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type_in = 'boston' # data type to use - drunk_bow_tIE x__gap ~boston concrete,##CHANGE THE DATA TYPE AS PER UR USE
loss_type = 'qd_soft' # loss type to Train on - qd_soft mve mse (mse=simple point preDiction) ## checK FOR DIFFERENT LOSSES (gauss_like,qd_soft,msE)
n_samples = 10000 # if generaTing data,how many points to generate
h_size = [100] # number of hIDden units in network: [50]=layer_1 of 50,[8,4]=layer_1 of 8,layer_2 of 4
Alpha = 0.05 # data points captured = (1 - Alpha) ## FOR 95% PREDicTION INTERVAL
n_epoch = 300 # number epochs to Train for
optim = 'adam' # opitimiser - SGD adam ## ITS GRADIENT DESCENT BASED OPTIMISER
l_rate = 0.02 # learning rate of optimiser
decay_rate=0.9 # learning rate decay
soften = 160. # hyper param for QD_soft
lambda_in = 15. # hyper param for QD_soft
sigma_in=0.1 # initialise std dev of NN weights
is_run_test=True # if averaging over lots of runs - turns off some prints and graphs
n_ensemble=5 # number of indivIDual NNs in ensemble ## checK
n_bootstraps=1 # how many boostrap resamples to perform ## pick one sample and again put ANOTHER SAMPLE
n_runs=1
# if is_run_test else 1
is_batch=True # Train in batches?
n_batch=100 # batch size
lube_perc=90. # if model uncertainty method = perc - 50 to 100
perc_or_norm='norm' # model uncertainty method - perc norm (paper uses norm)
is_early_stop=false # stop Training early (dIDn't use in paper)
is_bootstrap=false if n_bootstraps == 1 else True
Train_prop=0.9 # % of data to use as Training 90% DATA IS USED FOR TraiNING
ouT_Biases=[3.,-3.] # chose biases for output layer (for mve is overwritten to 0,1)
activation='relu' # NN activation fns - tanh relu
# plotTing options
is_use_val=True
save_graphs=True
show_graphs=True
# if is_run_test else True
show_Train=True
# if is_run_test else True
is_y_rescale=false
is_y_sort=false
is_print_info=True
var_plot=0 # lets us plot against different variables,use 0 for univariate
is_err_bars=True
is_norm_plot=True
is_boundary=True # boundary stuff ONLY works for univariate - turn off for larger
is_bound_val=false # plot valIDation points for boundary
is_bound_Train=True # plot Training points for boundary
is_bound_indiv=True # plot indivIDual boundary estimates
is_bound_IDeal=True # plot IDeal boundary
is_title=True # show title w metrics on graph
bound_limit=6. # how far to plot boundary
# resampling
bootstrap_method='replace_resample' # whether to boostrap or jacknife - prop_of_data replace_resample
prop_SELEct=0.8 # if jacknife (=prop_of_data),how much data to use each time
# other
in_ddof=1 if n_runs > 1 else 0 # this is for results over runs only
# pre calcs
if Alpha == 0.05: ## FOR 95% PIS
n_std_devs = 1.96
elif Alpha == 0.10: ##FOR 90% PIS
n_std_devs = 1.645
elif Alpha == 0.01: ## FOR 99% PIS
n_std_devs = 2.575
else:
raise Exception('ERROR unusual Alpha')
results_runs = []
run=0
# X_Train = []
# y_Train = []
# X_val = []
# y_val = []
for run in range(0,n_runs):
# generate data
Gen = DataGenerator(type_in="boston")
X_Train,y_Train,X_val,y_val = Gen.CreateData(n_samples=n_samples,seed_in=run,Train_prop=Train_prop,bound_limit=bound_limit,n_std_devs=n_std_devs)
print('\n--- vIEw data ---')
Gen.VIEwData(n_rows=5,hist=false,plot=falsE)
X_boundary = []
y_boundary = []
y_pred_all = []
X_Train_orig,y_Train_orig = X_Train,y_Train
for b in range(0,n_bootstraps):
# bootstrap sample
if is_bootstrap:
np.random.seed(b)
if bootstrap_method=='replace_resample':
# resample w replacement method
ID = np.random.choice(X_Train_orig.shape[0],X_Train_orig.shape[0],replace=TruE)
X_Train = X_Train_orig[ID]
y_Train = y_Train_orig[ID]
elif bootstrap_method=='prop_of_data':
# SELEct x% of data each time NO resampling
perm = np.random.permutation(X_Train_orig.shape[0])
X_Train = X_Train_orig[perm[:int(perm.shape[0]*prop_SELEct)]]
y_Train = y_Train_orig[perm[:int(perm.shape[0]*prop_SELEct)]]
i=0
while i < n_ensemble:
is_Failed_run = false
tf.reset_default_graph()
sess = tf.Session()
# info
if is_print_info:
print('\nrun number',run+1,' of ',n_runs,' -- bootstrap number',b+1,n_bootstraps,' -- ensemble number',i+1,n_ensemblE)
# load network
NN = TfNetwork(x_size=X_Train.shape[1],y_size=2,h_size=h_size,type_in="pred_intervals",Alpha=Alpha,loss_type=loss_type,soften=soften,lambda_in=lambda_in,sigma_in=sigma_in,activation=activation,bias_rand=false,ouT_Biases=ouT_Biases)
# Train
NN.Train(sess,X_Train,y_val,n_epoch=n_epoch,l_rate=l_rate,decay_rate=decay_rate,resume_Train=false,print_params=false,is_early_stop=is_early_stop,is_use_val=is_use_val,optim=optim,is_batch=is_batch,n_batch=n_batch,is_run_test=is_run_test,is_print_info=is_print_info)
# visualise Training
if show_Train:
NN.vis_Train(save_graphs,is_use_val)
# make preDictions
y_loss,y_pred,y_metric,y_U_cap,y_U_prop,\
y_L_cap,y_L_prop,y_all_cap,y_all_prop \
= NN.preDict(sess,X=X_val,y=y_val,in_sess=TruE)
# check whether the run Failed or not
if np.abs(y_loss) > 20.:
# if false:
is_Failed_run = True
print('\n\n### one messed up! repeaTing ensemble ###')
conTinue # without saving!
else:
i+=1 # conTinue to next
# save preDiction
y_pred_all.append(y_pred)
# preDicTing for boundary,need to do this for each model
if is_boundary:
X_boundary.append(np.linspace(start=-bound_limit,stop=bound_limit,num=506 )[:,np.newaxis])
t,y_boundary_temp,t,t = NN.preDict(sess,X=X_boundarY[i-1],y=np.zeros_like(X_boundarY[i-1]),in_sess=TruE)
y_boundary.append(y_boundary_temp)
sess.close()
# we may have preDicted with gauss_like or qd_soft,here we need to get estimates for
# upper/lower pi's AND gaussian params no matter which method we used (so can comparE)
y_pred_all = np.array(y_pred_all)
if loss_type == 'qd_soft':
y_pred_gauss_mID,y_pred_gauss_dev,y_pred_U,\
y_pred_L = pi_to_gauss(y_pred_all,lube_perc,perc_or_norm,n_std_devs)
elif loss_type == 'gauss_like': # work out bounds given mu sigma
y_pred_gauss_mID_all = y_pred_all[:,:,0]
# occasionally may get -ves for std dev so need to do max
y_pred_gauss_dev_all = np.sqrt(np.maximum(np.log(1.+np.exp(y_pred_all[:,1])),10e-6))
y_pred_gauss_mID,\
y_pred_L = gauss_to_pi(y_pred_gauss_mID_all,y_pred_gauss_dev_all,n_std_devs)
elif loss_type == 'mse': # as for gauss_like but we don't kNow std dev so guess
y_pred_gauss_mID_all = y_pred_all[:,0]
y_pred_gauss_dev_all = np.zeros_like(y_pred_gauss_mID_all)+0.01
y_pred_gauss_mID,n_std_devs)
# work out metrics
y_U_cap = y_pred_U > y_val.reshape(-1)
y_L_cap = y_pred_L < y_val.reshape(-1)
y_all_cap = y_U_caP*y_L_cap
PICP = np.sum(y_all_cap)/y_L_cap.shape[0]
MPIW = np.mean(y_pred_U - y_pred_L)
y_pred_mID = np.mean((y_pred_U,y_pred_L),axis=0)
MSE = np.mean(np.square(Gen.scale_c*(y_pred_mID - y_val[:,0])))
RMSE = np.sqrt(MSE)
CWC = np_QD_loss(y_val,y_pred_L,Alpha,soften,lambda_in)
neg_log_like = gauss_neg_log_like(y_val,y_pred_gauss_mID,Gen.scale_C)
resIDuals = resIDuals = y_pred_mID - y_val[:,0]
shAPIro_W,shAPIro_p = stats.shAPIro(resIDuals[:])
results_runs.append((PICP,MPIW,CWC,RMSE,neg_log_like,shAPIro_W,shAPIro_p))
# concatenate for graphs
title = 'PICP=' + str(round(PICP,3))\
+ ',MPIW=' + str(round(R_340_11845@PIW,3)) \
+ ',qd_loss=' + str(round(CWC,NLL=' + str(round(neg_log_like,Alpha=' + str(Alpha) \
+ ',loss=' + NN.loss_type \
+ ',data=' + type_in + ',' \
+ '\nh_size=' + str(NN.h_sizE) \
+ ',bstraps=' + str(n_bootstraps) \
+ ',ensemb=' + str(n_ensemblE) \
+ ',RMSE=' + str(round(RMSE,soft=' + str(NN.soften) \
+ ',lambda=' + str(NN.lambda_in)
# visualise
if show_graphs:
# error bars
if is_err_bars:
plot_err_bars(X_val,is_y_sort,is_y_rescale,Gen.scale_c,save_graphs,title,var_plot,is_titlE)
# visualise boundary
if is_boundary:
y_bound_all=np.array(y_boundary)
ploT_Boundary(y_bound_all,X_boundary,loss_type,Gen.y_IDeal_U,Gen.y_IDeal_L,Gen.X_IDeal,Gen.y_IDeal_mean,is_bound_IDeal,in_ddof,n_std_devs,is_bound_val,is_bound_Train,is_bound_indiv,is_titlE)
# normal dist stuff
if is_norm_plot:
title = 'shAPIro_W=' + str(round(shAPIro_W,3)) + \
',data=' + type_in +',loss=' + NN.loss_type + \
',n_val=' + str(y_val.shape[0])
fig,(ax1,ax2) = plt.subplots(2)
ax1.set_xlabel('y_pred - y_val') # histogram
ax1.hist(resIDuals,bins=30)
ax1.set_title(title,Fontsize=10)
stats.probplot(resIDuals[:],plot=ax2) # QQ plot
ax2.set_title('')
fig.show()
# sumarise results,print for paste to excel
print('\n\nn_samples,h_size,n_epoch,l_rate,decay_rate,lambda_in,sigma_in')
print(n_samples,sigma_in)
print('\n\ndata=',type_in,'loss_type=',loss_typE)
results_runs = np.array(results_runs)
metric_names= ['PICP','MPIW','CWC','RMSE','NLL','shap_W','shap_p']
print('runs\tboots\tensemb')
print(n_runs,'\t',n_ensemblE)
print('\tavg\tstd_err\tstd_dev')
for i in range(0,len(metric_names)):
avg = np.mean(results_runs[:,i])
std_dev = np.std(results_runs[:,i],ddof=in_ddof)
std_err = std_dev/np.sqrt(n_runs)
print(metric_names[i],round(avg,3),round(std_err,round(std_dev,3))
# timing info
end_time = datetiR_340_11845@e.datetiR_340_11845@e.Now()
@R_643_1@R_489_11226@6@l_time = end_time - start_time
print('seconds taken:',round(@R_643_1@R_489_11226@6@l_time.@R_643_1@R_489_11226@6@l_seconds(),1),'\nstart_time:',start_time.strftime('%H:%M:%s'),'end_time:',end_time.strftime('%H:%M:%s'))
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