# be expensive, so I recommend running it once per dataset and using those
# hyperparameter values in train() to save computational time
# make predictions
m = model.predict(x_test) #returns the median prediction if more than one tree
#evaluate results
acc = sklearn.metrics.accuracy_score(y_test, m.flatten())
print('Accuracy', acc)
#close model
model.close_model()
print("Reload model and continue training")
# reload model; can also open it using cPickle.load()
model2 = djinn.load(model_name="class_djinn_test")
#continue training for 20 epochs using same learning rate, etc as before
model2.continue_training(x_train,
y_train,
20,
learnrate,
batchsize,
random_state=1)
#make updated predictions
m2 = model2.predict(x_test)
#evaluate results
acc = sklearn.metrics.accuracy_score(y_test, m.flatten())
print('Accuracy', acc)
# evaluate results
for i in [0, 1]:
mse = sklearn.metrics.mean_squared_error(y_test[:, i], m[:, i])
mabs = sklearn.metrics.mean_absolute_error(y_test[:, i], m[:, i])
exvar = sklearn.metrics.explained_variance_score(y_test[:, i], m[:, i])
print('MSE', mse)
print('M Abs Err', mabs)
print('Expl. Var.', exvar)
# close model
model.close_model()
print("Reload model and continue training for 50 epochs")
# reload model and continue training for 50 more epochs
model2 = djinn.load(model_name="multireg_djinn_test")
model2.continue_training(x_train, y_train, 50, learnrate, batchsize)
m2 = model2.predict(x_test)
# evaluate results
mse2 = sklearn.metrics.mean_squared_error(y_test, m2)
mabs2 = sklearn.metrics.mean_absolute_error(y_test, m2)
exvar2 = sklearn.metrics.explained_variance_score(y_test, m2)
print('MSE', mse2)
print('M Abs Err', mabs2)
print('Expl. Var.', exvar2)
print("Create Bayesian-DJINN model with multiple outputs")
modelname = "multireg_bdjinn_test" #name the model
m = model.predict(x_test) #returns the median prediction if more than one tree
#evaluate results
mse = sklearn.metrics.mean_squared_error(y_test, m)
mabs = sklearn.metrics.mean_absolute_error(y_test, m)
exvar = sklearn.metrics.explained_variance_score(y_test, m)
print('MSE', mse)
print('M Abs Err', mabs)
print('Expl. Var.', exvar)
#close model
model.close_model()
print("Reload model and continue training for 10 epochs")
# reload model; can also open it using cPickle.load()
model2 = djinn.load(model_name="djinn_test")
#continue training for 20 epochs using same learning rate, etc as before
model2.continue_training(x_train,
y_train,
20,
learnrate,
batchsize,
random_state=1)
#make updated predictions
m2 = model2.predict(x_test)
#evaluate results
mse2 = sklearn.metrics.mean_squared_error(y_test, m2)
mabs2 = sklearn.metrics.mean_absolute_error(y_test, m2)
def computeMSE(x_train, x_test, y_train, y_test, feature):
x_train[:, feature] = 0
x_test[:, feature] = 0
print("djinn example")
modelname = "reg_djinn_test" # name the model
ntrees = 1 # number of trees = number of neural nets in ensemble
maxdepth = 4 # max depth of tree -- optimize this for each data set
dropout_keep = 1.0 # dropout typically set to 1 for non-Bayesian models
#initialize the model
model = djinn.DJINN_Regressor(ntrees, maxdepth, dropout_keep)
# find optimal settings: this function returns dict with hyper-parameters
# each djinn function accepts random seeds for reproducible behavior
optimal = model.get_hyperparameters(x_train, y_train, random_state=1)
batchsize = optimal['batch_size']
learnrate = optimal['learn_rate']
epochs = optimal['epochs']
# train the model with hyperparameters determined above
model.train(x_train,
y_train,
epochs=epochs,
learn_rate=learnrate,
batch_size=batchsize,
display_step=1,
save_files=True,
file_name=modelname,
save_model=True,
model_name=modelname,
random_state=1)
# *note there is a function model.fit(x_train,y_train, ... ) that wraps
# get_hyperparameters() and train(), so that you do not have to manually
# pass hyperparameters to train(). However, get_hyperparameters() can
# be expensive, so I recommend running it once per dataset and using those
# hyperparameter values in train() to save computational time
# make predictions
m = model.predict(
x_test) #returns the median prediction if more than one tree
#evaluate results
mse = sklearn.metrics.mean_squared_error(y_test, m)
mabs = sklearn.metrics.mean_absolute_error(y_test, m)
exvar = sklearn.metrics.explained_variance_score(y_test, m)
print('MSE', mse)
print('M Abs Err', mabs)
print('Expl. Var.', exvar)
#close model
model.close_model()
print("Reload model and continue training for 20 epochs")
# reload model; can also open it using cPickle.load()
model2 = djinn.load(model_name="reg_djinn_test")
#continue training for 20 epochs using same learning rate, etc as before
model2.continue_training(x_train,
y_train,
20,
learnrate,
batchsize,
random_state=1)
#make updated predictions
m2 = model2.predict(x_test)
#evaluate results
mse2 = sklearn.metrics.mean_squared_error(y_test, m2)
mabs2 = sklearn.metrics.mean_absolute_error(y_test, m2)
exvar2 = sklearn.metrics.explained_variance_score(y_test, m2)
print('MSE', mse2)
print('M Abs Err', mabs2)
print('Expl. Var.', exvar2)
# Bayesian formulation with dropout. Recommend dropout keep
# probability ~0.95, 5-10 trees.
print("Bayesian djinn example")
ntrees = 10
dropout_keep = 0.95
modelname = "reg_bdjinn_test"
# initialize a model
bmodel = djinn.DJINN_Regressor(ntrees, maxdepth, dropout_keep)
# "fit()" does what get_hyperparameters + train does, in one step:
bmodel.fit(x_train,
y_train,
display_step=1,
save_files=True,
file_name=modelname,
save_model=True,
model_name=modelname,
random_state=1)
# evaluate: niters is the number of times you evaluate the network for
# a single sample. higher niters = better resolved distribution of predictions
niters = 100
bl, bm, bu, results = bmodel.bayesian_predict(x_test,
n_iters=niters,
random_state=1)
# bayesian_predict returns 25, 50, 75 percentile and results dict with all predictions
# evaluate performance on median predictions
mse = sklearn.metrics.mean_squared_error(y_test, bm)
mabs = sklearn.metrics.mean_absolute_error(y_test, bm)
exvar = sklearn.metrics.explained_variance_score(y_test, bm)
print('MSE', mse)
print('M Abs Err', mabs)
print('Expl. Var.', exvar)
# # make a pretty plot
# g=np.linspace(np.min(y_test),np.max(y_test),10)
# fig, axs = plt.subplots(1,1, figsize=(8,8), facecolor='w', edgecolor='k')
# fig.subplots_adjust(hspace = .15, wspace=.1)
# sc=axs.scatter(y_test, bm, linewidth=0,s=6,
# alpha=0.8, c='#68d1ca')
# a,b,c=axs.errorbar(y_test, bm, yerr=[bm-bl,bu-bm], marker='',ls='',zorder=0,
# alpha=0.5, ecolor='black')
# axs.set_xlabel("True")
# axs.set_ylabel("B-DJINN Prediction")
# axs.plot(g,g,color='red')
# # axs.text(0.5, 0.9,filename, fontsize = 28, ha='center', va='center', transform=axs.transAxes)
# axs.text(0.4, 0.75,'mesTest=%.5f' %mse, fontsize = 28, ha='center', va='center', transform=axs.transAxes)
# # plt.show()
# plt.savefig(resultPath+'allTest', bbox_inches="tight", dpi = 300)
# # collect_tree_predictions gathers predictions in results dict
# # in a more intuitive way for easy plotting, etc
# p=bmodel.collect_tree_predictions(results['predictions'])
return mse