class ML(LP):
'''
Meta-Label Classifier
--------------------------
Essentially 'RAkELd'; need to add pruning option (inherit PS instead of LP),
to make it a generic meta-label classifier.
'''
k = 3
def fit(self, X, Y):
Yy, self.reverse = transform_BR2ML(Y, self.k)
N, self.L = Y.shape
N_, L_ = Yy.shape
from BR import BR
self.h = BR(L_, copy.deepcopy(self.h))
self.h.fit(X, Yy)
return self
def predict(self, X):
'''
return predictions for X
'''
Yy = self.h.predict(X)
N, D = X.shape
Y = transform_ML2BR(Yy, self.reverse, self.L, self.k)
return Y
class DRBM() :
"""
DRBM
-------------------
Meta-RBM: Train an RBM, and then stick BR on top.
"""
W = None
H = 10
L = -1
h = None
rbm = None
def __init__(self, num_hidden=10, h=linear_model.LogisticRegression()):
''' for H hidden units, and base classifier 'h' '''
self.H = num_hidden
self.h = h
def train(self, X, Y, W_e=None, E=500, lr=0.1):
'''
X: input
Y: output
h: base classifier
'''
# 0. Extract Dimensions
N,D = X.shape
self.L = Y.shape[1]
# 1. RBM
from RBME import *
self.rbm = RBM(num_visible = D, num_hidden = self.H, learning_rate = lr)
self.rbm.train(X, max_epochs = E)
Z = self.rbm.run_visible(X)
#rng = random.RandomState(123)
#from RBM import *
#self.rbm = RBM(input=X,n_visible=D,n_hidden=self.H,numpy_rng=rng)
#for epoch in xrange(100):
# self.rbm.contrastive_divergence(lr=0.01, k=1)
#A,Z = self.rbm.sample_h_given_v(X)
#print Z
#from sklearn.neural_network import BernoulliRBM
#self.rbm = BernoulliRBM(self.H, learning_rate = 0.01, n_iter = 10000)
#self.rbm.fit(X)
#Z = self.rbm.transform(X)
#print Z
# 2. Train final layer, h : YFX -> Y
self.h = BR(self.L,self.h)
self.h.train(Z,Y)
def predict(self, X):
'''
return predictions for X
'''
N,D = X.shape
A = self.rbm.run_visible(X)
#A,Z = self.rbm.sample_h_given_v(X)
#print A,Z
#Z = self.rbm.transform(X)
# Propagate to final layer
Y = self.h.predict(A > 0.5)
return Y
class DRBM():
"""
DRBM
-------------------
Meta-RBM: Train an RBM, and then stick BR on top.
"""
W = None
H = 10
L = -1
h = None
rbm = None
def __init__(self, num_hidden=10, h=linear_model.LogisticRegression()):
''' for H hidden units, and base classifier 'h' '''
self.H = num_hidden
self.h = h
def train(self, X, Y, W_e=None, E=500, lr=0.1):
'''
X: input
Y: output
h: base classifier
'''
# 0. Extract Dimensions
N, D = X.shape
self.L = Y.shape[1]
# 1. RBM
from RBME import *
self.rbm = RBM(num_visible=D, num_hidden=self.H, learning_rate=lr)
self.rbm.train(X, max_epochs=E)
Z = self.rbm.run_visible(X)
#rng = random.RandomState(123)
#from RBM import *
#self.rbm = RBM(input=X,n_visible=D,n_hidden=self.H,numpy_rng=rng)
#for epoch in xrange(100):
# self.rbm.contrastive_divergence(lr=0.01, k=1)
#A,Z = self.rbm.sample_h_given_v(X)
#print Z
#from sklearn.neural_network import BernoulliRBM
#self.rbm = BernoulliRBM(self.H, learning_rate = 0.01, n_iter = 10000)
#self.rbm.fit(X)
#Z = self.rbm.transform(X)
#print Z
# 2. Train final layer, h : YFX -> Y
self.h = BR(self.L, self.h)
self.h.train(Z, Y)
def predict(self, X):
'''
return predictions for X
'''
N, D = X.shape
A = self.rbm.run_visible(X)
#A,Z = self.rbm.sample_h_given_v(X)
#print A,Z
#Z = self.rbm.transform(X)
# Propagate to final layer
Y = self.h.predict(A > 0.5)
return Y
