def test_bivariate_model():
spamsDict = {
"lambda1": 0.1,
"it0": 3,
"max_it": 10,
"intercept" : True
}
spamsDict['compute_gram'] = True
spamsDict["loss"] = "square"
spamsDict["regul"] = "l1l2"
learner = BatchBivariateLearner(**spamsDict)
gen = BillMatlabGenerator(MATLAB_FILE_LOC,98,True)
for i in range(10):
X,Y = gen.generate()
learner.process(X,Y)
def runExperiment():
spamsDict = {
"numThreads": 1,
"lambda1": 0.0001,
"bivar_it0": 3,
"bivar_max_it": 10,
"max_it": 500,
"tol": 1e-3,
"intercept": True,
}
spamsDict["compute_gram"] = True
spamsDict["loss"] = "square"
spamsDict["regul"] = "l1l2"
learner = BatchBivariateLearner(**spamsDict)
gen = BillMatlabGenerator(MATLAB_DATA, 98, True)
evaluator = RootMeanSumSquareEval(learner)
for i in range(35):
logger.info("New Item Seen: %d" % i)
X, Y = gen.generate()
if learner.w is not None and learner.u is not None:
loss = evaluator.evaluate(X, Y)
logger.info("Loss: %2.5f" % loss)
logger.info("W sparcity: %2.2f" % learner._wsparcity())
logger.info("U sparcity: %2.2f" % learner._usparcity())
logger.info("Bias: %s" % learner.bias)
logger.info("The predictions:")
for t in range(Y.shape[1]):
dotproduct = learner.u[:, t : t + 1].T.dot(X.T).dot(learner.w[:, t : t + 1])[0, 0]
withoutbias = dotproduct
dotproduct += learner.bias[0, t]
logger.info(
"task=%d,y=%2.5f,v=%2.5f,vb=%2.5f,rse=%2.5f"
% (t, Y[0, t], dotproduct, withoutbias, sqrt(pow(Y[0, t] - dotproduct, 2)))
)
# calculate loss of Y for new X
pass
learner.process(X, Y)
loss = evaluator.evaluate(X, Y)
logger.info("Loss (post addition): %2.5f" % loss)
def runExperiment():
spamsDict = {
"numThreads": 1,
"L0": 0.1,
"lambda1": 0.001,
"it0":100,
"max_it":500,
"tol":1e-3,
"intercept":True,
"bivar_it0": 3,
"bivar_max_it": 10,
}
spamsDict['compute_gram'] = True
spamsDict["loss"] = "square"
spamsDict["regul"] = "l1l2"
learner = BatchBivariateLearner(**spamsDict)
gen = BillMatlabGenerator(MATLAB_DATA,98,True)
evaluator = RootMeanSumSquareEval(learner)
foldN = 0;
for fold in gen.folds:
logger.info("Performing fold: %d"%foldN)
X,Y = gen.fromFold(fold['training'])
learner.process(X,Y)
if learner.w is not None and learner.u is not None:
Xtest,Ytest = gen.fromFold(fold['test'])
logger.info("W sparcity: %2.2f"%learner._wsparcity())
logger.info("U sparcity: %2.2f"%learner._usparcity())
logger.info("Bias: %s"%learner.bias)
loss = evaluator.evaluate(Xtest,Ytest)
logger.info("Loss: %2.5f"%loss)
logger.info("The predictions:")
for i in range(len(Xtest)):
for t in range(Y.shape[1]):
dotproduct = learner.u[:,t:t+1].T.dot(Xtest[i].T).dot(learner.w[:,t:t+1])[0,0]
withoutbias = dotproduct
if learner.bias is not None: dotproduct += learner.bias[0,t]
logger.info("task=%d,i=%d,y=%2.5f,v=%2.5f,vb=%2.5f,delta=%3.5f"%(
t,i,Ytest[i,t],dotproduct,withoutbias,
pow(Ytest[i,t]-dotproduct,2)
))
# calculate loss of Y for new X
pass
foldN +=1
break
def runExperiment():
spamsDict = {
"numThreads": 1,
"lambda1": 0.0001,
"bivar_it0": 3,
"bivar_max_it": 10,
"max_it":500,
"tol":1e-3,
"intercept":True,
}
spamsDict['compute_gram'] = True
spamsDict["loss"] = "square"
spamsDict["regul"] = "l1l2"
learner = BatchBivariateLearner(**spamsDict)
gen = BillMatlabGenerator(MATLAB_DATA,98,True)
evaluator = RootMeanSumSquareEval(learner)
for i in range(35):
logger.info("New Item Seen: %d"%i)
X,Y = gen.generate()
if learner.w is not None and learner.u is not None:
loss = evaluator.evaluate(X,Y)
logger.info("Loss: %2.5f"%loss)
logger.info("W sparcity: %2.2f"%learner._wsparcity())
logger.info("U sparcity: %2.2f"%learner._usparcity())
logger.info("Bias: %s"%learner.bias)
logger.info("The predictions:")
for t in range(Y.shape[1]):
dotproduct = learner.u[:,t:t+1].T.dot(X.T).dot(learner.w[:,t:t+1])[0,0]
withoutbias = dotproduct
dotproduct += learner.bias[0,t]
logger.info("task=%d,y=%2.5f,v=%2.5f,vb=%2.5f,rse=%2.5f"%(
t,Y[0,t],dotproduct,withoutbias,
sqrt(pow(Y[0,t]-dotproduct,2))
))
# calculate loss of Y for new X
pass
learner.process(X,Y)
loss = evaluator.evaluate(X,Y)
logger.info("Loss (post addition): %2.5f"%loss)
