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NodeOptimize.py
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NodeOptimize.py
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import numpy
import theano
import sys
import theano.tensor as T
from theano import pp
from sklearn.cross_validation import train_test_split
import IPython
def OptimalNode(x_train, y_train, Regression=True, Classification=False,
bias=False, n_iter=5, alpha=0.01, minibatch=False):
'''
inputs
x_train: training features
y_train: response variable
n_iter: # of iterations for SGD
alpha: strength of L2 penalty (default penalty for now)
outputs
Node: dictionary with Node parameters an predict method
'''
rng = numpy.random
feats = len(x_train[0, :])
D = [x_train, y_train]
training_steps = n_iter
#print "training steps: ", training_steps
#print "penalty strength: ", alpha
#print "Uses bias: ", bias
# Declare Theano symbolic variables
x = T.matrix("x")
y = T.vector("y")
w = theano.shared(rng.uniform(low=-0.25, high=0.25, size=feats), name="w")
b = theano.shared(rng.randn(1)[0], name="b")
a = theano.shared(abs(rng.randn(1)[0]), name="a")
#print "Initialize node as:"
#print w.get_value(), b.get_value(), a.get_value()
# Construct Theano expression graph
if bias:
p_1 = -0.5 + a / (1 + T.exp(-T.dot(x, w) - b))
else:
p_1 = a / (1 + T.exp(-T.dot(x, w)))
prediction = p_1 > 0.5
if Classification:
xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss
elif Regression:
xent = 0.5 * (y - p_1)**2
if alpha == 0:
cost = xent.mean() # The cost to minimize
else:
cost = xent.mean() + alpha * ((w ** 2).sum())
if bias:
gw, gb, ga = T.grad(cost, [w, b, a])
else:
gw, ga = T.grad(cost, [w, a]) # Compute the gradient of the cost
# Compile
Node = {}
Node['Path'] = {}
NodePath = Node['Path']
if bias:
train = theano.function(inputs=[x, y], outputs=[prediction, xent],
updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb),
(a, a - 0.1 * ga)))
else:
train = theano.function(inputs=[x, y], outputs=[prediction, xent],
updates=((w, w - 0.1 * gw), (a, a - 0.1 * ga)))
predict = theano.function(inputs=[x], outputs=p_1)
# Train
for i in range(training_steps):
if minibatch:
batch_split = train_test_split(x_train, y_train, test_size=0.2)
_, D[0], _, D[1] = batch_split
pred, err = train(D[0], D[1])
elif not minibatch:
pred, err = train(D[0], D[1])
NodePath[str(i)] = {}
NodePath[str(i)]['w'] = w.get_value()
NodePath[str(i)]['b'] = b.get_value()
NodePath[str(i)]['a'] = a.get_value()
Node['w'] = w.get_value()
Node['b'] = b.get_value()
Node['a'] = a.get_value()
Node['predict'] = predict
return Node
def OptimalGaussian(x_train, y_train, Regression=True, Classification=False,
bias=False, n_iter=5, alpha=0.01, minibatch=False):
'''
inputs
x_train: training features
y_train: response variable
n_iter: # of iterations for SGD
alpha: strength of L2 penalty (default penalty for now)
outputs
Gaussian Node: dictionary with Node parameters an predict method
'''
rng = numpy.random
feats = len(x_train[0, :])
N = len(x_train[:, 0])
D = [x_train, y_train]
training_steps = n_iter
#print "training steps: ", training_steps
#print "penalty strength: ", alpha
#print "Uses bias: ", bias
# Declare Theano symbolic variables
x = T.matrix("x")
y = T.vector("y")
w = theano.shared(rng.uniform(low=-0.25, high=0.25, size=feats), name="w")
b = theano.shared(abs(rng.randn(1)[0]), name="b")
a = theano.shared(abs(rng.randn(1)[0]), name="a")
rep = theano.shared(numpy.asarray([1]*N), name="rep")
#print "Initialize node as:"
#print w.get_value(), b.get_value(), a.get_value()
# Construct Theano expression graph
W = T.outer(rep, w)
if bias:
p_1 = a * T.exp(-0.5 / (b**2) * T.dot((x - w).T, (x - w)))
else:
p_1 = a * T.exp(-0.5 / (1**2) * T.diagonal(T.dot((x - W), (x - W).T)))
prediction = p_1 > 0.5
if Regression:
xent = 0.5 * (y - p_1)**2
if alpha == 0:
cost = xent.mean() # The cost to minimize
else:
cost = xent.mean() + alpha * ((w ** 2).sum())
if bias:
gw, gb, ga = T.grad(cost, [w, b, a])
else:
gw, ga = T.grad(cost, [w, a]) # Compute the gradient of the cost
# Compile
Node = {}
Node['Path'] = {}
NodePath = Node['Path']
if bias:
train = theano.function(inputs=[x, y], outputs=[prediction, xent],
updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb),
(a, a - 0.1 * ga)))
else:
train = theano.function(inputs=[x, y], outputs=[prediction, xent],
updates=((w, w - 0.1 * gw), (a, a - 0.1 * ga)))
predict = theano.function(inputs=[x], outputs=p_1)
# Train
for i in range(training_steps):
if minibatch:
batch_split = train_test_split(x_train, y_train, test_size=0.2)
_, D[0], _, D[1] = batch_split
#IPython.embed()
pred, err = train(D[0], D[1])
elif not minibatch:
pred, err = train(D[0], D[1])
NodePath[str(i)] = {}
NodePath[str(i)]['w'] = w.get_value()
NodePath[str(i)]['b'] = b.get_value()
NodePath[str(i)]['a'] = a.get_value()
Node['w'] = w.get_value()
Node['b'] = b.get_value()
Node['a'] = a.get_value()
Node['predict'] = predict
return Node
def EarlyStopNode(Node, x_validate, y_validate):
'''
Creates validation set
Evaluates Node's path on validation set
Chooses optimal w in Node's path based on validation set
'''
x = T.matrix("x")
y = T.vector("y")
w = T.vector("w")
b = T.dscalar("b")
a = T.dscalar("a")
p_1 = -0.5 + a / (1 + T.exp(-T.dot(x, w) - b))
xent = 0.5 * (y - p_1)**2
cost = xent.mean()
loss = theano.function(inputs=[x, y, w, b, a], outputs=cost)
Path = Node['Path'].keys()
Path = map(int, Path)
Path.sort()
best_node = {}
best_node_ind = 0
best_loss = numpy.mean(y_validate**2)
losses = []
for ind in Path:
node = Node['Path'][str(ind)]
l = loss(x_validate, y_validate, node['w'], node['b'], node['a'])
losses.append(l)
if l < best_loss:
best_node = node
best_node_ind = ind
best_loss = l
#print "path losses: ", losses
#print "best path index: ", best_node_ind
#print "best loss: ", best_loss
#IPython.embed()
Node['w'] = best_node['w']
Node['b'] = best_node['b']
Node['a'] = best_node['a']
return Node