def test():
# base data
X = np.random.randn( 1000, 1 ) * 10 + 50
Y = X * 2 - 10
# add noise
X += np.random.randn( 1000, 1 ) * 2
Y += np.random.randn( 1000, 1 ) * 2
# split
trainX = X[ :900 ]
trainY = Y[ :900 ]
testX = X[ 900: ]
testY = Y[ 900: ]
# for prediction line
plotX = np.array( [ min( X ), max( X ) ] )
iters = 2000
name = [ "RMSProp", "Momentum", "Nesterov", "SGD", "Rprop", "Adam" ]
model = [ LLS( 1, 1, update=Update.RmsProp() ),
LLS( 1, 1, update=Update.Momentum( 1e-7 ) ),
LLS( 1, 1, update=Update.NesterovMomentum( 1e-7 ) ),
LLS( 1, 1, update=Update.Sgd( 1e-7 ) ),
LLS( 1, 1, update=Update.Rprop() ),
LLS( 1, 1, update=Update.Adam() ) ]
error = np.zeros( ( len( model ), iters ) )
for i in range( iters ):
for m in range( len( model ) ):
error[ m, i ] = model[ m ].partial_fit( trainX, trainY )
print( i + 1, "complete" )
# plot results
plt.figure()
plt.title( 'Data Space' )
plt.scatter( trainX, trainY, label='train' )
plt.scatter( testX, testY, label='test' )
plt.plot( plotX, model[ 4 ].predict( plotX ).x_, label='prediction' )
plt.legend()
plt.figure()
plt.title( 'Error Curves' )
for m in range( len( model ) ):
plt.semilogy( error[ m ], label=name[ m ] )
plt.legend()
plt.show()
def main():
cal_housing = fetch_california_housing()
X, y = cal_housing.data, cal_housing.target
names = cal_housing.feature_names
# Center target to avoid gradient boosting init bias: gradient boosting
# with the 'recursion' method does not account for the initial estimator
# (here the average target, by default)
y -= y.mean()
print("Training SNN_Regressor...")
est = SNN_Regressor(8,
1,
10,
10,
hiddenAct=Activation.Tanh(),
error=Error.Mse(),
update=Update.RmsProp(0.001, rateDecay=0.9))
t = [
(3, lambda e: e.cool()), # cool
(6, lambda e: Trainer.prune(e, X, y)), # prune
# ( 18, lambda e: e.cool() ), # cool
(9,
lambda e: Trainer.grow(e, max(1, 1 + int(np.log(e.hiddenSize_ + 1))))
), # grow
# ( 11, lambda e: e.cool() ), # cool
]
growLoss = Trainer.train(est, X, y, batch=1, maxIter=100, triggers=t)
est.maxIter_ = 1000
plt.semilogy(growLoss, label='Grow')
plt.legend()
# plt.show()
# pdb.set_trace()
print("SNN weights:", est.weight_)
print("SNN dweight:", est.dWeight_)
print("SNN nHidden:", est.hiddenSize_)
print('Computing partial dependence plots...')
# We don't compute the 2-way PDP (5, 1) here, because it is a lot slower
# with the brute method.
features = [0, 5, 1, 2]
plot_partial_dependence(est,
X,
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig = plt.gcf()
fig.suptitle('Partial dependence of house value on non-location features\n'
'for the California housing dataset, with SNN_Regressor...')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print("Training MLPRegressor...")
est = MLPRegressor(activation='logistic')
est.fit(X, y)
print('MLP Loss: ', np.average(Error.Mse().f(y, est.predict(X))))
print('Computing partial dependence plots...')
# We don't compute the 2-way PDP (5, 1) here, because it is a lot slower
# with the brute method.
features = [0, 5, 1, 2]
plot_partial_dependence(est,
X,
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig = plt.gcf()
fig.suptitle('Partial dependence of house value on non-location features\n'
'for the California housing dataset, with MLPRegressor')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print("Training GradientBoostingRegressor...")
est = GradientBoostingRegressor(n_estimators=100,
max_depth=4,
learning_rate=0.1,
loss='huber',
random_state=1)
est.fit(X, y)
print('Computing partial dependence plots...')
features = [0, 5, 1, 2, (5, 1)]
plot_partial_dependence(est,
X,
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig = plt.gcf()
fig.suptitle('Partial dependence of house value on non-location features\n'
'for the California housing dataset, with Gradient Boosting')
plt.subplots_adjust(top=0.9)
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (1, 5)
pdp, axes = partial_dependence(est, X, target_feature, grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu,
edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median\n'
'age and average occupancy, with Gradient Boosting')
plt.subplots_adjust(top=0.9)
plt.show()
