ih, weights_hh), axis=1)
biases = np.concatenate((biases_ih, biases_hh), axis=1)
neurons = np.concatenate((weights, biases), axis=1)
cax = ax.imshow(neurons,
interpolation="nearest",
cmap=cmap,
vmin=-2,
vmax=2)
all_axes.append(ax)
for i in range(N_Normal_layers):
layer = model.LinearModels[i]
if ("linear" in type(layer).__name__.lower()):
ax = gl.subplot2grid((1, N_Bayesian_layers + N_Normal_layers),
(0, N_Bayesian_layers + i),
rowspan=1,
colspan=1)
weights = layer.weight.detach().cpu().numpy()
biases = layer.bias.detach().cpu().numpy().reshape(-1, 1)
neurons = np.concatenate((weights, biases), axis=1)
cax = ax.imshow(neurons,
interpolation="nearest",
cmap=cmap,
vmin=-2,
vmax=2)
all_axes.append(ax)
else:
ax = gl.subplot2grid((1, N_Bayesian_layers + N_Normal_layers),
(0, N_Bayesian_layers + i),
rowspan=1,
colspan=1)
weights_ih = layer.weight_ih.detach().cpu().numpy()
biases_ih = layer.bias_ih.detach().cpu().numpy().reshape(-1, 1)
weights_hh = layer.weight_hh.detach().cpu().numpy()
biases_hh = layer.bias_hh.detach().cpu().numpy().reshape(-1, 1)
weights = np.concatenate((weights_ih, weights_hh), axis=1)
biases = np.concatenate((biases_ih, biases_hh), axis=1)
neurons = np.concatenate((weights, biases), axis=1)
cax = ax.imshow(neurons,
interpolation="nearest",
cmap=cmap,
vmin=-2,
vmax=2)
all_axes.append(ax)
# plt.xticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='vertical')
# plt.yticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='horizontal')
plt.colorbar(cax)
# plt.colorbar(cax2)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Weights ')
# 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
plt.show()
gl.set_fontSizes(ax=[all_axes],
title=20,
xlabel=20,
ylabel=20,
legend=20,
xticks=12,
yticks=12)
# Set final properties and save figure
gl.subplots_adjust(left=.09,
bottom=.10,
right=.90,
top=.95,
wspace=.30,
hspace=0.30)
gl.savefig(video_fotograms_folder2 + '%i.png' % epoch_i,
dpi=100,
sizeInches=[14, 10],
close=True,
bbox_inches=None)
################### PLOTTING THE DATA #########################
################################################################
## Plotting plotting
plotting_some = 1
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
plt.xlim([-1, X.shape[1]])
plt.stem(range(X.shape[1]), pvalues[indices], label="p-value")
plt.show()
gl.savefig(folder_images + 'Feauture_importance_LM.png',
dpi=100,
sizeInches=[2 * 8, 2 * 2])
############## Plot the correlation matrix #######################
if (plot_correlation):
Cov_tr = np.corrcoef(data_df_train.T)
from matplotlib import cm as cm
#
gl.init_figure()
ax1 = gl.subplot2grid((1, 4), (0, 0), rowspan=1, colspan=4)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(Cov_tr, interpolation="nearest", cmap=cmap)
plt.xticks(range(data_df_train.shape[1]),
data_df_train.columns,
rotation='vertical')
plt.yticks(range(data_df_train.shape[1]),
data_df_train.columns,
rotation='horizontal')
plt.colorbar(cax)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Correlation matrix of variables')
# labels=[str(x) for x in range(Nshow )]
# ax1.set_xticklabels(labels,fontsize=20)
def init_figure(self):
"""
This function initializes the chart, with its widgets and everything
"""
button_height = 0.030;
textbox_length0 = 0.02
textbox_length1 = 0.04
textbox_length2 = 0.05
fig = gl.init_figure();
## Set the image to full screen
fig_manager = plt.get_current_fig_manager()
if hasattr(fig_manager, 'window'):
fig_manager.window.showMaximized()
data_axes = gl.subplot2grid((1,4), (0,0), rowspan=1, colspan=3)
self.fig = fig; self.data_axes = data_axes;
#### Logo Images !!
logo_path = self.output_folder + "images_IoTubes/IoTubes_logo.png"
image = mpimg.imread(logo_path)
ax_img = plt.axes([0.725, 0.75, 0.2, 0.2])
ax_img.imshow(image)
ax_img.axis("off")
################## Widgets Axes #####################
widgets_x = 0.76
widgets_x2 = 0.85
widgets_x3 = 0.90
w1_x, w2_x, w3_x = 0.73, 0.8,0.87
base_y = 0.69
administration_y = base_y
monitoring_y = administration_y - 0.12
chart_s_y = monitoring_y - 0.12
chart_s_y2 = chart_s_y -0.05
chart_start_stop_y = chart_s_y2 - 0.05
output_y = chart_start_stop_y - 0.12
diff_headline_content = 0.052
## Administration !
headlines_x = 0.705
text = self.fig.text(headlines_x, administration_y + diff_headline_content, 'Administration:', size=20) # ha='center', va='center', size=20)
axbox_machineID = plt.axes([widgets_x, administration_y, textbox_length1, button_height])
axbox_pipingID = plt.axes([widgets_x2, administration_y, textbox_length1, button_height])
### Monitoring
text = self.fig.text(headlines_x, monitoring_y + diff_headline_content, 'PH Monitoring:', size=20) # ha='center', va='center', size=20)
axbox_desired_value = plt.axes([widgets_x, monitoring_y, textbox_length0, button_height])
axbox_range_warning = plt.axes([widgets_x2, monitoring_y, textbox_length0, button_height])
## Sampling and plotting
text = self.fig.text(headlines_x, output_y + diff_headline_content, 'Output Generation:', size=20) # ha='center', va='center', size=20)
axbox_sample_period = plt.axes([widgets_x, chart_s_y, textbox_length1, button_height])
axbox_plot_period = plt.axes([widgets_x2, chart_s_y, textbox_length1, button_height])
axbox_Nsamples_show = plt.axes([widgets_x, chart_s_y2, textbox_length1, button_height])
ax_start = plt.axes([widgets_x,chart_start_stop_y, 0.04, button_height])
ax_stop = plt.axes([widgets_x2, chart_start_stop_y, 0.04, button_height])
## Output
text = self.fig.text(headlines_x, chart_s_y + diff_headline_content, 'Sampling and plotting:', size=20) # ha='center', va='center', size=20)
axsave_disk = plt.axes([w1_x, output_y, 0.055, button_height])
axsave_DDBB = plt.axes([w2_x, output_y, 0.055, button_height])
axreport = plt.axes([w3_x, output_y, 0.055, button_height])
################## Add functionalities ###########################
################ Chart AXES ################:
bstop = Button(ax_stop, 'Stop')
bstop.on_clicked(self.stop_reading_data)
bstart = Button(ax_start, 'Start')
bstart.on_clicked(self.start_reading_data)
# bprev.on_clicked(self.auto_update_test)
#### Text input Period ####
initial_text = str(int(self.period_sampling * 1000));
text_box_sample_period = TextBox(axbox_sample_period, 'Sample(ms) ', initial=initial_text)
text_box_sample_period.on_submit(self.submit_sample_period)
initial_text = str(int(self.period_plotting * 1000));
text_box_plotting_period = TextBox(axbox_plot_period, 'Plot(ms) ', initial=initial_text)
text_box_plotting_period.on_submit(self.submit_plotting_period)
#### Text input N samples ####
initial_text = str(int(self.show_window));
text_Nsamples_show = TextBox(axbox_Nsamples_show, 'Samples Chart ', initial=initial_text)
text_Nsamples_show.on_submit(self.submit_show_window)
################ Data generation widgets ################
bpsave_disk = Button(axsave_disk, 'Save Disk')
bpsave_disk.on_clicked(self.save_to_disk)
bpsave_DDBB = Button(axsave_DDBB, 'Save DDBB')
bpsave_DDBB.on_clicked(self.send_buffer_to_DDBB)
bpsave_report = Button(axreport, 'Report')
bpsave_report.on_clicked(self.generate_report)
################ Cleaning input widgets ################
## Text input MAchine ID
initial_text = self.machine_ID
text_box_machine = TextBox(axbox_machineID, 'Machine ID ', initial=initial_text)
text_box_machine.on_submit(self.submit_machineID)
initial_text = self.piping_ID
text_box_piping = TextBox(axbox_pipingID, 'Piping ID ', initial=initial_text)
text_box_piping.on_submit(self.submit_pipingID)
################ MONITORING variables ################
initial_text = str(self.Monitor.desired_value);
text_desired_value = TextBox(axbox_desired_value, 'Desired PH ', initial=initial_text)
text_desired_value.on_submit(self.submit_desired_value)
initial_text = str(self.Monitor.range_warning);
text_range_warning = TextBox(axbox_range_warning, 'Warning Range ', initial=initial_text)
text_range_warning.on_submit(self.submit_range_warning)
# I think we needed to keep them in memory of they would die
self.buttons = [bstart, bstop, bpsave_disk,bpsave_DDBB,text_box_machine,
text_box_sample_period,text_box_plotting_period,
text_Nsamples_show,
text_desired_value, text_range_warning,bpsave_report, text_box_piping]
self.initial_text_data = gl.add_text(positionXY = [0.35,0.5], text = r'Waiting for data',fontsize = 30, ax = data_axes)
gl.subplots_adjust(left=.09, bottom=.20, right=.90, top=.90, wspace=.20, hspace=0)
self.monitoring_y = monitoring_y
return_lazy_loading_size, return_hidden_size, return_betas,
return_initialization_index
]
#Standard_values_alyses = [0.001,0.2,40,
# 0.2,10000,100,
# 0.9, "whatever"]
Standard_values_alyses = [0.001, 0.2, 30, 0.2, 10000, 100, 0.9, "whatever"]
"""
Get the data from the training_logger for the charts for Batch info
"""
N_analyses = len(List_analyses)
for an_i in range(N_analyses):
gl.init_figure()
ax1 = gl.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((1, 2), (0, 1),
rowspan=1,
colspan=1,
sharex=ax1,
sharey=ax1)
## Subselect models to plot !!!
## Models with Lazy loading True, 35 epochs
Selected_training_logger_list = []
Selected_cf_a_list = []
for i in range(Nmodels):
training_logger = training_logger_list[i]
cf_a = cf_a_list[i]
MACD, MACDsign, MACDdiff = timeData.MACD()
TRIX = timeData.TRIX()
gl.set_subplots(3,1)
gl.plot(dates, price , nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price"])
gl.plot(dates, [MACD, MACDsign, MACDdiff], nf = 1, na = 0,
legend = ["MACD"])
gl.plot(dates, TRIX , nf = 1, na = 0,
legend = [ "TRIX"])
gl.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
gl.create_axes(position = [0.3,0.4,0.3,0.4])
# gl.subplot2grid((10,10),)
# gl.subplot2grid((6,4), (5,0), ro)
gl.plot(dates, TRIX , nf = 0, na = 0,legend = [ "TRIX"])
# gl.plot(dates, ADX , nf = 1, na = 0,
# legend = ["ADX"])
#
# gl.plot(dates, ACCDIST , nf = 1, na = 0,
# legend = ["ACCDIST"])
#
trading_indicators_complex = 0
nMA1 = 10
nMA2 = 20
SMAw = indl.get_SMA(bMA.delta(nMA1), nMA1, cval = 1)
EMAw = indl.get_EMA(bMA.delta(nMA1), nMA1, cval = 1)
WMAw = indl.get_WMA(bMA.delta(nMA1), nMA1, cval = 1)
SMAw2 = indl.get_SMA(bMA.delta(nMA2), nMA2, cval = 1)
EMAw2 = indl.get_EMA(bMA.delta(nMA2), nMA2, cval = 1)
WMAw2 = indl.get_WMA(bMA.delta(nMA2), nMA2, cval = 1)
SMA = timeData.SMA(n = nMA1)
""" 1st GRAPH """
############## Average and Window ################
ax1 = gl.subplot2grid((1,5), (0,0), rowspan=1, colspan=3)
title = "Price and SMA. " + str(symbols[0]) + "(" + ul.period_dic[timeData.period]+ ")"
gl.plot(dates, [price, SMA] ,AxesStyle = "Normal",
labels = [title,"",r"Price ($\$$)"],
legend = ["Price", "SMA(%i)"%nMA1])
ax2 = gl.subplot2grid((1,5), (0,3), rowspan=1, colspan=2)
gl.stem([], SMAw, nf = 0, AxesStyle = "Normal2",
labels = ["SMA Window","lag",""],
legend = ["SMAw(%i)"%nMA1],
xlimPad = [0.1,0.3], ylimPad = [0.1,0.4],
marker = [".",10,None])
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.35, hspace=0)
gl.savefig(folder_images +'basicSMA.png',
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")
ygrid_real_func, folder_images)
if (plot_weights):
####### PLOT ANALYSIS OF THE WEIGHTS ###########
pf.plot_weights_network(myGeneralVBModel, folder_images)
pf.plot_Variational_weights_network(myGeneralVBModel, folder_images)
saved_models.append(myGeneralVBModel)
if (1):
### USED TO GENERATE THE CHART FOR THE INTRO:
gl.init_figure()
Nmodels = len(saved_models)
all_axes = []
for i in range(Nmodels):
gl.colorIndex = 0
model = saved_models[i]
ax1 = gl.subplot2grid((2, Nmodels), (0, i), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((2, Nmodels), (1, i), rowspan=1, colspan=1)
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"
x_grid, all_y_grid, most_likely_ygrid = pf.compute_regression_1D_data(
model, X_data_tr, X_data_val, Nsamples=100)
pf.plot_data_regression_1d_2axes(
X_data_tr, Y_data_tr, xgrid_real_func, ygrid_real_func, X_data_val,
def create_Bayesian_analysis_charts(model,
X_data_tr,
X_data_val,
tr_data_loss,
val_data_loss,
KL_loss_tr,
KL_loss_val,
final_loss_tr,
final_loss_val,
folder_images,
epoch_i=None):
# Configurations of the plots
alpha_points = 0.2
color_points_train = "dark navy blue"
color_points_val = "amber"
color_truth = "k"
color_mean = "b"
color_most_likey = "y"
################################ 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)
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)
"""
############################# Data computation #######################
"""
Xtrain_sample_cpu, Xtrain_reconstruction,Xtrain_reconstruction_samples = \
compute_reconstruction_data( model,X_data_tr, Nsamples = 100, sample_index = 2)
plot_reconstruction_data(Xtrain_sample_cpu, Xtrain_reconstruction,
Xtrain_reconstruction_samples, ax1, ax2)
"""
############## ax3 ax4 ax5: Loss Evolution !! ######################
"""
plot_losses_evolution_epoch(tr_data_loss, val_data_loss, KL_loss_tr,
KL_loss_val, final_loss_tr, final_loss_val,
ax3, ax4, ax5)
"""
############## ax6 ax7: Projecitons Weights !! ######################
"""
plot_projections_VAE(model, X_data_tr, ax6)
## Plot in chart 7 the acceptable mu = 2sigma -> sigma = |mu|/2sigma
# 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")
def generate_images_iterations_ll(Xs, mus, covs, Ks, myDManager, logl,
theta_list, model_theta_list,
folder_images_gif):
# os.remove(folder_images_gif) # Remove previous images if existing
"""
WARNING: MEANT FOR ONLY 3 Distributions due to the color RGB
"""
import shutil
ul.create_folder_if_needed(folder_images_gif)
shutil.rmtree(folder_images_gif)
ul.create_folder_if_needed(folder_images_gif)
######## Plot the original data #####
Xdata = np.concatenate(Xs, axis=1).T
colors = ["r", "b", "g"]
K_G, K_W, K_vMF = Ks
### FOR EACH ITERATION
for i in range(len(theta_list)): # theta_list
indx = i
gl.init_figure()
ax1 = gl.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=1)
## Get the relative ll of the Gaussian denoising cluster.
ll = myDManager.pdf_log_K(Xdata, theta_list[indx])
N, K = ll.shape
# print ll.shape
for j in range(N): # For every sample
#TODO: Can this not be done without a for ?
# Normalize the probability of the sample being generated by the clusters
Marginal_xi_probability = gf.sum_logs(ll[j, :])
ll[j, :] = ll[j, :] - Marginal_xi_probability
ax1 = gl.scatter(
Xdata[j, 0],
Xdata[j, 1],
labels=[
'EM Evolution. Kg:' + str(K_G) + ', Kw:' + str(K_W) +
', K_vMF:' + str(K_vMF), "X1", "X2"
],
color=(np.exp(ll[j, 1]), np.exp(ll[j, 0]),
np.exp(ll[j, 2])), ### np.exp(ll[j,2])
alpha=1,
nf=0)
# Only doable if the clusters dont die
for k_c in myDManager.clusterk_to_Dname.keys():
k = myDManager.clusterk_to_thetak[k_c]
distribution_name = myDManager.clusterk_to_Dname[k_c] # G W
if (distribution_name == "Gaussian"):
## Plot the ecolution of the mu
#### Plot the Covariance of the clusters !
mean, w, h, theta = bMA.get_gaussian_ellipse_params(
mu=theta_list[indx][k][0],
Sigma=theta_list[indx][k][1],
Chi2val=2.4477)
r_ellipse = bMA.get_ellipse_points(mean, w, h, theta)
gl.plot(r_ellipse[:, 0],
r_ellipse[:, 1],
ax=ax1,
ls="-.",
lw=3,
AxesStyle="Normal2",
legend=[
"Kg(%i). pi:%0.2f" %
(k, float(model_theta_list[indx][0][0, k]))
])
elif (distribution_name == "Watson"):
#### Plot the pdf of the distributino !
## Distribution parameters for Watson
kappa = float(theta_list[indx][k][1])
mu = theta_list[-1][k][0]
Nsa = 1000
# Draw 2D samples as transformation of the angle
Xalpha = np.linspace(0, 2 * np.pi, Nsa)
Xgrid = np.array([np.cos(Xalpha), np.sin(Xalpha)])
probs = [] # Vector with probabilities
for i in range(Nsa):
probs.append(
np.exp(Wad.Watson_pdf_log(Xgrid[:, i], [mu, kappa])))
probs = np.array(probs)
# Plot it in polar coordinates
X1_w = (1 + probs) * np.cos(Xalpha)
X2_w = (1 + probs) * np.sin(Xalpha)
gl.plot(X1_w,
X2_w,
alpha=1,
lw=3,
ls="-.",
legend=[
"Kw(%i). pi:%0.2f" %
(k, float(model_theta_list[indx][0][0, k]))
])
elif (distribution_name == "vonMisesFisher"):
#### Plot the pdf of the distributino !
## Distribution parameters for Watson
kappa = float(theta_list[indx][k][1])
mu = theta_list[indx][k][0]
Nsa = 1000
# Draw 2D samples as transformation of the angle
Xalpha = np.linspace(0, 2 * np.pi, Nsa)
Xgrid = np.array([np.cos(Xalpha), np.sin(Xalpha)])
probs = [] # Vector with probabilities
for i in range(Nsa):
probs.append(
np.exp(
vMFd.vonMisesFisher_pdf_log(
Xgrid[:, i], [mu, kappa])))
probs = np.array(probs)
probs = probs.reshape((probs.size, 1)).T
# Plot it in polar coordinates
X1_w = (1 + probs) * np.cos(Xalpha)
X2_w = (1 + probs) * np.sin(Xalpha)
# print X1_w.shape, X2_w.shape
gl.plot(X1_w,
X2_w,
alpha=1,
lw=3,
ls="-.",
legend=[
"Kvmf(%i). pi:%0.2f" %
(k, float(model_theta_list[indx][0][0, k]))
])
gl.set_zoom(xlim=[-6, 6], ylim=[-6, 6], ax=ax1)
ax2 = gl.subplot2grid((1, 2), (0, 1), rowspan=1, colspan=1)
if (indx == 0):
gl.add_text(positionXY=[0.1, .5],
text=r' Initilization Incomplete LogLike: %.2f' %
(logl[0]),
fontsize=15)
pass
elif (indx >= 1):
gl.plot(
range(1,
np.array(logl).flatten()[1:].size + 1),
np.array(logl).flatten()[1:(indx + 1)],
ax=ax2,
legend=["Iteration %i, Incom LL: %.2f" % (indx, logl[indx])],
labels=[
"Convergence of LL with generated data", "Iterations", "LL"
],
lw=2)
gl.scatter(1, logl[1], lw=2)
pt = 0.05
gl.set_zoom(xlim=[0, len(logl)],
ylim=[
logl[1] - (logl[-1] - logl[1]) * pt,
logl[-1] + (logl[-1] - logl[1]) * pt
],
ax=ax2)
gl.subplots_adjust(left=.09,
bottom=.10,
right=.90,
top=.95,
wspace=.2,
hspace=0.01)
gl.savefig(folder_images_gif + 'gif_' + str(indx) + '.png',
dpi=100,
sizeInches=[16, 8],
close="yes",
bbox_inches=None)
gl.close("all")
MACD, MACDsign, MACDdiff = timeData.MACD()
TRIX = timeData.TRIX()
gl.set_subplots(3,1)
gl.plot(dates, price , nf = 1,
labels = ["Averages","Time","Value"],
legend = ["Price"])
gl.plot(dates, [MACD, MACDsign, MACDdiff], nf = 1, na = 0,
legend = ["MACD"])
gl.plot(dates, TRIX , nf = 1, na = 0,
legend = [ "TRIX"])
gl.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
gl.create_axes(position = [0.3,0.4,0.3,0.4])
# gl.subplot2grid((10,10),)
# gl.subplot2grid((6,4), (5,0), ro)
gl.plot(dates, TRIX , nf = 0, na = 0,legend = [ "TRIX"])
# gl.plot(dates, ADX , nf = 1, na = 0,
# legend = ["ADX"])
#
# gl.plot(dates, ACCDIST , nf = 1, na = 0,
# legend = ["ACCDIST"])
#
###########################################################################
############## Moving Averages ############################################
color = gl.get_color()
gl.scatter(t_val_abs, t.pdf(t_val_abs, N-1) , color = color,
legend = ["T: %.2f, p-v: %.3f"%(t_val,p_val)])
gl.plot(x_grid, t_pdf, color = color, fill = 1, alpha = 0.2)
gl.set_zoom(ax = ax1, X = x_grid_all,xlimPad = [0.1,0.1])
gl.savefig(folder_images +'t-StatSig.png',
dpi = 100, sizeInches = [14,6])
##### Plot the both sided one !!!
gl.init_figure()
ax0 = gl.subplot2grid((1,2), (0,0), rowspan=1, colspan=1)
t_pdf = t.pdf(x_grid_all, df)
gl.plot(x_grid_all, t_pdf, alpha = 1, lw = 3, AxesStyle = "Normal",
legend = ["df %i"%df],color = color,
labels = ["right-tail event","t","pdf(t)"])
## Plot the right sided
for i in days:
## Day 1
D = ret1[i*26:(i+1)*26]
N,mu_hat,S = D.size,np.mean(D),np.std(D)
t_val = (mu_hat - mu_0)/(S/np.sqrt(N))
x_grid = np.linspace(t_val,4,100)
t_pdf = t.pdf(x_grid, N-1)
p_val = 1 - t.cdf(t_val, N-1)
gl.plot(dates, priceA, ax=axP, nf=0, na=0, legend=["Average price"])
# Formating the legend
maLeg = axP.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
borderaxespad=0.)
maLeg.get_frame().set_alpha(0.4)
textEd = axP.get_legend().get_texts()
pylab.setp(textEd[0:5], color='w')
## Plot other indicators
pos = 0
gl.subplot2grid((Ndiv, 4), (HPV + pos, 0),
rowspan=1,
colspan=4,
sharex=axP,
axisbg='#07000d')
gl.plotMACD(timeData, ax=None)
## Plot other indicators
pos = pos + 1
gl.subplot2grid((Ndiv, 4), (HPV + pos, 0),
rowspan=1,
colspan=4,
sharex=axP)
MOM = timeData.MOM(n=1)
ROC = timeData.ROC(n=2)
gl.plot(dates, MOM, nf=0, na=0, legend=["Momentum"])
gl.plot(dates, ROC, nf=0, na=1, legend=["ROC"])
dates2 = Cartera.get_timeData(symbolIDs[symbol_ID_indx2],periods[0]).get_dates()
symbolIDs = cmplist.iloc[0:30]["Symbol"].tolist()
symbol_ID1 = symbolIDs[symbol_ID_indx1]
symbol_ID2 = symbolIDs[symbol_ID_indx2]
#print dates[0], dates[26], dates[27]
################# Plotting the data #################
if(trading_graph):
# Trading plot of the points !
title = "CLOSE Price, Volume and Return evolution"
ax1 = gl.subplot2grid((4,1), (0,0), rowspan=1, colspan=1)
gl.tradingBarChart(Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]), ax = ax1,
dataTransform = dataTransform, AxesStyle = "Normal - No xaxis", color = "k",
labels = [title,"",symbolIDs[symbol_ID_indx1] +"(" +str(periods[0])+"M)"])
ax2 = gl.subplot2grid((4,1), (1,0), rowspan=1, colspan=1, sharex = ax1)
gl.tradingBarChart(Cartera.get_timeData(symbolIDs[symbol_ID_indx2],periods[0]), ax = ax2,
dataTransform = dataTransform, AxesStyle = "Normal - No xaxis", color = "k",
labels = ["","",symbolIDs[symbol_ID_indx2] +"(" +str(periods[0])+"M)"])
ax3 = gl.subplot2grid((4,1), (2,0), rowspan=1, colspan=1)
gl.stem(dates, ret1, ax = ax3, dataTransform = dataTransform,
AxesStyle = "Normal",
labels = ["","",symbolIDs[symbol_ID_indx1] +"("+ str(periods[0])+ "M)"], legend = ["Return"])
ax4 = gl.subplot2grid((4,1), (3,0), rowspan=1, colspan=1, sharex = ax1, sharey = ax3)
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")
dpi = 100, sizeInches = [14, 7])
if (plot_projections_2Assets):
X2D = X[:,[AAPL_id, GOOGL_id]]
componentsPCA = MSlib.get_components_PCA(X2D, n_components = 2)
Xproj = MSlib.get_projected_PCA(X2D, n_components = 2)
gl.init_figure()
X_1,X_2 = X2D[:,[0]], X2D[:,[1]]
mu_1, mu_2 = np.mean(Xproj, axis =0)
std_1,std_2 = np.std(Xproj,axis =0)
cov = np.cov(np.concatenate((X_1,X_2),axis = 1).T).T
std_K = 3
## Do stuff now
ax0 = gl.subplot2grid((1,2), (0,0), rowspan=1, colspan=1)
gl.scatter(X_1,X_2, alpha = 0.5, ax = ax0, lw = 4, AxesStyle = "Normal",
labels = ["Original 2D data","X1: %s" %(symbolIDs[AAPL_id]), "X2: %s" %(symbolIDs[GOOGL_id])])
ax0.axis('equal')
################# Draw the error ellipse #################
mean,w,h,theta = bMA.get_gaussian_ellipse_params(X2D, Chi2val = 2.4477)
corr = bMA.get_corrMatrix(X2D)
cov = bMA.get_covMatrix(X2D)
vecs,vals = bMA.get_eigenVectorsAndValues(X2D)
r_ellipse = bMA.get_ellipse_points(mean,w,h,theta)
gl.plot(r_ellipse[:,0], r_ellipse[:,1], ax = ax0, ls = "--",color = "k", lw = 2,
legend = ["Corr: %.2f"%(corr[0,1])])
gl.plot([mean[0], mean[0] + vecs[0,0]*w],
if (fill_data_f):
Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]).fill_data()
########## Obtain the data ###############
price = Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]).get_timeSeries(["Close"])
volume = Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]).get_timeSeries(["Volume"])
ret1 = Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]).get_timeSeriesReturn(["Close"])*100
dates = Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]).get_dates()
#print dates[0], dates[26], dates[27]
################# Plotting the data #################
if(trading_graph):
# Trading plot of the points !
title = "CLOSE Price, Volume and Return evolution"
ax1 = gl.subplot2grid((5,1), (0,0), rowspan=2, colspan=1)
gl.tradingBarChart(Cartera.get_timeData(symbolIDs[symbol_ID_indx1],periods[0]), ax = ax1,
dataTransform = dataTransform, AxesStyle = "Normal - No xaxis",
labels = [title,"",symbolIDs[symbol_ID_indx1] +"(" +str(periods[0])+")"])
ax2 = gl.subplot2grid((5,1), (2,0), rowspan=1, colspan=1, sharex = ax1)
gl.stem(dates, volume, ax = ax2, dataTransform = dataTransform,
AxesStyle = "Normal - No xaxis - Ny:4",
labels = ["","",symbolIDs[0] +"("+ str(periods[0])+ "M)"], legend = [ "Volume"])
ax3 = gl.subplot2grid((5,1), (3,0), rowspan=2, colspan=1, sharex = ax1)
gl.stem(dates, ret1, ax = ax3, dataTransform = dataTransform,
AxesStyle = "Normal",
labels = ["","",symbolIDs[0] +"("+ str(periods[0])+ "M)"], legend = ["Return"])
#
gl.set_fontSizes(ax = [ax1,ax2,ax3], title = 20, xlabel = 20, ylabel = 20,
################################################################
################### PLOTTING THE DATA #########################
################################################################
## Plotting plotting
plotting_some = 1
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)
plot_charts = 1
if (plot_charts):
if (True):
Nrows = 2
Ncols = 3
list_all_axes = []
gl.init_figure()
list_all_weights = []
list_all_labels = []
if (cf_a.VB_Linear_projection_ELMO):
# gl.colorIndex = 0
VBmodel = model._linear_projection_ELMO
ax1 = gl.subplot2grid((Nrows, Ncols), (0, 0), rowspan=1, colspan=1)
plot_VB_weights_mu_std_2D(VBmodel, ax1, "LinearVB",
"Linear proyection ELMo $W_{E}$")
list_all_axes.append(ax1)
list_all_labels.append("$W_{E}$")
list_all_weights.append(get_LinearVB_weights(VBmodel))
if (cf_a.VB_highway_layers):
# gl.colorIndex = 0
VBmodel = model._highway_layer._module.VBmodels[0]
ax1 = gl.subplot2grid((Nrows, Ncols), (0, 1), rowspan=1, colspan=1)
plot_VB_weights_mu_std_2D(VBmodel, ax1, "HighwayVB",
"Highway matrix weights $W_{H}$")
list_all_axes.append(ax1)
gl.set_subplots(Npeople, 1)
ax1 = None
legend = []
labels_title = "Cluster responsibility for the EM"
Ndiv = 4
for i in range(Npeople_plot):
if (myEM.clusters_relation == "independent"):
resp = r[N * i:N * (i + 1), :]
elif (myEM.clusters_relation == "MarkovChain1"):
resp = r[i]
Nclusters = resp.shape[1]
ax_ii = gl.subplot2grid((Npeople_plot, Ndiv), (i, 0),
rowspan=1,
colspan=Ndiv - 1)
ax1 = gl.plot_filled(time,
resp,
nf=0,
fill_mode="stacked",
legend=legend,
sharex=ax1,
sharey=ax1,
AxesStyle="Normal - No yaxis",
labels=[labels_title, "",
"%i" % i])
gl.colorIndex = 0
labels_title = ""
ax_i = gl.subplot2grid((Npeople_plot, Ndiv), (0, Ndiv - 1),
## Plot the covariance matrix !
# Show the Nshow first samples
Nshow = 20
## Plot realizations of the Gaussian process
Nrealizations = 10
flag = 1
legend = ["Realizations"]
labels = ["Gaussian Process noise e(t)", "t", "e(t)"]
# Plot the realizations
gl.init_figure()
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
plt.xticks(range(X.shape[1]), names, rotation='vertical')
plt.xlim([-1, X.shape[1]])
plt.stem(range(X.shape[1]), pvalues[indices], label = "p-value")
plt.show()
gl.savefig(folder_images +'Feauture_importance_LM.png',
dpi = 100, sizeInches = [2*8, 2*2])
############## Plot the correlation matrix #######################
if (plot_correlation):
Cov_tr = np.corrcoef(data_df_train.T)
from matplotlib import cm as cm
#
gl.init_figure();
ax1 = gl.subplot2grid((1,4), (0,0), rowspan=1, colspan=4)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(Cov_tr, interpolation="nearest", cmap=cmap)
plt.xticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='vertical')
plt.yticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='horizontal')
plt.colorbar(cax)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Correlation matrix of variables')
# 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
plt.show()
gl.savefig(folder_images +'Corr.png',
List_f_analyses = [
return_layers_dropout, return_hidden_size, return_etaKL, return_sigma2,
return_initialization_index
]
Standard_values_alyses = [0.0, 100, 0.01, 0.5, "whatever"]
images_prefix += "eta_KL_" + str(Standard_values_alyses[2]) + "_DRr_" + str(
Standard_values_alyses[0]) + "_sigma_" + str(Standard_values_alyses[3])
"""
Get the data from the training_logger for the charts for Batch info
"""
N_analyses = len(List_analyses)
for an_i in range(N_analyses):
gl.init_figure()
ax1 = gl.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((1, 2), (0, 1),
rowspan=1,
colspan=1,
sharex=ax1,
sharey=ax1)
## Subselect models to plot !!!
## Models with Lazy loading True, 35 epochs
Selected_training_logger_list = []
Selected_cf_a_list = []
for i in range(Nmodels):
training_logger = training_logger_list[i]
cf_a = cf_a_list[i]
Cartera.set_interval(sdate, edate)
Cartera.set_seriesNames(["Close"])
#######################################################
############# DANSKE BANK Stuff ###################
######################################################
timeDataObjs = Cartera.get_timeDataObj(period1)
Nsym = len(timeDataObjs)
gl.init_figure()
#ax1.xaxis_date()
i = 0
for tdo in timeDataObjs:
gl.subplot2grid((Nsym, 4), (i, 0), rowspan=1, colspan=4)
gl.tradingPV(tdo, color_mode=1, fontsize=15)
i = i + 1
ax = gl.get_axes()[-1]
all_axes = gl.get_axes()
for i in range(len(all_axes) - 2):
ax = all_axes[i]
plt.setp(ax.get_xticklabels(), visible=False)
#plt.setp(ax.get_xticklabels(), visible=True)
plt.suptitle("Danske Bank", color='k', fontsize=20)
plt.subplots_adjust(left=.09,
bottom=.10,
right=.90,
top=.95,
f = 2 * x_2 + x_1 * x_2 - 4 * x_1 - 4 * x_2 * x_2 - x_1 * x_1 + 5
print("---- Results after Iteration %i ------" % (i))
print("m1 = %.4f, m2 = %.4f" % (m_1, m_2))
print("x1 = %.4f, x2 = %.4f" % (x_1, x_2))
print("t = %.4f" % (t))
print("f(x): %.4f" % (f))
f_values.append(f)
t_values.append(t)
x1_values.append(x_1)
x2_values.append(x_2)
m1_values.append(m_1)
m2_values.append(m_2)
gl.init_figure()
ax1 = gl.subplot2grid((1, 4), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((1, 4), (0, 1), rowspan=1, colspan=1, sharex=ax1)
ax3 = gl.subplot2grid((1, 4), (0, 2), rowspan=1, colspan=1, sharex=ax1)
ax4 = gl.subplot2grid((1, 4), (0, 3), rowspan=1, colspan=1, sharex=ax1)
ax1 = gl.plot([],
f_values,
ax=ax1,
lw=3,
labels=["Objective function", "iterations", "f(X)"],
legend=["f(x)"],
AxesStyle="Normal ",
color="k")
ax2 = gl.plot([],
t_values,
# Bollinger Bands and ATR
nBB = 10; nBB1 = 20; nBB2 = 10
BB1 = timeData.BBANDS(seriesNames = ["Close"], n = nBB1)
SMA1 = timeData.SMA(n = nBB1)
BB2 = timeData.BBANDS(seriesNames = ["Close"], n = nBB2)
SMA2 = timeData.SMA(n = nBB2)
EMA1 = timeData.EMA(n = nBB1)
BBE1 = timeData.BBANDS(seriesNames = ["Close"], MA = SMA1, n = nBB1)
color1 = "k"
colorBB1 = "cobalt blue"
colorBB2 = "irish green"
## Plotting t the BB !!
ax1 = gl.subplot2grid((5,1), (0,0), rowspan=4, colspan=1)
# gl.plot(dates, price, ax = ax1, AxesStyle = "Normal - No xaxis",
# labels = ["Volatility indicator BB", "", "Price"],
# legend = ["Price"], color = "dark navy blue")
title = "Bollinger Bands. " + str(symbols[0]) + "(" + ul.period_dic[timeData.period]+ ")"
gl.barchart(dates, dataHLOC, ax = ax1, color = color1,
labels = [title, "", r"Price ($\$$)"],
legend = ["HLOC Price"])
BBs = np.concatenate([BB1[:,[0]],BB1[:,[1]]],axis = 1)
gl.plot(dates, SMA1, color = colorBB1,
legend = ["0.95 BB(%i)"%nBB1])
gl.plot(dates, BBs,
color = colorBB1, ls = "--")
gl.plot_filled(dates, BBs,
color = colorBB1, alpha = 0.1, AxesStyle = "Normal - No xaxis",)
if(distribution_graph_2D):
# Get the histogram and gaussian estimations !
## Scatter plot of the points
# gl.init_figure()
i_1 = 2
i_2 = 0
X_1,X_2 = X[:,[i_1]], X[:,[i_2]]
mu_1, mu_2 = mus[i_1],mus[i_2]
std_1, std_2 = stds[i_1],stds[i_2]
mu = mus[[i_1,i_2]]
cov = np.cov(np.concatenate((X_1,X_2),axis = 1).T).T
std_K = 3
## Do stuff now
ax1 = gl.subplot2grid((4,4), (1,0), rowspan=3, colspan=3)
gl.scatter(X_1,X_2, alpha = 0.5, ax = ax1, lw = 4, AxesStyle = "Normal",
labels = ["","X1", "X2"])
## X distribution
ax2 = gl.subplot2grid((4,4), (0,0), rowspan=1, colspan=3, sharex = ax1)
x_grid, y_val = bMA.gaussian1D_points(mean = mu_1, std = std_1, std_K = std_K)
gl.plot(x_grid, y_val, color = "k",
labels = ["","",""], legend = ["M: %.1f, std: %.1f"%(mu_1, std_1)],
AxesStyle = "Normal - No xaxis")
# Y distribution
ax3 = gl.subplot2grid((4,4), (1,3), rowspan=3, colspan=1,sharey = ax1,)
x_grid, y_val = bMA.gaussian1D_points(mean = mu_2, std = std_2, std_K = std_K)
return_betas,return_initialization_index]
#Standard_values_alyses = [0.001,0.2,40,
# 0.2,10000,100,
# 0.9, "whatever"]
Standard_values_alyses = [0.001,0.2,30,
0.2,10000,100,
0.9, "whatever"]
"""
Get the data from the training_logger for the charts for Batch info
"""
N_analyses = len(List_analyses)
for an_i in range(N_analyses):
gl.init_figure();
ax1 = gl.subplot2grid((1,2), (0,0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((1,2), (0,1), rowspan=1, colspan=1, sharex = ax1, sharey = ax1)
## Subselect models to plot !!!
## Models with Lazy loading True, 35 epochs
Selected_training_logger_list = []
Selected_cf_a_list = []
for i in range (Nmodels):
training_logger = training_logger_list[i]
cf_a = cf_a_list[i]
condition_analysis = True
# Substract one because the last one is the iterations one.
for an_j in range(N_analyses -1):
if(an_j != an_i):
nBB1 = 20
nBB2 = 10
BB1 = timeData.BBANDS(seriesNames=["Close"], n=nBB1)
SMA1 = timeData.SMA(n=nBB1)
BB2 = timeData.BBANDS(seriesNames=["Close"], n=nBB2)
SMA2 = timeData.SMA(n=nBB2)
EMA1 = timeData.EMA(n=nBB1)
BBE1 = timeData.BBANDS(seriesNames=["Close"], MA=SMA1, n=nBB1)
color1 = "k"
colorBB1 = "cobalt blue"
colorBB2 = "irish green"
## Plotting t the BB !!
ax1 = gl.subplot2grid((5, 1), (0, 0), rowspan=4, colspan=1)
# gl.plot(dates, price, ax = ax1, AxesStyle = "Normal - No xaxis",
# labels = ["Volatility indicator BB", "", "Price"],
# legend = ["Price"], color = "dark navy blue")
title = "Bollinger Bands. " + str(
symbols[0]) + "(" + ul5.period_dic[timeData.period] + ")"
##### The problem is that we are using dataTrasnform so the X axis is not being transformed into integers
##### but fed directly, we need to still make such a transformation for this to work?
gl.barchart(dates,
dataHLOC,
ax=ax1,
color=color1,
labels=[title, "", r"Price ($\$$)"],
legend=["HLOC Price"])
MACD, MACDsign, MACDdiff = timeData.MACD()
TRIX = timeData.TRIX()
gl.set_subplots(3, 1)
gl.plot(dates,
price,
nf=1,
labels=["Averages", "Time", "Value"],
legend=["Price"])
gl.plot(dates, [MACD, MACDsign, MACDdiff], nf=1, na=0, legend=["MACD"])
gl.plot(dates, TRIX, nf=1, na=0, legend=["TRIX"])
gl.subplot2grid((6, 4), (1, 0), rowspan=4, colspan=4, axisbg='#07000d')
gl.create_axes(position=[0.3, 0.4, 0.3, 0.4])
# gl.subplot2grid((10,10),)
# gl.subplot2grid((6,4), (5,0), ro)
gl.plot(dates, TRIX, nf=0, na=0, legend=["TRIX"])
# gl.plot(dates, ADX , nf = 1, na = 0,
# legend = ["ADX"])
#
# gl.plot(dates, ACCDIST , nf = 1, na = 0,
# legend = ["ACCDIST"])
#
###########################################################################
############## Moving Averages ############################################
###########################################################################
## Scatter plot of the points
# gl.init_figure()
i_1 = 2
i_2 = 0
X_1,X_2 = X[:,[i_1]], X[:,[i_2]]
mu_1, mu_2 = mus[i_1],mus[i_2]
Xjoint = np.concatenate((X_1,X_2), axis = 1).T
std_1, std_2 = stds[i_1],stds[i_2]
mu = mus[[i_1,i_2]]
cov = np.cov(Xjoint)
std_K = 3
## Do stuff now
ax1 = gl.subplot2grid((1,2), (0,0), rowspan=1, colspan=1)
gl.scatter(Xjoint[0,:],Xjoint[1,:], alpha = 0.5, ax = ax1, lw = 4, AxesStyle = "Normal",
labels = ["","U1", "U2"])
ax1.axis('equal')
xx, yy, zz = bMA.get_gaussian2D_pdf( xbins=40j, ybins=40j, mu = mu, cov = cov,
std_K = std_K, x_grid = None)
ax1.contour(xx, yy, zz, linewidths = 3, linestyles = "solid", alpha = 0.8,
colors = None, zorder = 100)
######## Transformation !!
A = np.array([[0.9,2],[0.8,0.7]])
mu = np.array([-1.5,2]).reshape(2,1)
Yjoint = A.dot(Xjoint) + mu
## Plot the function
Q_values = range (10000,100000,30)
T_values = []
S_values = []
q_max = []
negative = []
for Q in Q_values:
S = Q*((b*p + h*d)/((p+h)*b))
T = K*d/Q + c*d + p*b*(Q-S)*(Q-S)/(2*Q*(b-d)) + h*(b*S - d*Q)*(b*S - d*Q)/(2*b*Q*(b-d))
T_values.append(T)
q_max.append(S - Q*d/b)
negative.append(Q-S)
gl.init_figure();
ax1 = gl.subplot2grid((1,3), (0,0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((1,3), (0,1), rowspan=1, colspan=1, sharex = ax1)
ax3 = gl.subplot2grid((1,3), (0,2), rowspan=1, colspan=1, sharex = ax1)
ax1 = gl.plot(Q_values, T_values, ax = ax1, lw = 3, labels = ["Total Anual Cost", "Q","T(Q)"], legend = ["T(Q)"],
AxesStyle = "Normal ", color = "k")
ax2 = gl.plot(Q_values, q_max, ax = ax2, lw = 3, labels = ["Maximum inventory", "Q","qmax(Q)"], legend = ["qmax(Q)"],
AxesStyle = "Normal ", color = "k")
ax3 = gl.plot(Q_values, negative, ax = ax3, lw = 3, labels = ["Maximum Shortage", "Q","(Q-S)(Q)"], legend = ["(Q-S)(Q)"],
AxesStyle = "Normal ", color = "k")
nMA1 = 10
nMA2 = 20
SMAw = indl.get_SMA(bMA.delta(nMA1), nMA1, cval=1)
EMAw = indl.get_EMA(bMA.delta(nMA1), nMA1, cval=1)
WMAw = indl.get_WMA(bMA.delta(nMA1), nMA1, cval=1)
SMAw2 = indl.get_SMA(bMA.delta(nMA2), nMA2, cval=1)
EMAw2 = indl.get_EMA(bMA.delta(nMA2), nMA2, cval=1)
WMAw2 = indl.get_WMA(bMA.delta(nMA2), nMA2, cval=1)
SMA = timeData.SMA(n=nMA1)
""" 1st GRAPH """
############## Average and Window ################
ax1 = gl.subplot2grid((1, 5), (0, 0), rowspan=1, colspan=3)
title = "Price and SMA. " + str(
symbols[0]) + "(" + ul5.period_dic[timeData.period] + ")"
gl.plot(dates, [price, SMA],
AxesStyle="Normal",
labels=[title, "", r"Price ($\$$)"],
legend=["Price", "SMA(%i)" % nMA1])
ax2 = gl.subplot2grid((1, 5), (0, 3), rowspan=1, colspan=2)
gl.stem([],
SMAw,
nf=0,
AxesStyle="Normal2",
labels=["SMA Window", "lag", ""],
legend=["SMAw(%i)" % nMA1],
###### SET THE READING ########
ser = serial.Serial('/dev/ttyUSB0', 9600)
ser.readline()
for i in range(10):
print (float(ser.readline().decode("utf-8").split("\n")[0]))
#ser.close()
###### GENERATE FAKE DATA ############
data = np.random.randn(100,1) + 35
time = range(data.size)
###### GENERATE THE FIGURE ############
fig = gl.init_figure();
data_axes = gl.subplot2grid((1,4), (0,0), rowspan=1, colspan=3)
data = []
time = []
update_data.index = 0
print ("starting...")
## Define the class with all the info
class information():
## Serial port info
serial = ser # Serial port we get the info from
rt = None
## Data information
time = time
## Plot the covariance matrix !
# Show the Nshow first samples
Nshow = 20
## Plot realizations of the Gaussian process
Nrealizations = 10
flag = 1;
legend = ["Realizations"]
labels = ["Gaussian Process noise e(t)","t", "e(t)"]
# Plot the realizations
gl.init_figure();
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",
periods[0]).get_dates()
symbolIDs = cmplist.iloc[0:30]["Symbol"].tolist()
symbol_ID1 = symbolIDs[symbol_ID_indx1]
symbol_ID2 = symbolIDs[symbol_ID_indx2]
#print dates[0], dates[26], dates[27]
################# Plotting the data #################
if (trading_graph):
# Trading plot of the points !
title = "CLOSE Price, Volume and Return evolution"
ax1 = gl.subplot2grid((4, 1), (0, 0), rowspan=1, colspan=1)
gl.tradingBarChart(Cartera.get_timeData(symbolIDs[symbol_ID_indx1],
periods[0]),
ax=ax1,
dataTransform=dataTransform,
AxesStyle="Normal - No xaxis",
color="k",
labels=[
title, "", symbolIDs[symbol_ID_indx1] + "(" +
str(periods[0]) + "M)"
])
ax2 = gl.subplot2grid((4, 1), (1, 0), rowspan=1, colspan=1, sharex=ax1)
gl.tradingBarChart(
Cartera.get_timeData(symbolIDs[symbol_ID_indx2], periods[0]),
ax=ax2,
plot_charts = 1
if (plot_charts):
if (True):
Nrows = 2
Ncols = 3
list_all_axes = []
gl.init_figure()
list_all_weights = []
list_all_labels = []
if (cf_a.VB_Linear_projection_ELMO):
# gl.colorIndex = 0
VBmodel = model._linear_projection_ELMO
ax1 = gl.subplot2grid((Nrows,Ncols), (0,0), rowspan=1, colspan=1)
plot_VB_weights_mu_std_2D(VBmodel,ax1,"LinearVB", "Linear proyection ELMo $W_{E}$")
list_all_axes.append(ax1)
list_all_labels.append("$W_{E}$")
list_all_weights.append(get_LinearVB_weights(VBmodel))
if (cf_a.VB_highway_layers):
# gl.colorIndex = 0
VBmodel = model._highway_layer._module.VBmodels[0]
ax1 = gl.subplot2grid((Nrows,Ncols), (0,1), rowspan=1, colspan=1)
plot_VB_weights_mu_std_2D(VBmodel,ax1,"HighwayVB", "Highway matrix weights $W_{H}$")
list_all_axes.append(ax1)
list_all_labels.append("$W_{H}$")
list_all_weights.append(get_LinearVB_weights(VBmodel))
# %%
"""
##########################################################
############### PLOTTING #################################
##########################################################
"""
if (plotting_daily):
# Get the daily values of the remaining days
timeData_daily = tut.get_daily_timedata(timeData, symbols[0])
## Get the daily HLOC
H,L,O,C,V = np.array(timeData_daily.TD[["High","Low","Open","Close","Volume"]][:]).T
gl.init_figure()
ax1 = gl.subplot2grid((4,1), (0,0), rowspan=3, colspan=1)
title = "Bar Chart. " + str(symbols[0]) + "(" + ul.period_dic[1440]+ ")"
gl.tradingBarChart(timeData_daily, ax = ax1, legend = ["Close price"], color = "k",
labels = [title,"",r"Rate"], AxesStyle = "Normal - No xaxis")
ax2 = gl.subplot2grid((4,1), (3,0), rowspan=1, colspan=1, sharex = ax1)
gl.tradingVolume(timeData_daily, ax = ax2,legend = ["Volume"],
AxesStyle = "Normal", labels = ["","","Volume"], color = "#53868B")
gl.set_fontSizes(ax = [ax1,ax2], title = 20, xlabel = 20, ylabel = 20,
legend = 10, xticks = 10, yticks = 10)
image_name = "daily_data"
gl.savefig(folder_images + image_name,
dpi = 100, sizeInches = [20, 7])
def create_Bayesian_analysis_charts_simplified(model,
train_dataset,
validation_dataset,
tr_loss,
val_loss,
KL_loss,
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"
################################ 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)
####### ax1, ax2: Get confusion matrices ##########
labels_classes, confusion = model.get_confusion_matrix(train_dataset)
plot_confusion_matrix(confusion, labels_classes, ax1)
labels_classes, confusion = model.get_confusion_matrix(validation_dataset)
plot_confusion_matrix(confusion, labels_classes, ax2)
############## ax3 ax4 ax5: Loss Evolution !! ######################
## ax3: Evolutoin of the data loss
gl.plot([],
tr_loss,
ax=ax3,
lw=3,
labels=["Losses", "", "Data loss (MSE)"],
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([],
tr_loss,
ax=ax5,
lw=3,
labels=["", "epoch", "Total Loss (Bayes)"],
legend=["train"],
color=color_train_loss)
gl.plot([],
val_loss,
ax=ax5,
lw=3,
legend=["validation"],
color=color_val_loss)
############## ax6 ax7: Variational Weights !! ######################
create_plot_variational_weights(model, ax6, ax7)
gl.set_zoom(ax=ax6, ylim=[-0.1, 10])
gl.set_zoom(ax=ax7, xlim=[-2.5, 2.5], ylim=[-0.1, 0.5])
# 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 + 'Training_Example_Data_Bayesian.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")
dataCSV['Recent dividend payed (per share)'][comp],
edate,lastDivs[i], m_periods[i],
inflation = inflation)
# Check the currency
if (dataCSV['Dividend currency'][comp] == "USD"):
dividends = dividends * USDDKK
# print "%s changed currency" % comp
# Apply the TAXes
dividends = dividends * (1-TAX)
total_div += dividends
all_divs.append(total_div)
gl.subplot2grid((4,4), (0,0), rowspan=4, colspan=4)
gl.plot(range_months,all_divs,
legend = ["Inflation 0%"],
labels = ["Dividends","Months", "DKK"], nf = 0, fill =1, alpha = 0.5)
gl.set_xlim(range_months[0],range_months[-1])
if (1):
#TAX = 0.25
inflation = 0.02
all_divs = []
dataCSV.loc['HP Inc' , 'Amount (No.)'] += 1300
dataCSV.loc['Exxon Mobil Corp' ,'Amount (No.)'] -= 1000
model,
device,
dataset_iterable,
num_batches,
Evaluate_Model_Results,
bayesian_ensemble=bayesian_ensemble)
DataSet_statistics_list.append(DataSet_statistics)
DataSet_statistics = DataSet_statistics_list[0]
EM_list = []
F1_list = []
for i in range(len(DataSet_statistics_list)):
EM_list.append(100 * np.mean(DataSet_statistics_list[i]["em"]))
F1_list.append(100 * np.mean(DataSet_statistics_list[i]["f1"]))
gl.init_figure()
ax1 = gl.subplot2grid((3, 1), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((3, 1), (1, 0), rowspan=1, colspan=1, sharex=ax1)
ax3 = gl.subplot2grid((3, 1), (2, 0), rowspan=1, colspan=1, sharex=ax1)
## Accuracies !!
gl.plot(trim_mu_sigma,
EM_list,
ax=ax1,
legend=["EM"],
labels=[
"Trimming of the weights and biases by $|\mu_w|/\sigma_w$", "",
"Accuracy"
])
gl.plot(trim_mu_sigma, F1_list, ax=ax1, legend=["F1"])
## Systems !
###### SET THE READING ########
if (0):
ser = serial.Serial('/dev/ttyUSB0', 9600)
ser.readline()
for i in range(10):
print(float(ser.readline().decode("utf-8").split("\n")[0]))
#ser.close()
###### GENERATE FAKE DATA ############
data = np.random.randn(100, 1) + 35
time = range(data.size)
###### GENERATE THE FIGURE ############
fig = gl.init_figure()
data_axes = gl.subplot2grid((1, 4), (0, 0), rowspan=1, colspan=3)
data = []
time = []
update_data.index = 0
print("starting...")
## Define the class with all the info
class information():
## Serial port info
serial = ser # Serial port we get the info from
rt = None
## Data information
test_cross_entropy = Ytest * np.log(Ypredict_test_proba) + (
1 - Ytest) * np.log(1 - Ypredict_test_proba)
train_cross_entropy = Ytrain * np.log(Ypredict_train_proba) + (
1 - Ytrain) * np.log(1 - Ypredict_train_proba)
test_cross_entropy = -test_cross_entropy
train_cross_entropy = -train_cross_entropy
##############################################################
alpha_stem = 0.5
marker_stem = [".", 1, None]
train_color = "b"
test_color = "r"
gl.init_figure()
ax1 = gl.subplot2grid((4, 1), (0, 0), rowspan=1, colspan=1)
ax2 = gl.subplot2grid((4, 1), (1, 0), rowspan=1, colspan=1, sharex=ax1)
ax3 = gl.subplot2grid((4, 1), (2, 0), rowspan=1, colspan=1, sharex=ax1)
ax4 = gl.subplot2grid((4, 1), (3, 0), rowspan=1, colspan=1, sharex=ax1)
## Ax1 = Close price at the end of the sessions
gl.plot(days_keys,
C,
ax=ax1,
labels=["Results " + key_classifier, "", "Close Rate"],
AxesStyle="Normal - No xaxis",
legend=["Close Rate"])
## Ax2 = 1 if the stock has gone up, zero if it has gone down
gl.stem(dates_train,
Ytrain_reg,
def create_image_weights_epoch(model, video_fotograms_folder2, epoch_i):
"""
Creates the image of the training and validation accuracy
"""
N_Bayesian_layers = len(model.VBmodels)
N_Normal_layers = len(model.LinearModels)
# Compute the number of squares we will need:
# 1 x linear layers, 2 x LSTMS
gl.init_figure();
cmap = cm.get_cmap('coolwarm', 30)
all_axes = []
for i in range(N_Bayesian_layers):
layer = model.VBmodels[i]
# if (layer.type_layer == "linear"):
if ("linear" in type(layer).__name__.lower()):
ax = gl.subplot2grid((1,N_Bayesian_layers + N_Normal_layers), (0,i), rowspan=1, colspan=1)
weights = layer.weight.detach().cpu().numpy()
biases = layer.bias.detach().cpu().numpy().reshape(-1,1)
neurons = np.concatenate((weights, biases), axis = 1)
cax = ax.imshow(neurons, interpolation="nearest", cmap=cmap, vmin=-2, vmax=2)
all_axes.append(ax)
else:
ax = gl.subplot2grid((1,N_Bayesian_layers + N_Normal_layers), (0,i), rowspan=1, colspan=1)
weights_ih = layer.weight_ih.detach().cpu().numpy()
biases_ih = layer.bias_ih.detach().cpu().numpy().reshape(-1,1)
weights_hh = layer.weight_hh.detach().cpu().numpy()
biases_hh = layer.bias_hh.detach().cpu().numpy().reshape(-1,1)
weights = np.concatenate((weights_ih,weights_hh),axis = 1)
biases = np.concatenate((biases_ih,biases_hh),axis = 1)
neurons = np.concatenate((weights, biases), axis = 1)
cax = ax.imshow(neurons, interpolation="nearest", cmap=cmap, vmin=-2, vmax=2)
all_axes.append(ax)
for i in range(N_Normal_layers):
layer = model.LinearModels[i]
if ("linear" in type(layer).__name__.lower()):
ax = gl.subplot2grid((1,N_Bayesian_layers + N_Normal_layers), (0,N_Bayesian_layers +i), rowspan=1, colspan=1)
weights = layer.weight.detach().cpu().numpy()
biases = layer.bias.detach().cpu().numpy().reshape(-1,1)
neurons = np.concatenate((weights, biases), axis = 1)
cax = ax.imshow(neurons, interpolation="nearest", cmap=cmap, vmin=-2, vmax=2)
all_axes.append(ax)
else:
ax = gl.subplot2grid((1,N_Bayesian_layers + N_Normal_layers), (0,N_Bayesian_layers +i), rowspan=1, colspan=1)
weights_ih = layer.weight_ih.detach().cpu().numpy()
biases_ih = layer.bias_ih.detach().cpu().numpy().reshape(-1,1)
weights_hh = layer.weight_hh.detach().cpu().numpy()
biases_hh = layer.bias_hh.detach().cpu().numpy().reshape(-1,1)
weights = np.concatenate((weights_ih,weights_hh),axis = 1)
biases = np.concatenate((biases_ih,biases_hh),axis = 1)
neurons = np.concatenate((weights, biases), axis = 1)
cax = ax.imshow(neurons, interpolation="nearest", cmap=cmap, vmin=-2, vmax=2)
all_axes.append(ax)
# plt.xticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='vertical')
# plt.yticks(range(data_df_train.shape[1]), data_df_train.columns, rotation='horizontal')
plt.colorbar(cax)
# plt.colorbar(cax2)
# ax1.set_xticks(data_df_train.columns) # , rotation='vertical'
# ax1.grid(True)
plt.title('Weights ')
# 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
plt.show()
gl.set_fontSizes(ax = [all_axes], title = 20, xlabel = 20, ylabel = 20,
legend = 20, xticks = 12, yticks = 12)
# Set final properties and save figure
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.30, hspace=0.30)
gl.savefig(video_fotograms_folder2 +'%i.png'%epoch_i,
dpi = 100, sizeInches = [14, 10], close = True, bbox_inches = None)